1
|
Yang X, Hu JH, Fan LP, Peng HP, Shi HJ, Zhuang MY, Ji FH, Peng K. Intraoperative dexmedetomidine on postoperative sleep disturbance in older patients undergoing major abdominal surgery: A randomized controlled trial protocol. Heliyon 2024; 10:e31668. [PMID: 38845907 PMCID: PMC11153091 DOI: 10.1016/j.heliyon.2024.e31668] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2024] [Revised: 05/09/2024] [Accepted: 05/20/2024] [Indexed: 06/09/2024] Open
Abstract
Background Postoperative sleep disturbance (PSD) occurs frequently in patients who undergo major abdominal surgical procedures. Dexmedetomidine is a promising agent to improve the quality of sleep for surgical patients. We designed this trial to investigate the effects of two different doses of intraoperative dexmedetomidine on the occurrence of PSD in elderly patients who have major abdominal surgery. Methods In this randomized, double-blind, controlled trial, 210 elderly patients aged ≥65 years will be randomized, with an allocation ratio of 1:1:1, to two dexmedetomidine groups (intraoperative infusion of 0.3 or 0.6 μg/kg/h) and a normal saline placebo group. The primary endpoint is the occurrence of PSD on the first night after surgery, assessed using the Athens Insomnia Scale. The secondary endpoints are (1) the incidence of PSD during the 2nd, 3rd, 5th, 7th, and 30th nights postoperatively; (2) pain at rest and on movement at 24 and 48 h postoperatively, assessed using the Numerical Rating Scale; (3) the incidence of postoperative delirium during 0-7 days postoperatively or until hospital discharge, assessed using the 3-min Confusion Assessment Method; (4) depressive symptoms during 0-7 days postoperatively or until hospital discharge, assessed using the 15-items Geriatric Depression Scale; and (5) quality of recovery on postoperative days 1, 2, and 3, assessed using the 15-items Quality of Recovery Scale. Patients' sleep data will also be collected by Xiaomi Mi Band 7 for further analysis. Discussion The findings of this trial will provide clinical evidence for improving the quality of sleep among elderly patients undergoing major abdominal surgery. Ethics and dissemination This trial was approved by the Ethics Committee of the First Affiliated Hospital of Soochow University (No. 2023-160). The results will be published in a peer-reviewed journal. Trial registration Chinese Clinical Trial Registry (ChiCTR2300073163).
Collapse
Affiliation(s)
- Xiu Yang
- Department of Anesthesiology, First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China
- Institute of Anesthesiology, Soochow University, Suzhou, Jiangsu, China
| | - Jing-hui Hu
- Department of Anesthesiology, First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China
- Institute of Anesthesiology, Soochow University, Suzhou, Jiangsu, China
| | - Li-ping Fan
- Jintan Traditional Chinese Medicine Hospital, Changzhou, Jiangsu, China
| | - Hui-ping Peng
- Department of Anesthesiology, First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China
- Institute of Anesthesiology, Soochow University, Suzhou, Jiangsu, China
| | - Hai-jing Shi
- Department of Anesthesiology, First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China
- Institute of Anesthesiology, Soochow University, Suzhou, Jiangsu, China
| | - Min-yuan Zhuang
- Department of Anesthesiology, First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China
- Institute of Anesthesiology, Soochow University, Suzhou, Jiangsu, China
| | - Fu-hai Ji
- Department of Anesthesiology, First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China
- Institute of Anesthesiology, Soochow University, Suzhou, Jiangsu, China
| | - Ke Peng
- Department of Anesthesiology, First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China
- Institute of Anesthesiology, Soochow University, Suzhou, Jiangsu, China
| |
Collapse
|
2
|
Garbarino S, Bragazzi NL. Revolutionizing Sleep Health: The Emergence and Impact of Personalized Sleep Medicine. J Pers Med 2024; 14:598. [PMID: 38929819 PMCID: PMC11204813 DOI: 10.3390/jpm14060598] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2024] [Revised: 05/11/2024] [Accepted: 06/03/2024] [Indexed: 06/28/2024] Open
Abstract
Personalized sleep medicine represents a transformative shift in healthcare, emphasizing individualized approaches to optimizing sleep health, considering the bidirectional relationship between sleep and health. This field moves beyond conventional methods, tailoring care to the unique physiological and psychological needs of individuals to improve sleep quality and manage disorders. Key to this approach is the consideration of diverse factors like genetic predispositions, lifestyle habits, environmental factors, and underlying health conditions. This enables more accurate diagnoses, targeted treatments, and proactive management. Technological advancements play a pivotal role in this field: wearable devices, mobile health applications, and advanced diagnostic tools collect detailed sleep data for continuous monitoring and analysis. The integration of machine learning and artificial intelligence enhances data interpretation, offering personalized treatment plans based on individual sleep profiles. Moreover, research on circadian rhythms and sleep physiology is advancing our understanding of sleep's impact on overall health. The next generation of wearable technology will integrate more seamlessly with IoT and smart home systems, facilitating holistic sleep environment management. Telemedicine and virtual healthcare platforms will increase accessibility to specialized care, especially in remote areas. Advancements will also focus on integrating various data sources for comprehensive assessments and treatments. Genomic and molecular research could lead to breakthroughs in understanding individual sleep disorders, informing highly personalized treatment plans. Sophisticated methods for sleep stage estimation, including machine learning techniques, are improving diagnostic precision. Computational models, particularly for conditions like obstructive sleep apnea, are enabling patient-specific treatment strategies. The future of personalized sleep medicine will likely involve cross-disciplinary collaborations, integrating cognitive behavioral therapy and mental health interventions. Public awareness and education about personalized sleep approaches, alongside updated regulatory frameworks for data security and privacy, are essential. Longitudinal studies will provide insights into evolving sleep patterns, further refining treatment approaches. In conclusion, personalized sleep medicine is revolutionizing sleep disorder treatment, leveraging individual characteristics and advanced technologies for improved diagnosis, treatment, and management. This shift towards individualized care marks a significant advancement in healthcare, enhancing life quality for those with sleep disorders.
Collapse
Affiliation(s)
- Sergio Garbarino
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics and Maternal/Child Sciences (DINOGMI), University of Genoa, 16126 Genoa, Italy;
- Post-Graduate School of Occupational Health, Università Cattolica del Sacro Cuore, 00168 Rome, Italy
| | - Nicola Luigi Bragazzi
- Laboratory for Industrial and Applied Mathematics (LIAM), Department of Mathematics and Statistics, York University, Toronto, ON M3J 1P3, Canada
- Human Nutrition Unit (HNU), Department of Food and Drugs, University of Parma, 43125 Parma, Italy
| |
Collapse
|
3
|
Jones AM, Itti L, Sheth BR. Expert-level sleep staging using an electrocardiography-only feed-forward neural network. Comput Biol Med 2024; 176:108545. [PMID: 38749325 DOI: 10.1016/j.compbiomed.2024.108545] [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: 11/13/2023] [Revised: 04/05/2024] [Accepted: 04/28/2024] [Indexed: 05/31/2024]
Abstract
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.
Collapse
Affiliation(s)
- Adam M Jones
- Neuroscience Graduate Program, University of Southern California, Los Angeles, CA, USA.
| | - Laurent Itti
- Neuroscience Graduate Program, University of Southern California, Los Angeles, CA, USA
| | - Bhavin R Sheth
- Department of Electrical & Computer Engineering, University of Houston, Houston, TX, USA; Center for NeuroEngineering and Cognitive Systems, University of Houston, Houston, TX, USA
| |
Collapse
|
4
|
Li Y, Xu Z, Chen Z, Zhang Y, Zhang B. Insights from the 2nd China intelligent sleep staging competition. Sleep Breath 2024:10.1007/s11325-024-03055-8. [PMID: 38730204 DOI: 10.1007/s11325-024-03055-8] [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: 12/02/2023] [Revised: 04/25/2024] [Accepted: 05/08/2024] [Indexed: 05/12/2024]
Abstract
STUDY OBJECTIVES Artificial intelligence (AI) is quickly advancing in the field of sleep medicine, which bodes well for the potential of actual clinical use. In this study, an analysis of the 2nd China Intelligent Sleep Staging Competition was conducted to gain insights into the general level and constraints of AI-assisted sleep staging in China. METHODS The outcomes of 10 teams from the children's track and 13 teams from the adult track were investigated in this study. The analysis included overall performance, differences between five different sleep stages, variations across subjects, and performance during stage transitions. RESULTS The adult track's accuracy peaked at 80.46%, while the children's track's accuracy peaked at 88.96%. On average, accuracy rates stood at 71.43% for children and 68.40% for adults. All results were produced within a mere 5-min timeframe. The N1 stage was prone to misclassification as W, N2, and R stages. In the adult track, significant differences were apparent among subjects (p < 0.05), whereas in the children's track, such differences were not observed. Nonetheless, both tracks experienced a performance decline during stage transitions. CONCLUSIONS The computational speed of AI is remarkably fast, simultaneously holding the potential to surpass the accuracy of physicians. Improving the machine learning model's classification of the N1 stage and transitional periods between stages, along with bolstering its robustness to individual subject variations, is imperative for maximizing its ability in assisting clinical scoring.
Collapse
Affiliation(s)
- Yamei Li
- College of Electronic and Information Engineering, Southwest University, Chongqing, 400715, China
| | - Zhifei Xu
- Department of Respiratory Medicine, Beijing Children's Hospital, Capital Medical University, Beijing, 100045, China
| | - Zhiqiang Chen
- College of Electronic and Information Engineering, Southwest University, Chongqing, 400715, China
| | - Yuan Zhang
- College of Electronic and Information Engineering, Southwest University, Chongqing, 400715, China.
| | - Bin Zhang
- Department of Psychiatry, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China
| |
Collapse
|
5
|
Xing M, Zhang L, Li J, Li Z, Yu Q, Li W. Development and validation of a novel sleep health score in the sleep heart health study. Eur J Intern Med 2024:S0953-6205(24)00189-4. [PMID: 38729786 DOI: 10.1016/j.ejim.2024.05.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/11/2023] [Revised: 04/14/2024] [Accepted: 05/03/2024] [Indexed: 05/12/2024]
Abstract
BACKGROUND There is a lack of consensus in evaluating multidimensional sleep health, especially concerning its implication for mortality. A validated multidimensional sleep health score is the foundation of effective interventions. METHODS We obtained data from 5706 participants in the Sleep Heart Health Study. First, random forest-recursive feature elimination algorithm was used to select potential predictive variables. Second, a sleep composite score was developed based on the regression coefficients from a Cox proportional hazards model evaluating the associations between selected sleep-related variables and mortality. Last, we validated the score by constructing Cox proportional hazards models to assess its association with mortality. RESULTS The mean age of participants was 63.2 years old, and 47.6% (2715/5706) were male. Six sleep variables, including average oxygen saturation (%), spindle density (C3), sleep efficiency (%), spindle density (C4), percentage of fast spindles (%) and percentage of rapid eye movement (%) were selected to construct this multidimensional sleep health score. The average sleep composite score in participants was 6.8 of 22 (lower is better). Participants with a one-point increase in sleep composite score had an 10% higher risk of death (hazard ratio = 1.10, 95% confidence interval: 1.08-1.12). CONCLUSIONS This study constructed and validated a novel multidimensional sleep health score to better predict death based on sleep, with significant associations between sleep composite score and all-cause mortality. Integrating questionnaire information and sleep microstructures, our sleep composite score is more appropriately applied for mortality risk stratification.
Collapse
Affiliation(s)
- Muqi Xing
- Department of Big Data in Health Science, School of Public Health, and Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Lingzhi Zhang
- Department of Big Data in Health Science, School of Public Health, and Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Jiahui Li
- Department of Big Data in Health Science, School of Public Health, and Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Zihan Li
- Department of Big Data in Health Science, School of Public Health, and Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Qi Yu
- Department of Big Data in Health Science, School of Public Health, and Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Wenyuan Li
- Department of Big Data in Health Science, School of Public Health, and Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
| |
Collapse
|
6
|
Ogg M, Coon WG. Self-Supervised Transformer Model Training for a Sleep-EEG Foundation Model. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.18.576245. [PMID: 38293234 PMCID: PMC10827180 DOI: 10.1101/2024.01.18.576245] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2024]
Abstract
The American Academy of Sleep Medicine (AASM) recognizes five sleep/wake states (Wake, N1, N2, N3, REM), yet this classification schema provides only a high-level summary of sleep and likely overlooks important neurological or health information. New, data-driven approaches are needed to more deeply probe the information content of sleep signals. Here we present a self-supervised approach that learns the structure embedded in large quantities of neurophysiological sleep data. This masked transformer training procedure is inspired by high performing self-supervised methods developed for speech transcription. We show that self-supervised pre-training matches or outperforms supervised sleep stage classification, especially when labeled data or compute-power is limited. Perhaps more importantly, we also show that our pretrained model is flexible and can be fine-tuned to perform well on new tasks including distinguishing individuals and quantifying "brain age" (a potential health biomarker). This suggests that modern methods can automatically learn information that is potentially overlooked by the 5-class sleep staging schema, laying the groundwork for new schemas and further data-driven exploration of sleep.
Collapse
|
7
|
Kervezee L, Dashti HS, Pilz LK, Skarke C, Ruben MD. Using routinely collected clinical data for circadian medicine: A review of opportunities and challenges. PLOS DIGITAL HEALTH 2024; 3:e0000511. [PMID: 38781189 PMCID: PMC11115276 DOI: 10.1371/journal.pdig.0000511] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/25/2024]
Abstract
A wealth of data is available from electronic health records (EHR) that are collected as part of routine clinical care in hospitals worldwide. These rich, longitudinal data offer an attractive object of study for the field of circadian medicine, which aims to translate knowledge of circadian rhythms to improve patient health. This narrative review aims to discuss opportunities for EHR in studies of circadian medicine, highlight the methodological challenges, and provide recommendations for using these data to advance the field. In the existing literature, we find that data collected in real-world clinical settings have the potential to shed light on key questions in circadian medicine, including how 24-hour rhythms in clinical features are associated with-or even predictive of-health outcomes, whether the effect of medication or other clinical activities depend on time of day, and how circadian rhythms in physiology may influence clinical reference ranges or sampling protocols. However, optimal use of EHR to advance circadian medicine requires careful consideration of the limitations and sources of bias that are inherent to these data sources. In particular, time of day influences almost every interaction between a patient and the healthcare system, creating operational 24-hour patterns in the data that have little or nothing to do with biology. Addressing these challenges could help to expand the evidence base for the use of EHR in the field of circadian medicine.
