Personalized Scorer Modeling: A Learning-Based Framework for Deriving Robust Sleep Stage Labels from Multiple Experts
A new study proposes a learning-based hypnogram (LBH) framework that models each scorer's behavior with confusion matrices to construct more reliable reference sleep stage labels from multiple experts. On the DOD-H and DOD-O datasets, random forest with EEG+EMG achieved the best performance, reaching roughly 86% accuracy and F1-score, outperforming the dataset hypnogram and best-scorer hypnogram.
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[Submitted on 12 Aug 2026]
Title:Personalized Scorer Modeling: A Learning-Based Framework for Deriving Robust Sleep Stage Labels from Multiple Experts
View a PDF of the paper titled Personalized Scorer Modeling: A Learning-Based Framework for Deriving Robust Sleep Stage Labels from Multiple Experts, by Seyyed Ali Hoseini and 4 other authors
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Abstract:Sleep stage classification is important for the diagnosis and management of sleep disorders, yet most automatic staging studies evaluate models against a single reference hypnogram despite known inter-scorer variability. This study investigates whether multi-scored datasets can be used to construct more reliable reference labels from the collective behavior of multiple experts. We use the publicly available DOD-H and DOD-O datasets. EEG (C3-M2) and chin EMG signals were segmented into 30-s epochs, and 30 features were extracted from each modality, yielding 60 features for EEG+EMG. We propose a learning-based hypnogram (LBH) that models the stage-specific behavior of each scorer using confusion matrices derived from machine-learning models. After column normalization, these matrices estimate the probability of each true sleep stage given each scorer's label; probabilities are aggregated across scorers to assign the final label for each epoch. LBH was evaluated with random forest, support vector machine, and multilayer perceptron classifiers under EEG-only and EEG+EMG settings, and compared with the dataset hypnogram (DH) and best-scorer hypnogram (BSH). LBH consistently improved overall performance. The best results were obtained with random forest and EEG+EMG, reaching 86.07% accuracy, 85.46% precision, and 85.29% F1-score on DOD-H, and 86.04% accuracy, 85.21% precision, and 84.70% F1-score on DOD-O. These findings suggest that personalized scorer modeling can improve reference hypnogram construction without discarding information from individual experts.
Subjects:
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
Cite as: arXiv:2608.12446 [cs.LG]
(or arXiv:2608.12446v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2608.12446
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: AmirHossein Eshghi [view email] [v1] Wed, 12 Aug 2026 17:28:43 UTC (1,464 KB)
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