De-noising of ECG Signal and QRS Detection Using Hilbert Transform and Adaptive Thresholding

Abstract

Electrocardiogram (ECG) is a primary diagnostic tool for cardiac disorders. During acquisition of ECG signals different noises like instrument noise, muscle noise, motion artifacts and baseline wander are frequently mixed with signals in real-time situation. The segmentation and detection of R peaks in the ECG is the initial steps in HRV analysis. In this paper, we employ the discrete wavelet transform to remove noise components of the time - frequency domain in order to enhance the ECG signal and the Hilbert transform with the adaptive thresholding technique used to explore an optimal combination to detect R-peaks more accurately. The proposed method is evaluated on ECG signals from MIT database. The experimental results of present method show better signal to noise ratio (SNR) with lower mean square error (MSE). To evaluate the quality of physiological information preserved in the enhanced ECG signal, the R-peak detection was also tested. The performance of the proposed method is found to be better in detecting R-peaks having sensitivity of 99.71% and the positive predictability of 99.72% respectively with less detection error rate of 0.52%.