Nihal Fatma Güler

Recurrent neural networks employing Lyapunov exponents for EEG signals classification

There are a number of different quantitative models that can be used in a medical diagnostic decision support system including parametric methods, non-parametric methods and several neural network models. Unfortunately, there is no theory available to guide model selection. The aim of this study is to evaluate the diagnostic accuracy of the recurrent neural networks (RNNs) employing Lyapunov exponents trained with Levenberg-Marquardt algorithm on the electroencephalogram (EEG) signals. An approach based on the consideration that the EEG signals are chaotic signals was used in developing a reliable classification method for electroencephalographic changes. This consideration was tested successfully using the non-linear dynamics tools, like the computation of Lyapunov exponents. We explored the ability of designed and trained Elman RNNs, combined with the Lyapunov exponents, to discriminate the EEG signals (EEG signals recorded from healthy volunteers with eyes open, epilepsy patients in the epileptogenic zone during a seizure-free interval, and epilepsy patients during epileptic seizures). The RNNs achieved accuracy rates which were higher than that of the feedforward neural network models. The obtained results demonstrated that the proposed RNNs employing the Lyapunov exponents can be useful in analyzing long-term EEG signals for early detection of the electroencephalographic changes.

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.

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.

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.

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.

Comparison of FFT- and AR-based sonogram outputs of 20 MHz pulsed doppler data in real time

Real time sonogram outputs of autoregressive (AR) and Fast Fourier Transform (FFT) spectral analysis of 20 MHz pulsed ultrasonic Doppler blood flowmeter are presented. Data obtained from coronary, renal, iliac, digital and mesenteric arteries were processed using AR- and FFT-based spectral analysis techniques and interpretable sonograms were constructed. In comparison with the FFT-based sonogram outputs. AR-based sonogram outputs for 20 MHz pulsed Doppler data provide better results. Hence, the AR modeling is strongly recommended for small vessels with diameters between 1 and 2 mm.

Design and testing of a microprocessor-controlled portable thermoelectric medical cooling kit

Blood cells, vaccines, and other drugs and biological products should be kept at a temperature within certain range. In this paper, a medical thermoelectric cooling kit is introduced. The kit keeps medical products at temperatures between 6 and 10 °C while being stored or transported. This range is within the standards recommended by WHO to keep biological, medical and food products. Tests were carried out under various environmental temperatures in laboratory conditions as well as outside the laboratory and on a transportation vehicle. Under all these conditions, the kit provided cooling at a satisfactory level. This paper presents the technical specifications of the kit, theory of its operation, and test results. The kit is supported with a microprocessor unit. The block diagram of the microprocessor program is also presented in the paper.

Analysis of the Doppler signals using largest Lyapunov exponent and correlation dimension in healthy and stenosed internal carotid artery patients

Nonlinear analysis techniques have been applied for many applications on the physiological systems. In this research, the largest Lyapunov exponent (LLE) and correlation dimension (CD) calculations were performed to evaluate the chaotic behaviour of blood flow obtained from the healthy and stenosed internal carotid artery (ICA) using noninvasive Doppler ultrasonography technique. The Doppler signals were taken from 30 healthy, 8 mild, 8 moderate and 8 serious degree of stenosis ICA subjects. The LLE calculation was performed by using Wolf algorithm. The Grassberger-Procaccia algorithm was used for CD analysis. The calculated LLE and CD values for stenosed ICA Doppler signals were found as significantly high compared to the values belonging to the healthy subjects. It is found that, the LLEs and CDs of stenosed ICA Doppler signals increase with the increasing of the degree of stenosis. The results show that the LLE and CD can be used for diagnosis of the ICA stenosis and to determine the degree of stenosis.

The electronic detail of a pulsed doppler blood flow measurement system

Several techniques exist for determining blood flow to the organs. Most of these techniques are indirect and do not measure blood flow as an instantaneous function of time. A non-invasive technique for measuring blood flow in a specific vessel, transcutaneously, is based on ultrasonic Doppler methods. The authors examine the electronics of a pulsed Doppler flowmeter. All the electronic circuits of a directional 4 MHz pulse wave (PW) Doppler velocimeter with a peak frequency follower have been constructed and tested.

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.