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    Detection of amyotrophic lateral sclerosis disease by variational mode decomposition and convolution neural network methods from event-related potential signals
    (Tubitak Scientific & Technological Research Council Turkey, 2021) Latifoglu, Fatma; Orhanbulucu, Firat; Ileri, Ramis
    Amyotrophic lateral sclerosis (ALS), also known as motor neuron disease, is a neurological disease that occurs as a result of damage to the nerves in the brain and restriction of muscle movements. Electroencephalography (EEG) is the most common method used in brain imaging to study neurological disorders. Diagnosis of neurological disorders such as ALS, Parkinson's, attention deficit hyperactivity disorder is important in biomedical studies. In recent years, deep learning (DL) models have been started to be applied in the literature for the diagnosis of these diseases. In this study, event-related potentials (ERPs) were obtained from EEG signals obtained as a result of visual stimuli from ALS patients and healthy controls. As a new method, variational mode decomposition (VMD) is applied to the produced ERP signals and the signals are decomposed into subbands. In addition, empirical mode decomposition (EMD), one of the popular decomposition methods in the literature, was also analyzed, and ERP signals were divided into subbands and compared with the VMD method. Subband signals were classified in two stages with the one-dimensional convolutional neural network (1D CNN) model, which is one of the DL techniques proposed in the study. Accuracy, sensitivity, specificity, and F1-Score measurements were obtained using 5-and 10-fold cross-validation to evaluate classifier performance. In the first stage of classification, only VMD and EMD subband signals were used and 92.95% classification accuracy was obtained by the VMD method. In the second stage, VMD, EMD subband signals, and original ERP signals were all classified together with the VMD+ERP model achieving the maximum classification accuracy rate of 90.42%. It is thought that the results of the study will contribute to the diagnosis of similar neurological disorders such as ALS, attention studies based on visual stimuli, and the development of brain-computer interface (BCI) systems using the method applied to the proposed ERP signals.
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    Detection of amyotrophic lateral sclerosis disease from event-related potentials using variational mode decomposition method
    (Taylor & Francis Ltd, 2022) Orhanbulucu, Firat; Latifoglu, Fatma
    This study, it was aimed to contribute to the literature on Amyotrophic lateral sclerosis (ALS) diagnosis and Brain-Computer Interface (BCI) technologies by analyzing the electroencephalography (EEG) signals obtained as a result of visual stimuli and attention from ALS patients and healthy controls. It was observed that the success rate significantly increased both in the occipital and central regions in all classifiers, especially in the entropy features. The most successful classification was obtained with the Naive Bayes (NB) classifier using the Morphological Features (MF) + Variational Mode Decomposition (VMD) -Entropy features at 88.89% in the occipital region and 94.44% in the central region.
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    EEG signal analysis for the classification of Alzheimer's and frontotemporal dementia: a novel approach using artificial neural networks and cross-entropy techniques
    (Taylor & Francis Ltd, 2026) Latifoglu, Fatma; Orhanbulucu, Firat; Murugappan, Murugappan; Gurbuz, Suemeyye Nur; Cayir, Burcin; Avci, Fatma Zehra
    Dementia, a neurological disorder, can cause cognitive decline due to damage to the brain. Our study aims to contribute to the development of computer-aided diagnosis (CAD) systems to aid in the early diagnosis of Alzheimer's disease (AD) and frontotemporal dementia (FTD) by examining Electroencephalogram (EEG) signals. EEG signals of 36 AD, 23 FTD and 29 healthy control (HC) participants were analyzed and entropy measurement approaches were used to analyze the connectivity between EEG channel pairs. The cross-permutation entropy (CPE) method and the cross conditional entropy (CCE) method were analyzed separately and the fused cross entropy (FCE) method was also tested by combining these techniques to determine the most appropriate method for feature extraction from EEG signals. The features obtained from these techniques were then evaluated in the classification phase using two separate machine learning algorithms. According to the performance evaluation criteria, the FCE and artificial neural network (ANN) model showed the most successful performance in the classification of all groups. In terms of area under the curve (AUC) and accuracy rates, 99.85% AUC and 98.46% accuracy were obtained in AD&HC groups, 99.71% AUC and 98.10% accuracy in FTD&HC groups and 99.39% AUC, 96.61% accuracy in AD&FTD groups. With the same model, an AUC rate of 97.14% and accuracy rate of 73.86% was obtained for the classification of the triple group (AD&FTD&HC). It has been observed that the results of this study show successful performance compared to the results of similar studies.
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    Machine learning-based migraine analysis using retinal vessel diameters from optical coherence tomography: an alternative approach
    (Springer-Verlag Italia Srl, 2025) Orhanbulucu, Firat; Unlu, Metin; Sevim, Duygu Gulmez; Gultekin, Murat; Latifoglu, Fatma
    ObjectiveMigraine is a primary headache disorder characterised by attacks of headache that are usually unilateral and throbbing in nature, may be accompanied by neurological symptoms, and, due to its complex pathophysiology, can affect not only the central nervous system but also structures such as the retinal vascular system. In recent years, retinal imaging techniques have emerged as a promising method for studying neuro-ophthalmological diseases. In this study, we aimed to predict migraine by evaluating the measurements made from retinal images obtained with Optical Coherence Tomography (OCT).Materials and methodsIn the present study, 70 eyes of migraine patients and 38 eyes of healthy control group were examined. In cases where there was an imbalance between the classes, the data were balanced by applying the SMOTE method, which is widely preferred in studies. In addition to age and gender data, features such as retinal artery and vein diameters and choroidal thickness measurements were used as data. Pearson's Correlation Coefficient method was applied to calculate the linear relationship between the features.ResultsClassification results were evaluated with Area Under the Curve (AUC), Accuracy (Acc), Kappa statistic (KS), F1-score (F1), and Matthews Correlation Coefficient (MCC) parameters. The most successful result in the classification process between migraine and healthy control was obtained with the LightGBM algorithm with 93.28% AUC, 91.14% Acc, 86.67% F1, 0.74 KS, and 0.76 MCC rates.ConclusionThe presented research can be considered as a preliminary study. The results of the research on the application of machine learning algorithms showed an effective performance in migraine prediction from OCT data. Ensemble-based Boosting model classifiers were more successful than traditional machine learning classifiers.
