Saemaldahr, R.; Ilyas, M. Patient-Specific Preictal Pattern-Aware Epileptic Seizure Prediction with Federated Learning. Sensors2023, 23, 6578.
Saemaldahr, R.; Ilyas, M. Patient-Specific Preictal Pattern-Aware Epileptic Seizure Prediction with Federated Learning. Sensors 2023, 23, 6578.
Saemaldahr, R.; Ilyas, M. Patient-Specific Preictal Pattern-Aware Epileptic Seizure Prediction with Federated Learning. Sensors2023, 23, 6578.
Saemaldahr, R.; Ilyas, M. Patient-Specific Preictal Pattern-Aware Epileptic Seizure Prediction with Federated Learning. Sensors 2023, 23, 6578.
Abstract
Electroencephalography (EEG) signals are the primary source for discriminating the preictal from the interictal stage, enabling early warnings before the seizure onset. The epileptic seizure prediction models face significant challenges due to data scarcity, diversity, and privacy. This paper proposes a three-tier architecture for epileptic seizure prediction associated with the Federated Learning (FL) model that enhances the capability by utilizing the significant amount of seizure patterns from globally distributed patients with data privacy. The determination of the preictal state is influenced by global and local model-assisted decision-making by modeling the two-level edge layer. Integrating the Spiking Encoder (SE) with Graph Convolutional Neural Network (Spiking-GCNN) works as the local model trained using the bi-timescale approach. Each local model utilizes the aggregated seizure knowledge from the different medical centers through FL and determines the preictal probability in the coarse-grained personalization. The Adaptive Neuro-Fuzzy Inference System (ANFIS) is utilized in fine-grained personalization to recognize epileptic seizure patients by examining the outcomes of the FL model, heart rate variability features, and patient-specific clinical features. The proposed seizure prediction is evaluated using benchmark datasets by comparing them with the existing works to demonstrate the potential results.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
