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Cribbet MR, Thayer JF, Jarczok MN, Fischer JE. High-Frequency Heart Rate Variability Is Prospectively Associated With Sleep Complaints in a Healthy Working Cohort. Psychosom Med 2024; 86:342-348. [PMID: 38724040 PMCID: PMC11090416 DOI: 10.1097/psy.0000000000001302] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/15/2024]
Abstract
OBJECTIVE Vagus nerve functioning, as indexed by high-frequency heart rate variability (HF-HRV), has been implicated in a wide range of mental and physical health conditions, including sleep complaints. This study aimed to test associations between HF-HRV measured during sleep (sleep HF-HRV) and subjective sleep complaints 4 years later. METHODS One hundred forty-three healthy employees (91% male; MAge = 47.8 years [time 2], SD = 8.3 years) of an industrial company in Southern Germany completed the Jenkins Sleep Problems Scale, participated in a voluntary health assessment, and were given a 24-hour ambulatory heart rate recording device in 2007. Employees returned for a health assessment and completed the Jenkins Sleep Problems Scale 4 years later. RESULTS Hierarchical regression analyses showed that lower sleep HF-HRV measured in 2007 was associated with higher self-reported sleep complaints 4 years later after controlling for covariates (rab,c = -0.096, b = -0.108, 95% CI, -0.298 to 0.081, ΔR2 = 0.009, p = .050). CONCLUSIONS These data are the first to show that lower sleep HF-HRV predicted worse sleep 4 years later, highlighting the importance of vagus nerve functioning in adaptability and health.
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Affiliation(s)
- Matthew R. Cribbet
- Department of Psychology, The University of Alabama, Tuscaloosa, Alabama
| | - Julian F. Thayer
- Department of Psychological Science, The University of California at Irvine, Irvine, CA
| | - Marc N. Jarczok
- Clinic for Psychosomatic Medicine and Psychotherapy, University Hospital Ulm, Ulm, Germany
| | - Joachim E. Fischer
- General Medicine, Center for Preventive Medicine and Digital Health, Mannheim Medical Facility, Heidelberg University, Mannheim, Germany
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2
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Sargent KS, Martinez EL, Reed AC, Guha A, Bartholomew ME, Diehl CK, Chang CS, Salama S, Popov T, Thayer JF, Miller GA, Yee CM. Oscillatory Coupling Between Neural and Cardiac Rhythms. Psychol Sci 2024:9567976241235932. [PMID: 38568870 DOI: 10.1177/09567976241235932] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/05/2024] Open
Abstract
Oscillations serve a critical role in organizing biological systems. In the brain, oscillatory coupling is a fundamental mechanism of communication. The possibility that neural oscillations interact directly with slower physiological rhythms (e.g., heart rate, respiration) is largely unexplored and may have important implications for psychological functioning. Oscillations in heart rate, an aspect of heart rate variability (HRV), show remarkably robust associations with psychological health. Mather and Thayer proposed coupling between high-frequency HRV (HF-HRV) and neural oscillations as a mechanism that partially accounts for such relationships. We tested this hypothesis by measuring phase-amplitude coupling between HF-HRV and neural oscillations in 37 healthy adults at rest. Robust coupling was detected in all frequency bands. Granger causality analyses indicated stronger heart-to-brain than brain-to-heart effects in all frequency bands except gamma. These findings suggest that cardiac rhythms play a causal role in modulating neural oscillations, which may have important implications for mental health.
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Affiliation(s)
- Kaia S Sargent
- Department of Psychology, University of California, Los Angeles
| | | | | | - Anika Guha
- Department of Psychology, University of California, Los Angeles
| | | | | | | | - Sarah Salama
- Department of Psychology, University of California, Los Angeles
| | - Tzvetan Popov
- Department of Psychology, University of Konstanz
- Department of Psychology, University of Zurich
| | - Julian F Thayer
- Department of Psychological Science, University of California, Irvine
| | - Gregory A Miller
- Department of Psychology, University of California, Los Angeles
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles
| | - Cindy M Yee
- Department of Psychology, University of California, Los Angeles
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles
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3
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Thieux M, Guyon A, Seugnet L, Franco P. Salivary α-amylase as a marker of sleep disorders: A theoretical review. Sleep Med Rev 2024; 74:101894. [PMID: 38157687 DOI: 10.1016/j.smrv.2023.101894] [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: 07/27/2023] [Revised: 12/04/2023] [Accepted: 12/14/2023] [Indexed: 01/03/2024]
Abstract
Sleep disorders are commonplace in our modern societies. Specialized hospital departments are generally overloaded, and sleep assessment is an expensive process in terms of equipment, human resources, and time. Biomarkers would usefully complement current measures in the screening and follow-up of sleep disorders and their daytime repercussions. Among salivary markers, a growing body of literature suggests that salivary α-amylase (sAA) may be a cross-species marker of sleep debt. However, there is no consensus as to the direction of variation in sAA with sleep disorders. Herein, after describing the mechanisms of sAA secretion and its relationship with stress, studies assessing the relationship between sAA and sleep parameters are reviewed. Finally, the influence of confounding factors is discussed, along with methodological considerations, to better understand the fluctuations in sAA and facilitate future studies in the field.
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Affiliation(s)
- Marine Thieux
- Centre de Recherche en Neurosciences de Lyon (CRNL), INSERM, Lyon, France.
| | - Aurore Guyon
- Pediatric Sleep Unit and CRMR Narcolepsie-Hypersomnies Rares, Department of Pediatric Clinical Epileptology, Sleep Disorders and Functional Neurology, Hôpital Femme Mère Enfant, Hospices Civils de Lyon, Lyon, France
| | - Laurent Seugnet
- Centre de Recherche en Neurosciences de Lyon (CRNL), INSERM, Lyon, France
| | - Patricia Franco
- Centre de Recherche en Neurosciences de Lyon (CRNL), INSERM, Lyon, France; Pediatric Sleep Unit and CRMR Narcolepsie-Hypersomnies Rares, Department of Pediatric Clinical Epileptology, Sleep Disorders and Functional Neurology, Hôpital Femme Mère Enfant, Hospices Civils de Lyon, Lyon, France
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4
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von Jakitsch CB, Pinto Neto O, Pinho TOR, Ribeiro W, Pereira R, Baltatu OC, Osório RAL. High and low pitch sound stimuli effects on heart-brain coupling. Biomed Eng Lett 2024; 14:331-339. [PMID: 38374900 PMCID: PMC10874348 DOI: 10.1007/s13534-023-00340-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 11/27/2023] [Accepted: 12/01/2023] [Indexed: 02/21/2024] Open
Abstract
This study aimed to explore the influence of sound stimulation on heart rate and the potential coupling between cardiac and cerebral activities. Thirty-one participants underwent exposure to periods of silence and two distinct continuous, non-repetitive pure tone stimuli: low pitch (110 Hz) and high pitch (880 Hz). Electroencephalography (EEG) data from electrodes F3, F4, F7, F8, Fp1, Fp2, T3, T4, T5, and T6 were recorded, along with R-R interval data for heart rate. Heart-brain connectivity was assessed using wavelet coherence between heart rate variability (HRV) and EEG envelopes (EEGE). Heart rates were significantly lower during high and low-pitch sound periods than in silence (p < 0.002). HRV-EEGE coherence was significantly lower during high-pitch intervals than silence and low-pitch sound intervals (p < 0.048), specifically between the EEG Beta band and the low-frequency HRV range. These results imply a differential involvement of the frontal and temporal brain regions in response to varying auditory stimuli. Our findings highlight the essential nature of discerning the complex interrelations between sound frequencies and their implications for heart-brain connectivity. Such insights could have ramifications for conditions like seizures and sleep disturbances. A deeper exploration is warranted to decipher specific sound stimuli's potential advantages or drawbacks in diverse clinical scenarios.
