![\"Loading..\"](\"//bibbase.org/img/ajax-loader.gif\")
\n\n\n generated by\n \n ![\"bibbase.org\"\n](\"//bibbase.org/img/logo.svg\"\n "\\"BibBase")
\n \n
\n \n\n \n \n \n\n
\n\n\n - \n\n
- \n \n Group by\n \n \n
- \n
- \n Year\n \n
- \n Author\n \n
- \n Type\n \n
- \n Keyword\n \n
- \n Downloads\n \n
\n\n\n - \n \n \n \n \n
- \n
- \n \n Expand/Collapse All\n \n \n
- \n \n Download BibTeX\n \n \n
- \n \n RSS Feed\n \n \n
\n\n \n\n\n \n
\n\n Excellent! Next you can\n create a new website with this list, or\n embed it in an existing web page by copying & pasting\n any of the following snippets.\n\n
\n\n \n JavaScript\n (easiest)\n
\n \n
\n\n PHP\n \n <script src=\"https://bibbase.org/service/mendeley/aba9653c-d139-3f95-aad8-969c487ed2f3/group/17c612fe-279d-38a6-ac90-9228b535bc5e?jsonp=1&jsonp=1\"></script>\n \n \n
\n\n iFrame\n (not recommended)\n \n <?php\n $contents = file_get_contents(\"https://bibbase.org/service/mendeley/aba9653c-d139-3f95-aad8-969c487ed2f3/group/17c612fe-279d-38a6-ac90-9228b535bc5e?jsonp=1\");\n print_r($contents);\n ?>\n \n \n
\n\n \n <iframe src=\"https://bibbase.org/service/mendeley/aba9653c-d139-3f95-aad8-969c487ed2f3/group/17c612fe-279d-38a6-ac90-9228b535bc5e?jsonp=1\"></iframe>\n \n \n For more details see the documention.\n
\n\n\n This is a preview! To use this list on your own web site\n or create a new web site from it,\n create a free account. The file will be added\n and you will be able to edit it in the File Manager.\n We will show you instructions once you've created your account.\n
\n\n \n\n
\n\n To the site owner:
\n\nAction required! Mendeley is changing its\n API. In order to keep using Mendeley with BibBase past April\n 14th, you need to:\n
- \n
- renew the authorization for BibBase on Mendeley, and \n
- update the BibBase URL\n in your page the same way you did when you initially set up\n this page.\n \n
\n \n \n Fix it now\n
\n\n \n \n
\n\n\n \n\n \n \n \n \n\n\n
\n\n \n \n 2024\n \n \n (1)\n \n \n
\n \n \n \n
\n \n \n\n \n \n \n \n \n \n Instance Selection Framework for Alzheimer’s Disease Classification Using Multiple Regions of Interest and Atlas Integration.\n \n \n \n \n\n\n \n Castro-Silva., J., A.; Moreno-García., M.; Guachi-Guachi., L.; and Peluffo-Ordóñez., D., H.\n\n\n \n\n\n\n In Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods - ICPRAM, pages 453-460, 2024. SciTePress\n \n\n\n\n
\n\n\n\n \n \n![\"Instance](\"//bibbase.org/img/filetypes/link.svg\"\n\t "\\"Website\\"\n\t")
Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n
\n\n\n\n\n\n \n\n\n\n \n \n
Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n\n
\n\n\n@inproceedings{\n title = {Instance Selection Framework for Alzheimer’s Disease Classification Using Multiple Regions of Interest and Atlas Integration},\n type = {inproceedings},\n year = {2024},\n pages = {453-460},\n websites = {https://www.scitepress.org/Link.aspx?doi=10.5220/0012469600003654},\n publisher = {SciTePress},\n institution = {INSTICC},\n id = {79c5dd8f-bb10-3f28-8ac9-d1cdb68237e7},\n created = {2024-03-06T14:15:39.373Z},\n file_attached = {false},\n profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},\n group_id = {17c612fe-279d-38a6-ac90-9228b535bc5e},\n last_modified = {2024-03-06T14:15:39.373Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n citation_key = {icpram24},\n source_type = {conference},\n private_publication = {false},\n abstract = {Optimal selection of informative instances from a dataset is critical for constructing accurate predictive models. As databases expand, leveraging instance selection techniques becomes imperative to condense data into a more manageable size. This research unveils a novel framework designed to strategically identify and choose the most informative 2D brain image slices for Alzheimer’s disease classification. Such a framework integrates annotations from multiple regions of interest across multiple atlases. The proposed framework consists of six core components: 1) Atlas merging for ROI annotation and hemisphere separation. 2) Image preprocessing to extract informative slices. 3) Dataset construction to prevent data leakage, select subjects, and split data. 4) Data generation for memory-efficient batches. 5) Model construction for diverse classification training and testing. 6) Weighted ensemble for combining predictions from multiple models with a single learning algorithm. Our instanc e selection framework was applied to construct Transformer-based classification models, demonstrating an overall accuracy of approximately 98.33% in distinguishing between Cognitively Normal and Alzheimer’s cases at the subject level. It exhibited enhancements of 3.68%, 3.01%, 3.62% for sagittal, coronal, and axial planes respectively in comparison with the percentile technique.},\n bibtype = {inproceedings},\n author = {Castro-Silva., Juan A. and Moreno-García., Maria and Guachi-Guachi., Lorena and Peluffo-Ordóñez., Diego H.},\n doi = {10.5220/0012469600003654},\n booktitle = {Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods - ICPRAM}\n}\n\n Optimal selection of informative instances from a dataset is critical for constructing accurate predictive models. As databases expand, leveraging instance selection techniques becomes imperative to condense data into a more manageable size. This research unveils a novel framework designed to strategically identify and choose the most informative 2D brain image slices for Alzheimer’s disease classification. Such a framework integrates annotations from multiple regions of interest across multiple atlases. The proposed framework consists of six core components: 1) Atlas merging for ROI annotation and hemisphere separation. 2) Image preprocessing to extract informative slices. 3) Dataset construction to prevent data leakage, select subjects, and split data. 4) Data generation for memory-efficient batches. 5) Model construction for diverse classification training and testing. 6) Weighted ensemble for combining predictions from multiple models with a single learning algorithm. Our instanc e selection framework was applied to construct Transformer-based classification models, demonstrating an overall accuracy of approximately 98.33% in distinguishing between Cognitively Normal and Alzheimer’s cases at the subject level. It exhibited enhancements of 3.68%, 3.01%, 3.62% for sagittal, coronal, and axial planes respectively in comparison with the percentile technique.\n
\n\n\n\n
\n\n \n \n 2023\n \n \n (7)\n \n \n
\n \n \n \n
\n \n \n\n \n \n \n \n \n \n Recognition and Classification of Cardiac Arrhythmias Using Discrete Wavelet Transform (DWT) and Machine Learning Techniques.\n \n \n \n \n\n\n \n Ayala-Cucas, H., A.; Mora-Piscal, E., A.; Mayorca-Torres, D.; León-Salas, A., J.; and Peluffo-Ordoñez, D., H.\n\n\n \n\n\n\n In Botto-Tobar, M.; Gómez, O., S.; Rosero Miranda, R.; Díaz Cadena, A.; and Luna-Encalada, W., editor(s), Trends in Artificial Intelligence and Computer Engineering, pages 3-15, 2023. Springer Nature Switzerland\n \n\n\n\n
\n\n\n\n \n \nWebsite\n \n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n
\n\n\n \n\n\n\n \n \n
Website\n \n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n\n
\n\n\n@inproceedings{\n title = {Recognition and Classification of Cardiac Arrhythmias Using Discrete Wavelet Transform (DWT) and Machine Learning Techniques},\n type = {inproceedings},\n year = {2023},\n pages = {3-15},\n websites = {https://link.springer.com/chapter/10.1007/978-3-031-25942-5_1},\n publisher = {Springer Nature Switzerland},\n city = {Cham},\n id = {db6c182b-dabb-3edb-a7b8-0b5aede3237b},\n created = {2023-02-13T23:10:56.414Z},\n file_attached = {false},\n profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},\n group_id = {17c612fe-279d-38a6-ac90-9228b535bc5e},\n last_modified = {2023-02-13T23:10:56.414Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n citation_key = {10.1007/978-3-031-25942-5_1},\n source_type = {inproceedings},\n private_publication = {false},\n abstract = {Cardiac arrhythmias are heart rhythm problems that usually occur when the electrical impulses coordinated with the heartbeat do not work correctly. For this reason, detecting abnormalities in an electrocardiogram (ECG) plays a vital role in patient follow-up. Due to the presence of noise, the irregularity of the heartbeat, and the nonstationary nature of ECG signals, their interpretation can be difficult, requiring the use of advanced computer systems to support the diagnosis of cardiac disorders. Therefore, the development of assisted ECG analysis systems is a current topic of study, and the main challenge is to achieve adequate accuracy for application in the clinical setting. Therefore, this article describes a software tool for classifying ECG samples into the main classes of cardiac arrhythmias by removing noise from the ECG signal at the preprocessing stage using conventional digital filters; the location of the QRS complex is essential for the identification of the ECG signal. Therefore, the position and amplitude of the R peaks are determined in the segmentation stage. Then the selection of the most relevant features of the ECG signal is performed using the discrete wavelet transform (DWT). The ability of the extracted features to differentiate between different classes of data is tested using machine learning techniques such as k-Nearest Neighbors, Neural Networks, and Decision Trees with 10-fold cross-validation. These methods are evaluated and tested with the MIT-BIH arrhythmia database, achieving the best accuracy of 98.54\\% using the k-Nearest Neighbors classifier.},\n bibtype = {inproceedings},\n author = {Ayala-Cucas, Hermes Andrés and Mora-Piscal, Edison Alexander and Mayorca-Torres, Dagoberto and León-Salas, Alejandro José and Peluffo-Ordoñez, Diego Hernán},\n editor = {Botto-Tobar, Miguel and Gómez, Omar S and Rosero Miranda, Raul and Díaz Cadena, Angela and Luna-Encalada, Washington},\n booktitle = {Trends in Artificial Intelligence and Computer Engineering}\n}\n\n Cardiac arrhythmias are heart rhythm problems that usually occur when the electrical impulses coordinated with the heartbeat do not work correctly. For this reason, detecting abnormalities in an electrocardiogram (ECG) plays a vital role in patient follow-up. Due to the presence of noise, the irregularity of the heartbeat, and the nonstationary nature of ECG signals, their interpretation can be difficult, requiring the use of advanced computer systems to support the diagnosis of cardiac disorders. Therefore, the development of assisted ECG analysis systems is a current topic of study, and the main challenge is to achieve adequate accuracy for application in the clinical setting. Therefore, this article describes a software tool for classifying ECG samples into the main classes of cardiac arrhythmias by removing noise from the ECG signal at the preprocessing stage using conventional digital filters; the location of the QRS complex is essential for the identification of the ECG signal. Therefore, the position and amplitude of the R peaks are determined in the segmentation stage. Then the selection of the most relevant features of the ECG signal is performed using the discrete wavelet transform (DWT). The ability of the extracted features to differentiate between different classes of data is tested using machine learning techniques such as k-Nearest Neighbors, Neural Networks, and Decision Trees with 10-fold cross-validation. These methods are evaluated and tested with the MIT-BIH arrhythmia database, achieving the best accuracy of 98.54\\% using the k-Nearest Neighbors classifier.\n
\n\n\n\n \n\n \n \n \n \n \n \n Neural Networks on Noninvasive Electrocardiographic Imaging Reconstructions: Preliminary Results.\n \n \n \n \n\n\n \n Mayorca-Torres, D.; León-Salas, A., J.; and Peluffo-Ordoñez, D., H.\n\n\n \n\n\n\n In Botto-Tobar, M.; Gómez, O., S.; Rosero Miranda, R.; Díaz Cadena, A.; and Luna-Encalada, W., editor(s), Trends in Artificial Intelligence and Computer Engineering, pages 55-63, 2023. Springer Nature Switzerland\n \n\n\n\n
\n\n\n\n \n \nWebsite\n \n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n
\n\n\n \n\n\n\n \n \n
Website\n \n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n\n
