1
|
Willoughby AR, Golkashani HA, Ghorbani S, Wong KF, Chee NIYN, Ong JL, Chee MWL. Performance of wearable sleep trackers during nocturnal sleep and periods of simulated real-world smartphone use. Sleep Health 2024; 10:356-368. [PMID: 38570223 DOI: 10.1016/j.sleh.2024.02.007] [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: 01/18/2024] [Revised: 02/16/2024] [Accepted: 02/27/2024] [Indexed: 04/05/2024]
Abstract
GOAL AND AIMS To test sleep/wake transition detection of consumer sleep trackers and research-grade actigraphy during nocturnal sleep and simulated peri-sleep behavior involving minimal movement. FOCUS TECHNOLOGY Oura Ring Gen 3, Fitbit Sense, AXTRO Fit 3, Xiaomi Mi Band 7, and ActiGraph GT9X. REFERENCE TECHNOLOGY Polysomnography. SAMPLE Sixty-three participants (36 female) aged 20-68. DESIGN Participants engaged in common peri-sleep behavior (reading news articles, watching videos, and exchanging texts) on a smartphone before and after the sleep period. They were woken up during the night to complete a short questionnaire to simulate responding to an incoming message. CORE ANALYTICS Detection and timing accuracy for the sleep onset times and wake times. ADDITIONAL ANALYTICS AND EXPLORATORY ANALYSES Discrepancy analysis both including and excluding the peri-sleep activity periods. Epoch-by-epoch analysis of rate and extent of wake misclassification during peri-sleep activity periods. CORE OUTCOMES Oura and Fitbit were more accurate at detecting sleep/wake transitions than the actigraph and the lower-priced consumer sleep tracker devices. Detection accuracy was less reliable in participants with lower sleep efficiency. IMPORTANT ADDITIONAL OUTCOMES With inclusion of peri-sleep periods, specificity and Kappa improved significantly for Oura and Fitbit, but not ActiGraph. All devices misclassified motionless wake as sleep to some extent, but this was less prevalent for Oura and Fitbit. CORE CONCLUSIONS Performance of Oura and Fitbit is robust on nights with suboptimal bedtime routines or minor sleep disturbances. Reduced performance on nights with low sleep efficiency bolsters concerns that these devices are less accurate for fragmented or disturbed sleep.
Collapse
Affiliation(s)
- Adrian R Willoughby
- Sleep and Cognition Laboratory, Centre for Sleep and Cognition, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Hosein Aghayan Golkashani
- Sleep and Cognition Laboratory, Centre for Sleep and Cognition, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Shohreh Ghorbani
- Sleep and Cognition Laboratory, Centre for Sleep and Cognition, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Kian F Wong
- Sleep and Cognition Laboratory, Centre for Sleep and Cognition, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Nicholas I Y N Chee
- Sleep and Cognition Laboratory, Centre for Sleep and Cognition, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Ju Lynn Ong
- Sleep and Cognition Laboratory, Centre for Sleep and Cognition, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Michael W L Chee
- Sleep and Cognition Laboratory, Centre for Sleep and Cognition, Yong Loo Lin School of Medicine, National University of Singapore, Singapore.
| |
Collapse
|
2
|
Guerrera CS, Boccaccio FM, Varrasi S, Platania GA, Coco M, Pirrone C, Castellano S, Caraci F, Ferri R, Lanza G. A narrative review on insomnia and hypersomnolence within Major Depressive Disorder and bipolar disorder: A proposal for a novel psychometric protocol. Neurosci Biobehav Rev 2024; 158:105575. [PMID: 38331126 DOI: 10.1016/j.neubiorev.2024.105575] [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: 07/03/2023] [Revised: 01/27/2024] [Accepted: 02/03/2024] [Indexed: 02/10/2024]
Abstract
Sleep disorders have become increasingly prevalent, with many adults worldwide reporting sleep dissatisfaction. Major Depressive Disorder (MDD) and Bipolar Disorder (BD) are common conditions associated with disrupted sleep patterns such as insomnia and hypersomnolence. These sleep disorders significantly affect the progression, severity, treatment, and outcome of unipolar and bipolar depression. While there is evidence of a connection between sleep disorders and depression, it remains unclear if sleep features differ between MDD and BD. In light of this, this narrative review aims to: (1) summarize findings on common sleep disorders like insomnia and hypersomnolence, strongly linked to MDD and BD; (2) propose a novel psychometric approach to assess sleep in individuals with depressive disorders. Despite insomnia seems to be more influent in unipolar depression, while hypersomnolence in bipolar one, there is no common agreement. So, it is essential adopting a comprehensive psychometric protocol for try to fill this gap. Understanding the relationship between sleep and MDD and BD disorders are crucial for effective management and better quality of life for those affected.
