Yazar "Özerdem, Mehmet Siraç" seçeneğine göre listele

Listeleniyor 1 - 20 / 48
  • Küçük Resim
    Öğe
    Automated detection of alzheimer’s disease using raw EEG time series via. DWT-CNN model
    (Dicle Üniversitesi Mühendislik Fakültesi, 2022) Şeker, Mesut; Özerdem, Mehmet Siraç
    Dementia is an age-related neurological disease and gives rise to profound cognitive decline in patients’ life. Alzheimer’s Disease (AD) is the progression of dementia and AD patients generally have memory loss and behavioral disorders. It is possible to determine the stage of dementia by developing automated systems via. signals obtained from patients. EEG is a popular brain monitoring system due to its cost effective, non-invasive implementation, and higher time resolution. In current study, we include participants of 24 HC (12 eyes open (EO), 12 eyes closed (EC)), and 24 AD (HC (12 eyes open (EO), 12 eyes closed (EC)). The aim of current study is to design a practical AD detection tool for AD/HC participants with a model called DWT-CNN. We performed Discrete Wavelet Transform (DWT) to extract EEG sub-bands. A Conv2D architecture is applied to raw samples of related EEG sub-bands. According to obtained performance metrics calculated from confusion matrices, all AD and HC time series are correctly classified for alpha band and full band range under both EO and EC. Classification rate of AD vs. HC increases under EO state in all cases even if EC is commonly preferred in other studies. We will add MCI patients with equal size and similar demographics and repeat the experimental steps to develop early alert system in future studies. Adding more participants will also increase generalization ability of method. It is also promising study to combine EEG with different modalities (2D TF image conversion, or MRI) in a multimodal approach.
  • Küçük Resim
    Öğe
    Automatic detection and localization of COVID-19 pneumonia using axial computed tomography images and deep convolutional neural networks
    (Wiley, 2021) Polat, Hasan; Özerdem, Mehmet Siraç; Ekici, Faysal; Akpolat, Veysi
    COVID-19 was first reported as an unknown group of pneumonia in Wuhan City, Hubei province of China in late December of 2019. The rapid increase in the number of cases diagnosed with COVID-19 and the lack of experienced radiologists can cause diagnostic errors in the interpretation of the images along with the exceptional workload occurring in this process. Therefore, the urgent development of automated diagnostic systems that can scan radiological images quickly and accurately is important in combating the pandemic. With this motivation, a deep convolutional neural network (CNN)-based model that can automatically detect patterns related to lesions caused by COVID-19 from chest computed tomography (CT) images is proposed in this study. In this context, the image ground-truth regarding the COVID-19 lesions scanned by the radiologist was evaluated as the main criteria of the segmentation process. A total of 16 040 CT image segments were obtained by applying segmentation to the raw 102 CT images. Then, 10 420 CT image segments related to healthy lung regions were labeled as COVID-negative, and 5620 CT image segments, in which the findings related to the lesions were detected in various forms, were labeled as COVID-positive. With the proposed CNN architecture, 93.26% diagnostic accuracy performance was achieved. The sensitivity and specificity performance metrics for the proposed automatic diagnosis model were 93.27% and 93.24%, respectively. Additionally, it has been shown that by scanning the small regions of the lungs, COVID-19 pneumonia can be localized automatically with high resolution and the lesion densities can be successfully evaluated quantitatively.
  • Küçük Resim
    Öğe
    Automatic detection of cursor movements from the EEG signals via deep learning approach
    (IEEE-Institute of Electrical Electronics Engineers INC., 2020) Polat, Hasan; Özerdem, Mehmet Siraç
    The classification of motor imagery (MI) tasks is one of the key objectives of EEC-based brain-computer interface (BC!) systems. To ensure successful classification performance to BCI systems, researchers endeavor to extract appropriate features. However, these challenges are based on the conventional method. In this study, EEG signals related to MI tasks are classified using the convolutional neural network (CNN), which does not need a separate feature extraction. EEG records, which are generally evaluated as one-dimensional in machine learning problems, were taken into consideration as the image representation by using a novel method. The datasets were taken from a healthy subject. The subject was asked to move a cursor up and down on a computer screen, while his cortical potentials were taken. The EEG signals recorded over the 3.5-second time interval were evaluated for both the whole time and sub time intervals. Thus, the most effective time interval that has distinguishing features for EEG recordings related to different cursor movements was tried to be determined as well. As a result, it has been shown that the proposed model based on deep learning approach can successfully classify EEG signals related to cursor movements.
