The goal of explaining black boxes in EEG seizure prediction is not to explain models' decisions.

ABSTRACT: Many state-of-the-art methods for seizure prediction, using the electroencephalogram, are based on machine learning models that are black boxes, weakening the trust of clinicians in them for high-risk decisions. Seizure prediction concerns a multidimensional time-series problem that performs continuous sliding window analysis and classification. In this work, we make a critical review of which explanations increase trust in models' decisions for predicting seizures. We developed three machine learning methodologies to explore their explainability potential. These contain different levels of model transparency: a logistic regression, an ensemble of 15 support vector machines, and an ensemble of three convolutional neural networks. For each methodology, we evaluated quasi-prospectively the p

SUBMITTER: Pinto MF 

PROVIDER: S-EPMC10235568 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature