Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.11851/12543
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Ebrahimpour Moghaddam Tasouj, Parisa | - |
dc.contributor.author | Soysal, Gokhan | - |
dc.contributor.author | Erogul, Osman | - |
dc.contributor.author | Yetkin, Sinan | - |
dc.date.accessioned | 2025-07-10T19:45:10Z | - |
dc.date.available | 2025-07-10T19:45:10Z | - |
dc.date.issued | 2025 | - |
dc.identifier.issn | 2075-4418 | - |
dc.identifier.uri | https://doi.org/10.3390/diagnostics15111414 | - |
dc.identifier.uri | https://hdl.handle.net/20.500.11851/12543 | - |
dc.description.abstract | Background: Post-traumatic stress disorder (PTSD) is a serious psychiatric condition that can lead to severe anxiety, depression, and cardiovascular complications if left untreated. Early and accurate diagnosis is critical. This study aims to develop and evaluate an artificial intelligence-based classification system using electrocardiogram (ECG) signals for the detection of PTSD. Methods: Raw ECG signals were transformed into time-frequency images using Continuous Wavelet Transform (CWT) to generate 2D scalogram representations. These images were classified using deep learning-based convolutional neural networks (CNNs), including AlexNet, GoogLeNet, and ResNet50. In parallel, statistical features were extracted directly from the ECG signals and used in traditional machine learning (ML) classifiers for performance comparison. Four different segment lengths (5 s, 10 s, 15 s, and 20 s) were tested to assess their effect on classification accuracy. Results: Among the tested models, ResNet50 achieved the highest classification accuracy of 94.92%, along with strong MCC, sensitivity, specificity, and precision metrics. The best performance was observed with 5-s signal segments. Deep learning (DL) models consistently outperformed traditional ML approaches. The area under the curve (AUC) for ResNet50 reached 0.99, indicating excellent classification capability. Conclusions: This study demonstrates that CNN-based models utilizing time-frequency representations of ECG signals can effectively classify PTSD with high accuracy. Segment length significantly influences model performance, with shorter segments providing more reliable results. The proposed method shows promise for non-invasive, ECG-based diagnostic support in PTSD detection. | en_US |
dc.language.iso | en | en_US |
dc.publisher | MDPI | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | Continuous Wavelet Transform | en_US |
dc.subject | Deep Learning | en_US |
dc.subject | Electrocardiogram | en_US |
dc.subject | Machine Learning | en_US |
dc.subject | PTSD | en_US |
dc.title | ECG Signal Analysis for Detection and Diagnosis of Post-Traumatic Stress Disorder: Leveraging Deep Learning and Machine Learning Techniques | en_US |
dc.type | Article | en_US |
dc.department | TOBB University of Economics and Technology | en_US |
dc.identifier.volume | 15 | en_US |
dc.identifier.issue | 11 | en_US |
dc.identifier.wos | WOS:001505930800001 | - |
dc.identifier.scopus | 2-s2.0-105007919336 | - |
dc.identifier.pmid | 40506986 | - |
dc.identifier.doi | 10.3390/diagnostics15111414 | - |
dc.authorwosid | Soysal, Gokhan/Aah-2516-2020 | - |
dc.authorwosid | Erogul, Osman/Aaw-3005-2021 | - |
dc.authorwosid | Yetki̇n, Si̇nan/Jvp-2317-2024 | - |
dc.authorscopusid | 59941187900 | - |
dc.authorscopusid | 16231589600 | - |
dc.authorscopusid | 56247443100 | - |
dc.authorscopusid | 56211425100 | - |
dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
dc.identifier.scopusquality | Q2 | - |
dc.identifier.wosquality | Q2 | - |
dc.description.woscitationindex | Science Citation Index Expanded | - |
item.cerifentitytype | Publications | - |
item.fulltext | No Fulltext | - |
item.grantfulltext | none | - |
item.languageiso639-1 | en | - |
item.openairetype | Article | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
crisitem.author.dept | 02.2. Department of Biomedical Engineering | - |
Appears in Collections: | PubMed İndeksli Yayınlar Koleksiyonu / PubMed Indexed Publications Collection Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection |
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