An open-source algorithm to detect onset of arterial blood pressure pulses

Abstract

In this paper; we present an effective algorithm for detecting the onset of anerial blood pressure (ABP) IntroductionThe ABP waveform contains rich information about the cardiovascular system, such as heart rate, systolic, mean, and diastolic arterial pressures, and it can be used to assess properties of the arterial vessel wall [l, 21. Reliable and accurate ABP pulse detection is crucial for beat-by-beat extraction and analysis of the information mentioned above. This task is rendered difficult, however, since the ABP measurement is prone to noise and artifacts (see Figure 1 (b) and (d)). Furthermore, the waveform morphology can change dramatically, even over short periods of time, in response to altered pathologic or physiologic stresses (Figure 1 (a) and (c)).Although it is the ABP pulse onset that denotes the arrival of the arterial pressure pulse at the recording site, most ABP pulse and pulse-component detection algorithms identify the peak of the ABP waveform as the fiducial

Estimation of blood pressure parameters using ex-Gaussian model

“…The beat localization can be obtained by using only the blood pressure waveform (for example by using the algorithm proposed in [3]), and the proposed model can be then used to obtain signal parameters.…”

“…Next, the characteristic points of a full beat of blood pressure signal should be estimated. A different method of estimation can be utilized including a windowed and weighted slope sum function [3], a filter bank with variable cutoff frequencies, rank-order nonlinear filters, and decision logic [4], inflection and zero-crossing points of blood pressure, and then combinatorial amplitude and interval criteria to select the onset and systolic peak [5], wavelets [6], principal components [7], waveform descriptor compared with a customized template [8], determined lines and polynomial approximation [9] or Fourier series interpolation [10].…”

Cardiac Condition Monitoring through Photoplethysmogram Signal Denoising Using Wearables: Can We Detect Coronary Artery Disease with Higher Performance Efficacy?

“…Input PPG signal is segmented using slope sum function [1]. From the segment set Ω , the most probable segment duration is computed using DBSCAN clustering.…”

“…The segments Ω k , k = 1,2, … , K are derived using slope sum function. First the PPG signal is filtered with narrow band pass filter and the filtered PPG is slope-adjusted and realigned through weighted slope sum function [1] to find the series of Ω k , k = 1,2, … , K, where K is the total number of complete heart cycles present in P s . Let , = 1,2, … , K be the segment length of Ω k .…”

Embedded System for ECG Biometrics

“…Two segmentation algorithms are studied, Hamilton [2] and Slope Sum Function (SSF) [10], being the SSF the one implemented on the embedded system for its real-time properties. Other particularity is that this SSF algorithm was first developed to detect onset waves of arterial blood pressure pulses and is herein adapted for the R complex detection.…”