Why the study?
Standard QRS detector benchmarking is flawed by large temporal error margins and noise-free databases that mask jitter and overestimate accuracy.
Standard ECG QRS detection benchmarking overestimates accuracy; using sample precision error margins and realistic noisy data reveals significant performance drops in popular algorithms, which is critical for downstream metrics like heart rate variability.
May inflate HRV reliability in clinical monitoring; challenges standard ECG benchmarks and leaves open noisy-data validation.
QRS detection within an electrocardiogram (ECG) is the basis of virtually any further processing and any error caused by this detection will propagate to further processing stages. However, standard benchmarking procedures of QRS detectors are seriously flawed because they report almost always close to 100% accuracy for any QRS detection algorithm. This is due to the use of large temporal error margins and noise-free ECG databases which grossly overestimate their performance. The use of a large fixed error margin masks temporal jitter between detection and ground truth measurements. Here, we present a new performance measure (JF) which combines temporal jitter with the F-score, and also an ECG database with decreasing levels of signal to noise ratios based on noise generated from different tasks. Our new performance measure JF fully encompasses all the types of errors that can occur, equally weights them and provides a percentage value which allows direct comparison between QRS detection algorithms. In combination with the new noisy ECG database, the JF performance measure now varies between 50% and 100% for different detectors and signal to noise conditions thereby making it possible to find the best detector for an application.
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Porr et al. (2019) studied this question.
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