Novel heuristic search for ventricular arrhythmia detection using normalized cut clustering. Castro-Ospina, A., E., Castro-Hoyos, C., Peluffo-Ordonez, D., & Castellanos-Dominguez, G. In 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pages 7076-7079, 7, 2013. IEEE. ![Novel heuristic search for ventricular arrhythmia detection using normalized cut clustering [link]](https://bibbase.org/img/filetypes/link.svg "link")
Website doi abstract bibtex Processing of the long-term ECG Holter recordings for accurate arrhythmia detection is a problem that has been addressed in several approaches. However, there is not an outright method for heartbeat classification able to handle problems such as the large amount of data and highly unbalanced classes. This work introduces a heuristic-search-based clustering to discriminate among ventricular cardiac arrhythmias in Holter recordings. The proposed method is posed under the normalized cut criterion, which iteratively seeks for the nodes to be grouped into the same cluster. Searching procedure is carried out in accordance to the introduced maximum similarity value. Since our approach is unsupervised, a procedure for setting the initial algorithm parameters is proposed by fixing the initial nodes using a kernel density estimator. Results are obtained from MIT/BIH arrhythmia database providing heartbeat labelling. As a result, proposed heuristic-search-based clustering shows an adequate performance, even in the presence of strong unbalanced classes. © 2013 IEEE.
@inproceedings{
title = {Novel heuristic search for ventricular arrhythmia detection using normalized cut clustering},
type = {inproceedings},
year = {2013},
keywords = {Cardiac arrhythmia,heuristic search,kernel density estimator,normalized cut clustering},
pages = {7076-7079},
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month = {7},
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abstract = {Processing of the long-term ECG Holter recordings for accurate arrhythmia detection is a problem that has been addressed in several approaches. However, there is not an outright method for heartbeat classification able to handle problems such as the large amount of data and highly unbalanced classes. This work introduces a heuristic-search-based clustering to discriminate among ventricular cardiac arrhythmias in Holter recordings. The proposed method is posed under the normalized cut criterion, which iteratively seeks for the nodes to be grouped into the same cluster. Searching procedure is carried out in accordance to the introduced maximum similarity value. Since our approach is unsupervised, a procedure for setting the initial algorithm parameters is proposed by fixing the initial nodes using a kernel density estimator. Results are obtained from MIT/BIH arrhythmia database providing heartbeat labelling. As a result, proposed heuristic-search-based clustering shows an adequate performance, even in the presence of strong unbalanced classes. © 2013 IEEE.},
bibtype = {inproceedings},
author = {Castro-Ospina, A. E. and Castro-Hoyos, C. and Peluffo-Ordonez, D. and Castellanos-Dominguez, G.},
doi = {10.1109/EMBC.2013.6611188},
booktitle = {2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)}
}
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