A review on current trends in automatic sleep staging through bio-signal recordings and future challenges

Chriskos, Panteleimon; Frantzidιs, Christos A.; Nday, Christiane M.; Gkivogkli, Polyxeni T.; Bamidis, Panagiotis D.; Kourtidou‐Papadeli, Chrysoula

doi:10.1016/j.smrv.2020.101377

Cited by 35 publications

(29 citation statements)

References 42 publications

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“…Although several methods of automatic sleep-stage detection are already available 32 – 34 , we have implemented, as a proof-of-concept, a first version of a Bayesian, hyper-parameter-based detector.…”

Section: Discussionmentioning

confidence: 99%

Sleep as a random walk: a super-statistical analysis of EEG data across sleep stages

et al. 2021

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In clinical practice, human sleep is classified into stages, each associated with different levels of muscular activity and marked by characteristic patterns in the EEG signals. It is however unclear whether this subdivision into discrete stages with sharply defined boundaries is truly reflecting the dynamics of human sleep. To address this question, we consider one-channel EEG signals as heterogeneous random walks: stochastic processes controlled by hyper-parameters that are themselves time-dependent. We first demonstrate the heterogeneity of the random process by showing that each sleep stage has a characteristic distribution and temporal correlation function of the raw EEG signals. Next, we perform a super-statistical analysis by computing hyper-parameters, such as the standard deviation, kurtosis, and skewness of the raw signal distributions, within subsequent 30-second epochs. It turns out that also the hyper-parameters have characteristic, sleep-stage-dependent distributions, which can be exploited for a simple Bayesian sleep stage detection. Moreover, we find that the hyper-parameters are not piece-wise constant, as the traditional hypnograms would suggest, but show rising or falling trends within and across sleep stages, pointing to an underlying continuous rather than sub-divided process that controls human sleep. Based on the hyper-parameters, we finally perform a pairwise similarity analysis between the different sleep stages, using a quantitative measure for the separability of data clusters in multi-dimensional spaces.

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Section: Discussionmentioning

confidence: 99%

Sleep as a random walk: a super-statistical analysis of EEG data across sleep stages

et al. 2021

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“…However, algorithms developed for similar signals obtained from different sensing modalities may not be compatible with each other [164]. Further, the limited availability of accessible data sets makes it difficult to develop and benchmark such algorithms [165]. Having a wearable system will make it easier to obtain and maintain large data sets of different sleep signals that could be helpful for researchers to assess and compare the accuracy of their algorithms.…”

Section: Challenges and Future Directionsmentioning

confidence: 99%

A Systematic Review of Sensing Technologies for Wearable Sleep Staging

Imtiaz

2021

Sensors

107

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Designing wearable systems for sleep detection and staging is extremely challenging due to the numerous constraints associated with sensing, usability, accuracy, and regulatory requirements. Several researchers have explored the use of signals from a subset of sensors that are used in polysomnography (PSG), whereas others have demonstrated the feasibility of using alternative sensing modalities. In this paper, a systematic review of the different sensing modalities that have been used for wearable sleep staging is presented. Based on a review of 90 papers, 13 different sensing modalities are identified. Each sensing modality is explored to identify signals that can be obtained from it, the sleep stages that can be reliably identified, the classification accuracy of systems and methods using the sensing modality, as well as the usability constraints of the sensor in a wearable system. It concludes that the two most common sensing modalities in use are those based on electroencephalography (EEG) and photoplethysmography (PPG). EEG-based systems are the most accurate, with EEG being the only sensing modality capable of identifying all the stages of sleep. PPG-based systems are much simpler to use and better suited for wearable monitoring but are unable to identify all the sleep stages.

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“…Consumers should be aware of the reliability of the device they use, “medical” (e.g., validated through academic research protocols) or “industrial” (usually not validated). This should also help avoid duplication in algorithm development and subsequent incompatibility of devices [ 71 ].…”

Section: Discussionmentioning

confidence: 99%

Photoplethysmography in Normal and Pathological Sleep

Vulcan

André

Bruyneel

2021

Sensors

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This article presents an overview of the advancements that have been made in the use of photoplethysmography (PPG) for unobtrusive sleep studies. PPG is included in the quickly evolving and very popular landscape of wearables but has specific interesting properties, particularly the ability to capture the modulation of the autonomic nervous system during sleep. Recent advances have been made in PPG signal acquisition and processing, including coupling it with accelerometry in order to construct hypnograms in normal and pathologic sleep and also to detect sleep-disordered breathing (SDB). The limitations of PPG (e.g., oxymetry signal failure, motion artefacts, signal processing) are reviewed as well as technical solutions to overcome these issues. The potential medical applications of PPG are numerous, including home-based detection of SDB (for triage purposes), and long-term monitoring of insomnia, circadian rhythm sleep disorders (to assess treatment effects), and treated SDB (to ensure disease control). New contact sensor combinations to improve future wearables seem promising, particularly tools that allow for the assessment of brain activity. In this way, in-ear EEG combined with PPG and actigraphy could be an interesting focus for future research.

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A review on current trends in automatic sleep staging through bio-signal recordings and future challenges

Cited by 35 publications

References 42 publications

Sleep as a random walk: a super-statistical analysis of EEG data across sleep stages

Sleep as a random walk: a super-statistical analysis of EEG data across sleep stages

A Systematic Review of Sensing Technologies for Wearable Sleep Staging

Photoplethysmography in Normal and Pathological Sleep

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