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de Gans CJ, Burger P, van den Ende ES, Hermanides J, Nanayakkara PWB, Gemke RJBJ, Rutters F, Stenvers DJ. Sleep assessment using EEG-based wearables - A systematic review. Sleep Med Rev 2024; 76:101951. [PMID: 38754209 DOI: 10.1016/j.smrv.2024.101951] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2023] [Revised: 04/26/2024] [Accepted: 05/06/2024] [Indexed: 05/18/2024]
Abstract
Polysomnography (PSG) is the reference standard of sleep measurement, but is burdensome for the participant and labor intensive. Affordable electroencephalography (EEG)-based wearables are easy to use and are gaining popularity, yet selecting the most suitable device is a challenge for clinicians and researchers. In this systematic review, we aim to provide a comprehensive overview of available EEG-based wearables to measure human sleep. For each wearable, an overview will be provided regarding validated population and reported measurement properties. A systematic search was conducted in the databases OVID MEDLINE, Embase.com and CINAHL. A machine learning algorithm (ASReview) was utilized to screen titles and abstracts for eligibility. In total, 60 papers were selected, covering 34 unique EEG-based wearables. Feasibility studies indicated good tolerance, high compliance, and success rates. The 42 included validation studies were conducted across diverse populations and showed consistently high accuracy in sleep staging detection. Therefore, the recent advancements in EEG-based wearables show great promise as alternative for PSG and for at-home sleep monitoring. Users should consider factors like user-friendliness, comfort, and costs, as these devices vary in features and pricing, impacting their suitability for individual needs.
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Affiliation(s)
- C J de Gans
- Department of Internal Medicine, Section General Internal Medicine Unit Acute Medicine, Amsterdam University Medical Center, Amsterdam, the Netherlands; Amsterdam Public Health Research Institute, Amsterdam University Medical Center, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands.
| | - P Burger
- Amsterdam Public Health Research Institute, Amsterdam University Medical Center, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands; Department of Pediatrics, Emma Children's Hospital, Amsterdam University Medical Center, Amsterdam, the Netherlands; Amsterdam Reproduction and Development Research Institute, Amsterdam, the Netherlands
| | - E S van den Ende
- Department of Internal Medicine, Section General Internal Medicine Unit Acute Medicine, Amsterdam University Medical Center, Amsterdam, the Netherlands; Amsterdam Public Health Research Institute, Amsterdam University Medical Center, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - J Hermanides
- Amsterdam Public Health Research Institute, Amsterdam University Medical Center, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands; Department of Anesthesiology, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, the Netherlands
| | - P W B Nanayakkara
- Department of Internal Medicine, Section General Internal Medicine Unit Acute Medicine, Amsterdam University Medical Center, Amsterdam, the Netherlands; Amsterdam Public Health Research Institute, Amsterdam University Medical Center, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - R J B J Gemke
- Amsterdam Public Health Research Institute, Amsterdam University Medical Center, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands; Department of Pediatrics, Emma Children's Hospital, Amsterdam University Medical Center, Amsterdam, the Netherlands; Amsterdam Reproduction and Development Research Institute, Amsterdam, the Netherlands
| | - F Rutters
- Amsterdam Public Health Research Institute, Amsterdam University Medical Center, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands; Department of Epidemiology and Data Science, Amsterdam University Medical Center, the Netherlands
| | - D J Stenvers
- Department of Endocrinology and Metabolism, Amsterdam UMC, University of Amsterdam, Department Meibergdreef 9, Amsterdam, the Netherlands; Amsterdam Gastroenterology Endocrinology and Metabolism (AGEM), Amsterdam, the Netherlands
