Akan, Aydın

Profile Picture
Profile URL
https://hdl.handle.net/20.500.14365/6663
Name Variants
Akan, Aydin
Akan, A
Akan, Aydan
Akan, Aydm
Job Title
Email Address
akan@istanbul.edu.tr
aydinakan@gmail.com
akan.aydin@ieu.edu.tr
aydin.akan@ikc.edu.tr
Main Affiliation
05.06. Electrical and Electronics Engineering
Status
Current Staff
Website
https://people.ieu.edu.tr/en/aydinakan
ORCID ID
0000-0001-8894-5794
Scopus Author ID
35617283100
Turkish CoHE Profile ID
3720A8A8B332E77A
Google Scholar ID
WSG0eK4AAAAJ
WoS Researcher ID
P-3068-2019
Files
Aydin_Akan.jpg (10.31 KB)

Sustainable Development Goals

5

GENDER EQUALITY
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9

INDUSTRY, INNOVATION AND INFRASTRUCTURE
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13

CLIMATE ACTION
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DECENT WORK AND ECONOMIC GROWTH
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14

LIFE BELOW WATER
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17

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NO POVERTY
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2

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QUALITY EDUCATION
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SUSTAINABLE CITIES AND COMMUNITIES
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16

PEACE, JUSTICE AND STRONG INSTITUTIONS
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3

GOOD HEALTH AND WELL-BEING
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CLEAN WATER AND SANITATION
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RESPONSIBLE CONSUMPTION AND PRODUCTION
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REDUCED INEQUALITIES
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LIFE ON LAND
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AFFORDABLE AND CLEAN ENERGY
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Documents

