ECG segmentation and fiducial point extraction using multi hidden Markov model

In this paper, we propose a novel method for extracting fiducial points (FPs) of electrocardiogram (ECG) signals. We propose the use of multi hidden Markov model (MultiHMM) as opposed to the traditional use of Classic HMM. In the MultiHMM method, each segment of an ECG beat is represented by a separate ergodic continuous density HMM. Each HMM has different state number and is trained separately. In the test step, the log-likelihood of two consecutive HMMs is compared and a path is estimated, which shows the correspondence of each part of the ECG signal to the HMM with the maximum log-likelihood.

Source: Computers in Biology and Medicine - September 27, 2016 Category: Bioinformatics Authors: Mahsa Akhbari, Mohammad B. Shamsollahi, Omid Sayadi, Antonis A. Armoundas, Christian Jutten Source Type: research