Ali Ozturk

A biomedical system based on fuzzy discrete hidden Markov model for the diagnosis of the brain diseases

Because it is a non-invasive, easy to apply and reliable technique, transcranial doppler (TCD) study of the adult intracerebral circulation has increased enormously in the last 10 years. In this study, a biomedical system has been implemented in order to classify the TCD signals recorded from the temporal region of the brain of 82 patients as well as of 24 healthy people. The diseases were investigated cerebral aneurysm, brain hemorrhage, cerebral oedema and brain tumor. The system is composed of feature extraction and classification parts, basically. In the feature extraction stage, the linear predictive coding analysis and cepstral analysis were applied in order to extract the cepstral and delta-cepstral coefficients in frame level as feature vectors. In the classification stage, discrete hidden Markov model (DHMM) based methods were used. In order to avoid loosing information due to vector quantization and to increase the classification performance, a fuzzy approach based similarity was applied to implement the DHMM. The performance of the proposed Fuzzy DHMM (FDHMM) was compared with some methods such as DHMM, artificial neural network (ANN), neuro-fuzzy approaches and obtained better classification performance than these methods.

Classification of transcranial Doppler signals using their chaotic invariant measures

In this study, chaos analysis was performed on the transcranial Doppler (TCD) signals recorded from the temporal region of the brain of 82 patients as well as of 24 healthy people. Two chaotic invariant measures, i.e. the maximum Lyapunov exponent and the correlation dimension, were calculated for the TCD signals after applying nonlinearity and stationarity tests to them. The sonograms obtained via Burg autoregressive (AR) method demonstrated that the chaotic invariant measures represented the unpredictability and complexity levels of the TCD signals. According to the multiple linear regression analysis, the chaotic invariant measures were found to be highly significant for the regression equation which fitted to the data. This result suggested that the chaotic invariant measures could be used for automatically differentiating various cerebrovascular conditions via an appropriate classifier. For comparison purposes, we investigated several different classification algorithms. The k-nearest neighbour algorithm outperformed all the other classifiers with a classification accuracy of 94.44% on the test data. We used the receiver operating characteristic (ROC) curves in order to assess the performance of the classifiers. The results suggested that the classification systems which use the chaotic invariant measures as input have potential in detecting the blood flow velocity changes due to various brain diseases.

Determination of Onset of Yield due to Material Properties in a Heat Generating Two-Layered Compound Cylinder

In this theoretical study, based on Trescas yield criterion and its associated flow rule, the elastic deformation of a centrally heated compound cylinder with fixed ends is investigated analytically by taking into consideration not only the geometrical but also the material parameters such as yield strength, modulus of elasticity, Poissons ratio, thermal conductivity and coefficient of thermal expansion. These material parameters are assumed to be independent of the temperature. The compound cylinder is assumed to be very long such that axisymmetric condition exists. Both of the constituent materials of the two layers are supposed to be elastic-perfectly plastic materials. There is heat generation in the interior solid cylinder but no heat generation in the outer hollow cylinder. Both of the cylinders are assumed to be bounded perfectly at the interface. Elastic stress analysis is performed to prevent yield in the compound cylinder. Keywords: Compound cylinder, elastic stress analysis, thermal stress, yield strength.

Short term prediction of aluminium strip thickness via Support Vector Machines

The fundamental principle of cold rolling process is the tension produced by the coiling and uncoiling motors of the rolling machine. If the tension is not properly regulated, the strip thickness will not be homogenous over the surface and even ruptures may occur. Therefore, short-term prediction of the aluminium strip thickness is important to control the tension. In this study, nonlinear time series analysis methods were applied to the recorded thickness data in order to obtain the embedding vectors with appropriate embedding dimension and time delay. For various prediction horizons, the embedding vector and corresponding thickness value pairs were used as the data set to assess the prediction performance of Support Vector Machines (SVM) with k-fold cross validation. The comparison results were given for Polynomial kernel with different exponent values, RBF kernel and Universal Pearson VII function (PUK) kernel. The SVM model with PUK kernel gave the most accurate results. The closest accuracy levels to PUK were belonging to Polynomial kernel of exponent p=3, but the time taken to build the SVM model with Polynomial kernel was very longer than the SVM model with PUK. The RBF kernel had the shortest SVM model building time with the worst accuracy levels.

Analytical Solution of Thermal Stresses in a Functionally Graded Solid Cylinder within Parabolic Continuous Grading

This paper presents analytical solutions of the thermal stresses in a functionally graded solid cylinder with fixed ends in elastic region. These thermal stresses are due to the uniform heat generation inside the cylinder. Material properties of the functionally graded (FG) cylinder vary radially according to a parabolic form. The material properties are assumed to be independent of the temperature which are yield strength, elasticity modulus, thermal conduction coefficient, thermal expansion coefficient and Poisson’s ratio. The solutions for the thermal stresses are valid for both homogeneous and functionally graded materials.

Neuro-fuzzy classification of transcranial Doppler signals with chaotic meaures and spectral parameters

Transcranial Doppler (TCD) is a non-invasive diagnosis method which is used in diagnosis of various brain diseases by measuring the blood flow velocities in brain arteries. In this study, chaos analysis of the TCD signals recorded from the middle arteries of the temporal region of brain of the 82 patients and 23 healthy people was investigated. Among 82 patients, 20 of them had cerebral aneurism, 10 had brain hemorrhage, 22 had cerebral oedema and the remaining 30 had brain tumor. Maximum Lyapunov exponent which is the strongest quantitative indicator of chaos was found to be positive for all TCD signals. The correlation dimension was found as greater than 2 and as fractional value for all TCD signals. These two features were used for training a NEFCLASS model. The NEFCLASS model had two input nodes for D2 and maximum Lyapunov exponent values and five output nodes representing the subject group to which the inputs belonged. In order to make k-fold cross-validation, the data set was randomly divided into 5 subsets of equal size. In an iterated manner, 4 of these subsets were used for training and the remaining 1 subset was used for testing. This operation was repeated for 3 times. The average accuracy for train and test set was found as %81 and %79, respectively. The performance of the NEFCLASS model was also assessed in the same manner with spectral parameters (i.e. resistivity index and pulsatility index) which were obtained from Doppler sonograms. The average accuracy was found as %67 and %63 for train and test set, respectively.

Texture Segmentation Using Fractal Dimension And Second Order Statistics

In this study, segmentation of textured images using four different textural features is examined. The first three features are fractal dimension (FD) of the original image, contrast-stretched image and top-hat transformed image, respectively. Contrast-stretching and top-hat transform are known as detail enhancement techniques in the presence of shading or poor illumination, thus it is assumed that the hidden structures in textures will be apparent after these transformations. The fourth feature, e.g. entropy, is one of the parameters estimated from spatial gray level co-occurence matrix statistics. For comparison purposes, two different feature smoothing methods are applied to the feature space before running k-ortalama clustering.