Biomedical Engineering: Applications, Basis and Communications

In the present paper a heat transfer (HT) model to estimate survival time of individual stranded in cold water such as at sea is proposed. The HT model was derived based on the assumption that the body specific heat capacity and thermal conductance are not time dependent. The solution to the HT model simulates expected survival time as a function of water temperature, metabolism rate, skin, muscle and fat thickness, insulation thermal conductivity and thickness, height and weight of the subject. Although, these predictions must be considered approximate due to the complex nature of the variables involved, the proposed HT model can be employed to determine supplemental body insulation such as personal protective clothing to meet a predefined survival time in any given water temperature. In particular, the results obtained are useful as a decision aid in search and rescue mission in predicting survival time for shipwreck victims at sea.

As the advantages of meditation have been outlined literally, scientific exploration of the meditation phenomena becomes significant. Meditation EEG may provide an access to the mental states beyond normal consciousness. It is the first attempt to score the meditation course by EEG. Wavelet analysis and fuzzy c-means (FCM) are applied in the automatic interpretation algorithm. However, FCM applied straightforward to quantitative feature vectors often results in an over-trifling interpretation. As a consequence, this paper presents novel cluster-managing strategies for achieving an interpretation closer to the result of naked-eye examination. The running gray-scale chart, derived by extracting, clustering, and coding the EEG features, reveals five different meditation scenarios differing from those of the controlled subjects.

Snoring analysis is important for the diagnosis and treatment of sleep-related breathing disorders (SRBD). Snoring has traditionally been assessed in clinical practice from subjective accounts by the snorer and his/her partner. The use of polysomnographic recording is a standard evaluation procedure for SRBD patients. However, it is expensive and is not suitable for long term monitoring. This paper describes the development of a portable microcontroller based device for long-term, home monitoring of snoring. By analyzing the temporal feature of the snoring sound, this device can output the total snoring count, average number of snores per hour, and the number of intermittent snoring. In our tests, the average success rate in identifying snores is over 85% in a lab environment and around 70% in a home.

This paper discusses the design of micro power Sigma-delta modulator with oversampling technology. This Sigma-delta modulator design is paid special attention to its low power application of portable electronic system in digitizing biomedical signals such as Electro-cardiogram (ECG), Electroencephalogram (EEG) etc. [1]. A high performance, low power second order Sigma-delta modulator is more useful in analog signal acquisition system. Using Sigma-delta modulator can reduce the power consumption and cost in the whole system. The original biomedical signal can be reconstructed by simply applying the digital bit stream from the modulator output through a low-pass filter. The loop filter of this modulator has been implemented by using switch capacitor (SC) integrators and using simple circuitry consists of OpAmps, Comparator and DAC. In general, the resolution of modulator is about 10 bits for biomedical application. In this two order Sigma-delta modulator simulation results of the 1.8V sigma delta modulator show a 68 dB signal-to-noise-and-distortion ratio (SNDR) in 4 kHz biomedical signal bandwidth and a sampling frequency equal to 1 MHz in the 0.18 μ m CMOS technology. The power consumption is 400 μ W. It is very suitable for low power application of biomedical instrument design.

Diabetes mellitus is a kind of chronic disease which can be effectively prevented and controlled only if the blood glucose level of the patient is constantly monitored, and the health education and professional medicine care is fully supported. In this paper a role-based intelligent diabetes mobile care system with alert mechanism in full diabetic care environment is proposed and implemented. The roles in our system include patients, physicians, nurses, and home care assistants. Each of the roles uses a mobile device such as a PDA with GSM module or a mobile phone to communicate with the server so that he or she can go around without restrictions. Our system provides alert management by using an automaticl urgency strategy to assure the information correctness and notification completeness so as to improve the quality of diabetes care. The reliability test and performance test have also shown that our system can provide fast and reliable assistance to the diabetes patients. With the help of our system, it is possible to set up a whole intelligent diabetes care chain in the care center.

In this study, a functional link network with genetic algorithm is employed for extracting evoked potentials. Evoked potentials are time-varying signals typically buried in relatively large noises of electroencephalogram. Recently, to extract evoked potentials more effectively from noises, adaptive filtering techniques are widely employed for evoked potentials. In general, least-mean-square algorithm is used to adapt filter weights. However, it is well-known that the selection of step-sizes is a trade-off on the convergence rate and steady state performance. In practice, an inappropriate step-size always causes deficiency. Therefore, the proposed method employs genetic algorithm to improve this issue. Genetic algorithm is basically a kind of evolutionary strategies. The step-size candidates are regularly generated and evaluated, and the fittest candidate is selected for subsequent adaptation. Results show that the proposed method is insensitive to the selection of step sizes and reference inputs. It is applicable for extracting EP in different noise levels.

In this paper, we propose a novel registration method based on Adaptive Genetic Algorithm (adaptive-GA) to accomplish the registration task of computer tomography (CT) and its corresponding facial surface data. First, chain code method is utilized to represent the facial curve such that facial surface data obtained by a face scanner is efficiently reduced. Next, based on the concept of the genetic algorithm in continuous space (GACS), we improve its evolutional mechanisms of chromosomes' crossover and mutation to speed up the convergent process of general GA. From the experimental results, it is proved that the proposed registration method can be used for non-fiducials stereotactic brain surgeries and help surgeons to diagnose and treat brain disease correctly and conveniently.

The application of microchip technology and an intelligent analysis algorithm to design a hierarchical system for monitoring mobility changes of the elderly at house is proposed in this paper. There are four levels which are environment, measurement & sensing, signal processing, and intelligent analyses to build for interpreting the mobility index of daily life according to three different sensors, such as infrared (IR) sensors for detecting human movement, mechanical switch sensors for identifying opening and closing of doors of the bookcases, and current sensors for detecting use of computer and lights. Hence, a smart box has been designed as a passive monitoring system (i.e., non-consciousness for human) where data is gathered from a number of physically distributed sensor points within the house, linked using the mains wiring as the communications medium, and then automatically transmitted over the modem system via internet to a monitoring and supervisory centre. The preliminary study has been tested successfully in volunteer's research room during six weeks in comparison with the questionnaire results. Hence, it can be seen as a demonstration of feasibility of the applicability of microchip technology and an intelligent analysis algorithm for monitoring mobility changes of the elderly at home. But, it still needs a longer series of real testing at home, perhaps to refine the rule-base, and certainly to see how widely they are applicable.