Accurate RR-interval extraction from single-lead, telehealth electrocardiogram signals

Devices that record single-lead ECGs, such as smartwatches and handheld ECG recorders, hold promise for detecting undiagnosed atrial fibrillation (AF). Accurately extracting RR-intervals from telehealth ECGs is key for heart rhythm assessment. The aim of this study was to develop an algo-rithm to extract RR-intervals from telehealth ECGs, and assess whether the extracted RR-intervals are accurate and therefore suitable for analysis. Two datasets of 30-second handheld ECGs were used: TELE ECG Database (250 ECGs) and SAFER ECG dataset (507 ECGs). One of three high-performance primary QRS detectors, selected based on previous evidence, was used to detect QRS complexes and extract RR-intervals. These detec-tions were compared to those from a secondary QRS detector to assess accu-racy. All pairs of 3 primary and 18 secondary QRS detectors were tested. Ac-curacy was quantified using mean absolute error (MAE) and the proportion of time RR-intervals were assessed as accurate (coverage). Best performance was achieved using unsw and nk as primary and secondary detectors, with MAEs of 19.8ms and 16.3ms, and coverages of 89% on TELE and SAFER respectively. Using a single detector alone produced higher MAEs (23.8ms and 43.9ms on TELE; 38.2ms and 41.7ms on SAFER). Accuracy was similar between AF and non-AF, but reduced on low-quality signals (50.8 vs. 7.7ms, p<0.001). In conclusion, the recommended algorithm produced more accu-rate RR-intervals than using a single QRS detector, maintaining accuracy during AF, although accuracy was reduced on low-quality signals. HighlightsO_LIAlgorithm extracts RR-intervals from ECGs and assesses their accuracy C_LIO_LIAlgorithm was developed using two datasets of ECGs collected using different devices C_LIO_LIThe impacts of arrhythmia and noise on algorithm performance were assessed C_LIO_LIThe algorithm uses a pair of openly available QRS detection algorithms C_LI