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Pun M, Guadagni V, Longman RS, Hanly PJ, Hill MD, Anderson TJ, Hogan DB, Rawling JM, Poulin M. Sex differences in the association of sleep spindle density and cognitive performance among community-dwelling middle-aged and older adults with obstructive sleep apnea. J Sleep Res 2024; 33:e14095. [PMID: 37963455 DOI: 10.1111/jsr.14095] [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: 05/15/2023] [Revised: 10/24/2023] [Accepted: 10/26/2023] [Indexed: 11/16/2023]
Abstract
Recent studies have found associations between obstructive sleep apnea and cognitive decline. The underlying mechanisms are still unclear. Here, we investigate the associations between changes in micro-architecture, specifically sleep spindles, and cognitive function in community-dwelling middle-aged and older adults, some with obstructive sleep apnea, with a focus on sex differences. A total of 125 voluntary participants (mean age 66.0 ± 6.4 years, 64 females) from a larger cohort (participants of the Brain in Motion Studies I and II) underwent 1 night of in-home polysomnography and a neuropsychological battery (sleep and cognitive testing were conducted within 2 weeks of each other). A semi-automatic computerized algorithm was used to score polysomnography data and detect spindle characteristics in non-rapid eye movement Stages 2 and 3 in both frontal and central electrodes. Based on their apnea-hypopnea index, participants were divided into those with no obstructive sleep apnea (apnea-hypopnea index < 5 per hr, n = 21), mild obstructive sleep apnea (5 ≥ apnea-hypopnea index < 15, n = 47), moderate obstructive sleep apnea (15 ≥ apnea-hypopnea index < 30, n = 34) and severe obstructive sleep apnea (apnea-hypopnea index ≥ 30, n = 23). There were no significant differences in spindle characteristics between the four obstructive sleep apnea severity groups. Spindle density and percentage of fast spindles were positively associated with some verbal fluency measures on the cognitive testing. Sex might be linked with these associations. Biological sex could play a role in the associations between spindle characteristics and some verbal fluency measures. Obstructive sleep apnea severity was not found to be a contributing factor in this non-clinical community-dwelling cohort.
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Affiliation(s)
- Matiram Pun
- Department of Physiology & Pharmacology, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
- Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
- Department of Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
| | - Veronica Guadagni
- Department of Physiology & Pharmacology, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
- Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
- Department of Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
- O'Brien Institute for Public Health, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
| | - Richard Stewart Longman
- Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
- Psychology Service, Foothills Medical Centre, Alberta Health Service, Calgary, Alberta, Canada
| | - Patrick J Hanly
- Department of Medicine, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
- Sleep Centre, Foothills Medical Centre, Calgary, Alberta, Canada
| | - Michael D Hill
- Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
- Department of Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
- O'Brien Institute for Public Health, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
- Department of Medicine, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
| | - Todd J Anderson
- Department of Cardiac Science, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
- Libin Cardiovascular Institute of Alberta, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
| | - David B Hogan
- Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
- Department of Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
- O'Brien Institute for Public Health, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
- Department of Medicine, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
| | - Jean M Rawling
- Department of Family Medicine, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
| | - Marc Poulin
- Department of Physiology & Pharmacology, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
- Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
- Department of Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
- O'Brien Institute for Public Health, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
- Libin Cardiovascular Institute of Alberta, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
- Faculty of Kinesiology, University of Calgary, Calgary, Alberta, Canada
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McMahon M, Goldin J, Kealy ES, Wicks DJ, Zilberg E, Freeman W, Aliahmad B. Performance Investigation of Somfit Sleep Staging Algorithm. Nat Sci Sleep 2024; 16:1027-1043. [PMID: 39071546 PMCID: PMC11277903 DOI: 10.2147/nss.s463026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/09/2024] [Accepted: 07/01/2024] [Indexed: 07/30/2024] Open
Abstract
Purpose To investigate accuracy of the sleep staging algorithm in a new miniaturized home sleep monitoring device - Compumedics® Somfit. Somfit is attached to patient's forehead and combines channels specified for a pulse arterial tonometry (PAT)-based home sleep apnea testing (HSAT) device with the neurological signals. Somfit sleep staging deep learning algorithm is based on convolutional neural network architecture. Patients and Methods One hundred and ten participants referred for sleep investigation with suspected or preexisting obstructive sleep apnea (OSA) in need of a review were enrolled into the study involving simultaneous recording of full overnight polysomnography (PSG) and Somfit data. The recordings were conducted at three centers in Australia. The reported statistics include standard measures of agreement between Somfit automatic hypnogram and consensus PSG hypnogram. Results Overall percent agreement across five sleep stages (N1, N2, N3, REM, and wake) between Somfit automatic and consensus PSG hypnograms was 76.14 (SE: 0.79). The percent agreements between different pairs of sleep technologists' PSG hypnograms varied from 74.36 (1.93) to 85.50 (0.64), with interscorer agreement being greater for scorers from the same sleep laboratory. The estimate of kappa between Somfit and consensus PSG was 0.672 (0.002). Percent agreement for sleep/wake discrimination was 89.30 (0.37). The accuracy of Somfit sleep staging algorithm varied with increasing OSA severity - percent agreement was 79.67 (1.87) for the normal subjects, 77.38 (1.06) for mild OSA, 74.83 (1.79) for moderate OSA and 72.93 (1.68) for severe OSA. Conclusion Agreement between Somfit and PSG hypnograms was non-inferior to PSG interscorer agreement for a number of scorers, thus confirming acceptability of electrode placement at the center of the forehead. The directions for algorithm improvement include additional arousal detection, integration of motion and oximetry signals and separate inference models for individual sleep stages.
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Affiliation(s)
- Marcus McMahon
- Department of Respiratory and Sleep Medicine, Epworth Hospital, Richmond, Victoria, Australia and Department of Respiratory and Sleep Medicine, Austin Health, Heidelberg, Victoria, Australia
| | - Jeremy Goldin
- Department of Respiratory and Sleep Medicine, Royal Melbourne Hospital, Parkvile, Victoria, Australia
| | | | | | - Eugene Zilberg
- Medical Innovations, Compumedics Limited, Abbotsford, Victoria, Australia
| | - Warwick Freeman
- Medical Innovations, Compumedics Limited, Abbotsford, Victoria, Australia
| | - Behzad Aliahmad
- Medical Innovations, Compumedics Limited, Abbotsford, Victoria, Australia
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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.
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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.
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Cordoza ML, Anderson BJ, Cevasco M, Diamond JM, Younes M, Gerardy B, Iroegbu C, Riegel B. Feasibility and Acceptability of Using Wireless Limited Polysomnography to Capture Sleep Before, During, and After Hospitalization for Patients With Planned Cardiothoracic Surgery. J Cardiovasc Nurs 2024:00005082-990000000-00180. [PMID: 38509035 DOI: 10.1097/jcn.0000000000001092] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/22/2024]
Abstract
BACKGROUND Sleep disruption, a common symptom among patients requiring cardiovascular surgery, is a potential risk factor for the development of postoperative delirium. Postoperative delirium is a disorder of acute disturbances in cognition associated with prolonged hospitalization, cognitive decline, and mortality. OBJECTIVE The aim of this study was to evaluate the feasibility and acceptability of using polysomnography (PSG) to capture sleep in patients with scheduled cardiothoracic surgery. METHODS Wireless limited PSG assessed sleep at baseline (presurgery at home), postoperatively in the intensive care unit, and at home post hospital discharge. Primary outcomes were quality and completeness of PSG signals, and acceptability by participants and nursing staff. RESULTS Among 15 patients, PSG data were of high quality, and mean percentage of unscorable data was 5.5% ± 11.1%, 3.7% ± 5.4%, and 3.7% ± 8.4% for baseline, intensive care unit, and posthospitalization measurements, respectively. Nurses and patients found the PSG monitor acceptable. CONCLUSIONS Wireless, limited PSG to capture sleep across the surgical continuum was feasible, and data were of high quality. Authors of future studies will evaluate associations of sleep indices and development of postoperative delirium in this high-risk population.