Collapse
Affiliation(s)
- Laura Kervezee
- Group of Circadian Medicine, Department of Cell and Chemical Biology, Leiden University Medical Center, Leiden, the Netherlands
| | - Hassan S. Dashti
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, United States of America
| | - Luísa K. Pilz
- Department of Anesthesiology and Intensive Care Medicine CCM / CVK, Charité–Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Berlin, Germany
- ECRC Experimental and Clinical Research Center, Charité–Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Berlin, Germany
| | - Carsten Skarke
- Institute for Translational Medicine and Therapeutics (ITMAT), University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, United States of America
- Chronobiology and Sleep Institute (CSI), University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, United States of America
- Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, United States of America
| | - Marc D. Ruben
- Divisions of Pulmonary and Sleep Medicine and Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio, United States of America
| |
Collapse
|
8
|
Garbarino S, Bragazzi NL. Evaluating the effectiveness of artificial intelligence-based tools in detecting and understanding sleep health misinformation: Comparative analysis using Google Bard and OpenAI ChatGPT-4. J Sleep Res 2024:e14210. [PMID: 38577714 DOI: 10.1111/jsr.14210] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2023] [Revised: 03/26/2024] [Accepted: 03/28/2024] [Indexed: 04/06/2024]
Abstract
This study evaluates the performance of two major artificial intelligence-based tools (ChatGPT-4 and Google Bard) in debunking sleep-related myths. More in detail, the present research assessed 20 sleep misconceptions using a 5-point Likert scale for falseness and public health significance, comparing responses of artificial intelligence tools with expert opinions. The results indicated that Google Bard correctly identified 19 out of 20 statements as false (95.0% accuracy), not differing from ChatGPT-4 (85.0% accuracy, Fisher's exact test p = 0.615). Google Bard's ratings of the falseness of the sleep misconceptions averaged 4.25 ± 0.70, showing a moderately negative skewness (-0.42) and kurtosis (-0.83), and suggesting a distribution with fewer extreme values compared with ChatGPT-4. In assessing public health significance, Google Bard's mean score was 2.4 ± 0.80, with skewness and kurtosis of 0.36 and -0.07, respectively, indicating a more normal distribution compared with ChatGPT-4. The inter-rater agreement between Google Bard and sleep experts had an intra-class correlation coefficient of 0.58 for falseness and 0.69 for public health significance, showing moderate alignment (p = 0.065 and p = 0.014, respectively). Text-mining analysis revealed Google Bard's focus on practical advice, while ChatGPT-4 concentrated on theoretical aspects of sleep. The readability analysis suggested Google Bard's responses were more accessible, aligning with 8th-grade level material, versus ChatGPT-4's 12th-grade level complexity. The study demonstrates the potential of artificial intelligence in public health education, especially in sleep health, and underscores the importance of accurate, reliable artificial intelligence-generated information, calling for further collaboration between artificial intelligence developers, sleep health professionals and educators to enhance the effectiveness of sleep health promotion.
Collapse
Affiliation(s)
- Sergio Garbarino
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics and Maternal, Child Sciences (DINOGMI), University of Genoa, Genoa, Italy
- Post-Graduate School of Occupational Health, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Nicola Luigi Bragazzi
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics and Maternal, Child Sciences (DINOGMI), University of Genoa, Genoa, Italy
- Laboratory for Industrial and Applied Mathematics (LIAM), Department of Mathematics and Statistics, York University, Toronto, Ontario, Canada
- Human Nutrition Unit (HNU), Department of Food and Drugs, University of Parma, Parma, Italy
| |
Collapse
|
9
|
Zhai B, Elder GJ, Godfrey A. Challenges and opportunities of deep learning for wearable-based objective sleep assessment. NPJ Digit Med 2024; 7:85. [PMID: 38575794 PMCID: PMC10995158 DOI: 10.1038/s41746-024-01086-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2024] [Accepted: 03/22/2024] [Indexed: 04/06/2024] Open
Affiliation(s)
- Bing Zhai
- Department of Computer and Information Sciences, Northumbria University, Newcastle, UK
| | - Greg J Elder
- Northumbria Sleep Research, Department of Psychology, Northumbria University, Newcastle upon Tyne, UK
| | - Alan Godfrey
- Department of Computer and Information Sciences, Northumbria University, Newcastle, UK.
| |
Collapse
|
10
|
Yue H, Chen Z, Guo W, Sun L, Dai Y, Wang Y, Ma W, Fan X, Wen W, Lei W. Research and application of deep learning-based sleep staging: Data, modeling, validation, and clinical practice. Sleep Med Rev 2024; 74:101897. [PMID: 38306788 DOI: 10.1016/j.smrv.2024.101897] [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: 10/02/2023] [Revised: 12/30/2023] [Accepted: 01/04/2024] [Indexed: 02/04/2024]
Abstract
Over the past few decades, researchers have attempted to simplify and accelerate the process of sleep stage classification through various approaches; however, only a few such approaches have gained widespread acceptance. Artificial intelligence technology, particularly deep learning, is promising for earning the trust of the sleep medicine community in automated sleep-staging systems, thus facilitating its application in clinical practice and integration into daily life. We aimed to comprehensively review the latest methods that are applying deep learning for enhancing sleep staging efficiency and accuracy. Starting from the requisite "data" for constructing deep learning algorithms, we elucidated the current landscape of this domain and summarized the fundamental modeling process, encompassing signal selection, data pre-processing, model architecture, classification tasks, and performance metrics. Furthermore, we reviewed the applications of automated sleep staging in scenarios such as sleep-disorder screening, diagnostic procedures, and health monitoring and management. Finally, we conducted an in-depth analysis and discussion of the challenges and future in intelligent sleep staging, particularly focusing on large-scale sleep datasets, interdisciplinary collaborations, and human-computer interactions.
Collapse
Affiliation(s)
- Huijun Yue
- Otorhinolaryngology Hospital, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, People's Republic of China
| | - Zhuqi Chen
- Otorhinolaryngology Hospital, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, People's Republic of China
| | - Wenbin Guo
- Otorhinolaryngology Hospital, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, People's Republic of China
| | - Lin Sun
- Otorhinolaryngology Hospital, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, People's Republic of China
| | - Yidan Dai
- School of Computer Science, South China Normal University, Guangzhou, People's Republic of China
| | - Yiming Wang
- Otorhinolaryngology Hospital, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, People's Republic of China
| | - Wenjun Ma
- School of Computer Science, South China Normal University, Guangzhou, People's Republic of China
| | - Xiaomao Fan
- College of Big Data and Internet, Shenzhen Technology University, Shenzhen, People's Republic of China
| | - Weiping Wen
- Otorhinolaryngology Hospital, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, People's Republic of China; Department of Otolaryngology, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, People's Republic of China.
| | - Wenbin Lei
- Otorhinolaryngology Hospital, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, People's Republic of China.
| |
Collapse
|
11
|
Meyer N, Lok R, Schmidt C, Kyle SD, McClung CA, Cajochen C, Scheer FAJL, Jones MW, Chellappa SL. The sleep-circadian interface: A window into mental disorders. Proc Natl Acad Sci U S A 2024; 121:e2214756121. [PMID: 38394243 PMCID: PMC10907245 DOI: 10.1073/pnas.2214756121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/25/2024] Open
Abstract
Sleep, circadian rhythms, and mental health are reciprocally interlinked. Disruption to the quality, continuity, and timing of sleep can precipitate or exacerbate psychiatric symptoms in susceptible individuals, while treatments that target sleep-circadian disturbances can alleviate psychopathology. Conversely, psychiatric symptoms can reciprocally exacerbate poor sleep and disrupt clock-controlled processes. Despite progress in elucidating underlying mechanisms, a cohesive approach that integrates the dynamic interactions between psychiatric disorder with both sleep and circadian processes is lacking. This review synthesizes recent evidence for sleep-circadian dysfunction as a transdiagnostic contributor to a range of psychiatric disorders, with an emphasis on biological mechanisms. We highlight observations from adolescent and young adults, who are at greatest risk of developing mental disorders, and for whom early detection and intervention promise the greatest benefit. In particular, we aim to a) integrate sleep and circadian factors implicated in the pathophysiology and treatment of mood, anxiety, and psychosis spectrum disorders, with a transdiagnostic perspective; b) highlight the need to reframe existing knowledge and adopt an integrated approach which recognizes the interaction between sleep and circadian factors; and c) identify important gaps and opportunities for further research.
Collapse
Affiliation(s)
- Nicholas Meyer
- Insomnia and Behavioural Sleep Medicine Clinic, University College London Hospitals NHS Foundation Trust, LondonWC1N 3HR, United Kingdom
- Department of Psychosis Studies, Institute of Psychology, Psychiatry, and Neuroscience, King’s College London, LondonSE5 8AF, United Kingdom
| | - Renske Lok
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA94305
| | - Christina Schmidt
- Sleep & Chronobiology Group, GIGA-Institute, CRC-In Vivo Imaging Unit, University of Liège, Liège, Belgium
- Psychology and Neuroscience of Cognition Research Unit, Faculty of Psychology, Speech and Language, University of Liège, Liège4000, Belgium
| | - Simon D. Kyle
- Sir Jules Thorn Sleep and Circadian Neuroscience Institute, Nuffield Department of Clinical Neurosciences, University of Oxford, OxfordOX1 3QU, United Kingdom
| | - Colleen A. McClung
- Translational Neuroscience Program, Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA15219
| | - Christian Cajochen
- Centre for Chronobiology, Department for Adult Psychiatry, Psychiatric Hospital of the University of Basel, BaselCH-4002, Switzerland
- Research Cluster Molecular and Cognitive Neurosciences, Department of Biomedicine, University of Basel, BaselCH-4055, Switzerland
| | - Frank A. J. L. Scheer
- Medical Chronobiology Program, Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women’s Hospital, Boston, MA02115
- Medical Chronobiology Program, Division of Sleep and Circadian Disorders, Department of Neurology, Brigham and Women’s Hospital, Boston, MA02115
- Division of Sleep Medicine, Harvard Medical School, Boston, MA02115
| | - Matthew W. Jones
- School of Physiology, Pharmacology and Neuroscience, Faculty of Health and Life Sciences, University of Bristol, BristolBS8 1TD, United Kingdom
| | - Sarah L. Chellappa
- School of Psychology, Faculty of Environmental and Life Sciences, University of Southampton, SouthamptonSO17 1BJ, United Kingdom
| |
Collapse
|
12
|
Çakar T, Son-Turan S, Girişken Y, Sayar A, Ertuğrul S, Filiz G, Tuna E. Unlocking the neural mechanisms of consumer loan evaluations: an fNIRS and ML-based consumer neuroscience study. Front Hum Neurosci 2024; 18:1286918. [PMID: 38375365 PMCID: PMC10875049 DOI: 10.3389/fnhum.2024.1286918] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Accepted: 01/11/2024] [Indexed: 02/21/2024] Open
Abstract
Introduction This study conducts a comprehensive exploration of the neurocognitive processes underlying consumer credit decision-making using cutting-edge techniques from neuroscience and machine learning (ML). Employing functional Near-Infrared Spectroscopy (fNIRS), the research examines the hemodynamic responses of participants while evaluating diverse credit offers. Methods The experimental phase of this study investigates the hemodynamic responses collected from 39 healthy participants with respect to different loan offers. This study integrates fNIRS data with advanced ML algorithms, specifically Extreme Gradient Boosting, CatBoost, Extra Tree Classifier, and Light Gradient Boosted Machine, to predict participants' credit decisions based on prefrontal cortex (PFC) activation patterns. Results Findings reveal distinctive PFC regions correlating with credit behaviors, including the dorsolateral prefrontal cortex (dlPFC) associated with strategic decision-making, the orbitofrontal cortex (OFC) linked to emotional valuations, and the ventromedial prefrontal cortex (vmPFC) reflecting brand integration and reward processing. Notably, the right dorsomedial prefrontal cortex (dmPFC) and the right vmPFC contribute to positive credit preferences. Discussion This interdisciplinary approach bridges neuroscience, machine learning and finance, offering unprecedented insights into the neural mechanisms guiding financial choices regarding different loan offers. The study's predictive model holds promise for refining financial services and illuminating human financial behavior within the burgeoning field of neurofinance. The work exemplifies the potential of interdisciplinary research to enhance our understanding of human financial decision-making.
Collapse
Affiliation(s)
- Tuna Çakar
- Department of Computer Engineering, MEF University, Istanbul, Türkiye
| | - Semen Son-Turan
- Department of Business Administration, MEF University, Maslak, Türkiye
| | - Yener Girişken
- Faculty of Economics and Administrative Sciences, Final International University, Istanbul, Türkiye
| | - Alperen Sayar
- Informatics Technologies Master Program, MEF University, Istanbul, Türkiye
| | - Seyit Ertuğrul
- Informatics Technologies Master Program, MEF University, Istanbul, Türkiye
| | - Gözde Filiz
- Computer Science and Engineering Ph.D. Program, MEF University, Istanbul, Türkiye
| | - Esin Tuna
- Department of Psychology, MEF University, Istanbul, Türkiye
| |
Collapse
|
13
|
Sun H, Adra N, Ayub MA, Ganglberger W, Ye E, Fernandes M, Paixao L, Fan Z, Gupta A, Ghanta M, Moura Junior VF, Rosand J, Westover MB, Thomas RJ. Assessing Risk of Health Outcomes From Brain Activity in Sleep: A Retrospective Cohort Study. Neurol Clin Pract 2024; 14:e200225. [PMID: 38173542 PMCID: PMC10759032 DOI: 10.1212/cpj.0000000000200225] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Accepted: 10/04/2023] [Indexed: 01/05/2024]
Abstract
Background and Objectives Patterns of electrical activity in the brain (EEG) during sleep are sensitive to various health conditions even at subclinical stages. The objective of this study was to estimate sleep EEG-predicted incidence of future neurologic, cardiovascular, psychiatric, and mortality outcomes. Methods This is a retrospective cohort study with 2 data sets. The Massachusetts General Hospital (MGH) sleep data set is a clinic-based cohort, used for model development. The Sleep Heart Health Study (SHHS) is a community-based cohort, used as the external validation cohort. Exposure is good, average, or poor sleep defined by quartiles of sleep EEG-predicted risk. The outcomes include ischemic stroke, intracranial hemorrhage, mild cognitive impairment, dementia, atrial fibrillation, myocardial infarction, type 2 diabetes, hypertension, bipolar disorder, depression, and mortality. Diagnoses were based on diagnosis codes, brain imaging reports, medications, cognitive scores, and hospital records. We used the Cox survival model with death as the competing risk. Results There were 8673 participants from MGH and 5650 from SHHS. For all outcomes, the model-predicted 10-year risk was within the 95% confidence interval of the ground truth, indicating good prediction performance. When comparing participants with poor, average, and good sleep, except for atrial fibrillation, all other 10-year risk ratios were significant. The model-predicted 10-year risk ratio closely matched the observed event rate in the external validation cohort. Discussion The incidence of health outcomes can be predicted by brain activity during sleep. The findings strengthen the concept of sleep as an accessible biological window into unfavorable brain and general health outcomes.
