A RECOGNITION METHOD OF R-PEAKS ON ELECTROCARDIOGRAMS BASED ON WAVELET TRANSFORM WITH PSEUDO-DIFFERENTIAL OPERATORS

Abstract

In this paper, we propose a recognition method of R-peaks on electrocardiograms (ECGs) based on wavelet transform with pseudo-differential operators. It is well known that the accurate recognition of R-peaks is highly importance for diagnosis of cardiac diseases and autonomic ataxia. However, the existing results for detection of R-peaks are not always accurate and can have missed peaks or false. Difficulties in accurate R-peaks detection is caused by presence of various noises in ECGs and the physiological variability of the QRS complex. From the above, we propose a more flexible and adaptive recognition method of R-peaks. In order to develop the proposed detection method, noises, artifacts, and baseline variation in ECGs are firstly suppressed by using the low-pass/high-pass filters, moving average, and MaMeMi filter. Next, the time-frequency domain's energy distribution is computed by using wavelet transform with pseudo-differential operators. Furthermore, we introduce a time-series index, -Normalized Spectrum Index ( f^p-NSI) obtained by scalograms based on the wavelet transform with pseudo-differential operators. Finally, R-peaks are recognized by taking the threshold toward the results of f^p-NSI. In this paper, we present the proposed recognition method of R-peaks on ECGs, and the effectiveness (accuracy) of the proposed method is evaluated.