By Topic

ECG signal analysis by using Hidden Markov model

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Shing-Tai Pan ; Dept. of Comput. Sci. & Inf. Eng., Nat. Univ. of Kaohsiung, Kaohsiung, Taiwan ; Tzung-Pei Hong ; Hung-Chin Chen

This paper explores the real-time normal and abnormal heartbeats recognition system mainly based on electrocardiogram (ECG). The recognition of heartbeats from electrocardiogram (ECG) is performed by a statis-tical model, Hidden Markov model (HMM), to immedi-ately determine the status of the patient's heartbeats. The ECG features developed by existing papers are used to train the HMM model. The same features of testing data are then fed into the trained HMM model for recognition. The four abnormal heartbeats include the left bundle branch block (LBBB), the right bundle branch block (RBBB), the ventricular premature contractions (VPC), and the atrial premature contractions (APC) are recognized for the ECG data in the MIT-BIH Arrhythmia Da-tabase. Experimental results in this paper shown that the proposed system performed well and had very excellent recognition rate for some heartbeat cases.

Published in:

Fuzzy Theory and it's Applications (iFUZZY), 2012 International Conference on

Date of Conference:

16-18 Nov. 2012