  • [ X ]
    Öğe
    Automatic determination of different soil types via several machine learning algorithms employing radarsat-2 SAR image polarization coefficients
    (Springer, 2022) Acar, Emrullah; Özerdem, Mehmet Siraç; 0000-0002-1897-9830; 0000-0002-9368-8902
    Synthetic aperture radar (SAR), which is one of the most popular remote sensing technologies, has been extensively employed for classification of various soil types, soil texture description, and its mapping. Determining the soil type is useful for rural and urban management. In the current study, several machine learning algorithms, which consist of the K-Nearest Neighbor (K-NN), Extreme Learning Machine (ELM), and Naive Bayes (NB), have been recommended by utilizing Radarsat-2 SAR data. A pilot region in the city of Diyarbakir, Turkey that spreads among 370 46’- 380 04’ N latitudes and 400 04’- 400 26’E longitudes was employed, and nearly, 156 soil samples were collected for classification of two soil types (Clayey and Clayey+Loamy). After that, four different Radarsat-2 SAR image polarization coefficients were computed for each soil sample, and these coefficients were utilized as inputs in the classification stage. Finally, the results showed that an overall accuracy of 91.1% with K-NN, 82.0% with ELM, and 85.2% with NB algorithm was computed for the classification of two soil types.
  • Küçük Resim
    Öğe
    A classification approach for focal/non-focal EEG detection using cepstral analysis
    (Dicle Üniversitesi Mühendislik Fakültesi, 2021) Şeker, Delal; Özerdem, Mehmet Siraç
    Electroencephalogram (EEG) is a convenient neuroimaging technique due to its non-invasive setup, practical usage, and high temporal resolution. EEG allows to detect brain electrical activity to diagnose neurological disorders. Epilepsy is a crucial neurologic disorder that is reasoned from occurrence of sudden and repeated seizures. The goal of this paper is to classify the focal (epileptogenic area) and non-focal (non-epileptogenic area) EEG records with cepstral coefficients and machine learning algorithms. Analysis is carried out using publicly available Bern-Barcelona EEG dataset. Mel Frequency Cepstral Coefficients (MFCC) are calculated from EEG epochs. Feature sets are normalized with z-score and dimension reduction is realized using Principal Component Analysis. Fine Tree, Quadratic Discriminant Analysis, Logistic Regression, Gaussian Naïve Bayes, Cubic Support Vector Machine, weighted k-nearest neighbors, and Bagged Trees are applied for classification stage. A value of k=10 is used for cross validation. All focal and non-focal EEG pairs are perfectly classified with acc., sen., spe., and F1-score of 100% and AUC with 1 via. Quadratic Discriminant Analysis, Logistic Regression, Cubic SVM and Weighted k-NN. Proposed work recommends MFCCs as a single marker and this provides less computation workload, practicality, and direct processing of focal / non-focal EEG time series. Proposed methodology in this paper serves one of the highest achievements to literature and can assist neurologist and physicians to validate their diagnosis.
