NDLI: Multiple Cardiac Arrhythmia Recognition Using Adaptive Wavelet Network

Content Provider	IEEE Xplore Digital Library
Author	Chia-Hung Lin Pei-Jarn Chen Yung-Fu Chen You-Yun Lee Tainsong Chen
Copyright Year	2005
Description	Author affiliation: Dept. of Electr. Eng., Kao-Yuan Inst. of Technol., Kaohsiung (Chia-Hung Lin)
Abstract	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
Starting Page	5655
Ending Page	5659
File Size	314682
Page Count	5
File Format	PDF
ISBN	0780387414
DOI	10.1109/IEMBS.2005.1615769
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-01-17
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Adaptive systems Electrocardiography Heart beat Feature extraction Pattern recognition Computer vision Neural networks Wavelet analysis Data mining Performance analysis Probabilistic Neural Network (PNN) Electrocardiogram (ECG) Adaptive Wavelet Network (AWN) Cardiac Arrhythmia Morlet Wavelet
Content Type	Text
Resource Type	Article

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