\n\n\n@inproceedings{\n title = {Neural Networks on Noninvasive Electrocardiographic Imaging Reconstructions: Preliminary Results},\n type = {inproceedings},\n year = {2023},\n pages = {55-63},\n websites = {https://link.springer.com/chapter/10.1007/978-3-031-25942-5_5},\n publisher = {Springer Nature Switzerland},\n city = {Cham},\n id = {9458992a-67f3-3799-9dd6-525e932e119c},\n created = {2023-02-13T23:42:39.700Z},\n file_attached = {false},\n profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},\n group_id = {17c612fe-279d-38a6-ac90-9228b535bc5e},\n last_modified = {2023-02-13T23:42:39.700Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n citation_key = {10.1007/978-3-031-25942-5_5},\n source_type = {inproceedings},\n private_publication = {false},\n abstract = {In the reverse electrocardiography (ECG) problem, the objective is to reconstruct the heart's electrical activity from a set of body surface potentials by solving the direct model and the geometry of the torso. Over the years, researchers have used various approaches to solve this problem, from direct, iterative, probabilistic, and those based on deep learning. The interest of the latter, among the wide range of techniques, is because the complexity of the problem can be significantly reduced while increasing the precision of the estimation. In this article, we evaluate the performance of a deep learning-based neural network compared to the Tikhonov method of zero order (ZOT), first (FOT), and second (SOT). Preliminary results show an improvement in performance over real data when Pearson's correlation coefficient (CC) and (RMSE) are calculated. The CC's mean value and standard deviation for the proposed method were 0.960 (0.065), well above ZOT, which was 0.864 (0.047).},\n bibtype = {inproceedings},\n author = {Mayorca-Torres, Dagoberto and León-Salas, Alejandro José and Peluffo-Ordoñez, Diego Hernán},\n editor = {Botto-Tobar, Miguel and Gómez, Omar S and Rosero Miranda, Raul and Díaz Cadena, Angela and Luna-Encalada, Washington},\n booktitle = {Trends in Artificial Intelligence and Computer Engineering}\n}\n\n In the reverse electrocardiography (ECG) problem, the objective is to reconstruct the heart's electrical activity from a set of body surface potentials by solving the direct model and the geometry of the torso. Over the years, researchers have used various approaches to solve this problem, from direct, iterative, probabilistic, and those based on deep learning. The interest of the latter, among the wide range of techniques, is because the complexity of the problem can be significantly reduced while increasing the precision of the estimation. In this article, we evaluate the performance of a deep learning-based neural network compared to the Tikhonov method of zero order (ZOT), first (FOT), and second (SOT). Preliminary results show an improvement in performance over real data when Pearson's correlation coefficient (CC) and (RMSE) are calculated. The CC's mean value and standard deviation for the proposed method were 0.960 (0.065), well above ZOT, which was 0.864 (0.047).\n
\n\n\n\n \n\n \n \n \n \n \n \n A 3D Printed, Bionic Hand Powered by EMG Signals and Controlled by an Online Neural Network.\n \n \n \n \n\n\n \n Avilés-Mendoza, K.; Gaibor-León, N., G.; Asanza, V.; Lorente-Leyva, L., L.; and Peluffo-Ordóñez, D., H.\n\n\n \n\n\n\n Biomimetics, 8(2). 2023.\n \n\n\n\n
\n\n\n\n \n \nWebsite\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n
\n\n\n \n\n\n\n \n \n
Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n\n
\n\n\n@article{\n title = {A 3D Printed, Bionic Hand Powered by EMG Signals and Controlled by an Online Neural Network},\n type = {article},\n year = {2023},\n volume = {8},\n websites = {https://www.mdpi.com/2313-7673/8/2/255},\n id = {30377fc4-0373-3f90-884b-4246e4d0df40},\n created = {2023-06-17T22:55:16.475Z},\n file_attached = {false},\n profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},\n group_id = {17c612fe-279d-38a6-ac90-9228b535bc5e},\n last_modified = {2023-06-17T22:55:16.475Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n citation_key = {biomimetics8020255},\n source_type = {article},\n private_publication = {false},\n abstract = {About 8% of the Ecuadorian population suffers some type of amputation of upper or lower limbs. Due to the high cost of a prosthesis and the fact that the salary of an average worker in the country reached 248 USD in August 2021, they experience a great labor disadvantage and only 17% of them are employed. Thanks to advances in 3D printing and the accessibility of bioelectric sensors, it is now possible to create economically accessible proposals. This work proposes the design of a hand prosthesis that uses electromyography (EMG) signals and neural networks for real-time control. The integrated system has a mechanical and electronic design, and the latter integrates artificial intelligence for control. To train the algorithm, an experimental methodology was developed to record muscle activity in upper extremities associated with specific tasks, using three EMG surface sensors. These data were used to train a five-layer neural network. the trained model was compressed and exported using TensorflowLite. The prosthesis consisted of a gripper and a pivot base, which were designed in Fusion 360 considering the movement restrictions and the maximum loads. It was actuated in real time thanks to the design of an electronic circuit that used an ESP32 development board, which was responsible for recording, processing and classifying the EMG signals associated with a motor intention, and to actuate the hand prosthesis. As a result of this work, a database with 60 electromyographic activity records from three tasks was released. The classification algorithm was able to detect the three muscle tasks with an accuracy of 78.67% and a response time of 80 ms. Finally, the 3D printed prosthesis was able to support a weight of 500 g with a safety factor equal to 15.},\n bibtype = {article},\n author = {Avilés-Mendoza, Karla and Gaibor-León, Neil George and Asanza, Víctor and Lorente-Leyva, Leandro L and Peluffo-Ordóñez, Diego H},\n doi = {10.3390/biomimetics8020255},\n journal = {Biomimetics},\n number = {2}\n}\n\n About 8% of the Ecuadorian population suffers some type of amputation of upper or lower limbs. Due to the high cost of a prosthesis and the fact that the salary of an average worker in the country reached 248 USD in August 2021, they experience a great labor disadvantage and only 17% of them are employed. Thanks to advances in 3D printing and the accessibility of bioelectric sensors, it is now possible to create economically accessible proposals. This work proposes the design of a hand prosthesis that uses electromyography (EMG) signals and neural networks for real-time control. The integrated system has a mechanical and electronic design, and the latter integrates artificial intelligence for control. To train the algorithm, an experimental methodology was developed to record muscle activity in upper extremities associated with specific tasks, using three EMG surface sensors. These data were used to train a five-layer neural network. the trained model was compressed and exported using TensorflowLite. The prosthesis consisted of a gripper and a pivot base, which were designed in Fusion 360 considering the movement restrictions and the maximum loads. It was actuated in real time thanks to the design of an electronic circuit that used an ESP32 development board, which was responsible for recording, processing and classifying the EMG signals associated with a motor intention, and to actuate the hand prosthesis. As a result of this work, a database with 60 electromyographic activity records from three tasks was released. The classification algorithm was able to detect the three muscle tasks with an accuracy of 78.67% and a response time of 80 ms. Finally, the 3D printed prosthesis was able to support a weight of 500 g with a safety factor equal to 15.\n
\n\n\n\n \n\n \n \n \n \n \n \n Construcción de una base de datos no estructurada para procesar datos espirométricos [Building an unstructured database to process spirometric data].\n \n \n \n \n\n\n \n Sierra-Martínez, Luz Marina; Tunubalá-Ramírez, Jorge Alfredo; Ordóñez, D., H., P.\n\n\n \n\n\n\n Revista Ibérica de Sistemas e Tecnologias de Informação, E57: 508 - 521. 2023.\n \n\n\n\n
\n\n\n\n \n \nWebsite\n \n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n
\n\n\n \n\n\n\n \n \n
Website\n \n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n\n
\n\n\n@article{\n title = {Construcción de una base de datos no estructurada para procesar datos espirométricos [Building an unstructured database to process spirometric data]},\n type = {article},\n year = {2023},\n pages = {508 - 521},\n volume = {E57},\n websites = {https://www.proquest.com/openview/e4c408ebe531be18eb89b8e5b66e734b/},\n id = {f9611905-d48c-352f-86f5-e73eaf6b9f96},\n created = {2023-07-05T03:34:33.452Z},\n file_attached = {false},\n profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},\n group_id = {17c612fe-279d-38a6-ac90-9228b535bc5e},\n last_modified = {2023-07-05T03:34:33.452Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {Non-SQL databases are becoming an option for information storage, and many healthcare applications are currently relying on this type of storage, given their ease of horizontal growth with low-cost hardware. Spirometric records are a useful tool for monitoring and diagnosing patients with respiratory conditions. Having these records in an easily accessible and easy to grow storage, such as No SQL DBs, favors both the loading and extraction of information for further processing. with machine learning tools that allow the identification, classification or prediction of situations associated with patients. Additionally, this study includes a brief literature review that involves a comparison between RDBs and Non-SQL databases, the most used engines, their performance, and some cases in the health sector, since no similar works for spirometric records to the best knowledge of the authors.},\n bibtype = {article},\n author = {Sierra-Martínez, Luz Marina; Tunubalá-Ramírez, Jorge Alfredo; Ordóñez, Diego H Peluffo.},\n journal = {Revista Ibérica de Sistemas e Tecnologias de Informação}\n}\n\n Non-SQL databases are becoming an option for information storage, and many healthcare applications are currently relying on this type of storage, given their ease of horizontal growth with low-cost hardware. Spirometric records are a useful tool for monitoring and diagnosing patients with respiratory conditions. Having these records in an easily accessible and easy to grow storage, such as No SQL DBs, favors both the loading and extraction of information for further processing. with machine learning tools that allow the identification, classification or prediction of situations associated with patients. Additionally, this study includes a brief literature review that involves a comparison between RDBs and Non-SQL databases, the most used engines, their performance, and some cases in the health sector, since no similar works for spirometric records to the best knowledge of the authors.\n
\n\n\n\n \n\n \n \n \n \n \n \n MILimbEEG: A dataset of EEG signals related to upper and lower limb execution of motor and motor imagery tasks.\n \n \n \n \n\n\n \n Asanza, V.; Lorente-Leyva, L., L.; Peluffo-Ordóñez, D., H.; Montoya, D.; and Gonzalez, K.\n\n\n \n\n\n\n Data in Brief, 50: 109540. 2023.\n \n\n\n\n
\n\n\n\n \n \nWebsite\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
\n\n\n \n\n\n\n \n \n
Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n\n