Collapse
Affiliation(s)
- Claudia Savia Guerrera
- Department of Educational Sciences, University of Catania, Via Biblioteca, 4, 95124 Catania, Italy; Department of Biomedical and Biotechnological Sciences, University of Catania, Torre Biologica, Via Santa Sofia, 97, 95123 Catania, Italy.
| | | | - Simone Varrasi
- Department of Educational Sciences, University of Catania, Via Biblioteca, 4, 95124 Catania, Italy
| | | | - Marinella Coco
- Department of Educational Sciences, University of Catania, Via Biblioteca, 4, 95124 Catania, Italy
| | - Concetta Pirrone
- Department of Educational Sciences, University of Catania, Via Biblioteca, 4, 95124 Catania, Italy
| | - Sabrina Castellano
- Department of Educational Sciences, University of Catania, Via Biblioteca, 4, 95124 Catania, Italy
| | - Filippo Caraci
- Department of Drug and Health Sciences, University of Catania, Cittadella Universitaria, Via Santa Sofia, 95123 Catania, Italy; Unit of Neuropharmacology and Translation Neurosciences, Oasi Research Institute - IRCCS, Via Conte Ruggero 73, 94018 Troina, En, Italy
| | - Raffaele Ferri
- Sleep Research Centre, Department of Neurology IC, Oasi Research Institute - IRCCS, Via Conte Ruggero 73, 94018 Troina, En, Italy
| | - Giuseppe Lanza
- Unit of Neuropharmacology and Translation Neurosciences, Oasi Research Institute - IRCCS, Via Conte Ruggero 73, 94018 Troina, En, Italy; Department of Surgery and Medical-Surgical Specialties, University of Catania, A.O.U. "Policlinico - San Marco", Via Santa Sofia, 78, 95123 Catania, Italy
| |
Collapse
|
3
|
Ilias L, Doukas G, Kontoulis M, Alexakis K, Michalitsi-Psarrou A, Ntanos C, Askounis D. Overview of methods and available tools used in complex brain disorders. OPEN RESEARCH EUROPE 2023; 3:152. [PMID: 38389699 PMCID: PMC10882203 DOI: 10.12688/openreseurope.16244.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 07/25/2023] [Indexed: 02/24/2024]
Abstract
Complex brain disorders, including Alzheimer's dementia, sleep disorders, and epilepsy, are chronic conditions that have high prevalence individually and in combination, increasing mortality risk, and contributing to the socioeconomic burden of patients, their families and, their communities at large. Although some literature reviews have been conducted mentioning the available methods and tools used for supporting the diagnosis of complex brain disorders and processing different files, there are still limitations. Specifically, these research works have focused primarily on one single brain disorder, i.e., sleep disorders or dementia or epilepsy. Additionally, existing research initiatives mentioning some tools, focus mainly on one single type of data, i.e., electroencephalography (EEG) signals or actigraphies or Magnetic Resonance Imaging, and so on. To tackle the aforementioned limitations, this is the first study conducting a comprehensive literature review of the available methods used for supporting the diagnosis of multiple complex brain disorders, i.e., Alzheimer's dementia, sleep disorders, epilepsy. Also, to the best of our knowledge, we present the first study conducting a comprehensive literature review of all the available tools, which can be exploited for processing multiple types of data, including EEG, actigraphies, and MRIs, and receiving valuable forms of information which can be used for differentiating people in a healthy control group and patients suffering from complex brain disorders. Additionally, the present study highlights both the benefits and limitations of the existing available tools.
