A review of signals used in sleep analysis

Abstract

This article presents a review of signals used for measuring physiology and activity during sleep and techniques for extracting information from these signals. We examine both clinical needs and biomedical signal processing approaches across a range of sensor types. Issues with recording and analysing the signals are discussed, together with their applicability to various clinical disorders. Both univariate and data fusion (exploiting the diverse characteristics of the primary recorded signals) approaches are discussed, together with a comparison of automated methods for analysing sleep.

Unobtrusive sleep state measurements in preterm infants – A review

“…To date, no automated video analysis has been conducted on preterm infants. Due to the similar vessel composition of preterm infants and adults, adaptations to the video analysis and actigraphy software might enable an automated analysis for preterm infants.Similar to HR and BR with BCG, actigraphy can be acquired by the use of pressure sensors or accelerometry embedded in, for example, the neonatal mattress (41,88), detecting sleep, wake or activity patterns(89). Pressure sensors can detect slight changes in the g force (> 0.05 g)(40,87), making it suitable for measuring the less intense movements of preterm infants.…”

The role of telemedicine and mobile health in the monitoring of sleep-breathing disorders: improving patient outcomes

“…23 Indeed, studies have found that actigraphy can be helpful for sleep research, because it tends to be less expensive and is more comfortable compared to PSG. 24,25 Similarly, many sleep-related apps that register body position and movement using built-in accelerometers have been developed.…”

Movement characterisation with accelerometry for improved paediatric sleep assessment

“…However, as arousal events without an associated movement tend to be shorter, it may be possible to detect lengthy arousal events and thereby estimate the severity of sleep disturbance. Indeed, literature suggests that longer arousals are a significant contributor to sleep disturbance [5], and that the incidence rate or length of arousal likely determines the extent of daytime dysfunction caused by sleep disruption [186].…”

“…Among many other detrimental factors, disrupted sleep often manifests as poor temperament, fatigue, decreased motor function and decreased cognitive ability. Sleep disorders are disruptive to sleep because they impair vital physiological processes, incite unwanted movements, or incite involuntary speech [4,5]. One such sleep disorder is obstructive sleep apnoea syndrome (OSA), which a↵ects approximately 2 4% of children [6,7].…”

“…This summary activity value was defined as the activity count ZC T RI normalised for the duration t of the movement segment, 5) where ZC A M represents the zero-crossing value for each movement during arousal. A threshold T n (varied between 0 and the maximum movement duration or maximum ZC A M ) was applied to the movement duration and the summarised activity counts ZC A M to predict arousal events.…”