Generalisability of sleep stage classification based on interbeat intervals: validating three machine learning approaches on self-recorded test data

Classifying sleep stages is an important basis for neuroscience, health sciences, psychology and many other fields. However, the manual determination of sleep stages is tedious and time consuming. Therefore, the development of automatic sleep stage classifiers based on data collected with low-cost sensor systems is an important research area. This study aims to analyse the generalisability of different machine learning approaches for sleep stage classification. We train three different models (random forest, CNN-LSTM and seq2seq) for classifying three as well as four sleep stages, with the MESA data set. For validation, we use a fivefold cross-validation and further validate the models with one new self-recorded test data set to analyse the models’ generalisability to a completely new cohort with different characteristics with regard to age and health status. Our results show that the two deep learning approaches performed better than the random forest. Moreover, all models are generalisable and therefore suitable for sleep stage classification on a new three-stage classification data set. However, generalisability for the four-stage classification task shows poorer performance, and therefore requires new approaches such as transfer learning or a larger data set to train the models.