A real-time QRS detector based on discrete wavelet transform and cubic spline interpolation.

QRS detection is an important step in electrocardiogram signal processing and analysis. Despite a lot of research effort, robustness and high detection accuracy still remain open problems. Here we present a real-time QRS detector, based on wavelet decomposition and spline interpolation, which is working in our portable health monitor system (PHMS). The discrete wavelet transform combined with the Cubic Spline Interpolation is used as the preprocessor. An improved dynamic weights adjusting strategy is adopted to enhance the detection robustness. Finally, peak detector and adaptive threshold detector are used to determine the R fiducial point. We tested the algorithm against the Massachusetts Institute of Technology-Beth Israel Hosptial (MIT-BIH) arrhythmia database, and achieved a sensitivity of 99.75% and positive prediction of 99.83%. Further experiments carried out in the PHMS showed the robustness and sound performance in processing real-time sampled signal despite heavy noise. Time accuracy was also taken into consideration in the test and the total root mean square error was 16.03 ms.