Details
| Originalsprache | Englisch |
|---|---|
| Aufsatznummer | 005 |
| Seitenumfang | 3 |
| Fachzeitschrift | Proceedings on Automation in Medical Engineering |
| Jahrgang | 1 |
| Ausgabenummer | 1 |
| Publikationsstatus | Veröffentlicht - 2020 |
| Extern publiziert | Ja |
Abstract
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in: Proceedings on Automation in Medical Engineering, Jahrgang 1, Nr. 1, 005, 2020.
Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › Peer-Review
}
TY - JOUR
T1 - A self-calibrating and learning control system for non-invasive continuous perioperative blood pressure measurement
AU - Borchers, Patrick
AU - Laidig, Daniel
AU - Geus, Paul
AU - Schauer, Thomas
AU - Seel, Thomas
N1 - Funding Information: The project RadialisPeriOP(FKZ 13GW0190F)is supported by the German Federal Ministry of Education and Research.Conflict of interest: Authors state no conflictof interest.
PY - 2020
Y1 - 2020
N2 - State-of-the-artnon-invasive blood pressure measurement devices according to Riva-Rocci only allow for non-continuous measurementsevery few minutes.Thispaper presents a non-invasive system that can measure the blood pressurecontinuously.The systemcontainsa pressure control loop as well asan Iterative Learning Controlloop.The pressure controllerinitiallyperforms a calibration procedure to adapt itself to different pressuredynamics. Furthermore, time-scale transformation is applied for the Iterative Learning Controlto enable the system to deal with varying heart rates. Thementioned system properties render it well suited forblood pressure monitoring during surgery.
AB - State-of-the-artnon-invasive blood pressure measurement devices according to Riva-Rocci only allow for non-continuous measurementsevery few minutes.Thispaper presents a non-invasive system that can measure the blood pressurecontinuously.The systemcontainsa pressure control loop as well asan Iterative Learning Controlloop.The pressure controllerinitiallyperforms a calibration procedure to adapt itself to different pressuredynamics. Furthermore, time-scale transformation is applied for the Iterative Learning Controlto enable the system to deal with varying heart rates. Thementioned system properties render it well suited forblood pressure monitoring during surgery.
U2 - 10.18416/AUTOMED.2020
DO - 10.18416/AUTOMED.2020
M3 - Article
VL - 1
JO - Proceedings on Automation in Medical Engineering
JF - Proceedings on Automation in Medical Engineering
IS - 1
M1 - 005
ER -