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Hammour G, Davies H, Atzori G, Della Monica C, Ravindran KKG, Revell V, Dijk DJ, Mandic DP. From Scalp to Ear-EEG: A Generalizable Transfer Learning Model for Automatic Sleep Scoring in Older People. IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE 2024; 12:448-456. [PMID: 38765887 PMCID: PMC11100860 DOI: 10.1109/jtehm.2024.3388852] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Revised: 03/21/2024] [Accepted: 04/10/2024] [Indexed: 05/22/2024]
Abstract
OBJECTIVE Sleep monitoring has extensively utilized electroencephalogram (EEG) data collected from the scalp, yielding very large data repositories and well-trained analysis models. Yet, this wealth of data is lacking for emerging, less intrusive modalities, such as ear-EEG. METHODS AND PROCEDURES The current study seeks to harness the abundance of open-source scalp EEG datasets by applying models pre-trained on data, either directly or with minimal fine-tuning; this is achieved in the context of effective sleep analysis from ear-EEG data that was recorded using a single in-ear electrode, referenced to the ipsilateral mastoid, and developed in-house as described in our previous work. Unlike previous studies, our research uniquely focuses on an older cohort (17 subjects aged 65-83, mean age 71.8 years, some with health conditions), and employs LightGBM for transfer learning, diverging from previous deep learning approaches. RESULTS Results show that the initial accuracy of the pre-trained model on ear-EEG was 70.1%, but fine-tuning the model with ear-EEG data improved its classification accuracy to 73.7%. The fine-tuned model exhibited a statistically significant improvement (p < 0.05, dependent t-test) for 10 out of the 13 participants, as reflected by an enhanced average Cohen's kappa score (a statistical measure of inter-rater agreement for categorical items) of 0.639, indicating a stronger agreement between automated and expert classifications of sleep stages. Comparative SHAP value analysis revealed a shift in feature importance for the N3 sleep stage, underscoring the effectiveness of the fine-tuning process. CONCLUSION Our findings underscore the potential of fine-tuning pre-trained scalp EEG models on ear-EEG data to enhance classification accuracy, particularly within an older population and using feature-based methods for transfer learning. This approach presents a promising avenue for ear-EEG analysis in sleep studies, offering new insights into the applicability of transfer learning across different populations and computational techniques. CLINICAL IMPACT An enhanced ear-EEG method could be pivotal in remote monitoring settings, allowing for continuous, non-invasive sleep quality assessment in elderly patients with conditions like dementia or sleep apnea.
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Affiliation(s)
- Ghena Hammour
- Department of Electrical and Electronic EngineeringImperial College LondonSW7 2BTLondonU.K.
- U.K. Dementia Research Institute, Care Research and Technology CentreSW7 2BTLondonU.K.
| | - Harry Davies
- Department of Electrical and Electronic EngineeringImperial College LondonSW7 2BTLondonU.K.
- U.K. Dementia Research Institute, Care Research and Technology CentreSW7 2BTLondonU.K.
| | - Giuseppe Atzori
- 2Surrey Sleep Research Centre, School of Biosciences, Faculty of Health and Medical SciencesUniversity of SurreyGU2 7XHGuildfordU.K.
- U.K. Dementia Research Institute, Care Research and Technology CentreSW7 2BTLondonU.K.
| | - Ciro Della Monica
- 2Surrey Sleep Research Centre, School of Biosciences, Faculty of Health and Medical SciencesUniversity of SurreyGU2 7XHGuildfordU.K.
- U.K. Dementia Research Institute, Care Research and Technology CentreSW7 2BTLondonU.K.
| | - Kiran K. G. Ravindran
- 2Surrey Sleep Research Centre, School of Biosciences, Faculty of Health and Medical SciencesUniversity of SurreyGU2 7XHGuildfordU.K.
- U.K. Dementia Research Institute, Care Research and Technology CentreSW7 2BTLondonU.K.
| | - Victoria Revell
- 2Surrey Sleep Research Centre, School of Biosciences, Faculty of Health and Medical SciencesUniversity of SurreyGU2 7XHGuildfordU.K.
| | - Derk-Jan Dijk
- 2Surrey Sleep Research Centre, School of Biosciences, Faculty of Health and Medical SciencesUniversity of SurreyGU2 7XHGuildfordU.K.
- U.K. Dementia Research Institute, Care Research and Technology CentreSW7 2BTLondonU.K.
| | - Danilo P. Mandic
- Department of Electrical and Electronic EngineeringImperial College LondonSW7 2BTLondonU.K.
- U.K. Dementia Research Institute, Care Research and Technology CentreSW7 2BTLondonU.K.