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Affiliation(s)
| | - Osmar Pinto Neto
- Center of Innovation, Technology and Education (CITE), Anhembi Morumbi University – Anima Institute, São José dos Campos Technology Park, São José dos Campos, Brazil
- Arena235 Research Lab, São José dos Campos, Brazil
| | | | | | - Rafael Pereira
- Integrative Physiology Research Center, Department of Biological Sciences, Universidade Estadual do Sudoeste da Bahia (UESB), Jequie, Brazil
| | - Ovidiu Constantin Baltatu
- Center of Innovation, Technology and Education (CITE), Anhembi Morumbi University – Anima Institute, São José dos Campos Technology Park, São José dos Campos, Brazil
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5
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Akita T, Kurono Y, Yamada A, Hayano J, Minagawa M. Effects of Acupuncture on Autonomic Nervous Functions During Sleep: Comparison with Nonacupuncture Site Stimulation Using a Crossover Design. JOURNAL OF INTEGRATIVE AND COMPLEMENTARY MEDICINE 2022; 28:791-798. [PMID: 35895512 DOI: 10.1089/jicm.2022.0526] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Objectives: Although many studies have shown that acupuncture can improve sleep quality, there is no clear evidence by objective physiological measures. The authors investigated the effects of acupuncture on the autonomic indices of heart rate variability (HRV) during sleep. Design: The authors applied true acupuncture and sham-site stimulations in 10 healthy adult males (mean ± standard deviation age, 40 ± 9 years) and compared autonomic nerve indices of HRV during each sleep stage in a crossover design. The sleep stages were estimated by the combined analysis of an HRV maker of non-rapid eye movement (REM) sleep (HRV sleep index [Hsi]) and actigraphic body movement. Results: Heart rate was lower (true vs. sham acupuncture, mean ± standard error of the mean, 60.9 ± 1.8 vs. 61.7 ± 1.7 bpm, p < 0.0001) and the power of low-frequency and high-frequency components of HRV was higher (35.6 ± 2.0 vs. 34.7 ± 2.0 msec, p = 0.04 and 26.7 ± 3.2 vs. 25.8 ± 3.2 msec, p < 0.0001, respectively) after the true acupuncture compared with the sham-site stimulation throughout sleep. During non-REM sleep, heart rate was lower (59.6 ± 1.8 vs. 60.1 ± 1.8 bpm, p = 0.0004) and the power of low-frequency and high-frequency components were higher (27.7 ± 1.8 vs. 26.1 ± 1.8 msec p = 0.0004 and 28.4 ± 3.5 vs. 27.7 ± 3.5 msec, p = 0.004) after the true acupuncture than the sham-site stimulation. Whereas during REM sleep, there was no significant difference in either HRV indices between them, while heart rate was lower after the true acupuncture than the sham-site stimulation (60.8 ± 1.6 vs. 61.7 ± 1.6 bpm, p < 0.0001). Conclusions: Acupuncture increases parasympathetic HRV indices during sleep, especially during the non-REM stage.
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Affiliation(s)
| | | | | | - Junichiro Hayano
- Heart Beat Science Lab, Co., Ltd., Sendai, Japan
- Nagoya City University, Nagoya, Japan
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6
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Barthelemy JC, Pichot V, Hupin D, Berger M, Celle S, Mouhli L, Bäck M, Lacour JR, Roche F. Targeting autonomic nervous system as a biomarker of well-ageing in the prevention of stroke. Front Aging Neurosci 2022; 14:969352. [PMID: 36185479 PMCID: PMC9521604 DOI: 10.3389/fnagi.2022.969352] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Accepted: 08/25/2022] [Indexed: 11/13/2022] Open
Abstract
Stroke prediction is a key health issue for preventive medicine. Atrial fibrillation (AF) detection is well established and the importance of obstructive sleep apneas (OSA) has emerged in recent years. Although autonomic nervous system (ANS) appears strongly implicated in stroke occurrence, this factor is more rarely considered. However, the consequences of decreased parasympathetic activity explored in large cohort studies through measurement of ANS activity indicate that an ability to improve its activity level and equilibrium may prevent stroke. In support of these observations, a compensatory neurostimulation has already proved beneficial on endothelium function. The available data on stroke predictions from ANS is based on many long-term stroke cohorts. These data underline the need of repeated ANS evaluation for the general population, in a medical environment, and remotely by emerging telemedicine digital tools. This would help uncovering the reasons behind the ANS imbalance that would need to be medically adjusted to decrease the risk of stroke. This ANS unbalance help to draw attention on clinical or non-clinical evidence, disclosing the vascular risk, as ANS activity integrates the cumulated risk from many factors of which most are modifiable, such as metabolic inadaptation in diabetes and obesity, sleep ventilatory disorders, hypertension, inflammation, and lack of physical activity. Treating these factors may determine ANS recovery through the appropriate management of these conditions. Natural aging also decreases ANS activity. ANS recovery will decrease global circulating inflammation, which will reinforce endothelial function and thus protect the vessels and the associated organs. ANS is the whistle-blower of vascular risk and the actor of vascular health. Such as, ANS should be regularly checked to help draw attention on vascular risk and help follow the improvements in response to our interventions. While today prediction of stroke relies on classical cardiovascular risk factors, adding autonomic biomarkers as HRV parameters may significantly increase the prediction of stroke.
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Affiliation(s)
- Jean-Claude Barthelemy
- Physical Exercise and Clinical Physiology Department, CHU Nord, Saint-Étienne, France
- INSERM U1059 Santé Ingénierie Biologie, Université Jean Monnet, Saint-Étienne, France
- *Correspondence: Jean-Claude Barthelemy,
| | - Vincent Pichot
- Physical Exercise and Clinical Physiology Department, CHU Nord, Saint-Étienne, France
- INSERM U1059 Santé Ingénierie Biologie, Université Jean Monnet, Saint-Étienne, France
| | - David Hupin
- Physical Exercise and Clinical Physiology Department, CHU Nord, Saint-Étienne, France
- INSERM U1059 Santé Ingénierie Biologie, Université Jean Monnet, Saint-Étienne, France
- Section of Translational Cardiology, Department of Medicine, Solna, Karolinska Institutet, Stockholm, Sweden
| | - Mathieu Berger
- Physical Exercise and Clinical Physiology Department, CHU Nord, Saint-Étienne, France
- INSERM U1059 Santé Ingénierie Biologie, Université Jean Monnet, Saint-Étienne, France
- Centre d’Investigation et de Recherche sur le Sommeil, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland
| | - Sébastien Celle
- Physical Exercise and Clinical Physiology Department, CHU Nord, Saint-Étienne, France
- INSERM U1059 Santé Ingénierie Biologie, Université Jean Monnet, Saint-Étienne, France
| | - Lytissia Mouhli
- Physical Exercise and Clinical Physiology Department, CHU Nord, Saint-Étienne, France
- Département de Neurologie, Hôpital Universitaire Nord, Saint-Étienne, France
| | - Magnus Bäck
- Section of Translational Cardiology, Department of Medicine, Solna, Karolinska Institutet, Stockholm, Sweden
- Department of Cardiology, Karolinska University Hospital, Stockholm, Sweden
| | - Jean-René Lacour
- Laboratoire de Physiologie, Faculté de Médecine Lyon-Sud, Oullins, France
| | - Frederic Roche
- Physical Exercise and Clinical Physiology Department, CHU Nord, Saint-Étienne, France
- INSERM U1059 Santé Ingénierie Biologie, Université Jean Monnet, Saint-Étienne, France
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7
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Huwiler S, Carro Dominguez M, Huwyler S, Kiener L, Stich FM, Sala R, Aziri F, Trippel A, Schmied C, Huber R, Wenderoth N, Lustenberger C. Effects of auditory sleep modulation approaches on brain oscillatory and cardiovascular dynamics. Sleep 2022; 45:6632997. [PMID: 35793672 PMCID: PMC9453626 DOI: 10.1093/sleep/zsac155] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Revised: 06/01/2022] [Indexed: 11/14/2022] Open
Abstract
Abstract
Slow waves, the hallmark feature of deep nonrapid eye movement sleep, do potentially drive restorative effects of sleep on brain and body functions. Sleep modulation techniques to elucidate the functional role of slow waves thus have gained large interest. Auditory slow wave stimulation is a promising tool; however, directly comparing auditory stimulation approaches within a night and analyzing induced dynamic brain and cardiovascular effects are yet missing. Here, we tested various auditory stimulation approaches in a windowed, 10 s ON (stimulations) followed by 10 s OFF (no stimulations), within-night stimulation design and compared them to a SHAM control condition. We report the results of three studies and a total of 51 included nights and found a large and global increase in slow-wave activity (SWA) in the stimulation window compared to SHAM. Furthermore, slow-wave dynamics were most pronouncedly increased at the start of the stimulation and declined across the stimulation window. Beyond the changes in brain oscillations, we observed, for some conditions, a significant increase in the mean interval between two heartbeats within a stimulation window, indicating a slowing of the heart rate, and increased heart rate variability derived parasympathetic activity. Those cardiovascular changes were positively correlated with the change in SWA, and thus, our findings provide insight into the potential of auditory slow wave enhancement to modulate cardiovascular restorative conditions during sleep. However, future studies need to investigate whether the potentially increased restorative capacity through slow-wave enhancements translates into a more rested cardiovascular system on a subsequent day.