Collapse
Affiliation(s)
- Loukas Ilias
- Decision Support Systems Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, Athens, 15773, Greece
| | - George Doukas
- Decision Support Systems Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, Athens, 15773, Greece
| | - Michael Kontoulis
- Decision Support Systems Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, Athens, 15773, Greece
| | - Konstantinos Alexakis
- Decision Support Systems Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, Athens, 15773, Greece
| | - Ariadni Michalitsi-Psarrou
- Decision Support Systems Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, Athens, 15773, Greece
| | - Christos Ntanos
- Decision Support Systems Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, Athens, 15773, Greece
| | - Dimitris Askounis
- Decision Support Systems Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, Athens, 15773, Greece
| |
Collapse
|
4
|
Ahmed A, Garcia-Agundez A, Petrovic I, Radaei F, Fife J, Zhou J, Karas H, Moody S, Drake J, Jones RN, Eickhoff C, Reznik ME. Delirium detection using wearable sensors and machine learning in patients with intracerebral hemorrhage. Front Neurol 2023; 14:1135472. [PMID: 37360342 PMCID: PMC10288850 DOI: 10.3389/fneur.2023.1135472] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2022] [Accepted: 04/24/2023] [Indexed: 06/28/2023] Open
Abstract
Objective Delirium is associated with worse outcomes in patients with stroke and neurocritical illness, but delirium detection in these patients can be challenging with existing screening tools. To address this gap, we aimed to develop and evaluate machine learning models that detect episodes of post-stroke delirium based on data from wearable activity monitors in conjunction with stroke-related clinical features. Design Prospective observational cohort study. Setting Neurocritical Care and Stroke Units at an academic medical center. Patients We recruited 39 patients with moderate-to-severe acute intracerebral hemorrhage (ICH) and hemiparesis over a 1-year period [mean (SD) age 71.3 (12.20), 54% male, median (IQR) initial NIH Stroke Scale 14.5 (6), median (IQR) ICH score 2 (1)]. Measurements and main results Each patient received daily assessments for delirium by an attending neurologist, while activity data were recorded throughout each patient's hospitalization using wrist-worn actigraph devices (on both paretic and non-paretic arms). We compared the predictive accuracy of Random Forest, SVM and XGBoost machine learning methods in classifying daily delirium status using clinical information alone and combined with actigraph data. Among our study cohort, 85% of patients (n = 33) had at least one delirium episode, while 71% of monitoring days (n = 209) were rated as days with delirium. Clinical information alone had a low accuracy in detecting delirium on a day-to-day basis [accuracy mean (SD) 62% (18%), F1 score mean (SD) 50% (17%)]. Prediction performance improved significantly (p < 0.001) with the addition of actigraph data [accuracy mean (SD) 74% (10%), F1 score 65% (10%)]. Among actigraphy features, night-time actigraph data were especially relevant for classification accuracy. Conclusions We found that actigraphy in conjunction with machine learning models improves clinical detection of delirium in patients with stroke, thus paving the way to make actigraph-assisted predictions clinically actionable.
Collapse
Affiliation(s)
- Abdullah Ahmed
- Brown Center for Biomedical Informatics, Brown University, Providence, RI, United States
| | - Augusto Garcia-Agundez
- Brown Center for Biomedical Informatics, Brown University, Providence, RI, United States
- IMDEA Networks Institute, Madrid, Spain
| | - Ivana Petrovic
- Brown Center for Biomedical Informatics, Brown University, Providence, RI, United States
| | - Fatemeh Radaei
- Brown Center for Biomedical Informatics, Brown University, Providence, RI, United States
| | - James Fife
- Brown Center for Biomedical Informatics, Brown University, Providence, RI, United States
| | - John Zhou
- Brown Center for Biomedical Informatics, Brown University, Providence, RI, United States
| | - Hunter Karas
- Brown Center for Biomedical Informatics, Brown University, Providence, RI, United States
| | - Scott Moody
- Department of Neurology, Brown University, Providence, RI, United States
| | - Jonathan Drake
- Department of Neurology, Brown University, Providence, RI, United States
| | - Richard N. Jones
- Department of Psychiatry, Brown University, Providence, RI, United States
| | - Carsten Eickhoff
- Brown Center for Biomedical Informatics, Brown University, Providence, RI, United States
| | - Michael E. Reznik
- Department of Neurology, Brown University, Providence, RI, United States
| |
Collapse
|
5
|
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
|
6
|
Bitkina OV, Park J, Kim J. Modeling Sleep Quality Depending on Objective Actigraphic Indicators Based on Machine Learning Methods. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:9890. [PMID: 36011524 PMCID: PMC9408084 DOI: 10.3390/ijerph19169890] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Revised: 08/02/2022] [Accepted: 08/08/2022] [Indexed: 06/15/2023]
Abstract
According to data from the World Health Organization and medical research centers, the frequency and severity of various sleep disorders, including insomnia, are increasing steadily. This dynamic is associated with increased daily stress, anxiety, and depressive disorders. Poor sleep quality affects people's productivity and activity and their perception of quality of life in general. Therefore, predicting and classifying sleep quality is vital to improving the quality and duration of human life. This study offers a model for assessing sleep quality based on the indications of an actigraph, which was used by 22 participants in the experiment for 24 h. Objective indicators of the actigraph include the amount of time spent in bed, sleep duration, number of awakenings, and duration of awakenings. The resulting classification model was evaluated using several machine learning methods and showed a satisfactory accuracy of approximately 80-86%. The results of this study can be used to treat sleep disorders, develop and design new systems to assess and track sleep quality, and improve existing electronic devices and sensors.
