Does a novel automatic beat detection algorithm accurately identify beats in pressure signals compared to expert annotations in pediatric intensive care unit patients?
A newly developed automatic beat detection algorithm for pressure signals demonstrated high sensitivity and positive predictivity compared to expert annotations in pediatric ICU patients.
Beat detection algorithms have many clinical applications including pulse oximetry, cardiac arrhythmia detection, and cardiac output monitoring. Most of these algorithms have been developed by medical device companies and are proprietary. Thus, researchers who wish to investigate pulse contour analysis must rely on manual annotations or develop their own algorithms. We designed an automatic detection algorithm for pressure signals that locates the first peak following each heart beat. This is called the percussion peak in intracranial pressure (ICP) signals and the systolic peak in arterial blood pressure (ABP) and pulse oximetry (SpO2) signals. The algorithm incorporates a filter bank with variable cutoff frequencies, spectral estimates of the heart rate, rank-order nonlinear filters, and decision logic. We prospectively measured the performance of the algorithm compared to expert annotations of ICP, ABP, and SpO2 signals acquired from pediatric intensive care unit patients. The algorithm achieved a sensitivity of 99.36% and positive predictivity of 98.43% on a dataset consisting of 42,539 beats.
Aboy et al. (2005) studied this question.