40 years of actigraphy in sleep medicine and current state of the art algorithms

Review of 40 years of actigraphy sleep scoring plus a head-to-head of legacy count algorithms, raw-acceleration methods and MESA-trained deep count models on the open Newcastle PSG set (newcastle-psg, all 28 participants). Argues F1 is misleading on sleep-dominated data (all-sleep scores F1 79.6 with 0% wake specificity) and selects algorithms meeting sensitivity ≥75% and specificity ≥45%. Best wake specificity among those: Oakley rescored 62.8%; best F1: van Hees 79.1.

• Form factor: wrist

For the last 40 years, actigraphy or wearable accelerometry has provided an objective, low-burden and ecologically valid approach to assess real-world sleep and circadian patterns, contributing valuable data to epidemiological and clinical insights on sleep and sleep disorders. The proper use of wearable technology in sleep research requires validated algorithms that can derive sleep outcomes from the sensor data. Since the publication of the first automated scoring algorithm by Webster in 1982, a variety of sleep algorithms have been developed and contributed to sleep research, including many recent ones that leverage machine learning and / or deep learning approaches. However, it remains unclear how these algorithms compare to each other on the same data set and if these modern data science approaches improve the analytical validity of sleep outcomes based on wrist-worn acceleration data. This work provides a systematic evaluation across 8 state-of-the-art sleep algorithms on a common sleep data set with polysomnography (PSG) as ground truth. Despite the inclusion of recently published complex algorithms, simple regression-based and heuristic algorithms demonstrated slightly superior performance in sleep-wake classification and sleep outcome estimation. The performance of complex machine learning and deep learning models seem to suffer from poor generalization. This independent and systematic analytical validation of sleep algorithms provides key evidence on the use of wearable digital health technologies for sleep research and care.

Verbatim from the publisher's DOI record (opens in a new tab) · Publisher licence: https://creativecommons.org/licenses/by/4.0

1 row is credited to this paper. Ranks apply within each leaderboard only, as reported by the source.

• Newcastle PSG + GENEActiv both wrists (sleep clinic, 2015)Evaluation28 subjects · IMU