EPDD: Electrocardiogram-based Pulmonary Disease Detector Using Machine Learning

EPDD: Electrocardiogram-based Pulmonary Disease Detector Using Machine Learning. Vanaparthi*, H. S. L. V., Interlichia*, N., Liu, X., Nasseri, M., & Helgeson, S. In Proceedings of the IEEE international conference on Data Science and Advanced Analytics (DSAA), 2025. IEEE (CORE A, Full paper acceptance rate: 29.2%).

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EPDD: Electrocardiogram-based Pulmonary Disease Detector Using Machine Learning [link]

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Pulmonary diseases, such as chronic obstructive pulmonary disease(COPD) and asthma, are among the leading causes of death in the US.These lung diseases often are diagnosed by pulmonologists using physical exam (e.g., lung auscultation) and objective measurement of lung function with pulmonary function testing. These extensive tests, often happen at a later stage when patients have shown more progression and can be inaccessible to many patients due to limited resources and availability. Nowadays hand-held medical devices (e.g., electrocardiogram (ECG) monitors) are already available to such patients and can yield ECG data that potentially could be used for diagnosis. To this end, we introduce EPDD: an ECG-based pulmonary disease detector using machine learning to detect pulmonary disease. In this paper, we focus on one particular lung disease called obstructive lung disease (OLD) to explore the use of easily accessible ECGs to train machine learning models to classify whether a patient has normal or severe OLD. Not only do we utilize the time-series raw ECG directly, we also define and extract features from the PQRST waves of the ECGs. We propose to use traditional machine learning models (such as Random Forest, Logistic Regression, XG Boost, Support Vector Machine, K-Nearest Neighbors, and ensemble of them), as well as deep learning models (Convolutional Neural Network, Long Short-Term Memory, and Vision Transformer). Using a dataset of ECGs we collected from 11,346 patients, our experiments show that all of our proposed models significantly outperform our two baseline models – Visual Geometry Group-16 (VGG-16) and Residual Network-50 (ResNet-50) trained on ECG images generated by various signal processing methods, and that the traditional models trained on extracted features from ECGs outperform the deep learning models trained on ECGs and extracted features. Finally, with the goal of providing accessible and affordable healthcare, we design and develop a mobile app based prototype of EPDD to visualize predictions of the selected trained models to patients and pulmonologists.

@inproceedings{conf/dsaa2025/VardhiniILNH,
  author = {Hari Sree Lalitha Vardhini Vanaparthi* and Natasha Interlichia* and Xudong Liu and Mona Nasseri and Scott Helgeson},
  booktitle = {Proceedings of the IEEE international conference on Data Science and Advanced Analytics (DSAA)},
  publisher = {IEEE (<font color="red">CORE A</font>, Full paper acceptance rate: <font color="red">29.2%</font>)},
  abstract = {Pulmonary diseases, such as chronic obstructive pulmonary disease(COPD) and asthma, are among the leading causes of death in the US.These lung diseases often are diagnosed by pulmonologists using physical exam (e.g., lung auscultation) and objective measurement of lung function with pulmonary function testing. These extensive tests, often happen at a later stage when patients have shown more progression and can be inaccessible to many patients due to limited resources and availability. Nowadays hand-held medical devices (e.g., electrocardiogram (ECG) monitors) are already available to such patients and can yield ECG data that potentially could be used for diagnosis. To this end, we introduce EPDD: an ECG-based pulmonary disease detector using machine learning to detect pulmonary disease. In this paper, we focus on one particular lung disease called obstructive lung disease (OLD) to explore the use of easily accessible ECGs to train machine learning models to classify whether a patient has normal or severe OLD. Not only do we utilize the time-series raw ECG directly, we also define and extract features from the PQRST waves of the ECGs. We propose to use traditional machine learning models (such as Random Forest, Logistic Regression, XG Boost, Support Vector Machine, K-Nearest Neighbors, and ensemble of them), as well as deep learning models (Convolutional Neural Network, Long Short-Term Memory, and Vision Transformer). Using a dataset of ECGs we collected from 11,346 patients, our experiments show that all of our proposed models significantly outperform our two baseline models -- Visual Geometry Group-16 (VGG-16) and Residual Network-50 (ResNet-50) trained on ECG images generated by various signal processing methods, and that the traditional models trained on extracted features from ECGs outperform the deep learning models trained on ECGs and extracted features. Finally, with the goal of providing accessible and affordable healthcare, we design and develop a mobile app based prototype of EPDD to visualize predictions of the selected trained models to patients and pulmonologists.},
  title = {EPDD: Electrocardiogram-based Pulmonary Disease Detector Using Machine Learning},
  url_Paper = {http://xudongliu.domains.unf.edu/resources/EPDD_dsaa25.pdf},
  url = {https://ieeexplore.ieee.org/document/11247978},
  year = 2025
}

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