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Development and validation of a novel fusion algorithm for continuous, accurate, and automated R-wave detection and calculation of signal-derived metrics.

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.

journal_name

J Crit Care

journal_title

Journal of critical care

authors

Liu NT,Batchinsky AI,Cancio LC,Baker WL Jr,Salinas J

doi

10.1016/j.jcrc.2013.02.015

subject

Has Abstract

pub_date

2013-10-01 00:00:00

pages

885.e9-18

issue

5

eissn

0883-9441

issn

1557-8615

pii

S0883-9441(13)00059-2

journal_volume

28

pub_type

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