\n\n\n@article{\n title = {MILimbEEG: A dataset of EEG signals related to upper and lower limb execution of motor and motor imagery tasks},\n type = {article},\n year = {2023},\n keywords = {Brain–computer interface,Electroencephalography,Experimental methodology,Motor imagery task,Motor task,OpenBCI},\n pages = {109540},\n volume = {50},\n websites = {https://www.sciencedirect.com/science/article/pii/S2352340923006406},\n id = {3ec68e53-cbfa-3c98-a4cd-49184a37aaeb},\n created = {2023-09-22T18:07:54.271Z},\n file_attached = {false},\n profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},\n group_id = {17c612fe-279d-38a6-ac90-9228b535bc5e},\n last_modified = {2023-09-22T18:07:54.271Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n citation_key = {ASANZA2023109540},\n source_type = {article},\n private_publication = {false},\n abstract = {Biomedical Electroencephalography (EEG) signals are the result of measuring the electric potential difference generated on the scalp surface by neural activity corresponding to each brain area. Accurate and automatic detection of neural activity from the upper and lower limbs using EEG may be helpful in rehabilitating people suffering from mobility limitations or disabilities. This article presents a dataset containing 7440 CSV files from 60 test subjects during motor and motor imagery tasks. The motor and motor imagery tasks performed by the test subjects were: Closing Left Hand (CLH), Closing Right Hand (CRH), Dorsal flexion of Left Foot (DLF), Plantar flexion of Left Foot (PLF), Dorsal flexion of Right Foot (DRF), Plantar flexion of Right Foot (PRF) and Resting in between tasks (Rest). The volunteers were recruited from research colleagues at ESPOL and patients at the Luis Vernaza Hospital in Guayaquil, Ecuador. Each CSV file has 501 rows, of which the first one lists the electrodes from 0 to 15, and the remaining 500 rows correspond to 500 data recorded during the task is performed due to sample rate. In addition, each file has 17 columns, of which the first one indicates the sampling number and the remaining 16 columns represent 16 surface EEG electrodes. As a data recording equipment, the OpenBCI is used in a monopolar setup with a sampling rate of 125 Hz. This work includes statistical measures about the demographic information of all recruited test subjects. Finally, we outline the experimental methodology used to record EEG signals during upper and lower limb task execution. This dataset is called MILimbEEG and contains microvolt (µV) EEG signals acquired during motor and motor imagery tasks. The collected data may facilitate the evaluation of EEG signal detection and classification models dedicated to task recognition.},\n bibtype = {article},\n author = {Asanza, Víctor and Lorente-Leyva, Leandro L and Peluffo-Ordóñez, Diego H and Montoya, Daniel and Gonzalez, Kleber},\n doi = {https://doi.org/10.1016/j.dib.2023.109540},\n journal = {Data in Brief}\n}\n\n Biomedical Electroencephalography (EEG) signals are the result of measuring the electric potential difference generated on the scalp surface by neural activity corresponding to each brain area. Accurate and automatic detection of neural activity from the upper and lower limbs using EEG may be helpful in rehabilitating people suffering from mobility limitations or disabilities. This article presents a dataset containing 7440 CSV files from 60 test subjects during motor and motor imagery tasks. The motor and motor imagery tasks performed by the test subjects were: Closing Left Hand (CLH), Closing Right Hand (CRH), Dorsal flexion of Left Foot (DLF), Plantar flexion of Left Foot (PLF), Dorsal flexion of Right Foot (DRF), Plantar flexion of Right Foot (PRF) and Resting in between tasks (Rest). The volunteers were recruited from research colleagues at ESPOL and patients at the Luis Vernaza Hospital in Guayaquil, Ecuador. Each CSV file has 501 rows, of which the first one lists the electrodes from 0 to 15, and the remaining 500 rows correspond to 500 data recorded during the task is performed due to sample rate. In addition, each file has 17 columns, of which the first one indicates the sampling number and the remaining 16 columns represent 16 surface EEG electrodes. As a data recording equipment, the OpenBCI is used in a monopolar setup with a sampling rate of 125 Hz. This work includes statistical measures about the demographic information of all recruited test subjects. Finally, we outline the experimental methodology used to record EEG signals during upper and lower limb task execution. This dataset is called MILimbEEG and contains microvolt (µV) EEG signals acquired during motor and motor imagery tasks. The collected data may facilitate the evaluation of EEG signal detection and classification models dedicated to task recognition.\n
\n\n\n\n \n\n \n \n \n \n \n \n Myoelectric Prosthesis Using Sensor Fusion Between Electromyography and Pulse Oximetry Signals.\n \n \n \n \n\n\n \n Torres, K., Espinoza, J., Asanza, V., Lorente-Leyva, L.L., Peluffo-Ordóñez, D.\n\n\n \n\n\n\n Journal Européen des Systèmes Automatisés, 56(4): 641-649. 2023.\n \n\n\n\n
\n\n\n\n \n \nWebsite\n \n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
\n\n\n \n\n\n\n \n \n
Website\n \n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n\n
\n\n\n@article{\n title = {Myoelectric Prosthesis Using Sensor Fusion Between Electromyography and Pulse Oximetry Signals},\n type = {article},\n year = {2023},\n keywords = {artificial,bioelectric signal,electromyography,intelligence,myoelectric prosthesis,neural network,sensor fusion},\n pages = {641-649},\n volume = {56},\n websites = {https://www.iieta.org/journals/jesa/paper/10.18280/jesa.560413},\n id = {ce9d2762-9a97-3cf0-baf8-f8a298d55a52},\n created = {2023-10-02T16:22:16.903Z},\n file_attached = {false},\n profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},\n group_id = {17c612fe-279d-38a6-ac90-9228b535bc5e},\n last_modified = {2023-10-02T16:29:49.250Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n citation_key = {TorresK.EspinozaJ.AsanzaV.Lorente-LeyvaL.L.Peluffo-Ordonez2023},\n private_publication = {false},\n abstract = {Approximately 215,156 people in Ecuador grapple with physical disabilities, of whom nearly half fall within the 30 to 49% disability range, and a considerable number lack limbs. Moreover, there's been a surge in amputation cases, a trend linked to the increasing diabetes prevalence estimated at around 537 million cases by 2021 as per the International Diabetes Federation (IDF). While prosthetic solutions exist, they might incur high costs or offer constrained movement, even when more affordable. Thus, an alternative is proposed: a myoelectric upper limb prosthesis. This prosthesis would be maneuvered through electromyography and pulse oximetry signals, leveraging artificial intelligence methods. Employing a multi-layer neural network model, a structure comprising an input layer, four hidden layers, and an output layer, yields an impressive 93% prediction accuracy for user movement intentions. For AI model training, data from EMG and PPG sensors were recorded and scrutinized, leading to the condensation of classes from four to three. The model was embedded within an ESP32 C3 DevKit-M1 development board, and open-source blueprints facilitated the prosthesis's creation, complemented by supplementary components for electronics integration. The model attains a 93% precision in predicting classes, while the prosthesis's endurance spans approximately three hours and costs $295, equipped to handle diverse lightweight objects.},\n bibtype = {article},\n author = {Torres, K., Espinoza, J., Asanza, V., Lorente-Leyva, L.L., Peluffo-Ordóñez, D.H.},\n journal = {Journal Européen des Systèmes Automatisés},\n number = {4}\n}\n\n Approximately 215,156 people in Ecuador grapple with physical disabilities, of whom nearly half fall within the 30 to 49% disability range, and a considerable number lack limbs. Moreover, there's been a surge in amputation cases, a trend linked to the increasing diabetes prevalence estimated at around 537 million cases by 2021 as per the International Diabetes Federation (IDF). While prosthetic solutions exist, they might incur high costs or offer constrained movement, even when more affordable. Thus, an alternative is proposed: a myoelectric upper limb prosthesis. This prosthesis would be maneuvered through electromyography and pulse oximetry signals, leveraging artificial intelligence methods. Employing a multi-layer neural network model, a structure comprising an input layer, four hidden layers, and an output layer, yields an impressive 93% prediction accuracy for user movement intentions. For AI model training, data from EMG and PPG sensors were recorded and scrutinized, leading to the condensation of classes from four to three. The model was embedded within an ESP32 C3 DevKit-M1 development board, and open-source blueprints facilitated the prosthesis's creation, complemented by supplementary components for electronics integration. The model attains a 93% precision in predicting classes, while the prosthesis's endurance spans approximately three hours and costs $295, equipped to handle diverse lightweight objects.\n
\n\n\n\n \n\n \n \n \n \n \n \n Metodología para la identificación biométrica a partir de señales EEG en múltiples estados emocionales.\n \n \n \n \n\n\n \n Duque-Mejía, C.; Castro, A.; Duque, E.; Serna-Guarín, L.; Lorente-Leyva, L., L.; Peluffo-Ordóñez, D.; and Becerra, M., A.\n\n\n \n\n\n\n Revista Ibérica de Sistemas e Tecnologias de Informação, (E62): 281-288. 2023.\n \n\n\n\n
\n\n\n\n \n \nWebsite\n \n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n
\n\n\n\n\n\n \n\n\n\n \n \n
Website\n \n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n\n
\n\n\n@article{\n title = {Metodología para la identificación biométrica a partir de señales EEG en múltiples estados emocionales},\n type = {article},\n year = {2023},\n pages = {281-288},\n websites = {https://www.proquest.com/docview/2880949468?fromopenview=true&pq-origsite=gscholar},\n publisher = {Associação Ibérica de Sistemas e Tecnologias de Informacao},\n id = {d46236e1-20ba-3bdd-b5d6-f7a1c628b4c5},\n created = {2023-11-02T20:55:17.531Z},\n file_attached = {false},\n profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},\n group_id = {17c612fe-279d-38a6-ac90-9228b535bc5e},\n last_modified = {2023-11-02T20:55:17.531Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n citation_key = {duque2023metodologia},\n source_type = {article},\n private_publication = {false},\n abstract = {Biometric identification is in constant development as well as the systems that violate it, so it is an open field of research that requires new analysis and application of techniques to identify its vulnerabilities and improve its reliability levels. In this work we propose a biometric identification system based on EEG signals in multiple emotional states considering that the underlying dynamics of EEG signals vary according to the emotional state, which may affect the accuracy of classification models. El algoritmo de bosques aleatorios demostró la mayor exactitud (94%) superando los demás algoritmos. 2. Diferentes medidas fueron obtenidas para cada cada una de las señales EEG, a partir usando características derivadas de moving average, linear dynamic systems (LDS), rational asymmetry (RASM), computed differential symmetry (DASM), differential entropy (DE), differential caudality (DCAU), power spectral density (PSD), asymmetry (ASM).},\n bibtype = {article},\n author = {Duque-Mejía, Carolina and Castro, Andrés and Duque, Eduardo and Serna-Guarín, Leonardo and Lorente-Leyva, Leandro L and Peluffo-Ordóñez, Diego and Becerra, Miguel A},\n journal = {Revista Ibérica de Sistemas e Tecnologias de Informação},\n number = {E62}\n}\n\n Biometric identification is in constant development as well as the systems that violate it, so it is an open field of research that requires new analysis and application of techniques to identify its vulnerabilities and improve its reliability levels. In this work we propose a biometric identification system based on EEG signals in multiple emotional states considering that the underlying dynamics of EEG signals vary according to the emotional state, which may affect the accuracy of classification models. El algoritmo de bosques aleatorios demostró la mayor exactitud (94%) superando los demás algoritmos. 2. Diferentes medidas fueron obtenidas para cada cada una de las señales EEG, a partir usando características derivadas de moving average, linear dynamic systems (LDS), rational asymmetry (RASM), computed differential symmetry (DASM), differential entropy (DE), differential caudality (DCAU), power spectral density (PSD), asymmetry (ASM).\n
\n\n\n\n
\n\n \n \n 2022\n \n \n (5)\n \n \n
\n \n \n \n
