Necaattin Barişçi

Application of FFT analyzed cardiac Doppler signals to fuzzy algorithm.

Doppler signals, recorded from the output of tricuspid, mitral, and aorta valves of 60 patients, were transferred to a personal computer via 16-bit sound card. The fast Fourier transform (FFT) method was applied to the recorded signal from each patient. Since FFT method inherently cannot offer a good spectral resolution at highly turbulent blood flows, it sometimes leads to wrong interpretation of cardiac Doppler signals. In order to avoid this problem, firstly six known diseased heart signals such as hypertension, mitral stenosis, mitral failure, tricuspid stenosis, aorta stenosis, aorta insufficiency were introduced to fuzzy algorithm. Then, the unknown heart diseases from 15 patients were applied to the same fuzzy algorithm in order to detect the kinds of diseases. It is observed that the fuzzy algorithm gives true results for detecting the kind of diseases.

Prediction of Minor Head Injured Patients Using Logistic Regression and MLP Neural Network

In this study it is aimed to assess the posttraumatic cerebral hemodynamia in minor head injured patients. Eighty patients with minor head injury (Group 1) evaluated in the early 8 h of posttraumatic period between July 2003 and February 2004. The control group (Group 2) has composed of 32 healthy people. Bilateral blood flow velocities of middle cerebral arteries (MCA) had measured using transtemporal technique while internal carotid arteries were evaluated by submandibular examination. Two different mathematical models such as the traditional statistical method on the basis of logistic regression and a multi-layer perceptron (MLP) neural network are used to classify the age, sex, velocitiy parameters of MCA, mean velocity of extracranial ICAs and VMCA/ VICA ratios. The neural network was trained, cross-validated and tested with subject’s transcranial Doppler signals. As a result of these classifications, we found the success rate of logistic regression, the success rate of MLP neural network is 88.2 and 89.1%, respectively. The classification results show that MLP neural network is offering the best results in the case of diagnosis.

Control of Pitch Angle of Wind Turbine by Fuzzy Pid Controller

AbstractThis article presents a study on set of PID parameters of blade pitch angle controller of wind turbine with fuzzy logic algorithm. Three individual control methods were used to control the wind turbine pitch angle. These control methods are conventional PI, fuzzy and fuzzy PID. With the use of these control methods, the system was protected from possible harms in high wind speed region and maintained changing of nominal output power. It was aimed to the control the wind turbine blade pitch angle in different wind speeds and to hold the output power stable in the set point by simulation of controllers with Matlab/Simulink Software. By evaluating the steady state time of output power received from the simulation results and steady state errors, the performances of the control systems have been measured and compared with one another. As a result of these simulation comparisons, it is clear that fuzzy PID controller performed better than PI and Fuzzy Controller.

The Examination of the Effects of Obesity on a Number of Arteries and Body Mass Index by Using Expert Systems

In this study, the areas affected from obesity were examined by classifying divergent arteries and body mass index (BMI) of 30 healthy persons and 52 obese persons by using expert systems, and the classifying performances of NEFCLASS and CANFIS, which are expert systems were compared. As a result of this comparison, it is observed that the classifying performance of NEFCLASS is better than that of CANFIS, and the causes of this are examined. Furthermore, it is observed that after these classifications, obesity affects the BMI rather than divergent arteries.

The Adaptive ARMA Analysis of EMG Signals

In this study, Adaptive auto regressive-moving average (A-ARMA) analysis of EMG signals recorded on the ulnar nerve region of the right hand in resting position was performed. A-ARMA method, especially in the calculation of the spectrums of stationary signals, is used for frequency analysis of signals, which give frequency response as sharp peaks and valleys. In this study, as the result of A-ARMA method analysis of EMG signals frequency–time domain, frequency spectrum curves (histogram curves) were obtained. As the images belonging to these histograms were evaluated, fibrillation potential widths of the muscle fibers of the ulnar nerve region of the people (material of the study) were examined. According to the degeneration degrees of the motor nerves, 22 people had myopathy, 43 had neuropathy, and 28 were normal.

Classification of aorta doppler signals using variable coded-hierarchical genetic fuzzy system

In this study, Doppler signals, recorded from the output of aorta valve of 80 patients, were transferred to personal computer via 16 bit sound card. The fast Fourier transform (FFT) method was applied to the recorded signal from each patient. Since FFT method inherently cannot offer a good spectral resolution at highly turbulent blood flows, it sometimes causes wrong interpretation of cardiac Doppler signals. In order to avoid this problem, two known diseased heart signals such as aorta stenosis and aorta insufficiency were introduced to two different genetic fuzzy systems. The disadvantages arise from these two different genetic fuzzy systems were eliminated by using the new genetic fuzzy system which is proposed in this study. The proposed genetic fuzzy system is called as variable coded-hierarchical genetic fuzzy system. As a result, it is shown that the proposed system decreases the computational time since it uses less genes. q 2003 Elsevier Ltd. All rights reserved.

Application of an adaptive neuro‐fuzzy inference system for classification of Behcet disease using the fast Fourier transform method

: In this study, ophthalmic arterial Doppler signals were obtained from 200 subjects, 100 of whom suffered from ocular Behcet disease while the rest were healthy subjects. An adaptive neuro-fuzzy inference system (ANFIS) was used to detect the presence of ocular Behcet disease. Spectral analysis of the ophthalmic arterial Doppler signals was performed by the fast Fourier transform method for determining the ANFIS inputs. The ANFIS was trained with a training set and tested with a testing set. All these data sets were obtained from ophthalmic arteries of healthy subjects and subjects suffering from ocular Behcet disease. Performance indicators and statistical measures were used for evaluating the ANFIS. The correct classification rate was 94% for healthy subjects and 90% for unhealthy subjects suffering from ocular Behcet disease. The classification results showed that the ANFIS was effective at detecting ophthalmic arterial Doppler signals from subjects with Behcet disease.

Comparison of Multi Layer Perceptron and Jordan Elman Neural Networks for Diagnosis of Hypertension

In this study, from 150 individuals over the age of 30 taken no drugs, sex, age, height, weight, HDL, LDL, Triglyceride, smoking and uric acid were measured. 65 of them are normal but 85 consist of the patients. This data was transferred to the computer by processing methods of quantitative analysis. Data obtained of each patient was applied Artificial Neural Network (ANN) models. The results obtained will be classified as either normal or the patient. Using Multi Layer Perceptron (MLP) neural network, 80.4% of patient individuals and 81.8% of normal individuals were classified correctly. Using Jordan Elman neural network, 85.3% of the patient individuals and 87.8% of normal individuals were classified correctly.