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      Computer-assisted analysis of routine EEG to identify hidden biomarkers of epilepsy: A systematic review

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          Abstract

          Background

          Computational analysis of routine electroencephalogram (rEEG) could improve the accuracy of epilepsy diagnosis. We aim to systematically assess the diagnostic performances of computed biomarkers for epilepsy in individuals undergoing rEEG.

          Methods

          We searched MEDLINE, EMBASE, EBM reviews, IEEE Explore and the grey literature for studies published between January 1961 and December 2022. We included studies reporting a computational method to diagnose epilepsy based on rEEG without relying on the identification of interictal epileptiform discharges or seizures. Diagnosis of epilepsy as per a treating physician was the reference standard. We assessed the risk of bias using an adapted QUADAS-2 tool.

          Results

          We screened 10 166 studies, and 37 were included. The sample size ranged from 8 to 192 (mean=54). The computed biomarkers were based on linear (43%), non-linear (27%), connectivity (38%), and convolutional neural networks (10%) models. The risk of bias was high or unclear in all studies, more commonly from spectrum effect and data leakage. Diagnostic accuracy ranged between 64% and 100%. We observed high methodological heterogeneity, preventing pooling of accuracy measures.

          Conclusion

          The current literature provides insufficient evidence to reliably assess the diagnostic yield of computational analysis of rEEG.

          Significance

          We provide guidelines regarding patient selection, reference standard, algorithms, and performance validation.

          Graphical Abstract

          Highlights

          • There is insufficient evidence to assess the diagnostic accuracy of computational analysis of routine EEG for epilepsy.

          • Studies are at high risk of bias, mostly due to issues in patient selection and performance validation.

          • We suggest guidelines for future studies on patient selection, reference standard, algorithms, and performance validation.

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          Most cited references135

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          QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies.

          In 2003, the QUADAS tool for systematic reviews of diagnostic accuracy studies was developed. Experience, anecdotal reports, and feedback suggested areas for improvement; therefore, QUADAS-2 was developed. This tool comprises 4 domains: patient selection, index test, reference standard, and flow and timing. Each domain is assessed in terms of risk of bias, and the first 3 domains are also assessed in terms of concerns regarding applicability. Signalling questions are included to help judge risk of bias. The QUADAS-2 tool is applied in 4 phases: summarize the review question, tailor the tool and produce review-specific guidance, construct a flow diagram for the primary study, and judge bias and applicability. This tool will allow for more transparent rating of bias and applicability of primary diagnostic accuracy studies.
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              Complex network measures of brain connectivity: uses and interpretations.

              Brain connectivity datasets comprise networks of brain regions connected by anatomical tracts or by functional associations. Complex network analysis-a new multidisciplinary approach to the study of complex systems-aims to characterize these brain networks with a small number of neurobiologically meaningful and easily computable measures. In this article, we discuss construction of brain networks from connectivity data and describe the most commonly used network measures of structural and functional connectivity. We describe measures that variously detect functional integration and segregation, quantify centrality of individual brain regions or pathways, characterize patterns of local anatomical circuitry, and test resilience of networks to insult. We discuss the issues surrounding comparison of structural and functional network connectivity, as well as comparison of networks across subjects. Finally, we describe a Matlab toolbox (http://www.brain-connectivity-toolbox.net) accompanying this article and containing a collection of complex network measures and large-scale neuroanatomical connectivity datasets. Copyright (c) 2009 Elsevier Inc. All rights reserved.
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                Author and article information

                Contributors
                Journal
                Comput Struct Biotechnol J
                Comput Struct Biotechnol J
                Computational and Structural Biotechnology Journal
                Research Network of Computational and Structural Biotechnology
                2001-0370
                10 December 2023
                December 2024
                10 December 2023
                : 24
                : 66-86
                Affiliations
                [a ]Department of Neurosciences, University of Montreal, Canada
                [b ]Institute of biomedical engineering, Polytechnique Montreal, Canada
                [c ]University of Montreal Hospital Center’s Research Center, Canada
                [d ]Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
                [e ]School of Public Health, University of Montreal, Canada
                [f ]Stichting Epilepsie Instellingen Nederland (SEIN), Heemstede, the Netherlands
                Author notes
                [* ]Correspondence to: 1051 rue Sanguinet, Montréal, Québec H2×3E4, Canada. emile.lemoine@ 123456umontreal.ca
                Article
                S2001-0370(23)00480-4
                10.1016/j.csbj.2023.12.006
                10776381
                38204455
                751b5875-d899-41ed-bfb6-d1a6218b38ce
                © 2023 The Authors

                This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

                History
                : 26 September 2023
                : 5 December 2023
                : 5 December 2023
                Categories
                Review Article

                epilepsy,electroencephalogram,machine learning,diagnosis,computer-assisted,biomarker

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