Robust algorithm to locate heart beats from multiple physiological waveforms by individual signal detector voting.

Alarm fatigue is a top medical device hazard in patient monitoring that could be reduced by merging physiological information from multiple sensors, minimizing the impact of a single sensor failing. We developed a heart beat detection algorithm that utilizes multi-modal physiological signals (e.g. electrocardiogram, blood pressure, stroke volume, photoplethysmogram and electro-encephalogram) by merging the heart beats obtained from signal-specific detectors. We used the PhysioNet/Computing in Cardiology Challenge 2014 training set to develop the algorithm, and we refined it with a mix of signals from the multiparameter intelligent monitoring in intensive care (MIMIC II) database and artificially disrupted waveforms. The algorithm had an average sensitivity of 95.67% and positive predictive value (PPV) of 92.28% when applied to the PhysioNet/Computing in Cardiology Challenge 2014 200 record training set. On a refined dataset obtained by removing 5 records with arrhythmias and inconsistent reference annotations we obtained an average sensitivity of 97.43% and PPV of 94.17%. Algorithm performance was assessed with the Physionet Challenge 2014 test set that consisted of 200 records (each up to 10 min length) containing multiple physiological signals and reference annotations verified by the PhysioNet/Computing in Cardiology Challenge 2014 organizers. Our algorithm had a sensitivity of 92.74% and PPV of 87.37% computed over all annotated beats, and a record average sensitivity of 91.08%, PPV of 86.96% and an overall score (average of all 4 measures) of 89.53%. Our algorithm is an example of a data fusion approach that can improve patient monitoring and reduce false alarms by reducing the effect of individual signal failures.