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Ahrens E, Jennum P, Duun-Henriksen J, Borregaard HWS, Nielsen SS, Taptiklis N, Cormack F, Djurhuus BD, Homøe P, Kjær TW, Hemmsen MC. The Ultra-Long-Term Sleep study: Design, rationale, data stability and user perspective. J Sleep Res 2024:e14197. [PMID: 38572813 DOI: 10.1111/jsr.14197] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Revised: 02/26/2024] [Accepted: 03/04/2024] [Indexed: 04/05/2024]
Abstract
Sleep deprivation and poor sleep quality are significant societal challenges that negatively impact individuals' health. The interaction between subjective sleep quality, objective sleep measures, physical and cognitive performance, and their day-to-day variations remains poorly understood. Our year-long study of 20 healthy individuals, using subcutaneous electroencephalography, aimed to elucidate these interactions, assessing data stability and participant satisfaction, usability, well-being and adherence. In the study, 25 participants were fitted with a minimally invasive subcutaneous electroencephalography lead, with 20 completing the year of subcutaneous electroencephalography recording. Signal stability was measured using covariance of variation. Participant satisfaction, usability and well-being were measured with questionnaires: Perceived Ease of Use questionnaire, System Usability Scale, Headache questionnaire, Major Depression Inventory, World Health Organization 5-item Well-Being Index, and interviews. The subcutaneous electroencephalography signals remained stable for the entire year, with an average participant adherence rate of 91%. Participants rated their satisfaction with the subcutaneous electroencephalography device as easy to use with minimal or no discomfort. The System Usability Scale score was high at 86.3 ± 10.1, and interviews highlighted that participants understood how to use the subcutaneous electroencephalography device and described a period of acclimatization to sleeping with the device. This study provides compelling evidence for the feasibility of longitudinal sleep monitoring during everyday life utilizing subcutaneous electroencephalography in healthy subjects, showcasing excellent signal stability, adherence and user experience. The amassed subcutaneous electroencephalography data constitutes the largest dataset of its kind, and is poised to significantly advance our understanding of day-to-day variations in normal sleep and provide key insights into subjective and objective sleep quality.
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Affiliation(s)
- Esben Ahrens
- T&W Engineering A/S, Denmark
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Poul Jennum
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
- Danish Center for Sleep Medicine, Department of Clinical Neurophysiology, Glostrup, Denmark
| | | | | | | | | | - Francesca Cormack
- Cambridge Cognition Ltd, Cambridge, UK
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - Bjarki Ditlev Djurhuus
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
- Department of Otorhinolaryngology and Maxillofacial Surgery, Zealand University Hospital, Køge, Denmark
| | - Preben Homøe
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
- Department of Otorhinolaryngology and Maxillofacial Surgery, Zealand University Hospital, Køge, Denmark
| | - Troels W Kjær
- T&W Engineering A/S, Denmark
- UNEEG medical A/S, Lillerød, Lillerød, Denmark
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Warsito IF, Komosar M, Bernhard MA, Fiedler P, Haueisen J. Flower electrodes for comfortable dry electroencephalography. Sci Rep 2023; 13:16589. [PMID: 37789022 PMCID: PMC10547758 DOI: 10.1038/s41598-023-42732-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Accepted: 09/14/2023] [Indexed: 10/05/2023] Open
Abstract
Dry electroencephalography (EEG) electrodes provide rapid, gel-free, and easy EEG preparation, but with limited wearing comfort. We propose a novel dry electrode comprising multiple tilted pins in a flower-like arrangement. The novel Flower electrode increases wearing comfort and contact area while maintaining ease of use. In a study with 20 volunteers, we compare the performance of a novel 64-channel dry Flower electrode cap to a commercial dry Multipin electrode cap in sitting and supine positions. The wearing comfort of the Flower cap was rated as significantly improved both in sitting and supine positions. The channel reliability and average impedances of both electrode systems were comparable. Averaged VEP components showed no considerable differences in global field power amplitude and latency, as well as in signal-to-noise ratio and topography. No considerable differences were found in the power spectral density of the resting state EEGs between 1 and 40 Hz. Overall, our findings provide evidence for equivalent channel reliability and signal characteristics of the compared cap systems in the sitting and supine positions. The reliability, signal quality, and significantly improved wearing comfort of the Flower electrode allow new fields of applications for dry EEG in long-term monitoring, sensitive populations, and recording in supine position.
