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Öğe Detection of Parkinson's disease by Shifted One Dimensional Local Binary Patterns from gait(Elsevier, 2016-09) Ertuğrul, Ömer Faruk; Kaya, Yılmaz; Tekin, Ramazan; Almalı, Mehmet NuriThe Parkinson's disease (PD) is one of the most common diseases, especially in elderly people. Although the previous studies showed that the PD can be diagnosed by expert systems through its cardinal symptoms such as the tremor, muscular rigidity, disorders of movements and voice, it was reported that the presented approaches, which utilize simple motor tasks, were limited and lack of standardization. To achieve a standard approach in PD detection, an approach, which is built on shifted one-dimensional local binary patterns (Shifted 1D-LBP) and machine learning methods, was proposed. Shifted 1D-LBP is built on 1D-LBP, which is sensitive to local changes in a signal. In 1D-LBP the positions of neighbors around center data are constant and therefore, the number of patterns that can be exacted by it is limited. This drawback was solved by Shifted 1D-LBP by changeable positions of neighbors. In evaluation and validation stages, the Gait in Parkinson's Disease (gaitpdb) dataset, which consists of three gait datasets that were recorded in different tasks or experiment protocols, were employed. Statistical features were exacted from formed histograms of gait signals transformed by Shifted 1D-LBP. Whole features and selected features were classified by machine learning methods. Obtained results were compared with statistical features exacted from signals in both time and frequency domains and results reported in the literature. Achieved results showed that the proposed approach can be successfully employed in PD detection from gait. This work is not only an attempt to develop a PD detection method, but also a general-purpose approach that is based on detecting local changes in time ordered signals.Öğe EMG sinyallerinin aşırı ögrenme makinesi ile sınıflandırılması(IEEE, 2013-06-13) Ertuğrul, Ömer Faruk; Tağluk, Mehmet Emin; Kaya, Yılmaz; Tekin, Ramazan; Batman Üniversitesi Mühendislik - Mimarlık Fakültesi Bilgisayar Mühendisliği BölümüFrom disease detection to action assessment EMG signals are used variety of field. Miscellaneous studies have been conducted toward analysis of EMG signals. In this study some statistical features of signal were derived, the best evocative features were selected via Linear Discriminant Analysis (LDA) and feature vectors were constructed. This analytic feature vectors were classified through Extreme Learning Machine (ELM). 8 channel EMG signals recorded from 10 normal and 10 aggressive actions were used as an example. By cross-comparison of the obtained results to the ones obtained via various feature identifying methods (AR coefficients, wavelet energy and entropy) and classification methods (NB, SVM, LR, ANN, PART, Jrip, J48 and LMT) the success of the proposed method was determined.Öğe Hari̇ci̇ uyartı akımı ve i̇yoni̇k konsantrasyonların Hodgki̇n-Huxley si̇ni̇r modeli̇ üzeri̇ndeki̇ etki̇leri̇(IEEE, 2012-05-30) Tekin, Ramazan; Tağluk, Mehmet Emin; Ertuğrul, Ömer FarukHodgkin-Huxley (HH) nöron modeli teorik sinirbilimde çok önemli bir yere sahiptir. Çalışmada HH nöron modelini esas alınarak uyartı akım değerinin ve hücre içi ve dışı iyonik sodyum ve potasyum konsantrasyonlarının Aksiyon Potansiyeli (AP) formu üzerindeki etkileri incelenmiştir. Sodyum konsantrasyonu daha çok AP’nin genliğini etkilerken, Potasyumun AP’nin dinlenim, eşik ve hiper-polarizasyon durumunu etkilediği tespit edildi. Uyartı akım Şiddetinin artışı ise AP’nin oluşumunu daha erken tetiklediği ve bu nedenle AP/saniye sayısında artış olduğu gözlenmiştir. Bu gözlemlerin teorik nörobilimde kullanılabileceği düşünülmektedir.Öğe İki kanal yüzey EMG işareti ile el aç/kapa ve el parmaklarının sınıflandırılması(IEEE, 2017-11-02) Sezgin, Necmettin; Ertuğrul, Ömer Faruk; Tekin, Ramazan; Tağluk, Mehmet EminIn this study, two-channel surface electromyogram (sEMG) signals were used to classify hand open/close with fingers. The bispectrum analysis of the sEMG signal recorded with surface electrodes near the region of the muscle bundles on the front and back of the forearm was classified by extreme learning machines (ELM) based on phase matches in the EMG signal. EMG signals belonging to 17 persons, 8 males and 9 females, with an average age of 24 were used in the study. The fingers were classified using ELM algorithm with 94.60% accuracy in average. From the information obtained through this study, it seems possible to control finger movements and hand opening/closing by using muscle activities of the forearm which we hope to lead to control of intelligent prosthesis hands with high degree of freedom.