Collapse
Affiliation(s)
- Haoqi Sun
- Department of Neurology (HS, NA, MAA, WG, EY, MF, LP, ZF, AG, MG, VFMJ, JR, MBW), Massachusetts General Hospital; Henry and Allison McCance Center for Brain Health at Mass General (HS, VFMJ, JR, MBW); Department of Neurology (HS, WG, AG, MG, VFMJ, MBW), Beth Israel Deaconess Medical Center, Boston, MA; Department of Neurology (MAA), Louisiana State University Health Sciences Center, Shreveport, LA; Department of Neurology (LP), Washington University School of Medicine in St. Louis, MO; and Division of Pulmonary (RJT), Critical Care and Sleep, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA
| | - Noor Adra
- Department of Neurology (HS, NA, MAA, WG, EY, MF, LP, ZF, AG, MG, VFMJ, JR, MBW), Massachusetts General Hospital; Henry and Allison McCance Center for Brain Health at Mass General (HS, VFMJ, JR, MBW); Department of Neurology (HS, WG, AG, MG, VFMJ, MBW), Beth Israel Deaconess Medical Center, Boston, MA; Department of Neurology (MAA), Louisiana State University Health Sciences Center, Shreveport, LA; Department of Neurology (LP), Washington University School of Medicine in St. Louis, MO; and Division of Pulmonary (RJT), Critical Care and Sleep, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA
| | - Muhammad Abubakar Ayub
- Department of Neurology (HS, NA, MAA, WG, EY, MF, LP, ZF, AG, MG, VFMJ, JR, MBW), Massachusetts General Hospital; Henry and Allison McCance Center for Brain Health at Mass General (HS, VFMJ, JR, MBW); Department of Neurology (HS, WG, AG, MG, VFMJ, MBW), Beth Israel Deaconess Medical Center, Boston, MA; Department of Neurology (MAA), Louisiana State University Health Sciences Center, Shreveport, LA; Department of Neurology (LP), Washington University School of Medicine in St. Louis, MO; and Division of Pulmonary (RJT), Critical Care and Sleep, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA
| | - Wolfgang Ganglberger
- Department of Neurology (HS, NA, MAA, WG, EY, MF, LP, ZF, AG, MG, VFMJ, JR, MBW), Massachusetts General Hospital; Henry and Allison McCance Center for Brain Health at Mass General (HS, VFMJ, JR, MBW); Department of Neurology (HS, WG, AG, MG, VFMJ, MBW), Beth Israel Deaconess Medical Center, Boston, MA; Department of Neurology (MAA), Louisiana State University Health Sciences Center, Shreveport, LA; Department of Neurology (LP), Washington University School of Medicine in St. Louis, MO; and Division of Pulmonary (RJT), Critical Care and Sleep, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA
| | - Elissa Ye
- Department of Neurology (HS, NA, MAA, WG, EY, MF, LP, ZF, AG, MG, VFMJ, JR, MBW), Massachusetts General Hospital; Henry and Allison McCance Center for Brain Health at Mass General (HS, VFMJ, JR, MBW); Department of Neurology (HS, WG, AG, MG, VFMJ, MBW), Beth Israel Deaconess Medical Center, Boston, MA; Department of Neurology (MAA), Louisiana State University Health Sciences Center, Shreveport, LA; Department of Neurology (LP), Washington University School of Medicine in St. Louis, MO; and Division of Pulmonary (RJT), Critical Care and Sleep, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA
| | - Marta Fernandes
- Department of Neurology (HS, NA, MAA, WG, EY, MF, LP, ZF, AG, MG, VFMJ, JR, MBW), Massachusetts General Hospital; Henry and Allison McCance Center for Brain Health at Mass General (HS, VFMJ, JR, MBW); Department of Neurology (HS, WG, AG, MG, VFMJ, MBW), Beth Israel Deaconess Medical Center, Boston, MA; Department of Neurology (MAA), Louisiana State University Health Sciences Center, Shreveport, LA; Department of Neurology (LP), Washington University School of Medicine in St. Louis, MO; and Division of Pulmonary (RJT), Critical Care and Sleep, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA
| | - Luis Paixao
- Department of Neurology (HS, NA, MAA, WG, EY, MF, LP, ZF, AG, MG, VFMJ, JR, MBW), Massachusetts General Hospital; Henry and Allison McCance Center for Brain Health at Mass General (HS, VFMJ, JR, MBW); Department of Neurology (HS, WG, AG, MG, VFMJ, MBW), Beth Israel Deaconess Medical Center, Boston, MA; Department of Neurology (MAA), Louisiana State University Health Sciences Center, Shreveport, LA; Department of Neurology (LP), Washington University School of Medicine in St. Louis, MO; and Division of Pulmonary (RJT), Critical Care and Sleep, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA
| | - Ziwei Fan
- Department of Neurology (HS, NA, MAA, WG, EY, MF, LP, ZF, AG, MG, VFMJ, JR, MBW), Massachusetts General Hospital; Henry and Allison McCance Center for Brain Health at Mass General (HS, VFMJ, JR, MBW); Department of Neurology (HS, WG, AG, MG, VFMJ, MBW), Beth Israel Deaconess Medical Center, Boston, MA; Department of Neurology (MAA), Louisiana State University Health Sciences Center, Shreveport, LA; Department of Neurology (LP), Washington University School of Medicine in St. Louis, MO; and Division of Pulmonary (RJT), Critical Care and Sleep, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA
| | - Aditya Gupta
- Department of Neurology (HS, NA, MAA, WG, EY, MF, LP, ZF, AG, MG, VFMJ, JR, MBW), Massachusetts General Hospital; Henry and Allison McCance Center for Brain Health at Mass General (HS, VFMJ, JR, MBW); Department of Neurology (HS, WG, AG, MG, VFMJ, MBW), Beth Israel Deaconess Medical Center, Boston, MA; Department of Neurology (MAA), Louisiana State University Health Sciences Center, Shreveport, LA; Department of Neurology (LP), Washington University School of Medicine in St. Louis, MO; and Division of Pulmonary (RJT), Critical Care and Sleep, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA
| | - Manohar Ghanta
- Department of Neurology (HS, NA, MAA, WG, EY, MF, LP, ZF, AG, MG, VFMJ, JR, MBW), Massachusetts General Hospital; Henry and Allison McCance Center for Brain Health at Mass General (HS, VFMJ, JR, MBW); Department of Neurology (HS, WG, AG, MG, VFMJ, MBW), Beth Israel Deaconess Medical Center, Boston, MA; Department of Neurology (MAA), Louisiana State University Health Sciences Center, Shreveport, LA; Department of Neurology (LP), Washington University School of Medicine in St. Louis, MO; and Division of Pulmonary (RJT), Critical Care and Sleep, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA
| | - Valdery F Moura Junior
- Department of Neurology (HS, NA, MAA, WG, EY, MF, LP, ZF, AG, MG, VFMJ, JR, MBW), Massachusetts General Hospital; Henry and Allison McCance Center for Brain Health at Mass General (HS, VFMJ, JR, MBW); Department of Neurology (HS, WG, AG, MG, VFMJ, MBW), Beth Israel Deaconess Medical Center, Boston, MA; Department of Neurology (MAA), Louisiana State University Health Sciences Center, Shreveport, LA; Department of Neurology (LP), Washington University School of Medicine in St. Louis, MO; and Division of Pulmonary (RJT), Critical Care and Sleep, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA
| | - Jonathan Rosand
- Department of Neurology (HS, NA, MAA, WG, EY, MF, LP, ZF, AG, MG, VFMJ, JR, MBW), Massachusetts General Hospital; Henry and Allison McCance Center for Brain Health at Mass General (HS, VFMJ, JR, MBW); Department of Neurology (HS, WG, AG, MG, VFMJ, MBW), Beth Israel Deaconess Medical Center, Boston, MA; Department of Neurology (MAA), Louisiana State University Health Sciences Center, Shreveport, LA; Department of Neurology (LP), Washington University School of Medicine in St. Louis, MO; and Division of Pulmonary (RJT), Critical Care and Sleep, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA
| | - M Brandon Westover
- Department of Neurology (HS, NA, MAA, WG, EY, MF, LP, ZF, AG, MG, VFMJ, JR, MBW), Massachusetts General Hospital; Henry and Allison McCance Center for Brain Health at Mass General (HS, VFMJ, JR, MBW); Department of Neurology (HS, WG, AG, MG, VFMJ, MBW), Beth Israel Deaconess Medical Center, Boston, MA; Department of Neurology (MAA), Louisiana State University Health Sciences Center, Shreveport, LA; Department of Neurology (LP), Washington University School of Medicine in St. Louis, MO; and Division of Pulmonary (RJT), Critical Care and Sleep, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA
| | - Robert J Thomas
- Department of Neurology (HS, NA, MAA, WG, EY, MF, LP, ZF, AG, MG, VFMJ, JR, MBW), Massachusetts General Hospital; Henry and Allison McCance Center for Brain Health at Mass General (HS, VFMJ, JR, MBW); Department of Neurology (HS, WG, AG, MG, VFMJ, MBW), Beth Israel Deaconess Medical Center, Boston, MA; Department of Neurology (MAA), Louisiana State University Health Sciences Center, Shreveport, LA; Department of Neurology (LP), Washington University School of Medicine in St. Louis, MO; and Division of Pulmonary (RJT), Critical Care and Sleep, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA
| |
Collapse
|
14
|
LaGoy AD, Kubala AG, Seech TR, Jameson JT, Markwald RR, Russell DW. Steps toward developing a comprehensive fatigue monitoring and mitigation solution: perspectives from a cohort of United States Naval Surface Force officers. SLEEP ADVANCES : A JOURNAL OF THE SLEEP RESEARCH SOCIETY 2024; 5:zpae008. [PMID: 38425454 PMCID: PMC10904103 DOI: 10.1093/sleepadvances/zpae008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Revised: 01/12/2024] [Indexed: 03/02/2024]
Abstract
Study Objectives This study analyzed fatigue and its management in US Naval Surface Force warships, focusing on understanding current practices and barriers, and examining the influence of organizational and individual factors on managing chronic fatigue. Furthermore, this study explored the impact of organizational and individual factors on fatigue management. Methods As part of a larger study, 154 naval officers (mean ± standard deviation; 31.5 ± 7.0 years; 8.8 ± 6.8 years of service; 125 male, and 29 female) completed a fatigue survey. The survey addressed (1) self-reported fatigue, (2) fatigue observed in others, (3) fatigue monitoring strategies, (4) fatigue mitigation strategies, and (5) barriers to fatigue mitigation. Logistic and ordinal regressions were performed to examine the effect of individual (i.e. sleep quality and years in military service) and organizational (i.e. ship-class) factors on fatigue outcomes. Results Fatigue was frequently experienced and observed by 23% and 54% of officers, respectively. Of note, officers often monitored fatigue reactively (i.e. 65% observed others nodding off and 55% observed behavioral impairments). Still, officers did not frequently implement fatigue mitigation strategies, citing few operationally feasible mitigation strategies (62.3%), being too busy (61.7%), and not having clear thresholds for action (48.7%). Fatigue management varies across organizational factors, which must be considered when further developing fatigue management strategies. Conclusions Fatigue remains a critical concern aboard surface force ships and it may be better addressed through development of objective sleep and fatigue monitoring tools that could inform leadership decision-making.
Collapse
Affiliation(s)
- Alice D LaGoy
- Sleep, Tactical Efficiency, and Endurance Laboratory, Warfighter Performance Department, Naval Health Research Center, San Diego, CA, USA
- Military and Veterans Health Solutions, Leidos, Inc., San Diego, CA, USA
| | - Andrew G Kubala
- Sleep, Tactical Efficiency, and Endurance Laboratory, Warfighter Performance Department, Naval Health Research Center, San Diego, CA, USA
- Military and Veterans Health Solutions, Leidos, Inc., San Diego, CA, USA
| | - Todd R Seech
- Commander, Naval Surface Force, U.S. Pacific Fleet, Coronado, CA, USA
| | - Jason T Jameson
- Sleep, Tactical Efficiency, and Endurance Laboratory, Warfighter Performance Department, Naval Health Research Center, San Diego, CA, USA
- Military and Veterans Health Solutions, Leidos, Inc., San Diego, CA, USA
| | - Rachel R Markwald
- Sleep, Tactical Efficiency, and Endurance Laboratory, Warfighter Performance Department, Naval Health Research Center, San Diego, CA, USA
| | - Dale W Russell
- Commander, Naval Surface Force, U.S. Pacific Fleet, Coronado, CA, USA
- Department of Psychiatry, Uniformed Services University of the Health Sciences, Bethesda, MD, USA
| |
Collapse
|
15
|
Tapia-Rivas NI, Estévez PA, Cortes-Briones JA. A robust deep learning detector for sleep spindles and K-complexes: towards population norms. Sci Rep 2024; 14:263. [PMID: 38167626 PMCID: PMC10762090 DOI: 10.1038/s41598-023-50736-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Accepted: 12/24/2023] [Indexed: 01/05/2024] Open
Abstract
Sleep spindles (SSs) and K-complexes (KCs) are brain patterns involved in cognitive functions that appear during sleep. Large-scale sleep studies would benefit from precise and robust automatic sleep event detectors, capable of adapting the variability in both electroencephalography (EEG) signals and expert annotation rules. We introduce the Sleep EEG Event Detector (SEED), a deep learning system that outperforms existing approaches in SS and KC detection, reaching an F1-score of 80.5% and 83.7%, respectively, on the MASS2 dataset. SEED transfers well and requires minimal fine-tuning for new datasets and annotation styles. Remarkably, SEED substantially reduces the required amount of annotated data by using a novel pretraining approach that leverages the rule-based detector A7. An analysis of 11,224 subjects revealed that SEED's detections provide better estimates of SS population statistics than existing approaches. SEED is a powerful resource for obtaining sleep-event statistics that could be useful for establishing population norms.
Collapse
Affiliation(s)
| | - Pablo A Estévez
- Department of Electrical Engineering, University of Chile, Santiago, Chile.
- Millennium Institute of Intelligent Healthcare Engineering, Santiago, Chile.
- IMPACT, Center of Interventional Medicine for Precision and Advanced Cellular Therapy, Santiago, Chile.
| | - José A Cortes-Briones
- Schizophrenia and Neuropharmacology Research Group at Yale (SNRGY), Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Abraham Ribicoff Research Facilities, Connecticut Mental Health Center, New Haven, CT, USA
- VA Connecticut Healthcare System, West Haven, CT, USA
| |
Collapse
|
16
|
Chen K, Duan Y, Huang Y, Hu W, Xie Y. A Deep Learning Method of Human Identification from Radar Signal for Daily Sleep Health Monitoring. Bioengineering (Basel) 2023; 11:2. [PMID: 38275570 PMCID: PMC10813377 DOI: 10.3390/bioengineering11010002] [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: 11/20/2023] [Revised: 12/15/2023] [Accepted: 12/18/2023] [Indexed: 01/27/2024] Open
Abstract
Radar signal has been shown as a promising source for human identification. In daily home sleep-monitoring scenarios, large-scale motion features may not always be practical, and the heart motion or respiration data may not be as ideal as they are in a controlled laboratory setting. Human identification from radar sequences is still a challenging task. Furthermore, there is a need to address the open-set recognition problem for radar sequences, which has not been sufficiently studied. In this paper, we propose a deep learning-based approach for human identification using radar sequences captured during sleep in a daily home-monitoring setup. To enhance robustness, we preprocess the sequences to mitigate environmental interference before employing a deep convolution neural network for human identification. We introduce a Principal Component Space feature representation to detect unknown sequences. Our method is rigorously evaluated using both a public data set and a set of experimentally acquired radar sequences. We report a labeling accuracy of 98.2% and 96.8% on average for the two data sets, respectively, which outperforms the state-of-the-art techniques. Our method excels at accurately distinguishing unknown sequences from labeled ones, with nearly 100% detection of unknown samples and minimal misclassification of labeled samples as unknown.
Collapse
Affiliation(s)
- Ken Chen
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; (K.C.); (Y.D.)
| | - Yulong Duan
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; (K.C.); (Y.D.)
| | - Yi Huang
- Shenzhen HUAYI Medical Technologies Co., Ltd., Shenzhen 518055, China; (Y.H.); (W.H.)
| | - Wei Hu
- Shenzhen HUAYI Medical Technologies Co., Ltd., Shenzhen 518055, China; (Y.H.); (W.H.)
| | - Yaoqin Xie
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; (K.C.); (Y.D.)
| |
Collapse
|
17
|
Themelis K, Tang NKY. The Management of Chronic Pain: Re-Centring Person-Centred Care. J Clin Med 2023; 12:6957. [PMID: 38002572 PMCID: PMC10672376 DOI: 10.3390/jcm12226957] [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: 09/27/2023] [Revised: 11/03/2023] [Accepted: 11/05/2023] [Indexed: 11/26/2023] Open
Abstract
The drive for a more person-centred approach in the broader field of clinical medicine is also gaining traction in chronic pain treatment. Despite current advances, a further departure from 'business as usual' is required to ensure that the care offered or received is not only effective but also considers personal values, goals, abilities, and day-to-day realities. Existing work typically focuses on explaining pain symptoms and the development of standardised interventions, at the risk of overlooking the broader consequences of pain in individuals' lives and individual differences in pain responses. This review underscores the importance of considering additional factors, such as the influence of chronic pain on an individual's sense of self. It explores innovative approaches to chronic pain management that have the potential to optimise effectiveness and offer person-centred care. Furthermore, it delves into research applying hybrid and individual formulations, along with self-monitoring technologies, to enhance pain assessment and the tailoring of management strategies. In conclusion, this review advocates for chronic pain management approaches that align with an individual's priorities and realities while fostering their active involvement in self-monitoring and self-management.
