Cardiovascular control and stabilization via inclination and mobilization during bed rest

Cardiovascular deconditioning has long been recognized as a characteristic of the physiological adaptation to long-term bed rest in patients. The process is thought to contribute to orthostatic intolerance and enhance secondary complications in a significant way. Mobilization is a cost-effective and simple method to maintain the cardiovascular parameters (i.e., blood pressure, heart rate) stable, counter orthostatic intolerance and reduce the risk of secondary problems in patients during long-term immobilization. The aim of this project is to control the cardiovascular parameters such as heart rate and blood pressure of bed rest patients via automated leg mobilization and body tilting. In a first step, a nonlinear model predictive control strategy was designed and evaluated on five healthy subjects and 11 bed rest patients. In a next step, a clinically feasible study was conducted on two patients. The mean values differed on average less than 1bpm from the predetermined heart rate and less than 2.5mmHg from the desired blood pressure values. These results of the feasibility study are promising, although heterogeneous disease etiologies and individual medication strongly influence the mechanically induced reactions. The long-term goal is an automation of the control of physiological signals and the mobilization of bed rest patients in an early phase of the rehabilitation process. Therefore, this new approach could help to strengthen the cardiovascular system and prevent secondary health problems arising from long-term bed rest

Cardiovascular control and stabilization via inclination and mobilization during bed rest

Cardiovascular deconditioning has long been recognized as a characteristic of the physiological adaptation to long-term bed rest in patients. The process is thought to contribute to orthostatic intolerance and enhance secondary complications in a significant way. Mobilization is a cost-effective and simple method to maintain the cardiovascular parameters (i.e., blood pressure, heart rate) stable, counter orthostatic intolerance and reduce the risk of secondary problems in patients during long-term immobilization. The aim of this project is to control the cardiovascular parameters such as heart rate and blood pressure of bed rest patients via automated leg mobilization and body tilting. In a first step, a nonlinear model predictive control strategy was designed and evaluated on five healthy subjects and 11 bed rest patients. In a next step, a clinically feasible study was conducted on two patients. The mean values differed on average less than 1 bpm from the predetermined heart rate and less than 2.5 mmHg from the desired blood pressure values. These results of the feasibility study are promising, although heterogeneous disease etiologies and individual medication strongly influence the mechanically induced reactions. The long-term goal is an automation of the control of physiological signals and the mobilization of bed rest patients in an early phase of the rehabilitation process. Therefore, this new approach could help to strengthen the cardiovascular system and prevent secondary health problems arising from long-term bed rest

Sleep-wake behaviour and the EEG in altered states of consciousness

Abstract presented at the 23rd Australasian Society for Psychophysiology Conference, 20-22 Nov 2013, Wollongong, Australia

Quantification of clinical scores through physiological recordings in low-responsive patients: a feasibility study

<p>Abstract</p> <p>Clinical scores represent the gold standard in characterizing the clinical condition of patients in vegetative or minimally conscious state. However, they suffer from problems of sensitivity, specificity, subjectivity and inter-rater reliability.</p> <p>In this feasibility study, objective measures including physiological and neurophysiological signals are used to quantify the clinical state of 13 low-responsive patients. A linear regression method was applied in nine patients to obtain fixed regression coefficients for the description of the clinical state. The statistical model was extended and evaluated with four patients of another hospital. A linear mixed models approach was introduced to handle the challenges of data sets obtained from different locations.</p> <p>Using linear backward regression 12 variables were sufficient to explain 74.4% of the variability in the change of the clinical scores. Variables based on event-related potentials and electrocardiogram account for most of the variability.</p> <p>These preliminary results are promising considering that this is the first attempt to describe the clinical state of low-responsive patients in such a global and quantitative way. This new model could complement the clinical scores based on objective measurements in order to increase diagnostic reliability. Nevertheless, more patients are necessary to prove the conclusions of a statistical model with 12 variables.</p