A Lightweight and Inexpensive In-ear Sensing System For Automatic Whole-night Sleep Stage Monitoring

Nguyen, Anh Duy; Alqurashi, Raghda; Raghebi, Zohreh; Banaei-Kashani, Farnoush; Halbower, Ann C.; Vu, Tam

doi:10.1145/2994551.2994562

Cited by 58 publications

(30 citation statements)

References 37 publications

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“…Recording the EEG from within the ear has been investigated by different research groups. 48 , 49 However, there are distinct differences between our in-ear EEG device and other devices. Nguyen et al 48 have used an off-the-shelf foam-based earplug that can be inserted in both ears simultaneously.…”

Section: Discussionmentioning

confidence: 99%

A novel in-ear sensor to determine sleep latency during the Multiple Sleep Latency Test in healthy adults with and without sleep restriction

Alqurashi¹,

Nakamura²,

Goverdovsky³

et al. 2018

NSS

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ObjectivesDetecting sleep latency during the Multiple Sleep Latency Test (MSLT) using electroencephalogram (scalp-EEG) is time-consuming. The aim of this study was to evaluate the efficacy of a novel in-ear sensor (in-ear EEG) to detect the sleep latency, compared to scalp-EEG, during MSLT in healthy adults, with and without sleep restriction.MethodsWe recruited 25 healthy adults (28.5±5.3 years) who participated in two MSLTs with simultaneous recording of scalp and in-ear EEG. Each test followed a randomly assigned sleep restriction (≤5 hours sleep) or usual night sleep (≥7 hours sleep). Reaction time and Stroop test were used to assess the functional impact of the sleep restriction. The EEGs were scored blind to the mode of measurement and study conditions, using American Academy of Sleep Medicine 2012 criteria. The Agreement between the scalp and in-ear EEG was assessed using Bland-Altman analysis.ResultsTechnically acceptable data were obtained from 23 adults during 69 out of 92 naps in the sleep restriction condition and 25 adults during 85 out of 100 naps in the usual night sleep. Meaningful sleep restrictions were confirmed by an increase in the reaction time (mean ± SD: 238±30 ms vs 228±27 ms; P=0.045). In the sleep restriction condition, the in-ear EEG exhibited a sensitivity of 0.93 and specificity of 0.80 for detecting sleep latency, with a substantial agreement (κ=0.71), whereas after the usual night’s sleep, the in-ear EEG exhibited a sensitivity of 0.91 and specificity of 0.89, again with a substantial agreement (κ=0.79).ConclusionThe in-ear sensor was able to detect reduced sleep latency following sleep restriction, which was sufficient to impair both the reaction time and cognitive function. Substantial agreement was observed between the scalp and in-ear EEG when measuring sleep latency. This new in-ear EEG technology is shown to have a significant value as a convenient measure for sleep latency.

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Section: Discussionmentioning

confidence: 99%

A novel in-ear sensor to determine sleep latency during the Multiple Sleep Latency Test in healthy adults with and without sleep restriction

Alqurashi¹,

Nakamura²,

Goverdovsky³

et al. 2018

NSS

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“…The authors of Rofouei et al (2011) developed a wearable neck cuff system for monitoring physiological signals in real time. The authors of Nguyen et al (2016) have developed a lightweight and inexpensive in-ear wearable sensing system that can capture electrical activities of the brain and eye and facial muscles. Nguyen et al (2016) have used a supervised non negative matrix factorization algorithm to adaptively analyze the signals.…”

Section: Related Workmentioning

confidence: 99%

“…The authors of Nguyen et al (2016) have developed a lightweight and inexpensive in-ear wearable sensing system that can capture electrical activities of the brain and eye and facial muscles. Nguyen et al (2016) have used a supervised non negative matrix factorization algorithm to adaptively analyze the signals. A sleep monitoring model using image analysis has been proposed in Nakajima et al (2000), but it has proved inefficient in case of low light conditions at night.…”

Section: Related Workmentioning

confidence: 99%

An Active Sleep Monitoring Framework Using Wearables

Hossain

Ramamurthy

Khan

et al. 2018

ACM Trans. Interact. Intell. Syst.