Collapse
Affiliation(s)
- Kristy Themelis
- Department of Psychology, University of Warwick, Coventry CV4 7AL, UK
| | - Nicole K. Y. Tang
- Department of Psychology, University of Warwick, Coventry CV4 7AL, UK
| |
Collapse
|
18
|
G Ravindran KK, Della Monica C, Atzori G, Lambert D, Hassanin H, Revell V, Dijk DJ. Three Contactless Sleep Technologies Compared With Actigraphy and Polysomnography in a Heterogeneous Group of Older Men and Women in a Model of Mild Sleep Disturbance: Sleep Laboratory Study. JMIR Mhealth Uhealth 2023; 11:e46338. [PMID: 37878360 PMCID: PMC10632916 DOI: 10.2196/46338] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Revised: 07/11/2023] [Accepted: 08/25/2023] [Indexed: 10/26/2023] Open
Abstract
BACKGROUND Contactless sleep technologies (CSTs) hold promise for longitudinal, unobtrusive sleep monitoring in the community and at scale. They may be particularly useful in older populations wherein sleep disturbance, which may be indicative of the deterioration of physical and mental health, is highly prevalent. However, few CSTs have been evaluated in older people. OBJECTIVE This study evaluated the performance of 3 CSTs compared to polysomnography (PSG) and actigraphy in an older population. METHODS Overall, 35 older men and women (age: mean 70.8, SD 4.9 y; women: n=14, 40%), several of whom had comorbidities, including sleep apnea, participated in the study. Sleep was recorded simultaneously using a bedside radar (Somnofy [Vital Things]: n=17), 2 undermattress devices (Withings sleep analyzer [WSA; Withings Inc]: n=35; Emfit-QS [Emfit; Emfit Ltd]: n=17), PSG (n=35), and actigraphy (Actiwatch Spectrum [Philips Respironics]: n=18) during the first night in a 10-hour time-in-bed protocol conducted in a sleep laboratory. The devices were evaluated through performance metrics for summary measures and epoch-by-epoch classification. PSG served as the gold standard. RESULTS The protocol induced mild sleep disturbance with a mean sleep efficiency (SEFF) of 70.9% (SD 10.4%; range 52.27%-92.60%). All 3 CSTs overestimated the total sleep time (TST; bias: >90 min) and SEFF (bias: >13%) and underestimated wake after sleep onset (bias: >50 min). Sleep onset latency was accurately detected by the bedside radar (bias: <6 min) but overestimated by the undermattress devices (bias: >16 min). CSTs did not perform as well as actigraphy in estimating the all-night sleep summary measures. In an epoch-by-epoch concordance analysis, the bedside radar performed better in discriminating sleep versus wake (Matthew correlation coefficient [MCC]: mean 0.63, SD 0.12, 95% CI 0.57-0.69) than the undermattress devices (MCC of WSA: mean 0.41, SD 0.15, 95% CI 0.36-0.46; MCC of Emfit: mean 0.35, SD 0.16, 95% CI 0.26-0.43). The accuracy of identifying rapid eye movement and light sleep was poor across all CSTs, whereas deep sleep (ie, slow wave sleep) was predicted with moderate accuracy (MCC: >0.45) by both Somnofy and WSA. The deep sleep duration estimates of Somnofy correlated (r2=0.60; P<.01) with electroencephalography slow wave activity (0.75-4.5 Hz) derived from PSG, whereas for the undermattress devices, this correlation was not significant (WSA: r2=0.0096, P=.58; Emfit: r2=0.11, P=.21). CONCLUSIONS These CSTs overestimated the TST, and sleep stage prediction was unsatisfactory in this group of older people in whom SEFF was relatively low. Although it was previously shown that CSTs provide useful information on bed occupancy, which may be useful for particular use cases, the performance of these CSTs with respect to the TST and sleep stage estimation requires improvement before they can serve as an alternative to PSG in estimating most sleep variables in older individuals.
Collapse
Affiliation(s)
- Kiran K G Ravindran
- Surrey Sleep Research Centre, School of Biosciences, Faculty of Health and Medical Sciences, University of Surrey, Guildford, United Kingdom
- UK Dementia Research Institute, Care Research and Technology Centre at Imperial College, London and the University of Surrey, Guildford, United Kingdom
| | - Ciro Della Monica
- Surrey Sleep Research Centre, School of Biosciences, Faculty of Health and Medical Sciences, University of Surrey, Guildford, United Kingdom
- UK Dementia Research Institute, Care Research and Technology Centre at Imperial College, London and the University of Surrey, Guildford, United Kingdom
| | - Giuseppe Atzori
- Surrey Sleep Research Centre, School of Biosciences, Faculty of Health and Medical Sciences, University of Surrey, Guildford, United Kingdom
- UK Dementia Research Institute, Care Research and Technology Centre at Imperial College, London and the University of Surrey, Guildford, United Kingdom
| | - Damion Lambert
- Surrey Sleep Research Centre, School of Biosciences, Faculty of Health and Medical Sciences, University of Surrey, Guildford, United Kingdom
- UK Dementia Research Institute, Care Research and Technology Centre at Imperial College, London and the University of Surrey, Guildford, United Kingdom
| | - Hana Hassanin
- UK Dementia Research Institute, Care Research and Technology Centre at Imperial College, London and the University of Surrey, Guildford, United Kingdom
- Surrey Clinical Research Facility, School of Biosciences, Faculty of Health and Medical Sciences, Guildford, United Kingdom
- National Institute for Health Research - Royal Surrey Clinical Research Facility, Guildford, United Kingdom
| | - Victoria Revell
- Surrey Sleep Research Centre, School of Biosciences, Faculty of Health and Medical Sciences, University of Surrey, Guildford, United Kingdom
- UK Dementia Research Institute, Care Research and Technology Centre at Imperial College, London and the University of Surrey, Guildford, United Kingdom
| | - Derk-Jan Dijk
- Surrey Sleep Research Centre, School of Biosciences, Faculty of Health and Medical Sciences, University of Surrey, Guildford, United Kingdom
- UK Dementia Research Institute, Care Research and Technology Centre at Imperial College, London and the University of Surrey, Guildford, United Kingdom
| |
Collapse
|
19
|
Morokuma S, Hayashi T, Kanegae M, Mizukami Y, Asano S, Kimura I, Tateizumi Y, Ueno H, Ikeda S, Niizeki K. Deep learning-based sleep stage classification with cardiorespiratory and body movement activities in individuals with suspected sleep disorders. Sci Rep 2023; 13:17730. [PMID: 37853134 PMCID: PMC10584883 DOI: 10.1038/s41598-023-45020-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Accepted: 10/14/2023] [Indexed: 10/20/2023] Open
Abstract
Deep learning methods have gained significant attention in sleep science. This study aimed to assess the performance of a deep learning-based sleep stage classification model constructed using fewer physiological parameters derived from cardiorespiratory and body movement data. Overnight polysomnography (PSG) data from 123 participants (age: 19-82 years) with suspected sleep disorders were analyzed. Multivariate time series data, including heart rate, respiratory rate, cardiorespiratory coupling, and body movement frequency, were input into a bidirectional long short-term memory (biLSTM) network model to train and predict five-class sleep stages. The trained model's performance was evaluated using balanced accuracy, Cohen's κ coefficient, and F1 scores on an epoch-per-epoch basis and compared with the ground truth using the leave-one-out cross-validation scheme. The model achieved an accuracy of 71.2 ± 5.8%, Cohen's κ of 0.425 ± 0.115, and an F1 score of 0.650 ± 0.083 across all sleep stages, and all metrics were negatively correlated with the apnea-hypopnea index, as well as age, but positively correlated with sleep efficiency. Moreover, the model performance varied for each sleep stage, with the highest F1 score observed for N2 and the lowest for N3. Regression and Bland-Altman analyses between sleep parameters of interest derived from deep learning and PSG showed substantial correlations (r = 0.33-0.60) with low bias. The findings demonstrate the efficacy of the biLSTM deep learning model in accurately classifying sleep stages and in estimating sleep parameters for sleep structure analysis using a reduced set of physiological parameters. The current model without using EEG information may expand the application of unobtrusive in-home monitoring to clinically assess the prevalence of sleep disorders outside of a sleep laboratory.
Collapse
Affiliation(s)
- Seiichi Morokuma
- Department of Health Sciences, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan.
| | | | | | | | | | | | - Yuji Tateizumi
- Department of Electrical Engineering, National Institute of Technology, Tokyo College, Tokyo, Japan
| | - Hitoshi Ueno
- Tokyo Information Design Professional University, Tokyo, Japan
| | - Subaru Ikeda
- Department of Health Sciences, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Kyuichi Niizeki
- Department of Biosystems Engineering, Graduate School of Science and Engineering, Yamagata University (Emeritus), Yonezawa, Japan
| |
Collapse
|
20
|
Song YM, Choi SJ, Park SH, Lee SJ, Joo EY, Kim JK. A real-time, personalized sleep intervention using mathematical modeling and wearable devices. Sleep 2023; 46:zsad179. [PMID: 37422720 DOI: 10.1093/sleep/zsad179] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Revised: 06/03/2023] [Indexed: 07/10/2023] Open
Abstract
The prevalence of artificial light exposure has enabled us to be active any time of the day or night, leading to the need for high alertness outside of traditional daytime hours. To address this need, we developed a personalized sleep intervention framework that analyzes real-world sleep-wake patterns obtained from wearable devices to maximize alertness during specific target periods. Our framework utilizes a mathematical model that tracks the dynamic sleep pressure and circadian rhythm based on the user's sleep history. In this way, the model accurately predicts real-time alertness, even for shift workers with complex sleep and work schedules (N = 71, t = 13~21 days). This allowed us to discover a new sleep-wake pattern called the adaptive circadian split sleep, which incorporates a main sleep period and a late nap to enable high alertness during both work and non-work periods of shift workers. We further developed a mobile application that integrates this framework to recommend practical, personalized sleep schedules for individual users to maximize their alertness during a targeted activity time based on their desired sleep onset and available sleep duration. This can reduce the risk of errors for those who require high alertness during nontraditional activity times and improve the health and quality of life for those leading shift work-like lifestyles.
Collapse
Affiliation(s)
- Yun Min Song
- Department of Mathematical Sciences, KAIST, Daejeon, Republic of Korea
- Biomedical Mathematics Group, Institute for Basic Science, Daejeon, Republic of Korea
| | - Su Jung Choi
- Graduate School of Clinical Nursing Science, Sungkyunkwan University, Seoul, Republic of Korea
| | - Se Ho Park
- Biomedical Mathematics Group, Institute for Basic Science, Daejeon, Republic of Korea
- Department of Mathematics, University of Wisconsin-Madison, Madison, WI, USA
| | - Soo Jin Lee
- Department of Neurology, Neuroscience Center, Samsung Medical Center, Samsung Biomedical Research Institute, School of Medicine, Sungkyunkwan University, Seoul, Republic of Korea
| | - Eun Yeon Joo
- Department of Neurology, Neuroscience Center, Samsung Medical Center, Samsung Biomedical Research Institute, School of Medicine, Sungkyunkwan University, Seoul, Republic of Korea
| | - Jae Kyoung Kim
- Department of Mathematical Sciences, KAIST, Daejeon, Republic of Korea
- Biomedical Mathematics Group, Institute for Basic Science, Daejeon, Republic of Korea
| |
Collapse
|
21
|
Mo W, Yamakawa M, Takahashi S, Liu X, Nobuhara K, Kurakami H, Takeya Y, Ikeda M. Effect of sleep report feedback using information and communication technology combined with health guidance on improving sleep indicators in community-dwelling older people: a pilot trial. Psychogeriatrics 2023; 23:763-772. [PMID: 37312423 DOI: 10.1111/psyg.12994] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Revised: 05/08/2023] [Accepted: 05/29/2023] [Indexed: 06/15/2023]
Abstract
BACKGROUND This study evaluated the preliminary effect of an integrated novel intervention comprising visualised sleep report feedback using information and communication technology and periodic health guidance on improving sleep indicators among community-dwelling older people. METHODS The intervention was implemented among 29 older people in Sakai City, Japan, in a 3 months pilot trial. Non-worn actigraph devices were placed under participants' bedding to continuously measure their sleep state, and they received monthly sleep reports in writing. Sleep efficiency, total sleep time, sleep latency, and the number of times away from bed were recorded. A trained nurse expertly interpreted participants' sleep data and provided telephone health guidance. The first month's data were used as the baseline (T1), the second month provided data for the first intervention (T2), and the third month provided data for the second intervention (T3). Friedman tests and Wilcoxon signed-rank tests were used to examine differences in sleep outcomes between different time points. RESULTS Participants' mean age was 78.97 ± 5.15 years, and 51.72% (15/29) were female. Comparison of T2 and T1 showed the intervention decreased participants' sleep latency at T2 (P = 0.038). Compared with T1, the intervention significantly decreased sleep latency (P = 0.004), increased total sleep time (P < 0.001), and improved sleep efficiency (P < 0.001) at T3. When T3 was compared with T2, only total sleep time was significantly increased (P < 0.001). There were no significant differences in the number of times away from bed across the three time points (P > 0.05). CONCLUSIONS This visualised sleep report feedback and periodic health guidance intervention for community-dwelling older people showed promising, albeit small preliminary effects on sleep. A fully powered randomised controlled trial is required to verify the significance of this effect.
Collapse
Affiliation(s)
- Wenping Mo
- Department of Evidence-Based Clinical Nursing, Division of Health Sciences, Graduate School of Medicine, Osaka University, Osaka, Japan
| | - Miyae Yamakawa
- Department of Evidence-Based Clinical Nursing, Division of Health Sciences, Graduate School of Medicine, Osaka University, Osaka, Japan
- The Japan Centre for Evidence-Based Practice: A JBI Centre of Excellence, Osaka, Japan
| | - Shimpei Takahashi
- Department of Evidence-Based Clinical Nursing, Division of Health Sciences, Graduate School of Medicine, Osaka University, Osaka, Japan
| | - Xiaoji Liu
- Department of Evidence-Based Clinical Nursing, Division of Health Sciences, Graduate School of Medicine, Osaka University, Osaka, Japan
| | | | - Hiroyuki Kurakami
- Institute for Clinical and Translational Science, Nara Medical University, Nara, Japan
| | - Yasushi Takeya
- Department of Evidence-Based Clinical Nursing, Division of Health Sciences, Graduate School of Medicine, Osaka University, Osaka, Japan
| | - Manabu Ikeda
- Department of Psychiatry, Graduate School of Medicine, Osaka University, Osaka, Japan
| |
Collapse
|
22
|
Hrozanova M, Haugan JA, Saksvik-Lehouillier I, Skalická V, Krondorf L, Stenseng F, Moen F. The potential impact of adolescent sleep patterns on adulthood fertility: A call for longitudinal studies. Sleep Med 2023; 109:301-302. [PMID: 37474425 DOI: 10.1016/j.sleep.2023.07.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Accepted: 07/07/2023] [Indexed: 07/22/2023]
Affiliation(s)
- Maria Hrozanova
- Department of Public Health and Nursing, Norwegian University of Science and Technology, Trondheim, Norway.
| | - Jan Arvid Haugan
- Department of Teacher Education, Norwegian University of Science and Technology, Trondheim, Norway
| | | | - Věra Skalická
- Department of Psychology, Norwegian University of Science and Technology, Trondheim, Norway
| | - Lukas Krondorf
- Department of Data Science and Research, VitalThings AS, Trondheim, Norway
| | - Frode Stenseng
- Department of Education and Lifelong Learning, Norwegian University of Science and Technology, Trondheim, Norway
| | - Frode Moen
- Department of Teacher Education, Norwegian University of Science and Technology, Trondheim, Norway; Olympiatoppen Midt-Norge, Trondheim, Norway
| |
Collapse
|
23
|
Chiang AA, Khosla S. Consumer Wearable Sleep Trackers: Are They Ready for Clinical Use? Sleep Med Clin 2023; 18:311-330. [PMID: 37532372 DOI: 10.1016/j.jsmc.2023.05.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/04/2023]
Abstract
As the importance of good sleep continues to gain public recognition, the market for sleep-monitoring devices continues to grow. Modern technology has shifted from simple sleep tracking to a more granular sleep health assessment. We examine the available functionalities of consumer wearable sleep trackers (CWSTs) and how they perform in healthy individuals and disease states. Additionally, the continuum of sleep technology from consumer-grade to medical-grade is detailed. As this trend invariably grows, we urge professional societies to develop guidelines encompassing the practical clinical use of CWSTs and how best to incorporate them into patient care plans.
