An adaptive thresholding technique for QRS-complex detection in ECG signal based on empirical wavelet transform

Since the QRS complex varies with different cardiac health conditions, therefore efficient and automatic detection of QRS complex and is essential for reliable health condition monitoring. In this work an empirical wavelet transform (EWT)-based algorithm has been used for accurate detection of QRS complex. EWT is one of the adaptive time-frequency data analysis method. In the first step, this method decomposes the ECG signal into set of the AM-FM components called modes. Later, adaptive thresholding is applied to its last mode to detection of QRS-complexes. Last mode is nearly the same as that of the original signal if we look at it visually. The proposed algorithm has been tested on the standard. The performance of proposed method has been measured on the basis of statistical parameters and gives the positive predictivity 99.82%, sensitivity 99.93%, and error rate 0.24%. The proposed method is also tested on self-recorded dataset and achieves 100% sensitivity and positive predictivity and zero error rates.