In vitro calibration of a system for measurement of in vivo convective heat transfer coefficient in animals

BACKGROUND: We need a sensor to measure the convective heat transfer coefficient during ablation of the heart or liver. METHODS: We built a minimally invasive instrument to measure the in vivo convective heat transfer coefficient, h in animals, using a Wheatstone-bridge circuit, similar to a hot-wire anemometer circuit. One arm is connected to a steerable catheter sensor whose tip is a 1.9 mm × 3.2 mm thin film resistive temperature detector (RTD) sensor. We used a circulation system to simulate different flow rates at 39°C for in vitro experiments using distilled water, tap water and saline. We heated the sensor approximately 5°C above the fluid temperature. We measured the power consumed by the sensor and the resistance of the sensor during the experiments and analyzed these data to determine the value of the convective heat transfer coefficient at various flow rates. RESULTS: From 0 to 5 L/min, experimental values of h in W/(m(2)·K) were for distilled water 5100 to 13000, for tap water 5500 to 12300, and for saline 5400 to 13600. Theoretical values were 1900 to 10700. CONCLUSION: We believe this system is the smallest, most accurate method of minimally invasive measurement of in vivo h in animals and provides the least disturbance of flow

Designing and implementing an electronic system to control moving orthosis virtual mechanical system to emulate lower limb

This work is intended to individuals that have paraplegia as a result of spinal cord injury (SCI) and lost motion mobility, so the goal of this paper is to restore some degree of legged mobility to those people. The paper investigates the development of kinematic and dynamic model for the leg. Modeling and simulation of the system under investigation is implemented and evaluated using MATLAB/ SIMULINK®. Practical results obtained from under investigating lower limb model are compared with their counter parts of normal gait pattern. The error between the desired gait pattern and the obtained results is corrected using optimal control approach. It’s shown that the obtained results have validated the proposed approach. Both practical and simulation results have demonstrated the stability of the proposed control approach. It is believed that the proposed approach will help to establish an integrated system which emulates precisely the normal gait of human

A portable Raspberry Pi-based system for diagnosis of heart valve diseases using automatic segmentation and artificial neural networks

This study proposes a Raspberry Pi-based system for the diagnosis of heart valve diseases as a primary tool to improve the diagnostic accuracy of physicians. The proposed system is able to detect and classify nine common valvular heart cases encompassing eight types of heart valve diseases as well as the normal case of valves. The design and development of the proposed system are mainly divided into two phases, namely development of a disease classification approach, and design and implementation of the diagnostic hardware system. The developed disease classification approach is comprised of five stages, namely obtaining phonocardiogram (PCG) signals, preprocessing, segmentation using a proposed automatic algorithm, feature extraction in three domains (time, frequency, and wavelet decomposition domains) and classification using a backpropagation neural network. The hardware of the diagnostic system consists of a PCG signal acquisition module connected to a processing and displaying unit, which is represented by a Raspberry Pi connected to a touch screen. Where the developed disease classification approach is implemented in the software of the Raspberry Pi to enable it to detect the diseases in real time and fully automatically. The proposed system was clinically tested on 50 real subjects encompassing the nine cases. The performance of the diagnostic system is obtained with an accuracy of 96%, sensitivity of 95.23%, and specificity of 100%