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. Highlights Algorithm extracts RR-intervals from ECGs and assesses their accuracy Algorithm was developed using two datasets of ECGs collected using different devices The impacts of arrhythmia and noise on algorithm performance were assessed The algorithm uses a pair of openly available QRS detection algorithms