Collapse
Affiliation(s)
- Ambrose A Chiang
- Division of Sleep Medicine, Louis Stokes Cleveland VA Medical Center, 10701 East Blvd, Suite 2B-129, Cleveland, OH 44106, USA; Division of Pulmonary, Critical Care, and Sleep Medicine, University Hospitals Cleveland Medical Center, Cleveland, OH, USA; Department of Medicine, Case Western Reserve University, Cleveland, OH, USA.
| | - Seema Khosla
- North Dakota Center for Sleep, 1531 32nd Avenue S Ste 103, Fargo, ND 58103, USA
| |
Collapse
|
24
|
LaGoy AD, Kubala AG, Deering S, Germain A, Markwald RR. Dawn of a New Dawn: Advances in Sleep Health to Optimize Performance. Sleep Med Clin 2023; 18:361-371. [PMID: 37532375 DOI: 10.1016/j.jsmc.2023.05.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/04/2023]
Abstract
Optimal sleep health is a critical component to high-level performance. In populations such as the military, public service (eg, firefighters), and health care, achieving optimal sleep health is difficult and subsequently deficiencies in sleep health may lead to performance decrements. However, advances in sleep monitoring technologies and mitigation strategies for poor sleep health show promise for further ecological scientific investigation within these populations. The current review briefly outlines the relationship between sleep health and performance as well as current advances in behavioral and technological approaches to improving sleep health for performance.
Collapse
Affiliation(s)
- Alice D LaGoy
- Sleep, Tactical Efficiency, and Endurance Laboratory, Warfighter Performance Department, Naval Health Research Center, 140 Sylvester Road, San Diego, CA 92106, USA; Leidos, Inc., San Diego, CA, USA
| | - Andrew G Kubala
- Sleep, Tactical Efficiency, and Endurance Laboratory, Warfighter Performance Department, Naval Health Research Center, 140 Sylvester Road, San Diego, CA 92106, USA; Leidos, Inc., San Diego, CA, USA
| | - Sean Deering
- Sleep, Tactical Efficiency, and Endurance Laboratory, Warfighter Performance Department, Naval Health Research Center, 140 Sylvester Road, San Diego, CA 92106, USA; Leidos, Inc., San Diego, CA, USA
| | | | - Rachel R Markwald
- Sleep, Tactical Efficiency, and Endurance Laboratory, Warfighter Performance Department, Naval Health Research Center, 140 Sylvester Road, San Diego, CA 92106, USA.
| |
Collapse
|
25
|
Yoon H, Choi SH. Technologies for sleep monitoring at home: wearables and nearables. Biomed Eng Lett 2023; 13:313-327. [PMID: 37519880 PMCID: PMC10382403 DOI: 10.1007/s13534-023-00305-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Revised: 06/17/2023] [Accepted: 07/03/2023] [Indexed: 08/01/2023] Open
Abstract
Sleep is an essential part of our lives and daily sleep monitoring is crucial for maintaining good health and well-being. Traditionally, the gold standard method for sleep monitoring is polysomnography using various sensors attached to the body; however, it is limited with regards to long-term sleep monitoring in a home environment. Recent advancements in wearable and nearable technology have made it possible to monitor sleep at home. In this review paper, the technologies that are currently available for sleep stages and sleep disorder monitoring at home are reviewed using wearable and nearable devices. Wearables are devices that are worn on the body, while nearables are placed near the body. These devices can accurately monitor sleep stages and sleep disorder in a home environment. In this study, the benefits and limitations of each technology are discussed, along with their potential to improve sleep quality.
Collapse
Affiliation(s)
- Heenam Yoon
- Department of Human-Centered Artificial Intelligence, Sangmyung University, Seoul, 03016 Korea
| | - Sang Ho Choi
- School of Computer and Information Engineering, Kwangwoon University, Seoul, 01897 Korea
| |
Collapse
|
26
|
Strumpf Z, Gu W, Tsai CW, Chen PL, Yeh E, Leung L, Cheung C, Wu IC, Strohl KP, Tsai T, Folz RJ, Chiang AA. Belun Ring (Belun Sleep System BLS-100): Deep learning-facilitated wearable enables obstructive sleep apnea detection, apnea severity categorization, and sleep stage classification in patients suspected of obstructive sleep apnea. Sleep Health 2023; 9:430-440. [PMID: 37380590 DOI: 10.1016/j.sleh.2023.05.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Revised: 03/25/2023] [Accepted: 05/03/2023] [Indexed: 06/30/2023]
Abstract
GOAL AND AIMS Our objective was to evaluate the performance of Belun Ring with second-generation deep learning algorithms in obstructive sleep apnea (OSA) detection, OSA severity categorization, and sleep stage classification. FOCUS TECHNOLOGY Belun Ring with second-generation deep learning algorithms REFERENCE TECHNOLOGY: In-lab polysomnography (PSG) SAMPLE: Eighty-four subjects (M: F = 1:1) referred for an overnight sleep study were eligible. Of these, 26% had PSG-AHI<5; 24% had PSG-AHI 5-15; 23% had PSG-AHI 15-30; 27% had PSG-AHI ≥ 30. DESIGN Rigorous performance evaluation by comparing Belun Ring to concurrent in-lab PSG using the 4% rule. CORE ANALYTICS Pearson's correlation coefficient, Student's paired t-test, diagnostic accuracy, sensitivity, specificity, positive predictive value, negative predictive value, positive likelihood ratio, negative likelihood ratio, Cohen's kappa coefficient (kappa), Bland-Altman plots with bias and limits of agreement, receiver operating characteristics curves with area under the curve, and confusion matrix. CORE OUTCOMES The accuracy, sensitivity, specificity, and kappa in categorizing AHI ≥ 5 were 0.85, 0.92, 0.64, and 0.58, respectively. The accuracy, sensitivity, specificity, and Kappa in categorizing AHI ≥ 15 were 0.89, 0.91, 0.88, and 0.79, respectively. The accuracy, sensitivity, specificity, and Kappa in categorizing AHI ≥ 30 were 0.91, 0.83, 0.93, and 0.76, respectively. BSP2 also achieved an accuracy of 0.88 in detecting wake, 0.82 in detecting NREM, and 0.90 in detecting REM sleep. CORE CONCLUSION Belun Ring with second-generation algorithms detected OSA with good accuracy and demonstrated a moderate-to-substantial agreement in categorizing OSA severity and classifying sleep stages.
Collapse
Affiliation(s)
- Zachary Strumpf
- Division of Pulmonary, Critical Care, and Sleep Medicine, University Hospitals Cleveland Medical Center, Cleveland, OH, USA
| | - Wenbo Gu
- Belun Technology Company Limited, Hong Kong; Department of Computer Science, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
| | | | | | - Eric Yeh
- Division of Pulmonary, Critical Care, and Sleep Medicine, University Hospitals Cleveland Medical Center, Cleveland, OH, USA; Department of Medicine, Case Western Reserve University, Cleveland, OH, USA
| | | | | | - I-Chen Wu
- Department of Computer Science, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
| | - Kingman P Strohl
- Division of Pulmonary, Critical Care, and Sleep Medicine, University Hospitals Cleveland Medical Center, Cleveland, OH, USA; Department of Medicine, Case Western Reserve University, Cleveland, OH, USA; Division of Sleep Medicine, Louis Stokes Cleveland VA Medical Center, Cleveland, OH, USA
| | - Tiffany Tsai
- Case Western Reserve University, Cleveland, OH, USA
| | - Rodney J Folz
- Division of Pulmonary, Critical Care, and Sleep Medicine, Houston Methodist Hospital, Houston, TX, USA
| | - Ambrose A Chiang
- Division of Pulmonary, Critical Care, and Sleep Medicine, University Hospitals Cleveland Medical Center, Cleveland, OH, USA; Department of Medicine, Case Western Reserve University, Cleveland, OH, USA; Division of Sleep Medicine, Louis Stokes Cleveland VA Medical Center, Cleveland, OH, USA.
| |
Collapse
|
27
|
Park JH, Lee JD. A Customized Deep Sleep Recommender System Using Hybrid Deep Learning. SENSORS (BASEL, SWITZERLAND) 2023; 23:6670. [PMID: 37571454 PMCID: PMC10422391 DOI: 10.3390/s23156670] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Revised: 07/16/2023] [Accepted: 07/24/2023] [Indexed: 08/13/2023]
Abstract
This paper proposes a recommendation system based on a hybrid learning approach for a personal deep sleep service, called the Customized Deep Sleep Recommender System (CDSRS). Sleep is one of the most important factors for human life in modern society. Optimal sleep contributes to increasing work efficiency and controlling overall well-being. Therefore, a sleep recommendation service is considered a necessary service for modern individuals. Accurate sleep analysis and data are required to provide such a personalized sleep service. However, given the variations in sleep patterns between individuals, there is currently no international standard for sleep. Additionally, service platforms face a cold start problem when dealing with new users. To address these challenges, this study utilizes K-means clustering analysis to define sleep patterns and employs a hybrid learning algorithm to evaluate recommendations by combining user-based and collaborative filtering methods. It also incorporates feedback top-N classification processing for user profile learning and recommendations. The behavior of the study model is as follows. Using personal information received through mobile devices and data, such as snoring, sleep time, movement, and noise collected through AI motion beds, we recommend sleep and receive user evaluations of recommended sleep. This assessment reconstructs the profile and, finally, makes recommendations using top-N classification. The experimental results were evaluated using two absolute error measurement methods: mean squared error (MSE) and mean absolute percentage error (MAPE). The research results regarding the hybrid learning methods show 13.2% fewer errors than collaborative filtering (CF) and 10.2% fewer errors than content-based filtering (CBF) on an MSE basis. According to the MAPE, the methods are 14.7% more accurate than the CF model and 9.2% better than the CBF model. These results demonstrate that CDSRS systems can provide more accurate recommendations and customized sleep services to users than CF, CBF, and combination models. As a result, CDSRS, a hybrid learning method, can better reflect a user's evaluation than traditional methods and can increase the accuracy of recommendations as the number of users increases.
Collapse
Affiliation(s)
| | - Jae-Dong Lee
- Department of Computer Science, Dankook University, 152 Jukjeon-ro Campus, Suji-gu, Yongin-si 16890, Republic of Korea;
| |
Collapse
|
28
|
Bappi MH, Prottay AAS, Kamli H, Sonia FA, Mia MN, Akbor MS, Hossen MM, Awadallah S, Mubarak MS, Islam MT. Quercetin Antagonizes the Sedative Effects of Linalool, Possibly through the GABAergic Interaction Pathway. Molecules 2023; 28:5616. [PMID: 37513487 PMCID: PMC10384931 DOI: 10.3390/molecules28145616] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Revised: 07/15/2023] [Accepted: 07/20/2023] [Indexed: 07/30/2023] Open
Abstract
Sedatives promote calmness or sleepiness during surgery or severely stressful events. In addition, depression is a mental health issue that negatively affects emotional well-being. A group of drugs called anti-depressants is used to treat major depressive illnesses. The aim of the present work was to evaluate the effects of quercetin (QUR) and linalool (LIN) on thiopental sodium (TS)-induced sleeping mice and to investigate the combined effects of these compounds using a conventional co-treatment strategy and in silico studies. For this, the TS-induced sleeping mice were monitored to compare the occurrence, latency, and duration of the sleep-in response to QUR (10, 25, 50 mg/kg), LIN (10, 25, 50 mg/kg), and diazepam (DZP, 3 mg/kg, i.p.). Moreover, an in silico investigation was undertaken to assess this study's putative modulatory sedation mechanism. For this, we observed the ability of test and standard medications to interact with various gamma-aminobutyric acid A receptor (GABAA) subunits. Results revealed that QUR and LIN cause dose-dependent antidepressant-like and sedative-like effects in animals, respectively. In addition, QUR-50 mg/kg and LIN-50 mg/kg and/or DZP-3 mg/kg combined were associated with an increased latency period and reduced sleeping times in animals. Results of the in silico studies demonstrated that QUR has better binding interaction with GABAA α3, β1, and γ2 subunits when compared with DZP, whereas LIN showed moderate affinity with the GABAA receptor. Taken together, the sleep duration of LIN and DZP is opposed by QUR in TS-induced sleeping mice, suggesting that QUR may be responsible for providing sedation-antagonizing effects through the GABAergic interaction pathway.
Collapse
Affiliation(s)
- Mehedi Hasan Bappi
- Department of Pharmacy, Bangabandhu Sheikh Mujibur Rahman Science and Technology University, Gopalganj 8100, Bangladesh
| | - Abdullah Al Shamsh Prottay
- Department of Pharmacy, Bangabandhu Sheikh Mujibur Rahman Science and Technology University, Gopalganj 8100, Bangladesh
| | - Hossam Kamli
- Department of Clinical Laboratory Sciences, College of Applied Medical Sciences, King Khalid University, Abha 61421, Saudi Arabia
| | - Fatema Akter Sonia
- Department of Pharmacy, Bangabandhu Sheikh Mujibur Rahman Science and Technology University, Gopalganj 8100, Bangladesh
| | - Md Nayem Mia
- Department of Pharmacy, Bangabandhu Sheikh Mujibur Rahman Science and Technology University, Gopalganj 8100, Bangladesh
| | - Md Showkoth Akbor
- Department of Pharmacy, Bangabandhu Sheikh Mujibur Rahman Science and Technology University, Gopalganj 8100, Bangladesh
| | - Md Munnaf Hossen
- School of Health and Biomedical Sciences, RMIT University, Bundoora, VIC 3083, Australia
| | - Samir Awadallah
- Department of Medical Lab Sciences, Faculty of Allied Medical Sciences, Zarqa University, Zarqa 13110, Jordan
| | | | - Muhammad Torequl Islam
- Department of Pharmacy, Bangabandhu Sheikh Mujibur Rahman Science and Technology University, Gopalganj 8100, Bangladesh
| |
Collapse
|
29
|
Gagnon-Turcotte G, Cote-Allard U, Mascret Q, Torresen J, Gosselin B. Photoplethysmography-based derivation of physiological information using the BioPoint. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-5. [PMID: 38083646 DOI: 10.1109/embc40787.2023.10340642] [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
The BioPoint is a new wireless and wearable device, targeting both the ambulatory and on-site monitoring of biosignals. It is described as being capable of streaming and recording the i) electromyography, ii) electrocardiography, iii) electrodermal activity, iv) photoplethysmography, v) skin temperature and vi) actigraphy simultaneously, while making the raw signals recorded by the sensors readily available. However, an in-depth assessment of the biophysical signals recorded by this device, as well as its ability to derive vital signs and other health metrics, remains to be carried out. Consequently, this work proposes a preliminary study to evaluate the quality of the signals that can be acquired by this wearable with a focus on the derivation of heart rate and peripheral blood oxygenation via photoplethysmography. The device is quantitatively compared to the medical-grade pulse oximeter NoninConnect 3245, by Nonin inc. This study was performed with participants wearing the BioPoint at different positions on the body (finger, wrist, forearm, biceps and plantar arch), while the NoninConnect was worn on the fingertip and used as the ground truth. The results show that the BioPoint can accurately determine both heart rate and oxygen saturation from various locations on the body. However, as the BioPoint's photoplethysmograph is not calibrated it cannot be used for medical purposes (non-medical-grade).
