Objective The development and application of data-driven decision-support systems for medical triage, diagnostics, and prognostics pose special requirements on physiologic data. In particular, that data are reliable in order to produce meaningful results. We describe a method that automatically estimates the reliability of reference heart rates (HRr) derived from electrocardiogram (ECG) waveforms and photoplethysmogram (PPG) waveforms recorded by vital-signs monitors. The reliability is quantitatively expressed through a quality index (QI) for each HRr.

Design The proposed method estimates the reliability of heart rates from vital-signs monitors by: (1) assessing the quality of the ECG and PPG waveforms, (2) separately computing heart rates from these waveforms, and (3) concisely combining this information into a QI, which considers the physical redundancy of the signal sources and independence of heart rate calculations. The assessment of the waveforms is performed by a Support Vector Machine classifier and the independent computation of heart rate from the waveforms is performed by an adaptive peak identification technique, termed ADAPIT, which is designed to filter out motion-induced noise.

Results We evaluate the method against 158 randomly selected data samples of trauma patients collected during helicopter transport, each sample consisting of seven-second ECG and PPG waveform segments and their associated HRr. We compare the results of the algorithm against manual analysis performed by human experts, and find that in 92% of the cases the algorithm either matches or is more conservative than the human's QI qualification. In the remaining 8% of the cases, the algorithm infers a less conservative QI, though in most cases this was because of algorithm/human disagreement over ambiguous waveform quality. Should these ambiguous waveforms be re-labeled, the misclassification rate would drop from 8 to 3%.

Conclusion This method provides a robust approach for automatically assessing the reliability of large quantities of heart rate data and the waveforms from which they are derived.