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ST-segment analysis using hidden Markov Model beat segmentation: application to ischemia detection

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4 Author(s)
Andreao, R.V. ; Inst. Nat. des Telecommun., Evry, France ; Dorizzi, B. ; Boudy, J. ; Mota, J.C.M.

In this work, we propose an ECG analysis system to ischemia detection. This system is based on an original markovian approach for online beat detection and segmentation, providing a precise localization of all beat waves and particularly of the PQ and ST segments. Our approach addresses a large panel of topics never studied before in others HMM related works: multichannel beat detection and segmentation, waveform models and unsupervised patient adaptation. Thanks to the use of some heuristic rules defined by cardiologists, our system performs a reliable ischemic episode detection, showing to be a helpful tool to ambulatory ECG analysis. The performance was evaluated on the two-channel European ST-T database, following its ST episode definitions. The experimentation was performed over 48 files extracted from 90. Our best average statistic results are 83% sensitivity and 85% positive predictivity. Performance compares favorably to others reported in the literature.

Published in:

Computers in Cardiology, 2004

Date of Conference:

19-22 Sept. 2004

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