  • Küçük Resim
    Öğe
    Complexity of EEG dynamics for early diagnosis of alzheimer's disease using permutation entropy neuromarker
    (Elsevier, 2021) Şeker, Mesut; Özbek, Yağmur; Yener, Görsev; Özerdem, Mehmet Siraç
    Background and objective Electroencephalogram (EEG) is one of the most demanded screening tools that investigates the effects of Alzheimer's Disease (AD) on human brain. Identification of AD in early stage gives rise to efficient treatment in dementia. Mild Cognitive Impairment (MCI) is considered as a conversion stage. Reducing EEG complexity can be used as a marker to detect AD. The aim of this study is to develop a 3-way diagnostic classification using EEG complexity in the detection of MCI/AD in clinical practice. This study also investigates the effects of different eyes states, i.e. eyes-open, eyes-closed on classification performance. Methods EEG recordings from 85 AD, 85 MCI subjects, and 85 Healthy Controls with eyes-open and eyes- closed are analyzed. Permutation Entropy (PE) values are computed from frontal, central, parietal, temporal, and occipital regions for each EEG epoch. Distribution of PE values are visualized to observe discrimination of MCI/AD with HC. Visual investigations are combined with statistical analysis using ANOVA to determine whether groups are significant or not. Multinomial Logistic Regression model is applied to feature sets in order to classify participants individually. Results Distribution of measured PE shows that EEG complexity is lower in AD and higher in HC group. MCI group is observed as an intermediate form due to heterogeneous values. Results from 3-way classification indicate that F1-scores and rates of sensitivity and specificity achieve the highest overall discrimination rates reaching up to 100% for at TP8 for eyes-closed condition; and C3, C4, T8, O2 electrodes for eyes-open condition. Classification of HC from both patient groups is achieved best. Eyes-open state increases discrimination of MCI and AD. Conclusions This nonlinear EEG methodology study contributes to literature with high discrimination rates for identification of AD. PE is recommended as a practical diagnostic neuro-marker for AD studies. Resting state EEG at eyes-open condition can be more advantageous over eyes-closed EEG recordings for diagnosis of AD.
  • Küçük Resim
    Öğe
    The convolutional neural network approach from electroencephalogram signals in emotional detection
    (Wiley, 2021) Türk, Ömer; Özerdem, Mehmet Siraç
    Although brain-computer interfaces (BCI) progress rapidly, the desired success has not been achieved yet. One of these BCI is to detect emotional states in humans. An emotional state is a brain activity consisting of hormonal and mental reasons in the face of events. Emotions can be detected by electroencephalogram (EEG) signals due to these activities. Being able to detect the emotional state from EEG signals is important in terms of both time and cost. In this study, a method is proposed for the detection of the emotional state by using EEG signals. In the proposed method, we aim to classify EEG signals without any transform (Fourier transform, wavelet transform, etc.) or feature extraction method as a pre-processing. For this purpose, convolutional neural networks (CNNs) are used as classifiers, together with SEED EEG dataset containing three different emotional (positive, negative, and neutral) states. The records used in the study were taken from 15 participants in three sessions. In the proposed method, raw channel-time EEG recordings are converted into 28 x 28 size pattern segments without pre-processing. The obtained patterns are then classified in the CNN. As a result of the classification, three emotion performance averages of all participants are found to be 88.84%. Based on the participants, the highest classification performance is 93.91%, while the lowest classification performance is 77.70%. Also, the average f-score is found to be 0.88 for positive emotion, 0.87 for negative emotion, and 0.89 for neutral emotion. Likewise, the average kappa value is 0.82 for positive emotion, 0.81 for negative emotion, and 0.83 for neutral emotion. The results of the method proposed in the study are compared with the results of similar studies in the literature. We conclude that the proposed method has an acceptable level of performance
  • Küçük Resim
    Öğe
    Dahilerin ilham kaynağı El-Cezerî'nin günümüze yansıyan mirası
    (Dicle Üniversitesi, 2023) Özerdem, Mehmet Siraç; Yıldız, İrfan
    İslam âleminin önemli mucitlerinden ve bilginlerinden olan el- Cezerî, kültür- sanat ve aynı zamanda saray kültürünün olduğu bir şehir olan Diyarbakır’da yaşamıştır. 1183 yılından itibaren Amid şehrine gelen el- Cezerî, sarayın başmühendisi ve aynı zamanda baş müderrisi olarak görev yapmaya başlamıştır. El- Cezerî, Amid Artuklu Sarayı’nda 25 yıl çalışmıştır. Çalışmalarını ağırlıklı olarak Amid Artuklu Sultanı Melîk Salîh Nâsîrüddîn Ebû’l- Feth Mahmûd bin Muhammed döneminde yapmıştır. Ancak el-Cezerî Melîk Salih’in kardeşi Kutbeddin II. Sökmen ve babası Nureddin Muhammed bin Kara Aslan zamanında da Artuklu Sarayı’nda çalışmıştır. Cezerî çalışmalarını Melîk Salîh Nâsîrüddîn Ebû’l- Feth Mahmûd bin Muhammed’in destekleri ile “El Cami Beyne’l-ilm ve’l-Ameli’n-Nâfi fî Sınaâti’l-Hiyel” adlı kitabında toplamıştır. El-Cezerî’nin yaşadığı Artuklu Sarayı Diyarbakır merkez içkalede Amida (Amidi-Amedi) Höyüğün üstündedir. Sarayda kazı çalışmalarına 1961-62 yıllarında başlanmış daha sonra ara verilen kazılara Dicle Üniversitesi öncülüğünde 2018 yılında tekrar başlanmıştır. Yapılan kazılarda elCezerî’nin yaşadığı Artuklu Sarayı’nın birçok mekânı gün yüzüne çıkarılmıştır.
