TY - JOUR
T1 - An automatic beat detection algorithm for pressure signals
AU - Aboy, Mateo
AU - McNames, James
AU - Thong, Tran
AU - Tsunami, Daniel
AU - Ellenby, Miles S.
AU - Goldstein, Brahm
N1 - Funding Information:
The authors wish to acknowledge the support of the North-west Health Foundation and the Doernbecher Children’s Hospital Foundation.
Funding Information:
Manuscript received October 30, 2003; revised November 14, 2004. This work was supported in part by the Thrasher Research Foundation, in part by the Northwest Health Foundation, and in part by the Doernbecher Children’s Hospital Foundation. Asterisk indicates corresponding author. *M. Aboy is with the Electronics Engineering Technology Department, Oregon Institute of Technology, Portland, OR 97229 USA and also with the Biomedical Signal Processing Laboratory, Department of Electrical and Computer Engineering at Portland State University, 1900 SW 4th Ave., Portland, OR 97201 USA (e-mail: mateoaboy@ieee.org).
PY - 2005/10
Y1 - 2005/10
N2 - 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 (SpO 2) 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 SpO 2 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.
AB - 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 (SpO 2) 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 SpO 2 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.
KW - Arterial blood pressure (ABP)
KW - Component detection
KW - Intracranial pressure (ICP)
KW - Pressure beat detection
KW - Pulse contour analysis
KW - Pulse oximetry (SpO )
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U2 - 10.1109/TBME.2005.855725
DO - 10.1109/TBME.2005.855725
M3 - Article
C2 - 16235652
AN - SCOPUS:26444594252
SN - 0018-9294
VL - 52
SP - 1662
EP - 1670
JO - IEEE Transactions on Biomedical Engineering
JF - IEEE Transactions on Biomedical Engineering
IS - 10
ER -