Improving Systems Dynamics by Means of Advanced Signal Processing -Mathematical, Laboratorial and Clinical Evaluation of Propofol Monitoring in Breathing Gas

Abstract: Electrochemical sensors are used in various gas measurement applications and are available for different gases. Depending on the application, the sensor might need to be installed far away from the actual measurement site, requiring the use of long sampling lines. Examples are portable gas measurement devices in which remote locations like tanks and chemical reactors need to be monitored. But also medical applications, where the sensors cannot be positioned in close vicinity to the patient, are common like, e.g., the side-stream measurement of breathing gas. Due to the characteristics of electrochemical sensors and to the adsorption and desorption behavior of sampling lines for different gases, the electrical sensor signal may indicate long response times. In this paper, we propose an on-line signal processing algorithm which is capable to significantly improve the performance. After characterizing the dynamic behavior of the sensor system, a properly designed deconvolution filter is used to reduce response time and signal noise. Within this article, we also provide an http://www.sensorsportal.com/HTML/DIGEST/P_2747.htm Sensors & Transducers, Vol. 193, Issue 10, October 2015, pp. 145-153 146 example of this algorithm for a novel electrochemical sensor for the measurement of the anesthetic agent propofol in exhaled air. For this application, the acceleration is prerequisite for the measurement chain to be of practical use in a clinical setting. Our goals, to establish measurement dynamics to record the physiologic parameter and to reduce non-physiological disturbances, were achieved with additional reserves. This article is based o

An analysis of variance type method to describe and compare steady states in clinical data

Identifying and comparing different steady states is an important task for clinical decision making. Data from unequal sources, comprising diverse patient status information, have to be interpreted. In order to compare results an expressive representation is the key. In this contribution we suggest a criterion to calculate a context-sensitive value based on variance analysis and discuss its advantages and limitations referring to a clinical data example obtained during anesthesia. Different drug plasma target levels of the anesthetic propofol were preset to reach and maintain clinically desirable steady state conditions with target controlled infusion (TCI). At the same time systolic blood pressure was monitored, depth of anesthesia was recorded using the bispectral index (BIS) and propofol plasma concentrations were determined in venous blood samples. The presented analysis of variance (ANOVA) is used to quantify how accurately steady states can be monitored and compared using the three methods of measurement

Improving Systems Dynamics by Means of Advanced Signal Processing – Mathematical, Laboratorial and Clinical Evaluation of Propofol Monitoring in Breathing Gas

Electrochemical sensors are used in various gas measurement applications and are available for different gases. Depending on the application, the sensor might need to be installed far away from the actual measurement site, requiring the use of long sampling lines. Examples are portable gas measurement devices in which remote locations like tanks and chemical reactors need to be monitored. But also medical applications, where the sensors cannot be positioned in close vicinity to the patient, are common like, e.g., the side-stream measurement of breathing gas. Due to the characteristics of electrochemical sensors and to the adsorption and desorption behavior of sampling lines for different gases, the electrical sensor signal may indicate long response times. In this paper, we propose an on-line signal processing algorithm which is capable to significantly improve the performance. After characterizing the dynamic behavior of the sensor system, a properly designed deconvolution filter is used to reduce response time and signal noise. Within this article, we also provide an example of this algorithm for a novel electrochemical sensor for the measurement of the anesthetic agent propofol in exhaled air. For this application, the acceleration is prerequisite for the measurement chain to be of practical use in a clinical setting. Our goals, to establish measurement dynamics to record the physiologic parameter and to reduce non-physiological disturbances, were achieved with additional reserves. This article is based on 1 and is extended by original clinical data. As an example, we present propofol monitoring in breath of one patient in order to demonstrate the performance of the introduced algorithm in a real clinical application. We proved that the electrochemical sensor, associated with the provided algorithm, is capable for real-time monitoring in a clinical setting