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Recent simulation studies have shown that a technique of multi-frequency microwave radiometry is feasible for non-invasive measurement of deep brain temperatures in the new-born infants. A five-band microwave radiometer system has been developed, and its operation in a normal electromagnetic environment is checked. Five receivers operating with a waveguide antenna and at center frequencies of 1.2, 1.65, 2.3, 3.0 and 3.6 GHz (0.4 GHz bandwidth) are calibrated using a temperature-controlled water-bath. Temperature resolutions obtained for each receiver are 0.183, 0.273, 0.148, 0.108 and 0.118 K, respectively. A temperature retrieval simulation based on these resolutions and the previously proposed algorithm shows that the confidence interval, as produced by thermal noise, is 0.62 K for the retrieved central brain temperature. If the conductivity of brain is estimated wrong by 10 %, this will result in an error of 0.3-0.4 K. The result of this work is encouraging for realization of radiometric measurement of temperature profile in a baby's head.

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In this study we present a design for a multi-frequency microwave radiometer aimed at prolonged monitoring of deep brain temperature in newborn infants and suitable for use during hypothermic neural rescue therapy. We identify appropriate hardware to measure brightness temperature and evaluate the accuracy of the measurements. We describe a method to estimate the tissue temperature distribution from measured brightness temperatures which uses the results of numerical simulations of the tissue temperature as well as the propagation of the microwaves in a realistic detailed three-dimensional infant head model. The temperature retrieval method is then used to evaluate how the statistical fluctuations in the measured brightness temperatures limit the confidence interval for the estimated temperature: for an 18 degrees C temperature differential between cooled surface and deep brain we found a standard error in the estimated central brain temperature of 0.75 degrees C. Evaluation of the systematic errors arising from inaccuracies in model parameters showed that realistic deviations in tissue parameters have little impact compared to uncertainty in the thickness of the bolus between the receiving antenna and the infant's head or in the skull thickness. This highlights the need to pay particular attention to these latter parameters in future practical implementation of the technique.

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The problem of parametric signal restoration given its blurred/nonlinearly distorted version contaminated by additive noise is discussed. It is postulated that feedforward artificial neural networks can be used to find a solution to this problem. The proposed estimator does not require iterative calculations that are normally performed using numerical methods for signal parameter estimation. Thus high speed is the main advantage of this approach. A two-stage neural network-based estimator architecture is considered in which the vector of measurements is projected on the signal subspace and the resulting features form the input to a feedforward neural network. The effect of noise on the estimator performance is analyzed and compared to the least-squares technique. It is shown, for low and moderate noise levels, that the two estimators are similar to each other in terms of their noise performance, provided the neural network approximates the inverse mapping from the measurement space to the parameter space with a negligible error. However, if the neural network is trained on noisy signal observations, the proposed technique is superior to the least-squares estimate (LSE) model fitting. Numerical examples are presented to support the analytical results. Problems for future research are addressed.

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The present status of the development of a non-invasive thermometer based on microwave radiometry at our laboratory is reported. We have developed a model fitting technique combined with a Monte Carlo technique to retrieve temperature-depth profiles from multi (4-6)-frequency-band microwave radiometric data along with confidence intervals (2-sigma) of tissue temperatures as a function of depth. In order to make the radiometric technique compatible with the heating, brightness temperatures are measured through a 1 cm thick water bolus. Results of phantom experiments are presented to demonstrate the above capabilities of the method. Numerical simulation studies have shown that 2-sigma intervals would be 1.0 K or less over a 0-4 cm range and 1.4 K at 5 cm from the surface with using a six-band, 1-5 GHz radiometer having brightness temperature resolution of 0.03 K (3 s integration time). The six-band instrument is currently being assembled at our laboratory.

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Fuzzy theory was applied to the rate control of a cardiac pacemaker which uses two parameters, respiratory rate and temperature, as the parameters for rate regulation. Using 25 fuzzy reasoning rules derived from five mongrel dogs, the pacing rates in three animals were calculated and compared with the intrinsic heart rates. It is concluded that the fuzzy method is well suited for the rate determination of a multi-parameter rate-responsive cardiac pacemaker.

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A new method using respiratory rate and temperature as the guides for optimal pacing is proposed. A pacemaker was fabricated which senses these two parameters simultaneously. The pacemaker functions by calculating the cardiac rate, which would be derived from the respiratory rate and the blood temperature. The higher of the two rates is adopted as the cardiac pacing rate, i.e., at which stimuli will be delivered. The operation was tested in a mongrel dog with complete atrioventricular block. After the induction of anesthesia, a thermistor temperature probe was inserted into right atrium and a respiratory rate sensor was attached around the chest. After administration of a pyrogenic drug, both respiratory rate and blood temperature increased. The pacing rate was increased from 178 beats/minute(bpm) at 36.4 degrees C, blood temperature, and 26.5 acts/minute(apm), respiratory rate, to 233 bpm at 40.1 degrees C and 40.0 apm. Cardiac output was increased from 2.15 liters/minute(l/pm) at the beginning to 2.50 l/pm at maximum. The transition of the guide from respiratory rate to temperature was observed at about 38 degrees C.

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A microcomputer-based temperature-sensitive cardiac pacemaker system and some preliminary experiments are described. The system consists of a microcomputer, a twin 5 in floppy disk unit, expansion and interface units, a visual display unit and a printer. It senses the blood temperature in the right atrium, determines the pacing rate and supplies the heart with stimulating pulses. System-heart interfacing is performed by the separate pacing and sensing units which communicate with the computer via a peripheral interface IC in an expansion unit. The pacing rate is determined by software, rather than a combination of hardware elements. The temperature response time of the system, from 25 degrees C to 40 degrees C is about 26 s, and this would seem to be satisfactory given that smaller and slower changes in blood temperature are normal.

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An artificial cardiac pacemaker which is sensitive to the temperature of blood in the right atrium has been fabricated. For a temperature change of 20 degrees C the circuit achieves 90% of its final response within a period of 18 s. In the authors' opinion this is satisfactory since changes in blood temperature are generally small. Cardiac output in dogs rose from 2.37 +/- 0.65 to 4.54 +/- 1.15 l/min when the rate was increased from 202.6 beats/min(b.p.m.) at 37.6 degrees C to 231.6 b.p.m. at 41 degrees C. Cardiac output was found, from statistical observation, to be improved at temperatures over 39.6 degrees C.

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