Catheter-manometer system damped blood pressures detected by neural nets

A. Prentza, K.H. Wesseling

Research output: Contribution to journalArticleAcademicpeer-review

8 Citations (Scopus)

Abstract

AbstractwDegraded catheter-manometer systems cause distortion of blood pressure waveforms, often leading to erroneously resonant or damped waveforms, requiring waveform qua/iW control We have tried muitilayer perceptron back-propagation trained neural nets of varying architecture to detect damping on sets of normal and artificially damped brachia/ 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.
Original languageEnglish
Pages (from-to)589-595
Number of pages7
JournalMedical and Biological Engineering and Computing
Volume33
DOIs
Publication statusPublished - 1995

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Manometers
Catheters
Blood pressure
Neural networks
Damping
Blood

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title = "Catheter-manometer system damped blood pressures detected by neural nets",
abstract = "AbstractwDegraded catheter-manometer systems cause distortion of blood pressure waveforms, often leading to erroneously resonant or damped waveforms, requiring waveform qua/iW control We have tried muitilayer perceptron back-propagation trained neural nets of varying architecture to detect damping on sets of normal and artificially damped brachia/ 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.",
author = "A. Prentza and K.H. Wesseling",
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doi = "10.1007/BF02522519",
language = "English",
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journal = "Medical and Biological Engineering and Computing",
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Catheter-manometer system damped blood pressures detected by neural nets. / Prentza, A.; Wesseling, K.H.

In: Medical and Biological Engineering and Computing, Vol. 33, 1995, p. 589-595.

Research output: Contribution to journalArticleAcademicpeer-review

TY - JOUR

T1 - Catheter-manometer system damped blood pressures detected by neural nets

AU - Prentza, A.

AU - Wesseling, K.H.

PY - 1995

Y1 - 1995

N2 - AbstractwDegraded catheter-manometer systems cause distortion of blood pressure waveforms, often leading to erroneously resonant or damped waveforms, requiring waveform qua/iW control We have tried muitilayer perceptron back-propagation trained neural nets of varying architecture to detect damping on sets of normal and artificially damped brachia/ 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.

AB - AbstractwDegraded catheter-manometer systems cause distortion of blood pressure waveforms, often leading to erroneously resonant or damped waveforms, requiring waveform qua/iW control We have tried muitilayer perceptron back-propagation trained neural nets of varying architecture to detect damping on sets of normal and artificially damped brachia/ 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.

U2 - 10.1007/BF02522519

DO - 10.1007/BF02522519

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VL - 33

SP - 589

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JO - Medical and Biological Engineering and Computing

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