Sürdürülebilir Kalkınma Amaçları
Yayınlar
- MakaleAçık Erişim2019Scopus SJRQ2A deep learning model for automated sleep stages classification using PSG signals
Yildirim, Ozal; Baloglu, Ulas Baran; Acharya, U Rajendra
Sleep disorder is a symptom of many neurological diseases that may significantly affect the quality of daily life. Traditional methods are time-consuming and involve the manual scoring of polysomnogram (PSG) signals obtained in a laboratory environment. However, the automated monitoring of sleep stages can help detect neurological disorders accurately as well. In this study, a flexible deep learning model is proposed using raw PSG signals. A one-dimensional convolutional neural network (1D-CNN) is developed using electroencephalogram (EEG) and electrooculogram (EOG) signals for the classification of sleep stages. The performance of the system is evaluated using two public databases (sleep-edf and sleep-edfx). The developed model yielded the highest accuracies of 98.06%, 94.64%, 92.36%, 91.22%, and 91.00% for two to six sleep classes, respectively, using the sleep-edf database. Further, the proposed model obtained the highest accuracies of 97.62%, 94.34%, 92.33%, 90.98%, and 89.54%, respectively for the same two to six sleep classes using the sleep-edfx dataset. The developed deep learning model is ready for clinical usage, and can be tested with big PSG data.
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İncele - MakaleAçık Erişim2019Scopus SJRQ2Automated Depression Detection Using Deep Representation and Sequence Learning with EEG Signals
Ay, Betul; Yildirim, Ozal; Talo, Muhammed; Baloglu, Ulas Baran; Aydin, Galip; Puthankattil, Subha D.; Acharya, U. Rajendra
Depression affects large number of people across the world today and it is considered as the global problem. It is a mood disorder which can be detected using electroencephalogram (EEG) signals. The manual detection of depression by analyzing the EEG signals requires lot of experience, tedious and time consuming. Hence, a fully automated depression diagnosis system developed using EEG signals will help the clinicians. Therefore, we propose a deep hybrid model developed using convolutional neural network (CNN) and long-short term memory (LSTM) architectures to detect depression using EEG signals. In the deep model, temporal properties of the signals are learned with CNN layers and the sequence learning process is provided through the LSTM layers. In this work, we have used EEG signals obtained from left and right hemispheres of the brain. Our work has provided 99.12% and 97.66% classification accuracies for the right and left hemisphere EEG signals respectively. Hence, we can conclude that the developed CNN-LSTM model is accurate and fast in detecting the depression using EEG signals. It can be employed in psychiatry wards of the hospitals to detect the depression using EEG signals accurately and thus aid the psychiatrists.
AtıfScopus279OpenAlex291İncele - MakaleYalnız künye2019Scopus SJRQ4ECG BEAT DETECTION and CLASSIFICATION SYSTEM USING WAVELET TRANSFORM and ONLINE SEQUENTIAL ELM
Yildirim, Ozal
Electrocardiogram (ECG) signals consist of data containing measurements of electrical activity in the heartbeats. These signals include relevant information used to detect abnormalities such as arrhythmia. In this study, a recognition system is proposed for detection and classification of heartbeats in ECG signals. Heartbeats in the ECG data were detected by using the wavelet transform (WT) method and these beats are segmented with determined periods. For obtaining distinctive features from the beats, multi-resolution WT is applied to these segmented signals, and wavelet coefficients are obtained from different frequency levels. Feature vectors are generated on these coefficients by using various statistical methods. The proposed recognition system is trained on feature vectors by using the Online Sequential Extreme Learning Machine (OSELM) classifier during the learning phase to automatically recognize the signals. Five different beat types were obtained from the MIT-BIH arrhythmia dataset. The multi-class dataset that includes five classes and the binary-class dataset that includes two classes were created among these beat types. Performance tests of the proposed wavelet-based-OSELM (W-OSELM) method were realized with these two datasets. The proposed recognition system provided 97.29% correct beat detection rate from raw ECG signals. The classification accuracy is 99.44% for the binary-class dataset and 98.51% for the multi-class dataset. Furthermore, the proposed classifier has shown very fast recognition performance on ECG signals.