  • Küçük Resim
    Öğe
    Deep insights into MCI diagnosis: A comparative deep learning analysis of EEG time series
    (Elsevier, Mart 2024) Şeker, Mesut; Özerdem, Mehmet Siraç
    Background Individuals in the early stages of Alzheimer’s Disease (AD) are typically diagnosed with Mild Cognitive Impairment (MCI). MCI represents a transitional phase between normal cognitive function and AD. Electroencephalography (EEG) records carry valuable insights into cerebral cortex brain activities to analyze neuronal degeneration. To enhance the precision of dementia diagnosis, automatic and intelligent methods are required for the analysis and processing of EEG signals. New methods This paper aims to address the challenges associated with MCI diagnosis by leveraging EEG signals and deep learning techniques. The analysis in this study focuses on processing the information embedded within the sequence of raw EEG time series data. EEG recordings are collected from 10 Healthy Controls (HC) and 10 MCI participants using 19 electrodes during a 30 min eyes-closed session. EEG time series are transformed into 2 separate formats of input tensors and applied to deep neural network architectures. Convolutional Neural Network (CNN) and ResNet from scratch are performed with 2D time series with different segment lengths. Furthermore, EEGNet and DeepConvNet architectures are utilized for 1D time series. Results ResNet demonstrates superior effectiveness in detecting MCI when compared to CNN architecture. Complete discrimination is achieved using EEGNet and DeepConvNet for noisy segments. Comparison with existing methods ResNet has yielded a 3 % higher accuracy rate compared to CNN. None of the architectures in the literature have achieved 100 % accuracy except proposed EEGNet and DeepConvnet. Conclusion Deep learning architectures hold great promise in enhancing the accuracy of early MCI detection.
  • Küçük Resim
    Öğe
    Derin öğrenme tabanlı YOLOv5 nesne tespiti yöntemi kullanılarak gaz tüpü tespiti
    (Institute of Electrical and Electronics Engineers Inc., 2022) Albayrak, Abdulkadir; Özerdem, Mehmet Siraç
    Detection and tracking of objects has critical importance in terms of speeding up the process and facilitating the work in many areas. Especially in the process of counting objects, which is difficult and time-consuming for experts. In this paper, a study was carried out to detect gas cylinders with different colors and shapes using the deep learning-based Yolov5 method. The process of counting cylinders in the stock area or in the filling facilities can be difficult for the specialist due to the different sizes, arrangement and large number of cylinders. Within the scope of the study, a data set containing different types of cylinders in gas filling facilities was created. When the obtained results are evaluated, it has been observed that the Yolov5 algorithm detects the gas cylinders with different color and shape properties with a high success rate of 96.16%. In addition to the detection success, it has been observed that the method is also successful in different objective detections such as precision, sensitivity and box intersection.