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Younes M. New insights and potential clinical implications of the odds ratio product. Front Neurol 2023; 14:1273623. [PMID: 37885480 PMCID: PMC10598615 DOI: 10.3389/fneur.2023.1273623] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2023] [Accepted: 09/11/2023] [Indexed: 10/28/2023] Open
Abstract
The odds ratio product (ORP) is a continuous metric of sleep depth that ranges from 0 (very deep sleep) to 2. 5 (full wakefulness). Its advantage over the conventional method recommended by AASM is that it discloses different levels of stage wake (sleep propensity) and different sleep depths within the same sleep stage. As such, it can be used to identify differences in sleep depth between subjects, and in the same subjects under different circumstances, when differences are not discernible by conventional staging. It also identifies different sleep depths within stage rapid-eye-movement sleep, with possible implications to disorders during this stage. Epoch-by-epoch ORP can be displayed graphically across the night or as average values in conventional sleep stages. In addition, ORP can be reported as % of recording time in specific ORP ranges (e.g., deciles of the total ORP range) where it produces distinct distribution patterns (ORP-architecture) that have been associated with different clinical disorders and outcomes. These patterns offer unique research opportunities to identify different mechanisms and potential therapy for various sleep complaints and disorders. In this review I will discuss how ORP is measured, its validation, differences from delta power, and the various phenotypes, and their postulated mechanisms, identified by ORP architecture and the opportunities for research to advance management of sleep-disordered breathing, insomnia and idiopathic hypersomnia.
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Affiliation(s)
- Magdy Younes
- Department of Medicine, University of Manitoba, Winnipeg, MB, Canada
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Abstract
Automatic polysomnography analysis can be leveraged to shorten scoring times, reduce associated costs, and ultimately improve the overall diagnosis of sleep disorders. Multiple and diverse strategies have been attempted for implementation of this technology at scale in the routine workflow of sleep centers. The field, however, is complex and presents unsolved challenges in a number of areas. Recent developments in computer science and artificial intelligence are nevertheless closing the gap. Technological advances are also opening new pathways for expanding our current understanding of the domain and its analysis.
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Affiliation(s)
- Diego Alvarez-Estevez
- Center for Information and Communications Technology Research (CITIC), Universidade da Coruña, 15071 A Coruña, Spain.
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Cohen O, Shah NA, McAlpine CS. Sleep calibrates atherosclerotic cardiovascular disease. NATURE CARDIOVASCULAR RESEARCH 2022; 1:1104-1106. [PMID: 37384127 PMCID: PMC10306324 DOI: 10.1038/s44161-022-00190-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/30/2023]
Abstract
Sleep modulates cardiovascular health, and recent studies have begun to uncover underlying mechanistic links. An integrated translational approach that combines animal models and human trials will enrich scientific discovery, improve therapy, and help to alleviate the global burden of insufficient sleep and cardiovascular disease.
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Affiliation(s)
- Oren Cohen
- Division of Pulmonary, Critical Care and Sleep Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Neomi A. Shah
- Division of Pulmonary, Critical Care and Sleep Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Cameron S. McAlpine
- Cardiovascular Research Institute and the Department of Medicine, Cardiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Nash Family Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA
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8
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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]
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Lechat B, Hirotsu C, Appleton S, Younes M, Adams RJ, Vakulin A, Hansen K, Zajamsek B, Wittert G, Catcheside P, Heinzer R, Eckert DJ. A novel EEG marker predicts perceived sleepiness and poor sleep quality. Sleep 2022; 45:zsac051. [PMID: 35554584 DOI: 10.1093/sleep/zsac051] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Revised: 02/16/2022] [Indexed: 09/21/2023] Open
Abstract
STUDY OBJECTIVES To determine if a novel EEG-derived continuous index of sleep depth/alertness, the odds ratio product (ORP), predicts self-reported daytime sleepiness and poor sleep quality in two large population-based cohorts. METHODS ORP values which range from 0 (deep sleep) to 2.5 (fully alert) were calculated in 3s intervals during awake periods (ORPwake) and NREM sleep (ORPNREM) determined from home sleep studies in the HypnoLaus (N = 2162: 1106 females, 1056 males) and men androgen inflammation lifestyle environment and stress (MAILES) cohorts (N = 754 males). Logistic regression was used to examine associations between ORPwake, ORPNREM, and traditional polysomnography measures (as comparators) with excessive sleepiness (Epworth sleepiness scale >10) and poor sleep quality (Pittsburgh sleep quality index >5) and insomnia symptoms. RESULTS High ORPwake was associated with a ~30% increase in poor sleep quality in both HypnoLaus (odds ratio, OR, and 95% CI) 1.28 (1.09, 1.51), and MAILES 1.36 (1.10, 1.68). High ORPwake was also associated with a ~28% decrease in excessive daytime sleepiness in the MAILES dataset. ORPNREM was associated with a ~30% increase in poor sleep quality in HypnoLaus but not in MAILES. No consistent associations across cohorts were detected using traditional polysomnography markers. CONCLUSIONS ORP, a novel EEG-derived metric, measured during wake periods predicts poor sleep quality in two independent cohorts. Consistent with insomnia symptomatology of poor perceived sleep in the absence of excessive daytime sleepiness, ORPwake may provide valuable objective mechanistic insight into physiological hyperarousal.
