Turker Tekin Erguzel

Feature Selection and Classification of Electroencephalographic Signals An Artificial Neural Network and Genetic Algorithm Based Approach

Feature selection is an important step in many pattern recognition systems aiming to overcome the so-called curse of dimensionality. In this study, an optimized classification method was tested in 147 patients with major depressive disorder (MDD) treated with repetitive transcranial magnetic stimulation (rTMS). The performance of the combination of a genetic algorithm (GA) and a back-propagation (BP) neural network (BPNN) was evaluated using 6-channel pre-rTMS electroencephalographic (EEG) patterns of theta and delta frequency bands. The GA was first used to eliminate the redundant and less discriminant features to maximize classification performance. The BPNN was then applied to test the performance of the feature subset. Finally, classification performance using the subset was evaluated using 6-fold cross-validation. Although the slow bands of the frontal electrodes are widely used to collect EEG data for patients with MDD and provide quite satisfactory classification results, the outcomes of the proposed approach indicate noticeably increased overall accuracy of 89.12% and an area under the receiver operating characteristic (ROC) curve (AUC) of 0.904 using the reduced feature set.

Neural Network Based Response Prediction of rTMS in Major Depressive Disorder Using QEEG Cordance

Objective The combination of repetitive transcranial magnetic stimulation (rTMS), a non-pharmacological form of therapy for treating major depressive disorder (MDD), and electroencephalogram (EEG) is a valuable tool for investigating the functional connectivity in the brain. This study aims to explore whether pre-treating frontal quantitative EEG (QEEG) cordance is associated with response to rTMS treatment among MDD patients by using an artificial intelligence approach, artificial neural network (ANN). Methods The artificial neural network using pre-treatment cordance of frontal QEEG classification was carried out to identify responder or non-responder to rTMS treatment among 55 MDD subjects. The classification performance was evaluated using k-fold cross-validation. Results The ANN classification identified responders to rTMS treatment with a sensitivity of 93.33%, and its overall accuracy reached to 89.09%. Area under Receiver Operating Characteristic (ROC) curve (AUC) value for responder detection using 6, 8 and 10 fold cross validation were 0.917, 0.823 and 0.894 respectively. Conclusion Potential utility of ANN approach method can be used as a clinical tool in administering rTMS therapy to a targeted group of subjects suffering from MDD. This methodology is more potentially useful to the clinician as prediction is possible using EEG data collected before this treatment process is initiated. It is worth using feature selection algorithms to raise the sensitivity and accuracy values.

A wrapper-based approach for feature selection and classification of major depressive disorder-bipolar disorders

Feature selection (FS) and classification are consecutive artificial intelligence (AI) methods used in data analysis, pattern classification, data mining and medical informatics. Beside promising studies in the application of AI methods to health informatics, working with more informative features is crucial in order to contribute to early diagnosis. Being one of the prevalent psychiatric disorders, depressive episodes of bipolar disorder (BD) is often misdiagnosed as major depressive disorder (MDD), leading to suboptimal therapy and poor outcomes. Therefore discriminating MDD and BD at earlier stages of illness could help to facilitate efficient and specific treatment. In this study, a nature inspired and novel FS algorithm based on standard Ant Colony Optimization (ACO), called improved ACO (IACO), was used to reduce the number of features by removing irrelevant and redundant data. The selected features were then fed into support vector machine (SVM), a powerful mathematical tool for data classification, regression, function estimation and modeling processes, in order to classify MDD and BD subjects. Proposed method used coherence, a promising quantitative electroencephalography (EEG) biomarker, values calculated from alpha, theta and delta frequency bands. The noteworthy performance of novel IACO-SVM approach stated that it is possible to discriminate 46 BD and 55 MDD subjects using 22 of 48 features with 80.19% overall classification accuracy. The performance of IACO algorithm was also compared to the performance of standard ACO, genetic algorithm (GA) and particle swarm optimization (PSO) algorithms in terms of their classification accuracy and number of selected features. In order to provide an almost unbiased estimate of classification error, the validation process was performed using nested cross-validation (CV) procedure.

