@Article{cmes.2026.079305,
AUTHOR = {Ezz El-Din Hemdan, Haitham Elwahsh, Samah Alshathri, Amged Sayed},
TITLE = {Explainable Hybrid Deep Learning for Secured Seizure Detection Framework Based on EEG Signal in Medical IoT Systems},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {},
YEAR = {},
NUMBER = {},
PAGES = {{pages}},
URL = {http://www.techscience.com/CMES/online/detail/26712},
ISSN = {1526-1506},
ABSTRACT = {Ensuring robust methods for maintaining high levels of medical data security is crucial in the Medical Internet of Things (IoT) for the protection of sensitive patient data during real-time transmission and analysis. Electroencephalography (EEG) signals in medical IoT systems are transmitted through cloud and edge networks, which create risks of cyber threats, unauthorized access, and data breaches. Consequently, there is an urgent need for efficient encryption methods to ensure the confidentiality of EEG signals during classification and prediction processes, as several state-of-the-art models either neglect security during classification or suffer from increased computational overhead that limits real-time applicability. In this paper, an innovative framework is proposed for secured and accurate detection of seizures from EEG signals based on Quantum Hilbert Encryption-assisted hybrid deep learning and machine learning methods. Raw EEG signals are first converted into 2D spectrogram images to facilitate visual feature analysis. These spectrogram images are then encrypted using the newly developed Quantum Hilbert Encryption Scheme to preserve the sensitive medical data while processing or transmission occurs. In addition, deep learning methods used encrypted EEG representations to extract discriminant features while maintaining the signal integrity and data confidentiality. Then, machine learning classifiers are used for seizure classification, efficiently and accurately doing so. Experimental evaluations highlight the system’s strong performance: Support Vector Machine (SVM) achieved top accuracies of 87.63% with ResNet50 as features extractor and 83.51% with VGG19 as feature extractor, while RF excelled in precision with scores of 88.61% (SVM with ResNet50 as features extractor) and 87.91% (RF with Xception as features extractor). These results confirm the system’s superior capability in seizure detection using encrypted EEG data. To enhance model interpretability, we employed Gradient-weighted Class Activation Mapping (Grad-CAM) and Local Interpretable Model-agnostic Explanations (LIME) to visualize and explain the decision-making process of the proposed hybrid AI model. By combining cutting-edge encryption with hybrid AI models and Explainable AI, the proposed method holds promising potential for application in Medical IoT (MIoT) environments, where secure real-time automatic EEG analysis is paramount.},
DOI = {10.32604/cmes.2026.079305}
}