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Affiliation(s)
- Bastien Lechat
- College of Science and Engineering, Flinders University, Adelaide, SA, Australia
- Flinders Health and Medical Research Institute Sleep Health/Adelaide Institute for Sleep Health, Flinders University, College of Medicine and Public Health Adelaide, SA, Australia
| | - Camila Hirotsu
- Center for Investigation and Research in Sleep, Department of Medicine, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Sarah Appleton
- Flinders Health and Medical Research Institute Sleep Health/Adelaide Institute for Sleep Health, Flinders University, College of Medicine and Public Health Adelaide, SA, Australia
| | - Magdy Younes
- Department of Medicine, University of Manitoba, Winnipeg, MN, Canada
| | - Robert J Adams
- Flinders Health and Medical Research Institute Sleep Health/Adelaide Institute for Sleep Health, Flinders University, College of Medicine and Public Health Adelaide, SA, Australia
| | - Andrew Vakulin
- Flinders Health and Medical Research Institute Sleep Health/Adelaide Institute for Sleep Health, Flinders University, College of Medicine and Public Health Adelaide, SA, Australia
| | - Kristy Hansen
- College of Science and Engineering, Flinders University, Adelaide, SA, Australia
- Flinders Health and Medical Research Institute Sleep Health/Adelaide Institute for Sleep Health, Flinders University, College of Medicine and Public Health Adelaide, SA, Australia
| | - Branko Zajamsek
- College of Science and Engineering, Flinders University, Adelaide, SA, Australia
- Flinders Health and Medical Research Institute Sleep Health/Adelaide Institute for Sleep Health, Flinders University, College of Medicine and Public Health Adelaide, SA, Australia
| | - Gary Wittert
- Freemasons Centre for Male Health and Wellness, Adelaide University, Adelaide, SA, Australia
| | - Peter Catcheside
- Flinders Health and Medical Research Institute Sleep Health/Adelaide Institute for Sleep Health, Flinders University, College of Medicine and Public Health Adelaide, SA, Australia
| | - Raphael Heinzer
- Center for Investigation and Research in Sleep, Department of Medicine, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Danny J Eckert
- Flinders Health and Medical Research Institute Sleep Health/Adelaide Institute for Sleep Health, Flinders University, College of Medicine and Public Health Adelaide, SA, Australia
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Smith MG, Younes M, Aeschbach D, Elmenhorst EM, Müller U, Basner M. Traffic noise-induced changes in wake-propensity measured with the Odds-Ratio Product (ORP). THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 805:150191. [PMID: 34818802 DOI: 10.1016/j.scitotenv.2021.150191] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/14/2021] [Revised: 08/18/2021] [Accepted: 09/03/2021] [Indexed: 06/13/2023]
Abstract
Nocturnal traffic noise can disrupt sleep and impair physical and mental restoration, but classical sleep scoring techniques may not fully capture subtle yet clinically relevant alterations of sleep induced by noise. We used a validated continuous measure of sleep depth and quality based on automatic analysis of physiologic sleep data, termed Wake Propensity (WP), to investigate temporal changes of sleep in response to nocturnal noise events in 3-s epochs. Seventy-two healthy participants (mean age 40.3 years, range 18-71 years, 40 females, 32 males) slept for 11 nights in a laboratory, during which we measured sleep with polysomnography. In 8 nights, participants were exposed to 40, 80 or 120 road, rail and/or aircraft noise events with maximum noise levels of 45-65 dB LAS,max during 8-h sleep opportunities. We analyzed sleep macrostructure and event-related change of WP during noise exposure with linear mixed models. Nocturnal traffic noise led to event-related shifts towards wakefulness and less deep, more unstable sleep (increase in WP relative to pre-noise baseline ranging from +29.5% at 45 dB to +38.3% at 65 dB; type III effect p < 0.0001). Sleep depth decreased dynamically with increasing noise level, peaking when LAS,max was highest. This change in WP was stronger and occurred more quickly for events where the noise onset was more rapid (road and rail) compared to more gradually time-varying noise (aircraft). Sleep depth did not immediately recover to pre-noise WP, leading to decreased sleep stability across the night compared to quiet nights, which was greater with an increasing number of noise events (standardized β = 0.053, p = 0.003). Further, WP was more sensitive to noise than classical arousals. Results demonstrate the usefulness of WP as a measure of the effects of external stimuli on sleep, and show WP is a more sensitive measure of noise-induced sleep disruption than traditional methods of sleep analysis.
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Affiliation(s)
- Michael G Smith
- Unit for Experimental Psychiatry, Division of Sleep and Chronobiology, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Magdy Younes
- Sleep Disorders Center, University of Manitoba, Winnipeg, MB, Canada
| | - Daniel Aeschbach
- Department of Sleep and Human Factors Research, Institute of Aerospace Medicine, German Aerospace Center, Cologne, Germany; Faculty of Medicine, University of Bonn, Bonn, Germany
| | - Eva-Maria Elmenhorst
- Department of Sleep and Human Factors Research, Institute of Aerospace Medicine, German Aerospace Center, Cologne, Germany; Institute for Occupational and Social Medicine, Medical Faculty, RWTH Aachen University, Aachen, Germany
| | - Uwe Müller
- Department of Sleep and Human Factors Research, Institute of Aerospace Medicine, German Aerospace Center, Cologne, Germany
| | - Mathias Basner
- Unit for Experimental Psychiatry, Division of Sleep and Chronobiology, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
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Kang DY, DeYoung PN, Tantiongloc J, Coleman TP, Owens RL. Statistical uncertainty quantification to augment clinical decision support: a first implementation in sleep medicine. NPJ Digit Med 2021; 4:142. [PMID: 34593972 PMCID: PMC8484290 DOI: 10.1038/s41746-021-00515-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Accepted: 09/13/2021] [Indexed: 11/09/2022] Open
Abstract
Machine learning has the potential to change the practice of medicine, particularly in areas that require pattern recognition (e.g. radiology). Although automated classification is unlikely to be perfect, few modern machine learning tools have the ability to assess their own classification confidence to recognize uncertainty that might need human review. Using automated single-channel sleep staging as a first implementation, we demonstrated that uncertainty information (as quantified using Shannon entropy) can be utilized in a "human in the loop" methodology to promote targeted review of uncertain sleep stage classifications on an epoch-by-epoch basis. Across 20 sleep studies, this feedback methodology proved capable of improving scoring agreement with the gold standard over automated scoring alone (average improvement in Cohen's Kappa of 0.28), in a fraction of the scoring time compared to full manual review (60% reduction). In summary, our uncertainty-based clinician-in-the-loop framework promotes the improvement of medical classification accuracy/confidence in a cost-effective and economically resourceful manner.
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Affiliation(s)
- Dae Y Kang
- Department of Medicine, Division of Pulmonary, Critical Care, & Sleep Medicine, University of California, San Diego, 9500 Gilman Dr, La Jolla, CA, 92093, USA
| | - Pamela N DeYoung
- Department of Medicine, Division of Pulmonary, Critical Care, & Sleep Medicine, University of California, San Diego, 9500 Gilman Dr, La Jolla, CA, 92093, USA
| | - Justin Tantiongloc
- Department of Computer Science & Engineering, University of California, San Diego, 9500 Gilman Dr, La Jolla, CA, 92093, USA
| | - Todd P Coleman
- Department of Bioengineering, University of California, San Diego, 9500 Gilman Dr, La Jolla, CA, 92093, USA
| | - Robert L Owens
- Department of Medicine, Division of Pulmonary, Critical Care, & Sleep Medicine, University of California, San Diego, 9500 Gilman Dr, La Jolla, CA, 92093, USA.
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12
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Peter-Derex L, Berthomier C, Taillard J, Berthomier P, Bouet R, Mattout J, Brandewinder M, Bastuji H. Automatic analysis of single-channel sleep EEG in a large spectrum of sleep disorders. J Clin Sleep Med 2021; 17:393-402. [PMID: 33089777 DOI: 10.5664/jcsm.8864] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
STUDY OBJECTIVES To assess the performance of the single-channel automatic sleep staging (AS) software ASEEGA in adult patients diagnosed with various sleep disorders. METHODS Sleep recordings were included of 95 patients (38 women, 40.5 ± 13.7 years) diagnosed with insomnia (n = 23), idiopathic hypersomnia (n = 24), narcolepsy (n = 24), and obstructive sleep apnea (n = 24). Visual staging (VS) was performed by two experts (VS1 and VS2) according to the American Academy of Sleep Medicine rules. AS was based on the analysis of a single electroencephalogram channel (Cz-Pz), without any information from electro-oculography nor electromyography. The epoch-by-epoch agreement (concordance and Conger's coefficient [κ]) was compared pairwise (VS1-VS2, AS-VS1, AS-VS2) and between AS and consensual VS. Sleep parameters were also compared. RESULTS The pairwise agreements were: between AS and VS1, 78.6% (κ = 0.70); AS and VS2, 75.0% (0.65); and VS1 and VS2, 79.5% (0.72). Agreement between AS and consensual VS was 85.6% (0.80), with the following distribution: insomnia 85.5% (0.80), narcolepsy 83.8% (0.78), idiopathic hypersomnia 86.1% (0.68), and obstructive sleep disorder 87.2% (0.82). A significant low-amplitude scorer effect was observed for most sleep parameters, not always driven by the same scorer. Hypnograms obtained with AS and VS exhibited very close sleep organization, except for 80% of rapid eye movement sleep onset in the group diagnosed with narcolepsy missed by AS. CONCLUSIONS Agreement between AS and VS in sleep disorders is comparable to that reported in healthy individuals and to interexpert agreement in patients. ASEEGA could therefore be considered as a complementary sleep stage scoring tool in clinical practice, after improvement of rapid eye movement sleep onset detection.
