Classification of electrocardiogram using hidden Markov models

Author

Cheng, W.T. ; Chan, K.L.

Author_Institution

Dept. of Electron. Eng., City Polytech. of Hong Kong, Kowloon, Hong Kong

fYear

1998

fDate

29 Oct-1 Nov 1998

Firstpage

143

Abstract

The objective of this project is to develop models for the characterization of electrocardiogram (EGG). A fast and reliable QRS detection algorithm based on a one-pole filter has been developed. Automatic ECG classification using hidden Markov models (HMMs) is investigated. Models representing various types of beat are trained using the American Heart Association (AHA) ventricular arrhythmia ECG data. The types of beat being selected in the study are: normal (N), premature ventricular contraction (V), and fusion of ventricular and normal beats (F). Artificial ECG generated from the model shows that each model truly characterizes that particular type of beat. In the testing phase, ECG signals are classified using the trained models. The average classification accuracy is 93% for N beat, 65.55% for V beat, and 56.38% for F beat respectively

Keywords

electrocardiography; hidden Markov models; medical signal processing; pattern classification; physiological models; signal classification; ECG modelling; artificial ECG; automatic ECG classification; fusion of beats; hidden Markov models; normal beats; one-pole filter; premature ventricular contraction; reliable QRS detection algorithm; ventricular arrhythmia ECG data; Character generation; Detection algorithms; Electrocardiography; Electronic mail; Filters; Fusion power generation; Heart rate variability; Hidden Markov models; Reliability engineering; Testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Engineering in Medicine and Biology Society, 1998. Proceedings of the 20th Annual International Conference of the IEEE

Conference_Location

Hong Kong

ISSN

1094-687X

Print_ISBN

0-7803-5164-9

Type

conf

DOI

10.1109/IEMBS.1998.745850

Filename

745850