Adaptive wavelet network for multiple cardiac arrhythmias recognition

Chia Hung Lin, Yi Chun Du, Tainsong Chen

研究成果: Article同行評審

83 引文 斯高帕斯(Scopus)


This paper proposes a method for electrocardiogram (ECG) heartbeat detection and recognition using adaptive wavelet network (AWN). The ECG beat recognition can be divided into a sequence of stages, starting with feature extraction from QRS complexes, and then according to characteristic features to identify the cardiac arrhythmias including the supraventricular ectopic beat, bundle branch ectopic beat, and ventricular ectopic beat. The method of ECG beats is a two-subnetwork architecture, Morlet wavelets are used to enhance the features from each heartbeat, and probabilistic neural network (PNN) performs the recognition tasks. The AWN method is used for application in a dynamic environment, with add-in and delete-off features using automatic target adjustment and parameter tuning. The experimental results used from the MIT-BIH arrhythmia database demonstrate the efficiency of the proposed non-invasive method. Compared with conventional multi-layer neural networks, the test results also show accurate discrimination, fast learning, good adaptability, and faster processing time for detection.

頁(從 - 到)2601-2611
期刊Expert Systems With Applications
出版狀態Published - 2008 5月

All Science Journal Classification (ASJC) codes

  • 工程 (全部)
  • 電腦科學應用
  • 人工智慧


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