Mühendislik Fakültesi / Faculty of Engineering

Permanent URI for this collectionhttps://hdl.handle.net/11727/1401

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Author: search.filters.author.Aksahin, Mehmet FeyziSubject: search.filters.subject.EEG

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    Detection of Multiple Sclerosis Disease by EEG Coherence Analysis
    (2019) Karaca, Busra Kubra; Aksahin, Mehmet Feyzi; Ocal, Ruhsen; V-3553-2017
    Multiple sclerosis (MS) is a chronic and inflammatory disease affecting the brain and spinal cord. Although the exact cause of MS is not known, genetic, environmental and immunological factors are involved in the etiology of the disease. The lack of a single diagnostic test for early diagnosis of MS and the similarity of clinical features in MS to other diseases is a serious problem. Early detection of MS is important, and therefore a rapid and reliable pre-diagnosis of MS is important for the treatment and prognosis of the disease. Electroencephalography (EEG) signals provide important information about brain and nerve diseases. Therefore, in the proposed study, a decision support system has been developed which will contribute to the pre-diagnosis by using EEG signals. In this context, coherence analysis of bipolar channel pairs of EEG signals obtained from MS patients and healthy individuals was performed and feature extraction was performed from certain frequency bands. Using the obtained features, the "Subspace Discriminant" classifier was trained with 95.8% accuracy and then the system was tested. As a result, accuracy, sensitivity and specificity rates were 91.67%, 85.71% and 100%, respectively.
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    Neurofeedback System Design Based On Discrete Wavelet Transform
    (2018) Ozen, Sinasi Kutay; Aksahin, Mehmet Feyzi
    Neurofeedback is a learning strategy that helps to alter brain waves called electroencephalography (EEG). Neurofeedback technique is used in the treatment of various diseases such as epilepsy, sleep disorders, attention deficit and learning disability. In the first stage of this study, a device was developed in which the EEG signals are collected and transferred to the computer. In the second stage of the study, the obtained EEG signals were analyzed by the discrete wavelet transform method, and according to the analysis result, a system was designed by which the patients could be treated using visual feedback. The usability of the visual feedback part based on theta/ beta protocol with EEG subbands has been tested in real time.
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    Detection of Epilepsy Disease From EEG Signals With Artificial Neural Networks
    (2016) Ozkan, Cansu; Dogan, Seda; Kantar, Tugce; Aksahin, Mehmet Feyzi; Erdamar, Aykut; 0000-0001-8588-480X; AAA-6844-2019
    The diagnosis of the epilepsy diseases are made by physicians with analyzing the electroencephalography (EEG) records. The epilepsy diseases can be determined with observing the main properties of before and on-time seizure signals in time and frequency domain. Physicians are evaluating the results after some necessary scoring on EEG records. However, this evaluation is specialistic, time consuming processes and also may subjective results. At this point, to allow detection of epilepsy diseases, a decision support system can give more objective results to the physicians for diagnosing. The subject of the study is automatically diagnosing the epilepsy diseases on EEG signals. In the proposed study, analyses of EEG signals in time and frequency domain were done and features of diseases were obtained. As a result, using artificial neural network (ANN) and obtained features, a decision support system is realized to diagnose the epilepsy. The specificity and the sensitivity of the algorithm are 94% and 66% respectively.
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    A novel electroencephalography based approach for Alzheimer's disease and mild cognitive impairment detection
    (2021) Oltu, Burcu; Aksahin, Mehmet Feyzi; Kibaroglu, Seda; 0000-0002-3964-268X; AAJ-2956-2021
    Background and objective: Alzheimer's disease (AD) is characterized by cognitive, behavioral and intellectual deficits. The term mild cognitive impairment (MCI) is used to describe individuals whose cognitive impairment departing from their expectations for the age that does not interfere with daily activities. To diagnose these disorders, a combination of time-consuming, expensive tests that has difficulties for the target population are evaluated, moreover, the evaluation may yield subjective results. In the presented study, a novel methodology is developed for the automatic detection of AD and MCI using EEG signals. Methods: This study analyzed the EEGs of 35 subjects (16 MCI, 8 AD, 11 healthy control) with the developed algorithm. The algorithm consists of 3 methods for analysis, discrete wavelet transform(DWT), power spectral density (PSD) and coherence. In the first approach, DWT is applied to the signals to obtain major EEG sub-bands, afterward, PSD of each sub-band is calculated using Burg's method. In the second approach, interhemispheric coherence values are calculated. The variance and amplitude summation of each sub-bands' PSD and the amplitude summation of the coherence values corresponding to the major sub-bands are determined as features. Bagged Trees is selected as a classifier among the other tested classification algorithms. Data set is used to train the classifier with 5-fold cross-validation. Results: As a result, accuracy, sensitivity, and specificity of 96.5%, 96.21%, 97.96% are achieved respectively. Conclusion: In this study, we have investigated whether EEG can provide efficient clues about the neuropathology of Alzheimer's Disease and mild cognitive impairment for early and accurate diagnosis. Accordingly, a decision support system that produces reproducible and objective results with high accuracy is developed.