An automated method for detecting systolic peaks from arterial blood pressure signals

Abstract

In this paper, we present an automatic method for determining time-location of systolic peak in arterial blood pressure (ABP) signals. The method consists of four major steps: Gaussian derivative filtering, nonlinear peak amplification, Gaussian derivative based peak finding scheme, and peak position adjustment procedure. The method is tested and validated using the standard MIT-BIR Polysomnographic database containing a wide range of ABP signals, artifacts and high-frequency noises. Our results demonstrate that the proposed method can achieve better peak detection performance while maintaining very small detection error rates for both clean and noisy ABP signals. The method achieves an average sensitivity of 99.89% and positive predictivity of 99.59% on test ABP datasets consisting of 67,125 beats. Unlike other existing methods, our method is quite straightforward and simple in the sense that it does not use search-back algorithms with secondary thresholds.