  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.
