Description of Invention
Electroencephalography (EEG) signals are crucial for seizure diagnosis. The data provides detailed insights into brain activity which aids in epilepsy management. Artificial intelligence (AI) and deep learning are widely employed in the analysis of EEG signals to achieve promising classification performance. However, these AI models require centralized data processing, thereby raising privacy concerns. Thus, we propose FCEEG, a convolutional-based deep learning with federated learning (FL) to diagnose seizures with EEG signals while preserving data privacy. Unlike traditional approaches, our framework allows EEG data to be processed locally using Convolutional neural networks (CNNs) on client devices, eliminating the need to transmit sensitive raw EEG data to a central server. This decentralized process ensures the confidentiality and integrity of these sensitive health records. This balances data privacy with a promising performance. Additionally, this research involves experimenting federated learning. The empirical results demonstrate that our proposed framework FCEEG with Federated Proximal (FedProx) aggregation with the best aggregation methods for EEG signals in method can effectively utilize diverse local EEG data from local clients to perform reliable seizure detection with a promising performance with an accuracy of 87.66%, precision of 99.95%, specificity of 99.96%, recall rate of 75.86%, and F1-score of 86.25%.