Denoising of the ECG Signal using Kohonen Neural Network

1&2 Department of Electronics and Communication 1&2 Punjabi University 1&2 Rajpura Road, Patiala 1&2 INDIA Abstract:Noisy Electrocardiogram (ECG) signal can mask some of the important features of the original ECG signal. Therefore, it is necessary to remove the noise for proper analysis of the ECG signal. In this paper, the use of Kohonen Neural Network (KNN) for automatically identifying the cutoff frequency of ECG signal for lowpass filtering is presented. ECG signal having noise and baseline wandering is extracted from two classes: arrhythmia and supraventricular. Baseline wander is removed using the empirical mode decomposition method. Frequency spectrum of the baseline wandering removed ECG signal is used to train the KNN. The performance of the KNN with various parameters is investigated. The cutoff frequency identified using the KNN is applied to the low pass Finite Impulse Response filter and the resulting signal is compared with the conventional filtered ECG signal. The result show that the KNN based approach successfully denoised the ECG signals more effectively than the conventional method.