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Affiliation(s)
- Laure Peter-Derex
- Center for Sleep Medicine and Respiratory Diseases, Croix-Rousse Hospital, Lyon, France.,Lyon Neuroscience Research Center, CNRS 5292 INSERM U1028, Lyon, France.,Lyon 1 University, Lyon, France
| | | | - Jacques Taillard
- CNRS, Bordeaux University, USR 3413 SANPSY Sleep, Addiction and Neuropsychiatry, Bordeaux, France
| | | | - Romain Bouet
- Lyon Neuroscience Research Center, CNRS 5292 INSERM U1028, Lyon, France
| | - Jérémie Mattout
- Lyon Neuroscience Research Center, CNRS 5292 INSERM U1028, Lyon, France
| | | | - Hélène Bastuji
- Center for Sleep Medicine and Respiratory Diseases, Croix-Rousse Hospital, Lyon, France.,Lyon Neuroscience Research Center, CNRS 5292 INSERM U1028, Lyon, France.,Functional Neurology and Epilepsy Unit, Neurological Hospital, Hospices Civils de Lyon, Bron, France
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13
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Abou Jaoude M, Sun H, Pellerin KR, Pavlova M, Sarkis RA, Cash SS, Westover MB, Lam AD. Expert-level automated sleep staging of long-term scalp electroencephalography recordings using deep learning. Sleep 2021; 43:5849506. [PMID: 32478820 DOI: 10.1093/sleep/zsaa112] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2019] [Revised: 03/20/2020] [Indexed: 12/25/2022] Open
Abstract
STUDY OBJECTIVES Develop a high-performing, automated sleep scoring algorithm that can be applied to long-term scalp electroencephalography (EEG) recordings. METHODS Using a clinical dataset of polysomnograms from 6,431 patients (MGH-PSG dataset), we trained a deep neural network to classify sleep stages based on scalp EEG data. The algorithm consists of a convolutional neural network for feature extraction, followed by a recurrent neural network that extracts temporal dependencies of sleep stages. The algorithm's inputs are four scalp EEG bipolar channels (F3-C3, C3-O1, F4-C4, and C4-O2), which can be derived from any standard PSG or scalp EEG recording. We initially trained the algorithm on the MGH-PSG dataset and used transfer learning to fine-tune it on a dataset of long-term (24-72 h) scalp EEG recordings from 112 patients (scalpEEG dataset). RESULTS The algorithm achieved a Cohen's kappa of 0.74 on the MGH-PSG holdout testing set and cross-validated Cohen's kappa of 0.78 after optimization on the scalpEEG dataset. The algorithm also performed well on two publicly available PSG datasets, demonstrating high generalizability. Performance on all datasets was comparable to the inter-rater agreement of human sleep staging experts (Cohen's kappa ~ 0.75 ± 0.11). The algorithm's performance on long-term scalp EEGs was robust over a wide age range and across common EEG background abnormalities. CONCLUSION We developed a deep learning algorithm that achieves human expert level sleep staging performance on long-term scalp EEG recordings. This algorithm, which we have made publicly available, greatly facilitates the use of large long-term EEG clinical datasets for sleep-related research.
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Affiliation(s)
- Maurice Abou Jaoude
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA
| | - Haoqi Sun
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA
| | - Kyle R Pellerin
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA
| | - Milena Pavlova
- Department of Neurology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA
| | - Rani A Sarkis
- Department of Neurology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA
| | - Sydney S Cash
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA
| | - M Brandon Westover
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA
| | - Alice D Lam
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA
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14
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Leone MJ, Sun H, Boutros CL, Liu L, Ye E, Sullivan L, Thomas RJ, Robbins GK, Mukerji SS, Westover MB. HIV Increases Sleep-based Brain Age Despite Antiretroviral Therapy. Sleep 2021; 44:6204183. [PMID: 33783511 DOI: 10.1093/sleep/zsab058] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2020] [Revised: 01/06/2021] [Indexed: 11/12/2022] Open
Abstract
STUDY OBJECTIVES Age-related comorbidities and immune activation raise concern for advanced brain aging in people living with HIV (PLWH). The brain age index (BAI) is a machine learning model that quantifies deviations in brain activity during sleep relative to healthy individuals of the same age. High BAI was previously found to be associated with neurological, psychiatric, cardiometabolic diseases, and reduced life expectancy among people without HIV. Here, we estimated the effect of HIV infection on BAI by comparing PLWH and HIV-controls. METHODS Clinical data and sleep EEGs from 43 PLWH on antiretroviral therapy (HIV+) and 3,155 controls (HIV-) were collected from Massachusetts General Hospital. The effect of HIV infection on BAI, and on individual EEG features, was estimated using causal inference. RESULTS The average effect of HIV on BAI was estimated to be +3.35 years (p < 0.01, 95% CI = [0.67, 5.92]) using doubly robust estimation. Compared to HIV- controls, HIV+ participants exhibited a reduction in delta band power during deep sleep and rapid eye movement sleep. CONCLUSION We provide causal evidence that HIV contributes to advanced brain aging reflected in sleep EEG. A better understanding is greatly needed of potential therapeutic targets to mitigate the effect of HIV on brain health, potentially including sleep disorders and cardiovascular disease.
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Affiliation(s)
| | - Haoqi Sun
- Massachusetts General Hospital, Boston, MA, USA
| | | | - Lin Liu
- Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Elissa Ye
- Massachusetts General Hospital, Boston, MA, USA
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15
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Hof zum Berge A, Ferrauti A, Meyer T, Pfeiffer M, Kellmann M. Portable polysomnography for sleep monitoring in elite youth rowing: An athlete's gain or the sleep's thief? TRANSLATIONAL SPORTS MEDICINE 2020. [DOI: 10.1002/tsm2.205] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Affiliation(s)
| | | | - Tim Meyer
- Institute of Sports and Preventive Medicine Saarland University Saarbrücken Germany
| | - Mark Pfeiffer
- Institute of Sport Science Johannes‐Gutenberg University Mainz Germany
| | - Michael Kellmann
- Faculty of Sport Science Ruhr University Bochum Bochum Germany
- School of Human Movement and Nutrition Sciences The University of Queensland Brisbane Australia
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16
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Chriskos P, Frantzidis CA, Nday CM, Gkivogkli PT, Bamidis PD, Kourtidou-Papadeli C. A review on current trends in automatic sleep staging through bio-signal recordings and future challenges. Sleep Med Rev 2020; 55:101377. [PMID: 33017770 DOI: 10.1016/j.smrv.2020.101377] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2019] [Revised: 04/11/2020] [Accepted: 06/02/2020] [Indexed: 12/09/2022]
Abstract
Sleep staging is a vital process conducted in order to analyze polysomnographic data. To facilitate prompt interpretation of these recordings, many automatic sleep staging methods have been proposed. These methods rely on bio-signal recordings, which include electroencephalography, electrocardiography, electromyography, electrooculography, respiratory, pulse oximetry and others. However, advanced, uncomplicated and swift sleep-staging-evaluation is still needed in order to improve the existing polysomnographic data interpretation. The present review focuses on automatic sleep staging methods through bio-signal recording including current and future challenges.
