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What is Interpretable ML Models about?

The paper discusses a method for detecting epileptic seizures using interpretable machine learning models applied to electroencephalogram (EEG) data. It introduces a data augmentation technique called random channel ordering (RCO) to address class imbalance in seizure detection and employs models like LeNet, VGG, ResNet, and vision transformer (ViT) for classification. The study emphasizes the importance of model interpretability, providing clinical experts with intuitive insights into seizure detection and

Author

LEESHMA

Language

EN