Towards Sleep Scoring Generalization Through Self-Supervised Meta-Learning

2022/07/27 by Abdelhak Lemkhenter, Lemkhenter, Abdelhak, Paolo Favaro +1 · 1 citation
Computer Science · Medicine · #Context-Aware Activity Recognition Systems #FOS: Computer and information sciences #Machine Learning (cs.LG) #Obstructive Sleep Apnea Research

paper · pdf · doi:10.48550/arxiv.2207.13801

openalex publication_date 2022/07/27 · openalex created_date 2022/07/30 · openalex updated_date 2026/07/28

Abstract

In this work we introduce a novel meta-learning method for sleep scoring based on self-supervised learning. Our approach aims at building models for sleep scoring that can generalize across different patients and recording facilities, but do not require a further adaptation step to the target data. Towards this goal, we build our method on top of the Model Agnostic Meta-Learning (MAML) framework by incorporating a self-supervised learning (SSL) stage, and call it S2MAML. We show that S2MAML can significantly outperform MAML. The gain in performance comes from the SSL stage, which we base on a general purpose pseudo-task that limits the overfitting to the subject-specific patterns present in the training dataset. We show that S2MAML outperforms standard supervised learning and MAML on the SC, ST, ISRUC, UCD and CAP datasets.

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• MetaSTH-Sleep: Towards Effective Few-Shot Sleep Stage Classification for Health Management with Spatial-Temporal Hypergraph Enhanced Meta-Learning

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