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Affiliation(s)
- Stephanie Huwiler
- Department of Health Sciences and Technology, Neural Control of Movement Lab, Institute of Human Movement Sciences and Sport, ETH Zurich, Zurich, Switzerland
| | - Manuel Carro Dominguez
- Department of Health Sciences and Technology, Neural Control of Movement Lab, Institute of Human Movement Sciences and Sport, ETH Zurich, Zurich, Switzerland
| | - Silja Huwyler
- Department of Health Sciences and Technology, Neural Control of Movement Lab, Institute of Human Movement Sciences and Sport, ETH Zurich, Zurich, Switzerland
| | - Luca Kiener
- Department of Health Sciences and Technology, Neural Control of Movement Lab, Institute of Human Movement Sciences and Sport, ETH Zurich, Zurich, Switzerland
| | - Fabia M Stich
- Department of Health Sciences and Technology, Neural Control of Movement Lab, Institute of Human Movement Sciences and Sport, ETH Zurich, Zurich, Switzerland
| | - Rossella Sala
- Department of Health Sciences and Technology, Neural Control of Movement Lab, Institute of Human Movement Sciences and Sport, ETH Zurich, Zurich, Switzerland
| | - Florent Aziri
- Department of Health Sciences and Technology, Neural Control of Movement Lab, Institute of Human Movement Sciences and Sport, ETH Zurich, Zurich, Switzerland
| | - Anna Trippel
- Department of Health Sciences and Technology, Neural Control of Movement Lab, Institute of Human Movement Sciences and Sport, ETH Zurich, Zurich, Switzerland
| | - Christian Schmied
- Department of Cardiology, University Heart Center Zurich, University of Zurich, Zurich, Switzerland
| | - Reto Huber
- Center of Competence Sleep and Health Zurich, University of Zurich, Zurich, Switzerland
- Neuroscience Center Zurich (ZNZ), University of Zurich, ETH Zurich, Zurich, Switzerland
- Child Development Centre, University Children’s Hospital, University of Zurich, Zurich, Switzerland
- Department of Child and Adolescent Psychiatry and Psychotherapy, Psychiatric Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Nicole Wenderoth
- Department of Health Sciences and Technology, Neural Control of Movement Lab, Institute of Human Movement Sciences and Sport, ETH Zurich, Zurich, Switzerland
- Neuroscience Center Zurich (ZNZ), University of Zurich, ETH Zurich, Zurich, Switzerland
- Future Health Technologies, Singapore-ETH Center, Campus for Research Excellence and Technological Enterprise (CREATE), Singapore, Singapore
| | - Caroline Lustenberger
- Corresponding author. Caroline Lustenberger, Department of Health Sciences and Technology, Neural Control of Movement Lab, ETH Zurich, Zurich, 8092, Switzerland.
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8
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Inhibitory Control and Brain–Heart Interaction: An HRV-EEG Study. Brain Sci 2022; 12:brainsci12060740. [PMID: 35741625 PMCID: PMC9221218 DOI: 10.3390/brainsci12060740] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Revised: 06/01/2022] [Accepted: 06/02/2022] [Indexed: 12/10/2022] Open
Abstract
Background: Motor inhibition is a complex cognitive function regulated by specific brain regions and influenced by the activity of the Central Autonomic Network. We investigate the two-way Brain–Heart interaction during a Go/NoGo task. Spectral EEG ϑ, α powerbands, and HRV parameters (Complexity Index (CI), Low Frequency (LF) and High Frequency (HF) powers) were recorded. Methods: Fourteen healthy volunteers were enrolled. We used a modified version of the classical Go/NoGo task, based on Rule Shift Cards, characterized by a baseline and two different tasks of different complexity. The participants were divided into subjects with Good (GP) and Poor (PP) performances. Results: In the baseline, CI was negatively correlated with α/ϑ. In task 1, the CI was negatively correlated with the errors and α/ϑ, while the errors were positively correlated with α/ϑ. In task 2, CI was negatively correlated with the Reaction Time and positively with α, and the errors were negatively correlated with the Reaction Time and positively correlated with α/ϑ. The GP group showed, at baseline, a negative correlation between CI and α/ϑ. Conclusions: We provide a new combined Brain–Heart model underlying inhibitory control abilities. The results are consistent with the complementary role of α and ϑ oscillations in cognitive control.
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9
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Autonomic Central Coupling during Daytime Sleep Differs between Older and Younger People. Neurobiol Learn Mem 2022; 193:107646. [PMID: 35671980 DOI: 10.1016/j.nlm.2022.107646] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2021] [Revised: 04/12/2022] [Accepted: 05/28/2022] [Indexed: 11/24/2022]
Abstract
Decreased functioning in the elderly is mirrored by independent changes in central and autonomic nervous systems. Additionally, recent work suggests that the coupling of these systems may also serve an important role. In young adults, Autonomic and Central Events (ACEs), measured in the temporal coincidence of heart rate bursts (HRBs) and increased slow-wave-activity (SWA, 0.5-1Hz) and sigma activity (12-15Hz), followed by parasympathetic surge (RRHF) during non-rapid eye movement (NREM) sleep, predicted cognitive improvements. However, ACEs have not been examined in the elderly. Thus, the current study compared ACEs during wake and daytime sleep in older and younger adults and examined associations with working memory improvement before and after a nap. Compared to youngers, older adults showed lower amplitude of ACEs during NREM sleep, but not during wake. Furthermore, while younger adults demonstrated a parasympathetic surge after HRBs, older adults showed an earlier rise and longer maintenance of the RRHF. Taken together, our results demonstrate that autonomic-central coupling declines with age. Pathological aging implicates independent roles for decreased autonomic and central nervous system functioning, the current findings suggest that the coupling of these systems may also deserve attention.
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10
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Ichiba T, Kawamura A, Nagao K, Kurumai Y, Fujii A, Yoshimura A, Yoshiike T, Kuriyama K. Periocular Skin Warming Promotes Sleep Onset Through Heat Dissipation From Distal Skin in Patients With Insomnia Disorder. Front Psychiatry 2022; 13:844958. [PMID: 35599781 PMCID: PMC9114477 DOI: 10.3389/fpsyt.2022.844958] [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: 12/29/2021] [Accepted: 04/13/2022] [Indexed: 11/13/2022] Open
Abstract
STUDY OBJECTIVES Periocular skin warming before bedtime has been demonstrated to improve subjective sleep initiation in healthy adults with sleep difficulties scored six or higher in the Pittsburgh Sleep Questionnaire Index. This study aimed to investigate the effects of periocular skin warming on sleep initiation and thermoregulation processes in patients with insomnia disorder. METHODS Participants included those with sleep difficulty (n = 22) and those with insomnia disorder (n = 16). Individuals from both groups were assessed at baseline (habitual sleep-wake schedule) and after two intervention conditions (use of a warming eye mask or a sham eye mask before habitual bedtime). The subjective and electroencephalographic sleep onset latency, along with proximal and distal skin temperature after periocular skin warming, were evaluated. RESULTS Periocular skin warming reduced objective sleep onset latency in independently of the group. Foot temperature and foot-proximal temperature gradient after getting into bed increased with periocular skin warming in independently of the group. However, the increase in hand temperature was observed only in the insomnia disorder group. Periocular skin warming also increased the normalized high frequency component of heart rate variability in independently of the group. The reduction of objective sleep onset latency was strongly associated with heat dissipation from the foot skin region. CONCLUSION These results suggest that periocular skin warming promotes sleep initiation by enhancing heat dissipation from the distal skin regions in individuals with sleep difficulty and insomnia disorder. Periocular skin warming could thus be a novel non-pharmacological therapy for insomnia disorder.