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Affiliation(s)
- Panteleimon Chriskos
- Laboratory of Medical Physics, Medical School, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Christos A Frantzidis
- Laboratory of Medical Physics, Medical School, Aristotle University of Thessaloniki, Thessaloniki, Greece; Greek Aerospace Medical Association and Space Research (GASMA-SR), Thessaloniki, Greece
| | - Christiane M Nday
- Laboratory of Medical Physics, Medical School, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Polyxeni T Gkivogkli
- Laboratory of Medical Physics, Medical School, Aristotle University of Thessaloniki, Thessaloniki, Greece; Greek Aerospace Medical Association and Space Research (GASMA-SR), Thessaloniki, Greece
| | - Panagiotis D Bamidis
- Laboratory of Medical Physics, Medical School, Aristotle University of Thessaloniki, Thessaloniki, Greece; Greek Aerospace Medical Association and Space Research (GASMA-SR), Thessaloniki, Greece.
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17
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Younes M, Schweitzer PK, Griffin KS, Balshaw R, Walsh JK. Comparing two measures of sleep depth/intensity. Sleep 2020; 43:5867896. [DOI: 10.1093/sleep/zsaa127] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2019] [Revised: 05/20/2020] [Indexed: 01/05/2023] Open
Abstract
Abstract
Study Objectives
To compare delta spectral power (delta) and odds ratio product (ORP) as measures of sleep depth during sleep restriction with placebo or a drug that increases delta.
Methods
This is a secondary analysis of data from a study of 41 healthy participants randomized to receive placebo or gaboxadol 15 mg during sleep restriction. Participants underwent in-laboratory sleep studies on two baseline, four sleep restriction (5-h), and two recovery nights. Relation between delta or ORP and sleep depth was operationally defined as the degree of association of each metric to the probability of arousal or awakening occurring during the next 30 s (arousability).
Results
ORP values in wake, N1, N2, N3, and REM were significantly different. Delta differed between both N2 and N3 and other sleep stages but not between wake and N1 or N1 and REM. Epoch-by-epoch and individual correlations between ORP and delta power were modest or insignificant. The relation between ORP and arousability was linear across the entire ORP range. Delta also changed with arousability but only when delta values were less than 300 μV2. Receiver-operating-characteristic analysis found the ability to predict imminent arousal to be significantly greater with ORP than with log delta power for all experimental conditions. Changes in ORP, but not log delta, across the night correlated with next-day physiologic sleep tendency.
Conclusions
Compared to delta power, ORP is more discriminating among sleep stages, more sensitive to sleep restriction, and more closely associated with arousability. This evidence supports ORP as a measure of sleep depth/intensity.
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Affiliation(s)
- Magdy Younes
- Sleep Disorders Centre, Misericordia Health Centre, University of Manitoba, Winnipeg, Canada
| | | | - Kara S Griffin
- Sleep Medicine & Research Center, St. Luke’s Hospital, Chesterfield, MO
| | - Robert Balshaw
- Centre for Healthcare Innovation, Rady Faculty of Health Science, University of Manitoba, Winnipeg, Canada
| | - James K Walsh
- Sleep Medicine & Research Center, St. Luke’s Hospital, Chesterfield, MO
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18
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Zhang L, Fabbri D, Upender R, Kent D. Automated sleep stage scoring of the Sleep Heart Health Study using deep neural networks. Sleep 2020; 42:5530377. [PMID: 31289828 DOI: 10.1093/sleep/zsz159] [Citation(s) in RCA: 45] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2018] [Revised: 05/19/2019] [Indexed: 11/13/2022] Open
Abstract
STUDY OBJECTIVES Polysomnography (PSG) scoring is labor intensive and suffers from variability in inter- and intra-rater reliability. Automated PSG scoring has the potential to reduce the human labor costs and the variability inherent to this task. Deep learning is a form of machine learning that uses neural networks to recognize data patterns by inspecting many examples rather than by following explicit programming. METHODS A sleep staging classifier trained using deep learning methods scored PSG data from the Sleep Heart Health Study (SHHS). The training set was composed of 42 560 hours of PSG data from 5213 patients. To capture higher-order data, spectrograms were generated from electroencephalography, electrooculography, and electromyography data and then passed to the neural network. A holdout set of 580 PSGs not included in the training set was used to assess model accuracy and discrimination via weighted F1-score, per-stage accuracy, and Cohen's kappa (K). RESULTS The optimal neural network model was composed of spectrograms in the input layer feeding into convolutional neural network layers and a long short-term memory layer to achieve a weighted F1-score of 0.87 and K = 0.82. CONCLUSIONS The deep learning sleep stage classifier demonstrates excellent accuracy and agreement with expert sleep stage scoring, outperforming human agreement on sleep staging. It achieves comparable or better F1-scores, accuracy, and Cohen's kappa compared to literature for automated sleep stage scoring of PSG epochs. Accurate automated scoring of other PSG events may eventually allow for fully automated PSG scoring.
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Affiliation(s)
- Linda Zhang
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN
| | - Daniel Fabbri
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN
| | - Raghu Upender
- Department of Neurology, Sleep Disorders Division, Vanderbilt University School of Medicine, Nashville, TN
| | - David Kent
- Department of Otolaryngology, Vanderbilt University Medical Center, Nashville, TN
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19
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Younes M, Giannouli E. Mechanism of excessive wake time when associated with obstructive sleep apnea or periodic limb movements. J Clin Sleep Med 2020; 16:389-399. [PMID: 31992415 DOI: 10.5664/jcsm.8214] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
STUDY OBJECTIVES It is uncertain whether obstructive apnea (OSA) or periodic limb movements (PLMs) contribute to excessive wake time (EWT) when EWT and these disorders coexist. We hypothesized that such EWT is an independent disorder related to central regulation of sleep depth. Accordingly, we compared sleep depth in patients with EWT and OSA/PLMs (EWT+P) with patients with EWT and no OSA/PLMs (EWT-NP) and patients with a normal wake time. METHODS A total of 267 participants were divided into five groups: (1) EWT+P: n = 100 (wake time > 20% total recording time; TRT) with OSA (apnea-hypopnea index 5-110 events/h) and/or PLMs (PLM index 10-151 events/h); (2) EWT-NP: n = 49 (wake time > 20%TRT), no associated pathology; (3) normal wake time (NWT)+P: n = 54 (wake time < 20%TRT, with OSA/PLMs); (4) NWT-NP: n = 26; (5) Healthy participants: n = 38 (no sleep complaints, NWT and no OSA/PLMs). Sleep depth was evaluated by the odds ratio product (ORP; 0 = deep sleep, 2.5 = fully alert). We also measured ORP in the 9 seconds immediately following arousals (ORP-9) to distinguish between peripheral and central mechanisms of light sleep. RESULTS ORP during sleep was higher (lighter sleep) in both EWT groups than in the three NWT groups (P < 1E-11) with no difference between those with and those without OSA/PLMs. ORP-9 was also significantly higher in the EWT groups than in the NWT groups (P < 1E-19), also with no difference between those with and without OSA/PLMs, indicating that the lighter sleep was of central origin. There were highly significant correlations between wake time and ORP-9 across all groups (P < 1E-35). CONCLUSIONS EWT associated with OSA/PLMs is independent of OSA/PLMs and related to abnormal central regulation of sleep depth.