  • Küçük Resim
    Öğe
    Derin transfer öğrenimi yaklaşımı ile kamusal alanda medikal maske kullanımının otomatik kontrolü
    (Bingöl Üniversitesi Fen Bilimleri Enstitüsü, 2021) Polat, Hasan; Özerdem, Mehmet Siraç
    Uluslararası kamu sağlığı acil durumu olan COVID-19 hastalığının başlıca bulaşma yolları, solunum damlacıkları ve fiziksel temastır. Hastalığın yayılımını önlemek ve salgınla mücadele etmenin kapsamlı stratejilerinden biri olarak kamusal alanda medikal maske kullanımı birçok toplumda zorunlu kılınmıştır. Bu kapsamda, kamusal alanda medikal maske kullanımının otomatik olarak kontrolü, salgınla mücadelede önem arz etmektedir. Bu çalışmada, transfer öğrenimi yaklaşımı ile kamusal alandan alınan görüntülerden medikal maske kullanımının otomatik olarak tespit edilmesi amaçlanmıştır. Derin mimariye transfer öğrenimi yaklaşımı uygulanarak, öğrenilmiş parametrelerinin ince ayarı ile medikal maske tespitinde etkili çözümlerin elde edilmesi amaçlanmıştır. Medikal maske kullanımının otomatik olarak tespitinde, Human in the Loop (HITL) tarafından erişime açık olarak sunulan görüntüler kullanılmıştır. SqueezeNet tabanlı transfer öğrenimi yaklaşımı ile %99,20 oranında sınıflandırma doğruluğu elde edilmiştir. ROC eğrisi altında kalan alanın (AUC) büyüklüğü ise 0,998 olarak elde edilmiştir. Ayrıca, transfer öğrenimi yaklaşımının üstünlüğünü vurgulamak için eğitilmiş parametre içermeyen SqueezeNet mimarisi de aynı veri seti üzerinde uygulanmış ve elde edilen performans değerleri karşılaştırılmıştır. Sınırlı sayıda görüntü veri kümesi üzerinde eğitilen mimari ile sınıflandırma doğruluğu ve AUC performansları sırasıyla %94,75 ve 0,976 olarak elde edilmiştir. Transfer öğrenimi yaklaşımı ile çok kısa sürede eğitilen derin mimarinin medikal maske kullanımı tespitinde etkileyici bir performans sergilediği gözlemlenmiştir.
  • [ X ]
    Öğe
    Dicle Nehri Havzasında Toprak Nem Ölçümleri ile Radar Imgeleri Arasındaki Iliskiyi Saptama ve Bu Iliskiye Dayalı Toprak Neminin Tahmini
    (2018) Acar, Emrullah; Üstündağ, Burak Berk; Özerdem, Mehmet Siraç; Ekinci, Remzi
    Uzaktan algılama teknolojisi; yeryüzündeki arazi kullanımlarının tespiti, arazilerdeki hızlı degisimlerin izlenmesi amacıyla anlık kayıtlarının alınması, dogal kaynakların saptanması gibi birçok alanda kullanılmaktadır. Günümüzde ve gelecekte uzaktan algılamaya ihtiyaç duyulacak alanlardan biri de artan nüfus ve tarımsal alanlara paralel olarak ileride yetersiz kalabilecek su kaynaklarının tarımsal arazilerde dogru bir sekilde kullanılmasını saglamaktır. Topraktaki su içerigi, topragın geri saçılma katsayısını önemli ölçüde etkilediginden, yersel toprak nemi ölçümleri ile uzaktan algılama verileri arasındaki iliskilendirilme toprak neminin kısa sürede tahmin edilmesini saglayabilmektedir. Ayrıca, elektromanyetik spektrumun mikrodalga bölgesinde faaliyet gösteren SAR sensörleri toprak içerigindeki nem degisimlerine karsı hassas olduklarından dolayı, bu sensörlerin toprak nemi tahmininde kullanımı daha uygundur. Dolayısıyla, bir SAR radarı olan Radarsat-2 toprak neminin tahmini için bu çalısmada kullanılmıstır. Bu projenin temel amacı, yersel nem ölçümleri ile Radarsat?2 verileri arasındaki iliskiyi belirlemek; çorak ve/veya bitki örtüsü kaplı tarım alanları üzerindeki toprak rutubetini belirlenen iliskiye dayanarak tahmin etmektir. Çalısma bes asamadan olusmaktadır. Ilk asamada; Radarsat-2 verileri farklı tarihlerde elde edilmis ve yersel toprak nem ölçümleri bu verilerin temini ile aynı anda gerçeklestirilmistir. Ikinci asamada; Radarsat?2 verileri önisleme tabi tutulmus ve her bir toprak numunesinin alındıgı noktaların GPS koordinatları bu verilere aktarılmıstır. Sonraki