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.

Background: PTSD diagnosis is challenging. Symptoms overlap with depression and panic attacks. This causes misdiagnosis and delayed treatment. Current methods lack objective biomarkers. This study presents a hybrid AI framework. It combines CNNs and SVMs. The system detects PTSD from ECG signals. Methods: ECG data from 79 participants were analyzed. Four groups were included. PTSD patients numbered 20. Depression patients numbered 20. Panic attack patients numbered 19. Healthy controls numbered 20. Wavelet transform created scalograms. Three CNN models were tested. AlexNet, GoogLeNet, and ResNet50 were used. Deep features were extracted. SVMs classified the features. Five-fold validation was performed. Statistical tests confirmed significance. Results: Hybrid models performed robustly. ResNet50 + SVM and AlexNet + SVM achieved statistically equivalent results with accuracies of 97.05% and 97.26%, respectively. AUC reached 1.00 for multi-class tasks. PTSD detection was highly accurate. The system distinguished PTSD from other disorders. Hybrid models beat standalone CNNs. SVM integration improved results significantly. Conclusions: This is the first ECG-based AI for PTSD diagnosis. The hybrid approach achieves clinical-level accuracy. PTSD is distinguished from depression and panic attacks. Objective biomarkers support psychiatric assessment. Early intervention becomes possible.

Objectives: Fetal electrocardiography (FECG) is a valuable tool for assessing fetal health and identifying cardiac rhythm abnormalities. This study aimed to determine the convolutional neural network (CNN)-based deep learning (DL) model that provides the highest performance for binary classification of fetal arrhythmia and normal rhythm (ARR vs NR) in FECG signals. Methods: FECG signals were converted to time-frequency images using the Continuous Wave Transform (CWT), and these images were used as input to DL-based CNN architectures such as AlexNet, GoogleNet, and ResNet50. The effect of data length was investigated to achieve high accuracy with transfer learning. In addition, these DL models were compared with traditional machine learning methods. Results: Among the evaluated DL models, AlexNet achieved the highest performance on the 10-second segments. It outperformed the traditional machine learning approaches across all evaluated metrics. Among the conventional methods, the Trilayered Neural Network showed the best performance, with an accuracy of 87.50%. Conclusions: The findings suggest that scalogram-based CNN models have strong potential for classifying binary fetal arrhythmias from FECG signals. In particular, the AlexNet model showed promising performance under the experimental conditions of this study. However, the results should be interpreted with caution due to the limited dataset size, and further validation on larger, independent datasets is needed.

This paper proposes a lightweight polynomial-based encryption architecture for resource-constrained IoT and wireless communication hardware. Instead of substitution boxes, the method uses key-dependent polynomial multiplication with minimal polynomials over finite fields. Plaintext rows are XORed with secret keys and a nonce-dependent pseudorandom mask and multiplied by a minimal polynomial; multiplication by a complementary factor recovers the message exactly. A security analysis shows that, because the transformation is linear over $\mathrm{GF}\left(2\right)$, reuse of the same key and polynomial allows for known- and chosen-plaintext key recovery; per-message nonce masking is therefore required. Software experiments (four message lengths, 100 trials) verified lossless decryption. The encryption time of the key-assisted method was comparable to that of SPECK64/128 at the same implementation level, whereas AES-128 was faster for long messages, and the avalanche ratio of the proposed method (0.070) was well below the value of about 0.5 obtained by the reference ciphers. On a Xilinx Artix-7 FPGA, the proposed core used 1211 LUTs and 205 registers without block RAM or DSPs and reached 154.1 MHz, compared with 2305 LUTs and 2733 registers for an iterative AES-128 core. The method is a compact, S-box-free polynomial processing layer rather than a stand-alone replacement for standard ciphers.