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

A custom-built single-channel in-ear electroencephalography sensor for sleep phase detection: an interdependent solution for at-home sleep studies

Sleep is vital for health. It has regenerative and protective functions. Its disruption reduces the quality of life and increases susceptibility to disease. During sleep, there is a cyclicity of distinct phases that are studied for clinical purposes using polysomnography (PSG), a costly and technically demanding method that compromises the quality of natural sleep. The search for simpler devices for recording biological signals at home addresses some of these issues. We have reworked a single-channel in-ear electroencephalography (EEG) sensor grounded to a commercially available memory foam earplug with conductive tape. A total of 14 healthy volunteers underwent a full night of simultaneous PSG, in-ear EEG and actigraphy recordings. We analysed the performance of the methods in terms of sleep metrics and staging. In another group of 14 patients evaluated for sleep-related pathologies, PSG and in-ear EEG were recorded simultaneously, the latter in two different configurations (with and without a contralateral reference on the scalp). In both groups, the in-ear EEG sensor showed a strong correlation, agreement and reliability with the 'gold standard' of PSG and thus supported accurate sleep classification, which is not feasible with actigraphy. Single-channel in-ear EEG offers compelling prospects for simplifying sleep parameterisation in both healthy individuals and clinical patients and paves the way for reliable assessments in a broader range of clinical situations, namely by integrating Level 3 polysomnography devices. In addition, addressing the recognised overestimation of the apnea-hypopnea index, due to the lack of an EEG signal, and the sparse information on sleep metrics could prove fundamental for optimised clinical decision making.

Expert-level sleep staging using an electrocardiography-only feed-forward neural network

Reliable classification of sleep stages is crucial in sleep medicine and neuroscience research for providing valuable insights, diagnoses, and understanding of brain states. The current gold standard method for sleep stage classification is polysomnography (PSG). Unfortunately, PSG is an expensive and cumbersome process involving numerous electrodes, often conducted in an unfamiliar clinic and annotated by a professional. Although commercial devices like smartwatches track sleep, their performance is well below PSG. To address these disadvantages, we present a feed-forward neural network that achieves gold-standard levels of agreement using only a single lead of electrocardiography (ECG) data. Specifically, the median five-stage Cohen's kappa is 0.725 on a large, diverse dataset of 5 to 90-year-old subjects. Comparisons with a comprehensive meta-analysis of between-human inter-rater agreement confirm the non-inferior performance of our model. Finally, we developed a novel loss function to align the training objective with Cohen's kappa. Our method offers an inexpensive, automated, and convenient alternative for sleep stage classification-further enhanced by a real-time scoring option. Cardiosomnography, or a sleep study conducted with ECG only, could take expert-level sleep studies outside the confines of clinics and laboratories and into realistic settings. This advancement democratizes access to high-quality sleep studies, considerably enhancing the field of sleep medicine and neuroscience. It makes less-expensive, higher-quality studies accessible to a broader community, enabling improved sleep research and more personalized, accessible sleep-related healthcare interventions.

Advancements in Wearable EEG Technology for Improved Home-Based Sleep Monitoring and Assessment: A Review

Sleep is a fundamental aspect of daily life, profoundly impacting mental and emotional well-being. Optimal sleep quality is vital for overall health and quality of life, yet many individuals struggle with sleep-related difficulties. In the past, polysomnography (PSG) has served as the gold standard for assessing sleep, but its bulky nature, cost, and the need for expertise has made it cumbersome for widespread use. By recognizing the need for a more accessible and user-friendly approach, wearable home monitoring systems have emerged. EEG technology plays a pivotal role in sleep monitoring, as it captures crucial brain activity data during sleep and serves as a primary indicator of sleep stages and disorders. This review provides an overview of the most recent advancements in wearable sleep monitoring leveraging EEG technology. We summarize the latest EEG devices and systems available in the scientific literature, highlighting their design, form factors, materials, and methods of sleep assessment. By exploring these developments, we aim to offer insights into cutting-edge technologies, shedding light on wearable EEG sensors for advanced at-home sleep monitoring and assessment. This comprehensive review contributes to a broader perspective on enhancing sleep quality and overall health using wearable EEG sensors.