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Affiliation(s)
- Magdy Younes
- Sleep Disorders Centre, Misericordia Health Centre, University of Manitoba, Winnipeg, Canada
| | - Eleni Giannouli
- Sleep Disorders Centre, Misericordia Health Centre, University of Manitoba, Winnipeg, Canada
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20
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Biswal S, Sun H, Goparaju B, Westover MB, Sun J, Bianchi MT. Expert-level sleep scoring with deep neural networks. J Am Med Inform Assoc 2019; 25:1643-1650. [PMID: 30445569 PMCID: PMC6289549 DOI: 10.1093/jamia/ocy131] [Citation(s) in RCA: 118] [Impact Index Per Article: 23.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2018] [Accepted: 09/21/2018] [Indexed: 12/15/2022] Open
Abstract
Objectives Scoring laboratory polysomnography (PSG) data remains a manual task of visually annotating 3 primary categories: sleep stages, sleep disordered breathing, and limb movements. Attempts to automate this process have been hampered by the complexity of PSG signals and physiological heterogeneity between patients. Deep neural networks, which have recently achieved expert-level performance for other complex medical tasks, are ideally suited to PSG scoring, given sufficient training data. Methods We used a combination of deep recurrent and convolutional neural networks (RCNN) for supervised learning of clinical labels designating sleep stages, sleep apnea events, and limb movements. The data for testing and training were derived from 10 000 clinical PSGs and 5804 research PSGs. Results When trained on the clinical dataset, the RCNN reproduces PSG diagnostic scoring for sleep staging, sleep apnea, and limb movements with accuracies of 87.6%, 88.2% and 84.7% on held-out test data, a level of performance comparable to human experts. The RCNN model performs equally well when tested on the independent research PSG database. Only small reductions in accuracy were noted when training on limited channels to mimic at-home monitoring devices: frontal leads only for sleep staging, and thoracic belt signals only for the apnea-hypopnea index. Conclusions By creating accurate deep learning models for sleep scoring, our work opens the path toward broader and more timely access to sleep diagnostics. Accurate scoring automation can improve the utility and efficiency of in-lab and at-home approaches to sleep diagnostics, potentially extending the reach of sleep expertise beyond specialty clinics.
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Affiliation(s)
- Siddharth Biswal
- School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - Haoqi Sun
- Neurology Department, Massachusetts General Hospital, Wang 720, Boston, MA, USA
| | - Balaji Goparaju
- Neurology Department, Massachusetts General Hospital, Wang 720, Boston, MA, USA.,Division of Sleep Medicine, Harvard Medical School, Boston, MA, USA
| | - M Brandon Westover
- Neurology Department, Massachusetts General Hospital, Wang 720, Boston, MA, USA
| | - Jimeng Sun
- School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - Matt T Bianchi
- Neurology Department, Massachusetts General Hospital, Wang 720, Boston, MA, USA.,Division of Sleep Medicine, Harvard Medical School, Boston, MA, USA
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21
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Lee PL, Huang YH, Lin PC, Chiao YA, Hou JW, Liu HW, Huang YL, Liu YT, Chiueh TD. Automatic Sleep Staging in Patients With Obstructive Sleep Apnea Using Single-Channel Frontal EEG. J Clin Sleep Med 2019; 15:1411-1420. [PMID: 31596205 PMCID: PMC6778346 DOI: 10.5664/jcsm.7964] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2019] [Revised: 05/15/2019] [Accepted: 05/16/2019] [Indexed: 11/13/2022]
Abstract
STUDY OBJECTIVES Reliable sleep staging is difficult to obtain from home sleep testing for diagnosis of obstructive sleep apnea (OSA), especially when it is self-applied. Hence, the current study aimed to develop a single frontal electroencephalography-based automatic sleep staging system (ASSS). METHODS The ASSS system was developed on a clinical dataset, with a high percentage of participants with OSA. The F4-M1 signal extracted from 62 participants (62.9% having OSA) was used to build a four-stage classifier. Performance of the ASSS was tested in a holdout set of 58 patients (60.3% having OSA) with epoch-by-epoch and whole-night agreement for sleep staging compared with expert scoring of polysomnography. RESULTS Mean all-stage percentage agreement was 75.52% (95% confidence interval, 72.90 to 78.13) (kappa 0.62; 95% confidence interval, 0.58 to 0.65), with mean percentage agreement for wake, light sleep, deep sleep (DS), and rapid eye movement of 78.04%, 70.97%, 83.65%, and 75.00%, respectively. The whole-night agreement was good-excellent (intraclass correlation coefficient, 0.74 to 0.88) for sleep onset latency, wake after sleep onset, total sleep time, and sleep efficiency. Compared to the non-OSA subset, the OSA subset had lower agreement for DS. CONCLUSIONS Our results indicate that a single-channel F4-M1 based ASSS was sufficient for sleep staging in a population with a high percentage of participants with OSA.
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Affiliation(s)
- Pei-Lin Lee
- Center of Sleep Disorder, National Taiwan University Hospital, Taipei, Taiwan
- Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
- Center for Electronics Technology Integration, National Taiwan University
| | - Yi-Hao Huang
- Graduate Institute of Electronics Engineering, National Taiwan University, Taipei, Taiwan
| | - Po-Chen Lin
- Graduate Institute of Electronics Engineering, National Taiwan University, Taipei, Taiwan
| | - Yu-An Chiao
- Graduate Institute of Electronics Engineering, National Taiwan University, Taipei, Taiwan
| | - Jen-Wen Hou
- Graduate Institute of Electronics Engineering, National Taiwan University, Taipei, Taiwan
| | - Hsiang-Wen Liu
- Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
| | - Ya-Ling Huang
- Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
| | | | - Tzi-Dar Chiueh
- Graduate Institute of Electronics Engineering, National Taiwan University, Taipei, Taiwan
- Department of Electrical Engineering, National Taiwan University, Taipei, Taiwan
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22
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Hof Zum Berge A, Kellmann M, Kallweit U, Mir S, Gieselmann A, Meyer T, Ferrauti A, Pfeiffer M, Kölling S. Portable PSG for sleep stage monitoring in sports: Assessment of SOMNOwatch plus EEG. Eur J Sport Sci 2019; 20:713-721. [PMID: 31456506 DOI: 10.1080/17461391.2019.1659421] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Current sport-scientific studies mostly neglect the assessment of sleep architecture, although the distribution of different sleep stages is considered an essential component influencing an athlete's recovery and performance capabilities. A mobile, self-applied tool like the SOMNOwatch plus EEG might serve as an economical and time-friendly alternative to activity-based devices. However, self-application of SOMNOwatch plus EEG has not been validated against conventional polysomnography (PSG) yet. For evaluation purposes, 25 participants (15 female, 10 male; M age = 22.92 ± 2.03 years) slept in a sleep laboratory on two consecutive nights wearing both, conventional PSG and SOMNOwatch plus EEG electrodes. Sleep parameters and sleep stages were compared using paired t-tests and Bland-Altman plots. No significant differences were found between the recordings for Sleep Onset Latency, stages N1 to N3 as well as Rapid Eye Movement stage. Significant differences (Bias [95%-confidence interval]) were present between Total Sleep Time (9.95 min [-29.18, 49.08], d = 0.14), Total Wake Time (-13.12 min [-47.25, 23.85], d = -0.28), Wake after Sleep Onset (-11.70 min [-47.25, 23.85], d = -0.34) and Sleep Efficiency (2.18% [-7.98, 12.34], d = 0.02) with small effect sizes. Overall, SOMNOwatch plus EEG can be considered a valid and practical self-applied method for the examination of sleep. In sport-scientific research, it is a promising tool to assess sleep architecture in athletes; nonetheless, it cannot replace in-lab PSG for all clinical or scientific purposes.
