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An automatic beat detection algorithm for pressure signals
Mateo Aboy1, James McNames, Tran Thong
1Electronics Engineering Technology Department, Oregon Institute of Technology, Portland, OR 97229, USA. mateoaboy@ieee.org
Insights
A new automatic beat detection algorithm accurately identifies peaks in physiological signals like intracranial pressure (ICP) and arterial blood pressure (ABP). This tool aids researchers in pulse contour analysis without proprietary limitations.
Area of Science:
- Biomedical Engineering
- Physiological Signal Processing
- Critical Care Medicine
Background:
- Beat detection algorithms are crucial for clinical applications such as pulse oximetry, cardiac arrhythmia detection, and cardiac output monitoring.
- Existing algorithms are often proprietary, limiting independent research into pulse contour analysis.
- Researchers currently rely on manual annotations or developing their own algorithms, which is time-consuming and may lack standardization.
Purpose of the Study:
- To develop an automatic, non-proprietary algorithm for detecting the primary peak in physiological pressure signals.
- To enable researchers to perform pulse contour analysis without relying on commercial, closed-source algorithms.
- To provide a reliable tool for beat detection in intracranial pressure (ICP), arterial blood pressure (ABP), and pulse oximetry (SpO2) signals.
Main Methods:
- Designed an automatic detection algorithm focusing on identifying the first peak post-heartbeat (percussion peak in ICP, systolic peak in ABP/SpO2).
- Integrated a filter bank with adaptable cutoff frequencies, heart rate spectral estimation, rank-order nonlinear filters, and decision logic.
- Prospectively evaluated algorithm performance against expert annotations on ICP, ABP, and SpO2 signals from pediatric intensive care unit patients.
Main Results:
- The algorithm demonstrated high accuracy in beat detection across multiple physiological signal types.
- Achieved a sensitivity of 99.36% and a positive predictive value of 98.43%.
- Validated on a large dataset comprising 42,539 detected beats from pediatric ICU patients.
Conclusions:
- The developed automatic beat detection algorithm offers a robust and accurate solution for analyzing physiological pressure signals.
- This non-proprietary tool can significantly aid researchers in pulse contour analysis and related clinical investigations.
- The high performance suggests clinical utility in critical care settings for real-time physiological monitoring.
Abstract:
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.
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