\n \n \n\n \n \n \n \n \n \n Identification of Lower-Limb Motor Tasks via Brain-Computer Interfaces: A Topical Overview.\n \n \n \n \n\n\n \n Asanza, V.; Peláez, E.; Loayza, F.; Lorente-Leyva, L., L.; and Peluffo-Ordóñez, D., H.\n\n\n \n\n\n\n Sensors, 22(5). 2022.\n \n\n\n\n
\n\n\n\n \n \nWebsite\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n \n \n 5 downloads\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n
\n\n\n \n\n\n\n \n \n
Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n \n \n 5 downloads\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n\n
\n\n\n@article{\n title = {Identification of Lower-Limb Motor Tasks via Brain-Computer Interfaces: A Topical Overview},\n type = {article},\n year = {2022},\n volume = {22},\n websites = {https://www.mdpi.com/1424-8220/22/5/2028},\n id = {bbd45dcf-d5aa-3adc-873a-dec15f776564},\n created = {2022-03-05T01:20:50.626Z},\n file_attached = {false},\n profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},\n group_id = {17c612fe-279d-38a6-ac90-9228b535bc5e},\n last_modified = {2022-03-05T01:20:50.626Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n citation_key = {s22052028},\n source_type = {article},\n private_publication = {false},\n abstract = {Recent engineering and neuroscience applications have led to the development of brain–computer interface (BCI) systems that improve the quality of life of people with motor disabilities. In the same area, a significant number of studies have been conducted in identifying or classifying upper-limb movement intentions. On the contrary, few works have been concerned with movement intention identification for lower limbs. Notwithstanding, lower-limb neurorehabilitation is a major topic in medical settings, as some people suffer from mobility problems in their lower limbs, such as those diagnosed with neurodegenerative disorders, such as multiple sclerosis, and people with hemiplegia or quadriplegia. Particularly, the conventional pattern recognition (PR) systems are one of the most suitable computational tools for electroencephalography (EEG) signal analysis as the explicit knowledge of the features involved in the PR process itself is crucial for both improving signal classification performance and providing more interpretability. In this regard, there is a real need for outline and comparative studies gathering benchmark and state-of-art PR techniques that allow for a deeper understanding thereof and a proper selection of a specific technique. This study conducted a topical overview of specialized papers covering lower-limb motor task identification through PR-based BCI/EEG signal analysis systems. To do so, we first established search terms and inclusion and exclusion criteria to find the most relevant papers on the subject. As a result, we identified the 22 most relevant papers. Next, we reviewed their experimental methodologies for recording EEG signals during the execution of lower limb tasks. In addition, we review the algorithms used in the preprocessing, feature extraction, and classification stages. Finally, we compared all the algorithms and determined which of them are the most suitable in terms of accuracy.},\n bibtype = {article},\n author = {Asanza, Víctor and Peláez, Enrique and Loayza, Francis and Lorente-Leyva, Leandro L and Peluffo-Ordóñez, Diego H},\n doi = {10.3390/s22052028},\n journal = {Sensors},\n number = {5}\n}\n\n Recent engineering and neuroscience applications have led to the development of brain–computer interface (BCI) systems that improve the quality of life of people with motor disabilities. In the same area, a significant number of studies have been conducted in identifying or classifying upper-limb movement intentions. On the contrary, few works have been concerned with movement intention identification for lower limbs. Notwithstanding, lower-limb neurorehabilitation is a major topic in medical settings, as some people suffer from mobility problems in their lower limbs, such as those diagnosed with neurodegenerative disorders, such as multiple sclerosis, and people with hemiplegia or quadriplegia. Particularly, the conventional pattern recognition (PR) systems are one of the most suitable computational tools for electroencephalography (EEG) signal analysis as the explicit knowledge of the features involved in the PR process itself is crucial for both improving signal classification performance and providing more interpretability. In this regard, there is a real need for outline and comparative studies gathering benchmark and state-of-art PR techniques that allow for a deeper understanding thereof and a proper selection of a specific technique. This study conducted a topical overview of specialized papers covering lower-limb motor task identification through PR-based BCI/EEG signal analysis systems. To do so, we first established search terms and inclusion and exclusion criteria to find the most relevant papers on the subject. As a result, we identified the 22 most relevant papers. Next, we reviewed their experimental methodologies for recording EEG signals during the execution of lower limb tasks. In addition, we review the algorithms used in the preprocessing, feature extraction, and classification stages. Finally, we compared all the algorithms and determined which of them are the most suitable in terms of accuracy.\n
\n\n\n\n \n\n \n \n \n \n \n \n ECG-Based Heartbeat Classification for Arrhythmia Detection Using Artificial Neural Networks.\n \n \n \n \n\n\n \n Cepeda, E.; Sánchez-Pozo, N., N.; Peluffo-Ordóñez, D., H.; González-Vergara, J.; and Almeida-Galárraga, D.\n\n\n \n\n\n\n In Gervasi, O.; Murgante, B.; Hendrix, E., M., T.; Taniar, D.; and Apduhan, B., O., editor(s), Computational Science and Its Applications -- ICCSA 2022, pages 247-259, 2022. Springer International Publishing\n \n\n\n\n
\n\n\n\n \n \nWebsite\n \n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n \n \n 4 downloads\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n
\n\n\n \n\n\n\n \n \n
Website\n \n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n \n \n 4 downloads\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n\n
\n\n\n@inproceedings{\n title = {ECG-Based Heartbeat Classification for Arrhythmia Detection Using Artificial Neural Networks},\n type = {inproceedings},\n year = {2022},\n pages = {247-259},\n websites = {https://link.springer.com/chapter/10.1007/978-3-031-10450-3_20},\n publisher = {Springer International Publishing},\n city = {Cham},\n id = {0598d3e7-7f9d-3aaa-b28c-31d7bc140797},\n created = {2022-07-16T03:44:55.045Z},\n file_attached = {false},\n profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},\n group_id = {17c612fe-279d-38a6-ac90-9228b535bc5e},\n last_modified = {2022-07-16T03:44:55.045Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n citation_key = {10.1007/978-3-031-10450-3_20},\n source_type = {inproceedings},\n private_publication = {false},\n abstract = {Cardiovascular disease (CVD) has quickly grown in prevalence over the previous decade, becoming the major cause of human morbidity on a global scale. Due to the massive number of ECG data, manual analysis is regarded as a time-consuming, costly and prone to human error task. In the other hand, computational systems based on biomedical signal processing and machine learning techniques might be suited for supporting arrhythmia diagnostic processes, while solving some of those issues. In general, such systems involve five stages: acquisition, preprocessing, segmentation, characterization, and classification. Yet numerous fundamental aspects remain unresolved, including sensitivity to signal fluctuation, accuracy, computing cost, generalizability, and interpretability. In this context, the present study offers a comparative analysis of ECG signal classification using two artificial neural networks created by different machine learning frameworks. The neural nets were built into a pipeline that aims to strike an appropriate balance among signal robustness, variability, and accuracy. The proposed approach reaches up to 99\\% of overall accuracy for each register while keeping the computational cost low.},\n bibtype = {inproceedings},\n author = {Cepeda, Eduardo and Sánchez-Pozo, Nadia N and Peluffo-Ordóñez, Diego H and González-Vergara, Juan and Almeida-Galárraga, Diego},\n editor = {Gervasi, Osvaldo and Murgante, Beniamino and Hendrix, Eligius M T and Taniar, David and Apduhan, Bernady O},\n booktitle = {Computational Science and Its Applications -- ICCSA 2022}\n}\n\n Cardiovascular disease (CVD) has quickly grown in prevalence over the previous decade, becoming the major cause of human morbidity on a global scale. Due to the massive number of ECG data, manual analysis is regarded as a time-consuming, costly and prone to human error task. In the other hand, computational systems based on biomedical signal processing and machine learning techniques might be suited for supporting arrhythmia diagnostic processes, while solving some of those issues. In general, such systems involve five stages: acquisition, preprocessing, segmentation, characterization, and classification. Yet numerous fundamental aspects remain unresolved, including sensitivity to signal fluctuation, accuracy, computing cost, generalizability, and interpretability. In this context, the present study offers a comparative analysis of ECG signal classification using two artificial neural networks created by different machine learning frameworks. The neural nets were built into a pipeline that aims to strike an appropriate balance among signal robustness, variability, and accuracy. The proposed approach reaches up to 99\\% of overall accuracy for each register while keeping the computational cost low.\n
\n\n\n\n \n\n \n \n \n \n \n \n A Computer Vision Model to Identify the Incorrect Use of Face Masks for COVID-19 Awareness.\n \n \n \n \n\n\n \n Crespo, F.; Crespo, A.; Sierra-Martínez, L., M.; Peluffo-Ordóñez, D., H.; and Morocho-Cayamcela, M., E.\n\n\n \n\n\n\n Applied Sciences, 12(14). 2022.\n \n\n\n\n
\n\n\n\n \n \nWebsite\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n
\n\n\n \n\n\n\n \n \n
Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n\n
\n\n\n@article{\n title = {A Computer Vision Model to Identify the Incorrect Use of Face Masks for COVID-19 Awareness},\n type = {article},\n year = {2022},\n volume = {12},\n websites = {https://www.mdpi.com/2076-3417/12/14/6924},\n id = {629fb659-f68b-3e1e-9381-a9aac1b7c20d},\n created = {2022-07-24T01:25:37.860Z},\n file_attached = {false},\n profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},\n group_id = {17c612fe-279d-38a6-ac90-9228b535bc5e},\n last_modified = {2022-07-24T01:25:37.860Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n citation_key = {app12146924},\n source_type = {article},\n private_publication = {false},\n abstract = {Face mask detection has become a great challenge in computer vision, demanding the coalition of technology with COVID-19 awareness. Researchers have proposed deep learning models to detect the use of face masks. However, the incorrect use of a face mask can be as harmful as not wearing any protection at all. In this paper, we propose a compound convolutional neural network (CNN) architecture based on two computer vision tasks: object localization to discover faces in images/videos, followed by an image classification CNN to categorize the faces and show if someone is using a face mask correctly, incorrectly, or not at all. The first CNN is built upon RetinaFace, a model to detect faces in images, whereas the second CNN uses a ResNet-18 architecture as a classification backbone. Our model enables an accurate identification of people who are not correctly following the COVID-19 healthcare recommendations on face mask use. To enable further global use of our technology, we have released both the dataset used to train the classification model and our proposed computer vision pipeline to the public, and optimized it for embedded systems deployment.},\n bibtype = {article},\n author = {Crespo, Fabricio and Crespo, Anthony and Sierra-Martínez, Luz Marina and Peluffo-Ordóñez, Diego Hernán and Morocho-Cayamcela, Manuel Eugenio},\n doi = {10.3390/app12146924},\n journal = {Applied Sciences},\n number = {14}\n}\n\n Face mask detection has become a great challenge in computer vision, demanding the coalition of technology with COVID-19 awareness. Researchers have proposed deep learning models to detect the use of face masks. However, the incorrect use of a face mask can be as harmful as not wearing any protection at all. In this paper, we propose a compound convolutional neural network (CNN) architecture based on two computer vision tasks: object localization to discover faces in images/videos, followed by an image classification CNN to categorize the faces and show if someone is using a face mask correctly, incorrectly, or not at all. The first CNN is built upon RetinaFace, a model to detect faces in images, whereas the second CNN uses a ResNet-18 architecture as a classification backbone. Our model enables an accurate identification of people who are not correctly following the COVID-19 healthcare recommendations on face mask use. To enable further global use of our technology, we have released both the dataset used to train the classification model and our proposed computer vision pipeline to the public, and optimized it for embedded systems deployment.\n
