Patient-Adaptive Ectopic Beat Classification using Active Learning
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Guttag_Patient-adaptive.pdf
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95.32 KB
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Author(s)
Wiens, J.
Guttag, John V.
Date Issued
September 2010
Journal
Proceedings of the 2010 Computing in Cardiology
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
J. Wiens, J.V. Guttag. "Patient-Adaptive Ectopic Beat Classification using Active Learning" Proceedings of the 2010 Computing in Cardiology, IEEE. © Copyright 2010 IEEE
Version
Final published version
Abstract
A major challenge in applying machine learning techniques to the problem of heartbeat classification is dealing effectively with inter-patient differences in electrocardiograms (ECGs). Inter-patient differences create a need for patient-specific classifiers, since there is no a priori reason to assume that a classifier trained on data from one patient will yield useful results when applied to a different patient. Unfortunately, patient-specific classifiers come at a high cost, since they require a labeled training set. Using active learning, we show that one can drastically reduce the amount of patient-specific labeled training data required to build a highly accurate patient-specific binary heartbeat classifier for identifying ventricular ectopic beats. Tested on all 48 half-hour ECG recordings from the MIT-BIH Arrhythmia Database, our approach achieves an average sensitivity of 96.2% and specificity of 99.9%. The average number of beats needed to train each patient-specific classifier was less than 37 beats, approximately 30 seconds of data.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=5737921&contentType=Conference+Publications&sortType%3Dasc_p_Sequence%26filter%3DAND%28p_IS_Number%3A5737883%29%26rowsPerPage%3D50
