Investigating the Effect of Feature Distribution Shift on the Performance of Sleep Stage Classification with Consumer Sleep Trackers

The performance of consumer wearable sleep trackers could vary significantly from user to user. This paper presents a pilot study on the effect of the feature distribution shift between training and test datasets on the performance of sleep classification models. We used Anderson-Darling (AD) statistic to quantify the shift in feature distribution, which is then correlated to the several model performance metrics. Our results show that the distribution shift of some features were negatively correlated to the model accuracy for REM and NREM sleep classification. Future studies on consumer sleep tracking technologies may focus on addressing the dataset shift problem in the model training process.