\n\n\n\n \n\n \n \n \n \n \n \n Heart Rate Detection using a Piezoelectric Ceramic Sensor: Preliminary results.\n \n \n \n \n\n\n \n E Cepeda; Peluffo-Ordóñez, D., H.; Rosero-Montalvo, P.; Becerra, M., A.; Umaquinga-Criollo, A., C.; and Ramírez., L.\n\n\n \n\n\n\n Bionatura, 7(30). 2022.\n \n\n\n\n
\n\n\n\n \n \nWebsite\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n
\n\n\n \n\n\n\n \n \n
Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n\n
\n\n\n\n@article{\n title = {Heart Rate Detection using a Piezoelectric Ceramic Sensor: Preliminary results},\n type = {article},\n year = {2022},\n volume = {7},\n websites = {http://revistabionatura.com/2022.07.03.30.html},\n id = {12712cad-763d-397c-9567-2f378f4337c3},\n created = {2022-08-25T05:37:13.614Z},\n file_attached = {false},\n profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},\n group_id = {17c612fe-279d-38a6-ac90-9228b535bc5e},\n last_modified = {2022-08-25T05:37:13.614Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n citation_key = {bionatura2022},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {E Cepeda, undefined and Peluffo-Ordóñez, D H and Rosero-Montalvo, P and Becerra, M A and Umaquinga-Criollo, A C and Ramírez., L},\n doi = {10.21931/RB/2022.07.03.30},\n journal = {Bionatura},\n number = {30}\n}\n\n \n\n \n \n \n \n \n \n Impact of ECG Signal Preprocessing and Filtering on Arrhythmia Classification Using Machine Learning Techniques.\n \n \n \n \n\n\n \n Andrés Ayala-Cucas, H.; Mora-Piscal, E., A.; Mayorca-Torres, D.; Peluffo-Ordoñez, D., H.; and León-Salas, A., J.\n\n\n \n\n\n\n In Bicharra Garcia, A., C.; Ferro, M.; and Rodríguez Ribón, J., C., editor(s), Advances in Artificial Intelligence -- IBERAMIA 2022, pages 27-40, 2022. Springer International Publishing\n \n\n\n\n
\n\n\n\n \n \nWebsite\n \n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n
\n\n\n\n\n\n \n\n\n\n \n \n
Website\n \n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n\n
\n\n\n@inproceedings{\n title = {Impact of ECG Signal Preprocessing and Filtering on Arrhythmia Classification Using Machine Learning Techniques},\n type = {inproceedings},\n year = {2022},\n pages = {27-40},\n websites = {https://link.springer.com/chapter/10.1007/978-3-031-22419-5_3},\n publisher = {Springer International Publishing},\n city = {Cham},\n id = {7208c82f-19b0-374c-ac76-96fdc1449212},\n created = {2023-01-04T00:03:18.342Z},\n file_attached = {false},\n profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},\n group_id = {17c612fe-279d-38a6-ac90-9228b535bc5e},\n last_modified = {2023-01-04T00:03:18.342Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n citation_key = {10.1007/978-3-031-22419-5_3},\n source_type = {inproceedings},\n private_publication = {false},\n abstract = {Cardiac arrhythmias are heartbeat disorders in which the electrical impulses that coordinate the cardiac cycle malfunction. The heart's electrical activity is recorded using electrocardiography (ECG), a non-invasive method that helps diagnose several cardiovascular diseases. However, interpretation of ECG signals can be difficult due to the presence of noise, the irregularity of the heartbeat, and their nonstationary nature. Hence, the use of computational systems is required to support the diagnosis of cardiac arrhythmias. The main challenge in developing AI-assisted ECG systems is achieving accuracies suitable for application in clinical settings. Therefore, this paper introduces a software tool for classifying cardiac arrhythmias in ECG recordings that uses filtering, segmentation, and feature extraction of the QRS interval. We use the MIT-BIH Arrhythmia Database, which has 48 records of five different types of arrhythmias. We evaluate the data using supervised machine learning techniques such as k-Nearest Neighbors (KNN), Random Forest (RF), Multilayer Perceptron (MLP), and the Naive Bayesian classifier. This paper shows the impact of selecting and employing filtering and feature extraction methods on the performance of supervised machine learning algorithms compared with benchmark approaches.},\n bibtype = {inproceedings},\n author = {Andrés Ayala-Cucas, Hermes and Mora-Piscal, Edison Alexander and Mayorca-Torres, Dagoberto and Peluffo-Ordoñez, Diego Hernán and León-Salas, Alejandro J},\n editor = {Bicharra Garcia, Ana Cristina and Ferro, Mariza and Rodríguez Ribón, Julio Cesar},\n booktitle = {Advances in Artificial Intelligence -- IBERAMIA 2022}\n}\n\n Cardiac arrhythmias are heartbeat disorders in which the electrical impulses that coordinate the cardiac cycle malfunction. The heart's electrical activity is recorded using electrocardiography (ECG), a non-invasive method that helps diagnose several cardiovascular diseases. However, interpretation of ECG signals can be difficult due to the presence of noise, the irregularity of the heartbeat, and their nonstationary nature. Hence, the use of computational systems is required to support the diagnosis of cardiac arrhythmias. The main challenge in developing AI-assisted ECG systems is achieving accuracies suitable for application in clinical settings. Therefore, this paper introduces a software tool for classifying cardiac arrhythmias in ECG recordings that uses filtering, segmentation, and feature extraction of the QRS interval. We use the MIT-BIH Arrhythmia Database, which has 48 records of five different types of arrhythmias. We evaluate the data using supervised machine learning techniques such as k-Nearest Neighbors (KNN), Random Forest (RF), Multilayer Perceptron (MLP), and the Naive Bayesian classifier. This paper shows the impact of selecting and employing filtering and feature extraction methods on the performance of supervised machine learning algorithms compared with benchmark approaches.\n
\n\n\n\n
\n\n \n \n 2021\n \n \n (4)\n \n \n
\n \n \n \n
\n \n \n\n \n \n \n \n \n \n Design of a low computational cost prototype for cardiac arrhythmia detection [Diseño de un prototipo de bajo coste computacional para detección de arritmias cardiacas].\n \n \n \n \n\n\n \n Vargas-Muñoz, A., M.; Chamorro-Sangoquiza, D., C.; Umaquinga-Criollo, A., C.; Rosero-Montalvo, P., D.; Becerra, M., A.; Peluffo-Ordóñez, D., H.; and Revelo-Fuelagán, E., J.\n\n\n \n\n\n\n RISTI - Revista Iberica de Sistemas e Tecnologias de Informacao, 2021(E40): 470-479. 2021.\n \n\n\n\n
\n\n\n\n \n \nWebsite\n \n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n
\n\n\n \n\n\n\n \n \n
Website\n \n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n\n
\n\n\n@article{\n title = {Design of a low computational cost prototype for cardiac arrhythmia detection [Diseño de un prototipo de bajo coste computacional para detección de arritmias cardiacas]},\n type = {article},\n year = {2021},\n pages = {470-479},\n volume = {2021},\n websites = {https://search.proquest.com/openview/d9dffd8a726c99f54a47adaf372e13b8/1?pq-origsite=gscholar&cbl=1006393},\n id = {7aeb058d-c66e-30c1-9ea0-67357cdd8f76},\n created = {2022-02-02T07:53:26.261Z},\n file_attached = {false},\n profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},\n group_id = {17c612fe-279d-38a6-ac90-9228b535bc5e},\n last_modified = {2022-02-02T07:53:26.261Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n citation_key = {Vargas-Muñoz2021470},\n source_type = {article},\n private_publication = {false},\n abstract = {This research presents the design of a prototype for the detection of cardiac arrhythmias that incorporates an embedded low-cost computational system in an environment of limited computational resources capable of analyzing characteristics of the QRS complexes. To do this, a strategy for classifying normal and pathological heart beats is developed in long-term electrocardiographic recordings (Holter), which are representative waves of the beat and their analysis allows identifying ventricular arrhythmias. For the development of this initial prototype, it is found that the use of the k nearest neighbors (k-NN) algorithm together with a stage of selection of variables from the training set is a good alternative and represents an important contribution of this work to experimental level. The experiments were carried out on the basis of cardiac arrhythmia data from the Massachusetts Institute of Technology (MIT). The results are satisfactory and promising. © 2021, Associacao Iberica de Sistemas e Tecnologias de Informacao. All rights reserved.},\n bibtype = {article},\n author = {Vargas-Muñoz, A M and Chamorro-Sangoquiza, D C and Umaquinga-Criollo, A C and Rosero-Montalvo, P D and Becerra, M A and Peluffo-Ordóñez, D H and Revelo-Fuelagán, E J},\n journal = {RISTI - Revista Iberica de Sistemas e Tecnologias de Informacao},\n number = {E40}\n}\n\n This research presents the design of a prototype for the detection of cardiac arrhythmias that incorporates an embedded low-cost computational system in an environment of limited computational resources capable of analyzing characteristics of the QRS complexes. To do this, a strategy for classifying normal and pathological heart beats is developed in long-term electrocardiographic recordings (Holter), which are representative waves of the beat and their analysis allows identifying ventricular arrhythmias. For the development of this initial prototype, it is found that the use of the k nearest neighbors (k-NN) algorithm together with a stage of selection of variables from the training set is a good alternative and represents an important contribution of this work to experimental level. The experiments were carried out on the basis of cardiac arrhythmia data from the Massachusetts Institute of Technology (MIT). The results are satisfactory and promising. © 2021, Associacao Iberica de Sistemas e Tecnologias de Informacao. All rights reserved.\n
\n\n\n\n \n\n \n \n \n \n \n \n Addressing the Data Acquisition Paradigm in the Early Detection of Pediatric Foot Deformities.\n \n \n \n \n\n\n \n Rosero-montalvo, P., D.; Fuentes-hernández, E., A.; and Morocho-cayamcela, M., E.\n\n\n \n\n\n\n Sensors,1-17. 2021.\n \n\n\n\n
\n\n\n\n \n \n![\"Addressing](\"//bibbase.org/img/filetypes/pdf.svg\"\n\t "\\"Paper\\"\n\t")
Paper\n \n \n \n Website\n \n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
\n\n\n \n\n\n\n \n \n
Paper\n \n \n \n Website\n \n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n\n
