Research Article Open Access

Development of a Novel ECG signal Denoising System Using Extended Kalman Filter

Abstract

ECG signal plays a crucial role in diagnosis of a Varity of diseases. At the time of diagnosis the proper information from the ECG signals helps to make a proper and efficient diagnosis for the patient. Most often it is found that treatment of the patient suffers due to improper information of ECG signals. The cause behind this problem is the noise added in the ECG signals at the time of signal acquisition. Hence to overcome this problem efficient denoising of ECG signals is required. This paper presents efficient denoising scheme for electrocardiogram (ECG) signals based on extended Kalman filter (EKF) structure. The basic idea is to overcome the disadvantages of conventional techniques like median filter by utilizing the adaptive nature of EKF structure. For the comparative analysis this paper deploys three important parameters; mean square error (MSE), Peak signal to noise ratio (PSNR), and most importantly RR interval estimation. On the basis of the three parameters a comparative analysis has been presented to explore efficient denoising capability of EKF over median filter. The results obtained indicated that EKF provides very less MSE and very high PSNR as compare to median filter. On the other side the estimated RR interval obtained using EKF is the closest match with original signal RR intervals, while median filter provides so many RR intervals, which are not even presents in the original signal.

Nagendra Sen, Chinmay Chandrakar

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