Collapse
Affiliation(s)
- Olga Vl. Bitkina
- Department of Industrial and Management Engineering, Incheon National University (INU), Academy-ro 119, Incheon 22012, Korea
| | - Jaehyun Park
- Department of Industrial and Management Engineering, Incheon National University (INU), Academy-ro 119, Incheon 22012, Korea
| | - Jungyoon Kim
- Department of Computer Science, Kent State University, Kent, OH 44240, USA
| |
Collapse
|
7
|
de Zambotti M, Menghini L, Grandner MA, Redline S, Zhang Y, Wallace ML, Buxton OM. Rigorous performance evaluation (previously, "validation") for informed use of new technologies for sleep health measurement. Sleep Health 2022; 8:263-269. [PMID: 35513978 PMCID: PMC9338437 DOI: 10.1016/j.sleh.2022.02.006] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Revised: 02/12/2022] [Accepted: 02/28/2022] [Indexed: 11/25/2022]
Abstract
New sleep technologies have become pervasive in the consumer space, and are becoming highly common in research and clinical sleep settings. The rapid, widespread use of largely unregulated and unstandardized technology has enabled the quantification of many different facets of sleep health, driving scientific discovery. As sleep scientists, it is our responsibility to inform principles and practices for proper evaluation of any new technology used in the clinical and research settings, and by consumers. A current lack of standardized methods for evaluating technology performance challenges the rigor of our scientific methods for accurate representation of the sleep health facets of interest. This special article describes the rationale and priorities of an interdisciplinary effort for rigorous, standardized, and rapid performance evaluation (previously, "validation") of new sleep and sleep disorders related technologies of all kinds (eg, devices or algorithms), including an associated article template for a new initiative for publication in Sleep Health of empirical studies systematically evaluating the performance of new sleep technologies. A structured article type should streamline manuscript development and enable more rapid writing, review, and publication. The goal is to promote rapid and rigorous evaluation and dissemination of new sleep technology, to enhance sleep research integrity, and to standardize terminology used in Rigorous Performance Evaluation papers to prevent misinterpretation while facilitating comparisons across technologies.
Collapse
Affiliation(s)
| | - Luca Menghini
- Department of Psychology, University of Bologna, Italy
| | - Michael A Grandner
- Sleep and Health Research Program, Department of Psychiatry, University of Arizona College of Medicine, Tucson, AZ, USA
| | - Susan Redline
- Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA
| | - Ying Zhang
- Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | | | - Orfeu M Buxton
- Department of Biobehavioral Health, The Pennsylvania State University, University Park, PA, USA.
| |
Collapse
|
8
|
Geng D, Qin Z, Wang J, Gao Z, Zhao N. Personalized recognition of wake/sleep state based on the combined shapelets and K-means algorithm. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103132] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
|
9
|
Rahimi-Eichi H, Coombs Iii G, Vidal Bustamante CM, Onnela JP, Baker JT, Buckner RL. Open-source Longitudinal Sleep Analysis From Accelerometer Data (DPSleep): Algorithm Development and Validation. JMIR Mhealth Uhealth 2021; 9:e29849. [PMID: 34612831 PMCID: PMC8529474 DOI: 10.2196/29849] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Revised: 06/17/2021] [Accepted: 08/02/2021] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND Wearable devices are now widely available to collect continuous objective behavioral data from individuals and to measure sleep. OBJECTIVE This study aims to introduce a pipeline to infer sleep onset, duration, and quality from raw accelerometer data and then quantify the relationships between derived sleep metrics and other variables of interest. METHODS The pipeline released here for the deep phenotyping of sleep, as the DPSleep software package, uses a stepwise algorithm to detect missing data; within-individual, minute-based, spectral power percentiles of activity; and iterative, forward-and-backward-sliding windows to estimate the major Sleep Episode onset and offset. Software modules allow for manual quality control adjustment of the derived sleep features and correction for time zone changes. In this paper, we have illustrated the pipeline with data from participants studied for more than 200 days each. RESULTS Actigraphy-based measures of sleep duration were associated with self-reported sleep quality ratings. Simultaneous measures of smartphone use and GPS location data support the validity of the sleep timing inferences and reveal how phone measures of sleep timing can differ from actigraphy data. CONCLUSIONS We discuss the use of DPSleep in relation to other available sleep estimation approaches and provide example use cases that include multi-dimensional, deep longitudinal phenotyping, extended measurement of dynamics associated with mental illness, and the possibility of combining wearable actigraphy and personal electronic device data (eg, smartphones and tablets) to measure individual differences across a wide range of behavioral variations in health and disease. A new open-source pipeline for deep phenotyping of sleep, DPSleep, analyzes raw accelerometer data from wearable devices and estimates sleep onset and offset while allowing for manual quality control adjustments.
