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

NO POVERTY1
NO POVERTY
0
Research Products
ZERO HUNGER2
ZERO HUNGER
0
Research Products
GOOD HEALTH AND WELL-BEING3
GOOD HEALTH AND WELL-BEING
1
Research Products
QUALITY EDUCATION4
QUALITY EDUCATION
0
Research Products
GENDER EQUALITY5
GENDER EQUALITY
0
Research Products
CLEAN WATER AND SANITATION6
CLEAN WATER AND SANITATION
0
Research Products
AFFORDABLE AND CLEAN ENERGY7
AFFORDABLE AND CLEAN ENERGY
2
Research Products
DECENT WORK AND ECONOMIC GROWTH8
DECENT WORK AND ECONOMIC GROWTH
0
Research Products
INDUSTRY, INNOVATION AND INFRASTRUCTURE9
INDUSTRY, INNOVATION AND INFRASTRUCTURE
0
Research Products
REDUCED INEQUALITIES10
REDUCED INEQUALITIES
0
Research Products
SUSTAINABLE CITIES AND COMMUNITIES11
SUSTAINABLE CITIES AND COMMUNITIES
0
Research Products
RESPONSIBLE CONSUMPTION AND PRODUCTION12
RESPONSIBLE CONSUMPTION AND PRODUCTION
0
Research Products
CLIMATE ACTION13
CLIMATE ACTION
0
Research Products
LIFE BELOW WATER14
LIFE BELOW WATER
0
Research Products
LIFE ON LAND15
LIFE ON LAND
0
Research Products
PEACE, JUSTICE AND STRONG INSTITUTIONS16
PEACE, JUSTICE AND STRONG INSTITUTIONS
0
Research Products
PARTNERSHIPS FOR THE GOALS17
PARTNERSHIPS FOR THE GOALS
0
Research Products
Documents

339

Citations

3292

h-index

27

Go to Scopus profile
Documents

311

Citations

2382

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Scholarly Output

98

Articles

26

Views / Downloads

117/81

Supervised MSc Theses

4

Supervised PhD Theses

0

WoS Citation Count

733

Scopus Citation Count

976

Patents

0

Projects

3

WoS Citations per Publication

7.48

Scopus Citations per Publication

9.96

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
Internatıonal Journal of Neural Systems6
European Signal Processing Conference6
TIPTEKNO 2025 - Medical Technologies Congress, Proceedings -- 2025 Medical Technologies Congress, TIPTEKNO 2025 -- 26 October 2025 through 28 October 2025 -- Gazi Magusa -- 2178126
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Now showing 1 - 10 of 98
  • Thumbnail Image
    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.
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    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.; Akan, Aydin
    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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    Artificial Intelligence Based Post-Disaster Trauma Analysis and Prioritization System
    (Institute of Electrical and Electronics Engineers Inc., 2025) Seyrek, Nimet; Solmaz, Miray; Tuntas, Rumeysa; Bicici, Ceren; Ari, Levent; Akan, Aydin
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    Article
    Citation - WoS: 5
    Citation - Scopus: 7
    Detection of Olfactory Stimulus in Electroencephalogram Signals Using Machine and Deep Learning Methods
    (Istanbul University, 2024) Akbuğday, Burak; Akbugday, S.P.; Sadikzade, R.; Akan, A.; Unal, S.; Sadighzadeh, Reza
    The investigation of olfactory stimuli has become more prominent in the context of neuromarketing research over the last couple of years. Although a few studies suggest that olfactory stimuli are linked with consumer behavior and can be observed in various ways, such as via electroencephalogram (EEG), a universal method for the detection of olfactory stimuli has not been established yet. In this study, 14-channel EEG signals acquired from participants while they were presented with 2 identical boxes, scented and unscented, were processed to extract several linear and nonlinear features. Two approaches are presented for the classification of scented and unscented cases: i) using machine learning (ML) methods utilizing extracted features; ii) using deep learning (DL) methods utilizing relative sub-band power topographic heat map images. Experimental results suggest that the olfactory stimulus can be successfully detected with up to 92% accuracy by the proposed method. Furthermore, it is shown that topographic heat maps can accurately depict the response of the brain to olfactory stimuli. © 2024 Istanbul University. All rights reserved.
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    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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    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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    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.
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    Conference Object
    Citation - WoS: 9
    Citation - Scopus: 17
    Abnormal Ecg Beat Detection Based on Convolutional Neural Networks
    (Institute of Electrical and Electronics Engineers Inc., 2020) Ozdemir M.A.; Guren O.; Cura O.K.; Akan A.; Onan A.; Ozdemir, Mehmet Akif; Guren, Onan; Cura, Ozlem Karabiber; Akan, Aydin; Onan, Aytug
    The heart is the most critical organ for the sustainability of life. Arrhythmia is any irregularity of heart rate that causes an abnormality in your heart rhythm. Clinical analysis of Electrocardiogram (ECG) signals is not enough to quickly identify abnormalities in the heart rhythm. This paper proposes a deep learning method for the accurate detection of abnormal and normal heartbeats based on 2-D Convolutional Neural Network (CNN) architecture. Two channels of ECG signals were obtained from the MIT-BIH arrhythmia dataset. Each ECG signal is segmented into heartbeats, and each heartbeat is transformed into a 2-D grayscale heartbeat image as an input for CNN structure. Due to the success of image recognition, CNN architecture is utilized for binary classification of the 2-D image matrix. In this study, the effect of different CNN architectures is compared based on the classification rate. The accuracies of training and test data are found as 100.00% and 99.10%, respectively for the best CNN model. Experimental results demonstrate that CNN with ECG image representation yields the highest success rate for the binary classification of ECG beats compared to the traditional machine learning methods, and one-dimensional deep learning classifiers. © 2020 IEEE.
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    Analysis of Dementia EEG Signals Using Empirical Mode Decomposition Variants and Deep Learning
    (Institute of Electrical and Electronics Engineers Inc., 2025) Senol, Yahya Oguzhan; Akan, Aydin; Cura, Ozlem Karabiber
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    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.