Catheter-manometer system damped blood pressures detected by neural nets

Degraded catheter-manometer systems cause distortion of blood pressure waveforms, often leading to erroneously resonant or damped waveforms, requiring waveforms quality control. We have tried multilayer perceptron back-propagation trained neural nets of varying architecture to detect damping on sets...

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Bibliographic Details
Published in:Medical & biological engineering & computing 1995-07, Vol.33 (4), p.589-595
Main Authors: PRENTZA, A, WESSELING, K. H
Format: Article
Language:English
Subjects:
Adult
Aged
Biological and medical sciences
Blood Pressure Determination - methods
Brachial Artery - physiology
Catheterization, Peripheral
Computerized, statistical medical data processing and models in biomedicine
Humans
Manometry
Medical sciences
Middle Aged
Models and simulation
Neural Networks (Computer)
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Summary:Degraded catheter-manometer systems cause distortion of blood pressure waveforms, often leading to erroneously resonant or damped waveforms, requiring waveforms quality control. We have tried multilayer perceptron back-propagation trained neural nets of varying architecture to detect damping on sets of normal and artificially damped brachial arterial pressure waves. A second-order digital simulation of a catheter-manometer system is used to cause waveform distortion. Each beat in the waveforms is represented by an 11 parameter input vector. From a group of normotensive or (borderline) hypertensive subjects, pressure waves are used to statistically test and train the neural nets. For each patient and category 5-10 waves are available. The best neural nets correctly classify about 75-85% of the individual beats as either adequate or damped. Using a single majority vote classification per subject per damped or adequate situation, the best neural nets correctly classify at least 16 of the 18 situations in nine test subjects (binomial P = 0.001). More importantly, these neural nets can always detect damping before clinically relevant parameters such as systolic pressure and computed stroke volume are reduced by more than 2%. Neural nets seem remarkably well adapted to solving such subtle problems as detecting a slight damping of arterial pressure waves before it affects waveforms to a clinically relevant degree.
ISSN:0140-0118
1741-0444
DOI:10.1007/BF02522519