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Yao W. Permutation time irreversibility in sleep electroencephalograms: Dependence on sleep stage and the effect of equal values. Phys Rev E 2024; 109:054104. [PMID: 38907450 DOI: 10.1103/physreve.109.054104] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Accepted: 04/05/2024] [Indexed: 06/24/2024]
Abstract
Time irreversibility (TIR) refers to the manifestation of nonequilibrium brain activity influenced by various physiological conditions; however, the influence of sleep on electroencephalogram (EEG) TIR has not been sufficiently investigated. In this paper, a comprehensive study on permutation TIR (pTIR) of EEG data under different sleep stages is conducted. Two basic ordinal patterns (i.e., the original and amplitude permutations) are distinguished to simplify sleep EEGs, and then the influences of equal values and forbidden permutation on pTIR are elucidated. To detect pTIR of brain electric signals, five groups of EEGs in the awake, stages I, II, III, and rapid eye movement (REM) stages are collected from the public Polysomnographic Database in PhysioNet. Test results suggested that the pTIR of sleep EEGs significantly decreases as the sleep stage increases (p<0.001), with the awake and REM EEGs demonstrating greater differences than others. Comparative analysis and numerical simulations support the importance of equal values. Distribution of equal states, a simple quantification of amplitude fluctuations, significantly increases with the sleep stage (p<0.001). If these equalities are ignored, incorrect probabilistic differences may arise in the forward-backward and symmetric permutations of TIR, leading to contradictory results; moreover, the ascending and descending orders for symmetric permutations also lead different outcomes in sleep EEGs. Overall, pTIR in sleep EEGs contributes to our understanding of quantitative TIR and classification of sleep EEGs.
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Affiliation(s)
- Wenpo Yao
- State Key Laboratory of Organic Electronics and Information Displays, Institute of Advanced Materials, School of Chemistry and Life Sciences, School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing 210023, China and Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai 200433, China
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Yang Q, Liu L, Wang J, Zhang Y, Jiang N, Zhang M. Wavelet Entropy Analysis of Electroencephalogram Signals During Wake and Different Sleep Stages in Patients with Insomnia Disorder. Nat Sci Sleep 2024; 16:347-358. [PMID: 38606372 PMCID: PMC11007398 DOI: 10.2147/nss.s452017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Accepted: 03/22/2024] [Indexed: 04/13/2024] Open
Abstract
Objective To investigate the changes in the wavelet entropy during wake and different sleep stages in patients with insomnia disorder. Methods Sixteen patients with insomnia disorder and sixteen normal controls were enrolled. They underwent scale assessment and two consecutive nights of polysomnography (PSG). Wavelet entropy analysis of electroencephalogram (EEG) signals recorded from all participants in the two groups was performed. The changes in the integral wavelet entropy (En) and individual-scale wavelet entropy (En(a)) during wake and different sleep stages in the two groups were observed, and the differences between the two groups were compared. Results The insomnia disorder group exhibited lower En during the wake stage, and higher En during the N3 stage compared with the normal control group (all P < 0.001). In terms of En(a), patients with insomnia disorder exhibited lower En(a) in the β and α frequency bands during the wake stage compared with normal controls (β band, P < 0.01; α band, P < 0.001), whereas they showed higher En(a) in the β and α frequency bands during the N3 stage than normal controls (β band, P < 0.001; α band, P < 0.001). Conclusion Wavelet entropy can reflect the changes in the complexity of EEG signals during wake and different sleep stages in patients with insomnia disorder, which provides a new method and insights about understanding of pathophysiological mechanisms of insomnia disorder. Wavelet entropy provides an objective indicator for assessing sleep quality.