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Affiliation(s)
- Annika Hof Zum Berge
- Faculty of Sport Science, Ruhr University Bochum, Bochum, Germany.,Department of Clinical Psychology, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Michael Kellmann
- Faculty of Sport Science, Ruhr University Bochum, Bochum, Germany.,School of Human Movement and Nutrition Sciences, The University of Queensland, Brisbane, Australia
| | - Ulf Kallweit
- Institute of Clinical Sleep and Neuroimmunology, University Witten/Herdecke, Witten, Germany.,Institute of Sleep Medicine, Helios Klinik Hagen-Ambrock, Hagen, Germany
| | - Shakila Mir
- Institute of Sleep Medicine, Helios Klinik Hagen-Ambrock, Hagen, Germany.,Institute of Pharmacology and Clinical Pharmacology, Universitätsklinikum Düsseldorf, Düsseldorf, Germany
| | - Annika Gieselmann
- Department of Clinical Psychology, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Tim Meyer
- Institute of Sports and Preventive Medicine, Saarland University, Saarbrücken, Germany
| | | | - Mark Pfeiffer
- Institute of Sport Science, Johannes-Gutenberg University, Mainz, Germany
| | - Sarah Kölling
- Faculty of Sport Science, Ruhr University Bochum, Bochum, Germany.,Department of Sport Science, Stellenbosch University, Stellenbosch, South Africa
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23
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Mazzotti DR, Lim DC, Sutherland K, Bittencourt L, Mindel JW, Magalang U, Pack AI, de Chazal P, Penzel T. Opportunities for utilizing polysomnography signals to characterize obstructive sleep apnea subtypes and severity. Physiol Meas 2018; 39:09TR01. [PMID: 30047487 DOI: 10.1088/1361-6579/aad5fe] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
BACKGROUND Obstructive sleep apnea (OSA) is a heterogeneous sleep disorder with many pathophysiological pathways to disease. Currently, the diagnosis and classification of OSA is based on the apnea-hypopnea index, which poorly correlates to underlying pathology and clinical consequences. A large number of in-laboratory sleep studies are performed around the world every year, already collecting an enormous amount of physiological data within an individual. Clinically, we have not yet fully taken advantage of this data, but combined with existing analytical approaches, we have the potential to transform the way OSA is managed within an individual patient. Currently, respiratory signals are used to count apneas and hypopneas, but patterns such as inspiratory flow signals can be used to predict optimal OSA treatment. Electrocardiographic data can reveal arrhythmias, but patterns such as heart rate variability can also be used to detect and classify OSA. Electroencephalography is used to score sleep stages and arousals, but specific patterns such as the odds-ratio product can be used to classify how OSA patients responds differently to arousals. OBJECTIVE In this review, we examine these and many other existing computer-aided polysomnography signal processing algorithms and how they can reflect an individual's manifestation of OSA. SIGNIFICANCE Together with current technological advance, it is only a matter of time before advanced automatic signal processing and analysis is widely applied to precision medicine of OSA in the clinical setting.
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Affiliation(s)
- Diego R Mazzotti
- Center for Sleep and Circadian Neurobiology, University of Pennsylvania, Philadelphia, United States of America
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24
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Younes M, Kuna ST, Pack AI, Walsh JK, Kushida CA, Staley B, Pien GW. Reliability of the American Academy of Sleep Medicine Rules for Assessing Sleep Depth in Clinical Practice. J Clin Sleep Med 2018; 14:205-213. [PMID: 29351821 PMCID: PMC5786839 DOI: 10.5664/jcsm.6934] [Citation(s) in RCA: 37] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2017] [Revised: 10/08/2017] [Accepted: 10/18/2017] [Indexed: 01/09/2023]
Abstract
STUDY OBJECTIVES The American Academy of Sleep Medicine has published manuals for scoring polysomnograms that recommend time spent in non-rapid eye movement sleep stages (stage N1, N2, and N3 sleep) be reported. Given the well-established large interrater variability in scoring stage N1 and N3 sleep, we determined the range of time in stage N1 and N3 sleep scored by a large number of technologists when compared to reasonably estimated true values. METHODS Polysomnograms of 70 females were scored by 10 highly trained sleep technologists, two each from five different academic sleep laboratories. Range and confidence interval (CI = difference between the 5th and 95th percentiles) of the 10 times spent in stage N1 and N3 sleep assigned in each polysomnogram were determined. Average values of times spent in stage N1 and N3 sleep generated by the 10 technologists in each polysomnogram were considered representative of the true values for the individual polysomnogram. Accuracy of different technologists in estimating delta wave duration was determined by comparing their scores to digitally determined durations. RESULTS The CI range of the ten N1 scores was 4 to 39 percent of total sleep time (% TST) in different polysomnograms (mean CI ± standard deviation = 11.1 ± 7.1 % TST). Corresponding range for N3 was 1 to 28 % TST (14.4 ± 6.1 % TST). For stage N1 and N3 sleep, very low or very high values were reported for virtually all polysomnograms by different technologists. Technologists varied widely in their assignment of stage N3 sleep, scoring that stage when the digitally determined time of delta waves ranged from 3 to 17 seconds. CONCLUSIONS Manual scoring of non-rapid eye movement sleep stages is highly unreliable among highly trained, experienced technologists. Measures of sleep continuity and depth that are reliable and clinically relevant should be a focus of clinical research.
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Affiliation(s)
- Magdy Younes
- Department of Medicine, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Samuel T. Kuna
- Department of Medicine and Center for Sleep and Circadian Neurobiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
- Department of Medicine, Corporal Michael J. Crescenz VA Medical Center, Philadelphia, Pennsylvania
| | - Allan I. Pack
- Department of Medicine and Center for Sleep and Circadian Neurobiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - James K. Walsh
- Sleep Medicine and Research Center, St. Luke's Hospital, Chesterfield, Missouri
| | - Clete A. Kushida
- Department of Psychiatry, Stanford University, Palo Alto, California
| | - Bethany Staley
- Department of Medicine and Center for Sleep and Circadian Neurobiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Grace W. Pien
- Division of Pulmonary and Critical Care Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland
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Assessment of intervention-related changes in non-rapid-eye-movement sleep depth: importance of sleep depth changes within stage 2. Sleep Med 2017; 40:84-93. [DOI: 10.1016/j.sleep.2017.09.022] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/24/2017] [Revised: 07/16/2017] [Accepted: 09/08/2017] [Indexed: 01/15/2023]
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Sun H, Jia J, Goparaju B, Huang GB, Sourina O, Bianchi MT, Westover MB. Large-Scale Automated Sleep Staging. Sleep 2017; 40:4209286. [PMID: 29029305 PMCID: PMC6251659 DOI: 10.1093/sleep/zsx139] [Citation(s) in RCA: 66] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
Study Objectives Automated sleep staging has been previously limited by a combination of clinical and physiological heterogeneity. Both factors are in principle addressable with large data sets that enable robust calibration. However, the impact of sample size remains uncertain. The objectives are to investigate the extent to which machine learning methods can approximate the performance of human scorers when supplied with sufficient training cases and to investigate how staging performance depends on the number of training patients, contextual information, model complexity, and imbalance between sleep stage proportions. Methods A total of 102 features were extracted from six electroencephalography (EEG) channels in routine polysomnography. Two thousand nights were partitioned into equal (n = 1000) training and testing sets for validation. We used epoch-by-epoch Cohen's kappa statistics to measure the agreement between classifier output and human scorer according to American Academy of Sleep Medicine scoring criteria. Results Epoch-by-epoch Cohen's kappa improved with increasing training EEG recordings until saturation occurred (n = ~300). The kappa value was further improved by accounting for contextual (temporal) information, increasing model complexity, and adjusting the model training procedure to account for the imbalance of stage proportions. The final kappa on the testing set was 0.68. Testing on more EEG recordings leads to kappa estimates with lower variance. Conclusion Training with a large data set enables automated sleep staging that compares favorably with human scorers. Because testing was performed on a large and heterogeneous data set, the performance estimate has low variance and is likely to generalize broadly.
