Aydin Akan

Izmir University of Economics, Department of Electrical and Electronics Engineering, Faculty Member

Istanbul University, Electrical and Electronics Engineering, Faculty Member

Izmir Katip Celebi, Biomedical Engineering, Faculty Member

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Aydin Akan received his B.Sc. degree in Electronics Engineering from the University of Uludag, Bursa, in 1988, the M.Sc. degree in Electronics and Telecommunications Program from the Technical University of Istanbul, Turkey in 1991, and the Ph.D. degree in Electrical Engineering from the University of Pittsburgh, Pittsburgh, PA, USA, in 1996.Currently he holds a Professor position at the Department of Electrical and Electronics Engineering, Izmir University of Economics where he serves as the Department Chair.
Phone: +905333586626
Address: Dept. of Electrical and Electronics Eng.
Izmir University of Economics
Sakarya St, No. 156, Balcova 35330
Izmir, TURKEY

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Analysis of reduced EEG channels based on emotional stimulus

2018 26th Signal Processing and Communications Applications Conference (SIU), 2018

Electroencephalogram (EEG) signals are widely used to investigate the electrical activities in th... more Electroencephalogram (EEG) signals are widely used to investigate the electrical activities in the brain an to get information for these activities. In this work, samples are selected from 3 type (Negative, Neutral, Positive) emotional pictures from ‘Nencki Affective Picture System’ and shown to subjects. EEG signals are collected according to 10–20 international electrode system standards. Collected signals are reduced to 3 type channels (Frontal, Parieto-Occipital, Central). Power Spectral Density (PSD) of the corresponding EEG signals are investigated and separated into EEG frequency bands (Delta, Theta, Alpha, Beta). Welch Spectral Estimation Method is applied into the acquired data and their PSDs are obtained after taking the expectation of their frequencies. Using PSDs, total power is calculated for each frequency band and channel. Then ANOVA analysis is performed and statistical differences are investigated. Our analyses show that statistically significant differences exist between each frequency band and 3 type of channels and among reduced channels themselves.

Detection of chronic kidney disease by using ensemble classifiers

2017 10th International Conference on Electrical and Electronics Engineering (ELECO), 2017

Chronic kidney disease is a major health problem that affect the lives of millions of people arou... more Chronic kidney disease is a major health problem that affect the lives of millions of people around the world and causes serious economical, social and medical problems. Chronic kidney disease can be detected with several automatic diagnosis systems. In this study, we apply Adaboost, Bagging and Random Subspaces ensemble learning algorithms for the diagnosis of chronic kidney diseases. Decision tree based classifiers are used in the decision stage. The classification performances are evaluated with kappa and accuracy criteria. Considering the performance analyses of the proposed systems, it is observed that ensemble learning classifiers provide better classification performance than individual classifiers.

Investigation of Emotional Changes Using Features of EEG-Gamma Band and Different Classifiers

2019 Medical Technologies Congress (TIPTEKNO), 2019

This study focuses on a signal processing method, which qualifies the relation between the emotio... more This study focuses on a signal processing method, which qualifies the relation between the emotional stimulation and emotional changes in healthy participants. For this purpose, an emotional EEG-based database was created by using stimuli which represented to the participants by using Nencki Affective Picture System (NAPS) for the scope of this study. Then, signal processing which includes Power Spectral Density (PSD) and a number of classification analysis is investigated. PSD was analyzed for the emotion connectivity between three types of emotional stimuli to the sub frequency band (gamma band) of the collected EEG-based data. In recent times, most of the studies commonly concentrated on the Brain-Computer Interface (BCI) and we aim that this study performed on emotional changes analyses based BCI systems will be estimable work for the researchers working in this scope.

Arrhythmia Detection on ECG Signals by Using Empirical Mode Decomposition

2018 Medical Technologies National Congress (TIPTEKNO), 2018

One of the main causes of sudden deaths is heart disease. Early detection and treatment of cardia... more One of the main causes of sudden deaths is heart disease. Early detection and treatment of cardiac arrhythmias prevent the problem from reaching sudden deaths. The purpose of this study is to develop an arrhythmia detection algorithm based on Empirical Mode Decomposition (EMD). This algorithm consists of four steps: Preprocessing, Empirical Mode Decomposition, feature extraction and classification. Six arrhythmia types were used for differentiate normal and arrhythmic signals obtained from the MIT-BIH Arrhythmia database. These are normal (N), left bundle branch block (LBBB), right bundle branch block (RBBB), premature ventricular contraction (PVC), paced beat and atrial premature beats (APB). Three different classifiers were used to classify ECG signals. The method achieves better result with accuracy of 87% using linear discriminant analysis (LDA) classifier for detection of normal and arrhythmic signals.

