The ability of artificial intelligence to distinguish abnormal from normal EEG in patients suspected of epilepsy – updated literature review

Kopka, Marcin

doi:10.2478/joepi-2024-0003

Abstract

Introduction

In patients suspected of epilepsy, electroencephalography (EEG) is an essential tool in the diagnostic workup. Currently, visual analysis of interictal epileptiform discharges by experts is the gold standard. Neurophysiologists perform an analysis of EEG through visual inspection. This is very time-consuming and inefficient. There is an increasing need to develop reliable and validated automated EEG analysis methods. Methods based on artificial intelligence can potentially help achieve this task.

Aim

The present review paper aims to present the current state of knowledge regarding the ability of artificial intelligence to distinguish abnormal from normal EEG in patients suspected of epilepsy.

Material and methods

This review covers the most relevant and recent papers identified using the PubMed database.

Results and discussion

Artificial intelligence (AI) has the potential to improve the management of epilepsy. It was shown that AI could distinguish normal from abnormal recordings, detect seizures, or detect interictal epileptiform discharges. The AI model (SCORE-AI) was developed and validated to assess routine clinical EEGs comprehensively. The sensitivity of SCORE-AI (86.7%) was lower than the sensitivity of the human experts (93.3%) and two models, Encevis (96.7%) and Persyst (100%) but higher than the sensitivity of SpikeNet (66.7%). The accuracy of SCORE-AI was similar to that of human experts and higher than that of the three previously published AI models. SCORE-AI achieves high specificity similar to the human raters and significantly higher accuracy than the three previously published AI models.

Conclusion

Methods based on artificial intelligence can potentially be helpful in EEG analysis. SCORE-AI may reduce excessive workloads for experts who interpret high volumes of EEG recordings. The SCORE-AI is the first model capable of completing a fully automated and comprehensive clinically relevant assessment of routine EEGs.

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