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Affiliation(s)
- Tomohisa Ichiba
- Personal Health Care Laboratory, Kao Corporation, Tokyo, Japan
| | - Aoi Kawamura
- Department of Psychiatry, Shiga University of Medical Science, Otsu, Japan.,Department of Sleep-Wake Disorders, National Center of Neurology and Psychiatry, National Institute of Mental Health, Kodaira, Japan
| | - Kentaro Nagao
- Department of Psychiatry, Shiga University of Medical Science, Otsu, Japan.,Department of Sleep-Wake Disorders, National Center of Neurology and Psychiatry, National Institute of Mental Health, Kodaira, Japan
| | - Yuichi Kurumai
- Department of Psychiatry, Shiga University of Medical Science, Otsu, Japan
| | - Akio Fujii
- Department of Psychiatry, Shiga University of Medical Science, Otsu, Japan
| | - Atsushi Yoshimura
- Department of Psychiatry, Shiga University of Medical Science, Otsu, Japan
| | - Takuya Yoshiike
- Department of Psychiatry, Shiga University of Medical Science, Otsu, Japan.,Department of Sleep-Wake Disorders, National Center of Neurology and Psychiatry, National Institute of Mental Health, Kodaira, Japan
| | - Kenichi Kuriyama
- Department of Psychiatry, Shiga University of Medical Science, Otsu, Japan.,Department of Sleep-Wake Disorders, National Center of Neurology and Psychiatry, National Institute of Mental Health, Kodaira, Japan
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11
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González C, Garcia-Hernando G, Jensen EW, Vallverdú-Ferrer M. Assessing rheoencephalography dynamics through analysis of the interactions among brain and cardiac networks during general anesthesia. FRONTIERS IN NETWORK PHYSIOLOGY 2022; 2:912733. [PMID: 36926077 PMCID: PMC10013012 DOI: 10.3389/fnetp.2022.912733] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Accepted: 07/08/2022] [Indexed: 11/13/2022]
Abstract
Cerebral blood flow (CBF) reflects the rate of delivery of arterial blood to the brain. Since no nutrients, oxygen or water can be stored in the cranial cavity due to space and pressure restrictions, a continuous perfusion of the brain is critical for survival. Anesthetic procedures are known to affect cerebral hemodynamics, but CBF is only monitored in critical patients due, among others, to the lack of a continuous and affordable bedside monitor for this purpose. A potential solution through bioelectrical impedance technology, also known as rheoencephalography (REG), is proposed, that could fill the existing gap for a low-cost and effective CBF monitoring tool. The underlying hypothesis is that REG signals carry information on CBF that might be recovered by means of the application of advanced signal processing techniques, allowing to track CBF alterations during anesthetic procedures. The analysis of REG signals was based on geometric features extracted from the time domain in the first place, since this is the standard processing strategy for this type of physiological data. Geometric features were tested to distinguish between different anesthetic depths, and they proved to be capable of tracking cerebral hemodynamic changes during anesthesia. Furthermore, an approach based on Poincaré plot features was proposed, where the reconstructed attractors form REG signals showed significant differences between different anesthetic states. This was a key finding, providing an alternative to standard processing of REG signals and supporting the hypothesis that REG signals do carry CBF information. Furthermore, the analysis of cerebral hemodynamics during anesthetic procedures was performed by means of studying causal relationships between global hemodynamics, cerebral hemodynamics and electroencephalogram (EEG) based-parameters. Interactions were detected during anesthetic drug infusion and patient positioning (Trendelenburg positioning and passive leg raise), providing evidence of the causal coupling between hemodynamics and brain activity. The provided alternative of REG signal processing confirmed the hypothesis that REG signals carry information on CBF. The simplicity of the technology, together with its low cost and easily interpretable outcomes, should provide a new opportunity for REG to reach standard clinical practice. Moreover, causal relationships among the hemodynamic physiological signals and brain activity were assessed, suggesting that the inclusion of REG information in depth of anesthesia monitors could be of valuable use to prevent unwanted CBF alterations during anesthetic procedures.
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Affiliation(s)
- Carmen González
- Biomedical Engineering Research Centre, CIBER of Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Universitat Politècnica de Catalunya, Barcelona, Spain.,Research and Development Department, Quantium Medical, Mataró, Spain
| | - Gabriel Garcia-Hernando
- Biomedical Engineering Research Centre, CIBER of Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Universitat Politècnica de Catalunya, Barcelona, Spain.,Research and Development Department, Quantium Medical, Mataró, Spain
| | - Erik W Jensen
- Research and Development Department, Quantium Medical, Mataró, Spain
| | - Montserrat Vallverdú-Ferrer
- Biomedical Engineering Research Centre, CIBER of Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Universitat Politècnica de Catalunya, Barcelona, Spain
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12
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Hartmann S, Ferri R, Bruni O, Baumert M. Causality of cortical and cardiovascular activity during cyclic alternating pattern in non-rapid eye movement sleep. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2021; 379:20200248. [PMID: 34689628 DOI: 10.1098/rsta.2020.0248] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 02/01/2021] [Indexed: 06/13/2023]
Abstract
The dynamic interplay between central and autonomic nervous system activities plays a pivotal role in orchestrating sleep. Macrostructural changes such as sleep-stage transitions or phasic, brief cortical events elicit fluctuations in neural outflow to the cardiovascular system, but the causal relationships between cortical and cardiovascular activities underpinning the microstructure of sleep are largely unknown. Here, we investigate cortical-cardiovascular interactions during the cyclic alternating pattern (CAP) of non-rapid eye movement sleep in a diverse set of overnight polysomnograms. We determine the Granger causality in both 507 CAP and 507 matched non-CAP sequences to assess the causal relationships between electroencephalography (EEG) frequency bands and respiratory and cardiovascular variables (heart period, respiratory period, pulse arrival time and pulse wave amplitude) during CAP. We observe a significantly stronger influence of delta activity on vascular variables during CAP sequences where slow, low-amplitude EEG activation phases (A1) dominate than during non-CAP sequences. We also show that rapid, high-amplitude EEG activation phases (A3) provoke a more pronounced change in autonomic activity than A1 and A2 phases. Our analysis provides the first evidence on the causal interplay between cortical and cardiovascular activities during CAP. Granger causality analysis may also be useful for probing the level of decoupling in sleep disorders. This article is part of the theme issue 'Advanced computation in cardiovascular physiology: new challenges and opportunities'.
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Affiliation(s)
- Simon Hartmann
- School of Electrical and Electronic Engineering, University of Adelaide, Adelaide, Australia
| | - Raffaele Ferri
- Sleep Research Center, Department of Neurology IC, Oasi Research Institute-IRCCS, Troina, Italy
| | - Oliviero Bruni
- Department of Social and Developmental Psychology, Sapienza University, Rome, Italy
| | - Mathias Baumert
- School of Electrical and Electronic Engineering, University of Adelaide, Adelaide, Australia
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13
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Mikutta C, Wenke M, Spiegelhalder K, Hertenstein E, Maier JG, Schneider CL, Fehér K, Koenig J, Altorfer A, Riemann D, Nissen C, Feige B. Co-ordination of brain and heart oscillations during non-rapid eye movement sleep. J Sleep Res 2021; 31:e13466. [PMID: 34467582 PMCID: PMC9285890 DOI: 10.1111/jsr.13466] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2021] [Revised: 05/26/2021] [Accepted: 07/23/2021] [Indexed: 12/25/2022]
Abstract
Oscillatory activities of the brain and heart show a strong variation across wakefulness and sleep. Separate lines of research indicate that non‐rapid eye movement (NREM) sleep is characterised by electroencephalographic slow oscillations (SO), sleep spindles, and phase–amplitude coupling of these oscillations (SO–spindle coupling), as well as an increase in high‐frequency heart rate variability (HF‐HRV), reflecting enhanced parasympathetic activity. The present study aimed to investigate further the potential coordination between brain and heart oscillations during NREM sleep. Data were derived from one sleep laboratory night with polysomnographic monitoring in 45 healthy participants (22 male, 23 female; mean age 37 years). The associations between the strength (modulation index [MI]) and phase direction of SO–spindle coupling (circular measure) and HF‐HRV during NREM sleep were investigated using linear modelling. First, a significant SO–spindle coupling (MI) was observed for all participants during NREM sleep, with spindle peaks preferentially occurring during the SO upstate (phase direction). Second, linear model analyses of NREM sleep showed a significant relationship between the MI and HF‐HRV (F = 20.1, r2 = 0.30, p < 0.001) and a tentative circular‐linear correlation between phase direction and HF‐HRV (F = 3.07, r2 = 0.12, p = 0.056). We demonstrated a co‐ordination between SO–spindle phase–amplitude coupling and HF‐HRV during NREM sleep, presumably related to parallel central nervous and peripheral vegetative arousal systems regulation. Further investigating the fine‐graded co‐ordination of brain and heart oscillations might improve our understanding of the links between sleep and cardiovascular health.
