Ten common QRS detection algorithms showed high accuracy (F1 >99%) on high-quality ECGs but decreased significantly (<80%) on poor-quality and dynamic telehealth signals.
While common QRS detection algorithms perform excellently on high-quality ECGs, their accuracy drops significantly on poor-quality and dynamic telehealth signals, highlighting the need for improved algorithms for real-world wearable data.
A systematical evaluation work was performed on ten widely used and high-efficient QRS detection algorithms in this study, aiming at verifying their performances and usefulness in different application situations. Four experiments were carried on six internationally recognized databases. Firstly, in the test of high-quality ECG database versus low-quality ECG database, for high signal quality database, all ten QRS detection algorithms had very high detection accuracy ( F1 >99%), whereas the F1 results decrease significantly for the poor signal-quality ECG signals (all 95% except RS slope algorithm with 94.24% on normal ECG database and 94.44% on arrhythmia database). Thirdly, for the paced rhythm ECG database, all ten algorithms were immune to the paced beats (>94%) except the RS slope method, which only output a low F1 result of 78.99%. At last, the detection accuracies had obvious decreases when dealing with the dynamic telehealth ECG signals (all <80%) except OKB algorithm with 80.43%. Furthermore, the time costs from analyzing a 10 s ECG segment were given as the quantitative index of the computational complexity. All ten algorithms had high numerical efficiency (all <4 ms) except RS slope (94.07 ms) and sixth power algorithms (8.25 ms). And OKB algorithm had the highest numerical efficiency (1.54 ms).
Liu et al. (2018) studied ECG signal analysis. Ten QRS detection algorithms vs. Different ECG databases and signal qualities was evaluated on Detection accuracy (F1 score). Ten common QRS detection algorithms showed high accuracy (F1 >99%) on high-quality ECGs but decreased significantly (<80%) on poor-quality and dynamic telehealth signals.