The proposed ET and PD control-based adaptive threshold algorithm achieved 99.90% sensitivity, 99.92% positive predictivity, and 99.82% accuracy for QRS detection on MIT-BIH databases.
Does the proposed ET and PD controlled threshold strategy algorithm accurately and rapidly detect QRS complexes in ECG signals?
The proposed ET and PD controlled threshold strategy provides highly accurate and fast real-time QRS detection suitable for wearable heart rate monitoring and automatic ECG analysis.
As one of the important components of electrocardiogram (ECG) signals, QRS signal represents the basic characteristics of ECG signals. The detection of QRS waves is also an essential step for ECG signal analysis. In order to further meet the clinical needs for the accuracy and real-time detection of QRS waves, a simple, fast, reliable, and hardware-friendly algorithm for real-time QRS detection is proposed. The exponential transform (ET) and proportional-derivative (PD) control-based adaptive threshold are designed to detect QRS-complex. The proposed ET can effectively narrow the magnitude difference of QRS peaks, and the PD control-based method can adaptively adjust the current threshold for QRS detection according to thresholds of previous two windows and predefined minimal threshold. The ECG signals from MIT-BIH databases are used to evaluate the performance of the proposed algorithm. The overall sensitivity, positive predictivity, and accuracy for QRS detection are 99.90%, 99.92%, and 99.82%, respectively. It is also implemented on Altera Cyclone V 5CSEMA5F31C6 Field Programmable Gate Array (FPGA). The time consumed for a 30-min ECG record is approximately 1.3 s. It indicates that the proposed algorithm can be used for wearable heart rate monitoring and automatic ECG analysis.
Chen et al. (2020) studied ECG signal analysis. ET and PD control-based adaptive threshold algorithm was evaluated on QRS detection performance (sensitivity, positive predictivity, and accuracy). The proposed ET and PD control-based adaptive threshold algorithm achieved 99.90% sensitivity, 99.92% positive predictivity, and 99.82% accuracy for QRS detection on MIT-BIH databases.