Collapse
|
30
|
Nan X, Xu Z, Cao X, Hao J, Wang X, Duan Q, Wu G, Hu L, Zhao Y, Yang Z, Gao L. A Review of Epidermal Flexible Pressure Sensing Arrays. BIOSENSORS 2023; 13:656. [PMID: 37367021 DOI: 10.3390/bios13060656] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Revised: 06/11/2023] [Accepted: 06/14/2023] [Indexed: 06/28/2023]
Abstract
In recent years, flexible pressure sensing arrays applied in medical monitoring, human-machine interaction, and the Internet of Things have received a lot of attention for their excellent performance. Epidermal sensing arrays can enable the sensing of physiological information, pressure, and other information such as haptics, providing new avenues for the development of wearable devices. This paper reviews the recent research progress on epidermal flexible pressure sensing arrays. Firstly, the fantastic performance materials currently used to prepare flexible pressure sensing arrays are outlined in terms of substrate layer, electrode layer, and sensitive layer. In addition, the general fabrication processes of the materials are summarized, including three-dimensional (3D) printing, screen printing, and laser engraving. Subsequently, the electrode layer structures and sensitive layer microstructures used to further improve the performance design of sensing arrays are discussed based on the limitations of the materials. Furthermore, we present recent advances in the application of fantastic-performance epidermal flexible pressure sensing arrays and their integration with back-end circuits. Finally, the potential challenges and development prospects of flexible pressure sensing arrays are discussed in a comprehensive manner.
Collapse
Affiliation(s)
- Xueli Nan
- School of Automation and Software Engineering, Shanxi University, Taiyuan 030006, China
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China
| | - Zhikuan Xu
- School of Automation and Software Engineering, Shanxi University, Taiyuan 030006, China
| | - Xinxin Cao
- School of Automation and Software Engineering, Shanxi University, Taiyuan 030006, China
| | - Jinjin Hao
- School of Automation and Software Engineering, Shanxi University, Taiyuan 030006, China
| | - Xin Wang
- School of Automation and Software Engineering, Shanxi University, Taiyuan 030006, China
| | - Qikai Duan
- School of Automation and Software Engineering, Shanxi University, Taiyuan 030006, China
| | - Guirong Wu
- Pen-Tung Sah Institute of Micro-Nano Science and Technology, Xiamen University, Xiamen 361102, China
| | - Liangwei Hu
- Pen-Tung Sah Institute of Micro-Nano Science and Technology, Xiamen University, Xiamen 361102, China
| | - Yunlong Zhao
- Pen-Tung Sah Institute of Micro-Nano Science and Technology, Xiamen University, Xiamen 361102, China
- Discipline of Intelligent Instrument and Equipment, Xiamen University, Xiamen 361102, China
| | - Zekun Yang
- Key Laboratory of Instrumentation Science and Dynamic Measurement Ministry of Education, North University of China, Taiyuan 030051, China
| | - Libo Gao
- Pen-Tung Sah Institute of Micro-Nano Science and Technology, Xiamen University, Xiamen 361102, China
- Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen 361005, China
| |
Collapse
|
31
|
Park S, Zhunis A, Constantinides M, Aiello LM, Quercia D, Cha M. Social dimensions impact individual sleep quantity and quality. Sci Rep 2023; 13:9681. [PMID: 37322226 PMCID: PMC10272146 DOI: 10.1038/s41598-023-36762-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Accepted: 06/09/2023] [Indexed: 06/17/2023] Open
Abstract
While sleep positively impacts well-being, health, and productivity, the effects of societal factors on sleep remain underexplored. Here we analyze the sleep of 30,082 individuals across 11 countries using 52 million activity records from wearable devices. Our data are consistent with past studies of gender and age-associated sleep characteristics. However, our analysis of wearable device data uncovers differences in recorded vs. self-reported bedtime and sleep duration. The dataset allowed us to study how country-specific metrics such as GDP and cultural indices relate to sleep in groups and individuals. Our analysis indicates that diverse sleep metrics can be represented by two dimensions: sleep quantity and quality. We find that 55% of the variation in sleep quality, and 63% in sleep quantity, are explained by societal factors. Within a societal boundary, individual sleep experience was modified by factors like exercise. Increased exercise or daily steps were associated with better sleep quality (for example, faster sleep onset and less time awake in bed), especially in countries like the U.S. and Finland. Understanding how social norms relate to sleep will help create strategies and policies that enhance the positive impacts of sleep on health, such as productivity and well-being.
Collapse
Affiliation(s)
- Sungkyu Park
- Department of AI Convergence, Kangwon National University, Chuncheon, 24341, Republic of Korea
- Data Science Group, Institute for Basic Science, Daejeon, 34126, Republic of Korea
| | - Assem Zhunis
- Data Science Group, Institute for Basic Science, Daejeon, 34126, Republic of Korea
- School of Computing, KAIST, Daejeon, 34141, Republic of Korea
| | | | - Luca Maria Aiello
- IT University, Copenhagen, Denmark
- Pioneer Centre for AI, Copenhagen, Denmark
| | - Daniele Quercia
- Nokia Bell Labs, Cambridge, CB3 0FA, UK.
- Centre for Urban Science and Progress, King's College London, London, UK.
| | - Meeyoung Cha
- Data Science Group, Institute for Basic Science, Daejeon, 34126, Republic of Korea.
- School of Computing, KAIST, Daejeon, 34141, Republic of Korea.
| |
Collapse
|
32
|
Benis A, Haghi M, Deserno TM, Tamburis O. One Digital Health Intervention for Monitoring Human and Animal Welfare in Smart Cities: Viewpoint and Use Case. JMIR Med Inform 2023; 11:e43871. [PMID: 36305540 DOI: 10.2196/43871] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Revised: 03/15/2023] [Accepted: 04/18/2023] [Indexed: 05/20/2023] Open
Abstract
Smart cities and digital public health are closely related. Managing digital transformation in urbanization and living spaces is challenging. It is critical to prioritize the emotional and physical health and well-being of humans and their animals in the dynamic and ever-changing environment they share. Human-animal bonds are continuous as they live together or share urban spaces and have a mutual impact on each other's health as well as the surrounding environment. In addition, sensors embedded in the Internet of Things are everywhere in smart cities. They monitor events and provide appropriate responses. In this regard, accident and emergency informatics (A&EI) offers tools to identify and manage overtime hazards and disruptive events. Such manifold focuses fit with One Digital Health (ODH), which aims to transform health ecosystems with digital technology by proposing a comprehensive framework to manage data and support health-oriented policies. We showed and discussed how, by developing the concept of ODH intervention, the ODH framework can support the comprehensive monitoring and analysis of daily life events of humans and animals in technologically integrated environments such as smart homes and smart cities. We developed an ODH intervention use case in which A&EI mechanisms run in the background. The ODH framework structures the related data collection and analysis to enhance the understanding of human, animal, and environment interactions and associated outcomes. The use case looks at the daily journey of Tracy, a healthy woman aged 27 years, and her dog Mego. Using medical Internet of Things, their activities are continuously monitored and analyzed to prevent or manage any kind of health-related abnormality. We reported and commented on an ODH intervention as an example of a real-life ODH implementation. We gave the reader examples of a "how-to" analysis of Tracy and Mego's daily life activities as part of a timely implementation of the ODH framework. For each activity, relationships to the ODH dimensions were scored, and relevant technical fields were evaluated in light of the Findable, Accessible, Interoperable, and Reusable principles. This "how-to" can be used as a template for further analyses. An ODH intervention is based on Findable, Accessible, Interoperable, and Reusable data and real-time processing for global health monitoring, emergency management, and research. The data should be collected and analyzed continuously in a spatial-temporal domain to detect changes in behavior, trends, and emergencies. The information periodically gathered should serve human, animal, and environmental health interventions by providing professionals and caregivers with inputs and "how-to's" to improve health, welfare, and risk prevention at the individual and population levels. Thus, ODH complementarily combined with A&EI is meant to enhance policies and systems and modernize emergency management.
Collapse
Affiliation(s)
- Arriel Benis
- Department of Digital Medical Technologies, Holon Institute of Technology, Holon, Israel
- Working Group "One Digital Health", European Federation for Medical Informatics (EFMI), Le Mont-sur-Lausanne, Switzerland
- Working Group "One Digital Health", International Medical Informatics Association (IMIA), Chene-Bourg, Geneva, Switzerland
| | - Mostafa Haghi
- Ubiquitous Computing Laboratory, Department of Computer Science, HTWG Konstanz - University of Applied Sciences, Konstanz, Germany
- Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School, Braunschweig, Germany
- Working Group "Accident & Emergency Informatics", International Medical Informatics Association (IMIA), Chene-Bourg, Geneva, Switzerland
| | - Thomas M Deserno
- Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School, Braunschweig, Germany
- Working Group "Accident & Emergency Informatics", International Medical Informatics Association (IMIA), Chene-Bourg, Geneva, Switzerland
| | - Oscar Tamburis
- Working Group "One Digital Health", European Federation for Medical Informatics (EFMI), Le Mont-sur-Lausanne, Switzerland
- Working Group "One Digital Health", International Medical Informatics Association (IMIA), Chene-Bourg, Geneva, Switzerland
- Institute of Biostructures and Bioimaging, National Research Council, Naples, Italy
| |
Collapse
|
33
|
Song TA, Chowdhury SR, Malekzadeh M, Harrison S, Hoge TB, Redline S, Stone KL, Saxena R, Purcell SM, Dutta J. AI-Driven sleep staging from actigraphy and heart rate. PLoS One 2023; 18:e0285703. [PMID: 37195925 PMCID: PMC10191307 DOI: 10.1371/journal.pone.0285703] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Accepted: 05/02/2023] [Indexed: 05/19/2023] Open
Abstract
Sleep is an important indicator of a person's health, and its accurate and cost-effective quantification is of great value in healthcare. The gold standard for sleep assessment and the clinical diagnosis of sleep disorders is polysomnography (PSG). However, PSG requires an overnight clinic visit and trained technicians to score the obtained multimodality data. Wrist-worn consumer devices, such as smartwatches, are a promising alternative to PSG because of their small form factor, continuous monitoring capability, and popularity. Unlike PSG, however, wearables-derived data are noisier and far less information-rich because of the fewer number of modalities and less accurate measurements due to their small form factor. Given these challenges, most consumer devices perform two-stage (i.e., sleep-wake) classification, which is inadequate for deep insights into a person's sleep health. The challenging multi-class (three, four, or five-class) staging of sleep using data from wrist-worn wearables remains unresolved. The difference in the data quality between consumer-grade wearables and lab-grade clinical equipment is the motivation behind this study. In this paper, we present an artificial intelligence (AI) technique termed sequence-to-sequence LSTM for automated mobile sleep staging (SLAMSS), which can perform three-class (wake, NREM, REM) and four-class (wake, light, deep, REM) sleep classification from activity (i.e., wrist-accelerometry-derived locomotion) and two coarse heart rate measures-both of which can be reliably obtained from a consumer-grade wrist-wearable device. Our method relies on raw time-series datasets and obviates the need for manual feature selection. We validated our model using actigraphy and coarse heart rate data from two independent study populations: the Multi-Ethnic Study of Atherosclerosis (MESA; N = 808) cohort and the Osteoporotic Fractures in Men (MrOS; N = 817) cohort. SLAMSS achieves an overall accuracy of 79%, weighted F1 score of 0.80, 77% sensitivity, and 89% specificity for three-class sleep staging and an overall accuracy of 70-72%, weighted F1 score of 0.72-0.73, 64-66% sensitivity, and 89-90% specificity for four-class sleep staging in the MESA cohort. It yielded an overall accuracy of 77%, weighted F1 score of 0.77, 74% sensitivity, and 88% specificity for three-class sleep staging and an overall accuracy of 68-69%, weighted F1 score of 0.68-0.69, 60-63% sensitivity, and 88-89% specificity for four-class sleep staging in the MrOS cohort. These results were achieved with feature-poor inputs with a low temporal resolution. In addition, we extended our three-class staging model to an unrelated Apple Watch dataset. Importantly, SLAMSS predicts the duration of each sleep stage with high accuracy. This is especially significant for four-class sleep staging, where deep sleep is severely underrepresented. We show that, by appropriately choosing the loss function to address the inherent class imbalance, our method can accurately estimate deep sleep time (SLAMSS/MESA: 0.61±0.69 hours, PSG/MESA ground truth: 0.60±0.60 hours; SLAMSS/MrOS: 0.53±0.66 hours, PSG/MrOS ground truth: 0.55±0.57 hours;). Deep sleep quality and quantity are vital metrics and early indicators for a number of diseases. Our method, which enables accurate deep sleep estimation from wearables-derived data, is therefore promising for a variety of clinical applications requiring long-term deep sleep monitoring.
Collapse
Affiliation(s)
- Tzu-An Song
- University of Massachusetts Amherst, Amherst, MA, United States of America
| | | | - Masoud Malekzadeh
- University of Massachusetts Amherst, Amherst, MA, United States of America
| | - Stephanie Harrison
- California Pacific Medical Center Research Institute, San Francisco, CA, United States of America
| | - Terri Blackwell Hoge
- California Pacific Medical Center Research Institute, San Francisco, CA, United States of America
| | - Susan Redline
- Brigham and Women’s Hospital, Boston, MA, United States of America
| | - Katie L. Stone
- California Pacific Medical Center Research Institute, San Francisco, CA, United States of America
| | - Richa Saxena
- Massachusetts General Hospital, Boston, MA, United States of America
| | - Shaun M. Purcell
- Brigham and Women’s Hospital, Boston, MA, United States of America
| | - Joyita Dutta
- University of Massachusetts Amherst, Amherst, MA, United States of America
| |
Collapse
|
34
|
Haghi M, Asadov A, Boiko A, Ortega JA, Martínez Madrid N, Seepold R. Validating Force Sensitive Resistor Strip Sensors for Cardiorespiratory Measurement during Sleep: A Preliminary Study. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23083973. [PMID: 37112315 PMCID: PMC10141142 DOI: 10.3390/s23083973] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Revised: 04/05/2023] [Accepted: 04/12/2023] [Indexed: 06/12/2023]
Abstract
Sleep disorders can impact daily life, affecting physical, emotional, and cognitive well-being. Due to the time-consuming, highly obtrusive, and expensive nature of using the standard approaches such as polysomnography, it is of great interest to develop a noninvasive and unobtrusive in-home sleep monitoring system that can reliably and accurately measure cardiorespiratory parameters while causing minimal discomfort to the user's sleep. We developed a low-cost Out of Center Sleep Testing (OCST) system with low complexity to measure cardiorespiratory parameters. We tested and validated two force-sensitive resistor strip sensors under the bed mattress covering the thoracic and abdominal regions. Twenty subjects were recruited, including 12 males and 8 females. The ballistocardiogram signal was processed using the 4th smooth level of the discrete wavelet transform and the 2nd order of the Butterworth bandpass filter to measure the heart rate and respiration rate, respectively. We reached a total error (concerning the reference sensors) of 3.24 beats per minute and 2.32 rates for heart rate and respiration rate, respectively. For males and females, heart rate errors were 3.47 and 2.68, and respiration rate errors were 2.32 and 2.33, respectively. We developed and verified the reliability and applicability of the system. It showed a minor dependency on sleeping positions, one of the major cumbersome sleep measurements. We identified the sensor under the thoracic region as the optimal configuration for cardiorespiratory measurement. Although testing the system with healthy subjects and regular patterns of cardiorespiratory parameters showed promising results, further investigation is required with the bandwidth frequency and validation of the system with larger groups of subjects, including patients.