A comprehensive study on the efficacy of a wearable sleep aid device featuring closed-loop real-time acoustic stimulation

Difficulty falling asleep is one of the typical insomnia symptoms. However, intervention therapies available nowadays, ranging from pharmaceutical to hi-tech tailored solutions, remain ineffective due to their lack of precise real-time sleep tracking, in-time feedback on the therapies, and an ability to keep people asleep during the night. This paper aims to enhance the efficacy of such an intervention by proposing a novel sleep aid system that can sense multiple physiological signals continuously and simultaneously control auditory stimulation to evoke appropriate brain responses for fast sleep promotion. The system, a lightweight, comfortable, and user-friendly headband, employs a comprehensive set of algorithms and dedicated own-designed audio stimuli. Compared to the gold-standard device in 883 sleep studies on 377 subjects, the proposed system achieves (1) a strong correlation (0.89 ± 0.03) between the physiological signals acquired by ours and those from the gold-standard PSG, (2) an 87.8% agreement on automatic sleep scoring with the consensus scored by sleep technicians, and (3) a successful non-pharmacological real-time stimulation to shorten the duration of sleep falling by 24.1 min. Conclusively, our solution exceeds existing ones in promoting fast falling asleep, tracking sleep state accurately, and achieving high social acceptance through a reliable large-scale evaluation.

The future of wearable EEG: a review of ear-EEG technology and its applications

Objective.This review paper provides a comprehensive overview of ear-electroencephalogram (EEG) technology, which involves recording EEG signals from electrodes placed in or around the ear, and its applications in the field of neural engineering.Approach.We conducted a thorough literature search using multiple databases to identify relevant studies related to ear-EEG technology and its various applications. We selected 123 publications and synthesized the information to highlight the main findings and trends in this field.Main results.Our review highlights the potential of ear-EEG technology as the future of wearable EEG technology. We discuss the advantages and limitations of ear-EEG compared to traditional scalp-based EEG and methods to overcome those limitations. Through our review, we found that ear-EEG is a promising method that produces comparable results to conventional scalp-based methods. We review the development of ear-EEG sensing devices, including the design, types of sensors, and materials. We also review the current state of research on ear-EEG in different application areas such as brain-computer interfaces, and clinical monitoring.Significance.This review paper is the first to focus solely on reviewing ear-EEG research articles. As such, it serves as a valuable resource for researchers, clinicians, and engineers working in the field of neural engineering. Our review sheds light on the exciting future prospects of ear-EEG, and its potential to advance neural engineering research and become the future of wearable EEG technology.

Advances in Electrode Materials for Scalp, Forehead, and Ear EEG: A Mini-Review

Electroencephalogram (EEG) records the electrical activity of neurons in the cerebral cortex and is used extensively to diagnose, treat, and monitor psychiatric and neurological conditions. Reliable contact between the skin and the electrodes is essential for achieving consistency and for obtaining electroencephalographic information. There has been an increasing demand for effective equipment and electrodes to overcome the time-consuming and cumbersome application of traditional systems. Recently, ear-centered EEG has met with growing interest since it can provide good signal quality due to the proximity of the ear to the brain. In addition, it can facilitate mobile and unobtrusive usage due to its smaller size and ease of use, since it can be used without interfering with the patient's daily activities. The purpose of this mini-review is to first introduce the broad range of electrodes used in conventional (scalp) EEG and subsequently discuss the state-of-the-art literature about around- and in-the-ear EEG.

The past, present, and future of sleep quality assessment and monitoring

Sleep quality is considered to be an individual's self-satisfaction with all aspects of the sleep experience. Good sleep not only improves a person's physical, mental and daily functional health, but also improves the quality-of-life level to some extent. In contrast, chronic sleep deprivation can increase the risk of diseases such as cardiovascular diseases, metabolic dysfunction and cognitive and emotional dysfunction, and can even lead to increased mortality. The scientific evaluation and monitoring of sleep quality is an important prerequisite for safeguarding and promoting the physiological health of the body. Therefore, we have compiled and reviewed the existing methods and emerging technologies commonly used for subjective and objective evaluation and monitoring of sleep quality, and found that subjective sleep evaluation is suitable for clinical screening and large-scale studies, while objective evaluation results are more intuitive and scientific, and in the comprehensive evaluation of sleep, if we want to get more scientific monitoring results, we should combine subjective and objective monitoring and dynamic monitoring.