asamada; öznitelik çıkarma islemi için ön islemi tamamlanmıs Radarsat?2 verilerine standart sigma geri saçılma katsayıları ile Freeman-Durden ve H / A / ? polarimetrik ayrısma modelleri uygulanmıs; her örüntü için 10 geri saçılma katsayısına sahip bir öznitelik vektörü olusturulmustur. Dördüncü asamada, elde edilen özellik vektörlerinden bölgesel toprak nemini elde etmek için dogrusal olmayan bir makine ögrenme modeli: Genellestirilmis Regresyon Sinir Agı (GRNN) kullanılmıstır. Son asamada ise GRNN girislerinde kullanılan en uygun özelliklerin belirlenebilmesi için literatürde çok yeni bir yöntem olan Asırı Ögrenme Makineleri tabanlı özellik seçme yöntemi (ELM-FS) uygulanmıstır. Yapılan çalısmalar neticesinde, önerilen sistem ile çorak ve bitkisel tarım alanları üzerinde Cbantlı SAR verileri iyi sonuçlar vermistir
  • [ X ]
    Öğe
    EEG based Schizophrenia Detection using SPWVD-ViT Model
    (Hibetullah KILIÇ, 2022) Şeker, Mesut; Özerdem, Mehmet Siraç
    Schizophrenia is a typical neurological disease that affects patients’ mental state, and daily behaviours. Combining image generation techniques with effective machine learning algorithms may accelerate treatment process, and possible early alert systems prevents diseases from reaching out crucial phase. The purpose of current study is to develop an automated EEG based schizophrenia detection with the Vision Transformer (ViT) model using Smoothed Pseudo Wigner Ville Distribution (SPWVD) time-frequency input images. EEG recordings from 35 schizophrenia (sch) and 35 healthy conditions (hc) are analyzed. We have used 5-fold cross validation for evaluation and testing of the method. Classification task is carried out as subject-independent and subject-dependent method. We reached out overall accuracy of 87% for subject-independent and 100% for subject-dependent approach for binary classification. While ViT has ben extensively used in Natural Language Processing (NLP) field, dividing input images within a sequence of embedded image patches via. transformer encoder is a practical way for medical image learning and developing diagnostic tools. SPWVD-ViT model is recommended as a disease detection tool not only for schizophrenia but other neurological symptoms.
  • Küçük Resim
    Öğe
    EEG channel selection using differential evolution algorithm and particle swarm optimization for classification of odorant-stimulated records
    (INESEG Yayıncılık, 2021) Şeker, Mesut; Özerdem, Mehmet Siraç
    A significant advancement has been made in the evolutionary computing and swarm intelligence methods in past decades. These methods have been commonly used to calculate well optimized solutions. Methods select the best elements or cases among set of alternatives. In EEG signal processing applications, efficient channel selection algorithms are required to reduce high dimensionality and remove redundant features. To do this, we examined optimal 5 electrodes out of 14 using Particle Swarm Optimization (PSO) and Differential Evolution Algorithm (DEA). The proposed work is related with pleasant- unpleasant EEG odors classification problem. Classification error rates were calculated by Linear Discriminant Analysis (LDA), k-NN (k Nearest Neighbour), Naive Bayes (NB), Regression Tree (RegTree) classifiers and used as fitness function for optimization algorithms. The results showed that PSO with selected 5 channels gave lowest error rates compared with DEA for all runs. RegTree classifier generated optimal fitness function value among other classifiers. PSO algorithm can effectively support channel selection problem to identify the best channels to maximize classification performance.
