Abstract
In this work, an efficient non-supervised algorithm for clustering of ECG signals is presented. The method is assessed over a set of records from MIT/BIH arrhythmia database with different types of heartbeats, including normal (N) heartbeats, as well as the arrhythmia heartbeats recommended by the AAMI, usually found in Holter recordings: ventricular extra systoles (VE), left and right branch bundles blocks (LBBB and RBBB) and atrial premature beats (APB). The results are assessed by means the sensitivity and specificity measures, taking advantage of the database labels. Also, unsupervised performance measures are used. Finally, the performance of the algorithm is in average 95%, improving results reported by previous works of the literature.
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Acknowledgements
This work is part of project number 249-028, funded by the Universidad Autónoma de Manizales.
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Rodríguez-Sotelo, J.L., Peluffo-Ordoñez, D.H., López-Londoño, D., Castro-Ospina, A. (2017). Segment Clustering for Holter Recordings Analysis. In: Ferrández Vicente, J., Álvarez-Sánchez, J., de la Paz López, F., Toledo Moreo, J., Adeli, H. (eds) Natural and Artificial Computation for Biomedicine and Neuroscience. IWINAC 2017. Lecture Notes in Computer Science(), vol 10337. Springer, Cham. https://doi.org/10.1007/978-3-319-59740-9_45
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DOI: https://doi.org/10.1007/978-3-319-59740-9_45
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