AtıfScopus25WoS17OpenAlex26İncele - BildiriYalnız künye2018Geometric Methods in Deep Learning
Ünal, Inan; Yildirim, Ozal; Ozyildirim, BM; Yildirim, T
Deep learning algorithms have recently become the most widely used machine learning approaches. Remarkable approaches have emerged in the field of machine learning studies with the use of geometric methods for deep learning. The manifold theory, which is simply regarded as the general state of an Euclidean space, has become applicable in the field of deep learning and this field has been named Geometric Deep Learning. In this study, applications of manifold theory and differential geometry to deep learning is presented.
- MakaleAçık Erişim2018Scopus SJRQ1FPGA-based online power quality monitoring system for electrical distribution network
Yildirim, Ozal; Eristi, Bekis; Eristi, Huseyin; Unal, Sencer; Erol, Yavuz; Demir, Yakup
In this paper, a new generation Internet-based Power Quality Monitoring System (IPQMS) that transmits real-time power quality (PQ) data over the internet has been developed. The monitoring system has the ability to measure PQ parameters in accordance with the related standards. This monitoring system includes many hardware and software designs and presents an efficient structure. PQ parameters are determined by signal processing algorithms that are applied to the current and voltage signals continuously obtained from the electrical power network. These signal processing algorithms are performed as embedded functions in the FPGA device. The PQ data obtained from the measurement points are transmitted to a server by UDP/IP communication protocol that is implemented in the FPGA device. The monitoring, reporting and permanently storing tasks are accomplished with the real-time automation software and web applications that running on the server computer. With its innovative software and hardware designs, the proposed monitoring approach presents a very useful monitoring structure that can be used in the PQ field.
AtıfScopus37WoS31İncele - MakaleAçık Erişim2018Scopus SJRQ2A new embedded power quality event classification system based on the wavelet transform
Eristi, Belkis; Yildirim, Ozal; Eristi, Huseyin; Demir, Yakup
In this paper, a real time embedded intelligent recognition system is proposed for diagnosing the power quality problems. The intelligent recognition system has a structure capable of classifying and detecting the power quality problems in real time. Hardware applications of the wavelet transform and decision tree classifier are realized inside the recognition system using with field programmable gate array (FPGA) device. These methods operating as embedded in the FPGA environment provide real-time diagnosis of power quality problems. A new approach of this recognition system is its capability to simultaneously detect a power quality event in the power systems and power quality disturbances occurring on each phase following the event. In this paper, 2 different recognition systems that online and offline are presented. The online recognition system operates in the fields with signal processing and classification structures embedded. In the offline system, the distinctive features of the event signals obtained from the online system are used and these signals are classified in the computer environment by means of the least square support vector machines. A prototype model of the power system is created in the laboratory environment in order to test the FPGA-based online intelligent recognition system, determine accuracy rates and evaluate its performance. Power quality event types are created in wide parameter ranges on this model. Obtained results from both recognition systems indicated the hardware and software designs of our embedded systems are quite effective, fast, and have high success performance.
AtıfScopus48WoS35OpenAlex50İncele - MakaleAçık Erişim2018Scopus SJRQ3Recognition of road type and quality for advanced driver assistance systems with deep learning
Tumen, Vedat; Yildirim, Ozal; Ergen, Burhan
To develop effective advanced driving assistance systems, it is important to accurately recognize current driving environments and make critical decisions about driving processes. Preventing accidents through the interaction between the driving assistance systems and the environment and ensuring optimum driving dynamics are the main topics in this field. Vehicles need to recognize the road type and quality at a high accuracy to ensure the most suitable driving for the road type. It is also important to use both uncomplicated and cost-effective systems when performing this detection. In this study, a deep learning-based approach that can be used in vehicle driver assistance systems is proposed to automatically recognize road type and quality. Using this approach, it is possible to determine the road type and the quality of the road using only driving images as the input data. A new convolutional neural network model is designed for classification of the driving images. Driving images obtained from Google Street View are used to evaluate the recognition system for an actual driving environment. The proposed approach shows that the road types were determined with accuracy of 91.41 %, and the pothole road–smooth road distinction was successful at 91.07 %. It can be said that the proposed method is an effective structure that can be used for advanced driving support systems, V2I communications systems, and similar intelligent transportation systems.
