Computer-assisted analysis of routine EEG to identify hidden biomarkers of epilepsy: protocol for a systematic review

Abstract

Background: The diagnosis of epilepsy frequently relies on the visual interpretation of the electroencephalogram (EEG) by a neurologist. The hallmark of epilepsy on EEG is the interictal epileptiform discharge (IED). This marker lacks sensitivity: it is only captured in a small percentage of 30-minute routine EEGs in patients with epilepsy. In the past three decades, there has been growing interest in the use of computational methods to analyze the EEG without relying on the detection of IEDs, but none have made it to the clinical practice. We aim to review the diagnostic accuracy of quantitative methods applied to ambulatory EEG analysis to guide the diagnosis and management of epilepsy. Methods: The protocol complies with the recommendations for systematic reviews of diagnostic test accuracy by Cochrane. We will search MEDLINE, EMBASE, EBM reviews, IEEE Explore along with grey literature for articles, conference papers and conference abstracts published after 1961. We will include observational studies that present a computational method to analyze the EEG for the diagnosis of epilepsy in adults or children without relying on the identification of IEDs or seizures. The reference standard is the diagnosis of epilepsy by a physician. We will report the estimated pooled sensitivity and specificity, and receiver operating characteristic area-under-the-curve (ROC AUC) for each marker. If possible, we will perform a meta-analysis of the sensitivity and specificity and ROC AUC for each individual marker. We will assess the risk of bias using an adapted QUADAS-2 tool. We will also describe the algorithms used for signal processing, feature extraction and predictive modeling, and comment on the reproducibility of the different studies. Discussion: Despite the promise to unveil epileptiform patterns that cannot be seen by the naked eye, computational analysis of ambulatory EEG has not yet been successfully translated to the clinical setting. We hope to produce recommendations for future studies on computer-assisted EEG interpretation for the diagnosis and management of epilepsy. Systematic review registration: PROSPERO #292261