Clinical translation of machine learning algorithms for seizure detection in scalp electroencephalography: a systematic review

arXiv:2404.15332v1 Announce Type: cross
Abstract: 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 …

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