\n\n\n@article{\n title = {Addressing the Data Acquisition Paradigm in the Early Detection of Pediatric Foot Deformities},\n type = {article},\n year = {2021},\n keywords = {academic editor,children,data analysis,embedded systems,emmanouil,machine learning,plantar pressure},\n pages = {1-17},\n websites = {https://www.mdpi.com/1424-8220/21/13/4422},\n id = {852b389f-cb76-30d2-95fa-9a60111154ef},\n created = {2022-02-02T07:53:26.630Z},\n file_attached = {true},\n profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},\n group_id = {17c612fe-279d-38a6-ac90-9228b535bc5e},\n last_modified = {2022-02-02T07:53:31.285Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {The analysis of plantar pressure through podometry has allowed analyzing and detecting different types of disorders and treatments in child patients. Early detection of an inadequate distribution of the patient’s weight can prevent serious injuries to the knees and lower spine. In this paper, an embedded system capable of detecting the presence of normal, flat, or arched footprints using resistive pressure sensors was proposed. For this purpose, both hardware- and software-related criteria were studied for an improved data acquisition through signal coupling and filtering processes. Subsequently, learning algorithms allowed us to estimate the type of footprint biomechanics in preschool and school children volunteers. As a result, the proposed algorithm achieved an overall classification accuracy of 97.2%. A flat feet share of 60% was encountered in a sample of 1000 preschool children. Similarly, flat feet were observed in 52% of a sample of 600 school children.},\n bibtype = {article},\n author = {Rosero-montalvo, Paul D and Fuentes-hernández, Edison A and Morocho-cayamcela, Manuel E},\n journal = {Sensors}\n}\n\n The analysis of plantar pressure through podometry has allowed analyzing and detecting different types of disorders and treatments in child patients. Early detection of an inadequate distribution of the patient’s weight can prevent serious injuries to the knees and lower spine. In this paper, an embedded system capable of detecting the presence of normal, flat, or arched footprints using resistive pressure sensors was proposed. For this purpose, both hardware- and software-related criteria were studied for an improved data acquisition through signal coupling and filtering processes. Subsequently, learning algorithms allowed us to estimate the type of footprint biomechanics in preschool and school children volunteers. As a result, the proposed algorithm achieved an overall classification accuracy of 97.2%. A flat feet share of 60% was encountered in a sample of 1000 preschool children. Similarly, flat feet were observed in 52% of a sample of 600 school children.\n
\n\n\n\n \n\n \n \n \n \n \n \n Classification of Subjects with Parkinson’s Disease using Finger Tapping Dataset.\n \n \n \n \n\n\n \n Asanza, V.; Sánchez-Pozo, N., N.; Lorente-Leyva, L., L.; Peluffo-Ordóñez, D., H.; Loayza, F., R.; and Peláez, E.\n\n\n \n\n\n\n IFAC-PapersOnLine, 54(15): 376-381. 2021.\n \n\n\n\n
\n\n\n\n \n \nWebsite\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
\n\n\n \n\n\n\n \n \n
Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n\n\n\n\n
\n\n\n@article{\n title = {Classification of Subjects with Parkinson’s Disease using Finger Tapping Dataset},\n type = {article},\n year = {2021},\n keywords = {Classification,Finger Tapping,Machine Learning,Parkinson’s disease},\n pages = {376-381},\n volume = {54},\n websites = {https://www.sciencedirect.com/science/article/pii/S2405896321016906},\n id = {3c438045-54ea-3759-9692-44d0aef7a3d8},\n created = {2022-02-02T07:53:26.900Z},\n file_attached = {false},\n profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},\n group_id = {17c612fe-279d-38a6-ac90-9228b535bc5e},\n last_modified = {2022-02-02T07:53:26.900Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n citation_key = {ASANZA2021376},\n source_type = {article},\n private_publication = {false},\n abstract = {Parkinson’s disease is the second most common neurodegenerative disorder and affects more than 7 million people globally. In this work, we classify subjects with Parkinson’s disease using data from finger-tapping on a keyboard. We use a free database by Physionet with more than 9 million records, preprocessed to delete atypical data. In the feature extraction stage, we obtained 48 features. We use Google Colaboratory to train, validate, and test nine supervised learning algorithms that detect the disease. As a result, we achieve a degree of accuracy higher than 98%.},\n bibtype = {article},\n author = {Asanza, Víctor and Sánchez-Pozo, Nadia N and Lorente-Leyva, Leandro L and Peluffo-Ordóñez, Diego Hernan and Loayza, Fancis R and Peláez, Enrique},\n doi = {https://doi.org/10.1016/j.ifacol.2021.10.285},\n journal = {IFAC-PapersOnLine},\n number = {15}\n}\n\n Parkinson’s disease is the second most common neurodegenerative disorder and affects more than 7 million people globally. In this work, we classify subjects with Parkinson’s disease using data from finger-tapping on a keyboard. We use a free database by Physionet with more than 9 million records, preprocessed to delete atypical data. In the feature extraction stage, we obtained 48 features. We use Google Colaboratory to train, validate, and test nine supervised learning algorithms that detect the disease. As a result, we achieve a degree of accuracy higher than 98%.\n
\n\n\n\n \n\n \n \n \n \n \n \n Comparison of current deep convolutional neural networks for the segmentation of breast masses in mammograms.\n \n \n \n \n\n\n \n Anaya-Isaza, A.; Mera-Jiménez, L.; Cabrera-Chavarro, J.; Guachi-Guachi, L.; Peluffo-Ordóñez, D.; and Rios-Patiño, J.\n\n\n \n\n\n\n IEEE Access. 2021.\n \n\n\n\n
\n\n\n\n \n \nWebsite\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n
\n\n\n\n\n\n \n\n\n\n \n \n
Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n\n
\n\n\n@article{\n title = {Comparison of current deep convolutional neural networks for the segmentation of breast masses in mammograms},\n type = {article},\n year = {2021},\n websites = {https://ieeexplore.ieee.org/document/9614200},\n id = {54d43dc8-44de-3ed4-8550-6a20805f7648},\n created = {2022-02-02T07:53:27.309Z},\n file_attached = {false},\n profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},\n group_id = {17c612fe-279d-38a6-ac90-9228b535bc5e},\n last_modified = {2022-02-02T07:53:27.309Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n citation_key = {9614200},\n source_type = {article},\n private_publication = {false},\n abstract = {Breast cancer causes approximately 684,996 deaths worldwide, making it the leading cause of female cancer mortality. However, these figures can be reduced with early diagnosis through mammographic imaging, allowing for the timely and effective treatment of this disease. To establish the best tools for contributing to the automatic diagnosis of breast cancer, different deep learning (DL) architectures were compared in terms of breast lesion segmentation, lesion type classification, and degree of suspicion of malignancy tests. The tasks were completed with state-of-the-art architectures and backbones. Initially, during segmentation, the base UNet, Visual Geometry Group 19 (VGG19), InceptionResNetV2, EfficientNet, MobileNetv2, ResNet, ResNeXt, MultiResUNet, linkNet-VGG19, DenseNet, SEResNet and SeResNeXt architectures were compared, where “Res” denotes a residual network. In addition, training was performed with 5 of the most advanced loss functions and validated by the Dice coefficient, sensitivity, and specificity. The proposed models achieved Dice values above 90%, with the EfficientNet architecture achieving 94.75% and 99% accuracy on the two tasks. Subsequently, classification was addressed with the ResNet50V2, VGG19, InceptionResNetV2, DenseNet121, InceptionV3, Xception and EfficientNetB7 networks. The proposed models achieved 96.97% and 97.73% accuracy through the VGG19 and ResNet50V2 networks on the lesion classification and degree of suspicion tasks, respectively. All three tasks were addressed with open-access databases, including the Digital Database for Screening Mammography (DDSM), the Mammographic Image Analysis Society (MIAS) database, and INbreast.},\n bibtype = {article},\n author = {Anaya-Isaza, Andrés and Mera-Jiménez, Leonel and Cabrera-Chavarro, Johan and Guachi-Guachi, Lorena and Peluffo-Ordóñez, Diego and Rios-Patiño, Jorge},\n doi = {10.1109/ACCESS.2021.3127862},\n journal = {IEEE Access}\n}\n\n Breast cancer causes approximately 684,996 deaths worldwide, making it the leading cause of female cancer mortality. However, these figures can be reduced with early diagnosis through mammographic imaging, allowing for the timely and effective treatment of this disease. To establish the best tools for contributing to the automatic diagnosis of breast cancer, different deep learning (DL) architectures were compared in terms of breast lesion segmentation, lesion type classification, and degree of suspicion of malignancy tests. The tasks were completed with state-of-the-art architectures and backbones. Initially, during segmentation, the base UNet, Visual Geometry Group 19 (VGG19), InceptionResNetV2, EfficientNet, MobileNetv2, ResNet, ResNeXt, MultiResUNet, linkNet-VGG19, DenseNet, SEResNet and SeResNeXt architectures were compared, where “Res” denotes a residual network. In addition, training was performed with 5 of the most advanced loss functions and validated by the Dice coefficient, sensitivity, and specificity. The proposed models achieved Dice values above 90%, with the EfficientNet architecture achieving 94.75% and 99% accuracy on the two tasks. Subsequently, classification was addressed with the ResNet50V2, VGG19, InceptionResNetV2, DenseNet121, InceptionV3, Xception and EfficientNetB7 networks. The proposed models achieved 96.97% and 97.73% accuracy through the VGG19 and ResNet50V2 networks on the lesion classification and degree of suspicion tasks, respectively. All three tasks were addressed with open-access databases, including the Digital Database for Screening Mammography (DDSM), the Mammographic Image Analysis Society (MIAS) database, and INbreast.\n
\n\n\n\n
\n\n \n \n 2020\n \n \n (1)\n \n \n
\n \n \n \n
\n \n \n\n \n \n \n \n \n \n Stochastic-and neuro-fuzzy-analysis-based characterization and classification of 4-channel phonocardiograms for cardiac murmur detection.\n \n \n \n \n\n\n \n Becerra, M., A.; Delgado-Trejos, E.; Mejía-Arboleda, C.; Peluffo-Ordóñez, D., H.; and Umaquinga-Criollo, A., C.\n\n\n \n\n\n\n RISTI - Revista Iberica de Sistemas e Tecnologias de Informacao. 2020.\n \n\n\n\n
\n\n\n\n \n \nWebsite\n \n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n \n \n 3 downloads\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
\n\n\n\n\n\n \n\n\n\n \n \n
Website\n \n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n \n \n 3 downloads\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n\n
\n\n\n@article{\n title = {Stochastic-and neuro-fuzzy-analysis-based characterization and classification of 4-channel phonocardiograms for cardiac murmur detection},\n type = {article},\n year = {2020},\n keywords = {ANFIS,Cardiac murmur,Empirical mode decomposition,Hidden markov models,Phonocardiogram},\n websites = {https://search.proquest.com/docview/2451419849/fulltextPDF/F4AF5E590BD14D5EPQ/8},\n id = {8995bd7f-4e40-3962-9bd1-cbbcc4a3f782},\n created = {2022-02-02T07:53:28.044Z},\n file_attached = {false},\n profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},\n group_id = {17c612fe-279d-38a6-ac90-9228b535bc5e},\n last_modified = {2022-02-02T07:53:28.044Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n citation_key = {Becerra2020},\n private_publication = {false},\n abstract = {Cardiac murmurs (CMs) are the most common heart’s diseases that are typically diagnosed from phonocardiogram (PCG) and echocardiogram tests-often supported by computerized systems. Research works have traditionally addressed the automatic CM diagnosis with no distinctively use of the four auscultation areas (one of each cardiac valve), resulting-most probably-in a constrained, nonimpartial diagnostic procedure. This study presents a comparison among four different CM detection systems from a 4-channel PCG. We first evaluate the acoustic characteristics derived from Mel-Frequency Cepstral Coefficients, Empirical Mode Decomposition (EMD), and statistical measures. Secondly, a relevance analysis is carried out using Fuzzy Rough Feature Selection. Thirdly, Hidden Markov Models (HMM), Adaptative Neuro-Fuzzy Inference System (ANFIS), Naïve Bayes, and Gaussian Mixture Model were applied for classification and validated using a 50fold cross-validation procedure with a 70/30 split demonstrating the functionality and capability of EMD, Hidden Markov Model and ANFIS for CM classification.},\n bibtype = {article},\n author = {Becerra, Miguel A. and Delgado-Trejos, Edilson and Mejía-Arboleda, Cristian and Peluffo-Ordóñez, Diego H. and Umaquinga-Criollo, Ana C.},\n journal = {RISTI - Revista Iberica de Sistemas e Tecnologias de Informacao}\n}\n\n Cardiac murmurs (CMs) are the most common heart’s diseases that are typically diagnosed from phonocardiogram (PCG) and echocardiogram tests-often supported by computerized systems. Research works have traditionally addressed the automatic CM diagnosis with no distinctively use of the four auscultation areas (one of each cardiac valve), resulting-most probably-in a constrained, nonimpartial diagnostic procedure. This study presents a comparison among four different CM detection systems from a 4-channel PCG. We first evaluate the acoustic characteristics derived from Mel-Frequency Cepstral Coefficients, Empirical Mode Decomposition (EMD), and statistical measures. Secondly, a relevance analysis is carried out using Fuzzy Rough Feature Selection. Thirdly, Hidden Markov Models (HMM), Adaptative Neuro-Fuzzy Inference System (ANFIS), Naïve Bayes, and Gaussian Mixture Model were applied for classification and validated using a 50fold cross-validation procedure with a 70/30 split demonstrating the functionality and capability of EMD, Hidden Markov Model and ANFIS for CM classification.\n