Collapse
Affiliation(s)
- Habiballah Rahimi-Eichi
- Department of Psychology, Harvard University, Cambridge, MA, United States.,Institute for Technology in Psychiatry, McLean Hospital, Belmont, MA, United States.,Department of Psychiatry, Harvard Medical School, Boston, MA, United States
| | - Garth Coombs Iii
- Department of Psychology, Harvard University, Cambridge, MA, United States
| | | | - Jukka-Pekka Onnela
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, United States
| | - Justin T Baker
- Institute for Technology in Psychiatry, McLean Hospital, Belmont, MA, United States.,Department of Psychiatry, Harvard Medical School, Boston, MA, United States
| | - Randy L Buckner
- Department of Psychology, Harvard University, Cambridge, MA, United States.,Department of Psychiatry, Harvard Medical School, Boston, MA, United States.,Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States
| |
Collapse
|
10
|
On the Unification of Common Actigraphic Data Scoring Algorithms. SENSORS 2021; 21:s21186313. [PMID: 34577520 PMCID: PMC8472753 DOI: 10.3390/s21186313] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 08/25/2021] [Accepted: 09/17/2021] [Indexed: 11/30/2022]
Abstract
Actigraphy is a well-known, inexpensive method to investigate human movement patterns. Sleep and circadian rhythm studies are among the most popular applications of actigraphy. In this study, we investigate seven common sleep-wake scoring algorithms designed for actigraphic data, namely Cole-Kripke algorithm, two versions of Sadeh algorithm, Sazonov algorithm, Webster algorithm, UCSD algorithm and Scripps Clinic algorithm. We propose a unified mathematical framework describing five of them. One of the observed novelties is that five of these algorithms are in fact equivalent to low-pass FIR filters with very similar characteristics. We also provide explanations about the role of some factors defining these algorithms, as none were given by their Authors who followed empirical procedures. Proposed framework provides a robust mathematical description of discussed algorithms, which for the first time allows one to fully understand their operation and basics.
Collapse
|
11
|
Prasad B, Agarwal C, Schonfeld E, Schonfeld D, Mokhlesi B. Deep learning applied to polysomnography to predict blood pressure in obstructive sleep apnea and obesity hypoventilation: a proof-of-concept study. J Clin Sleep Med 2021; 16:1797-1803. [PMID: 32484157 DOI: 10.5664/jcsm.8608] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
STUDY OBJECTIVES Nocturnal blood pressure (BP) profile shows characteristic abnormalities in OSA, namely acute postapnea BP surges and nondipping BP. These abnormal BP profiles provide prognostic clues indicating increased cardiovascular disease risk. We developed a deep neural network model to perform computerized analysis of polysomnography data and predict nocturnal BP profile. METHODS We analyzed concurrently performed polysomnography and noninvasive beat-to-beat BP measurement with a deep neural network model to predict nocturnal BP profiles from polysomnography data in 13 patients with severe OSA. RESULTS A good correlation was noted between measured and predicted postapnea systolic and diastolic BP (Pearson r ≥ .75). Moreover, Bland-Altman analyses showed good agreement between the 2 values. Continuous systolic and diastolic BP prediction by the deep neural network model was also well correlated with measured BP values (Pearson r ≥ .83). CONCLUSIONS We developed a deep neural network model to predict nocturnal BP profile from clinical polysomnography signals and provide a potential prognostic tool in OSA. Validation of the model in larger samples and examination of its utility in predicting CVD risk in future studies is warranted.
