Interpretable End-to-End Epileptic Seizure Detection via Linear and Nonlinear Filtering Networks
This paper presents CL-LNFNet, a contrastive learning framework for interpreting EEG signals to detect seizures by decomposing complex brain activity into linear and nonlinear components. The model aims to improve upon existing deep learning approaches by providing transparent decision pathways suitable for clinical diagnostics.
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- MED — Wed Jan 28 2026 00:00:00 GMT+0000 (Coordinated Universal Time) · Read full article (translated)
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