Akan, Aydın
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Akan, Aydin
Akan, A
Akan, Aydan
Akan, Aydm
Akan, A
Akan, Aydan
Akan, Aydm
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Email Address
akan@istanbul.edu.tr
aydinakan@gmail.com
akan.aydin@ieu.edu.tr
aydin.akan@ikc.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
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WoS Researcher ID
Sustainable Development Goals
1NO POVERTY
0
Research Products
2ZERO HUNGER
0
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3GOOD HEALTH AND WELL-BEING
1
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4QUALITY EDUCATION
0
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5GENDER EQUALITY
0
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6CLEAN WATER AND SANITATION
0
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7AFFORDABLE AND CLEAN ENERGY
2
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8DECENT WORK AND ECONOMIC GROWTH
0
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9INDUSTRY, INNOVATION AND INFRASTRUCTURE
0
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10REDUCED INEQUALITIES
0
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11SUSTAINABLE CITIES AND COMMUNITIES
0
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12RESPONSIBLE CONSUMPTION AND PRODUCTION
0
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13CLIMATE ACTION
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14LIFE BELOW WATER
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15LIFE ON LAND
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16PEACE, JUSTICE AND STRONG INSTITUTIONS
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17PARTNERSHIPS FOR THE GOALS
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Documents
339
Citations
3292
h-index
27
Documents
311
Citations
2382
Scholarly Output
99
Articles
27
Views / Downloads
458/320
Supervised MSc Theses
4
Supervised PhD Theses
0
WoS Citation Count
733
Scopus Citation Count
976
Patents
0
Projects
3
WoS Citations per Publication
7.40
Scopus Citations per Publication
9.86
Open Access Source
9
Supervised Theses
4
| Journal | Count |
|---|---|
| TIPTEKNO 2020 - Tip Teknolojileri Kongresi - 2020 Medical Technologies Congress, TIPTEKNO 2020 | 9 |
| 2022 Medıcal Technologıes Congress (Tıptekno'22) | 6 |
| Internatıonal Journal of Neural Systems | 6 |
| European Signal Processing Conference | 6 |
| TIPTEKNO 2025 - Medical Technologies Congress, Proceedings -- 2025 Medical Technologies Congress, TIPTEKNO 2025 -- 26 October 2025 through 28 October 2025 -- Gazi Magusa -- 217812 | 6 |
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99 results
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Now showing 1 - 10 of 99
Article Citation - WoS: 6Citation - Scopus: 9Null Subcarrier Index Modulation in Ofdm Systems for 6g and Beyond(Mdpi, 2021-10-31) Eren, Tuncay; Akan, AydinComputational 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.Conference Object Citation - WoS: 7Citation - Scopus: 6Synchrosqueezing Transform in Biomedical Applications: a Mini Review(Institute of Electrical and Electronics Engineers Inc., 2020-11-19) Değirmenci, Duygu; Yalçın, Melike; Özdemir, Mehmet Akif; Akan A.; Akan, AydinTime-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.Conference Object Analysis of Dementia EEG Signals Using Empirical Mode Decomposition Variants and Deep Learning(Institute of Electrical and Electronics Engineers Inc., 2025-10-26) Senol, Yahya Oguzhan; Akan, Aydin; Cura, Ozlem KarabiberDementia is among the most frequent neurological diseases that result in worsening cognitive functions and damaging consequences on quality of life. In this study, a novel method is proposed by using electroencephalogram (EEG) signals and signal decomposition methods to detect Alzheimer's disease (AD). Intrinsic Mode Functions (IMFs) are obtained by utilizing Empirical Mode Decomposition (EMD) and Ensemble Empirical Mode Decomposition (EEMD) methods. Five time-domain and five spectral-domain features are extracted from one-minute and five-second EEG segments, as well as from the corresponding IMF segments, using EEG recording. Topographic heat maps are generated for each feature. These feature maps give temporal and spatial information simultaneously on a single image. Feature map images are classified with two-dimensional convolutional neural network (2D-CNN). Three CNN architectures are used for classification and comparison between networks. The