Collapse
Affiliation(s)
- Bharati Prasad
- Department of Medicine, University of Illinois at Chicago, Chicago, Illinois.,Jesse Brown VA Medical Center, Chicago, Illinois
| | - Chirag Agarwal
- Department of Electrical and Computer Engineering, University of Illinois at Chicago, Chicago, Illinois
| | - Elan Schonfeld
- Department of Electrical and Computer Engineering, University of Illinois at Chicago, Chicago, Illinois
| | - Dan Schonfeld
- Department of Electrical and Computer Engineering, University of Illinois at Chicago, Chicago, Illinois
| | - Babak Mokhlesi
- Section of Pulmonary and Critical Care, Sleep Disorders Center, University of Chicago, Chicago, Illinois
| |
Collapse
|
12
|
Watson NF, Fernandez CR. Artificial intelligence and sleep: Advancing sleep medicine. Sleep Med Rev 2021; 59:101512. [PMID: 34166990 DOI: 10.1016/j.smrv.2021.101512] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2020] [Revised: 05/20/2021] [Accepted: 05/21/2021] [Indexed: 02/07/2023]
Abstract
Artificial intelligence (AI) allows analysis of "big data" combining clinical, environmental and laboratory based objective measures to allow a deeper understanding of sleep and sleep disorders. This development has the potential to transform sleep medicine in coming years to the betterment of patient care and our collective understanding of human sleep. This review addresses the current state of the field starting with a broad definition of the various components and analytic methods deployed in AI. We review examples of AI use in screening, endotyping, diagnosing, and treating sleep disorders and place this in the context of precision/personalized sleep medicine. We explore the opportunities for AI to both facilitate and extend providers' clinical impact and present ethical considerations regarding AI derived prognostic information. We cover early adopting specialties of AI in the clinical realm, such as radiology and pathology, to provide a road map for the challenges sleep medicine is likely to face when deploying this technology. Finally, we discuss pitfalls to ensure clinical AI implementation proceeds in the safest and most effective manner possible.
Collapse
Affiliation(s)
- Nathaniel F Watson
- Department of Neurology, University of Washington (UW) School of Medicine, USA; UW Medicine Sleep Center, USA.
| | | |
Collapse
|
13
|
Espinosa C, Becker M, Marić I, Wong RJ, Shaw GM, Gaudilliere B, Aghaeepour N, Stevenson DK. Data-Driven Modeling of Pregnancy-Related Complications. Trends Mol Med 2021; 27:762-776. [PMID: 33573911 DOI: 10.1016/j.molmed.2021.01.007] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2020] [Revised: 12/01/2020] [Accepted: 01/20/2021] [Indexed: 12/11/2022]
Abstract
A healthy pregnancy depends on complex interrelated biological adaptations involving placentation, maternal immune responses, and hormonal homeostasis. Recent advances in high-throughput technologies have provided access to multiomics biological data that, combined with clinical and social data, can provide a deeper understanding of normal and abnormal pregnancies. Integration of these heterogeneous datasets using state-of-the-art machine-learning methods can enable the prediction of short- and long-term health trajectories for a mother and offspring and the development of treatments to prevent or minimize complications. We review advanced machine-learning methods that could: provide deeper biological insights into a pregnancy not yet unveiled by current methodologies; clarify the etiologies and heterogeneity of pathologies that affect a pregnancy; and suggest the best approaches to address disparities in outcomes affecting vulnerable populations.
Collapse
Affiliation(s)
- Camilo Espinosa
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA; Department of Biomedical Data Sciences, Stanford University, Stanford, CA, USA
| | - Martin Becker
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA; Department of Biomedical Data Sciences, Stanford University, Stanford, CA, USA
| | - Ivana Marić
- Department of Pediatrics, Division of Neonatal and Developmental Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Ronald J Wong
- Department of Pediatrics, Division of Neonatal and Developmental Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Gary M Shaw
- Department of Pediatrics, Division of Neonatal and Developmental Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Brice Gaudilliere
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA; Department of Pediatrics, Division of Neonatal and Developmental Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Nima Aghaeepour
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA; Department of Biomedical Data Sciences, Stanford University, Stanford, CA, USA; Department of Pediatrics, Division of Neonatal and Developmental Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - David K Stevenson
- Department of Pediatrics, Division of Neonatal and Developmental Medicine, Stanford University School of Medicine, Stanford, CA, USA.
