QRS Detection Based on Multiscale Mathematical Morphology for Wearable ECG Devices in Body Area Networks

A novel wearable electrocardiograph (ECG) QRS detection algorithm for wearable ECG devices in body area networks is presented in this paper, which utilizes the multistage multiscale mathematical morphology filtering to suppress the impulsive noise and uses the multiframe differential modulus accumulation to remove the baseline drift and enhance the signal. The proposed algorithm, verified with data from the MIT/BIH Arrhythmia Database and wearable ECG devices, achieves an average QRS detection rate of 99.61%, a sensitivity of 99.81%, and a positive prediction of 99.80%. It compares favorably to the published methods.