Online HMM adaptation applied to ECG signal analysis

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Abstract

The online HMMs (Hidden Markov Model) adaptation has been introduced by this work1 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.

Original languageEnglish
Title of host publicationInternational Symposium on Industrial Electronics 2006, ISIE 2006
Pages511-514
Number of pages4
DOIs
Publication statusPublished - 1 Dec 2006
Externally publishedYes
EventInternational Symposium on Industrial Electronics 2006, ISIE 2006 - Montreal, QC, Canada
Duration: 9 Jul 200613 Jul 2006

Publication series

NameIEEE International Symposium on Industrial Electronics
Volume1

Conference

ConferenceInternational Symposium on Industrial Electronics 2006, ISIE 2006
Country/TerritoryCanada
CityMontreal, QC
Period9/07/0613/07/06

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