  • Küçük Resim
    Öğe
    The effect of low magnitude high frequency vibration on bone healing by clamp method in nonunion tibial fractures
    (Dicle Üniversitesi Tıp Fakültesi, 2022) Çelik, Ferhat; Bilgin, Hakkı Murat; Akkoç, Hasan; Özkul, Emin; Gem, Mehmet; Özerdem, Mehmet Siraç; Karıksız, Mesut; Erdem, Mustafa; Elçi, Serhat
    Introduction: This study aimed to investigate the clinical effect of Low Magnitude High Frequency Vibration (LMHFV) on nonunion tibial fractures, noninvasively. Methods: The Experimental (n=5) and control (n=5) groups were age-matched and pooled based on the Nonunion Tibia Score System (NUSS) (p>0.05). LMHFV (0.35g, 50 Hz, 20 minutes x 4/day) was applied to the experimental group for three months by a mechanical stimulator that we developed using a ‘clamp method’. The control group was followed during three months without any application other than routine treatment. The results were evaluated using the Radiographic Union Score for Tibial Fractures (RUST) and American Orthopedics Foot and Ankle Score (AOFAS). No statistically significant difference was observed between the groups at the beginning and in the end of the 3- month application for RUST and AOFAS scores (p>0.05). Results: Pain and function assessment, at the beginning and end of the study, as a part of The AOFAS scorewere not statistically different (p>0.05) in the control group. However, increases in pain and function AOFAS scores were statistically significant in the experimental group at the end of the 3- month application (p<0.034 and p<0.043, respectively). Conclusion: In this study, LMHFV contributed to the pain and function parameters of AOFAS in the experimental group; however, there was no significant difference between the groups in terms of total RUST and AOFAS scores.
  • Küçük Resim
    Öğe
    Effects of local vibration and pulsed electromagnetic field on bone fracture: A comparative study
    (Wiley, 2017) Bilgin, Hakkı Murat; Çelik, Ferhat; Gem, Mehmet; Akpolat, Veysi; Yıldız, İsmail; Ekinci, Aysun; Özerdem, Mehmet Siraç; Tunik, Selçuk; 0000-0002-6040-9989; 0000-0003-2585-9981; 0000-0002-2435-7800; 0000-0002-0547-4139; 0000-0001-5505-838X; 0000-0002-9368-8902; 0000-0002-0549-8472
    The effectiveness of various therapeutic methods on bone fracture has been demonstrated in several studies. In the present study, we tried to evaluate the effect of local low-magnitude, high-frequency vibration (LMHFV) on rat tibia fracture in comparison with pulsed electromagnetic fields (PEMF) during the healing process. Mid-diaphysis tibiae fractures were induced in 30 Sprague-Dawley rats. The rats were assigned into groups such as control (CONT), LMHFV (15 min/day, 7 days/week), and PEMF (3.5 h/day, 7 days/week) for a three-week treatment. Nothing was applied to control group. Radiographs, serum osteocalcin levels, and stereological bone analyses of the three groups were compared. The X-rays of tibiae were taken 21 days after the end of the healing process. PEMF and LMHFV groups had more callus formation when compared to CONT group; however, the difference was not statistically significant (P = 0.375). Serum osteocalcin levels were elevated in the experimental groups compared to CONT (P <= 0.001). Stereological tests also showed higher osteogenic results in experimental groups, especially in LMHFV group. The results of the present study suggest that application of direct local LMHFV on fracture has promoted bone formation, showing great potential in improving fracture outcome. (C) 2017 Wiley Periodicals, Inc.
  • Küçük Resim
    Öğe
    Effects of pharmacological treatments in alzheimer’s disease: Permutation entropy-based EEG complexity study
    (Springer, 2023) Fide, Ezgi; Polat, Hasan; Yener, Görsev; Özerdem, Mehmet Siraç
    Alzheimer’s disease (AD) is a neurodegenerative brain disease affecting cognitive and physical functioning. The currently available pharmacological treatments for AD mainly contain cholinesterase inhibitors (AChE-I) and N-methyl-d-aspartic acid (NMDA) receptor antagonists (i.e., memantine). Because brain signals have complex nonlinear dynamics, there has been an increase in interest in researching complexity changes in the time series of brain signals in individuals with AD. In this study, we explore the electroencephalographic (EEG) complexity for making better observation of pharmacological therapy-based treatment effects on AD patients using the permutation entropy (PE) method. We examined EEG sub-band (delta, theta, alpha, beta, and gamma) complexity in de-novo, monotherapy (AChE-I), dual therapy (AChE-I and memantine) receiving AD participants compared with healthy elderly controls. We showed that each frequency band depicts its own complexity profile, which is regionally altered between groups. These alterations were also found to be associated with global cognitive scores. Overall, our findings indicate that entropy measures could be useful to show medication effects in AD.