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Affiliation(s)
- Qian Yang
- Tianjin Union Medical Center, Tianjin Medical University, Tianjin, 300070, People’s Republic of China
| | - Lingfeng Liu
- Tianjin Union Medical Center, Tianjin Medical University, Tianjin, 300070, People’s Republic of China
| | - Jing Wang
- Department of Neurology, Tianjin Union Medical Center, Tianjin, 300121, People’s Republic of China
| | - Ying Zhang
- Department of Neurology, Tianjin Union Medical Center, Tianjin, 300121, People’s Republic of China
| | - Nan Jiang
- School of Mechanical Engineering, Tianjin University, Tianjin, 300354, People’s Republic of China
| | - Meiyun Zhang
- Department of Neurology, Tianjin Union Medical Center, Tianjin, 300121, People’s Republic of China
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Bosl WJ, Bosquet Enlow M, Lock EF, Nelson CA. A biomarker discovery framework for childhood anxiety. Front Psychiatry 2023; 14:1158569. [PMID: 37533889 PMCID: PMC10393248 DOI: 10.3389/fpsyt.2023.1158569] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/04/2023] [Accepted: 07/04/2023] [Indexed: 08/04/2023] Open
Abstract
Introduction Anxiety is the most common manifestation of psychopathology in youth, negatively affecting academic, social, and adaptive functioning and increasing risk for mental health problems into adulthood. Anxiety disorders are diagnosed only after clinical symptoms emerge, potentially missing opportunities to intervene during critical early prodromal periods. In this study, we used a new empirical approach to extracting nonlinear features of the electroencephalogram (EEG), with the goal of discovering differences in brain electrodynamics that distinguish children with anxiety disorders from healthy children. Additionally, we examined whether this approach could distinguish children with externalizing disorders from healthy children and children with anxiety. Methods We used a novel supervised tensor factorization method to extract latent factors from repeated multifrequency nonlinear EEG measures in a longitudinal sample of children assessed in infancy and at ages 3, 5, and 7 years of age. We first examined the validity of this method by showing that calendar age is highly correlated with latent EEG complexity factors (r = 0.77). We then computed latent factors separately for distinguishing children with anxiety disorders from healthy controls using a 5-fold cross validation scheme and similarly for distinguishing children with externalizing disorders from healthy controls. Results We found that latent factors derived from EEG recordings at age 7 years were required to distinguish children with an anxiety disorder from healthy controls; recordings from infancy, 3 years, or 5 years alone were insufficient. However, recordings from two (5, 7 years) or three (3, 5, 7 years) recordings gave much better results than 7 year recordings alone. Externalizing disorders could be detected using 3- and 5 years EEG data, also giving better results with two or three recordings than any single snapshot. Further, sex assigned at birth was an important covariate that improved accuracy for both disorder groups, and birthweight as a covariate modestly improved accuracy for externalizing disorders. Recordings from infant EEG did not contribute to the classification accuracy for either anxiety or externalizing disorders. Conclusion This study suggests that latent factors extracted from EEG recordings in childhood are promising candidate biomarkers for anxiety and for externalizing disorders if chosen at appropriate ages.
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Affiliation(s)
- William J. Bosl
- Center for AI & Medicine, University of San Francisco, San Francisco, CA, United States
- Computational Health Informatics Program, Boston Children’s Hospital, Boston, MA, United States
- Department of Pediatrics, Harvard Medical School, Boston, MA, United States
| | - Michelle Bosquet Enlow
- Department of Psychiatry and Behavioral Sciences, Boston Children’s Hospital, Boston, MA, United States
- Department of Psychiatry, Harvard Medical School, Boston, MA, United States
| | - Eric F. Lock
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN, United States
| | - Charles A. Nelson
- Department of Pediatrics, Harvard Medical School, Boston, MA, United States
- Laboratories of Cognitive Neuroscience, Division of Developmental Medicine, Boston Children’s Hospital, Boston, MA, United States
- Harvard Graduate School of Education, Cambridge, MA, United States
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Bosl WJ. Ellen R. Grass Lecture: The Future of Neurodiagnostics and Emergence of a New Science. Neurodiagn J 2023; 63:1-13. [PMID: 37023375 DOI: 10.1080/21646821.2023.2183012] [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] [Accepted: 01/12/2023] [Indexed: 06/19/2023]
Abstract
Electroencepholography (EEG) is the oldest and original brain measurement technology. Since EEG was first used in clinical settings, the role of neurodiagnostic professionals has focused on two principal tasks that require specialized training. These include collecting the EEG recording, performed primarily by EEG Technologists, and interpreting the recording, generally done by physicians with proper specialization. Emerging technology appears to enable non-specialists to contribute to these tasks. Neurotechnologists may feel vulnerable to being displaced by new technology. A similar shift occurred in the last century when human "computers," employed to perform repetitive calculations needed to solve complex mathematics for the Manhattan and Apollo Projects, were displaced by new electronic computing machines. Many human "computers" seized on the opportunity created by the new computing technology to become the first computer programmers and create the new field of computer science. That transition offers insights for the future of neurodiagnostics. From its inception, neurodiagnostics has been an information processing discipline. Advances in dynamical systems theory, cognitive neuroscience, and biomedical informatics have created an opportunity for neurodiagnostic professionals to help create a new science of functional brain monitoring. A new generation of advanced neurodiagnostic professionals that bring together knowledge and skills in clinical neuroscience and biomedical informatics will benefit psychiatry, neurology, and precision healthcare, lead to preventive brain health through the lifespan, and lead the establishment of a new science of clinical neuroinformatics.