Electrogastrography in patients with diabetic gastroparesis

2017 Medical Technologies National Congress (TIPTEKNO), 2017

Electrogastrography (EGG) is an experimental non-invasive method that reflects the myoelectrical ... more Electrogastrography (EGG) is an experimental non-invasive method that reflects the myoelectrical activity of the diabetic gastroparesis (D-GP) and healthy subjects gastric system. In clinical world, endoscopy and delayed gastric emptying diagnosis test are using for understand the D-GP patient's condition which are invasive, quite expensive and uncomfortable. Therefore our aim is to evaluate the Electrogastrography (EGG) features to discriminate the healthy subjects from patients with D-GP in real clinic. Total 25 patients D-GP and twenty 25 healthy subjects (HS) were included in this study. The recordings EGG parameters dominant frequency (DF) were analyzed and compared. The results we obtained from analysis of EGG signals proved that pre-fed (p= 0.048) and post-fed (p= 0.003) DF values were statistically significant between the D-GP and HS groups. This study proved that it is possible to distinguish D-GP patients from healthy subject's with a high accuracy and a great succ...

Low-Complexity Channel Estimation for OFDM Systems with Guard Subcarriers

2006 First International Conference on Communications and Networking in China, 2006

ABSTRACT In the actual orthogonal frequency division multiplexing (OFDM) systems since guard subc... more ABSTRACT In the actual orthogonal frequency division multiplexing (OFDM) systems since guard subcarriers are not used for transmission, some conventional channel estimators are not applicable. This paper, based on the criteria of least square (LS), proposes a novel channel estimation method for OFDM systems with guard subcarriers. The discrete Fourier transform (DFT)-based channel estimator can cause the aliasing error and high-frequency distortion when there exist guard subcarriers, but our proposed method can mitigate this problem and attain the better estimation performance with fewer pilots. Furthermore, compared with the conventional LS estimator, the proposed method has close performance but lower complexity and can be realized in the actual OFDM systems. Analyses and simulations demonstrate the effectiveness of the proposed approach.

A Dynamic Mode Decomposition Based Approach for Epileptic EEG Classification

by Mehmet Akif Özdemir and Aydin Akan

Epilepsy is a neurological disorder that affects many people all around the world, and its early ... more Epilepsy is a neurological disorder that affects many people all around the world, and its early detection is a topic of research widely studied in signal processing community. In this paper, a new technique that was introduced to solve problems of fluid dynamics called Dynamic Mode Decomposition (DMD), is used to classify seizure and non-seizure epileptic EEG signals. The DMD decomposes a given signal into the intrinsic oscillations called modes which are used to define a DMD spectrum. In the proposed approach, the DMD spectrum is obtained by applying either multi-channel or single-channel based DMD technique. Then, subband and total power features extracted from the DMD spectrum and various classifiers are utilized to classify seizure and non-seizure epileptic EEG segments. Outstanding classification results are achieved by both the single-channel based (96.7%), and the multi-channel based (96%) DMD approaches.

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Interference Excision in Spread Spectrum Communications Using Adaptive Positive Time-Frequency Analysis

EURASIP Journal on Wireless Communications and Networking, 2007

This paper introduces a novel algorithm to excise single and multicomponent chirp-like interferen... more This paper introduces a novel algorithm to excise single and multicomponent chirp-like interferences in direct sequence spread spectrum (DSSS) communications. The excision algorithm consists of two stages: adaptive signal decomposition stage and directional element detection stage based on the Hough-Radon transform (HRT). Initially, the received spread spectrum signal is decomposed into its time-frequency (TF) functions using an adaptive signal decomposition algorithm, and the resulting TF functions are mapped onto the TF plane. We then use a line detection algorithm based on the HRT that operates on the image of the TF plane and detects energy varying directional elements that satisfy a parametric constraint. Interference is modeled by reconstructing the corresponding TF functions detected by the HRT, and subtracted from the received signal. The proposed technique has two main advantages: (i) it localizes the interferences on the TF plane with no cross-terms, thus facilitating simple filtering techniques based on thresholding of the TF functions, and is an efficient way to excise the interference; (ii) it can be used for the detection of any directional interferences that can be parameterized. Simulation results with synthetic models have shown successful performance with linear and quadratic chirp interferences for single and multicomponent interference cases. The proposed method excises the interference even under very low SNR conditions of −10 dB, and the technique could be easily extended to any interferences that could be represented by a parametric equation in the TF plane.