| | | |
Collapse
|
14
|
Banfi T, Valigi N, di Galante M, d'Ascanio P, Ciuti G, Faraguna U. Efficient embedded sleep wake classification for open-source actigraphy. Sci Rep 2021; 11:345. [PMID: 33431918 PMCID: PMC7801620 DOI: 10.1038/s41598-020-79294-y] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2020] [Accepted: 12/04/2020] [Indexed: 11/09/2022] Open
Abstract
This study presents a thorough analysis of sleep/wake detection algorithms for efficient on-device sleep tracking using wearable accelerometric devices. It develops a novel end-to-end algorithm using convolutional neural network applied to raw accelerometric signals recorded by an open-source wrist-worn actigraph. The aim of the study is to develop an automatic classifier that: (1) is highly generalizable to heterogenous subjects, (2) would not require manual features' extraction, (3) is computationally lightweight, embeddable on a sleep tracking device, and (4) is suitable for a wide assortment of actigraphs. Hereby, authors analyze sleep parameters, such as total sleep time, waking after sleep onset and sleep efficiency, by comparing the outcomes of the proposed algorithm to the gold standard polysomnographic concurrent recordings. The relatively substantial agreement (Cohen's kappa coefficient, median, equal to 0.78 ± 0.07) and the low-computational cost (2727 floating-point operations) make this solution suitable for an on-board sleep-detection approach.
Collapse
Affiliation(s)
- Tommaso Banfi
- The BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy. .,Department of Excellence in Robotics & AI, Scuola Superiore Sant'Anna, Pisa, Italy. .,sleepActa S.R.L, Pontedera, Italy.
| | | | - Marco di Galante
- sleepActa S.R.L, Pontedera, Italy.,Department of Developmental Neuroscience, IRCCS Stella Maris, Pisa, Italy
| | - Paola d'Ascanio
- Department of Translational Research and of New Medical and Surgical Technologies, University of Pisa, Pisa, Italy
| | - Gastone Ciuti
- The BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy.,Department of Excellence in Robotics & AI, Scuola Superiore Sant'Anna, Pisa, Italy
| | - Ugo Faraguna
- sleepActa S.R.L, Pontedera, Italy.,Department of Developmental Neuroscience, IRCCS Stella Maris, Pisa, Italy.,Department of Translational Research and of New Medical and Surgical Technologies, University of Pisa, Pisa, Italy
| |
Collapse
|
15
|
Lüdtke S, Hermann W, Kirste T, Beneš H, Teipel S. An algorithm for actigraphy-based sleep/wake scoring: Comparison with polysomnography. Clin Neurophysiol 2021; 132:137-145. [DOI: 10.1016/j.clinph.2020.10.019] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2020] [Revised: 10/09/2020] [Accepted: 10/21/2020] [Indexed: 12/30/2022]
|
16
|
Hammam N, Sadeghi D, Carson V, Tamana SK, Ezeugwu VE, Chikuma J, van Eeden C, Brook JR, Lefebvre DL, Moraes TJ, Subbarao P, Becker AB, Turvey SE, Sears MR, Mandhane PJ. The relationship between machine-learning-derived sleep parameters and behavior problems in 3- and 5-year-old children: results from the CHILD Cohort study. Sleep 2020; 43:5856695. [PMID: 32531021 DOI: 10.1093/sleep/zsaa117] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2019] [Revised: 05/09/2020] [Indexed: 11/12/2022] Open
Abstract
STUDY OBJECTIVES Machine learning (ML) may provide insights into the underlying sleep stages of accelerometer-assessed sleep duration. We examined associations between ML-sleep patterns and behavior problems among preschool children. METHODS Children from the CHILD Cohort Edmonton site with actigraphy and behavior data at 3-years (n = 330) and 5-years (n = 304) were included. Parent-reported behavior problems were assessed by the Child Behavior Checklist. The Hidden Markov Model (HMM) classification method was used for ML analysis of the accelerometer sleep period. The average time each participant spent in each HMM-derived sleep state was expressed in hours per day. We analyzed associations between sleep and behavior problems stratified by children with and without sleep-disordered breathing (SDB). RESULTS Four hidden sleep states were identified at 3 years and six hidden sleep states at 5 years using HMM. The first sleep state identified for both ages (HMM-0) had zero counts (no movement). The remaining hidden states were merged together (HMM-mov). Children spent an average of 8.2 ± 1.2 h/day in HMM-0 and 2.6 ± 0.8 h/day in HMM-mov at 3 years. At age 5, children spent an average of 8.2 ± 0.9 h/day in HMM-0 and 1.9 ± 0.7 h/day in HMM-mov. Among SDB children, each hour in HMM-0 was associated with 0.79-point reduced externalizing behavior problems (95% CI -1.4, -0.12; p < 0.05), and a 1.27-point lower internalizing behavior problems (95% CI -2.02, -0.53; p < 0.01). CONCLUSIONS ML-sleep states were not associated with behavior problems in the general population of children. Children with SDB who had greater sleep duration without movement had lower behavioral problems. The ML-sleep states require validation with polysomnography.
