QRS Detection Based on Morphological Filter and Energy Envelope for Applications in Body Sensor Networks

Abstract

Emerging body sensor networks (BSN) provide solutions for continuous health monitoring at anytime and from anywhere. The implementation of these monitoring solutions requires wearable sensor devices and thus creates new technology challenges in both software and hardware. This paper presents a QRS detection method for wearable Electrocardiogram (ECG) sensor in body sensor networks. The success of proposed method is based on the combination of two computationally efficient procedures, i.e., single-scale mathematical morphological (MM) filter and approximated envelope. The MM filter removes baseline wandering, impulsive noise and the offset of DC component while the approximated envelope enhances the QRS complexes. The performance of the algorithm is verified with standard MIT-BIH arrhythmia database as well as exercise ECG data. It achieves a low detection error rate of 0.42% based on the MIT-BIH database.