Öğe Kortikal bir ağ modelinin çıkış verisindeki karmaşıklık ve uyumluluk analizi(IEEE, 2013-06-13) Tekin, Ramazan; Tağluk, Mehmet Emin; Ertuğrul, Ömer Faruk; Sezgin, NecmettinDepending on the complex interconnection of billions of neurons forming cortical network excitation times and the emergence of action potentials or spike trains becomes complex and irregular. The effect of various parameters such as synaptic connections, conductivity and voltage dependent channels on the output of the network has become of research issues. In this study, based on Hodgkin-Huxley neuron model an artificial cortical network that simulates a local region of cortex was designed and the effect of probabilistic values of network parameters used in this model on irregularity and complexity of the spike trains at the neurons' output were investigated. Approximation Entropy, Spectral Entropy and Magnitude Squared Coherence methods were used for irregularity analysis.Öğe A novel approach for SEMG signal classification with adaptive local binary pattern(Springer Nature, 2015-12-31) Ertuğrul, Ömer Faruk; Kaya, Yılmaz; Tekin, RamazanFeature extraction plays a major role in the pattern recognition process, and this paper presents a novel feature extraction approach, adaptive local binary pattern (aLBP). aLBP is built on the local binary pattern (LBP), which is an image processing method, and one-dimensional local binary pattern (1D-LBP). In LBP, each pixel is compared with its neighbors. Similarly, in 1D-LBP, each data in the raw is judged against its neighbors. 1D-LBP extracts feature based on local changes in the signal. Therefore, it has high a potential to be employed in medical purposes. Since, each action or abnormality, which is recorded in SEMG signals, has its own pattern, and via the 1D-LBP these (hidden) patterns may be detected. But, the positions of the neighbors in 1D-LBP are constant depending on the position of the data in the raw. Also, both LBP and 1D-LBP are very sensitive to noise. Therefore, its capacity in detecting hidden patterns is limited. To overcome these drawbacks, aLBP was proposed. In aLBP, the positions of the neighbors and their values can be assigned adaptively via the down-sampling and the smoothing coefficients. Therefore, the potential to detect (hidden) patterns, which may express an illness or an action, is really increased. To validate the proposed feature extraction approach, two different datasets were employed. Achieved accuracies by the proposed approach were higher than obtained results by employed popular feature extraction approaches and the reported results in the literature. Obtained accuracy results were brought out that the proposed method can be employed to investigate SEMG signals. In summary, this work attempts to develop an adaptive feature extraction scheme that can be utilized for extracting features from local changes in different categories of time-varying signals.Öğe Örüntü tanımada hopfield ağının kullanılması(Batman Üniversitesi, 2012) Sezgin, Necmettin; Tekin, Ramazan; Çalışkan, AbidinBilgisayar teknolojisinin hızlı bir şekilde gelişmesi akıllı sistemlerin insan yaşamının birçok alanında kullanılmasını artırmıştır. Bu alanlardan birisi de alfa nümerik karakterlerin otomatik olarak doğru bir şekilde tanınması, istenen bir objenin tespit edilmesi ve seçilmesidir. Hopfield ağı, gürültülü veya bozuk olan desenin kısmi ipuçlarından ve önceden depolanmış desenlerden yararlanarak bu deseni düzeltebilen karakteristik bir yapıya sahiptir. Bu süreçte ağ, girdi örüntüsünde yapılan her ufak değişimin ardından örüntü enerjisini yeniden hesaplayarak morfolojik dönüşümünün kontrolünü sağlar ve bu örüntünün daha önce öğrendiği başka bir örüntüye yakınsamasını zorlar. Bu benzetişim işlemi, enerjideki değişkenlik durağan olana dek sürer. Nesnelerin otomatik olarak tanınması, seçilmesi ve işlenmesi gibi işlemden sorumlu bir ağın kullanıldığı akıllı sistemler özellikle robotik alanında önemli bir yere sahiptir. Bu çalışmada Hopfield ağ yapısını kullanarak örüntü tanıyan bir sistem geliştirilmeye çalışılmıştır.Öğe Randomized feed-forward artificial neural networks in estimating short-term power load of a small house: A case study(IEEE, 2017-11-02) Ertuğrul, Ömer Faruk; Tekin, Ramazan; Kaya, YılmazRandomized feed-forward artificial neural networks (ANNs) have been employed in various domains. This paper was written in order to assess the efficiency of the basic forms of randomized feed-forward ANNs, which are randomized weight artificial neural network, random vector functional link network, extreme learning machine, and radial bases function neural network. In order to compare these methods, a complex dataset, which is the power load of a small house dataset, was used. Obtained results showed that lower training error rates were achieved by randomized vector functional link network. On the other hand, lower test error rates were achieved by ELM. Furthermore, ELM has faster training and test stages than the other employed randomized ANNs.