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    Migraine Analysis with Cross Entropy Based Connectivity Feature: Investigation of Sensory Stimulus Conditions
    (Ieee, 2025) Orhanbulucu, Firat; Latifoglu, Fatma
    Migraine is one of the most common neurological disorders. Despite its high prevalence, the lack of research on migraine compared to other neurological disorders is striking. Visual or auditory sensory stimuli are effective in triggering migraine attacks. The relationships between different brain regions depending on the stimuli can be analyzed with brain connectivity properties by creating a connectivity matrix. In this study, the effect of sensory stimuli in migraine patients is analyzed by entropy-based connectivity analysis and machine learning algorithms Support Vector Machine (SVM) and Artificial Neural Network (ANN) algorithms. At the same time, the classification process was carried out with a healthy control group according to the stimulus conditions in order to contribute to the development of systems that can help diagnose migraine. Unlike previous studies, calculations were made between electrode pairs by applying permutation and conditional Cross Entropy (CE) techniques to Electroencephalography (EEG) signals and connectivity feature matrix or map showing the connection between electrodes was created. In the analysis of the stimulus effect in migraine patients, the most successful classification was obtained between resting and auditory stimulus conditions in the ANN algorithm (Accuracy: 83.68%). In the classification of migraine patients with healthy control group, the ANN algorithm and auditory stimulus condition gave the most successful accuracy rate (Accuracy: 85.71%). According to the analyses in this study, it was determined that the auditory stimulus condition may show significant differences in certain channels in certain regions of the brain of migraine patients compared to visual stimulus and resting conditions and healthy participants. The proposed approach (Fused CE+ANN) has shown successful performance in analyzing the stimulus effect and predicting migraine in migraine patients. Cross entropy techniques can help to discover brain connectivity features that may occur in the brain activity information that may occur especially under stimulus effect.
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    A New Hybrid Approach Based on Time Frequency Images and Deep Learning Methods for Diagnosis of Migraine Disease and Investigation of Stimulus Effect
    (Mdpi, 2023) Orhanbulucu, Firat; Latifoglu, Fatma; Baydemir, Recep
    Migraine is a neurological disorder that is associated with severe headaches and seriously affects the lives of patients. Diagnosing Migraine Disease (MD) can be laborious and time-consuming for specialists. For this reason, systems that can assist specialists in the early diagnosis of MD are important. Although migraine is one of the most common neurological diseases, there are very few studies on the diagnosis of MD, especially electroencephalogram (EEG)-and deep learning (DL)-based studies. For this reason, in this study, a new system has been proposed for the early diagnosis of EEG- and DL-based MD. In the proposed study, EEG signals obtained from the resting state (R), visual stimulus (V), and auditory stimulus (A) from 18 migraine patients and 21 healthy control (HC) groups were used. By applying continuous wavelet transform (CWT) and short-time Fourier transform (STFT) methods to these EEG signals, scalogram-spectrogram images were obtained in the time-frequency (T-F) plane. Then, these images were applied as inputs in three different convolutional neural networks (CNN) architectures (AlexNet, ResNet50, SqueezeNet) that proposed deep convolutional neural network (DCNN) models and classification was performed. The results of the classification process were evaluated, taking into account accuracy (acc.), sensitivity (sens.), specificity (spec.), and performance criteria, and the performances of the preferred methods and models in this study were compared. In this way, the situation, method, and model that showed the most successful performance for the early diagnosis of MD were determined. Although the classification results are close to each other, the resting state, CWT method, and AlexNet classifier showed the most successful performance (Acc: 99.74%, Sens: 99.9%, Spec: 99.52%). We think that the results obtained in this study are promising for the early diagnosis of MD and can be of help to experts.
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    A novel approach for Parkinson's disease detection using Vold-Kalman order filtering and machine learning algorithms
    (Springer London Ltd, 2024) Latifoglu, Fatma; Penekli, Sultan; Orhanbulucu, Firat; Chowdhury, Muhammad E. H.
    Parkinson's disease (PD) is the second most common neurological disorder caused by damage to dopaminergic neurons. Therefore, it is important to develop systems for early and automatic diagnosis of PD. For this purpose, a study that will contribute to the development of systems for the automatic diagnosis of PD is presented. The Electroencephalography (EEG) signals were decomposed into sub-bands using adaptive decomposition methods, such as empirical mode decomposition, variational mode decomposition, and Vold-Kalman order filtering (VKF). Various features were extracted from the sub-band decomposed signals, and the significant ones were determined by Chi-squared test. These important features were applied as input to support vector machine (SVM), fitch neural network (FNN), k-nearest neighbours (KNN), and decision trees (DT), machine learning (ML) models and classification was performed. We analysed the performance of ML models by obtaining accuracy, sensitivity, specificity, positive predictive value, negative predictive values, F1-score, false-positive rate, kappa statistics, and area under the curve. The classification process was performed for two cases: PD ON-HC and PD OFF-HC groups. The most successful method in this study was the VKF method, which was applied for the first time in this field with the approach specified for both cases. In both instances, the SVM algorithm was employed as the ML model, with classifier performance criterion values close to 100%. The results obtained in this study seem to be successful compared to the results of recent research on the diagnosis of PD.