| Literature DB >> 16716445 |
Szi-Wen Chen1, Hsiao-Chen Chen, Hsiao-Lung Chan.
Abstract
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.Entities:
Mesh：
Year: 2006 PMID： 16716445 DOI： 10.1016/j.cmpb.2005.11.012
Source DB: PubMed Journal: Comput Methods Programs Biomed ISSN： 0169-2607 Impact factor: 5.428