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Affiliation(s)
- Christian Mikutta
- University Hospital of Psychiatry and Psychotherapy, University of Bern, Switzerland.,Privatklinik Meiringen, Meiringen, Switzerland
| | - Marion Wenke
- University Hospital of Psychiatry and Psychotherapy, University of Bern, Switzerland
| | - Kai Spiegelhalder
- Department of Psychiatry and Psychotherapy, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Elisabeth Hertenstein
- University Hospital of Psychiatry and Psychotherapy, University of Bern, Switzerland
| | - Jonathan G Maier
- University Hospital of Psychiatry and Psychotherapy, University of Bern, Switzerland
| | - Carlotta L Schneider
- University Hospital of Psychiatry and Psychotherapy, University of Bern, Switzerland
| | - Kristoffer Fehér
- University Hospital of Psychiatry and Psychotherapy, University of Bern, Switzerland
| | - Julian Koenig
- University Hospital of Child and Adolescent Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
| | - Andreas Altorfer
- University Hospital of Psychiatry and Psychotherapy, University of Bern, Switzerland
| | - Dieter Riemann
- Department of Psychiatry and Psychotherapy, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Christoph Nissen
- University Hospital of Psychiatry and Psychotherapy, University of Bern, Switzerland.,Department of Psychiatry and Psychotherapy, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Bernd Feige
- Department of Psychiatry and Psychotherapy, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
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14
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Son DY, Kwon HB, Lee DS, Jin HW, Jeong JH, Kim J, Choi SH, Yoon H, Lee MH, Lee YJ, Park KS. Changes in physiological network connectivity of body system in narcolepsy during REM sleep. Comput Biol Med 2021; 136:104762. [PMID: 34399195 DOI: 10.1016/j.compbiomed.2021.104762] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2021] [Revised: 08/09/2021] [Accepted: 08/09/2021] [Indexed: 10/20/2022]
Abstract
BACKGROUND Narcolepsy is marked by pathologic symptoms including excessive daytime drowsiness and lethargy, even with sufficient nocturnal sleep. There are two types of narcolepsy: type 1 (with cataplexy) and type 2 (without cataplexy). Unlike type 1, for which hypocretin is a biomarker, type 2 narcolepsy has no adequate biomarker to identify the causality of narcoleptic phenomenon. Therefore, we aimed to establish new biomarkers for narcolepsy using the body's systemic networks. METHOD Thirty participants (15 with type 2 narcolepsy, 15 healthy controls) were included. We used the time delay stability (TDS) method to examine temporal information and determine relationships among multiple signals. We quantified and analyzed the network connectivity of nine biosignals (brainwaves, cardiac and respiratory information, muscle and eye movements) during nocturnal sleep. In particular, we focused on the differences in network connectivity between groups according to sleep stages and investigated whether the differences could be potential biomarkers to classify both groups by using a support vector machine. RESULT In rapid eye movement sleep, the narcolepsy group displayed more connections than the control group (narcolepsy connections: 24.47 ± 2.87, control connections: 21.34 ± 3.49; p = 0.022). The differences were observed in movement and cardiac activity. The performance of the classifier based on connectivity differences was a 0.93 for sensitivity, specificity and accuracy, respectively. CONCLUSION Network connectivity with the TDS method may be used as a biomarker to identify differences in the systemic networks of patients with narcolepsy type 2 and healthy controls.
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Affiliation(s)
- Dong Yeon Son
- Interdisciplinary Program in Bioengineering, College of Engineering, Seoul National University, Seoul, 03080, South Korea; Integrated Major in Innovative Medical Science, College of Medicine, Seoul National University, Seoul, 03080, South Korea
| | - Hyun Bin Kwon
- Interdisciplinary Program in Bioengineering, College of Engineering, Seoul National University, Seoul, 03080, South Korea
| | - Dong Seok Lee
- Interdisciplinary Program in Bioengineering, College of Engineering, Seoul National University, Seoul, 03080, South Korea
| | - Hyung Won Jin
- Interdisciplinary Program in Bioengineering, College of Engineering, Seoul National University, Seoul, 03080, South Korea; Institute of Medical and Biological Engineering, Medical Research Center, Seoul National University, Seoul, 03080, South Korea
| | - Jong Hyeok Jeong
- Interdisciplinary Program in Bioengineering, College of Engineering, Seoul National University, Seoul, 03080, South Korea; Integrated Major in Innovative Medical Science, College of Medicine, Seoul National University, Seoul, 03080, South Korea
| | - Jeehoon Kim
- Department of Biomedical Engineering, College of Medicine, Seoul National University, Seoul, 03080, South Korea
| | - Sang Ho Choi
- School of Computer and Information Engineering, Kwangwoon University, Seoul, 01897, South Korea
| | - Heenam Yoon
- Department of Human-Centered Artificial Intelligence, Sangmyung University, Seoul, 03016, South Korea
| | - Mi Hyun Lee
- Department of Neuropsychiatry and Center for Sleep and Chronobiology, Seoul National University Hospital, Seoul, 03080, South Korea
| | - Yu Jin Lee
- Department of Neuropsychiatry and Center for Sleep and Chronobiology, Seoul National University Hospital, Seoul, 03080, South Korea
| | - Kwang Suk Park
- Institute of Medical and Biological Engineering, Medical Research Center, Seoul National University, Seoul, 03080, South Korea; Department of Biomedical Engineering, College of Medicine, Seoul National University, Seoul, 03080, South Korea.
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15
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Ebrahimi F, Alizadeh I. Automatic sleep staging by cardiorespiratory signals: a systematic review. Sleep Breath 2021; 26:965-981. [PMID: 34322822 DOI: 10.1007/s11325-021-02435-8] [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] [Received: 01/09/2021] [Revised: 06/22/2021] [Accepted: 07/06/2021] [Indexed: 11/29/2022]
Abstract
BACKGROUND Because of problems with the recording and analysis of the EEG signal, automatic sleep staging using cardiorespiratory signals has been employed as an alternative. This study reports on certain critical points which hold considerable promise for the improvement of the results of the automatic sleep staging using cardiorespiratory signals. METHODS A systematic review. RESULTS The review and analysis of the literature in this area revealed four outstanding points: (1) the feature extraction epoch length, denoting that the standard 30-s segments of cardiorespiratory signals do not carry enough information for automatic sleep staging and that a 4.5-min length segment centering on each 30-s segment is proper for staging, (2) the time delay between the EEG signal extracted from the central nervous system activity and the cardiorespiratory signals extracted from the autonomic nervous system activity should be considered in the automatic sleep staging using cardiorespiratory signals, (3) the information in the morphology of ECG signals can contribute to the improvement of sleep staging, and (4) applying convolutional neural network (CNN) and long short-term memory network (LSTM) deep structures simultaneously to a large PSG recording database can lead to more reliable automatic sleep staging results. CONCLUSIONS Considering the above-mentioned points simultaneously can improve automatic sleep staging by cardiorespiratory signals. It is hoped that by considering the points, staging sleep automatically using cardiorespiratory signals, which does not have problems with the recording and analysis of EEG signals, yields results acceptably close to the results of automatic sleep staging by EEG signals.
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Affiliation(s)
- Farideh Ebrahimi
- Faculty of Electrical and Computer Engineering, Babol Noshirvani University of Technology, Babol, Mazandaran, Iran.
| | - Iman Alizadeh
- English Language Department, School of Paramedical Sciences, Guilan University of Medical Sciences, Rasht, Iran
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16
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Ganesan A, Paul A, Nagabushnam G, Gul MJJ. Human-in-the-Loop Predictive Analytics Using Statistical Learning. JOURNAL OF HEALTHCARE ENGINEERING 2021; 2021:9955635. [PMID: 34367543 PMCID: PMC8346319 DOI: 10.1155/2021/9955635] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Revised: 06/23/2021] [Accepted: 07/18/2021] [Indexed: 12/27/2022]
Abstract
The human-in-the-loop cyber-physical system provides numerous solutions for the challenges faced by the doctors or medical practitioners. There is a linear trend of advancement and automation in the medical field for the early diagnosis of several diseases. One of the critical and challenging diseases in the medical field is coma. In the medical research field, currently, the prediction of these diseases is performed only using the data gathered from the devices only; however, the human's input is much essential to accurately understand their health condition to take appropriate decision on time. Therefore, we have proposed a healthcare framework involving the concept of artificial intelligence in the human-in- the-loop cyber-physical system. This model works via a response loop in which the human's intention is concluded by gathering biological signals and context data, and then, the decision is interpreted to a system action that is recognizable to the human in the physical environment, thereby completing the loop. In this paper, we have designed a model for early prognosis of coma using the electroencephalogram dataset. In the proposed approach, we have achieved the best results using a statistical learning algorithm called autoregressive integrated moving average in comparison to artificial neural networks and long short-term memory models. In order to measure the efficiency of our model, we have used the root mean squared error (RMSE), mean absolute error (MAE), and mean squared error (MSE) value to evaluate the linear models as it gives the difference between the measured value and true or correct value. We have achieved the least possible error value for our dataset. To conduct this experiment, we used the dataset available in the phsyionet opensource community.
