Şen, Sena Yağmur

Profile Picture
Profile URL
https://hdl.handle.net/20.500.14365/6778
Name Variants
Sen, Sena Yagmur
Job Title
Email Address
sena.yagmur@ieu.edu.tr
Main Affiliation
05.06. Electrical and Electronics Engineering
Status
Current Staff
Website
ORCID ID
0000-0002-0667-9603
Scopus Author ID
57215314563
Turkish CoHE Profile ID
77C9B3BAFB3DCE93
Google Scholar ID
L4wew-IAAAAJ
WoS Researcher ID
IUP-8865-2023
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Sena_Yagmur_Sen.jpg (8.68 KB)

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8

Citations

120

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4

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Documents

4

Citations

17

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Scholarly Output

5

Articles

1

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5/11

Supervised MSc Theses

0

Supervised PhD Theses

0

WoS Citation Count

6

Scopus Citation Count

19

WoS h-index

1

Scopus h-index

3

Patents

0

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0

WoS Citations per Publication

1.20

Scopus Citations per Publication

3.80

Open Access Source

0

Supervised Theses

0

JournalCount
2023 Innovations in Intelligent Systems and Applications Conference, ASYU 20231
32nd European Signal Processing Conference (EUSIPCO) -- AUG 26-30, 2024 -- Lyon, FRANCE1
9th 2023 International Conference on Control, Decision and Information Technologies, CoDIT 20231
European Signal Processing Conference1
Transactions of the Institute of Measurement and Control1
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Now showing 1 - 5 of 5
  • Thumbnail Image
    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.
  • Thumbnail Image
    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.
  • Thumbnail Image
    Conference Object
    Citation - Scopus: 3
    Classification of Dementia Eeg Signals by Using Time-Frequency Images for 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 impairs cognitive functions and significantly diminishes the quality of life. In this research, a deep learning method is introduced for detecting and monitoring Alzheimer's Dementia (AD) by analyzing Electroencephalography (EEG) signals. To accomplish this, a signal decomposition technique known as Intrinsic Time Scale Decomposition (ITD) is employed to classify EEG segments obtained from both AD patients and control subjects. The analysis specifically concentrates on 5-second EEG segments, utilizing ITD to extract Proper Rotation Components (PRCs) from these segments. The PRCs are subsequently transformed into Time-Frequency (TF) images using the Short-Time Fourier Transform (STFT) spectrogram. These TF images serve as training data for a 2-Dimensional Convolutional Neural Network (2D CNN). The proposed approach is compared with the classification of the spectrogram of 5-second EEG segments using the same CNN architecture. The experimental results conclusively demonstrate the superior classification performance of the ITD-based approach when compared to the utilization of raw EEG signals. © 2023 IEEE.
  • Thumbnail Image
    Article
    Citation - WoS: 5
    Citation - Scopus: 7
    Classification of Alzheimer’s Dementia Eeg Signals Using Deep Learning
    (SAGE Publications Ltd, 2025) Sen, S.Y.; Cura, O.K.; Yilmaz, G.C.; Akan, A.
    Alzheimer’s dementia (AD) is a predominant neurological disorder arising from corruptions in brain functions and is characterized by a chronic or progressive nature. While the precise etiology of dementia remains incompletely elucidated, its manifestation is frequently associated with discernible structural and chemical alterations in the brain. Living with dementia significantly impacts individuals’ daily lives due to the resultant loss of cognitive functions. This study presents a novel method to monitor and detect AD using advanced signal processing applied to electroencephalography (EEG) signals. The intrinsic time-scale decomposition (ITD) algorithm is employed to extract proper rotation components (PRCs) from EEG signals, utilizing a 5-second EEG segment duration. The proposed method is compared with the detection of 5-second raw EEG segments using a custom one-dimensional convolutional neural network (1D CNN). Additionally, four different quartiles (Quartile 1 (Q1), Q2, Q3, and Q4) of EEG signals are considered to identify the most significant contributor to AD. Experimental results demonstrate that the ITD-based approach yields better detection performance compared to using raw EEG signals. The most promising result is achieved by the EEG-PRCs method in Q1, with an accuracy of 94.00%, sensitivity of 93.50%, and specificity of 93.90%. In contrast, the highest-performing result of the raw EEG segments method is in Q2, with an accuracy of 88.40%, sensitivity of 89.10%, and specificity of 87.60% in terms of detecting AD. © The Author(s) 2024.
  • Thumbnail Image
    Conference Object
    Citation - Scopus: 5
    Classification of Adhd by Using Multiple Feature Maps of Eeg Signals and Deep Feature Extraction
    (European Signal Processing Conference, EUSIPCO, 2023) Cura, O.K.; Atli, S.K.; Şen, Sena Yağmur; Akan, Aydın
    Attention Deficit Hyperactivity Disorder (ADHD) is a neurological condition, typically manifesting in childhood. Behavioral studies are used to treat the illness, but there is no conclusive way to diagnose it. In order to comprehend changes in the brain, electroencephalography (EEG) signals of ADHD patients are frequently examined. In the proposed study, we introduced EEG feature maps (EEG-FM)-based image construction to be used as input to CNN architectures. To demonstrate the effectiveness of the proposed method, EEG data of 15 ADHD patients and 18 control subjects are analyzed and ADHD detection performance is demonstrated. EEG-FM-based images are obtained using both time domain features such as Hjorth parameters (activity, mobility, complexity), skewness, kurtosis, and peak-to-peak, and nonlinear features such as largest Lyapunov Exponent, correlation dimension, Hurst exponent, Katz fractal dimension, Higuchi fractal dimension, and approximation entropy. ResNet18 is trained using EEG-FM-based images and deep features are extracted for each image subset. Using the SVM classifier, the ADHD detection performance of the proposed approach is evaluated. Experimental results revealed that using EEG-FM-based images as input to ResNet architecture offers important benefits in identifying ADHD. © 2023 European Signal Processing Conference, EUSIPCO. All rights reserved.