Sleep Quality and Associated Factors Among Adult Cancer Patients on Treatments at Tikur Anbessa Specialized Hospital Oncology Unit, Addis Ababa, Ethiopia, 2021

BACKGROUND

Patients with cancer frequently reported sleep problems during their treatments which can affect their sleep quality have an impact on patients' quality of life (QOL).

OBJECTIVE

to assess the prevalence of sleep quality and associated factors in adult cancer patients on treatment in the Oncology unit of Tikur Anbessa Specialized Hospital, Addis Ababa, Ethiopia, 2021.

METHODS

institutional-based cross-sectional study design was used and data was collected by using face-to-face structured interview questionnaires from March 1 to April 1, 2021. Sleep Quality Index (PSQI) consisted of 19 items, the social support scale (OSS-3) consisted of 3 items, and the Hospital Anxiety and Depression Scale (HADS) consisted of 14 items were applied. Logistic regression including bivariate and multivariate analysis was done to examine the association between dependent and independent variables, and P< 0.05 was considered the level of significance for associations.

RESULTS

A total of 264 sampled adult cancer patients on treatments were included in this study, with a response rate of 93.61%. About 26.5% of the participants' age distribution was between 40 to 49 years, and 68.6% were female. 59.8% of the study participants were married. Concerning education, about 48.9% of participants attended primary and secondary school and 45% of participants were unemployed. Overall, 53.79% of individuals had poor sleep quality. Low income ((AOR=5.36 CI 95% (2.23, 12.90), fatigue (AOR=2.89 CI 95(1.32, 6.33), pain (AOR 3.82 C I95 % (1.84, 7.93), poor of social support (AOR =3.20 CI 95% (1.43, 6.74), anxiety (AOR=3.48 CI 95% (1.44, 8.38) and depression (AOR 2.87 CI 95 % (1.05-7.391) were all associated with poor sleep quality.

CONCLUSION

This study revealed a high prevalence of poor sleep quality, which was significantly associated with factors like low income, fatigue, pain, poor social support, anxiety, and depression among cancer patients on treatments.

Mapping of Spatiotemporal Auricular Electrophysiological Signals Reveals Human Biometric Clusters

Underneath the ear skin there are rich vascular network and sensory nerve branches. Hence, the 3D mapping of auricular electrophysiological signals can provide new biomedical perspectives. However, it is still extremely challenging for current sensing techniques to cover the entire ultra-curved auricle. Here, a 3D graphene-based ear-conformable sensing device with embedded and distributed 3D electrodes for full-auricle physiological monitoring is reported. As a proof-of-concept, spatiotemporal auricular electrical skin resistance (AESR) mapping is demonstrated for the first time, and human subject-specific AESR distributions are observed. From the data of more than 30 ears (both right and left ears), the auricular region-specific AESR changes after cycling exercise are observed in 98% of the tests and are clustered into four groups via machine learning-based data analyses. Correlations of AESR with heart rate and blood pressure are also studied. This 3D electronic platform and AESR-based biometrical findings show promising biomedical applications.

Design of hydrogel-based wearable EEG electrodes for medical applications

The electroencephalogram (EEG) is considered to be a promising method for studying brain disorders. Because of its non-invasive nature, subjects take a lower risk compared to some other invasive methods, while the systems record the brain signal. With the technological advancement of neural and material engineering, we are in the process of achieving continuous monitoring of neural activity through wearable EEG. In this article, we first give a brief introduction to EEG bands, circuits, wired/wireless EEG systems, and analysis algorithms. Then, we review the most recent advances in the interfaces used for EEG recordings, focusing on hydrogel-based EEG electrodes. Specifically, the advances for important figures of merit for EEG electrodes are reviewed. Finally, we summarize the potential medical application of wearable EEG systems.

Recent advances in wearable sensors and portable electronics for sleep monitoring

Despite the increasing awareness of the importance of sleep, the number of people suffering from insufficient sleep has increased every year. The gold-standard sleep assessment uses polysomnography (PSG) with various sensors to identify sleep patterns and disorders. However, due to the high cost of PSG and limited availability, many people with sleep disorders are left undiagnosed. Recent wearable sensors and electronics enable portable, continuous monitoring of sleep at home, overcoming the limitations of PSG. This report reviews the advances in wearable sensors, miniaturized electronics, and system packaging for home sleep monitoring. New devices available in the market and systems are collectively summarized based on their overall structure, form factor, materials, and sleep assessment method. It is expected that this review provides a comprehensive view of newly developed technologies and broad insights on wearable sensors and portable electronics toward advanced sleep monitoring as well as at-home sleep assessment.

