Title :
Heartbeat Recognition from ECG Signals Using Hidden Markov Model with Adaptive Features
Author :
Shing-Tai Pan ; Yi-Heng Wu ; Yi-Lan Kung ; Hung-Chin Chen
Author_Institution :
Dept. of Comput. Sci. & Inf. Eng., Nat. Univ. of Kaohsiung, Kaohsiung, Taiwan
Abstract :
A heartbeats recognition system for recognizing four different cardiac diseases was developed based on electrocardiogram (ECG) in this paper. The Hidden Markov model (HMM) was applied to the recognition of heartbeats from electrocardiogram (ECG). The ECG features developed by existing papers are used to train the HMM model. However, since different set of features are suitable to recognize different cardiac diseases, this paper proposed a strategy of using adaptive features to recognize different set of cardiac disease. The four abnormal heartbeats including 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 from the ECG data in the MIT-BIH Arrhythmia Database. Experimental results in this paper shown that the proposed strategy performed well and had very excellent recognition rate for some heartbeat cases.
Keywords :
diseases; electrocardiography; feature extraction; hidden Markov models; medical signal processing; APC; ECG data; ECG features; ECG signals; HMM model; LBBB; MIT-BIH Arrhythmia Database; RBBB; VPC; abnormal heartbeats; adaptive features; atrial premature contractions; cardiac disease; electrocardiogram; heartbeat recognition system; hidden Markov model; left bundle branch block; right bundle branch block; ventricular premature contractions; Databases; Electrocardiography; Heart beat; Hidden Markov models; Mathematical model; Testing; Training; ECG; HMM; Heart Beat; MIT-BIH Arrhythmia Database; cardiac arrhythmia;
Conference_Titel :
Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD), 2013 14th ACIS International Conference on
Conference_Location :
Honolulu, HI
DOI :
10.1109/SNPD.2013.59