Development and validation of a novel fusion algorithm for continuous, accurate, and automated R-wave detection and calculation of signal-derived metrics.
Journal of Critical Care · 2013-04-22 · Method & validation
Abstract
PURPOSE: Previous studies have shown that heart rate complexity may be a useful indicator of patient status in the critical care environment but will require continuous, accurate, and automated R-wave detection (RWD) in the electrocardiogram (ECG). Although numerous RWD algorithms exist, accurate detection remains a challenge. The purpose of this study was to develop and validate a novel fusion algorithm (Automated Electrocardiogram Selection of Peaks, or AESOP) that combines the strengths of several well-known algorithms to provide a more reliable real-time solution to the RWD problem. MATERIALS AND METHODS: This study involved the ECGs of 108 prehospital patient records and 32 ECGs from a conscious sedated porcine model of hemorrhagic shock. The criterion standard for validation was manual verification of R waves. RESULTS: For 108 human ECG records, the AESOP algorithm overall outperformed each of its component algorithms. In addition, for 32 swine ECG records, AESOP achieved an R-wave sensitivity of 97.9% and a positive predictive value of 97.5%, again outperforming its component algorithms. CONCLUSION: By fusing several best algorithms, AESOP uses the strengths of each algorithm to perform more robustly and reliably in real time. The AESOP algorithm will be integrated into a real-time heart rate complexity software program for decision support and triage in critically ill patients.