A hybrid CNN-LSTM deep learning framework using multimodal EEG and PPG signals demonstrated superior performance for automated epileptic seizure detection compared to existing state-of-the-art models.
Does a hybrid CNN-LSTM model using multimodal EEG and PPG signals improve automated epileptic seizure detection compared to existing models?
A hybrid CNN-LSTM model utilizing both EEG and PPG signals provides a reliable and efficient solution for automated, real-time epileptic seizure detection.
Epilepsy is a chronic neurological disorder characterized by recurrent and unpredictable seizures that significantly affect patients' health and quality of life. Conventional diagnosis relies heavily on continuous electroencephalogram (EEG) monitoring, which requires clinical expertise and is not well suited for real-time detection. To address these challenges, this paper presents a hybrid deep learning framework that integrates Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) models for automated epileptic seizure detection using multimodal EEG and Photoplethysmogram (PPG) signals. Unlike EEG-only approaches, the inclusion of PPG provides complementary physiological information such as autonomic fluctuations, seizure-induced heart rate variability changes, and peripheral vascular responses-which strengthens the model's discriminative capability, particularly in cases where EEG signatures alone are subtle or ambiguous. In the proposed framework, CNNs effectively extract spatial patterns from the preprocessed biosignals, while LSTMs capture temporal dependencies associated with seizure evolution. Data preprocessing steps including filtering, normalization, segmentation, and augmentation are applied to enhance signal quality and model generalization. The hybrid CNN-LSTM model is evaluated on benchmark EEG-PPG datasets using accuracy, precision, recall, F1-score, Cohen's Kappa, Matthews Correlation Coefficient (MCC), and Critical Success Index (CSI). Comparative analysis with existing state-of-the-art models demonstrates superior performance and robustness. Overall, the proposed multimodal deep learning system offers a reliable and efficient solution for real-time seizure detection, with strong potential for deployment in wearable and clinical healthcare platforms.
Aruna Devi B (2026) studied Epilepsy. Hybrid CNN-LSTM deep learning framework using multimodal EEG and PPG signals vs. Existing state-of-the-art models and EEG-only approaches was evaluated on Seizure detection performance (accuracy, precision, recall, F1-score, Cohen's Kappa, MCC, CSI). A hybrid CNN-LSTM deep learning framework using multimodal EEG and PPG signals demonstrated superior performance for automated epileptic seizure detection compared to existing state-of-the-art models.