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Affiliation(s)
- Anusha Ganesan
- The School of Computer Science and Engineering, Kyungpook National University, Daegu, Republic of Korea
| | - Anand Paul
- The School of Computer Science and Engineering, Kyungpook National University, Daegu, Republic of Korea
| | - Ganesan Nagabushnam
- The School of Computer Science and Engineering, Kyungpook National University, Daegu, Republic of Korea
| | - Malik Junaid Jami Gul
- The School of Computer Science and Engineering, Kyungpook National University, Daegu, Republic of Korea
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17
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Wang T, Yang J, Song Y, Pang F, Guo X, Luo Y. Interactions of central and autonomic nervous systems in patients with sleep apnea-hypopnea syndrome during sleep. Sleep Breath 2021; 26:621-631. [PMID: 34231085 DOI: 10.1007/s11325-021-02429-6] [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: 02/07/2021] [Revised: 06/21/2021] [Accepted: 06/23/2021] [Indexed: 11/24/2022]
Abstract
PURPOSE Sleep apnea-hypopnea syndrome (SAHS) is an independent risk factor for various cardiovascular and cerebrovascular diseases, but the underlying relationship of its physiological subsystems remains unclear. Thus, we aimed to investigate the effect of SAHS on central and autonomic nervous system (CNS-ANS) interactions during sleep. METHODS Thirty-five patients with SAHS and 19 healthy age-matched controls underwent overnight polysomnography. The absolute spectral powers of five frequency bands from six EEG channels and ECG morphological features (HR, PR interval, QT interval) were calculated. Multivariable transfer entropy was applied to analyze the differences of the CNS-ANS network interactions between patients with SAHS of different severities and healthy controls during deep, light, and rapid eye movement sleep. RESULTS The CNS-ANS network interacted bidirectionally in all researched groups, with the cardiac information modulating the brain activity. The information strength from QT to most EEG components and PR to some EEG components was significantly affected by SAHS severity during light sleep, which indicates the coupling features of QT-brain nodes are important indicators. The driver effects from the β-band significantly increased in patients with SAHS. CONCLUSIONS Respiratory events may be the main reason for the CNS-ANS interaction changes in SAHS. These findings help explain the physiological regulation process of SAHS and provide valuable information for analysis of the development of SAHS-related cardiovascular and chronic diseases.
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Affiliation(s)
- Tingting Wang
- School of Biomedical Engineering, Sun Yat-Sen University, Guangzhou, China
| | - Juan Yang
- School of Biomedical Engineering, Sun Yat-Sen University, Guangzhou, China
| | - Yingjie Song
- School of Biomedical Engineering, Sun Yat-Sen University, Guangzhou, China
| | - Feng Pang
- Sleep-Disordered Breathing Center, the Sixth Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Xinwen Guo
- Psychology Department, Guangdong 999 Brain Hospital, Guangzhou, China
| | - Yuxi Luo
- School of Biomedical Engineering, Sun Yat-Sen University, Guangzhou, China.
- Key Laboratory of Sensing Technology and Biomedical Instrument of Guangdong Province, Sun Yat-Sen University, Guangzhou, China.
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18
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Choi SH, Kwon HB, Jin HW, Yoon H, Lee MH, Lee YJ, Park KS. Weak closed-loop vibrational stimulation improves the depth of slow-wave sleep and declarative memory consolidation. Sleep 2021; 44:6047580. [PMID: 33367712 DOI: 10.1093/sleep/zsaa285] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2020] [Revised: 12/11/2020] [Indexed: 11/12/2022] Open
Abstract
Sleep is a unique behavioral state that affects body functions and memory. Although previous studies suggested stimulation methods to enhance sleep, a new method is required that is practical for long-term and unconstrained use by people. In this study, we used a novel closed-loop vibration stimulation method that delivers a stimulus in interaction with the intrinsic heart rhythm and examined the effects of stimulation on sleep and memory. Twelve volunteers participated in the experiment and each underwent one adaptation night and two experimental conditions-a stimulation condition (STIM) and a no-stimulation condition (SHAM). The heart rate variability analysis showed a significant increase in the normalized high frequency and the normalized low frequency significantly decreased under the STIM during the slow-wave sleep (SWS) stage. Furthermore, the synchronization ratio between the heartbeat and the stimulus significantly increased under the STIM in the SWS stage. From the electroencephalogram (EEG) spectral analysis, EEG relative powers of slow-wave activity and theta frequency bands showed a significant increase during the STIM in the SWS stage. Additionally, memory retention significantly increased under the STIM compared to the SHAM. These findings suggest that the closed-loop stimulation improves the SWS-stage depth and memory retention, and further provides a new technique for sleep enhancement.
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Affiliation(s)
- Sang Ho Choi
- Interdisciplinary Program in Bioengineering, Graduate School, Seoul National University, Seoul, Republic of Korea
| | - Hyun Bin Kwon
- Interdisciplinary Program in Bioengineering, Graduate School, Seoul National University, Seoul, Republic of Korea.,Institute of Medical and Biological Engineering, Medical Research Center, Seoul National University, Seoul, Republic of Korea
| | - Hyung Won Jin
- Interdisciplinary Program in Bioengineering, Graduate School, Seoul National University, Seoul, Republic of Korea
| | - Heenam Yoon
- Department of Human-Centered Artificial Intelligence, Sangmyung University, Seoul, Republic of Korea
| | - Mi Hyun Lee
- Department of Neuropsychiatry and Center for Sleep and Chronobiology, Seoul National University Hospital, Seoul, Republic of Korea
| | - Yu Jin Lee
- Department of Neuropsychiatry and Center for Sleep and Chronobiology, Seoul National University Hospital, Seoul, Republic of Korea
| | - Kwang Suk Park
- Institute of Medical and Biological Engineering, Medical Research Center, Seoul National University, Seoul, Republic of Korea.,Department of Biomedical Engineering, College of Medicine, Seoul National University, Seoul, Republic of Korea
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19
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Salvati L, d’Amore M, Fiorentino A, Pellegrino A, Sena P, Villecco F. On-Road Detection of Driver Fatigue and Drowsiness during Medium-Distance Journeys. ENTROPY (BASEL, SWITZERLAND) 2021; 23:135. [PMID: 33494447 PMCID: PMC7912473 DOI: 10.3390/e23020135] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Revised: 01/19/2021] [Accepted: 01/19/2021] [Indexed: 11/23/2022]
Abstract
BACKGROUND The detection of driver fatigue as a cause of sleepiness is a key technology capable of preventing fatal accidents. This research uses a fatigue-related sleepiness detection algorithm based on the analysis of the pulse rate variability generated by the heartbeat and validates the proposed method by comparing it with an objective indicator of sleepiness (PERCLOS). Methods: changes in alert conditions affect the autonomic nervous system (ANS) and therefore heart rate variability (HRV), modulated in the form of a wave and monitored to detect long-term changes in the driver's condition using real-time control. Results: the performance of the algorithm was evaluated through an experiment carried out in a road vehicle. In this experiment, data was recorded by three participants during different driving sessions and their conditions of fatigue and sleepiness were documented on both a subjective and objective basis. The validation of the results through PERCLOS showed a 63% adherence to the experimental findings. Conclusions: the present study confirms the possibility of continuously monitoring the driver's status through the detection of the activation/deactivation states of the ANS based on HRV. The proposed method can help prevent accidents caused by drowsiness while driving.
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Affiliation(s)
- Luca Salvati
- Department of Industrial Engineering, University of Salerno, Via Giovanni Paolo II 132, 84084 Fisciano, Italy; (L.S.); (A.P.)
| | - Matteo d’Amore
- Department of Pharmacy, University of Salerno, Via Giovanni Paolo II 132, 84084 Fisciano, Italy; (M.d.); (P.S.)
| | - Anita Fiorentino
- Pomigliano Technical Center, Fiat Chrysler Automobiles, Via Ex Aeroporto, 80038 Pomigliano d’Arco (NA), Italy;
| | - Arcangelo Pellegrino
- Department of Industrial Engineering, University of Salerno, Via Giovanni Paolo II 132, 84084 Fisciano, Italy; (L.S.); (A.P.)
| | - Pasquale Sena
- Department of Pharmacy, University of Salerno, Via Giovanni Paolo II 132, 84084 Fisciano, Italy; (M.d.); (P.S.)
| | - Francesco Villecco
- Department of Industrial Engineering, University of Salerno, Via Giovanni Paolo II 132, 84084 Fisciano, Italy; (L.S.); (A.P.)
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20
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Qin H, Steenbergen N, Glos M, Wessel N, Kraemer JF, Vaquerizo-Villar F, Penzel T. The Different Facets of Heart Rate Variability in Obstructive Sleep Apnea. Front Psychiatry 2021; 12:642333. [PMID: 34366907 PMCID: PMC8339263 DOI: 10.3389/fpsyt.2021.642333] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Accepted: 06/14/2021] [Indexed: 12/15/2022] Open
Abstract
Obstructive sleep apnea (OSA), a heterogeneous and multifactorial sleep related breathing disorder with high prevalence, is a recognized risk factor for cardiovascular morbidity and mortality. Autonomic dysfunction leads to adverse cardiovascular outcomes in diverse pathways. Heart rate is a complex physiological process involving neurovisceral networks and relative regulatory mechanisms such as thermoregulation, renin-angiotensin-aldosterone mechanisms, and metabolic mechanisms. Heart rate variability (HRV) is considered as a reliable and non-invasive measure of autonomic modulation response and adaptation to endogenous and exogenous stimuli. HRV measures may add a new dimension to help understand the interplay between cardiac and nervous system involvement in OSA. The aim of this review is to introduce the various applications of HRV in different aspects of OSA to examine the impaired neuro-cardiac modulation. More specifically, the topics covered include: HRV time windows, sleep staging, arousal, sleepiness, hypoxia, mental illness, and mortality and morbidity. All of these aspects show pathways in the clinical implementation of HRV to screen, diagnose, classify, and predict patients as a reasonable and more convenient alternative to current measures.
