A real-time QRS detection method based on moving-averaging incorporating with wavelet denoising.
In this paper, a simple moving average-based computing method for real-time QRS detection is proposed. In addition, for signal preprocessing our detection algorithm also incorporates a wavelet-based denoising procedure to effectively reduce the noise level for electrocardiogram (ECG) data. The overall computational structure of the proposed algorithm allows the QRS detection to be performed and implemented in real-time with high time- and memory-efficiency. Algorithm performance was evaluated against the MIT-BIH Arrhythmia Database. The numerical results indicated that the novel algorithm finally achieved about 99.5% of the detection rate for the standard database, and also, it could function reliably even under the condition of poor signal quality in the measured ECG data.
Chen
Szi-Wen
SW
Department of Electronic Engineering, Chang Gung University, Kwei-Shan, Tao-Yuan 333, Taiwan. chensw@mail.cgu.edu.tw
Chen
Hsiao-Chen
HC
Chan
Hsiao-Lung
HL
D000465:Algorithms; D001145:Arrhythmias, Cardiac; D003198:Computer Simulation; D003205:Computing Methodologies; D019992:Databases as Topic; D004562:Electrocardiography / Q000379:methods*; D006339:Heart Rate; D006801:Humans; D012815:Signal Processing, Computer-Assisted; D013223:Statistics as Topic