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Emotion Recognition from EEG Signals by Using Empirical Mode Decomposition

by Reza Sadighzadeh and Aydin Akan

2018 Medical Technologies National Congress (TIPTEKNO), 2018

This study investigates improved properties of empirical mode decomposition (EMD) for emotion rec... more This study investigates improved properties of empirical mode decomposition (EMD) for emotion recognition by using electroencephalogram (EEG) signals. The emotion recognition from EEG signals is a difficult study by the reason of nonstationary behavior of the signals. These signals are affected from complicated neural activity of brain. To analyze EEG signals, advanced signal processing techniques are required. In our study, data are collected from one channeled BIOPAC lab system. EEG signals were obtained from visual evoked potentials of 13 female and 13 male volunteers for 12 pleasant and 12 unpleasant pictures. To analyze nonlinear and nonstationary characteristics of EEG signals, an EMD-based method is proposed for emotion recognition. Various time and frequency domain techniques such as power spectral density (PSD), and higher order statistics (HOS) are used to analyze the IMFs extracted by EMD. Support vector machine (SVM), Linear discriminant analysis (LDA), and Naive Bayes classifiers are utilized for the classification of features extracted from the IMFs, and their performances are compared.

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David Helbert David Thornley Dhiya Al-Jumeily

Real Time Emotion Recognition from Facial Expressions Using CNN Architecture

2019 Medical Technologies Congress (TIPTEKNO), 2019

Emotion is an important topic in different fields such as biomedical engineering, psychology, neu... more Emotion is an important topic in different fields such as biomedical engineering, psychology, neuroscience and health. Emotion recognition could be useful for diagnosis of brain and psychological disorders. In recent years, deep learning has progressed much in the field of image classification. In this study, we proposed a Convolutional Neural Network (CNN) based LeNet architecture for facial expression recognition. First of all, we merged 3 datasets (JAFFE, KDEF and our custom dataset). Then we trained our LeNet architecture for emotion states classification. In this study, we achieved accuracy of 96.43% and validation accuracy of 91.81% for classification of 7 different emotions through facial expressions.

Synchronization Analysis In Epileptic EEG Signals Via State Transfer Networks Based On Visibility Graph Technique

International Journal of Neural Systems, 2021

Epilepsy is a persistent and recurring neurological condition in a community of brain neurons tha... more Epilepsy is a persistent and recurring neurological condition in a community of brain neurons that results from sudden and abnormal electrical discharges. This paper introduces a new form of assessment and interpretation of the changes in electroencephalography (EEG) recordings from different brain regions in epilepsy disorders based on graph analysis and statistical rescale range analysis. In this study, two different states of epilepsy EEG data (preictal and ictal phases), obtained from 17 subjects (18 channels each), were analyzed by a new method called state transfer network (STN). The analysis performed by STN yields a network metric called motifs, which are averaged over all channels and subjects in terms of their persistence level in the network. The results showed an increase of overall motif persistence during the ictal over the preictal phase, reflecting the synchronization increase during the seizure phase (ictal). An evaluation of intermotif cross-correlation indicated a...

EEG-based emotion recognition with deep convolutional neural networks

Biomedical Engineering / Biomedizinische Technik, 2020

The emotional state of people plays a key role in physiological and behavioral human interaction.... more The emotional state of people plays a key role in physiological and behavioral human interaction. Emotional state analysis entails many fields such as neuroscience, cognitive sciences, and biomedical engineering because the parameters of interest contain the complex neuronal activities of the brain. Electroencephalogram (EEG) signals are processed to communicate brain signals with external systems and make predictions over emotional states. This paper proposes a novel method for emotion recognition based on deep convolutional neural networks (CNNs) that are used to classify Valence, Arousal, Dominance, and Liking emotional states. Hence, a novel approach is proposed for emotion recognition with time series of multi-channel EEG signals from a Database for Emotion Analysis and Using Physiological Signals (DEAP). We propose a new approach to emotional state estimation utilizing CNN-based classification of multi-spectral topology images obtained from EEG signals. In contrast to most of ...