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Affiliation(s)
- Indhika Fauzhan Warsito
- Institute of Biomedical Engineering and Informatics at the Technische Universität Ilmenau, Ilmenau, Germany
| | - Milana Komosar
- Institute of Biomedical Engineering and Informatics at the Technische Universität Ilmenau, Ilmenau, Germany
| | - Maria Anne Bernhard
- Institute of Biomedical Engineering and Informatics at the Technische Universität Ilmenau, Ilmenau, Germany
| | - Patrique Fiedler
- Institute of Biomedical Engineering and Informatics at the Technische Universität Ilmenau, Ilmenau, Germany
| | - Jens Haueisen
- Institute of Biomedical Engineering and Informatics at the Technische Universität Ilmenau, Ilmenau, Germany.
- Department of Neurology, Biomagnetic Center, University Hospital Jena, Jena, Germany.
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Jørgensen SD, Kidmose P, Mikkelsen K, Blech M, Hemmsen MC, Rank ML, Kjaer TW. Long-term ear-EEG monitoring of sleep - A case study during shift work. J Sleep Res 2023; 32:e13853. [PMID: 36889935 DOI: 10.1111/jsr.13853] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 01/26/2023] [Accepted: 01/26/2023] [Indexed: 03/10/2023]
Abstract
The interest in sleep as a potential clinical biomarker is growing, but the standard method of sleep assessment, polysomnography, is expensive, time consuming, and requires a lot of expert assistance for both set-up and interpretation. To make sleep analysis more available both in research and in the clinic, there is a need for a reliable wearable device for sleep staging. In this case study, we test ear-electroencephalography. A wearable, where electrodes are placed in the outer ear, as a platform for longitudinal at-home recording of sleep. We explore the usability of the ear-electroencephalography in a shift work case with alternating sleep conditions. We find the ear-electroencephalography platform to be reliable both in terms of showing substantial agreement to polysomnography after long-time use (with an overall agreement, using Cohen's kappa, of 0.72) and by being unobtrusive enough to wear during night shift conditions. We find that fractions of non-rapid eye movement sleep and transition probability between sleep stages show great potential as sleep metrics when exploring quantitative differences in sleep architecture between shifting sleep conditions. This study shows that the ear-electroencephalography platform holds great potential as a reliable wearable for quantifying sleep "in the wild", pushing this technology further towards clinical adaptation.
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Affiliation(s)
| | - Preben Kidmose
- Department of Electrical and Computer Engineering, Aarhus University, Aarhus, Denmark
| | - Kaare Mikkelsen
- Department of Electrical and Computer Engineering, Aarhus University, Aarhus, Denmark
| | | | | | | | - Troels Wesenberg Kjaer
- Department of Neurology, Zealand University Hospital, Roskilde, Denmark
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
- Department of Neuroscience, University of Copenhagen, Copenhagen, Denmark
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Hammour G, Atzori G, Monica CD, Ravindran KKG, Revell V, Dijk DJ, Mandic DP. Hearables: Automatic Sleep Scoring from Single-Channel Ear-EEG in Older Adults. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38083340 DOI: 10.1109/embc40787.2023.10340253] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Sleep disorders are a prevalent problem among older adults, yet obtaining an accurate and reliable assessment of sleep quality can be challenging. Traditional polysomnography (PSG) is the gold standard for sleep staging, but is obtrusive, expensive, and requires expert assistance. To this end, we propose a minimally invasive single-channel single ear-EEG automatic sleep staging method for older adults. The method employs features from the frequency, time, and structural complexity domains, which provide a robust classification of sleep stages from a standardised viscoelastic earpiece. Our method is verified on a dataset of older adults and achieves a kappa value of at least 0.61, indicating substantial agreement. This paves the way for a non-invasive, cost-effective, and portable alternative to traditional PSG for sleep staging.
