Epileptic EEG patterns recognition through machine learning techniques and relevant time-frequency features

Biomed Tech (Berl). 2023 Oct 30. doi: 10.1515/bmt-2023-0332. Online ahead of print.ABSTRACTOBJECTIVES: The present study is designed to explore the process of epileptic patterns' automatic detection, specifically, epileptic spikes and high-frequency oscillations (HFOs), via a selection of machine learning (ML) techniques. The primary motivation for conducting such a research lies mainly in the need to investigate the long-term electroencephalography (EEG) recordings' visual examination process, often considered as a time-consuming and potentially error-prone procedure, requiring a great deal of mental focus and highly experimented neurologists. On attempting to resolve such a challenge, a number of state-of-the-art ML algorithms have been evaluated and compare in terms of performance, to pinpoint the most effective algorithm fit for accurately extracting epileptic EEG patterns.CONTENT: Based on intracranial as well as simulated EEG data, the attained findings turn out to reveal that the randomforest (RF) method proved to be the most consistently effective approach, significantly outperforming the entirety of examined methods in terms of EEG recordings epileptic-pattern identification. Indeed, the RF classifier appeared to record an average balanced classification rate (BCR) of 92.38 % in regard to spikes recognition process, and 78.77 % in terms of HFOs detection.SUMMARY: Compared to other approaches, our results provide valuable insights into the RF classifier's effectivenes...
Source: Biomedizinische Technik/Biomedical Engineering - Category: Biomedical Engineering Authors: Source Type: research