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Sleep is the most important aspect of healthy and active living. The right amount of sleep at the right time helps an individual to protect his or her physical, mental, and cognitive health and maintain his or her quality of life. The most durative of the Activities of Daily Living (ADL), sleep has a major synergic influence on a person's fuctional, behavioral, and cognitive health. A deep understanding of sleep behavior and its relationship with its physiological signals, and contexts (such as eye or body movements), is necessary to design and develop a robust intelligent sleep monitoring system. In this article, we propose an intelligent algorithm to detect the microscopic states of sleep that fundamentally constitute the components of good and bad sleeping behaviors and thus help shape the formative assessment of sleep quality. Our initial analysis includes the investigation of several classification techniques to identify and correlate the relationship of microscopic sleep states with overall sleep behavior. Subsequently, we also propose an online algorithm based on change point detection to process and classify the microscopic sleep states. We also develop a lightweight version of the proposed algorithm for real-time sleep monitoring, recognition, and assessment at scale. For a larger deployment of our proposed model across a community of individuals, we propose an active-learning-based methodology to reduce the effort of ground-truth data collection and labeling. Finally, we evaluate the performance of our proposed algorithms on real data traces and demonstrate the efficacy of our models for detecting and assessing the fine-grained sleep states beyond an individual.

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“…to distinguish between wakefulness and light sleep). Nguyen et al [15] conducted overnight sleep recordings over eight participants to evaluate their in-ear sensing system; their sensors were able to record the EEG, EOG, and EMG, key physiological variables for sleep monitoring. It is important to highlight that the sleep studies in [11][12][13][14][15], together with this study, were conducted using one-size-fits-all viscoelastic in-ear sensors, which are not optimised for a particular user but are convenient for wide deployment and promise an affordable out-of-clinic solution.…”

Section: Introductionmentioning

confidence: 99%

“…Nguyen et al [15] conducted overnight sleep recordings over eight participants to evaluate their in-ear sensing system; their sensors were able to record the EEG, EOG, and EMG, key physiological variables for sleep monitoring. It is important to highlight that the sleep studies in [11][12][13][14][15], together with this study, were conducted using one-size-fits-all viscoelastic in-ear sensors, which are not optimised for a particular user but are convenient for wide deployment and promise an affordable out-of-clinic solution. Owing to their flexibility and favourable stress-strain properties (memory foam) [28], these viscoelastic earpieces can be squeezed and shaped up to fit comfortably any ear; such a 'generic' in-ear sensor is readily applicable to a large population, a pre-requisite for the future eHealth in the community.…”

Section: Introductionmentioning

confidence: 99%

Hearables: Automatic Overnight Sleep Monitoring With Standardized In-Ear EEG Sensor

Nakamura

Alqurashi

Morrell

et al. 2020

IEEE Trans. Biomed. Eng.

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Objective: Advances in sensor miniaturisation and computational power have served as enabling technologies for monitoring human physiological conditions in real-world scenarios. Sleep disruption may impact neural function, and can be a symptom of both physical and mental disorders. This study proposes wearable in-ear electroencephalography (ear-EEG) for overnight sleep monitoring as a 24/7 continuous and unobtrusive technology for sleep quality assessment in the community. Methods: Twenty-two healthy participants took part in overnight sleep monitoring with simultaneous ear-EEG and conventional full polysomnography (PSG) recordings. The ear-EEG data were analysed in the both structural complexity and spectral domains; the extracted features were used for automatic sleep stage prediction through supervised machine learning, whereby the PSG data were manually scored by a sleep clinician. Results: The agreement between automatic sleep stage prediction based on ear-EEG from a single in-ear sensor and the hypnogram based on the full PSG was 74.1 % in the accuracy over five sleep stage classification; this is supported by a Substantial Agreement in the kappa metric (0.61). Conclusion: The in-ear sensor is both feasible for monitoring overnight sleep outside the sleep laboratory and mitigates technical difficulties associated with PSG. It therefore represents a 24/7 continuously wearable alternative to conventional cumbersome and expensive sleep monitoring. Significance: The 'standardised' one-size-fits-all viscoelastic in-ear sensor is a next generation solution to monitor sleep-this technology promises to be a viable method for readily wearable sleep monitoring in the community, a key to affordable healthcare and future eHealth.

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A Lightweight and Inexpensive In-ear Sensing System For Automatic Whole-night Sleep Stage Monitoring

Cited by 58 publications

References 37 publications

A novel in-ear sensor to determine sleep latency during the Multiple Sleep Latency Test in healthy adults with and without sleep restriction

A novel in-ear sensor to determine sleep latency during the Multiple Sleep Latency Test in healthy adults with and without sleep restriction

An Active Sleep Monitoring Framework Using Wearables

Hearables: Automatic Overnight Sleep Monitoring With Standardized In-Ear EEG Sensor

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