  • Küçük Resim
    Öğe
    The electricity price prediction of victoria city based on various regression algorithms
    (Institute of Electrical and Electronics Engineers Inc., 2022) Örenç, Sedat; Acar, Emrullah; Özerdem, Mehmet Siraç
    Precise electricity price prediction is extremely important for all markets especially for families' life conditions because the more demand the more electricity price increases, therefore it is vital to keep the balance between demand and supply. It is crucial to know how much electricity is needed for the future as it has a remarkable impact on economic circumstances. This article proposes four productive methods in order to forecast high-precision results. In the regression algorithms, it is used several methods which are called decision tree regressions, random forest regression, gradient boosting regression, and linear regression algorithms. The dataset is divided into three parts. Training, validation, and test are split into %70, %10, and %20 respectively. The empirical and efficient results show that these methods can be used and reduce errors. The article demonstrates that a novel forecasting model can be designed for the future.
  • Küçük Resim
    Öğe
    Emotion recognition based on EEG features in movie clips with channel selection
    (Springer Berlin Heidelberg, 2017) Özerdem, Mehmet Siraç; Polat, Hasan
    Emotion plays an important role in human interaction. People can explain their emotions in terms of word, voice intonation, facial expression, and body language. However, brain–computer interface (BCI) systems have not reached the desired level to interpret emotions. Automatic emotion recognition based on BCI systems has been a topic of great research in the last few decades. Electroencephalogram (EEG) signals are one of the most crucial resources for these systems. The main advantage of using EEG signals is that it reflects real emotion and can easily be processed by computer systems. In this study, EEG signals related to positive and negative emotions have been classified with preprocessing of channel selection. Self-Assessment Manikins was used to determine emotional states. We have employed discrete wavelet transform and machine learning techniques such as multilayer perceptron neural network (MLPNN) and k-nearest neighborhood (kNN) algorithm to classify EEG signals. The classifier algorithms were initially used for channel selection. EEG channels for each participant were evaluated separately, and five EEG channels that offered the best classification performance were determined. Thus, final feature vectors were obtained by combining the features of EEG segments belonging to these channels. The final feature vectors with related positive and negative emotions were classified separately using MLPNN and kNN algorithms. The classification performance obtained with both the algorithms are computed and compared. The average overall accuracies were obtained as 77.14 and 72.92% by using MLPNN and kNN, respectively.
  • Küçük Resim
    Öğe
    Epilepsy detection by using scalogram based convolutional neural network from eeg signals
    (MDPI AG, 2019) Türk, Ömer; Özerdem, Mehmet Siraç
    The studies implemented with Electroencephalogram (EEG) signals are progressing veryrapidly and brain computer interfaces (BCI) and disease determinations are carried out at certainsuccess rates thanks to new methods developed in this field. The effective use of these signals,especially in disease detection, is very important in terms of both time and cost. Currently, ingeneral, EEG studies are used in addition to conventional methods as well as deep learningnetworks that have recently achieved great success. The most important reason for this is that inconventional methods, increasing classification accuracy is based on too many human efforts asEEG is being processed, obtaining the features is the most important step. This stage is based onboth the time-consuming and the investigation of many feature methods. Therefore, there is a needfor methods that do not require human effort in this area and can learn the features themselves.Based on that, two-dimensional (2D) frequency-time scalograms were obtained in this study byapplying Continuous Wavelet Transform to EEG records containing five different classes.Convolutional Neural Network structure was used to learn the properties of these scalogram imagesand the classification performance of the structure was compared with the studies in the literature.In order to compare the performance of the proposed method, the data set of the University of Bonnwas used. The data set consists of five EEG records containing healthy and epilepsy disease whichare labeled as A, B, C, D, and E. In the study, A-E and B-E data sets were classified as 99.50%, A-Dand B-D data sets were classified as 100% in binary classifications, A-D-E data sets were 99.00% intriple classification, A-C-D-E data sets were 90.50%, B-C-D-E data sets were 91.50% in quaternaryclassification, and A-B-C-D-E data sets were in the fifth class classification with an accuracy of93.60%.