Multi-Feature Fusion-Based Convolutional Neural Networks for EEG Epileptic Seizure Prediction in Consumer Internet of Things

Early epileptic seizure prediction (ESP) has informative challenges due to the complexity of electroencephalogram (EEG) signals, patient variability, privacy and security issues regarding consumer health data, and the on-time alarm triggers before an upcoming seizure to provide sufficient time for the patients and caregivers to take appropriate action. Therefore, the proposed study presents a novel patient-specific seizure prediction framework with the Consumer Internet of Things (CIoT) for the smart healthcare system to anticipate the onset of upcoming seizures and improve the quality of healthcare and early treatment. Initially, the multi-handicraft and deep features are extracted in feature engineering modules and then concatenated in the fusion module. The fused feature fed into the Bidirectional Long Short-term Memory (BiLSTM) network to present the temporal dependency of the EEG signals. The Attention Mechanism is applied to reduce the dimension of the feature. Moreover, the CIoT module is integrated for real-time seizure prediction and sending alerts to doctors and emergency units through the cloud platform. Testing via the Leave-One-Out cross-validation method revealed the model's consistent performance across various seizure types, emphasizing real-time clinical applications. The model achieved 91.39 +/- 3.34% accuracy, 91.30 +/- 2.80% sensitivity, 90.06 +/- 4.84% specificity, and a false positive rate (FPR) of 0.12 +/- 0.04 h(-1) in the case of Seizure Alarm Horizon (SAH) of 10 minutes. The CIoT remotely monitoring the patients ensures timely treatment, maintains data security, privacy, and improves the performance of real-time applications in smart consumer healthcare systems.