Uçman Ergün

Combining Neural Network and Genetic Algorithm for Prediction of Lung Sounds

Recognition of lung sounds is an important goal in pulmonary medicine. In this work, we present a study for neural networks–genetic algorithm approach intended to aid in lung sound classification. Lung sound was captured from the chest wall of The subjects with different pulmonary diseases and also from the healthy subjects. Sound intervals with duration of 15–20 s were sampled from subjects. From each interval, full breath cycles were selected. Of each selected breath cycle, a 256-point Fourier Power Spectrum Density (PSD) was calculated. Total of 129 data values calculated by the spectral analysis are selected by genetic algorithm and applied to neural network. Multilayer perceptron (MLP) neural network employing backpropagation training algorithm was used to predict the presence or absence of adventitious sounds (wheeze and crackle). We used genetic algorithms to search for optimal structure and training parameters of neural network for a better predicting of lung sounds. This application resulted in designing of optimum network structure and, hence reducing the processing load and time.

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.

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.

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.

Classification of MCA Stenosis in Diabetes by MLP and RBF Neural Network

For the classification of Middle Cerebral Artery (MCA) stenosis, Doppler signals have been received from the diabetes and control group by using 2 MHz Transcranial Doppler. After the Fast Fourier Transform (FFT) analyses of the Doppler signals, Power Spectrum Density (PSD) estimations have been made and Multilayer Perceptron (MLP) and Radial Basis Function (RBF) have been dealt to apply to the neural networks. PSD estimations of Doppler signals received from MCA of 104 subjects have been successfully classified by MLP (correct classification = 94.2%) and RBF (correct classification = 88.4%) neural network. As we have seen in the area under ROC curve (AUC), MLP neural network (AUC = 0.934) has classified more successfully when compared with RBF neural network (AUC = 0.873).

Classification of the heart sounds via artificial neural network

Auscultation with stethoscope is a preferential method that the doctors use in order to differentiate normal cardiac systems from the abnormal ones that come out. On the other hand, the method of auscultation with stethoscope requires medical expert experience and careful listening. Because of the problems that can be faced, listening process with stethoscope, that is auscultation, falls behind in the search of the heart abnormalities. In this study, the frequency analysis of the heart sounds taken by the electronic stethoscope is implemented via a pocket computer. The parameters gained from the frequency analysis are again classified with the neural network on the pocket computer. Thus, hearing the heart sounds, medical doctors at the same time will be able to follow the information gained through the frequency and artificial neural network analysis on the pocket computer so that they will be able to make more accurate diagnoses.

The Classification of Obesity Disease in Logistic Regression and Neural Network Methods

The aim of this study is to establish an automated system to recognize and to follow-up obesity. In this study, the areas affected from obesity were examined with a classification considering the divergent arteries and body mass index of 30 healthy and 52 obese people by using two different mathematical models such as the traditional statistical method based on logistic regression and a multi-layer perception (MLP) neural network, and then classifying performances of logistic regression and neural network were compared. As a result of this comparison, it is observed that the classifying performance of neural network is better than logistic regression; also the reasons of this result were examined. Furthermore, after these classifications it is observed that in obesity the body mass index is more affected than the divergent arteries.

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.

Biomedical system based on the Discrete Hidden Markov Model using the Rocchio-Genetic approach for the classification of internal carotid artery Doppler signals

When the maximum likelihood approach (ML) is used during the calculation of the Discrete Hidden Markov Model (DHMM) parameters, DHMM parameters of the each class are only calculated using the training samples (positive training samples) of the same class. The training samples (negative training samples) not belonging to that class are not used in the calculation of DHMM model parameters. With the aim of supplying that deficiency, by involving the training samples of all classes in calculating processes, a Rocchio algorithm based approach is suggested. During the calculation period, in order to determine the most appropriate values of parameters for adjusting the relative effect of the positive and negative training samples, a Genetic algorithm is used as an optimization technique. The purposed method is used to classify the internal carotid artery Doppler signals recorded from 136 patients as well as of 55 healthy people. Our proposed method reached 97.38% classification accuracy with fivefold cross-validation (CV) technique. The classification results showed that the proposed method was effective for the classification of internal carotid artery Doppler signals.