QRS complex detection using stationary wavelet transform and adaptive thresholding

Abstract

Purpose- Electrocardiogram (ECG) signal is a record of the electrical activity of the heart and contains important clinical data about cardiovascular-related misfunctioning. The goal of the present work is to develop an improved QRS detection algorithm for the detection of heart abnormalities. Methods- In this present work stationary wavelet transforms (SWT) based method has been proposed for precise detection of QRS complex with ‘sym2’ mother wavelet. The stationary wavelet transform is a systematic mathematical tool to decompose the signal without downsampling using scale analysis and provides high detection of QRS complex and accurate localization of signal components. In the proposed method four level of decomposition is applied and the initial thresholding value is computed by the maximum amplitude of scale one at level four in SWT coefficients without the zero-crossing amplitude detection method. The multi-layered dynamic thresholding method has been applied to detect the true R-peak values and locate the QRS complex in the ECG signal. Results- For evaluation of results, the presented methodology is assessed on MIT-BIH, QTDB, and Noise stress test databases. In MIT-BIH, the sensitivity = 99.88%, positive predictivity = 99.93%, accuracy = 99.80% and detection error rate = 0.18% is achieved. In NSTD database, sensitivity = 97.46%, positive predictivity = 94.20%, accuracy = 91.95% and detection error rate = 8.47% and in QTDB, sensitivity = 99.95%, positive predictivity = 99.90%, accuracy = 99.71% and detection error rate = 0.16% is executed. Conclusion- In the presented proposed methodology, the computation complexity is low and exhibits a simple technique rather than an empirical approach. The proposed technique corroborates the performance for the detection of QRS complex with improved accuracy.

Electrocardiography signal compression using non-decimated stationary wavelet transform-based technique

“…By using the NSWT technique, the ECG signal can be decomposed into a few coefficients that contain the maximum energy of the signal. The wavelet transform level affects the number and location of peaks in a signal [47]. Choosing a level of decomposition depends on the number of frequencies necessary for signal analysis.…”

“…Choosing a level of decomposition depends on the number of frequencies necessary for signal analysis. According to NSWT, the number of signal samples must be a power of two; therefore, the following relationship must be satisfied [47]:…”

“…Where T thd is a threshold value of NSWT coefficients and  Nd is the detailed coefficient with root mean square values calculated in the ECG signals. The value of 0.45 is defined by the hit and trial method [47] at MITDB database. These threshold coefficients then passed through the dead zone quantization which summarized of sign and position of non-decimated stationary wavelet transform coefficients.…”

“…In the presented work, the detection of R-peak is based on the stationary wavelet transform method as explained in [47]. The fourth level is chosen for decomposition because QRS-complex lies in the frequency range of 0-20 Hz and the wavelet coefficients contain frequencies of 11.2-22.5 Hz respectively.…”

“…Additionally, several studies have proposed time-fRequency-based techniques for ECG compression [43][44][45][46]. Although the non-stationary wavelet transform (NSWT) has been widely used for denoising ECG signals and detecting QRS complex [47], its application for compressing ECG signals has received less attention. The previously published work gives low compression ratio and high complexity, resulting in large reconstruction errors.…”