Model Reference Control of Brain Temperature with 2 Degrees of Freedom for Brain Hypothermia Treatment

Mitaka

et al. 2010

THC

Automatic control systems of brain temperature for water surface-cooling were first-ever applied to the brain hypothermic treatment of patients. A patient in ICU was regarded as a unity controlled system with an input (temperature of water into blanket) and an output (tympanic membrane temperature). The proposed algorithm of optimal-adaptive and fuzzy control laws inclusive of our developed cooling and warming machine were well confirmed during the hypothermic course to keep brain temperature of patients within its allowable range. It was well controlled without much influence due to room temperature, metabolic and circulatory change caused by various medical treatments including the effect of nonlinear and timevarying characteristics of individual patients. The clinical control of brain temperature was almost continuously performed in around 10 days, under the brain temperature between 35 • C and 37 • C scheduled by physicians according to the state of patients. Their state had been monitored during the therapeutic course of pharmacological treatment with almost everyday examinations by CT imaging, referring various vital signs inclusive of the temperature of urinary bladder with continuous measurement of intracranial pressure by a catheter placement in cerebral sinus. The patients had good recovery to their rehabilitation after mild hypothermia by the proposed automatic control systems.

Section: Methods Based On Mathematical Background 421 Characteristmentioning

confidence: 99%

Section: Configuration Of Thermal Hardware Systemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Clinical system engineering of long-term automatic thermal control during brain hypothermia under changing conditions

Wakamatsu

Mitaka

et al. 2010

THC

“…Figure 3 represents the developed algorithm of brain temperature control by air-surface cooling. This algorithm consists of two feedback control loops similar to the automatic control system by water-surface cooling [7]. One is the optimal control loop regarding the characteristic model as controlled objects.…”

Section: Computermentioning

confidence: 99%

“…Thus, an automatic system of it was developed for not only precise control of brain temperature but also reduction of burden of clinical staff [7,8]. This system has an automatic algorithm on the basis of control theories, by which the temperature of water-cooling blankets is suitably calculated and automatically adjusted for brain temperature control.…”

Section: Introductionmentioning

confidence: 99%

Automatic control system of brain temperature by air-surface cooling for therapeutic hypothermia

2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

2013

An automatic control system of brain temperature by air-surface cooling was developed for therapeutic hypothermia, which is increasingly recommended for hypoxic-ischemic encephalopathy after cardiac arrest and neonatal asphyxia in several guidelines pertinent to resuscitation. Currently, water-surface cooling is the most widespread cooling method in therapeutic hypothermia. However, it requires large electric power for precise control and also needs water-cooling blankets which have potential for compression of patients by its own weight and for water leakage in ICU. Air-surface cooling does not have such problems and is more suitable for clinical use than water-surface cooling, because air has lower specific heat and density as well as the impossibility of the contamination in ICU by its leakage. In the present system, brain temperature of patients is automatically controlled by suitable adjustment of the temperature of the air blowing into the cooling blankets. This adjustment is carried out by the regulation of mixing cool and warm air using proportional control valves. The computer in the developed control apparatus suitably calculates the air temperature and rotation angle of the valves every sampling time on the basis of the optimal-adaptive control algorithm. Thus, the proposed system actualizes automatic control of brain temperature by the inputting only the clinically desired temperature of brain. The control performance of the suggested system was verified by the examination using the mannequin in substitution for an adult patient. In the result, the control error of the head temperature of the mannequin was 0.12 °C on average in spite of the lack of the production capacity of warm air after the re-warming period. Thus, this system serves as a model for the clinically applied system.

Development of Automatic Brain Temperature Controller Based on Conditions of Clinical Use

Wakamatsu

2014

Elect Comm in Japan

Self Cite

SUMMARY A new automatic brain temperature controller was developed based on the conditions required for clinical use from the viewpoint of various aspects of feasibility, in particular an electric power consumption of less than 1500 W in an intensive care unit. An adaptive algorithm was employed to deal with individual time‐varying characteristic changes of patients. The controller in water‐surface cooling hypothermia requires significant power for frequent regulation of the water temperature of cooling blankets. Thus, in this study, the power consumption of the controller was checked by several kinds of tests involving simulated control of brain temperature with a mannequin that had thermal characteristics similar to those of adult patients. The required accuracy for therapeutic brain hypothermia, specifically a control deviation within ±0.1 °C, was experimentally confirmed in terms of the root mean square control error, even though the present controller consumes less energy than a conventional controller. It can maintain a reserve power margin of more than 300 W even during full operation. The clinically required water temperature was also confirmed within the limits of the power supply. Thus, its practical application is highly desirable, since it will reduce the physical burden on medical staff and will offer greater usability and better medical cost performance.