A Smart IoT-Cloud Framework with Adaptive Deep Learning for Real-Time Epileptic Seizure Detection

Epileptic seizure recognition is of critical importance in determining the treatment and further management of epilepsy, which is a central nervous system disorder characterized by repeated seizures. Accurate and immediate diagnosis is critical in providing the necessary medical intervention and improving the lives of patients with epilepsy. The emergence of new technologies in signal processing, data engineering and machine learning has enabled dramatic improvement in the efficiency of using EEG for seizure recognition. The prospect of real-time seizure detection through a cloud-enabled Internet-of-Things (IoT) platform provides an opportunity to notify the patients experiencing a seizure with an impending onset, and thus quick response can be initiated. In this work, a new architecture for seizure state recognition from EEG signals in the context of an IoT architecture is presented, enabling the monitoring of patients remotely. The described model implements advanced and efficient feature selection by using a metaheuristic optimization technique and then adopting adaptive deep networks for classification. This IoT architecture enhances the surveillance of patients through the use of IoT SDKs coordinating IoT devices surrounding the patient with the help of Greengrass and mobile devices. It also lowers immobilization and enhances threat management and security and identity acknowledgment through device shadows and certificate security. Zhu stresses that the main IT functions are comprised of the management of collected information, model build-up, and maintenance as well as communication with medical consultants. Conclusively, because of the feature selection and development of the model, there is an improvement in the recognition over time. The results of the experiments support the correctness of the proposed architecture in the classification of clipped EEG-seizure states in comparison to existing solutions.