Sleep/wake state prediction and sleep parameter estimation using unsupervised classification via clustering

Sleep quality impacts virtually all aspects of life, including health, mood, emotions, cognition, memory, behavior, and performance. Actigraphy offers a lower-cost alternative to conventional polysomnography (PSG), the gold standard for measuring sleep quality. Effective use of actigraphy for assessing sleep quality requires reliable methods for detecting sleep/wake states from actigraphy measurements. Machine learning offers a promising approach to building sleep/wake state detectors from actigraphy data. However, current machine learning approaches rely on expert labeled training data that can be expensive and laborious to acquire. In this work, we introduce a novel approach for integrating unsupervised learning algorithms and domain knowledge heuristics, based on statistical properties of clustered sleep and wake epochs, to develop reliable sleep/wake state prediction models using unlabeled wrist actigraphy data. Experimental results using a dataset of 37 participants and covering 282 sleeping periods demonstrate the viability of the proposed approach on developing sleep/wake state detection models from unlabeled actigraphy data with a predictive performance that is comparable with the performance of models developed using some state-of-the-art supervised learning algorithms applied to labeled actigraphy data. Our results lay the groundwork for developing fully automated machine learning models for sleep/wake state prediction and sleep parameters estimations by eliminating the need for costly and labor-intensive expert annotations of PSG recordings for labeling actigraphy data.