339

Citations

3292

h-index

27

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Documents

311

Citations

2382

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Scholarly Output

88

Articles

25

Views / Downloads

12/8

Supervised MSc Theses

4

Supervised PhD Theses

0

WoS Citation Count

733

Scopus Citation Count

976

WoS h-index

13

Scopus h-index

15

Patents

0

Projects

3

WoS Citations per Publication

8.33

Scopus Citations per Publication

11.09

Open Access Source

8

Supervised Theses

4

JournalCount
TIPTEKNO 2020 - Tip Teknolojileri Kongresi - 2020 Medical Technologies Congress, TIPTEKNO 20209
2022 Medıcal Technologıes Congress (Tıptekno'22)6
European Signal Processing Conference6
Internatıonal Journal of Neural Systems6
2020 28th Signal Processing and Communications Applications Conference, SIU 2020 - Proceedings4
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Now showing 1 - 10 of 88
  • Thumbnail Image
    Conference Object
    Citation - WoS: 7
    Citation - Scopus: 6
    Synchrosqueezing Transform in Biomedical Applications: a Mini Review
    (Institute of Electrical and Electronics Engineers Inc., 2020) Değirmenci, Duygu; Yalçın, Melike; Özdemir, Mehmet Akif; Akan A.
    Time-frequency representation (TFR) provides a good analysis for periodic signals; however, they are insufficient for nonstationary signals. The synchrosqueezing transform (SST) provides a strong analysis of nonstationary signals. The signal has different synchrosqueezing transformations that are implemented using different TFR. This paper provides a review of the different SST methods implemented using different TFR available in the literature, a comparison of these, and their use with different techniques in biomedical signal processing applications. Adding different techniques to the applied SST method affects the signal processing and classification ability of the selected SST method. © 2020 IEEE.
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    Conference Object
    Liking State Estimation Using Time-Frequency Image Representation of EEG Signals
    (IEEE, 2025) Ceylan, Burak; Cekic, Yalcm; Akan, Aydin
    In recent years, there has been a significant increase in research on emotion and preference state estimation. In this study, a preference prediction method is proposed for use in neuromarketing studies by utilizing time-frequency (TF) energy distribution images derived from electroencephalogram (EEG) signals. EEG signals recorded while participants watched commercials from two different automobile brands were evaluated using deep learning techniques to estimate their preference states. After viewing the advertisements, participants were shown selected visual segments from the commercials (e. g., front view, dashboard, etc.) and asked to rate their preferences on a scale from 1 to 5. The EEG signals corresponding to these segments were transformed into two-dimensional RGB-scaled scalogram/spectrogram images using Continuous Wavelet Transform (CWT) and Short-Time Fourier Transform (STFT). Using deep learning models, the proposed method achieved maximum classification accuracies of 86.61% and 87.26%, respectively.
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    Conference Object
    A System Level Lifetime Enhancement Method for Power Converters of Type-4 Wind Turbines
    (IEEE, 2024) Alisar, Ibrahim; Demirok, Erhan; Akan, Aydin
    This study proposes a new system level lifetime enhancement method for back-to-back connected power converters of Type-4 wind turbines. The method is based on swapping both the position and roles of MSC (machine side converter) and GSC (grid side converter) periodically at ultra low frequency with additional mechanical switches so that the lifetime consumption of electrical switches (IGBTs and diodes) is shared by the inverter and rectifier operating modes. Moreover, the aging effect of the semiconductors have been considered and tested as an additional evaluation. The proposed method is tested with PSCAD (for the simulation of wind turbine and converter system) and MATLAB (for the development of thermal models, loss calculation and lifetime calculations) tools. The results show the effectiveness of the proposed method with significantly increased lifetime values.
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    Conference Object
    Detection of Attention Deficit Hyperactivity Disorder Using Eeg Signals and Douglas-Peucker Algorithm
    (IEEE, 2022) Cura, Ozlem Karabiber; Aydin, Gamze N.; Celen, Sibel; Atli, Sibel Kocaaslan; Akan, Aydin
    Attention Deficit Hyperactivity Disorder (ADHD) is a neurological disease that typically appears in childhood. The disease has three main symptoms in children: inattention, hyperactivity, and impulsivity. Treatment of the disease is based on behavioral studies; however, there is no definitive diagnosis method. Hence, the electroencephalography (EEG) signals of ADHD subjects are often investigated to understand changes in the brain. In the proposed study, it is aimed to process and reduce the EEG data of ADHD and control subjects (CS) by using the Douglas-Peucker algorithm and to investigate the effects of the algorithm on EEG signal analysis. EEG data obtained from 18 control subjects (4 boys, 14 girls, mean age 13) and 15 ADHD patients (7 boys, 8 girls, mean age 12) are collected. By using reduced EEG data; time features such as energy, skewness, kurtosis, mean absolute deviation (MAD), root mean square (RMS), peak to peak (PTP) value, Hjorth parameters, and non-linear features such as largest Lyapunov Exponent (LLE), correlation dimension (CD), Hurst exponent (HE), Katz fractal dimension (KFD), Higuchi fractal dimension (HFD), are calculated to examine different signal characteristics. Extracted features are used to distinguish the EEG data of ADHD and CS by using various machine learning algorithms.
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    Conference Object
    Citation - WoS: 1
    Citation - Scopus: 2
    Alzheimer's Dementia Detection: an Optimized Approach Using Itd of Eeg Signals
    (IEEE, 2024) Sen, Sena Yagmur; Akan, Aydin; Cura, Ozlem Karabiber