Collapse
Affiliation(s)
- Mostafa Haghi
- Ubiquitous Computing Laboratory, Department of Computer Science, HTWG Konstanz, 78462 Konstanz, Germany; (A.A.); (A.B.); (R.S.)
| | - Akhmadbek Asadov
- Ubiquitous Computing Laboratory, Department of Computer Science, HTWG Konstanz, 78462 Konstanz, Germany; (A.A.); (A.B.); (R.S.)
| | - Andrei Boiko
- Ubiquitous Computing Laboratory, Department of Computer Science, HTWG Konstanz, 78462 Konstanz, Germany; (A.A.); (A.B.); (R.S.)
| | | | - Natividad Martínez Madrid
- Internet of Things Laboratory, School of Informatics, Reutlingen University, 72762 Reutlingen, Germany;
| | - Ralf Seepold
- Ubiquitous Computing Laboratory, Department of Computer Science, HTWG Konstanz, 78462 Konstanz, Germany; (A.A.); (A.B.); (R.S.)
| |
Collapse
|
35
|
Pires GN, Arnardóttir ES, Islind AS, Leppänen T, McNicholas WT. Consumer sleep technology for the screening of obstructive sleep apnea and snoring: current status and a protocol for a systematic review and meta-analysis of diagnostic test accuracy. J Sleep Res 2023:e13819. [PMID: 36807680 DOI: 10.1111/jsr.13819] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Revised: 12/16/2022] [Accepted: 12/18/2022] [Indexed: 02/20/2023]
Abstract
There are concerns about the validation and accuracy of currently available consumer sleep technology for sleep-disordered breathing. The present report provides a background review of existing consumer sleep technologies and discloses the methods and procedures for a systematic review and meta-analysis of diagnostic test accuracy of these devices and apps for the detection of obstructive sleep apnea and snoring in comparison with polysomnography. The search will be performed in four databases (PubMed, Scopus, Web of Science, and the Cochrane Library). Studies will be selected in two steps, first by an analysis of abstracts followed by full-text analysis, and two independent reviewers will perform both phases. Primary outcomes include apnea-hypopnea index, respiratory disturbance index, respiratory event index, oxygen desaturation index, and snoring duration for both index and reference tests, as well as the number of true positives, false positives, true negatives, and false negatives for each threshold, as well as for epoch-by-epoch and event-by-event results, which will be considered for the calculation of surrogate measures (including sensitivity, specificity, and accuracy). Diagnostic test accuracy meta-analyses will be performed using the Chu and Cole bivariate binomial model. Mean difference meta-analysis will be performed for continuous outcomes using the DerSimonian and Laird random-effects model. Analyses will be performed independently for each outcome. Subgroup and sensitivity analyses will evaluate the effects of the types (wearables, nearables, bed sensors, smartphone applications), technologies (e.g., oximeter, microphone, arterial tonometry, accelerometer), the role of manufacturers, and the representativeness of the samples.
Collapse
Affiliation(s)
- Gabriel Natan Pires
- Departamento de Psicobiologia, Universidade Federal de São Paulo, São Paulo, Brazil.,European Sleep Research Society (ESRS), Regensburg, Germany
| | - Erna Sif Arnardóttir
- Reykjavik University Sleep Institute, Reykjavik University, Reykjavik, Iceland.,Landspitali-The National University Hospital of Iceland, Reykjavik, Iceland
| | - Anna Sigridur Islind
- Reykjavik University Sleep Institute, Reykjavik University, Reykjavik, Iceland.,Department of Computer Science, Reykjavik University, Reykjavik, Iceland
| | - Timo Leppänen
- Department of Technical Physics, University of Eastern Finland, Kuopio, Finland.,Diagnostic Imaging Center, Kuopio University Hospital, Kuopio, Finland.,School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Australia
| | - Walter T McNicholas
- Department of Respiratory and Sleep Medicine, St Vincent's Hospital Group, School of Medicine, University College Dublin, Dublin, Ireland
| |
Collapse
|
36
|
Grandner MA, Bromberg Z, Hadley A, Morrell Z, Graf A, Hutchison S, Freckleton D. Performance of a multisensor smart ring to evaluate sleep: in-lab and home-based evaluation of generalized and personalized algorithms. Sleep 2023; 46:6620808. [PMID: 35767600 DOI: 10.1093/sleep/zsac152] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Revised: 06/21/2022] [Indexed: 01/13/2023] Open
Abstract
STUDY OBJECTIVES Wearable sleep technology has rapidly expanded across the consumer market due to advances in technology and increased interest in personalized sleep assessment to improve health and mental performance. We tested the performance of a novel device, the Happy Ring, alongside other commercial wearables (Actiwatch 2, Fitbit Charge 4, Whoop 3.0, Oura Ring V2), against in-lab polysomnography (PSG) and at-home electroencephalography (EEG)-derived sleep monitoring device, the Dreem 2 Headband. METHODS Thirty-six healthy adults with no diagnosed sleep disorders and no recent use of medications or substances known to affect sleep patterns were assessed across 77 nights. Subjects participated in a single night of in-lab PSG and two nights of at-home data collection. The Happy Ring includes sensors for skin conductance, movement, heart rate, and skin temperature. The Happy Ring utilized two machine-learning derived scoring algorithms: a "generalized" algorithm that applied broadly to all users, and a "personalized" algorithm that adapted to individual subjects' data. Epoch-by-epoch analyses compared the wearable devices to in-lab PSG and to at-home EEG Headband. RESULTS Compared to in-lab PSG, the "generalized" and "personalized" algorithms demonstrated good sensitivity (94% and 93%, respectively) and specificity (70% and 83%, respectively). The Happy Personalized model demonstrated a lower bias and more narrow limits of agreement across Bland-Altman measures. CONCLUSION The Happy Ring performed well at home and in the lab, especially regarding sleep/wake detection. The personalized algorithm demonstrated improved detection accuracy over the generalized approach and other devices, suggesting that adaptable, dynamic algorithms can enhance sleep detection accuracy.
Collapse
Affiliation(s)
- Michael A Grandner
- Department of Psychiatry, University of Arizona College of Medicine, Tucson, AZ, USA
| | | | | | | | | | - Stephen Hutchison
- Department of Psychiatry, University of Arizona College of Medicine, Tucson, AZ, USA
| | | |
Collapse
|
37
|
Lane JM, Qian J, Mignot E, Redline S, Scheer FAJL, Saxena R. Genetics of circadian rhythms and sleep in human health and disease. Nat Rev Genet 2023; 24:4-20. [PMID: 36028773 PMCID: PMC10947799 DOI: 10.1038/s41576-022-00519-z] [Citation(s) in RCA: 50] [Impact Index Per Article: 50.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/30/2022] [Indexed: 12/13/2022]
Abstract
Circadian rhythms and sleep are fundamental biological processes integral to human health. Their disruption is associated with detrimental physiological consequences, including cognitive, metabolic, cardiovascular and immunological dysfunctions. Yet many of the molecular underpinnings of sleep regulation in health and disease have remained elusive. Given the moderate heritability of circadian and sleep traits, genetics offers an opportunity that complements insights from model organism studies to advance our fundamental molecular understanding of human circadian and sleep physiology and linked chronic disease biology. Here, we review recent discoveries of the genetics of circadian and sleep physiology and disorders with a focus on those that reveal causal contributions to complex diseases.
Collapse
Affiliation(s)
- Jacqueline M Lane
- Center for Genomic Medicine and Department of Anaesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital; and Division of Sleep Medicine, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, USA
| | - Jingyi Qian
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital; and Division of Sleep Medicine, Harvard Medical School, Boston, MA, USA
| | - Emmanuel Mignot
- Center for Narcolepsy, Stanford University, Palo Alto, California, USA
| | - Susan Redline
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital; and Division of Sleep Medicine, Harvard Medical School, Boston, MA, USA
| | - Frank A J L Scheer
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital; and Division of Sleep Medicine, Harvard Medical School, Boston, MA, USA.
| | - Richa Saxena
- Center for Genomic Medicine and Department of Anaesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital; and Division of Sleep Medicine, Harvard Medical School, Boston, MA, USA.
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, USA.
| |
Collapse
|
38
|
Budig M, Stoohs R, Keiner M. Validity of Two Consumer Multisport Activity Tracker and One Accelerometer against Polysomnography for Measuring Sleep Parameters and Vital Data in a Laboratory Setting in Sleep Patients. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22239540. [PMID: 36502241 PMCID: PMC9741062 DOI: 10.3390/s22239540] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Revised: 11/25/2022] [Accepted: 12/01/2022] [Indexed: 05/16/2023]
Abstract
Two commercial multisport activity trackers (Garmin Forerunner 945 and Polar Ignite) and the accelerometer ActiGraph GT9X were evaluated in measuring vital data, sleep stages and sleep/wake patterns against polysomnography (PSG). Forty-nine adult patients with suspected sleep disorders (30 males/19 females) completed a one-night PSG sleep examination followed by a multiple sleep latency test (MSLT). Sleep parameters, time in bed (TIB), total sleep time (TST), wake after sleep onset (WASO), sleep onset latency (SOL), awake time (WASO + SOL), sleep stages (light, deep, REM sleep) and the number of sleep cycles were compared. Both commercial trackers showed high accuracy in measuring vital data (HR, HRV, SpO2, respiratory rate), r > 0.92. For TIB and TST, all three trackers showed medium to high correlation, r > 0.42. Garmin had significant overestimation of TST, with MAE of 84.63 min and MAPE of 25.32%. Polar also had an overestimation of TST, with MAE of 45.08 min and MAPE of 13.80%. ActiGraph GT9X results were inconspicuous. The trackers significantly underestimated awake times (WASO + SOL) with weak correlation, r = 0.11−0.57. The highest MAE was 50.35 min and the highest MAPE was 83.02% for WASO for Garmin and ActiGraph GT9X; Polar had the highest MAE of 21.17 min and the highest MAPE of 141.61% for SOL. Garmin showed significant deviations for sleep stages (p < 0.045), while Polar only showed significant deviations for sleep cycle (p = 0.000), r < 0.50. Garmin and Polar overestimated light sleep and underestimated deep sleep, Garmin significantly, with MAE up to 64.94 min and MAPE up to 116.50%. Both commercial trackers Garmin and Polar did not detect any daytime sleep at all during the MSLT test. The use of the multisport activity trackers for sleep analysis can only be recommended for general daily use and for research purposes. If precise data on sleep stages and parameters are required, their use is limited. The accuracy of the vital data measurement was adequate. Further studies are needed to evaluate their use for medical purposes, inside and outside of the sleep laboratory. The accelerometer ActiGraph GT9X showed overall suitable accuracy in detecting sleep/wake patterns.
Collapse
Affiliation(s)
- Mario Budig
- Department of Sports Science, German University of Health & Sport, 85737 Ismaning, Germany
| | | | - Michael Keiner
- Department of Sports Science, German University of Health & Sport, 85737 Ismaning, Germany
- Correspondence:
| |
Collapse
|
39
|
Pfammatter AF, Hughes BO, Tucker B, Whitmore H, Spring B, Tasali E. The Development of a Novel mHealth Tool for Obstructive Sleep Apnea: Tracking Continuous Positive Airway Pressure Adherence as a Percentage of Time in Bed. J Med Internet Res 2022; 24:e39489. [PMID: 36469406 DOI: 10.2196/39489] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Revised: 10/07/2022] [Accepted: 10/24/2022] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Continuous positive airway pressure (CPAP) is the mainstay obstructive sleep apnea (OSA) treatment; however, poor adherence to CPAP is common. Current guidelines specify 4 hours of CPAP use per night as a target to define adequate treatment adherence. However, effective OSA treatment requires CPAP use during the entire time spent in bed to optimally treat respiratory events and prevent adverse health effects associated with the time spent sleeping without wearing a CPAP device. Nightly sleep patterns vary considerably, making it necessary to measure CPAP adherence relative to the time spent in bed. Weight loss is an important goal for patients with OSA. Tools are required to address these clinical challenges in patients with OSA. OBJECTIVE This study aimed to develop a mobile health tool that combined weight loss features with novel CPAP adherence tracking (ie, percentage of CPAP wear time relative to objectively assessed time spent in bed) for patients with OSA. METHODS We used an iterative, user-centered process to design a new CPAP adherence tracking module that integrated with an existing weight loss app. A total of 37 patients with OSA aged 20 to 65 years were recruited. In phase 1, patients with OSA who were receiving CPAP treatment (n=7) tested the weight loss app to track nutrition, activity, and weight for 10 days. Participants completed a usability and acceptability survey. In phase 2, patients with OSA who were receiving CPAP treatment (n=21) completed a web-based survey about their interpretations and preferences for wireframes of the CPAP tracking module. In phase 3, patients with recently diagnosed OSA who were CPAP naive (n=9) were prescribed a CPAP device (ResMed AirSense10 AutoSet) and tested the integrated app for 3 to 4 weeks. Participants completed a usability survey and provided feedback. RESULTS During phase 1, participants found the app to be mostly easy to use, except for some difficulty searching for specific foods. All participants found the connected devices (Fitbit activity tracker and Fitbit Aria scale) easy to use and helpful. During phase 2, participants correctly interpreted CPAP adherence success, expressed as percentage of wear time relative to time spent in bed, and preferred seeing a clearly stated percentage goal ("Goal: 100%"). In phase 3, participants found the integrated app easy to use and requested push notification reminders to wear CPAP before bedtime and to sync Fitbit in the morning. CONCLUSIONS We developed a mobile health tool that integrated a new CPAP adherence tracking module into an existing weight loss app. Novel features included addressing OSA-obesity comorbidity, CPAP adherence tracking via percentage of CPAP wear time relative to objectively assessed time spent in bed, and push notifications to foster adherence. Future research on the effectiveness of this tool in improving OSA treatment adherence is warranted.
Collapse
Affiliation(s)
- Angela Fidler Pfammatter
- Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Evanston, IL, United States
| | | | - Becky Tucker
- Department of Medicine, University of Chicago, Chicago, IL, United States
| | - Harry Whitmore
- Department of Medicine, University of Chicago, Chicago, IL, United States
| | - Bonnie Spring
- Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Evanston, IL, United States
| | - Esra Tasali
- Department of Medicine, University of Chicago, Chicago, IL, United States
| |
Collapse
|
40
|
Iacobelli P. Circadian dysregulation and Alzheimer’s disease: A comprehensive review. BRAIN SCIENCE ADVANCES 2022. [DOI: 10.26599/bsa.2022.9050021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/05/2022] Open
Abstract
Alzheimer’s disease (AD), the foremost variant of dementia, has been associated with a menagerie of risk factors, many of which are considered to be modifiable. Among these modifiable risk factors is circadian rhythm, the chronobiological system that regulates sleep‐wake cycles, food consumption timing, hydration timing, and immune responses amongst many other necessary physiological processes. Circadian rhythm at the level of the suprachiasmatic nucleus (SCN), is tightly regulated in the human body by a host of biomolecular substances, principally the hormones melatonin, cortisol, and serotonin. In addition, photic information projected along afferent pathways to the SCN and peripheral oscillators regulates the synthesis of these hormones and mediates the manner in which they act on the SCN and its substructures. Dysregulation of this cycle, whether induced by environmental changes involving irregular exposure to light, or through endogenous pathology, will have a negative impact on immune system optimization and will heighten the deposition of Aβ and the hyperphosphorylation of the tau protein. Given these correlations, it appears that there is a physiologic association between circadian rhythm dysregulation and AD. This review will explore the physiology of circadian dysregulation in the AD brain, and will propose a basic model for its role in AD‐typical pathology, derived from the literature compiled and referenced throughout.
