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An Automated Intelligent Approach for ECG Signal Noise Removal

Electrocardiogram (ECG) is an important biomedical tool for the diagnosis of heart disorders. However, the signal is susceptible to noise and it is essential to remove the noise especially when undertaking automated processing of the signal. In this paper, an intelligent approach based on moving median filter and Self-Organizing Map (SOM) neural network is proposed to identify the cutoff frequency of the noise, which is to be filtered out. In general, the spectrum of the ECG signal is derived, subsequently, baseline wander is removed using the moving median filter and finally, SOM is applied to the spectrum to calculate the cutoff frequency. The results of the proposed scheme are compared with the low-pass FIR filtering for ECG signal high frequency noise removal. Results show that using the proposed intelligent method, the calculated cutoff frequency will be equal or better than the classical results for ECG noise removal. Also, in all the cases of atrial fibrillation, arrhythmia and supraventricular ECG signals, the automatically calculated cutoff frequency produces very smoother signals than the classical low-pass filtering.