A Hidden Markov Model Based Unsupervised Algorithm for Sleep/Wake Identification Using Actigraphy

TL;DR

This study introduces an unsupervised Hidden Markov Model algorithm for sleep/wake detection using actigraphy data, demonstrating comparable or improved accuracy over existing methods and enabling individualized activity pattern analysis.

Contribution

The paper presents a novel unsupervised HMM-based algorithm for sleep/wake identification that automatically learns individual activity characteristics from actigraphy data.

Findings

HMM achieved 85.7% agreement with PSG, slightly outperforming Actiwatch software.

The method effectively differentiates active and sedentary individuals based on activity variability.

HMM provides a scalable, individualized approach for sleep analysis in large studies.

Abstract

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.