\n\n\n\n
\n\n \n \n 2019\n \n \n (2)\n \n \n
\n \n \n \n
\n \n \n\n \n \n \n \n \n \n Multi-target tracking for sperm motility measurement using the kalman filter and JPDAF: Preliminary results.\n \n \n \n \n\n\n \n Mayorca-Torres, D.; Guerrero-Chapal, H.; Mejía-Manzano, J.; Lopez-Mesa, D.; Peluffo-Ordoñez, D., H.; and Salazar-Castro, J., A.\n\n\n \n\n\n\n RISTI - Revista Iberica de Sistemas e Tecnologias de Informacao. 2019.\n \n\n\n\n
\n\n\n\n \n \nWebsite\n \n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
\n\n\n \n\n\n\n \n \n
Website\n \n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n\n
\n\n\n@article{\n title = {Multi-target tracking for sperm motility measurement using the kalman filter and JPDAF: Preliminary results},\n type = {article},\n year = {2019},\n keywords = {JPDAF,Kalman filter,Morphology,Motility,Spermatozoa},\n websites = {https://search.proquest.com/openview/69fcef4b61d6ec863099124a9c2fe66f},\n id = {22e43acc-2bb1-388e-a1a1-8a3f22708f29},\n created = {2022-02-02T07:53:28.681Z},\n file_attached = {false},\n profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},\n group_id = {17c612fe-279d-38a6-ac90-9228b535bc5e},\n last_modified = {2022-02-02T07:53:28.681Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n citation_key = {Mayorca-Torres2019a},\n private_publication = {false},\n abstract = {The determination of sperm motility characteristics is of great importance for the specification of fertility in men. The semengram is the main diagnostic test to confirm semen quality. Currently, many fertility laboratories use visual assistance techniques to evaluate by using the Makler counting chamber, where motility and sperm count analysis can be performed. This research project proposes a method that allows the quantification of motility through the use of the probabilistic filter (JPDAF) based on the Kalman filter. This research requires the stages of segmentation, feature extraction and development of tracking algorithms for the association of sperm trajectories when there are multiple objectives. A total of 200 individual sperm were selected and the effectiveness for sperm classification was determined according to the mobility categories established by the WHO, obtaining an average value of 93.5% for the categories (A, B, C and D).},\n bibtype = {article},\n author = {Mayorca-Torres, Dagoberto and Guerrero-Chapal, H. and Mejía-Manzano, Julio and Lopez-Mesa, Diana and Peluffo-Ordoñez, Diego H. and Salazar-Castro, José A.},\n journal = {RISTI - Revista Iberica de Sistemas e Tecnologias de Informacao}\n}\n\n The determination of sperm motility characteristics is of great importance for the specification of fertility in men. The semengram is the main diagnostic test to confirm semen quality. Currently, many fertility laboratories use visual assistance techniques to evaluate by using the Makler counting chamber, where motility and sperm count analysis can be performed. This research project proposes a method that allows the quantification of motility through the use of the probabilistic filter (JPDAF) based on the Kalman filter. This research requires the stages of segmentation, feature extraction and development of tracking algorithms for the association of sperm trajectories when there are multiple objectives. A total of 200 individual sperm were selected and the effectiveness for sperm classification was determined according to the mobility categories established by the WHO, obtaining an average value of 93.5% for the categories (A, B, C and D).\n
\n\n\n\n \n\n \n \n \n \n \n \n Cardiac Murmur Effects on Automatic Segmentation of ECG Signals for Biometric Identification: Preliminary Study.\n \n \n \n \n\n\n \n Duque-Mejía, C.; Becerra, M., A.; Zapata-Hernández, C.; Mejia-Arboleda, C.; Castro-Ospina, A., E.; Delgado-Trejos, E.; Peluffo-Ordóñez, D., H.; Rosero-Montalvo, P.; and Revelo-Fuelagán, J.\n\n\n \n\n\n\n In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pages 269-279, 2019. \n \n\n\n\n
\n\n\n\n \n \nWebsite\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
\n\n\n\n\n\n \n\n\n\n \n \n
Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n\n
\n\n\n@inproceedings{\n title = {Cardiac Murmur Effects on Automatic Segmentation of ECG Signals for Biometric Identification: Preliminary Study},\n type = {inproceedings},\n year = {2019},\n keywords = {Automatic segmentation,Biometric,Electrocardiographic signal,Heart murmur,Pattern recognition},\n pages = {269-279},\n websites = {https://link.springer.com/chapter/10.1007/978-3-030-14799-0_23,http://link.springer.com/10.1007/978-3-030-14799-0_23},\n id = {7711531c-ed94-3253-8df5-11b4e755c243},\n created = {2022-02-02T07:53:29.014Z},\n file_attached = {false},\n profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},\n group_id = {17c612fe-279d-38a6-ac90-9228b535bc5e},\n last_modified = {2022-02-02T07:53:29.014Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n citation_key = {Duque-Mejia2019},\n private_publication = {false},\n abstract = {Biometric identification or authentication is a pattern recognition process, which is carried out acquiring different measures of human beings to distinguish them. Fingerprint and eye iris are the most known and used biometric techniques; nevertheless, also they are the most vulnerable to counterfeiting. Consequently, nowadays research has been focused on physiological signals and behavioral traits for biometric identification because these allow not only the authentication but also determine that the subject is alive. Electrocardiographic signals (ECG-S) have been studied for biometric identification demonstrating their capability. Taking into account that some pathologies are detected using ECG-S, these can affect the results of biometric identification; nonetheless, some diseases such as cardiac murmurs are not detected by ECG-S, but they can distort their morphology. Therefore, these signals must be analyzed considering different pathologies. In this paper, a biometric study was carried out from 40 subjects (20 with cardiac murmurs and 20 without cardiac affections). First, the ECG-S were preprocessed and segmented using the fast method for detecting T waves with annotation of P and T waves, then feature extraction was carried out using discrete wavelet transform (DWT), maximal overlap DWT, cepstral coefficients, and statistical measures. Then, rough set and relief F algorithms were applied to datasets (pathological and normal signals) for attribute reduction. Finally, multiple classifiers and combinations of them were tested. The results of the segmentation were analyzed achieving low results for signals affected by cardiac murmurs. On the other hand, according to the cardiac murmur effects analyzed, the performance of the classifiers in cascade shown the best accuracy for human identification from ECG-S, minimizing the impact of variability generated on ECG-S by cardiac murmurs diseases.},\n bibtype = {inproceedings},\n author = {Duque-Mejía, C. and Becerra, M. A. and Zapata-Hernández, C. and Mejia-Arboleda, C. and Castro-Ospina, A. E. and Delgado-Trejos, E. and Peluffo-Ordóñez, Diego H. and Rosero-Montalvo, P. and Revelo-Fuelagán, Javier},\n doi = {10.1007/978-3-030-14799-0_23},\n booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}\n}\n\n Biometric identification or authentication is a pattern recognition process, which is carried out acquiring different measures of human beings to distinguish them. Fingerprint and eye iris are the most known and used biometric techniques; nevertheless, also they are the most vulnerable to counterfeiting. Consequently, nowadays research has been focused on physiological signals and behavioral traits for biometric identification because these allow not only the authentication but also determine that the subject is alive. Electrocardiographic signals (ECG-S) have been studied for biometric identification demonstrating their capability. Taking into account that some pathologies are detected using ECG-S, these can affect the results of biometric identification; nonetheless, some diseases such as cardiac murmurs are not detected by ECG-S, but they can distort their morphology. Therefore, these signals must be analyzed considering different pathologies. In this paper, a biometric study was carried out from 40 subjects (20 with cardiac murmurs and 20 without cardiac affections). First, the ECG-S were preprocessed and segmented using the fast method for detecting T waves with annotation of P and T waves, then feature extraction was carried out using discrete wavelet transform (DWT), maximal overlap DWT, cepstral coefficients, and statistical measures. Then, rough set and relief F algorithms were applied to datasets (pathological and normal signals) for attribute reduction. Finally, multiple classifiers and combinations of them were tested. The results of the segmentation were analyzed achieving low results for signals affected by cardiac murmurs. On the other hand, according to the cardiac murmur effects analyzed, the performance of the classifiers in cascade shown the best accuracy for human identification from ECG-S, minimizing the impact of variability generated on ECG-S by cardiac murmurs diseases.\n
\n\n\n\n
\n\n\n\n\n \n \n 2018\n \n \n (4)\n \n \n
\n \n \n \n
\n \n \n\n \n \n \n \n \n \n Physiological Signals Fusion Oriented to Diagnosis - A Review.\n \n \n \n \n\n\n \n Uribe, Y., F.; Alvarez-Uribe, K., C.; Peluffo-Ordoñez, D., H.; and Becerra, M., A.\n\n\n \n\n\n\n Communications in Computer and Information Science, pages 1-15. 2018.\n \n\n\n\n
\n\n\n\n \n \nWebsite\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n \n \n 3 downloads\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
\n\n\n \n\n\n\n \n \n
Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n \n \n 3 downloads\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n\n
\n\n\n@inbook{\n type = {inbook},\n year = {2018},\n keywords = {Data fusion,Diagnostic decision support,Multimodal fusion,Physiological signal,Signal processing},\n pages = {1-15},\n websites = {http://link.springer.com/10.1007/978-3-319-98998-3_1},\n id = {5c776bbe-8666-3ec8-a466-967c634c04d0},\n created = {2022-02-02T07:53:29.378Z},\n file_attached = {false},\n profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},\n group_id = {17c612fe-279d-38a6-ac90-9228b535bc5e},\n last_modified = {2022-02-02T07:53:29.378Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n citation_key = {Uribe2018},\n private_publication = {false},\n abstract = {The analysis of physiological signals is widely used for the development of diagnosis support tools in medicine, and it is currently an open research field. The use of multiple signals or physiological measures as a whole has been carried out using data fusion techniques commonly known as multimodal fusion, which has demonstrated its ability to improve the accuracy of diagnostic care systems. This paper presents a review of state of the art, putting in relief the main techniques, challenges, gaps, advantages, disadvantages, and practical considerations of data fusion applied to the analysis of physiological signals oriented to diagnosis decision support. Also, physiological signals data fusion architecture oriented to diagnosis is proposed.},\n bibtype = {inbook},\n author = {Uribe, Y. F. and Alvarez-Uribe, K. C. and Peluffo-Ordoñez, D. H. and Becerra, M. A.},\n doi = {10.1007/978-3-319-98998-3_1},\n chapter = {Physiological Signals Fusion Oriented to Diagnosis - A Review},\n title = {Communications in Computer and Information Science}\n}\n\n The analysis of physiological signals is widely used for the development of diagnosis support tools in medicine, and it is currently an open research field. The use of multiple signals or physiological measures as a whole has been carried out using data fusion techniques commonly known as multimodal fusion, which has demonstrated its ability to improve the accuracy of diagnostic care systems. This paper presents a review of state of the art, putting in relief the main techniques, challenges, gaps, advantages, disadvantages, and practical considerations of data fusion applied to the analysis of physiological signals oriented to diagnosis decision support. Also, physiological signals data fusion architecture oriented to diagnosis is proposed.\n
