Abstract

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).

 Keywords: ECG classification, discrete observation, hidden conditional random fields, hidden Markov models