Multiple cardiac arrhythmia recognition using adaptive wavelet network.
This paper proposes a method for electrocardiogram (ECG) heartbeat pattern recognition using adaptive wavelet network (AWN). The ECG beat recognition can be divided into a sequence of stages, starting from feature extraction and conversion of QRS complexes, and then identifying cardiac arrhythmias based on the detected features. The discrimination method of ECG beats is a two-subnetwork architecture, consisting of a wavelet layer and a probabilistic neural network (PNN). Morlet wavelets are used to extract the features from each heartbeat, and then PNN is used to analyze the meaningful features and perform discrimination tasks. The AWN is suitable for application in a dynamic environment, with add-in and delete-off features using automatic target adjustment and parameter tuning. The experimental results obtained by testing the data of the MIT-BIH arrhythmia database demonstrate the efficiency of the proposed method.
Lin
Chia-Hung
CH
Department of Electrical Engineering, Kao-Yuan Institute of Technology, Kaohsiung 821, Taiwan; Institute of Biomedical Engineering, National Cheng-Kung University, Tainan 701, Taiwan.
Chen
Pei-Jarn
PJ
Chen
Yung-Fu
YF
Lee
You-Yun
YY
Chen
Tainsong
T