A Robust Approach Toward Recognizing Valid Arterial-Blood-Pressure Pulses

We propose a projection method based on singular value decomposition (SVD) to validate arterial blood pressure (ABP) signal in order to avoid artifacts and noise in subsequent processing. The projection has been done on 567 validated ABP beats collected from 51 patients hospitalized in University of...

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Bibliographic Details
Published in:IEEE journal of biomedical and health informatics 2010-01, Vol.14 (1), p.166-172
Main Authors: Asgari, S., Bergsneider, M., Xiao Hu
Format: Article
Language:English
Subjects:
Adolescent
Adult
Aged
Aged, 80 and over
Algorithm design and analysis
Algorithms
Arterial blood pressure
Arterial blood pressure (ABP)
Biomedical engineering
Biomedical monitoring
Blood pressure
Blood Pressure - physiology
Cleaning
Female
Humans
Laboratories
Male
Middle Aged
Models, Biological
Neurosurgery
noise and artifacts
Noise robustness
Pattern Recognition, Automated - methods
Reproducibility of Results
signal abnormality index (SAI)
Signal processing
Signal Processing, Computer-Assisted
Singular value decomposition
singular value decomposition (SVD)
valid beat recognition
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Summary:We propose a projection method based on singular value decomposition (SVD) to validate arterial blood pressure (ABP) signal in order to avoid artifacts and noise in subsequent processing. The projection has been done on 567 validated ABP beats collected from 51 patients hospitalized in University of California, Los Angeles Medical Center. Then, we compare the performance of the proposed projection method with that of a previously developed algorithm, signal abnormality index (SAI), which is a value- and trend-based approach, and has shown to be effective in cleaning the ABP waveforms. The testing dataset consists of 1336 ten-second ABP segments (18 472 ABP beats) of both valid and invalid pulses selected randomly from multiparameter intelligent monitoring for intensive care II database. The proposed projection approach that validates the signal based on the shape of the waveform achieves a true positive rate (TPR) of 99.06%, 5.43% higher than that of the SAI, and a false positive rate (FPR) of 7.69%, 17.38% lower than that of SAI. Integration of some of the SAI-value-based abnormality conditions to the validation process of SVD-based method can further improve the performance by reducing the FPR to 3.92%, while keeping the TPR at the high rate of 99.05%.
ISSN:1089-7771
2168-2194
1558-0032
2168-2208
DOI:10.1109/TITB.2009.2034845