Real-time QRS detection method

Abstract

QRS detection is important because R peak is recognized as the most useful fiducial point in ECG segmentation. Despite of lot of research effort, the accuracy and robustness of QRS detection still remain open problems. Here we propose a novel QRS detector, which uses the Discrete Wavelet Transform (DWT) and Cubic Spline Interpolation as preprocessor, together with an improved dynamic weights adjusting strategy to enhance the detection robustness in noise condition. The algorithm was tested against MIT-BIH arrhythmia database, and achieved average sensitivity of 99.68% and positive prediction of 99.59%. Stable detection ratio for the heavy noised recordings has showed its robustness in severe conditions.