Collapse
Affiliation(s)
- Nevin Hammam
- Department of Pediatrics, University of Alberta, Edmonton, AB, Canada
| | - Dorna Sadeghi
- Department of Pediatrics, University of Alberta, Edmonton, AB, Canada
| | - Valerie Carson
- Faculty of Kinesiology, Sport, and Recreation, University of Alberta, Edmonton, AB, Canada
| | | | - Victor E Ezeugwu
- Department of Pediatrics, University of Alberta, Edmonton, AB, Canada
| | - Joyce Chikuma
- Department of Pediatrics, University of Alberta, Edmonton, AB, Canada
| | | | - Jeffrey R Brook
- Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
| | - Diana L Lefebvre
- Department of Medicine, McMaster University, Hamilton, ON, Canada
| | - Theo J Moraes
- Department of Paediatrics, University of Toronto, Toronto, ON, Canada
| | - Padmaja Subbarao
- Department of Paediatrics, University of Toronto, Toronto, ON, Canada
| | - Allan B Becker
- Department of Pediatrics and Child Health, University of Manitoba, Winnipeg, MB, Canada
| | - Stuart E Turvey
- Department of Pediatrics, University of British Columbia, Vancouver, BC, Canada
| | - Malcolm R Sears
- Department of Medicine, McMaster University, Hamilton, ON, Canada
| | | |
Collapse
|
17
|
Liu J, Zhao Y, Lai B, Wang H, Tsui KL. Wearable Device Heart Rate and Activity Data in an Unsupervised Approach to Personalized Sleep Monitoring: Algorithm Validation. JMIR Mhealth Uhealth 2020; 8:e18370. [PMID: 32755887 PMCID: PMC7439146 DOI: 10.2196/18370] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2020] [Revised: 05/12/2020] [Accepted: 05/20/2020] [Indexed: 12/11/2022] Open
Abstract
Background The proliferation of wearable devices that collect activity and heart rate data has facilitated new ways to measure sleeping and waking durations unobtrusively and longitudinally. Most existing sleep/wake identification algorithms are based on activity only and are trained on expensive and laboriously annotated polysomnography (PSG). Heart rate can also be reflective of sleep/wake transitions, which has motivated its investigation herein in an unsupervised algorithm. Moreover, it is necessary to develop a personalized approach to deal with interindividual variance in sleep/wake patterns. Objective We aimed to develop an unsupervised personalized sleep/wake identification algorithm using multifaceted data to explore the benefits of incorporating both heart rate and activity level in these types of algorithms and to compare this approach’s output with that of an existing commercial wearable device’s algorithms. Methods In this study, a total of 14 community-dwelling older adults wore wearable devices (Fitbit Alta; Fitbit Inc) 24 hours a day and 7 days a week over period of 3 months during which their heart rate and activity data were collected. After preprocessing the data, a model was developed to distinguish sleep/wake states based on each individual’s data. We proposed the use of hidden Markov models and compared different modeling schemes. With the best model selected, sleep/wake patterns were characterized by estimated parameters in hidden Markov models, and sleep/wake states were identified. Results When applying our proposed algorithm on a daily basis, we found there were significant differences in estimated parameters between weekday models and weekend models for some participants. Conclusions Our unsupervised approach can be effectively implemented based on an individual’s multifaceted sleep-related data from a commercial wearable device. A personalized model is shown to be necessary given the interindividual variability in estimated parameters.
Collapse
Affiliation(s)
- Jiaxing Liu
- School of Data Science, City University of Hong Kong, Kowloon, China (Hong Kong)
| | - Yang Zhao
- School of Data Science, City University of Hong Kong, Kowloon, China (Hong Kong).,Centre for Systems Informatics Engineering, City University of Hong Kong, Kowloon, China (Hong Kong)
| | - Boya Lai
- School of Data Science, City University of Hong Kong, Kowloon, China (Hong Kong)
| | - Hailiang Wang
- School of Data Science, City University of Hong Kong, Kowloon, China (Hong Kong).,Centre for Systems Informatics Engineering, City University of Hong Kong, Kowloon, China (Hong Kong)
| | - Kwok Leung Tsui
- School of Data Science, City University of Hong Kong, Kowloon, China (Hong Kong).,Centre for Systems Informatics Engineering, City University of Hong Kong, Kowloon, China (Hong Kong)
| |
Collapse
|