Key result
The adaptive thresholding QRS detection algorithm achieved 99.29% sensitivity and 99.68% positive predictivity on the MIT-BIH Arrhythmia Database, with lower processing load than counterparts.
A novel low-complexity QRS detection algorithm reduces computational load while maintaining high positive predictivity, though with a slight trade-off in sensitivity.
In this study, a QRS detection algorithm with a low processing load based on time-domain thresholding is proposed. The ECG signal is filtered only with a low pass filter to reduce the computational load. After the filtering, derivation and squaring are also performed. In the Thresholding stage, a linear decreasing threshold voltage method using addition operation instead of multiplication is proposed. Simulations on MIT-BIT Arrhythmia Database have yielded 99.2925% sensitivity (% Se) and 99.6759% positive predictivity (+ P). The proposed algorithm is compared with two similar algorithms in terms of both performance and processing load. It is shown that the proposed algorithm is better than its counterparts, especially in terms of processing load. However, it is observed that it gave worse results in terms of Sensitivity (% Se).
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Ertuğrul KARAKULAK (2022) studied Arrhythmia (ECG signal processing). Adaptive thresholding based low complexity QRS detection algorithm vs. Two similar algorithms was evaluated on Sensitivity and positive predictivity. The adaptive thresholding QRS detection algorithm achieved 99.29% sensitivity and 99.68% positive predictivity on the MIT-BIH Arrhythmia Database, with lower processing load than counterparts.
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