Collapse
Affiliation(s)
- Peter Iacobelli
- Department of Arts and Sciences, University of South Carolina, Columbia, USA
| |
Collapse
|
41
|
Djanian S, Bruun A, Nielsen TD. Sleep classification using Consumer Sleep Technologies and AI: A review of the current landscape. Sleep Med 2022; 100:390-403. [PMID: 36206600 DOI: 10.1016/j.sleep.2022.09.004] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Accepted: 09/05/2022] [Indexed: 01/11/2023]
Abstract
Classifying sleep stages in real-time represents considerable potential, for instance in enabling interactive noise masking in noisy environments when persons are in a state of light sleep or to support clinical staff in analyzing sleep patterns etc. However, the current gold standard for classifying sleep stages, Polysomnography (PSG), is too cumbersome to apply outside controlled hospital settings and requires manual as well as highly specialized knowledge to classify sleep stages. Using data from Consumer Sleep Technologies (CSTs) to inform machine learning algorithms represent a promising opportunity for automating the process of classifying sleep stages, also in settings outside the confinements of clinical expert settings. This study reviews 27 papers that use CSTs in combination with Artificial Intelligence (AI) models to classify sleep stages. AI models and their performance are described and compared to synthesize current state of the art in sleep stage classification with CSTs. Furthermore, gaps in the current approaches are shown and how these AI models could be improved in the near-future. Lastly, the challenges of designing interactions for users that are asleep are highlighted pointing towards avenues of more interactive sleep interventions based on AI-infused CSTs solutions.
Collapse
Affiliation(s)
- Shagen Djanian
- Aalborg University, Department of Computer Science, Denmark.
| | - Anders Bruun
- Aalborg University, Department of Computer Science, Denmark
| | | |
Collapse
|
42
|
LifeSnaps, a 4-month multi-modal dataset capturing unobtrusive snapshots of our lives in the wild. Sci Data 2022; 9:663. [PMID: 36316345 PMCID: PMC9622868 DOI: 10.1038/s41597-022-01764-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Accepted: 10/06/2022] [Indexed: 11/07/2022] Open
Abstract
Ubiquitous self-tracking technologies have penetrated various aspects of our lives, from physical and mental health monitoring to fitness and entertainment. Yet, limited data exist on the association between in the wild large-scale physical activity patterns, sleep, stress, and overall health, and behavioral and psychological patterns due to challenges in collecting and releasing such datasets, including waning user engagement or privacy considerations. In this paper, we present the LifeSnaps dataset, a multi-modal, longitudinal, and geographically-distributed dataset containing a plethora of anthropological data, collected unobtrusively for the total course of more than 4 months by n = 71 participants. LifeSnaps contains more than 35 different data types from second to daily granularity, totaling more than 71 M rows of data. The participants contributed their data through validated surveys, ecological momentary assessments, and a Fitbit Sense smartwatch and consented to make these data available to empower future research. We envision that releasing this large-scale dataset of multi-modal real-world data will open novel research opportunities and potential applications in multiple disciplines.
Collapse
|
43
|
Wang X, Wang R, Sheng F, Chen L. The effects of empathy by caregivers on healthcare service satisfaction. Front Psychol 2022; 13:912076. [PMID: 36275215 PMCID: PMC9582974 DOI: 10.3389/fpsyg.2022.912076] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Accepted: 09/13/2022] [Indexed: 01/10/2023] Open
Abstract
Healthcare service satisfaction focuses not only on the patients but also on the caregivers' perspectives. This study explored how caregivers' empathy toward patients affects their satisfaction with healthcare services through an electroencephalography (EEG) experiment. EEG mu rhythm was used as the neural indicator to reflect empathy. The results showed that empathy reduces caregivers' evaluation of healthcare service satisfaction because they share suffering with the patients. However, implementing physician-patient communication through a process-based informed consent (IC), compared to an event-based IC, can effectively alleviate such adverse effects.
Collapse
Affiliation(s)
- Xiaoyi Wang
- School of Management, Zhejiang University, Hangzhou, China
| | - Ruining Wang
- School of Management, Zhejiang University, Hangzhou, China
| | - Feng Sheng
- School of Management, Zhejiang University, Hangzhou, China
| | - Leyi Chen
- Faculty of Arts, McGill University, Montreal, QC, Canada
| |
Collapse
|
44
|
Bakewell N, Sabin CA, Negi R, Garcia-Leon A, Winston A, Sachikonye M, Doyle N, Redline S, Mallon PWG, Kunisaki KM. Biomarker associations with insomnia and secondary sleep outcomes in persons with and without HIV in the POPPY-Sleep substudy: a cohort study. Sleep 2022; 45:6698710. [PMID: 36104003 PMCID: PMC9742892 DOI: 10.1093/sleep/zsac212] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Revised: 08/11/2022] [Indexed: 12/15/2022] Open
Abstract
STUDY OBJECTIVES We investigated associations between inflammatory profiles/clusters and sleep measures in people living with HIV and demographically-/lifestyle-similar HIV-negative controls in the Pharmacokinetic and clinical Observations in PeoPle over fiftY (POPPY)-Sleep substudy. METHODS Primary outcome was insomnia (Insomnia Severity Index [ISI]>15). Secondary sleep outcomes included 7-day actigraphy (e.g. mean/standard deviation of sleep duration/efficiency), overnight oximetry (e.g. oxygen desaturation index [ODI]) and patient-reported measures (Patient-Reported Outcomes Measurement Information System (PROMIS) sleep questionnaires). Participants were grouped using Principal Component Analysis of 31 biomarkers across several inflammatory pathways followed by cluster analysis. Between-cluster differences in baseline characteristics and sleep outcomes were assessed using Kruskal-Wallis/logistic regression/Chi-squared/Fisher's exact tests. RESULTS Of the 465 participants included (74% people with HIV, median [interquartile range] age 54 [50-60] years), only 18% had insomnia and secondary sleep outcomes suggested generally good sleep (e.g. ODI 3.1/hr [1.5-6.4]). Three clusters with distinct inflammatory profiles were identified: "gut/immune activation" (n = 47), "neurovascular" (n = 209), and "reference" (relatively lower inflammation; n = 209). The "neurovascular" cluster included higher proportions of people with HIV, obesity (BMI>30 kg/m2), and previous cardiovascular disease, mental health disorder, and arthritis of knee/hip relative to the other two clusters. No clinically relevant between-cluster differences were observed in proportions with insomnia (17%, 18%, 20%) before (p = .76) or after (p = .75) adjustment for potential confounders. Few associations were observed among actigraphy, oximetry, and PROMIS measures. CONCLUSIONS Although associations could exist with other sleep measures or biomarker types not assessed, our findings do not support a strong association between sleep and inflammation in people with HIV.
Collapse
Affiliation(s)
| | - Caroline A Sabin
- Corresponding author. Centre for Clinical Research, Epidemiology, Modelling and Evaluation, Institute for Global Health, UCL, Royal Free Campus, Rowland Hill Street, London, NW3 2PFUK, UK.
| | - Riya Negi
- Centre for Experimental Pathogen Host Research, School of Medicine, University CollegeDublin, Ireland
| | - Alejandro Garcia-Leon
- Centre for Experimental Pathogen Host Research, School of Medicine, University CollegeDublin, Ireland
| | - Alan Winston
- Department of Infectious Disease, Imperial College London, London, UK
| | | | - Nicki Doyle
- Department of Infectious Disease, Imperial College London, London, UK
| | - Susan Redline
- Brigham and Women’s Hospital, Boston, USA,Harvard Medical School, Harvard University, Boston, USA,Beth Israel Deaconess Medical Center, Boston, USA
| | | | | |
Collapse
|
45
|
Feature matching as improved transfer learning technique for wearable EEG. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.104009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
|
46
|
Klier K, Wagner M. Agreement of Sleep Measures-A Comparison between a Sleep Diary and Three Consumer Wearable Devices. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22166189. [PMID: 36015949 PMCID: PMC9413956 DOI: 10.3390/s22166189] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Revised: 08/16/2022] [Accepted: 08/16/2022] [Indexed: 05/28/2023]
Abstract
Nowadays, self-tracking and optimization are widely spread. As sleep is essential for well-being, health, and peak performance, the number of available consumer technologies to assess individual sleep behavior is increasing rapidly. However, little is known about the consumer wearables' usability and reliability for sleep tracking. Therefore, the aim of the present study was to compare the sleep measures of wearable devices with a standardized sleep diary in young healthy adults in free-living conditions. We tracked night sleep from 30 participants (19 females, 11 males; 24.3 ± 4.2 years old). Each wore three wearables and simultaneously assessed individual sleep patterns for four consecutive nights. Wearables and diaries correlated substantially regarding time in bed (Range CCCLin: 0.74-0.84) and total sleep time (Range CCCLin: 0.76-0.85). There was no sufficient agreement regarding the measures of sleep efficiency (Range CCCLin: 0.05-0.34) and sleep interruptions (Range CCCLin: -0.02-0.10). Finally, these results show wearables to be an easy-to-handle, time- and cost-efficient alternative to tracking sleep in healthy populations. Future research should develop and empirically test the usability of such consumer sleep technologies.
Collapse
|
47
|
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]
|
48
|
Jenkins CA, Tiley LCF, Lay I, Hartmann JA, Chan JKM, Nicholas CL. Comparing GENEActiv against Actiwatch-2 over Seven Nights Using a Common Sleep Scoring Algorithm and Device-Specific Wake Thresholds. Behav Sleep Med 2022; 20:369-379. [PMID: 34096407 DOI: 10.1080/15402002.2021.1924175] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Abstract
Demonstrating inter-device reliability is essential to use devices interchangeably, and accurately integrate, interpret, or compare data from different actigraphs. Despite this, there is a paucity of comparative literature over a timeframe exceeding one night. The aims of this study were to determine an optimal wake threshold for GENEActiv and to evaluate the concordance between Actiwatch-2 and GENEActiv using a common algorithm (Phillips Respironics). Data were collected from 33 individuals (20 female) aged 20-35 years (M= 25.33, SD = 4.69) across a total 213 nights. Participants wore both devices simultaneously and continuously for seven days. The sleep parameters of interest were: total sleep time, sleep efficiency, sleep onset latency, and wake after sleep onset. Exploratory analyses of sensitivity, specificity, overall accuracy, mean bias, and paired samples t-tests indicated an optimal wake threshold of 115 for GENEActiv, compared with Actiwatch-2 at the 40 (medium, default) threshold. Using these thresholds, sensitivity, and overall accuracy of GENEActiv were both good (86% and 78%, respectively), however specificity was relatively low (40%). There were no significant inter-device differences for any sleep parameters, and all absolute mean biases were small. Overall, the findings from this study provide the first empirical evidence to support the reliability of GENEActiv against Actiwatch-2 over multiple nights using a common algorithm with device-specific wake thresholds.
Collapse
Affiliation(s)
- Claire A Jenkins
- Melbourne School of Psychological Sciences, The University of Melbourne, Parkville, Australia
| | - Lucy C F Tiley
- Melbourne School of Psychological Sciences, The University of Melbourne, Parkville, Australia
| | - Isabella Lay
- Melbourne School of Psychological Sciences, The University of Melbourne, Parkville, Australia
| | - Jessica A Hartmann
- Orygen, Parkville, Australia.,Centre For Youth Mental Health, The University of Melbourne, Parkville, Australia
| | - Julia K M Chan
- Melbourne School of Psychological Sciences, The University of Melbourne, Parkville, Australia
| | - Christian L Nicholas
- Melbourne School of Psychological Sciences, The University of Melbourne, Parkville, Australia.,Institute for Breathing and Sleep, Heidelberg, Australia
| |
Collapse
|
49
|
Huang B, Liu X, Wang Y, Li H, Si J, Wang D, Afzal K. Is the Discount Really Favorable? The Effect of Numeracy on Price Magnitude Judgment: Evidence From Electroencephalography. Front Neurosci 2022; 16:817450. [PMID: 35769701 PMCID: PMC9234211 DOI: 10.3389/fnins.2022.817450] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Accepted: 05/12/2022] [Indexed: 11/13/2022] Open
Abstract
Attractive price promotion will induce an unreasonable willingness to purchase, especially through shopping. However, it is not clear how numeracy, one of the essential abilities for understanding and applying numbers, influences the process of purchase judgment. In total, 61 participants were recruited to perform a price promotion task using electroencephalography. The results showed that consumers with low numeracy performed worse than their peers with high numeracy at the behavioral level, and they also had lower P3b amplitude and less alpha desynchronization, regardless of price promotion frameworks. These findings provided evidence on the processing of price information and provided further insights into how numeracy impacts price magnitude judgment.
Collapse
Affiliation(s)
| | | | | | | | - Jiwei Si
- School of Psychology, Shandong Normal University, Jinan, China
| | | | | |
Collapse
|
50
|
Perez-Pozuelo I, Posa M, Spathis D, Westgate K, Wareham N, Mascolo C, Brage S, Palotti J. Detecting sleep outside the clinic using wearable heart rate devices. Sci Rep 2022; 12:7956. [PMID: 35562527 PMCID: PMC9106748 DOI: 10.1038/s41598-022-11792-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Accepted: 04/04/2022] [Indexed: 02/02/2023] Open
Abstract
The adoption of multisensor wearables presents the opportunity of longitudinal monitoring of sleep in large populations. Personalized yet device-agnostic algorithms can sidestep laborious human annotations and objectify cross-cohort comparisons. We developed and tested a heart rate-based algorithm that captures inter- and intra-individual sleep differences in free-living conditions and does not require human input. We evaluated it on four study cohorts using different research- and consumer-grade devices for over 2000 nights. Recording periods included both 24 h free-living and conventional lab-based night-only data. We compared our optimized method against polysomnography, sleep diaries and sleep periods produced through a state-of-the-art acceleration based method. Against sleep diaries, the algorithm yielded a mean squared error of 0.04-0.06 and a total sleep time (TST) deviation of [Formula: see text]2.70 (± 5.74) and 12.80 (± 3.89) minutes, respectively. When evaluated with PSG lab studies, the MSE ranged between 0.06 and 0.11 yielding a time deviation between [Formula: see text]29.07 and [Formula: see text]55.04 minutes. These results showcase the value of this open-source, device-agnostic algorithm for the reliable inference of sleep in free-living conditions and in the absence of annotations.
Collapse
Affiliation(s)
- Ignacio Perez-Pozuelo
- MRC Epidemiology Unit, School of Clinical Medicine, University of Cambridge, Cambridge, UK.
- The Alan Turing Institute, London, UK.
| | - Marius Posa
- School of Clinical Medicine, University of Cambridge, Cambridge, UK
| | - Dimitris Spathis
- Department of Computer Science and Technology, University of Cambridge, Cambridge, UK
| | - Kate Westgate
- MRC Epidemiology Unit, School of Clinical Medicine, University of Cambridge, Cambridge, UK
| | - Nicholas Wareham
- MRC Epidemiology Unit, School of Clinical Medicine, University of Cambridge, Cambridge, UK
| | - Cecilia Mascolo
- Department of Computer Science and Technology, University of Cambridge, Cambridge, UK
| | - Søren Brage
- MRC Epidemiology Unit, School of Clinical Medicine, University of Cambridge, Cambridge, UK
| | - Joao Palotti
- Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar.
| |
Collapse
|