Abstract
Electrocardiogram (ECG) monitoring is developing fast and automatic analysis of ECG attracts increasing interest. This paper presents a novel QRS detection algorithm based on adaptive thresholding and particle swarm optimization. Two adaptive thresholds are used to check the amplitude and the RR interval respectively, and they are determined in a fully parameterized form where all parameters are automatically learned through particle swarm optimization, further reducing the manual intervention. A simple and effective preprocessing technique based on the slope information is also developed for the enhancement of QRS complexes. The method was evaluated on MITDB (MIT-BIH Arrhythmia Database), NSTDB (MIT-BIH Noise Stress Test Database) and TELE (Telehealth Database), and the sensitivity and positive predictivity are 99.62%/99.49%, 93.64%/87.66%, and 78.55%/91.17% respectively, suggesting the robustness and effectiveness of our method.
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