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Supratak A, Haddawy P. Quantifying the impact of data characteristics on the transferability of sleep stage scoring models. Artif Intell Med 2023; 139:102540. [PMID: 37100508 DOI: 10.1016/j.artmed.2023.102540] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Revised: 03/18/2023] [Accepted: 03/27/2023] [Indexed: 04/03/2023]
Abstract
Deep learning models for scoring sleep stages based on single-channel EEG have been proposed as a promising method for remote sleep monitoring. However, applying these models to new datasets, particularly from wearable devices, raises two questions. First, when annotations on a target dataset are unavailable, which different data characteristics affect the sleep stage scoring performance the most and by how much? Second, when annotations are available, which dataset should be used as the source of transfer learning to optimize performance? In this paper, we propose a novel method for computationally quantifying the impact of different data characteristics on the transferability of deep learning models. Quantification is accomplished by training and evaluating two models with significant architectural differences, TinySleepNet and U-Time, under various transfer configurations in which the source and target datasets have different recording channels, recording environments, and subject conditions. For the first question, the environment had the highest impact on sleep stage scoring performance, with performance degrading by over 14% when sleep annotations were unavailable. For the second question, the most useful transfer sources for TinySleepNet and the U-Time models were MASS-SS1 and ISRUC-SG1, containing a high percentage of N1 (the rarest sleep stage) relative to the others. The frontal and central EEGs were preferred for TinySleepNet. The proposed approach enables full utilization of existing sleep datasets for training and planning model transfer to maximize the sleep stage scoring performance on a target problem when sleep annotations are limited or unavailable, supporting the realization of remote sleep monitoring.
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Tabar YR, Mikkelsen KB, Shenton N, Kappel SL, Bertelsen AR, Nikbakht R, Toft HO, Henriksen CH, Hemmsen MC, Rank ML, Otto M, Kidmose P. At-home sleep monitoring using generic ear-EEG. Front Neurosci 2023; 17:987578. [PMID: 36816118 PMCID: PMC9928964 DOI: 10.3389/fnins.2023.987578] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Accepted: 01/09/2023] [Indexed: 02/04/2023] Open
Abstract
Introduction A device comprising two generic earpieces with embedded dry electrodes for ear-centered electroencephalography (ear-EEG) was developed. The objective was to provide ear-EEG based sleep monitoring to a wide range of the population without tailoring the device to the individual. Methods To validate the device ten healthy subjects were recruited for a 12-night sleep study. The study was divided into two parts; part A comprised two nights with both ear-EEG and polysomnography (PSG), and part B comprised 10 nights using only ear-EEG. In addition to the electrophysiological measurements, subjects filled out a questionnaire after each night of sleep. Results The subjects reported that the ear-EEG system was easy to use, and that the comfort was better in part B. The performance of the system was validated by comparing automatic sleep scoring based on ear-EEG with PSG-based sleep scoring performed by a professional trained sleep scorer. Cohen's kappa was used to assess the agreement between the manual and automatic sleep scorings, and the study showed an average kappa value of 0.71. The majority of the 20 recordings from part A yielded a kappa value above 0.7. The study was compared to a companioned study conducted with individualized earpieces. To compare the sleep across the two studies and two parts, 7 different sleeps metrics were calculated based on the automatic sleep scorings. The ear-EEG nights were validated through linear mixed model analysis in which the effects of equipment (individualized vs. generic earpieces), part (PSG and ear-EEG vs. only ear-EEG) and subject were investigated. We found that the subject effect was significant for all computed sleep metrics. Furthermore, the equipment did not show any statistical significant effect on any of the sleep metrics. Discussion These results corroborate that generic ear-EEG is a promising alternative to the gold standard PSG for sleep stage monitoring. This will allow sleep stage monitoring to be performed in a less obtrusive way and over longer periods of time, thereby enabling diagnosis and treatment of diseases with associated sleep disorders.