Ant Colony Optimization Based Feature Selection Method for QEEG Data Classification

Objective Many applications such as biomedical signals require selecting a subset of the input features in order to represent the whole set of features. A feature selection algorithm has recently been proposed as a new approach for feature subset selection. Methods Feature selection process using ant colony optimization (ACO) for 6 channel pre-treatment electroencephalogram (EEG) data from theta and delta frequency bands is combined with back propagation neural network (BPNN) classification method for 147 major depressive disorder (MDD) subjects. Results BPNN classified R subjects with 91.83% overall accuracy and 95.55% subjects detection sensitivity. Area under ROC curve (AUC) value after feature selection increased from 0.8531 to 0.911. The features selected by the optimization algorithm were Fp1, Fp2, F7, F8, F3 for theta frequency band and eliminated 7 features from 12 to 5 feature subset. Conclusion ACO feature selection algorithm improves the classification accuracy of BPNN. Using other feature selection algorithms or classifiers to compare the performance for each approach is important to underline the validity and versatility of the designed combination.

Analysis of Brain Functional Changes in High-Frequency Repetitive Transcranial Magnetic Stimulation in Treatment-Resistant Depression.

Repetitive transcranial magnetic stimulation (rTMS) is a treatment procedure that uses magnetic fields to stimulate nerve cells in the brain, and is associated with significant improvements in clinical symptoms of major depressive disorder (MDD). The effect of rTMS treatment on the brain can be evaluated by cordance, a quantitative electroencephalography (QEEG) method that extracts information from absolute and relative power of EEG spectra. In this study, to analyze brain functional changes, pre- and post-rTMS, QEEG data were collected from 6 frontal electrodes (Fp1, Fp2, F3, F4, F7, and F8) in 2 slow bands (delta and theta) for 55 MDD subjects. To examine brain changes, cordance scores were determined, using repeated-measures analysis of variance (ANOVA). High-frequency rTMS was associated with cordance decrease in left frontal and right prefrontal regions in both delta and theta for nonresponders; it was associated with cordance increase in all right and left frontal electrodes, except F8, for responders.

Process control using genetic algorithm and ant colony optimization algorithm

Artificial life uses biological knowledge and techniques to solve different engineering, management, control and computational problems. Natural systems teach us that very simple individual organisms can form systems capable of performing highly complex tasks by dynamically interacting with each other. In this study, artificial life based approaches are handled and incorporated to enable a real-time water level control. The process was first modelled using NARX type Artificial Neural Network. A fuzzy controller was then attached to the model. For a better performance, fuzzy controller membership function boundary values and action values were optimized simultaneously. The optimization process was performed using genetic algorithm and ant colony optimization algorithm, respectively. Finally, the performance of the controllers was discussed further by considering the system outputs. The developed structure replaces the tedious process of trial-and-error for better combination of fuzzy parameters and can settle the problem of designing fuzzy controller without an experts experience.

A hybrid artificial intelligence method to classify trichotillomania and obsessive compulsive disorder

Classification of psychiatric disorders is becoming one of the major focuses of research using artificial intelligence approach. The combination of feature selection and classification methods generates satisfactory outcomes using biological biomarkers. The use of quantitative electroencephalography (EEG) cordance has enhanced the clinical utility of the EEG in psychiatric and neurological subjects. Trichotillomania (TTM), a kind of body focused repetitive behavior, is defined as a disorder characterized by repetitive hair pulling that results in noticeable hair loss. Phenomenological observations underline similarities between hair-pulling behaviors and compulsions seen in obsessive-compulsive disorder (OCD). Despite the recognized similarities between OCD and TTM, there is evidence of important differences between these two disorders. In order to dichotomize the subjects of each disorder, artificial intelligence approach was employed using quantitative EEG (QEEG) cordance values with 19 electrodes from 10 brain regions (prefrontal, frontocentral, central, left temporal, right temporal, left parietal, occipital, midline, left frontal and right frontal) in 4 frequency bands (delta, theta, alpha and beta). Machine learning methods, artificial neural networks (ANN), support vector machine (SVM), k-nearest neighbor (k-NN) and Naive Bayes (NB), were used in order to classify 39 TTM and 40 OCD patients. SVM, with its relatively better performance, was then combined with an improved ant colony optimization (IACO) approach in order to select more informative features with less iterations. The noteworthy performance of the hybrid approach underline that it is possible to discriminate OCD and TTM subjects with 81.04% overall accuracy. We used cordance as a biomarker combining absolute and relative power of EEG spectra.We used ACO for feature selection of 19 electrodes from 10 brain regions in 4 frequency bands.We used an improved ACO (IACO) to reduce computational complexity.We used SVM to classify trichotillomania and OCD subjects using their cordance values.We increased overall classification accuracy from 67.12% to 81.04% and increased AUC value from 0.698 to 0.816 decreasing used features from 40 to 13.