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Affiliation(s)
- Hua Qin
- Interdisciplinary Center of Sleep Medicine, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | | | - Martin Glos
- Interdisciplinary Center of Sleep Medicine, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Niels Wessel
- Department of Physics, Humboldt Universität zu Berlin, Berlin, Germany
| | - Jan F Kraemer
- Department of Physics, Humboldt Universität zu Berlin, Berlin, Germany
| | - Fernando Vaquerizo-Villar
- Biomedical Engineering Group, Universidad de Valladolid, Valladolid, Spain.,Centro de Investigación Biomédica en Red-Bioingeniería, Biomateriales y Nanomedicina, Valladolid, Spain
| | - Thomas Penzel
- Interdisciplinary Center of Sleep Medicine, Charité-Universitätsmedizin Berlin, Berlin, Germany.,Saratov State University, Russian Federation, Saratov, Russia
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21
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Early posterior negativity indicates time dilation by arousal. Exp Brain Res 2020; 239:533-543. [PMID: 33277996 PMCID: PMC7936965 DOI: 10.1007/s00221-020-05991-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2020] [Accepted: 11/19/2020] [Indexed: 11/12/2022]
Abstract
We investigated whether Early Posterior Negativity (EPN) indicated the subjective dilation of time when judging the duration of arousing stimuli. Participants performed a visual temporal bisection task along with high-level and low-level arousing auditory stimuli, while we simultaneously recorded EEG. In accordance with previous studies, arousing stimuli were temporally overestimated and led to higher EPN amplitude. Yet, we observed that time dilation and EPN amplitude were significantly correlated and this effect cannot be explained by confounds from stimulus valence. We interpret our findings in terms of the pacemaker–accumulator model of human timing, and suggest that EPN indicates an arousal-based increasing of the speed of our mental clock.
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22
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Paquola C, Seidlitz J, Benkarim O, Royer J, Klimes P, Bethlehem RAI, Larivière S, Vos de Wael R, Rodríguez-Cruces R, Hall JA, Frauscher B, Smallwood J, Bernhardt BC. A multi-scale cortical wiring space links cellular architecture and functional dynamics in the human brain. PLoS Biol 2020; 18:e3000979. [PMID: 33253185 PMCID: PMC7728398 DOI: 10.1371/journal.pbio.3000979] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2020] [Revised: 12/10/2020] [Accepted: 11/02/2020] [Indexed: 12/11/2022] Open
Abstract
The vast net of fibres within and underneath the cortex is optimised to support the convergence of different levels of brain organisation. Here, we propose a novel coordinate system of the human cortex based on an advanced model of its connectivity. Our approach is inspired by seminal, but so far largely neglected models of cortico-cortical wiring established by postmortem anatomical studies and capitalises on cutting-edge in vivo neuroimaging and machine learning. The new model expands the currently prevailing diffusion magnetic resonance imaging (MRI) tractography approach by incorporation of additional features of cortical microstructure and cortico-cortical proximity. Studying several datasets and different parcellation schemes, we could show that our coordinate system robustly recapitulates established sensory-limbic and anterior-posterior dimensions of brain organisation. A series of validation experiments showed that the new wiring space reflects cortical microcircuit features (including pyramidal neuron depth and glial expression) and allowed for competitive simulations of functional connectivity and dynamics based on resting-state functional magnetic resonance imaging (rs-fMRI) and human intracranial electroencephalography (EEG) coherence. Our results advance our understanding of how cell-specific neurobiological gradients produce a hierarchical cortical wiring scheme that is concordant with increasing functional sophistication of human brain organisation. Our evaluations demonstrate the cortical wiring space bridges across scales of neural organisation and can be easily translated to single individuals.
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Affiliation(s)
- Casey Paquola
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Jakob Seidlitz
- Developmental Neurogenomics Unit, National Institute of Mental Health, Bethesda, Maryland, United States of America
| | - Oualid Benkarim
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Jessica Royer
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Petr Klimes
- Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | | | - Sara Larivière
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Reinder Vos de Wael
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Raul Rodríguez-Cruces
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Jeffery A. Hall
- Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Birgit Frauscher
- Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | | | - Boris C. Bernhardt
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
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Tripathy RK, Ghosh SK, Gajbhiye P, Acharya UR. Development of Automated Sleep Stage Classification System Using Multivariate Projection-Based Fixed Boundary Empirical Wavelet Transform and Entropy Features Extracted from Multichannel EEG Signals. ENTROPY 2020; 22:e22101141. [PMID: 33286910 PMCID: PMC7597285 DOI: 10.3390/e22101141] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/23/2020] [Revised: 10/02/2020] [Accepted: 10/05/2020] [Indexed: 12/13/2022]
Abstract
The categorization of sleep stages helps to diagnose different sleep-related ailments. In this paper, an entropy-based information–theoretic approach is introduced for the automated categorization of sleep stages using multi-channel electroencephalogram (EEG) signals. This approach comprises of three stages. First, the decomposition of multi-channel EEG signals into sub-band signals or modes is performed using a novel multivariate projection-based fixed boundary empirical wavelet transform (MPFBEWT) filter bank. Second, entropy features such as bubble and dispersion entropies are computed from the modes of multi-channel EEG signals. Third, a hybrid learning classifier based on class-specific residuals using sparse representation and distances from nearest neighbors is used to categorize sleep stages automatically using entropy-based features computed from MPFBEWT domain modes of multi-channel EEG signals. The proposed approach is evaluated using the multi-channel EEG signals obtained from the cyclic alternating pattern (CAP) sleep database. Our results reveal that the proposed sleep staging approach has obtained accuracies of 91.77%, 88.14%, 80.13%, and 73.88% for the automated categorization of wake vs. sleep, wake vs. rapid eye movement (REM) vs. Non-REM, wake vs. light sleep vs. deep sleep vs. REM sleep, and wake vs. S1-sleep vs. S2-sleep vs. S3-sleep vs. REM sleep schemes, respectively. The developed method has obtained the highest overall accuracy compared to the state-of-art approaches and is ready to be tested with more subjects before clinical application.
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Affiliation(s)
- Rajesh Kumar Tripathy
- Department of Electrical and Electronics Engineering, BITS-Pilani, Hyderabad Campus, Hyderabad 500078, India
| | - Samit Kumar Ghosh
- Department of Electrical and Electronics Engineering, BITS-Pilani, Hyderabad Campus, Hyderabad 500078, India
| | - Pranjali Gajbhiye
- Department of Electrical and Electronics Engineering, BITS-Pilani, Hyderabad Campus, Hyderabad 500078, India
| | - U Rajendra Acharya
- School of Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore
- Department of Bioinformatics and Medical Engineering, Asia University, Taichung 41354, Taiwan
- School of Management and Enterprise, University of Southern Queensland, Springfield 4300, Australia
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Chen PC, Whitehurst LN, Naji M, Mednick SC. Autonomic/central coupling benefits working memory in healthy young adults. Neurobiol Learn Mem 2020; 173:107267. [PMID: 32535198 DOI: 10.1016/j.nlm.2020.107267] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2020] [Revised: 05/13/2020] [Accepted: 06/08/2020] [Indexed: 02/01/2023]
Abstract
Working memory (WM) is an executive function that can improve with training. However, the precise mechanism for this improvement is not known. Studies have shown greater WM gains after a period of sleep than a similar period of wake, and correlations between WM improvement and slow wave activity (SWA; 0.5-1 Hz) during slow wave sleep (SWS). A different body of literature has suggested an important role for autonomic activity during wake for WM. A recent study from our group reported that the temporal coupling of Autonomic/CentralEvents (ACEs) during sleep was associated with memory consolidation. We found that heart rate bursts (HR bursts) during non-rapid eye movement (NREM) sleep are accompanied by increases in SWA and sigma (12-15 Hz) power, as well as increases in the high-frequency (HF) component of the RR interval, reflecting vagal rebound. In addition, ACEs predict long-term, episodic memory improvement. Building on these previous results, we examined whether ACEs also contribute to gains in WM. We tested 104 young adults in an operation span task (OSPAN) in the morning and evening, with either a nap (n = 53; with electroencephalography (EEG) and electrocardiography (ECG)) or wake (n = 51) between testing sessions. We identified HR bursts in the ECG and replicated the increases in SWA and sigma prior to peak of the HR burst, as well as vagal rebound after the peak. Furthermore, we showed sleep-dependent WM improvement, which was predicted by ACE activity. Using regression analyses, we discovered that significantly more variance in WM improvement could be explained with ACE variables than with overall sleep activity not time-locked with ECG. These results provide the first evidence that coordinated autonomic and central events play a significant role in sleep-related WM improvement and implicate the potential of autonomic interventions during sleep for cognitive enhancement.