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Affiliation(s)
- Yousef R. Tabar
- Department of Electrical and Computer Engineering, Aarhus University, Aarhus, Denmark
| | - Kaare B. Mikkelsen
- Department of Electrical and Computer Engineering, Aarhus University, Aarhus, Denmark
| | | | - Simon L. Kappel
- Department of Electrical and Computer Engineering, Aarhus University, Aarhus, Denmark
| | | | | | | | | | | | | | - Marit Otto
- Department of Clinical Neurophysiology, Aarhus University Hospital, Aarhus, Denmark
| | - Preben Kidmose
- Department of Electrical and Computer Engineering, Aarhus University, Aarhus, Denmark,*Correspondence: Preben Kidmose,
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Sleep Spindle Characteristics and Relationship with Memory Ability in Patients with Obstructive Sleep Apnea-Hypopnea Syndrome. J Clin Med 2023; 12:jcm12020634. [PMID: 36675563 PMCID: PMC9864739 DOI: 10.3390/jcm12020634] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Revised: 12/23/2022] [Accepted: 01/04/2023] [Indexed: 01/15/2023] Open
Abstract
Obstructive sleep apnea syndrome (OSAS) causes intermittent hypoxia and sleep disruption in the brain, resulting in cognitive dysfunction, but its pathogenesis is unclear. The sleep spindle wave is a transient neural event involved in sleep memory consolidation and synaptic plasticity. This study aimed to investigate the characteristics of sleep spindle activity and its relationship with memory ability in patients with OSAS. A total of 119 patients, who were divided into the OSAS group (n = 59, AHI ≥ 15) and control group (n = 60, AHI < 15) according to the Apnea Hypopnea Index (AHI), were enrolled and underwent polysomnography. Power spectral density (PSD) and omega complexity were used to analyze the characteristics of single and different brain regions of sleep spindles. Memory-related cognitive functions were assessed in all subjects, including logical memory, digit ordering, pattern recognition, spatial recognition and spatial working memory. The spindle PSD of the OSAS group was significantly slower than the control group, regardless of the slow, fast, or total spindle. The complexity of the spindles in the prefrontal and central region decreased significantly, whereas it increased in the occipital region. Sleep spindle PSD was positively correlated with logical memory and working memory. Spindle complexity was positively correlated with immediate logical and visual memory in the prefrontal region and positively correlated with immediate/delayed logical and working memory in the central region. In contrast, spindle complexity in the occipital region negatively correlated with delayed logical memory. Spindle hyperconnectivity in the prefrontal and central regions underlies declines in logical, visual and working memory and weak connections in the occipital spindles underlie the decline in delayed logical memory.
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da Silva EC, Carneiro JR, de Almeida Fonseca Viola PC, Confortin SC, da Silva AAM. Association of Food Intake with Sleep Durations in Adolescents from a Capital City in Northeastern Brazil. Nutrients 2022; 14:nu14235180. [PMID: 36501210 PMCID: PMC9735429 DOI: 10.3390/nu14235180] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Revised: 11/26/2022] [Accepted: 11/27/2022] [Indexed: 12/12/2022] Open
Abstract
(1) Background: During adolescence, there are significant changes in food consumption, such as reducing the consumption of in natura or minimally processed foods and increasing the consumption of ultra-processed foods. Thus, eating habits can influence sleep duration and, consequently, affect the quality of life of young people. This study thus aims to estimate the association of consumption of in natura or minimally processed, processed, and ultra-processed foods with sleep durations in adolescents. (2) Methods: This is a cross-sectional study including 964 adolescents (18 to 19 years old) from the 1997 to 1998 birth cohort in São Luís, Maranhão. Food consumption was assessed using the food frequency questionnaire (FFQ) and stratified based on the NOVA classification. Sleep duration was verified using accelerometry in hours. The analysis of the association between the consumption of in natura or minimally processed, processedand ultra-processed foods with sleep durations in adolescents used crude and adjusted linear regression (by gender, age, skin color, education, economic class, work, consumption of alcohol, smoking, screen time, physical activity, use of illicit drugs, anxiety, depressive symptoms, and lean and fat mass). A directed acyclic graph (DAG) was used to determine the minimum set of adjustment factors. (3) Results: Of the 964 individuals evaluated, 52.0% were female. The mean sleep duration was 6 h (± 0.95). In the crude and adjusted analyses, no association was observed between food consumption according to the degree of processing and adolescent sleep durations. (4) Conclusion: There was no association between the consumption of in natura or minimally processed, processed, and ultra-processed foods with sleep durations.