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Affiliation(s)
- William J Bosl
- Health Informatics Program, University of San Francisco, San Francisco, California
- Computational Health Informatics Program, Boston Children's Hospital, Boston, Massachusetts
- Department of Pediatrics, Harvard Medical School, Boston, Massachusetts
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Yan C, Li P, Yang M, Li Y, Li J, Zhang H, Liu C. Entropy Analysis of Heart Rate Variability in Different Sleep Stages. ENTROPY 2022; 24:e24030379. [PMID: 35327890 PMCID: PMC8947316 DOI: 10.3390/e24030379] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Revised: 03/01/2022] [Accepted: 03/05/2022] [Indexed: 01/02/2023]
Abstract
How the complexity or irregularity of heart rate variability (HRV) changes across different sleep stages and the importance of these features in sleep staging are not fully understood. This study aimed to investigate the complexity or irregularity of the RR interval time series in different sleep stages and explore their values in sleep staging. We performed approximate entropy (ApEn), sample entropy (SampEn), fuzzy entropy (FuzzyEn), distribution entropy (DistEn), conditional entropy (CE), and permutation entropy (PermEn) analyses on RR interval time series extracted from epochs that were constructed based on two methods: (1) 270-s epoch length and (2) 300-s epoch length. To test whether adding the entropy measures can improve the accuracy of sleep staging using linear HRV indices, XGBoost was used to examine the abilities to differentiate among: (i) 5 classes [Wake (W), non-rapid-eye-movement (NREM), which can be divide into 3 sub-stages: stage N1, stage N2, and stage N3, and rapid-eye-movement (REM)]; (ii) 4 classes [W, light sleep (combined N1 and N2), deep sleep (N3), and REM]; and (iii) 3 classes: (W, NREM, and REM). SampEn, FuzzyEn, and CE significantly increased from W to N3 and decreased in REM. DistEn increased from W to N1, decreased in N2, and further decreased in N3; it increased in REM. The average accuracy of the three tasks using linear and entropy features were 42.1%, 59.1%, and 60.8%, respectively, based on 270-s epoch length; all were significantly lower than the performance based on 300-s epoch length (i.e., 54.3%, 63.1%, and 67.5%, respectively). Adding entropy measures to the XGBoost model of linear parameters did not significantly improve the classification performance. However, entropy measures, especially PermEn, DistEn, and FuzzyEn, demonstrated greater importance than most of the linear parameters in the XGBoost model.300-s270-s.
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Affiliation(s)
- Chang Yan
- School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China; (M.Y.); (Y.L.); (J.L.)
- Correspondence: (C.Y.); (C.L.)
| | - Peng Li
- Division of Sleep and Circadian Disorders, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA;
| | - Meicheng Yang
- School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China; (M.Y.); (Y.L.); (J.L.)
| | - Yang Li
- School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China; (M.Y.); (Y.L.); (J.L.)
| | - Jianqing Li
- School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China; (M.Y.); (Y.L.); (J.L.)
| | - Hongxing Zhang
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences, Beijing Institute of Lifeomics, Beijing 102206, China;
| | - Chengyu Liu
- School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China; (M.Y.); (Y.L.); (J.L.)
- Correspondence: (C.Y.); (C.L.)
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ECG and Heart Rate Variability in Sleep-Related Breathing Disorders. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2022; 1384:159-183. [PMID: 36217084 DOI: 10.1007/978-3-031-06413-5_10] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Here we discuss the current perspectives of comprehensive heart rate variability (HRV) analysis in electrocardiogram (ECG) signals as a non-invasive and reliable measure to assess autonomic function in sleep-related breathing disorders (SDB). It is a tool of increasing interest as different facets of HRV can be implemented to screen and diagnose SDB, monitor treatment efficacy, and prognose adverse cardiovascular outcomes in patients with sleep apnea. In this context, the technical aspects, pathophysiological features, and clinical applications of HRV are discussed to explore its usefulness in better understanding SDB.
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Behar JA, Shamay Y, Álvarez D, Del Campo F, Penzel T. From sleep medicine to medicine during sleep. Physiol Meas 2021; 42. [PMID: 34964719 DOI: 10.1088/1361-6579/ac3e38] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2021] [Accepted: 11/29/2021] [Indexed: 02/02/2023]
Affiliation(s)
- Joachim A Behar
- Faculty of Biomedical Engineering, Technion Institute of Technology, Haifa, Israel
| | - Yosi Shamay
- Faculty of Biomedical Engineering, Technion Institute of Technology, Haifa, Israel
| | - Daniel Álvarez
- Río Hortega University Hospital Valladolid, Valladolid, Spain.,Biomedical Engineering Group, University of Valladolid, Valladolid, Spain.,Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Valladolid, Spain
| | - Félix Del Campo
- Río Hortega University Hospital Valladolid, Valladolid, Spain.,Biomedical Engineering Group, University of Valladolid, Valladolid, Spain.,Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Valladolid, Spain
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