Classification of major depressive disorder subjects using Pre-rTMS electroencephalography data with support vector machine approach

The combination of repetitive transcranial magnetic stimulation (rTMS) and electroencephalogram (EEG) is a valuable tool for investigating the functional connectivity in the brain. Using pre-treatment cordance, a relatively new quantitative EEG method combining complementary information from absolute and relative power of EEG spectra, 55 major depression disorder (MDD) subjects were classified into responder or non-responder classes. In order to predict the response of rTMS treatment, support vector machine (SVM) based classification was carried out on pre-treatment cordance and the classification performance was evaluated using 6, 8 and 10-fold cross-validation (CV). Promising findings indicate that it is possible to classify rTMS treatment responders with 85.45% overall accuracy with a sensitivity of 82.35% and 0.925 area under receiver operating characteristics (ROC) curve value.

Analysis of Time - Frequency EEG Feature Extraction Methods for Mental Task Classification

Many endogenous and external components may affect the physiological, mental and behavioral states in humans. Monitoring tools are required to evaluate biomarkers, identify biological events, and predict their outcomes. Being one of the valuable indicators, brain biomarkers derived from temporal or spectral electroencephalography (EEG) signals processing, allow for the classification of mental disorders and mental tasks. An EEG signal has a nonstationary nature and individual frequency feature, hence it can be concluded that each subject has peculiar timing and data to extract unique features. In order to classify data, which are collected by performing four mental task (reciting the alphabet backwards, imagination of rotation of a cube, imagination of right hand movements (open/close) and performing mathematical operations), discriminative features were extracted using four competitive time-frequency techniques; Wavelet Packet Decomposition (WPD), Morlet Wavelet Transform (MWT), Short Time Fourier Transform (STFT) and Wavelet Filter Bank (WFB), respectively. The extracted features using both time and frequency domain information were then reduced using a principal component analysis for subset reduction. Finally, the reduced subsets were fed into a multi-layer perceptron neural network (MP-NN) trained with back propagation (BP) algorithm to generate a predictive model. This study mainly focuses on comparing the relative performance of time-frequency feature extraction methods that are used to classify mental tasks. The real-time (RT) conducted experimental results underlined that the WPD feature extraction method outperforms with 92% classification accuracy compared to three other aforementioned methods for four different

Radiation shielding parameters of some antioxidants using Monte Carlo method

In this paper, radiation shielding parameters such as mass attenuation coefficients and half value layer (HVL) of some antioxidants are investigated using MCNPX (version 2.4.0). The validation of the generated MCNPX simulation geometry for antioxidant structures is provided by comparing the results with standard WinXcom data for radiation mass attenuation coefficients of antioxidants. Very good agreement between WİNXCOM and MCNPX was obtained. The results from the validated geometry were used to calculate the shielding parameters of different antioxidants. The radiation attenuation properties of each antioxidant were compared with each other. The results showed that, on average, the highest and the lowest radiation mass attenuation coefficients were observed on hesperidin and delphinidin chloride, respectively. It can be concluded that Monte Carlo simulation is a strong tool and an alternate method where experimental investigations are not possible and a standard simulation setup can be used in further studies for different biological structures. It can also be concluded that the obtained results from this study are very useful for radiology and radiotherapy applications where antioxidants are frequently used.