ECG Signal Analysis for Detection and Diagnosis of Post-Traumatic Stress Disorder: Leveraging Deep Learning and Machine Learning Techniques

Tasouj P. E. M., Soysal G., Eroğul O., Yetkin S.

DIAGNOSTICS, cilt.15, sa.11, ss.1-22, 2025 (SCI-Expanded)

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 15 Sayı: 11
  • Basım Tarihi: 2025
  • Doi Numarası: 10.3390/diagnostics15111414
  • Dergi Adı: DIAGNOSTICS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, EMBASE, INSPEC, Directory of Open Access Journals
  • Sayfa Sayıları: ss.1-22
  • Ankara Üniversitesi Adresli: Evet

Özet

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.