Automated RR Interval Detection and Quality Assessment in Telehealth Electrocardiograms

Mobile devices like smartwatches and handheld ECG recorders which can record single-lead ECGs which could be used to detect atrial fibrillation.A key step in using these ECGs is extracting RR-intervals.We aimed to develop an algorithm to automatically: (i) derive RRintervals from ECGs; and (ii) predict whether they are accurate enough for diagnosis.The TELE ECG Database was used, containing 250 ECGs recorded using a handheld ECG device with manual QRS annotations.An algorithm was designed to: (i) detect QRS complexes and extract RR-intervals using a primary QRS detector; and (ii) assess the reliability of each RR-interval through comparison with QRS complexes detected by a secondary QRS detector.Performance was assessed using each combination of three primary and 18 secondary open-source QRS detection algorithms.The algorithm with the lowest mean absolute error (MAE) in RR-intervals deemed to be reliable used 'unsw' (primary) and 'rpeak' (secondary) detectors, achieving a mean MAE of 24ms with 33% of RRintervals deemed reliable.The algorithm with the lowest mean MAE, whilst deeming at least 90% of RR-intervals reliable, used 'unsw' (primary) and 'nk' (secondary) detectors, achieving a mean MAE of 33ms with 92% of RRintervals deemed reliable.These algorithms accurately derive RR-intervals from telehealth ECGs.