proposed method achieves promising results, with accuracy up to 96%.Conference Object Detection of Attention Deficit Hyperactivity Disorder Using Eeg Signals and Douglas-Peucker Algorithm(IEEE, 2022-10-31) Cura, Ozlem Karabiber; Aydin, Gamze N.; Celen, Sibel; Atli, Sibel Kocaaslan; Akan, AydinAttention 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.Conference Object Detection of Alzheimer’s Dementia by Using EEG Feature Maps and Deep Learning(European Signal Processing Conference, EUSIPCO, 2024-08-26) Akbugday, Sude Pehlivan; Akbugday, Burak; Cura, Ozlem Karabiber; Akan, AydinConference Object Citation - WoS: 1Citation - Scopus: 2Alzheimer's Dementia Detection: an Optimized Approach Using Itd of Eeg Signals(IEEE, 2024-08-26) Sen, Sena Yagmur; Akan, Aydin; Cura, Ozlem KarabiberThis 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.Conference Object Artificial Intelligence Based Post-Disaster Trauma Analysis and Prioritization System(Institute of Electrical and Electronics Engineers Inc., 2025-10-26) Seyrek, Nimet; Solmaz, Miray; Tuntas, Rumeysa; Bicici, Ceren; Ari, Levent; Akan, AydinTo address medical emergencies resulting from high-impact incidents such as natural disasters and traffic collisions, a sophisticated mobile application-Artificial Intelligence-Based Post-Disaster Trauma Assessment and Prioritization System-has been developed to facilitate AI-driven rapid response. This system autonomously evaluates users' health conditions, classifies them according to the severity of their medical needs, and transmits real-time alerts to emergency healthcare providers. Compatible with both smartphones and smartwatches, the application monitors key physiological indicators, including heart rate, blood pressure, capillary refill time (CRT), and consciousness level. On smartphones, essential health data are obtained through transdermal imaging using the device's built-in camera and flash. In contrast, smartwatches enable continuous and more accurate data acquisition via PPG and ECG sensors. The system integrates several cutting-edge technologies, including blood pressure estimation via Pulse Transit Time (PTT), CRT measurement using HSV-based image processing, and a low-latency emergency notification infrastructure developed with Firebase. Collectively, these innovations enable efficient and timely medical intervention in the aftermath of large-scale emergencies.Article Citation - WoS: 5Citation - Scopus: 7Detection of Olfactory Stimulus in Electroencephalogram Signals Using Machine and Deep Learning Methods(Istanbul University, 2024-01-30) Akbuğday, Burak; Akbugday, S.P.; Sadikzade, R.; Akan, A.; Unal, S.; Sadighzadeh, RezaThe 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.Conference Object Citation - WoS: 38Citation - Scopus: 54Emg Based Hand Gesture Recognition Using Deep Learning(Institute of Electrical and Electronics Engineers Inc., 2020-11-19) Ozdemir M.A.; Kisa D.H.; Guren O.; Onan A.; Akan A.; Ozdemir, Mehmet Akif; Kisa, Deniz Hande; Guren, Onan; Onan, Aytug; Akan, AydinThe Electromyography (EMG) signal is a nonstationary bio-signal based on the measurement of the electrical activity of the muscles. EMG based recognition systems play an important role in many fields such as diagnosis of neuromuscular diseases, human-computer interactions, console games, sign language detection, virtual reality applications, and amputee device controls. In this study, a novel approach based on deep learning has been proposed to improve the accuracy rate in the prediction of hand movements. Firstly, 4-channel surface EMG (sEMG) signals have been measured while simulating 7 different hand gestures (Extension, Flexion, Open Hand, Punch, Radial Deviation, Rest, and Ulnar Deviation) from 30 participants. The obtained sEMG signals have been segmented into sections where each movement was found. Then, spectrogram images of the segmented sEMG signals have been created by means of ShortTime Fourier Transform (STFT). The created colored spectrogram images have trained with 50-layer Convolutional Neural Network (CNN) based on Residual Networks (ResNet) architecture. Owing to the proposed method, test accuracy of 99.59% and F1 Score of 99.57% have achieved for 7 different hand gesture classifications. © 2020 IEEE.Book Part Classification of Attention Deficit Hyperactivity Disorder (ADHD) by Using Statistical Features of Mr Images(Institute of Physics Publishing, 2020-12-01) 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.
