A point process local likelihood algorithm for robust and automated heart beat detection and correction

Robust and automated classification and correction of ECG-derived heart beats are a necessary prerequisite for an accurate real-time estimation of measures of heart rate variability and cardiovascular control. In particular, the low quality of the signal, as well as the presence of recurring arrhythmic events, may significantly affect estimation accuracy. We here present a novel point process based method for a real time R-R interval error detection and correction. Results of detection analysis over data from the benchmark MIT-BIH arrhythmia database demonstrate that the proposed algorithm achieves 99.97% accuracy (98.23% sensitivity, 99.98% specificity and 95.69% positive predictive value), outperforming state-of-the-art algorithms. Further results on simulated data demonstrate the efficacy of the detection and correction method.