PREDiction of Different Variants of Sleep Stages for the Diagnosis Support of Chronic Insomnia and Epilepsy

Evaluation of the deep learning model's performance in accurately classifying different sleep stages and sub-stages compared to expert manual scoring. The metrics used to characterize this outcome are the macro F1-score and/or Cohen's Kappa (κ) score, with a target prediction accuracy of >80%. The macro F1-score measures the model's ability to correctly recognize each sleep stage while compensating for the imbalance between frequent and rare classes. Cohen's Kappa quantifies the degree of agreement between automatic predictions and human annotations by correcting for the agreement expected by chance. The combination of these two metrics offers a robust and balanced evaluation.

Evaluation of the deep learning algorithms' accuracy in identifying and predicting chronic insomnia profiles based on the electroencephalographic (EEG) analysis of polysomnographies. Performance will be assessed by comparing the automated predictions against established clinical diagnoses using standard machine learning classification metrics (such as macro F1-score and Cohen's Kappa).

Evaluation of the deep learning algorithms' accuracy in identifying and predicting different epilepsy profiles based on the electroencephalographic (EEG) analysis of polysomnographies. Performance will be assessed by comparing the automated predictions against established clinical diagnoses using standard machine learning classification metrics (such as macro F1-score and Cohen's Kappa).