Hidden Conditional Random Fields for ECG Classification
نویسنده
چکیده
In this paper a novel approach to ECG signal classification is proposed. The approach is based on using hidden conditional random fields (HCRF) to model the ECG signal. Features used in training and testing the HCRF are based on time-frequency analysis of the ECG waveforms. Experimental results show that the HCRF model is promising and gives higher accuracy compared to maximum-likelihood (ML) trained hidden Markov models (HMM).
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تاریخ انتشار 2008