Did you know that poor sleep patterns are linked to the progression of many diseases, including depression, hypertension, obesity, and neurodegenerative diseases? For this reason, among others, it's known that sleep is an important aspect of health and is essential to the quality of life.
For people living with sleep disorders, an accurate and reliable assessment of sleep patterns is the first step to treatment development and proper. Polysomnography (PSG) is the gold standard for identifying apneas, hypopneas, and REM disorders. To reduce the need for costly sleep lab visits, the industry has introduced wearable devices allowing objective and reliable sleep assessments in the ecologically-valid home setting. These are complemented with self-reported sleep diaries that also offer valuable objective information.
However, in-lab PSG has low ecological validity for understanding sleeping behavior because patients sleep with EEG electrodes, ECG electrodes, a breathing belt, a SpO2 monitor, and other sensors in a controlled laboratory environment, which is not typically representative of sleeping behavior in the home environment.
Despite the differences in approaches to studying sleep disorders, the multifaceted nature of sleep, and the use of automated algorithms, sleep assessments using wearable data can be confusing to clinical researchers and physicians. To gain leverage on the benefit of wearable devices, explore these state-of-the-art data processing steps and algorithms used to obtain acceptable sleep outcomes from wrist acceleration data.