- 期刊
Intelligent Hybrid Methods for ECG Classification-A Review
並列摘要
With creasing computational power, sophisticated algorithms has e hero proposed to improve the prediction accuracy of ECG waveform classification systems. One such approach aims to combine different algorithms to create hybrid classification systems. Among the possible combinations, the best-known hybrid systems apply artificial neural networks (ANN) as the first step for decision-making. For example, fuzzy neural networks (FNN) combine a fuzzification algorithm with an ANN; the wavelet neural network (WNN) combines a wavelet transform with an ANN. Other research has combined adaptive resonance theory (ART) and principal component analysis (PCA) with ANN. Such integration has been shown to improve the learning and classification performance of several ECG analysis applications. This article reviews and compares key applications of hybrid systems for ECU waveform classification, with an emphasis on neural and fuzzy approaches.
延伸閱讀
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