Optimizing the Impact of Resampling on QRS Detection
QRS detection is an essential activity performed on the electrocardiogram signal for finding heartbeat features. Even though there is already a lot of literature on QRS detection, we set a research question to find the dependence of QRS detection performance on the sampling frequency, and, if possible, to find a QRS detector that will be highly efficient at different sampling rates. Our synthesis technique aims to find the optimal value of the threshold parameters that define if the detected peak is artifact, noise or real QRS peak. In addition, we conducted experimental research to find the dependence and estimate the optimal threshold values for the best QRS detection performance. Our approach results with increased QRS detection performance on the original sampling frequency by improving the original Hamilton algorithm. We tested with the MIT-BIH Arrhythmia database. Lastly, QRS detection sensitivity and positive predictive rate are used to evaluate the performance of the algorithm.
KeywordsECG QRS detection Hamilton Sampling frequency AD conversion bit resolution
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