  Actigraphy is widely used in sleep studies but lacks a universal unsupervised
algorithm for sleep/wake identification. In this study, we proposed a Hidden
Markov Model (HMM) based unsupervised algorithm that can automatically and
effectively infer sleep/wake states. It is an individualized data-driven
approach that analyzes actigraphy from each individual respectively to learn
activity characteristics and further separate sleep and wake states. We used
Actiwatch and polysomnography (PSG) data from 43 individuals in the
Multi-Ethnic Study of Atherosclerosis to evaluate the performance of our
method. Epoch-by-epoch comparisons were made between our HMM algorithm and that
embedded in the Actiwatch software (AS). The percent agreement between HMM and
PSG was 85.7%, and that between AS and PSG was 84.7%. Positive predictive
values for sleep epochs were 85.6% and 84.6% for HMM and AS, respectively, and
95.5% and 85.6% for wake epochs. Both methods have similar performance and tend
to overestimate sleep and underestimate wake compared to PSG. Our HMM approach
is able to quantify the variability in activity counts that allow us to
differentiate relatively active and sedentary individuals: individuals with
higher estimated variabilities tend to show more frequent sedentary behaviors.
In conclusion, our unsupervised data-driven HMM algorithm achieves slightly
better performance compared to the commonly used algorithm in the Actiwatch
software. HMM can help expand the application of actigraphy in large-scale
studies and in cases where intrusive PSG is hard to acquire or unavailable. In
addition, the estimated HMM parameters can characterize individual activity
patterns that can be utilized for further analysis.
