Electrocardiogram (ECG) signal has been widely used in cardiac pathology to detect heart disease. In this paper, wavelet neural network (WNN) is studied for ECG signal modeling and noise reduction. WNN combines the multi-resolution nature of wavelets and the adaptive learning ability of artificial neural networks, and is trained by a hybrid algorithm that includes the adaptive diversity learning particle swarm optimization (ADLPSO) and the gradient descent optimization. Computer simulation results demonstrate this proposed approach can successfully model the ECG signal and remove high-frequency noise.


Electrical and Computer Engineering

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