AtıfScopus22WoS16OpenAlex25İncele - MakaleYalnız künye2018Scopus SJRQ3An efficient compression of ECG signals using deep convolutional autoencoders
Yildirim, Ozal; Tan, Ru San; Acharya, U. Rajendra
Background and objective: Advances in information technology have facilitated the retrieval and processing of biomedical data. Especially with wearable technologies and mobile platforms, we are able to follow our healthcare data, such as electrocardiograms (ECG), in real time. However, the hardware resources of these technologies are limited. For this reason, the optimal storage and safe transmission of the personal health data is critical. This study proposes a new deep convolutional autoencoder (CAE) model for compressing ECG signals. Methods: In this paper, a deep network structure of 27 layers consisting of encoder and decoder parts is designed. In the encoder section of this model, the signals are reduced to low-dimensional vectors; and in the decoder section, the signals are reconstructed. The deep learning approach provides the representations of the low and high levels of signals in the hidden layers of the model. Hence, the original signal can be reconstructed with minimal loss. Very different from traditional linear transformation methods, a deep compression approach implies that it can learn to use different ECG records automatically. Results: The performance was evaluated on an experimental data set comprising 4800 ECG fragments from 48 unique clinical patients. The compression rate (CR) of the proposed model was 32.25, and the average PRD value was 2.73%. These favourable observation suggest that our deep model can allow secure data transfer in a low-dimensional form to remote medical centers. We present an effective compression approach that can potentially be used in wearable devices, e-health applications, telemetry and Holter systems.
AtıfScopus201OpenAlex206İncele - BildiriAçık Erişim2017Face recognition based on convolutional neural network
Coskun, Musab; Ucar, Aysegul; Yildirim, Ozal; Demir, Yakup
Face recognition is of great importance to real world applications such as video surveillance, human machine interaction and security systems. As compared to traditional machine learning approaches, deep learning based methods have shown better performances in terms of accuracy and speed of processing in image recognition. This paper proposes a modified Convolutional Neural Network (CNN) architecture by adding two normalization operations to two of the layers. The normalization operation which is batch normalization provided acceleration of the network. CNN architecture was employed to extract distinctive face features and Softmax classifier was used to classify faces in the fully connected layer of CNN. In the experiment part, Georgia Tech Database showed that the proposed approach has improved the face recognition performance with better recognition results.
AtıfScopus250OpenAlex295İncele - BildiriYalnız künye2016An Online Electric Power Quality Disturbance Detection System
Yildirim, Ozal; Eristi, Belkis; Eristi, Huseyin; Ünal, Sencer; Erol, Yavuz; Demir, Yakup
In this study, an FPGA based online monitoring system was developed for detection of power quality disturbances. The developed system has the capability to instantly detect commonly seen PQ disturbances such as voltage sag, voltage swell and interruption occurring on real-time three-phase voltage signals obtained from the electric power system. The data of the detected disturbance events are transferred to the visual software prepared in the computer environment by means of the UDP/IP communication module embedded in the FPGA. This PQ disturbance detection system was tested upon installation at three different measuring points across the campus of Tunceli University. It was seen from the test results examined that the detection system successfully identified the PQ disturbance events.
- BildiriYalnız künye2016A Real-Time Power Quality Disturbance Detection System Based On The Wavelet Transform
Eristi, Belkis; Yildirim, Ozal; Eristi, Huseyin; Demir, Yakup
Disturbances occurring on voltage signals following switching events or faults on power systems are defined as power quality problem. Power quality problems result in significant financial losses in the power systems in addition to faulty operation or breakdown of sensitive loads connected to the power system. Therefore, power quality problems should be swiftly detected and eliminated. This paper proposes a real-time embedded detection system for the purpose of identification of power quality disturbances. The detection system developed has been created using the wavelet transform in the Field Programmable Gate Array (FPGA) media. The general structure of FPGA-based detection system comprises two fundamental parts: hardware and software. Hardware structure comprises signal input card, FPGA and computer components. The software stage includes FPGA and graphical interface software. An experimental setup of the power system has been created in laboratory environment in order to test the FPGA-based detection system, determine accuracy rates and assess its success. Various power quality disturbances have been created over this model in wide parameter intervals. When the results obtained for the real-time power quality disturbance detection system developed under this study are examined, it has been seen that hardware and software designs are quite effective, fast and of high achievement performance.
- BildiriYalnız künye2015An FPGA Based Power Quality Monitoring System
Yildirim, Ozal; Eristi, Belkis; Eristi, Huseyin; Ünal, Sencer; Erol, Yavuz; Demir, Yakup
In this study, a new FPGA based monitoring system has been developed for monitoring the power quality. Designed system performs the calculations of power quality parameters on real-time voltages and currents datas that are obtained from the network. FPGA devices that have recently become more popular in digital signal processing field is used in the structure of monitoring system. Designed power quality monitoring system has been established on electrical distribution panels of Tunceli University Vocational Schools. When the obtained test results are evaluated, it has been observed that the proposed power quality monitoring system is successful.
