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Found 5 papers, 0 papers with code
Comparison analysis between standard polysomnographic data and in-ear-EEG signals: A preliminary study
We show that the similarity between PSG and in-ear-EEG signals is high (JSD-FSI: 0. 61 +/- 0. 06 in awake, 0. 60 +/- 0. 07 in NREM and 0. 51 +/- 0. 08 in REM), and in line with the similarity values computed independently on standard PSG-channel-combinations.
Bridging AI and Clinical Practice: Integrating Automated Sleep Scoring Algorithm with Uncertainty-Guided Physician Review
The confidence network excelled at identifying uncertain predictions, achieving AUROC scores of 85. 7% on ID and 82. 5-85. 6% on OOD data.
U-Sleep's resilience to AASM guidelines
Specifically, we demonstrate that U-Sleep, a state-of-the-art sleep scoring algorithm, can be strong enough to solve the scoring task even using clinically non-recommended or non-conventional derivations, and with no need to exploit information about the chronological age of the subjects.
Multi-Scored Sleep Databases: How to Exploit the Multiple-Labels in Automated Sleep Scoring
Results: The performance of the models improves on all the databases when we train the models with our LSSC.
DeepSleepNet-Lite: A Simplified Automatic Sleep Stage Scoring Model with Uncertainty Estimates
We exploit, for the first time in sleep scoring, the Monte Carlo dropout technique to enhance the performance of the architecture and to also detect the uncertain instances.
