Adaptive Neural Network-Based ECG Data Compression According to the Morphology

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Adaptive Neural Network-Based ECG Data Compression According to the Morphology

A novel method for the compression of dynamic electrocardiographic (ECG) data is presented.The method uses a neural network based on the adaptive resonance theory (ART).Because ART network architectures have the good features of the category recognitions,the ECG cyclical morphological patterns that have been stored in the network system can not only be quickly recognized,but a new pattern that was not learnt can be automatically recognized and stored in the system.Thus the data compression method may recognize the morphological changes of the dynamic ECG signals and achieves higher data compression rate (DCR) and precision.We have implemented simulation and experimental studies of the ECG data compression with the method.Results indicate this is a feasible and effective date compression algorithm of the dynamic ECG signals.