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Denoising Electrocardiogram Signal from Electromyogram Noise Using Adaptive Filter Combination

Received: 18 October 2019 Accepted: 20 December 2019 The electrocardiogram (ECG) signal is the most important diagnostic test of heart disease detection; it is characterized by low frequency and weak amplitude which makes it susceptible to different kinds of noises such as high/ low-frequency noises. Thus, the diagnostic quality is reduced. This paper introduces a new filter that take the output of wavelet wiener based filter to design a normalized Least Mean Square (NLMS) based filter in order to reduce the broadband mypotentials (EMG) noise in ECG signal. Testing is performed by taking ECG signal from standard MIT/BIH arrhythmia database sampled at 360 Hz-per second and the model of the EMG noise is generated by shaping white Gaussian noise before we add it to a clean ECG signal to get the noisy signal. The proposed method gives best noise reduction and preserves the ECG waves shape. In addition, the combination shows better results through increasing SNR and reducing Mean Square Error (MSE) compared to other existing techniques.