Clinical translation of machine learning algorithms for seizure detection in scalp electroencephalography: systematic review
Abstract
Machine learning algorithms for seizure detection have shown considerable diagnostic potential, with recent reported accuracies reaching 100%. Yet, only few published algorithms have fully addressed the requirements for successful clinical translation. This is, for example, because the properties of training data may limit the generalisability of algorithms, algorithm performance may vary depending on which electroencephalogram (EEG) acquisition hardware was used, or run-time processing costs may be prohibitive to real-time clinical use cases. To address these issues in a critical manner, we systematically review machine learning algorithms for seizure detection with a focus on clinical translatability, assessed by criteria including generalisability, run-time costs, explainability, and clinically-relevant performance metrics. For non-specialists, the domain-specific knowledge necessary to contextualise model development and evaluation is provided. It is our hope that such critical evaluation of machine learning algorithms with respect to their potential real-world effectiveness can help accelerate clinical translation and identify gaps in the current seizure detection literature.
Keywords
Cite
@article{arxiv.2404.15332,
title = {Clinical translation of machine learning algorithms for seizure detection in scalp electroencephalography: systematic review},
author = {Nina Moutonnet and Steven White and Benjamin P Campbell and Saeid Sanei and Toshihisa Tanaka and Hong Ji and Danilo Mandic and Gregory Scott},
journal= {arXiv preprint arXiv:2404.15332},
year = {2024}
}
Comments
60 pages, LaTeX; Addition of co-authors, keywords alphabetically sorted, text in figure 1 changed to black, references added ([9],[56] ), abbreviations defined (CNN, RNN), added section 6.4, corrected the referencing style, added a sentence about the existence of non-epileptic attacks, added an explanation about the drawback of the 10-20 system, removed bold from Figure/Table titles