Online HMM Adaptation Applied to ECG Signal Analysis

The online HMMs (Hidden Markov Model) adaptation has been introduced by this work for the patient ECG signal adaptation problem. Two adaptive methods were implemented, namely the incremental version of the expectation- maximization (EM) and segmental k-means algorithms. The algorithms were implemented in an ECG segmentation system which classificatory is based on HMM. The performance criteria adopted are waveform detection, segmentation precision, and ischemia detection. For the tests, were used the QT and ST-T databases. The experiments have shown that the system adaptation for each individual improves the system reliability and increases the system performance. Furthermore, our results compare favorably with other works in the literature.