Clinical translation of machine learning algorithms for seizure detection in scalp electroencephalography: a systematic review
Machine learning algorithms for seizure detection have shown great diagnostic potential, with recent reported accuracies reaching 100%. However, few published algorithms have fully addressed the requirements for successful clinical translation. For example, the properties of training data may critically limit the generalisability of algorithms, algorithms may be sensitive to variability across EEG acquisition hardware, and run-time processing costs may render them unfeasible for real-time clinical use cases. Here, we systematically review machine learning seizure detection algorithms with a focus on clinical translatability, assessed by criteria including generalisability, run-time costs, explainability, and clinically-relevant performance metrics. For non-specialists, we provide domain-specific knowledge necessary to contextualise model development and evaluation. Our 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.
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