Multichannel QRS Detection by Combinatorial Optimization

This study aims to show that multi-channel combinatorial optimization enables sinus rhythm RR interval tracking in electrocardiograms that are too noisy for existing algorithms to handle. The same detector achieves very high performance for arrhythmias and irregular QRS shapes in low/medium noise. To demonstrate high noise performance, results are given for the Non-Invasive Multimodal Foetal ECG-Doppler Dataset for Antenatal Cardiology Research ("NInFEA"). For 57 out of 60 NInFEA records, 84% and 95% of the estimated RR intervals were within 5 ms and 10 ms, respectively, of the ground truth RR intervals. The multi-channel methodology also produces state of the art results when applied to the MIT-BIH Arrhythmia Database ("MIT-BIH DB"), which includes many abnormal rhythms and QRS shapes. For the MIT-BIH DB, the sensitivity (SE) and positive predictive value (PPV) were 99.93% and 99.96% respectively. Although a rules based, Bayesian motivated algorithm is described, combinatorial optimization lends itself to neural network implementations. The strong performance in all noise conditions and flexible framework suggest a way forward for QRS detection in the new era of wearable devices.