A design and construction of 4 channel biotelemetry device employing indoor.

In this study, 4 channel biotelemetry device was designed and implemented for monitoring physiological and biological signals of the patients at indoor. Body temperature, respiratory rate, heart rate, electrocardiogram (ECG) and electromyogram (EMG) signals were transmitted using 10 mW 433 MHz and 500 mW 868 MHz FM/FSK transmitter with 9.6 Kps transmission speed. At indoor, receive signal strength indicator (RSSI) levels were measured and compared with 433 and 868 MHz carrier frequency. The placement of repeater at indoor was decided as a result of RSSI measurement.

Wireless transmission of ECG signal.

In this study, the ECG signal were measured as noiseless with Ag/AgCl electrodes, then converted digital data using pulse code modulation (PCM) technique. The digital data were then sent to receiver where it is in 433 MHz FM/FSK transmitter, which has 10 mW output power and 9.6 Kps transmission speed. At the receiver, the digital data were reconverted to analog signal to monitor and record on the PC with Sonic Foundry Sound Forge 6.0 program. The power spectrum of measured ECG signal was formed using Matlab program. As a result, the medical supporting system is realized for monitoring the ECG signal on PC of human with chronic heart disease in their living surroundings.

Classification of EMG signals using PCA and FFT.

In this study, the fast Fourier transform (FFT) analysis was applied to EMG signals recorded from ulnar nerves of 59 patients to interpret data. The data of the patients were diagnosed by the neurologists as 19 patients were normal, 20 patients had neuropathy and 20 patients had myopathy. The amount of FFT coefficients had been reduced by using principal components analysis (PCA). This would facilitate calculation and storage of EMG data. PCA coefficients were applied to multilayer perceptron (MLP) and support vector machine (SVM) and both classified systems of performance values were computed. Consequently, the results show that SVM has high anticipation level in the diagnosis of neuromuscular disorders. It is proved that its test performance is high compared with MLP.

Use of support vector machines and neural network in diagnosis of neuromuscular disorders.

In this study the performance of support vector machine (SVM)and back-propagation neural network were applied to analyze the classification of the electromyogram (EMG) signals obtained from normal, neuropathy and myopathy subjects. By using autoregressive (AR) modeling, AR coefficients were obtained from EMG signals. Moreover, the support vector machine and artificial neural network (ANN) were used as base classifiers. The AR coefficients were benefited as inputs for SVM and ANN. Besides, these coefficients were tested both in ANN and SVM. The results show that SVM has high anticipation level in the diagnosis of neuromuscular disorders. It is proved that its test performance is high compared with ANN.

Wavelet-based neural network analysis of ophthalmic artery Doppler signals.

In this study, ophthalmic artery Doppler signals were recorded from 115 subjects, 52 of whom had ophthalmic artery stenosis while the rest were healthy controls. Results were classified using a wavelet-based neural network. The wavelet-based neural network model, employing the multilayer perceptron, was used for analysis of ophthalmic artery Doppler signals. A multilayer perceptron neural network (MLPNN) trained with the Levenberg-Marquardt algorithm was used to detect stenosis in ophthalmic arteries. In order to determine the MLPNN inputs, spectral analysis of ophthalmic artery Doppler signals was performed using wavelet transform. The MLPNN was trained, cross validated, and tested with training, cross validation, and testing sets, respectively. All data sets were obtained from ophthalmic arteries of healthy subjects and subjects suffering from ophthalmic artery stenosis. The correct classification rate was 97.22% for healthy subjects, and 96.77% for subjects having ophthalmic artery stenosis. The classification results showed that the MLPNN trained with the Levenberg-Marquardt algorithm was effective to detect ophthalmic artery stenosis.

Theory and applications of telemedicine.

In this study, telemedicine and the use of advanced telemedicine technologies are explained. Telemedicine is the use of modern telecommunications and information technologies for the provision of clinical care to individuals at a distance, and transmission of information to provide that care. Telemedicine can be used for decision making, remote sensing, and collaborative arrangements for the real-time management of patients at a distance. The use of telecommunications and information technologies in providing health services is determined. Telemedicine is described as combination of topics from the fields of telecommunication, medicine, and informatics. The medical systems infrastructure consisting of the equipment and processes used to acquire and present clinical information and to store and retrieve data are explained in details. The challenges existing in telemedicine development in different countries are given. Technological, political, and professional barriers in applications of telemedicine are defined. An investigation of telemedicine applications in various fields is presented, and enormous impact of telemedicine systems on the future of medicine is determined.

Design of microcontroller-based EMG and the analysis of EMG signals.

In this work, a microcontroller-based EMG designed and tested on 40 patients. When the patients are in rest, the fast Fourier transform (FFT) analysis was applied to EMG signals recorded from right leg peroneal region. The histograms are constructed from the results of the FFT analysis. The analysis results shows that the amplitude of fibrillation potential of the muscle fiber of 30 patients measured from peroneal region is low and the duration is short. This is the reason why the motor nerves degenerated and 10 patients were found to be healthy.

Theory and applications of biotelemetry.

In this study, biotelemetry and its evolution is explained in detail. Bioelectric and physiological variables could be measured by biotelemetry systems. The development of a biotelemetry system and its principal operation are presented. The components of a biotelemetry system are explained. Biomedical data has been telemetered through every medium between two sites by using a variety of modulated energy forms. Designing of the link between transmitter and receiver is described. Important factors in designing a backpack or implanted telemeter are explained. The main features of implanted biotelemetry units are determined. Single-channel and multichannel biotelemetry systems are defined. The types of telemetry, and a comparison thereof, are given. The power sources of biotelemetry systems and features of different power sources are explained. A survey of biotelemetry applications on humans and animals is presented and advantages of using biotelemetry systems are determined.