Deep Learning Based Facial Emotion Recognition System

2020 Medical Technologies Congress (TIPTEKNO), 2020

Bu çalışmada derin öğrenme yöntemi kullanılarak yüz görüntülerinden duygu durum tespiti yapılması... more Bu çalışmada derin öğrenme yöntemi kullanılarak yüz görüntülerinden duygu durum tespiti yapılması hedeflenmiştir. Etik kurul onayı alınmış çalışmada, 7 farklı yüz ifadesini (mutlu, üzgün, şaşırmış, kızgın, iğrenmiş, korkmuş ve tarafsız) taklit ederken 20 adedi erkek ve 20 adedi kadın katılımcıdan alınan videolar kullanılarak özel veri seti oluşturulmuştur. Elde edilen videolar, önce resim karelerine ayrılmış sonrasında resim karelerinden Haar kütüphanesi kullanılarak yüz resimleri bölütlenmiştir. Resim ön işlemime sonrasında elde edilen özel veri setinin boyutu 25 bin resimden fazladır. LeNet ağının yapısından faydalanılarak önerilen evrişimsel sinir ağı (ESA) mimarisi bu özel veri seti ile eğitilmiştir. Önerilen ESA mimarisi deney sonuçlarına göre, eğitim kaybı 0,0115, eğitim doğruluğu %99,62 bulunmuş, doğrulama kaybı 0,0109 ve doğrulama doğruluğu %99,71 olarak bulunmuştur.

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Analysis of EMG signals in the Quadratus Lumborum muscle of healthy subject with functional leg length discrepancy

The purpose of this study was to analyze the electromyography (EMG) signals of the Quadratus Lumb... more The purpose of this study was to analyze the electromyography (EMG) signals of the Quadratus Lumborum (QL) muscle activity on leg length discrepancy (LLD) and pelvic asymmetry. So we investigated whether pelvic asymmetry might cause injuries in lumbar spine and lower extremity. This was a randomized control experiment, total 50 (25 males and 25 females) datas were analyzed. All participants were right handed. Iliac crest levels were assessed by manually and LLD measurement was used with tape. EMG signals of the QL muscle were taken in the resting position without any activity intentionally in the prone position. Analysis of the data revealed that the QL muscles activity were higher at the pelvic elevation on the right side than on the left side. While there was a shortness in the lower extremity 27% of the cases on the right condition but it was statistically determined that 23% of the left side was short. At the same time, 100% of the cases in the lower extremity on the right side ...

Emotional state detection based on common spatial patterns of EEG

Signal, Image and Video Processing, 2019

The application of EEG-based emotional states is one of the most vital phases in the context of n... more The application of EEG-based emotional states is one of the most vital phases in the context of neural response decoding. Emotional response mostly appears in the presence of visual, auditory, tactile, and gustatory arousals. In our work, we use visual stimuli to evaluate the emotional feedback. One of the best performing methods in emotion estimation applications is the common spatial patterns (CSP). We implement CSP method in addition to the conventional Welch power spectral density-based analysis. Experimental results and topographies on the collected EEG data show that the CSP spatial filtering method implies the relationship between EEG bands, EEG channels, neural efficiency and emotional stimuli types.

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Chronic Kidney Disease Prediction with Reduced Individual Classifiers

Electrica, 2018

Chronic kidney disease is a rising health problem and involves conditions that decrease the effic... more Chronic kidney disease is a rising health problem and involves conditions that decrease the efficiency of renal functions and that damage the kidneys. Chronic kidney disease may be detected with several classification techniques, and these have been classified using various features and classifier combinations. In this study, we applied seven different classifiers (Naïve Bayes, HoeffdingTree, RandomTree, REPTree, Random Subspaces, Adaboost, and IBk) for the diagnosis of chronic kidney disease. The classification performances are evaluated with five different performance metrics, i.e., accuracy, kappa, mean absolute error (MAE), root mean square error (RMSE), and F measures. Considering the classification performance analyses of these methods, six reduced features provide a better and more rapid classification performance. Seven individual classifiers are applied to the six features and the best results are obtained using individual random tree and IBk classifiers.

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Special issue: Time-frequency signal analysis and its applications

Journal of the Franklin Institute

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3-B Tomosentez Göruntulemede Cebirsel Geriçatma Yöntemi ile Toplam Degisintinin Kullanımı Reconstruction for 3D Tomosynthesis Images with ART and Total Variation

2B Seyrek Tomografik Görüntülemede Yerel Olmayan Ortalama ile Görüntü İyileştirilmesi Image Enhancement by using Non Local Means in 2D Sparse Tomographic Imaging

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