title: Heart signal recognition by Hidden Markov Models: the ECG case.
abstract: Wave recognition in ECG signals by Hidden Markov Models (HMMs) relies on the stationary assumption for the set of parameters used to describe ECG waves. This approach seems unnatural and consequently generates severe errors in practice. A new class of HMMs called Modified Continuous Variable Duration HMMs is proposed to account for the specific properties 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 compared to classical HMMs.
authors.affiliation: Laboratoire Traitement du Signal et de l'Image, Université de Rennes I, France.
authors.forename: L
authors.initials: L
authors.lastname: Thoraval
authors.forename: G
authors.initials: G
authors.lastname: Carrault
authors.forename: J J
authors.initials: JJ
authors.lastname: Bellanger
mesh_terms: D001185:Artificial Intelligence; D004562:Electrocardiography; D008390:Markov Chains; D010363:Pattern Recognition, Automated; D012815:Signal Processing, Computer-Assisted