A Hidden Markov Model Based Unsupervised Algorithm for Sleep/Wake Identification Using Actigraphy
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Study Objective: Actigraphy is widely used in sleep studies but lacks a universal unsupervised algorithm for sleep/wake identification. This study develops an automated algorithm to effectively infer sleep/wake states. Methods: We propose a Hidden Markov Model (HMM) based unsupervised algorithm that automatically categorizes epochs into sleep/wake states. To evaluate the performance, we applied our algorithm to an Actiwatch dataset collected from 82 2-year-old toddlers, where epoch-by-epoch comparisons were made between our algorithm and that in the Actiwatch software. Results: HMM identified more sleep epochs (earlier sleep onset and later wake-up) compared to the Actiwatch software for 386 (87.5 end respectively. For the discrepant sleep epochs, 47.5 mean activity count per epoch was 33.0 (SD=29.5), suggesting immobility. HMM identified more wake epochs at sleep start for 21 days (4.8 discrepant wake epochs were zeros and the mean activity count per epoch was 193.3 (SD=166.0), suggesting active epochs. The estimated HMM parameters can differentiate relatively active and sedentary individuals. A parameter denoted as σ for the wake state represents the variability in activity counts, and individuals with higher estimated σ values tend to show more frequent sedentary behavior. Conclusions: Our unsupervised data-driven algorithm overcomes the limitations of current ad hoc methods that often involve variable selection, threshold setting, and model training steps. Real data analysis suggests that it outperforms the Actiwatch software. In addition, the estimated HMM parameters can capture individual heterogeneities in activity patterns that can be utilized for further analysis.
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