\n\n\n\n \n\n \n \n \n \n \n \n Fingertips Segmentation of Thermal Images and Its Potential Use in Hand Thermoregulation Analysis.\n \n \n \n \n\n\n \n Castro-Ospina, A., E.; Correa-Mira, A., M.; Herrera-Granda, I., D.; Peluffo-Ordóñez, D., H.; and Fandiño-Toro, H., A.\n\n\n \n\n\n\n Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pages 455-463. 2018.\n \n\n\n\n
\n\n\n\n \n \nWebsite\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
\n\n\n \n\n\n\n \n \n
Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n\n\n\n\n
\n\n\n@inbook{\n type = {inbook},\n year = {2018},\n keywords = {Fingertip segmentation,NPR measurement,Thermal hand images,Thermorregulation},\n pages = {455-463},\n websites = {http://link.springer.com/10.1007/978-3-319-92639-1_38},\n id = {a251124f-56a3-31d5-a6e5-62bfa8efbd63},\n created = {2022-02-02T07:53:29.685Z},\n file_attached = {false},\n profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},\n group_id = {17c612fe-279d-38a6-ac90-9228b535bc5e},\n last_modified = {2022-02-02T07:53:29.685Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n citation_key = {Castro-Ospina2018},\n private_publication = {false},\n abstract = {Thermoregulation refers to the physiological processes that maintain stable the body temperatures. Infrared thermography is a non-invasive technique useful for visualizing these temperatures. Previous works suggest it is important to analyze thermoregulation in peripheral regions, such as the fingertips, because some disabling pathologies affect particularly the thermoregulation of these regions. This work proposes an algorithm for fingertip segmentation in thermal images of the hand. By using a supervised index, the results are compared against segmentations provided by humans. The results are outstanding even when the analyzed images are highly resized.},\n bibtype = {inbook},\n author = {Castro-Ospina, A. E. and Correa-Mira, A. M. and Herrera-Granda, I. D. and Peluffo-Ordóñez, D. H. and Fandiño-Toro, H. A.},\n doi = {10.1007/978-3-319-92639-1_38},\n chapter = {Fingertips Segmentation of Thermal Images and Its Potential Use in Hand Thermoregulation Analysis},\n title = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}\n}\n\n Thermoregulation refers to the physiological processes that maintain stable the body temperatures. Infrared thermography is a non-invasive technique useful for visualizing these temperatures. Previous works suggest it is important to analyze thermoregulation in peripheral regions, such as the fingertips, because some disabling pathologies affect particularly the thermoregulation of these regions. This work proposes an algorithm for fingertip segmentation in thermal images of the hand. By using a supervised index, the results are compared against segmentations provided by humans. The results are outstanding even when the analyzed images are highly resized.\n
\n\n\n\n \n\n \n \n \n \n \n \n Case-Based Reasoning Systems for Medical Applications with Improved Adaptation and Recovery Stages.\n \n \n \n \n\n\n \n Blanco Valencia, X.; Bastidas Torres, D.; Piñeros Rodriguez, C.; Peluffo-Ordóñez, D., H.; Becerra, M., A.; and Castro-Ospina, A., E.\n\n\n \n\n\n\n Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pages 26-38. 2018.\n \n\n\n\n
\n\n\n\n \n \nWebsite\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
\n\n\n \n\n\n\n \n \n
Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n\n\n\n\n
\n\n\n@inbook{\n type = {inbook},\n year = {2018},\n keywords = {Cascade classification,Case-based reasoning,Preprocessing,Probability},\n pages = {26-38},\n websites = {http://link.springer.com/10.1007/978-3-319-78723-7_3},\n id = {5e126f71-fbc1-3bcd-ae13-54f028a655b4},\n created = {2022-02-02T07:53:30.549Z},\n file_attached = {false},\n profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},\n group_id = {17c612fe-279d-38a6-ac90-9228b535bc5e},\n last_modified = {2022-02-02T07:53:30.549Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n citation_key = {BlancoValencia2018},\n private_publication = {false},\n abstract = {Case-Based Reasoning Systems (CBR) are in constant evolution, as a result, this article proposes improving the retrieve and adaption stages through a different approach. A series of experiments were made, divided in three sections: a proper pre-processing technique, a cascade classification, and a probability estimation procedure. Every stage offers an improvement, a better data representation, a more efficient classification, and a more precise probability estimation provided by a Support Vector Machine (SVM) estimator regarding more common approaches. Concluding, more complex techniques for classification and probability estimation are possible, improving CBR systems performance due to lower classification error in general cases.},\n bibtype = {inbook},\n author = {Blanco Valencia, X. and Bastidas Torres, D. and Piñeros Rodriguez, C. and Peluffo-Ordóñez, D. H. and Becerra, M. A. and Castro-Ospina, A. E.},\n doi = {10.1007/978-3-319-78723-7_3},\n chapter = {Case-Based Reasoning Systems for Medical Applications with Improved Adaptation and Recovery Stages},\n title = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}\n}\n\n Case-Based Reasoning Systems (CBR) are in constant evolution, as a result, this article proposes improving the retrieve and adaption stages through a different approach. A series of experiments were made, divided in three sections: a proper pre-processing technique, a cascade classification, and a probability estimation procedure. Every stage offers an improvement, a better data representation, a more efficient classification, and a more precise probability estimation provided by a Support Vector Machine (SVM) estimator regarding more common approaches. Concluding, more complex techniques for classification and probability estimation are possible, improving CBR systems performance due to lower classification error in general cases.\n
\n\n\n\n \n\n \n \n \n \n \n \n Exploratory Study of the Effects of Cardiac Murmurs on Electrocardiographic-Signal-Based Biometric Systems.\n \n \n \n \n\n\n \n Becerra, M., A.; Duque-Mejía, C.; Zapata-Hernández, C.; Peluffo-Ordóñez, D., H.; Serna-Guarín, L.; Delgado-Trejos, E.; Revelo-Fuelagán, E., J.; and Blanco Valencia, X., P.\n\n\n \n\n\n\n Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pages 410-418. 2018.\n \n\n\n\n
\n\n\n\n \n \nWebsite\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
\n\n\n\n\n\n \n\n\n\n \n \n
Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n\n\n\n\n
\n\n\n@inbook{\n type = {inbook},\n year = {2018},\n keywords = {Biometric identification,Cardiac murmur,Electrocardiographic signal,Signal processing},\n pages = {410-418},\n websites = {http://link.springer.com/10.1007/978-3-030-03493-1_43},\n id = {05f507c9-440a-3674-ae8a-6cba6f397164},\n created = {2022-02-02T07:53:30.911Z},\n file_attached = {false},\n profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},\n group_id = {17c612fe-279d-38a6-ac90-9228b535bc5e},\n last_modified = {2022-02-02T07:53:30.911Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n citation_key = {Becerra2018},\n private_publication = {false},\n abstract = {The process of distinguishing among human beings through the inspection of acquired data from physical or behavioral traits is known as biometric identification. Mostly, fingerprint- and iris-based biometric techniques are used. Nowadays, since such techniques are highly susceptible to be counterfeited, new biometric alternatives are explored mainly based on physiological signals and behavioral traits -which are useful not only for biometric identification purposes, but may also play a role as a vital signal indicator. In this connection, the electrocardiographic (ECG) signals have shown to be a suitable approach. Nonetheless, their informative components (morphology, rhythm, polarization, and among others) can be affected by the presence of a cardiac pathology. Even more, some other cardiac diseases cannot directly be detected by the ECG signal inspection but still have an effect on their waveform, that is the case of cardiac murmurs. Therefore, for biometric purposes, such signals should be analyzed submitted to the effects of pathologies. This paper presents a exploratory study aimed at assessing the influence of the presence of a pathology when analyzing ECG signals for implementing a biometric system. For experiments, a data base holding 20 healthy subjects and 20 pathological subjects (diagnosed with different types of cardiac murmurs) are considered. The proposed signal analysis consists of preprocessing, characterization (using wavelet features), feature selection and classification (five classifiers as well as a mixture of them are tested). As a result, through the performed comparison of the classification rates when testing pathological and normal ECG signals, the cardiac murmurs’ undesired effect on the identification mechanism performance is clearly unveiled.},\n bibtype = {inbook},\n author = {Becerra, M. A. and Duque-Mejía, C. and Zapata-Hernández, C. and Peluffo-Ordóñez, D. H. and Serna-Guarín, L. and Delgado-Trejos, Edilson and Revelo-Fuelagán, E. J. and Blanco Valencia, X. P.},\n doi = {10.1007/978-3-030-03493-1_43},\n chapter = {Exploratory Study of the Effects of Cardiac Murmurs on Electrocardiographic-Signal-Based Biometric Systems},\n title = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}\n}\n\n The process of distinguishing among human beings through the inspection of acquired data from physical or behavioral traits is known as biometric identification. Mostly, fingerprint- and iris-based biometric techniques are used. Nowadays, since such techniques are highly susceptible to be counterfeited, new biometric alternatives are explored mainly based on physiological signals and behavioral traits -which are useful not only for biometric identification purposes, but may also play a role as a vital signal indicator. In this connection, the electrocardiographic (ECG) signals have shown to be a suitable approach. Nonetheless, their informative components (morphology, rhythm, polarization, and among others) can be affected by the presence of a cardiac pathology. Even more, some other cardiac diseases cannot directly be detected by the ECG signal inspection but still have an effect on their waveform, that is the case of cardiac murmurs. Therefore, for biometric purposes, such signals should be analyzed submitted to the effects of pathologies. This paper presents a exploratory study aimed at assessing the influence of the presence of a pathology when analyzing ECG signals for implementing a biometric system. For experiments, a data base holding 20 healthy subjects and 20 pathological subjects (diagnosed with different types of cardiac murmurs) are considered. The proposed signal analysis consists of preprocessing, characterization (using wavelet features), feature selection and classification (five classifiers as well as a mixture of them are tested). As a result, through the performed comparison of the classification rates when testing pathological and normal ECG signals, the cardiac murmurs’ undesired effect on the identification mechanism performance is clearly unveiled.\n
\n\n\n