    This paper presents a novel early-stage Alzheimer's dementia (AD) disease detection based on convolutional neural networks (CNNs). As it is widely used in detection and classification of AD disease, a time-frequency (TF) method has been proposed for AD detection. It has been described to address the problem of detecting early-stage AD by combining TF and CNN methods. The method is developed by utilizing the well-known structural similarity index measure (SSIM) to obtain discriminative features in each TF image. Experimental results demonstrate that the proposed method outperforms the early-stage AD detection using advanced signal decomposition algorithm that is intrinsic time-scale decomposition (ITD), and it achieves a notable improvement in terms of the detection success rates compared to AD detection from TF images of raw EEG signals.
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    Book Part
    Classification of Attention Deficit Hyperactivity Disorder (ADHD) by Using Statistical Features of Mr Images
    (Institute of Physics Publishing, 2020) Cicek, G.; Akan, A.
    The aim of this study is to minimize the human effort in the diagnosis of ADHD, and to develop an objective and reliable tool that can help physicians. Towards this direction, a model has been proposed by utilizing MRI data obtained from NPIstanbul Hospital. © IOP Publishing Ltd 2020.
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    Article
    Citation - WoS: 6
    Citation - Scopus: 9
    Null Subcarrier Index Modulation in Ofdm Systems for 6g and Beyond
    (Mdpi, 2021) Eren, Tuncay; Akan, Aydin
    Computational complexity is one of the drawbacks of orthogonal frequency division multiplexing (OFDM)-index modulation (IM) systems. In this study, a novel IM technique is proposed for OFDM systems by considering the null subcarrier locations (NSC-OFDM-IM) within a predetermined group in the frequency domain. So far, a variety of index modulation techniques have been proposed for OFDM systems. However, they are almost always based on modulating the active subcarrier indices. We propose a novel index modulation technique by employing the part of the transmitted bit group into the null subcarrier location index within the predefined size of the subgroup. The novelty comes from modulating null subcarriers rather than actives and reducing the computational complexity of the index selection and index detection algorithms at the transmitter and receiver, respectively. The proposed method is physically straightforward and easy to implement owing to the size of the subgroups, which is defined as a power of two. Based on the results of our simulations, it appeared that the proposed NSC-OFDM-IM does not suffer from any performance degradation compared to the existing OFDM-IM, while achieving better bit error rate (BER) performance and improved spectral efficiency (SE) compared to conventional OFDM. Moreover, in terms of computational complexity, the proposed approach has a significantly reduced complexity over the traditional OFDM-IM scheme.
  • Thumbnail Image
    Conference Object
    Citation - Scopus: 4
    Eeg Based Epileptic Seizures Detection Using Intrinsic Time-Scale Decomposition
    (Institute of Electrical and Electronics Engineers Inc., 2020) Degirmenci M.; Akan A.
    Epilepsy is a type of neurological disorder that causes abnormal brain activities and creates epileptic seizures. Traditionally epileptic seizure prediction is realized with a visual examination of Electroencephalogram (EEG) signals. But this technique needs a long time EEG monitoring. So, the automatic epileptic seizures prediction schemes become a requirement at this point. This study proposes a method to classify epileptic seizures and normal EEG data by utilizing the Intrinsic Time-scale Decomposition (ITD)-based features. The dataset has been supplied from the database of the Epileptology Department of Bonn University. It contains 5 data groups A, B, C, D, E. The study aims to classify healthy and epileptic data, so data of groups A and E are used to perform evaluations of proposed methods. The EEG data are decomposed into Proper Rotation Components (PRCs) by ITD. The feature extraction methods are applied to the first five PRCs of each EEG data from healthy and epileptic individuals. These features are classified using K-Nearest Neighbors (KNN), Linear Discriminant Analysis (LDA), Naive Bayes, Support Vector Machine (SVM) and Logistic Regression classifiers. The results demonstrated that the epileptic data is differentiated from normal data by applying the nonlinear ITD with outstanding classification performance. © 2020 IEEE.
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    Conference Object
    Citation - Scopus: 2
    Detection of Alzheimer's Dementia Using Intrinsic Time Scale Decomposition of Eeg Signals and Deep Learning
    (Institute of Electrical and Electronics Engineers Inc., 2023) Şen, Sena Yağmur; Cura, O.K.; Akan, Aydın
    Dementia is a prevalent neurological disorder that results in cognitive function decline, significantly impacting the quality of life. In this study, a signal decomposition based method is proposed for the detection and follow-up Alzheimer's Dementia (AD) by using Electroencephalography (EEG) signals. The proposed approach uses the Intrinsic Time Scale Decomposition (ITD) to classify EEG segments of AD patients and control subjects. Signal decomposition process is conducted with 5 seconds EEG segment duration. Proper Rotation Components (PRCs) extracted from the EEG segments are used to train a 1-Dimensional Convolutional Neural Network (1D CNN). The proposed method is compared with classification of 5s duration EEG segments using the same CNN architecture. The experimental results demonstrate that utilizing ITD based approach yields better classification performance when compared to using the plain EEG signals. © 2023 IEEE.
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    Conference Object
    Citation - WoS: 3
    Citation - Scopus: 2
    An Eeg and Machine Learning Based Method for the Detection of Major Depressive Disorder
    (IEEE, 2021) Izci, Elif; Ozdemir, Mehmet Akif; Akan, Aydin; Ozcoban, Mehmet Akif; Arikan, Mehmet Kemal
    Major depressive disorder (MDD) is a common mood disorder encountered worldwide. Early diagnosis has great importance to prevent the negative effects on the person. The aim of this study is to develop an objective method to differentiate MDD patients from healthy controls. Electroencephalography (EEG) signals taken from 16 MDD patients and 16 healthy subjects are analyzed according to the regions of the brain, and time-domain, frequency-domain, and nonlinear features were extracted. The feature sets are classified using five different classification algorithms. As a result of the study, a classification accuracy of 89.5% was yielded using the Bagging classifier with 7 features calculated from the central EEG channels.