AtıfWoS1İncele - MakaleAçık Erişim2015Scopus SJRQ2Automatic classification of harmonic data using k-means and least square support vector machine
Eristi, Huseyin; Tümen, Vedat; Yildirim, Ozal; Eristi, Belkis; Demir, Yakup
In this paper, an effective classification approach to classify harmonic data has been proposed. In the proposed classifier approach, harmonic data obtained through a 3-phase system have been classified by using k-means and least square support vector machine (LS-SVM) models. In order to obtain class details regarding harmonic data, a k-means clustering algorithm has been applied to these data first. The training of the LS-SVM model has been realized with the class details obtained through the k-means algorithm. To increase the efficiency of the LS-SVM model, the regularization and kernel parameters of this model have been determined with a grid search method and the training phase has been realized. Backpropagation neural network and J48 decision tree classifiers have been applied to the same data and results have been obtained for the purpose of comparing the performance of the LS-SVM model. The real data obtained from the output of distribution system have been used to assess the performance of the proposed classifier system. The obtained results and comparisons suggest that the proposed classifier system approach is quite efficient at classifying harmonic data.
AtıfWoS1OpenAlex3İncele - MakaleAçık Erişim2014Scopus SJRQ1Automatic recognition system of underlying causes of power quality disturbances based on S-Transform and Extreme Learning Machine
Eristi, Huseyin; Yildirim, Ozal; Eristi, Belkis; Demir, Yakup
In this paper, a new S-Transform and Extreme Learning Machine (ST-ELM)-based event recognition approach for the purpose of classifying power quality (PQ) event signals automatically has been proposed. In this approach, the distinctive features of the PQ event signals have been obtained with the S-Transform-based feature extraction. The feature vector obtained with feature extraction has been applied as input to the ELM classifier. Ten different classification procedures were determined within the framework of this study to assess the performance of the ELM classifier on PQ event data. Real PQ event data and synthetic PQ event data obtained from MATIAB/Simulink environment have been used in these procedures. Also, three different PQ event data sets, which are formed by adding noises of 20, 30 and 50 dB to the synthetic PQ event data respectively, have been used in order to assess the performance of the proposed approach on noisy conditions. According to the results of performance evaluations, the proposed ST-ELM-based PQ event recognition system has a very high performance of recognizing PQ event data. Besides, classification of noisy data showed that the proposed approach is robust at recognizing noisy data. The performance of the ST-ELM-based recognition system on PQ data shows that this approach has an effective recognition structure that can be used in real power systems. (C) 2014 Elsevier Ltd. All rights reserved.
AtıfWoS63OpenAlex93İncele - MakaleAçık Erişim2013Scopus SJRQ1Optimal feature selection for classification of the power quality events using wavelet transform and least squares support vector machines
Eristi, Huseyin; Yildirim, Ozal; Eristi, Belkis; Demir, Yakup
In this paper, a new optimal feature selection based power quality event recognition system is proposed for the classification of power quality events. While Apriori algorithm is capable of processing categorical data, an effective feature vector, which represents distinctive features of digital power quality event data, has been obtained by means of the proposed k-means based Apriori algorithm feature selection approach. The proposed k-means based Apriori algorithm feature selection approach is presented with a power quality event recognition system. In the power quality event recognition system, normalization and segmentation processes have been applied to three-phase event voltage signals. Using 9-level multiresolution analysis, wavelet transform coefficients of the event signals have been obtained. By applying nine different feature extraction processes to these coefficients, a 90 dimensional feature vector belonging to three-phase event voltage signals has been extracted. Optimal feature vector has been obtained by applying the k-means based Apriori algorithm feature selection approach to the obtained feature vector, which has been applied as the last step to the input of the least squares support vector machine classifier and recognition performance results have been obtained. Real power quality event data have been used to evaluate the performance of the proposed feature selection approach and power quality event recognition system. According to the results, the proposed k-means based Apriori algorithm feature selection approach and power quality event recognition system are efficient, reliable and applicable and classify three-phase event types with a high degree of accuracy. (C) 2013 Elsevier Ltd. All rights reserved.
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