Our work tackling the generalization problem of automatic sleep scoring models got accepted to EMBC 2022. This is one of the main hurdles that limits the adoption of such models for clinical and research sleep studies.
Abdelhak Lemkhenter and Paolo Favaro, in EMBC, 2022.
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. In our analysis, we show that MAML can be significantly boosted in performance by incorporating a self-supervised learning (SSL) stage. This SSL stage is based on a general purpose pseudo-task that limits the overfitting to the subject-specific patterns present in the training dataset. We show that our proposed method outperforms the baseline methods and state of the art meta-learning methods on the Sleep Cassette, Sleep Telemetry, ISRUC, UCD and CAP datasets.