Automatic QRS Detection and Segmentation Using Short Time Fourier Transform and Feature Fusion

Abstract

QRS detection from an electrocardiogram (ECG) is potentially useful tool in many applications such as diagnosing cardiac diseases, bio-identification, bio-encryption, etc. In this paper, we present an automated algorithm for detecting QRS waves and segmenting ECG signal into separate beats using short time Fourier transform (STFT) and multi-channel ECG feature-based classification. We test the performance of our algorithm using ECG signals of 62 subjects from the ECG ID public database. The results show that our method is capable of extracting QRS waves with 99.45% average QRS segmentation accuracy.