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Affiliation(s)
- Emanuellen Coelho da Silva
- Department of Public Health, School of Nutrition, Federal University of Maranhão, São Luís 65020-905, MA, Brazil
- Correspondence: ; Tel.: +55-983-272-9670
| | - Juliana Ramos Carneiro
- Department of Public Health, School of Medicine, Federal University of Maranhão, São Luís 65020-905, MA, Brazil
| | - Poliana Cristina de Almeida Fonseca Viola
- Nutrition Department, Nutrition Teacher at the Health Sciences Center, Federal University of Piauí, Teresina 64049-550, MA, Brazil
- Postgraduation Program in Collective Health, Department of Public Health, Federal University of Maranhão, São Luís 65020-905, MA, Brazil
| | - Susana Cararo Confortin
- Postgraduation Program in Collective Health, Department of Public Health, Federal University of Maranhão, São Luís 65020-905, MA, Brazil
| | - Antônio Augusto Moura da Silva
- Postgraduation Program in Collective Health, Department of Public Health, Federal University of Maranhão, São Luís 65020-905, MA, Brazil
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A systematic review of the validity of non-invasive sleep-measuring devices in mid-to-late life adults: Future utility for Alzheimer's disease research. Sleep Med Rev 2022; 65:101665. [DOI: 10.1016/j.smrv.2022.101665] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Revised: 06/21/2022] [Accepted: 06/23/2022] [Indexed: 11/24/2022]
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Henao D, Navarrete M, Juez JY, Dinh H, Gómez R, Valderrama M, Le Van Quyen M. Auditory closed‐loop stimulation on sleep slow oscillations using in‐ear EEG sensors. J Sleep Res 2022; 31:e13555. [DOI: 10.1111/jsr.13555] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2021] [Revised: 01/14/2022] [Accepted: 01/17/2022] [Indexed: 11/30/2022]
Affiliation(s)
- David Henao
- Department of Biomedical Engineering Universidad de Los Andes Bogotá D.C. Colombia
| | - Miguel Navarrete
- Department of Biomedical Engineering Universidad de Los Andes Bogotá D.C. Colombia
- Cardiff University Brain Research Imaging Centre (CUBRIC) School of Psychology Cardiff University Cardiff UK
| | - José Yesith Juez
- Department of Biomedical Engineering Universidad de Los Andes Bogotá D.C. Colombia
| | | | - Rodrigo Gómez
- Department of Biomedical Engineering Universidad de Los Andes Bogotá D.C. Colombia
| | - Mario Valderrama
- Department of Biomedical Engineering Universidad de Los Andes Bogotá D.C. Colombia
| | - Michel Le Van Quyen
- Laboratoire d’Imagerie Biomédicale (LIB) Inserm U1146/Sorbonne Université UMCR2/UMR7371 CNRS Paris France
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Ruhnau P, Zaehle T. Transcranial Auricular Vagus Nerve Stimulation (taVNS) and Ear-EEG: Potential for Closed-Loop Portable Non-invasive Brain Stimulation. Front Hum Neurosci 2021; 15:699473. [PMID: 34194308 PMCID: PMC8236702 DOI: 10.3389/fnhum.2021.699473] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Accepted: 05/21/2021] [Indexed: 11/17/2022] Open
Abstract
No matter how hard we concentrate, our attention fluctuates – a fact that greatly affects our success in completing a current task. Here, we review work from two methods that, in a closed-loop manner, have the potential to ameliorate these fluctuations. Ear-EEG can measure electric brain activity from areas in or around the ear, using small and thus portable hardware. It has been shown to capture the state of attention with high temporal resolution. Transcutaneous auricular vagus nerve stimulation (taVNS) comes with the same advantages (small and light) and critically current research suggests that it is possible to influence ongoing brain activity that has been linked to attention. Following the review of current work on ear-EEG and taVNS we suggest that a combination of the two methods in a closed-loop system could serve as a potential application to modulate attention.
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Affiliation(s)
- Philipp Ruhnau
- Department of Neurology, Otto von Guericke University, Magdeburg, Germany.,Center for Behavioral Brain Sciences, Otto von Guericke University, Magdeburg, Germany
| | - Tino Zaehle
- Department of Neurology, Otto von Guericke University, Magdeburg, Germany.,Center for Behavioral Brain Sciences, Otto von Guericke University, Magdeburg, Germany
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