Single Channel QRS Detection Using Wavelet And Median Denoising With Adaptive Multilevel Thresholding

Abstract

The study of heartbeats in electrocardiogram (ECG) signals is very important to sustain good health. Any anomalies in the heart rhythm can be detected by carefully studying the ECG signal. The detection of the QRS is obstructed by external and internal sources of noise. Automatic detection of the QRS is achieved by diminishing these noises to a minimum by different types of filtering such as band-pass filtering, wavelet transform, and applying thresholds. This paper presents a new method of QRS detection using discrete wavelet transform (DWT), median filtering, and adaptive multilevel thresholding (AMT). The proposed method is tested for the MIT-BIH Arrhythmia database and shows a high sensitivity of 99.74%, positive predictivity of 99.88%, and a detection error rate of 0.38%. In addition to this, the proposed technique is quite robust and can adapt to signals with a low signal-to-noise ratio.