Scopus İndeksli Yayınlar Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/11727/4809

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Author: search.filters.author.Aksahin, Mehmet Feyzi

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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.
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    Brain tumor prediction on MR images with semantic segmentation by using deep learning network and 3D imaging of tumor region
    (2021) Karayegen, Gokay; Aksahin, Mehmet Feyzi; 0000-0001-6294-9279
    When it comes to medical image segmentation on brain MR images, using deep learning techniques has a significant impact to predict tumor existence. Manual segmentation of a brain tumor is a time-consuming task and depends on knowledge and experience of physicians. In this paper, we present a semantic segmentation method by utilizing convolutional neural network to automatically segment brain tumor on 3D Brain Tumor Segmentation (BraTS) image data sets that comprise four different imaging modalities (T1, T1C, T2 and Flair). In addition, our study includes 3D imaging of whole brain and comparison between ground truth and predicted labels in 3D. In order to obtain exact tumor region and dimensions such as height, width and depth, this method was successfully applied and images were displayed different planes including sagittal, coronal and axial. Evaluation results of semantic segmentation which was executed by a deep learning network are significantly promising in terms of tumor prediction. Mean prediction ratio was determined as 91.718. Mean IoU (Intersection over Union) and Mean BF score were calculated as 86.946 and 92.938, respectively. Finally, dice scores of the test images were showed significant similarity between ground truth and predicted labels. As a result, both semantic segmentation metrics and 3D imaging can be interpreted as meaningful for diagnosing brain tumor accurately.
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    Detection of multiple sclerosis from photic stimulation EEG signals
    (2021) Karaca, Busra Kubra; Aksahin, Mehmet Feyzi; Ocal, Ruhsen
    Background: Multiple Sclerosis (MS) is characterized as a chronic, autoimmune and inflammatory disease of the central nervous system. Early diagnosis of MS is of great importance for the treatment and course of the disease. In addition to the many methods, cost-effective and non-invasive electroencephalogram signals may contribute to the pre-diagnosis of MS. Objectives: The aim of this paper is to classify male subjects who have MS and who are healthy control using photic stimulation electroencephalogram signals. Methods: Firstly the continuous wavelet transformation (CWT) method was applied to electroencephalogram signals under photic stimulation with 5Hz, 10Hz, 15Hz, 20Hz, and 25Hz frequencies. The sum, maximum, minimum and standard deviation values of absolute CWT coefficients, corresponding to "1-4 Hz" and "4-13 Hz" frequency ranges, were extracted in each stimulation frequency region. The ratios of these values obtained from the frequency ranges "1-4Hz" and "4-13Hz" was decided as features. Finally, various machine learning classifiers were evaluated to test the effectivity of determined features. Results: Consequently, the overall accuracy, sensitivity, specificity and positive predictive value of the proposed algorithm were 80 %, 72.7 %, 88.9 %, and 88.9 %, respectively by using the Ensemble Subspace k-NN classifier algorithm. Conclusions: The results showed how photic stimulation electroencephalogram signals can contribute to the prediagnosis of MS.
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    Multi-scale classification of single-cell gel electrophoresis assay using deep learning algorithm
    (2020) Erdamar, Aykut; Aksahin, Mehmet Feyzi; 0000-0001-8588-480X; AAA-6844-2019
    Structural and functional integrity of deoxyribonucleic acid (DNA) is crucial for the maintenance of hereditary information. However, by-products of cellular metabolism and physical or chemical factors may cause spontaneous DNA damage. The alkaline single-cell gel electrophoresis or comet assay analysis is an easy and reliable method for the determination genotoxic effects of chemical and physical factors. Simply, it is the electrophoretic analysis of intact damaged DNA of a single cell on in a thin layer of agarose gel. The quantitative analysis of the comet assay images is performed manually by an expert researcher. In visual scoring, DNA nuclei are scored as 0, 1, 2, 3, and 4; and the correct scoring is crucial for the determination of the DNA damage. However, visual scoring depends on the professional experience of the researcher and it is a time consuming and exhausting task. Therefore, this evaluation is inevitable to have subjective results. To avoid this subjectivity and to show the effectiveness of deep learning algorithm on cell images, a Convolution Neural Network (CNN) based deep learning method is proposed to classify comet assay images. According to the results, CNN is trained and tested with high accuracy. The results show that CNN algorithm can successfully classify five different scores of comet assay images, and these results can also reduce the subjectivity. (C) 2019 Elsevier Ltd. All rights reserved.
