Application of the Hidden Markov Model for determining PQRST complexes in electrocardiograms

2020/05/10 by N. S. Shlyankin, Shlyankin, N. S., Andrey Gaidel +2
Computer Science · Engineering · Medicine · #Cardiac electrophysiology and arrhythmias #ECG Monitoring and Analysis #FOS: Computer and information sciences #FOS: Electrical engineering #Fault Detection and Control Systems #Machine Learning (cs.LG) #Signal Processing (eess.SP) #cs.LG #eess.SP #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2005.04723

in Russian

arxiv created 2020/05/10 · openalex publication_date 2020/05/10 · arxiv updated 2020/05/12 · openalex created_date 2020/05/13 · openalex updated_date 2026/07/28

Abstract

The application of the hidden Markov model with various parameters in the segmentation task of QRS, ST, T, P, PQ, ISO complexes of electrocardiograms is considered. Models were trained using the Viterbi algorithm using the QT Database. For comparison, the Pan-Tompkins algorithm for searching for the duration of QRS complexes was modified.

Citations

• PhysioBank, PhysioToolkit, and PhysioNet

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