Optimizing long-short term memory neural networks for electroencephalogram anomaly detection using variable neighborhood search with dynamic strategy change

Abstract

Electroencephalography (EEG) serves as a crucial neurodiagnostic tool by recording the electrical brain activity via attached electrodes on the patient’s head. While artificial intelligence (AI) exhibited considerable promise in medical diagnostics, its potential in the realm of neurodiagnostics remains underexplored. This research addresses this gap by proposing an innovative approach employing time-series classification of EEG data, leveraging long-short-term memory (LSTM) neural networks for the identification of abnormal brain activity, particularly seizures. To enhance the performance of the proposed model, metaheuristic algorithms were employed for optimizing hyperparameter collection. Additionally, a tailored modification of the variable neighborhood search (VNS) is introduced, specifically tailored for this neurodiagnostic application. The effectiveness of this methodology is evaluated using a carefully curated dataset comprising real-world EEG recordings from both healthy individuals and those affected by epilepsy. This software-based approach demonstrates noteworthy results, showcasing its efficacy in anomaly and seizure detection, even when working with relatively modest sample sizes. This research contributes to the field by illuminating the potential of AI in neurodiagnostics, presenting a methodology that enhances accuracy in identifying abnormal brain activities, with implications for improved patient care and diagnostic precision.