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Affiliation(s)
- Haoqi Sun
- Energy Research Institute @ NTU, Interdisciplinary Graduate School, Nanyang Technological University, 639798, Singapore
- Fraunhofer IDM @ NTU, Nanyang Technological University, 639798, Singapore
| | - Jian Jia
- School of Mathematics, Northwest University, Xi’an, Shaanxi, 710127China
| | - Balaji Goparaju
- Massachusetts General Hospital, Neurology Department,Boston, MA
| | - Guang-Bin Huang
- School of Electrical and Electronic Engineering, Nanyang Technological University, 639798,Singapore.
| | - Olga Sourina
- Fraunhofer IDM @ NTU, Nanyang Technological University, 639798, Singapore
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Kang DY, DeYoung PN, Malhotra A, Owens RL, Coleman TP. A State Space and Density Estimation Framework for Sleep Staging in Obstructive Sleep Apnea. IEEE Trans Biomed Eng 2017; 65:1201-1212. [PMID: 28499990 DOI: 10.1109/tbme.2017.2702123] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
OBJECTIVE Although the importance of sleep is increasingly recognized, the lack of robust and efficient algorithms hinders scalable sleep assessment in healthy persons and those with sleep disorders. Polysomnography (PSG) and visual/manual scoring remain the gold standard in sleep evaluation, but more efficient/automated systems are needed. Most previous works have demonstrated algorithms in high agreement with the gold standard in healthy/normal (HN) individuals-not those with sleep disorders. METHODS This paper presents a statistical framework that automatically estimates whole-night sleep architecture in patients with obstructive sleep apnea (OSA)-the most common sleep disorder. Single-channel frontal electroencephalography was extracted from 65 HN/OSA sleep studies, and decomposed into 11 spectral features in 60 903 30 s sleep epochs. The algorithm leveraged kernel density estimation to generate stage-specific likelihoods, and a 5-state hidden Markov model to estimate per-night sleep architecture. RESULTS Comparisons to full PSG expert scoring revealed the algorithm was in fair agreement with the gold standard (median Cohen's kappa = 0.53). Further, analysis revealed modest decreases in median scoring agreement as OSA severity increased from HN (kappa = 0.63) to severe (kappa = 0.47). A separate implementation on HN data from the Physionet Sleep-EDF Database resulted in a median kappa = 0.65, further indicating the algorithm's broad applicability. CONCLUSION Results of this work indicate the proposed single-channel framework can emulate expert-level scoring of sleep architecture in OSA. SIGNIFICANCE Algorithms constructed to more accurately model physiological variability during sleep may help advance automated sleep assessment, for practical and general use in sleep medicine.
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Younes M, Soiferman M, Thompson W, Giannouli E. Performance of a New Portable Wireless Sleep Monitor. J Clin Sleep Med 2017; 13:245-258. [PMID: 27784419 DOI: 10.5664/jcsm.6456] [Citation(s) in RCA: 37] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2016] [Accepted: 09/28/2016] [Indexed: 01/16/2023]
Abstract
STUDY OBJECTIVES To determine if signals generated by a new sleep monitor (Prodigy) are comparable to signals generated during in-laboratory polysomnography (PSG). METHODS Fifty-nine patients with various sleep disorders (25 with moderate/severe sleep apnea) were studied. Full PSG was performed using standard acquisition systems. Prodigy was attached to the forehead with four disposable snap electrodes. Four additional electrodes were attached to monitor eye movements and muscle activity, and to serve as reference (mastoid). One frontal EEG signal was outputted in real time from the monitor and stored in the PSG record along with the other PSG signals. PSG was scored for sleep variables manually, and monitor records were scored by a validated automatic system (MSS) (MSS-Prodigy). MSS-Prodigy was briefly edited following suggestions of an Editing Helper feature of MSS. RESULTS Technical failures resulted in one study being unusable and another with data for only 3 hours. Prodigy EEG signal stored in the PSG record was visually indistinguishable from the PSG-derived EEG signals. Important differences between manual scores and unedited MSS-Prodigy were seen in a few patients in some sleep variables (notably onset latencies and REM time). Editing Helper issued 2.1 ± 0.8 suggestions/file. Only these suggestions were pursued during editing. Intraclass correlation coefficients for manual vs. edited MSS-Prodigy were > 0.83 for all sleep variables except for stages N1 and N3 (0.57 and 0.58). CONCLUSIONS When scored with MSS, and with only very minor editing, the monitor's results show excellent agreement with manual scoring of polysomnography data, even in patients with severe sleep disorders.
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Affiliation(s)
- Magdy Younes
- Sleep Disorders Centre, Winnipeg, MB, Canada.,YRT Ltd, Winnipeg, MB, Canada
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Younes M. The case for using digital EEG analysis in clinical sleep medicine. SLEEP SCIENCE AND PRACTICE 2017. [DOI: 10.1186/s41606-016-0005-0] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
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Abstract
INTRODUCTION The utility of multiple sleep latency tests (MSLTs) is limited to determining sleep onset latency (SOL) and rapid eye movement sleep latency. The odds ratio product (ORP) is a continuous index of sleep depth with values of 0, 1.0, and 2.5 reflecting very deep sleep, light sleep, and full wakefulness, respectively. We determined the time course of sleep depth during MSLT naps expecting that this would enhance the test's clinical utility. METHODS Thirty MSLTs (150 naps) were performed for excessive somnolence. Patients indicated whether they slept (yes/no) after each nap. SOL was scored by two experienced technologists. Time course of ORP was determined with a commercial system. We determined ORP at SOL (ORPSOL), times ORP decreased <2.0, <1.5, <1.0 and <0.5 during the entire nap duration, and the integral of decrease in ORP over nap duration (ΔORPINT). RESULTS SOL occurred almost invariably when ORP was between 1.0 and 2.0. Of 47 naps (21 patients) with SOL <5 minutes, ORP decreased <1.0 (light sleep) in <5 minutes in only 13 naps (nine patients) and <0.5 (deep sleep) in only two naps in one patient. The relation between ORPINT and frequency of sleep perception was well defined, allowing determination of a threshold for sleep perception. This threshold ranged widely (5-50 ΔORP*epoch). CONCLUSION As currently identified, SOL reflects transition into a highly unstable state between wakefulness and sleep. Reporting the times of attaining different sleep depths may help better identify patients at high risk of vigilance loss. Furthermore, an ORPSOL outside the range 1.0-2.0 can help identify scoring errors.
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Affiliation(s)
- Sonia Meza-Vargas
- Sleep Disorders Centre, University of Manitoba, Winnipeg, MB, Canada
| | - Eleni Giannouli
- Sleep Disorders Centre, University of Manitoba, Winnipeg, MB, Canada
| | - Magdy Younes
- Sleep Disorders Centre, University of Manitoba, Winnipeg, MB, Canada
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