Analysis of sampling frequency and resolution in ECG signals

Abstract

There are various ways a computer can detect QRS complexes, all of which depend on two main factors — the algorithm, and the data. In practice, doctors can pinpoint patient's R-peaks in an electrocardiogram by analyzing the complex morphology of the ECG signal; computers accomplish the same by utilizing different QRS detection algorithms. In this paper, we address the data problem to select the sample rate and resolution that obtain the highest accuracy. For this purpose, we conduct experiments to find the impact of the sampling frequency and resolution on the quality of the QRS detection, by several open-source QRS detection algorithms with multiple variations of data representation. The final goal is to recommend the lowest sampling frequency and smallest sampling resolution (bit depth), that will have sufficient data representation to enable high QRS detection accuracy.