Adaptive wavelet network for multiple cardiac arrhythmias recognition

Chia Hung Lin, Yi Chun Du, Tainsong Chen

Research output: Contribution to journalArticlepeer-review

88 Citations (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.

Original languageEnglish
Pages (from-to)2601-2611
Number of pages11
JournalExpert Systems With Applications
Issue number4
Publication statusPublished - 2008 May

All Science Journal Classification (ASJC) codes

  • General Engineering
  • Computer Science Applications
  • Artificial Intelligence


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