HEART SIGNAL RECOGNITION BY HIDDEN MARKOV-MODELS - THE ECG CASE

Wave recognition in ECG signals by Hidden Markov Models (HMMs) relies on the stationary assumption for the set of parameters used to describ e ECG waves. This approach seems unnatural and consequently generates severe errors in practice. A new class of HMMs called Modified Continu ous Variable Duration HMMs is proposed to account for the specific pro perties of the ECG signal. An application of the latter, coupled with a multiresolution front-end analysis of the ECG is presented. Results show these methods can increase the performance of ECG recognition com pared to classical HMMs.