Performance evaluation of metaheuristics-tuned recurrent neural networks for electroencephalography anomaly detection.

ABSTRACT: Electroencephalography (EEG) serves as a diagnostic technique for measuring brain waves and brain activity. Despite its precision in capturing brain electrical activity, certain factors like environmental influences during the test can affect the objectivity and accuracy of EEG interpretations. Challenges associated with interpretation, even with advanced techniques to minimize artifact influences, can significantly impact the accurate interpretation of EEG findings. To address this issue, artificial intelligence (AI) has been utilized in this study to analyze anomalies in EEG signals for epilepsy detection. Recurrent neural networks (RNNs) are AI techniques specifically designed to handle sequential data, making them well-suited for precise time-series tasks. While AI methods, including R

SUBMITTER: Pilcevic D 

PROVIDER: S-EPMC10682794 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature