Fırat Hardalaç

Determination of aorta failure with the application of FFT, AR and wavelet methods to Doppler technique.

In this study, Doppler signals recorded from the output of aorta valve of 24 patients were transferred to a personal computer (PC) by using a 16-bit sound card. Doppler difference frequencies were recorded from each of the patients, and then analyzed using fast Fourier transform, autoregressive and wavelet transform analyzer to obtain their sonograms. These sonograms are then used to compare with the applied methods in terms of medical evaluation.

Classification of Transcranial Doppler Signals Using Artificial Neural Network

Transcranial Doppler signals, recorded from the temporal region of brain on 110 patients were transferred to a personal computer by using a 16-bit sound card. The fast Fourier transform (FFT) method was applied to the recorded signal from each patient. Since FFT method inherently can not offer a good spectral resolution at jet blood flows, it sometimes causes wrong interpretation of transcranial Doppler signals. To do a correct and rapid diagnosis, transcranial Doppler blood flow signals were statistically arranged so that they were classified in artificial neural network. Back propagation neural network and self-organization map algorithms of artificial neural network were used for training, whereas momentum and delta–bar-delta algorithms were used for learning. The results of these algorithms were compared in the case of classification and learning.

Determination of Behcet disease with the application of FFT and AR methods

In this study, Doppler signals recorded from ophthalmic artery of 86 patients were processed by personal computer using fast Fourier transform, Burg autoregressive (AR), and least-squares AR methods. By using these spectrum analysis techniques, the variations in the shape of the Doppler spectrums as a function of time were presented in the form of sonograms in order to obtain medical information. These sonograms were then used to compare the applied methods in terms of their frequency resolution and the effects in determination of Behcet disease.

Classification of carotid artery stenosis of patients with diabetes by neural network and logistic regression.

The blood flow hemodynamics of carotid arteries were obtained from carotid arteries of 168 individuals with diabetes using the 7.5 MHz ultrasound Doppler M-unit. Fast Fourier Transform (FFT) methods were used for feature extraction from the Doppler signals on the time-frequency domain. The parameters, obtained from the Doppler sonograms, were applied to the mathematical models that were constituted to analyze the effect of diabetes on internal carotid artery (ICA) stenosis. In this study, two different mathematical models such as the traditional statistical method based on logistic regression and a Multi-Layer Perceptron (MLP) neural network were used to classify the Doppler parameters. The correct classification of these data was performed by an expert radiologist using angiograpy before they were executed by logistic regression and MLP neural networks. We classified the carotid artery stenosis into two categories such as non-stenosis and stenosis and we achieved similar results (correctly classified (CC) = 92.8%) in both mathematical models. But, as the degree of stenosis had been increased to 4 (0-39%, 40-59%, 60-79% and 80-99% diameter stenosis), it was found that the neural network (CC = 73.9%) became more efficient than the logistic regression analysis (CC = 67.7%). These outcomes indicate that the Doppler sonograms taken from the carotid arteries may be classified successfully by neural network.

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.

Classification of educational backgrounds of students using musical intelligence and perception with the help of genetic neural networks

In this study we demonstrate that machine learning can be used to classify students who had backgrounds in positive sciences (including engineering, science and math disciplines) vs. social sciences (including arts and humanities disciplines) by the help of musical hearing and perception using artificial neural networks. Our 80 test subjects had an even mixture of both aforementioned disciplines. Each participant is asked to listen to a melody played on a piano and to repeat the melody himself verbally. Both the original melody and participants repetition is recorded and frequency and amplitude response is analyzed by using fast Fourier transform (FFT). This information is then used to train a neural network. Our results show, that by using musical perception, our neural network classifies students with positive and social science backgrounds at a success rate of 90% and 85%, respectively

Comparison of FFT and adaptive ARMA methods in transcranial Doppler signals recorded from the cerebral vessels

In this work, transcranial Doppler signals recorded from the temporal region of the brain on 35 patients were transferred to a personal computer by using a 16-bit sound card. Fast Fourier transform and adaptive auto regressive-moving average (A-ARMA) methods were applied to transcranial Doppler frequencies obtained from the middle cerebral artery in the temporal region. Spectral analyses were obtained to compare both methods for medical diagnoses. The sonograms obtained using A-ARMA method give better results for spectral resolution than the FFT method. The sonograms of A-ARMA method offer net envelope and better imaging, so that the determination of blood flow and brain pressure can be calculated more accurately. All diseases show higher resistance to flow than controls with no difference between males and females. Whereas values between disease classes differed, resistance within each class was remarkably constant.

Classification of the Frequency of Carotid Artery Stenosis with MLP and RBF Neural Networks in Patients with Coroner Artery Disease

For the classification of left and right Internal Carotid Arteries (ICA) stenosis, Doppler signals have been received from the patients with coroner arteries stenosis by using 6.2–8.4 MHz linear transducer. To be able to classify the data obtained from LICA and RICA in artificial intelligence, MLP and RBF neural networks were used. The number of obstructed veins from the coroner angiography, intimal thickness, and plaque formation from the power Doppler US and resistive index values were used as the input data for the neural networks. Our findings demonstrated that 87.5% correct classification rate was obtained from MLP neural network and 80% correct classification rate was obtained from RBF neural network. MLP neural network has classified more successfully when compared with RBF neural network.

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.

Examination of Electric Field Effects on Tissues by Using Back Propagation Neural Network

The aim of this study is to determine lipid peroxidation and antioxidant enzyme levels in spleen and testis tissues of guinea pigs which were exposed to different intensities and periods of DC (direct current) and AC (alternating current) electric fields. The experimental results are applied to neural networks as learning data and the training of the feed forward neural network is realized. At the end of this training; without applying electric field to the tissues, the determination of the effects of the electric field on tissues by using computer is predicted by the neural network. After the experiments, the prediction of the neural network is averagely 99%.