Ear-EEG for sleep assessment: a comparison with actigraphy and PSG

PURPOSE

To assess automatic sleep staging of three ear-EEG setups with different electrode configurations and compare performance with concurrent polysomnography and wrist-worn actigraphy recordings.

METHODS

Automatic sleep staging was performed for single-ear, single-ear with ipsilateral mastoid, and cross-ear electrode configurations, and for actigraphy data. The polysomnography data were manually scored and used as the gold standard. The automatic sleep staging was tested on 80 full-night recordings from 20 healthy subjects. The scoring performance and sleep metrics were determined for all ear-EEG setups and the actigraphy device.

RESULTS

The single-ear, the single-ear with ipsilateral mastoid setup, and the cross-ear setup performed five class sleep staging with kappa values 0.36, 0.63, and 0.72, respectively. For the single-ear with mastoid electrode and the cross-ear setup, the performance of the sleep metrics, in terms of mean absolute error, was better than the sleep metrics estimated from the actigraphy device in the current study, and also better than current state-of-the-art actigraphy studies.

CONCLUSION

A statistically significant improvement in both accuracy and kappa was observed from single-ear to single-ear with ipsilateral mastoid, and from single-ear with ipsilateral mastoid to cross-ear configurations for both two and five-sleep stage classification. In terms of sleep metrics, the results were more heterogeneous, but in general, actigraphy and single-ear with ipsilateral mastoid configuration were better than the single-ear configuration; and the cross-ear configuration was consistently better than both the actigraphy device and the single-ear configuration.

Discrimination of Sleep Spindles in Ear-EEG

Sleep spindles are brief oscillatory events observed in EEG measurements during sleep, related to both sleep staging and basic neuroscience. The objective of this study was to investigate to which extent sleep spindles are observable from ear-EEG. The analysis was based on single-night recordings from 12 subjects, wearing both a polysomnography setup and two light-weight mobile EEG devices (ear-EEG). By introducing a sleep spindle index capable of discriminating between epochs with distinct spindles and distinctly spindle-free epochs, we describe to which extent the most clear cut sleep spindles (as labeled using scalp EEG) can be detected using ear-EEG. We find that ear-EEG can be used to detect sleep spindles, at a performance level similar to scalp derivations. We speculate that part of the observed discrepancy between ear-EEG and the gold standard (scalp EEG) could be caused by the visibility of different spindles in the ear-EEG.

Accurate whole-night sleep monitoring with dry-contact ear-EEG

Sleep is a key phenomenon to both understanding, diagnosing and treatment of many illnesses, as well as for studying health and well being in general. Today, the only widely accepted method for clinically monitoring sleep is the polysomnography (PSG), which is, however, both expensive to perform and influences the sleep. This has led to investigations into light weight electroencephalography (EEG) alternatives. However, there has been a substantial performance gap between proposed alternatives and PSG. Here we show results from an extensive study of 80 full night recordings of healthy participants wearing both PSG equipment and ear-EEG. We obtain automatic sleep scoring with an accuracy close to that achieved by manual scoring of scalp EEG (the current gold standard), using only ear-EEG as input, attaining an average Cohen's kappa of 0.73. In addition, this high performance is present for all 20 subjects. Finally, 19/20 subjects found that the ear-EEG had little to no negative effect on their sleep, and subjects were generally able to apply the equipment without supervision. This finding marks a turning point on the road to clinical long term sleep monitoring: the question should no longer be whether ear-EEG could ever be used for clinical home sleep monitoring, but rather when it will be.

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

OBJECTIVE

Advances in sensor miniaturization 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

A total of 22 healthy participants took part in overnight sleep monitoring with simultaneous ear-EEG and conventional full polysomnography recordings. The ear-EEG data were analyzed in the both structural complexity and spectral domains. The extracted features were used for automatic sleep stage prediction through supervized 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 feasible for monitoring overnight sleep outside the sleep laboratory and also mitigates technical difficulties associated with PSG. It, therefore, represents a 24/7 continuously wearable alternative to conventional cumbersome and expensive sleep monitoring.

SIGNIFICANCE

The "standardized" 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.