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    Heart sound recording and automatic S1-S2 waves detecting system design
    (2020) Aksahin, Mehmet Feyzi; Oltu, Burcu; Karaca, Busra Kubra
    The second leading cause of death in the world is cardiovascular diseases. Diagnosis of vast majority of cardiovascular diseases is made by listening to heart sounds by specialists (auscultation method). However, since the method of auscultation depends on the experience and hearing ability of the specialist, obtained results can be subjective. Therefore, digitization and visualization of heart sounds enables accurate, rapid and economical diagnosis of cardiovascular diseases, especially heart valve diseases. For this purpose, a device prototype that collects the heart sound from human body and also amplifies, filters, displays and records collected data on digital environment was designed in the first part of this study. In order to test the working accuracy of the designed device, clinical applications were carried out with the permission of the ethics committee and as the result of this application 15 heart sound recordings from 5 different disease groups(mitral insufficiency, mitral-aortic insufficiency, mitral-tricuspid insufficiency, mitral-aortic tricuspid insufficiency and healthy heart sound recordings) were collected.and obtained recordings were examined. The most effective parameter for the diagnosis of heart valve diseases is the location of the S1-S2 heart sounds. For this reason, in the second part of the study, a medical decision support system was established to detect the S1-S2 locations to assist physicians in their diagnosis. In this context, heart sounds are first filtered by discrete wavelet transform. Then, the S1-S2 waves in the filtered signal are made evident by the teager energy operator and rule-based algorithm. As a result, S1-S2 locations in normal and pathological data were detected with 98.67% sensitivity, 97.69% specificity and 98.18% accuracy.
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    Quantitative sleep EEG synchronization analysis for automatic arousals detection
    (2020) Erdamar, Aykut; Aksahin, Mehmet Feyzi; 0000-0001-8588-480X; AAA-6844-2019
    Background and objective: Electroencephalographic arousals are considered to be the main reason for the interruption of sleep and are visually examined by sleep physicians. Visual scoring of all-night recordings has inter-scorer variability which may lead to subjective results. Hence, we aimed to develop a novel automated method to detect arousals from two electroencephalographic channels in terms of the synchronic events of the right and left hemispheres. Methods: In the context of the occurrence of arousal pattern, the relationship between two synchronic C3-A2 and C4-A1 channels were quantified using by coherence spectrum and mutual information. The power and the ratio values of the sub-bands of the coherence spectrum were selected as the five features. Furthermore, the mutual information value was determined as the sixth feature. The automatic detection performance was evaluated using six features and machine learning techniques, on five different patients' whole-night electroencephalography recordings. The presented method does not include any signal conditioning, pre-processing steps, any manual involvement, meta-rule-based approaches, and some empirical thresholds. Results: The significant increases were found in sub-bands of the coherence spectrum in case of arousal. Moreover, the mutual information of these channels was distinctive during the arousal state. Consequently, the overall accuracy, sensitivity, specificity, and PPV values were achieved as 99.5 %, 99.8 %, 99.6 %, and 99.3 %, respectively with using ensemble bagged tree. Conclusion: The novelty of the present study is the practical determination of the relationship between electroencephalographic synchronization and the occurrence of the arousals between the central regions of the right and left hemispheres. (C) 2020 Elsevier Ltd. All rights reserved.