{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,23]],"date-time":"2026-08-23T18:39:58Z","timestamp":1787510398375,"version":"build-2736575974"},"reference-count":59,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2025,6,2]],"date-time":"2025-06-02T00:00:00Z","timestamp":1748822400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>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\u2013frequency 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\u2013frequency 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.<\/jats:p>","DOI":"10.3390\/diagnostics15111414","type":"journal-article","created":{"date-parts":[[2025,6,2]],"date-time":"2025-06-02T03:19:38Z","timestamp":1748834378000},"page":"1414","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["ECG Signal Analysis for Detection and Diagnosis of Post-Traumatic Stress Disorder: Leveraging Deep Learning and Machine Learning Techniques"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-2268-7876","authenticated-orcid":false,"given":"Parisa","family":"Ebrahimpour Moghaddam Tasouj","sequence":"first","affiliation":[{"name":"Biomedical Device Technology, Vocational School of Health Services, Ankara Medipol University, Ankara 06050, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1397-8564","authenticated-orcid":false,"given":"G\u00f6khan","family":"Soysal","sequence":"additional","affiliation":[{"name":"Department of Electrical and Electronics Engineering, Ankara University, Ankara 06830, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4640-6570","authenticated-orcid":false,"given":"Osman","family":"Ero\u011ful","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, TOBB University of Economics and Technology, Ankara 06510, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7709-2837","authenticated-orcid":false,"given":"Sinan","family":"Yetkin","sequence":"additional","affiliation":[{"name":"Department of Psychiatry, Ankara G\u00fclhane Health Application and Research Hospital, University of Health Sciences, Ankara 34668, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,6,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"263","DOI":"10.31887\/DCNS.2011.13.2\/jsherin","article-title":"Post-traumatic stress disorder: The neurobiological impact of psychological trauma","volume":"13","author":"Sherin","year":"2011","journal-title":"Dialogues Clin. 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