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Affiliation(s)
- Pin-Chun Chen
- Department of Cognitive Science, University of California, Irvine USA
| | | | - Mohsen Naji
- Department of Medicine, University of California, San Diego, CA, USA
| | - Sara C Mednick
- Department of Cognitive Science, University of California, Irvine USA.
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25
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Orjuela-Cañón AD, Cerquera A, Freund JA, Juliá-Serdá G, Ravelo-García AG. Sleep apnea: Tracking effects of a first session of CPAP therapy by means of Granger causality. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2020; 187:105235. [PMID: 31812116 DOI: 10.1016/j.cmpb.2019.105235] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/27/2019] [Revised: 11/04/2019] [Accepted: 11/18/2019] [Indexed: 06/10/2023]
Abstract
Connectivity between physiological networks is an issue of particular importance for understanding the complex interaction brain-heart. In the present study, this interaction was analyzed in polysomnography recordings of 28 patients diagnosed with obstructive sleep apnea (OSA) and compared with a group of 10 control subjects. Electroencephalography and electrocardiography signals from these polysomnography time series were characterized employing Granger causality computation to measure the directed connectivity among five brain waves and three spectral subbands of heart rate variability. Polysomnography data from OSA patients were recorded before and during a first session of continuous positive air pressure (CPAP) therapy in a split-night study. Results showed that CPAP therapy allowed the recovery of inner brain connectivities, mainly in subsystems involving the theta wave. In addition, differences between control and OSA patients were established in connections that involve lower frequency ranges of heart rate variability. This information can be potentially useful in the initial diagnosis of OSA, and determine the role of cardiac activity in sleep dynamics based on the use of three subbands of heart rate variability.
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Affiliation(s)
- Alvaro D Orjuela-Cañón
- Facultad de Ingeniería Mecánica, Electrónica y Biomédica, Universidad Antonio Nariño, Bogotá D.C., Colombia; Biomedical Engineering Program, School of Medicine and Health Sciences, Universidad del Rosario, Bogotá D.C., Colombia.
| | - Alexander Cerquera
- Brain Dynamics Program, Wilder Center for Epilepsy Research. Department of Neurology-College of Medicine. University of Florida, Gainesville, FL, United States.
| | - Jan A Freund
- Carl von Ossietzky Universität Oldenburg. ICBM & Research Center Neurosensory Science. D-26111, Oldenburg, Germany.
| | - Gabriel Juliá-Serdá
- Pulmonary Medicine Department, Hospital Universitario de Gran Canaria Dr. Negrín, Las Palmas de Gran Canaria 35010, Spain.
| | - Antonio G Ravelo-García
- Institute for Technological Development and Innovation in Communications, Universidad de Las Palmas de Gran Canaria, Las Palmas de Gran Canaria 35017, Spain.
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26
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Geng DY, Zhao J, Wang CX, Ning Q. A decision support system for automatic sleep staging from HRV using wavelet packet decomposition and energy features. Biomed Signal Process Control 2020. [DOI: 10.1016/j.bspc.2019.101722] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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27
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Naji M, Krishnan GP, McDevitt EA, Bazhenov M, Mednick SC. Timing between Cortical Slow Oscillations and Heart Rate Bursts during Sleep Predicts Temporal Processing Speed, but Not Offline Consolidation. J Cogn Neurosci 2019; 31:1484-1490. [PMID: 31180264 DOI: 10.1162/jocn_a_01432] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
Central and autonomic nervous system activities are coupled during sleep. Cortical slow oscillations (SOs; <1 Hz) coincide with brief bursts in heart rate (HR), but the functional consequence of this coupling in cognition remains elusive. We measured SO-HR temporal coupling (i.e., the peak-to-peak interval between downstate of SO event and HR burst) during a daytime nap and asked whether this SO-HR timing measure was associated with temporal processing speed and learning on a texture discrimination task by testing participants before and after a nap. The coherence of SO-HR events during sleep strongly correlated with an individual's temporal processing speed in the morning and evening test sessions, but not with their change in performance after the nap (i.e., consolidation). We confirmed this result in two additional experimental visits and also discovered that this association was visit-specific, indicating a state (not trait) marker. Thus, we introduce a novel physiological index that may be a useful marker of state-dependent processing speed of an individual.
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28
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Effect of Closed-Loop Vibration Stimulation on Heart Rhythm during Naps. SENSORS 2019; 19:s19194136. [PMID: 31554268 PMCID: PMC6806257 DOI: 10.3390/s19194136] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/27/2019] [Revised: 09/18/2019] [Accepted: 09/21/2019] [Indexed: 11/16/2022]
Abstract
Sleep plays a primary function for health and sustains physical and cognitive performance. Although various stimulation systems for enhancing sleep have been developed, they are difficult to use on a long-term basis. This paper proposes a novel stimulation system and confirms its feasibility for sleep. Specifically, in this study, a closed-loop vibration stimulation system that detects the heart rate (HR) and applies −n% stimulus beats per minute (BPM) computed on the basis of the previous 5 min of HR data was developed. Ten subjects participated in the evaluation experiment, in which they took a nap for approximately 90 min. The experiment comprised one baseline and three stimulation conditions. HR variability analysis showed that the normalized low frequency (LF) and LF/high frequency (HF) parameters significantly decreased compared to the baseline condition, while the normalized HF parameter significantly increased under the −3% stimulation condition. In addition, the HR density around the stimulus BPM significantly increased under the −3% stimulation condition. The results confirm that the proposed stimulation system could influence heart rhythm and stabilize the autonomic nervous system. This study thus provides a new stimulation approach to enhance the quality of sleep and has the potential for enhancing health levels through sleep manipulation.
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Alteration of coupling between brain and heart induced by sedation with propofol and midazolam. PLoS One 2019; 14:e0219238. [PMID: 31314775 PMCID: PMC6636731 DOI: 10.1371/journal.pone.0219238] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2017] [Accepted: 06/20/2019] [Indexed: 11/19/2022] Open
Abstract
For a comprehensive understanding of the nervous system, several previous studies have examined the network connections between the brain and the heart in diverse conditions. In this study, we identified coupling between the brain and the heart along the continuum of sedation levels, but not in discrete sedation levels (e. g., wakefulness, conscious sedation, and deep sedation). To identify coupling between the brain and the heart during sedation, we induced several depths of sedation using patient-controlled sedation with propofol and midazolam. We performed electroencephalogram (EEG) spectral analysis and extracted the instantaneous heart rate (HR) from the electrocardiogram (ECG). EEG spectral power dynamics and mean HR were compared along the continuum of sedation levels. We found that EEG sigma power was the parameter most sensitive to changes in the sedation level and was correlated with the mean HR under the effect of sedative agents. Moreover, we calculated the Granger causality (GC) value to quantify brain-heart coupling at each sedation level. Additionally, the GC analysis revealed noticeably different strengths and directions of causality among different sedation levels. In all the sedation levels, GC values from the brain to the heart (GCb→h) were higher than GC values from the heart to the brain (GCh→b). Moreover, the mean GCb→h increased as the sedation became deeper, resulting in higher GCb→h values in deep sedation (1.97 ± 0.18 in propofol, 2.02 ± 0.15 in midazolam) than in pre-sedation (1.71 ± 0.13 in propofol, 1.75 ± 0.11 in midazolam; p < 0.001). These results show that coupling between brain and heart activities becomes stronger as sedation becomes deeper, and that this coupling is more attributable to the brain-heart direction than to the heart-brain direction. These findings provide a better understanding of the relationship between the brain and the heart under specific conditions, namely, different sedation states.
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Günther M, Bartsch RP, Miron-Shahar Y, Hassin-Baer S, Inzelberg R, Kurths J, Plotnik M, Kantelhardt JW. Coupling Between Leg Muscle Activation and EEG During Normal Walking, Intentional Stops, and Freezing of Gait in Parkinson's Disease. Front Physiol 2019; 10:870. [PMID: 31354521 PMCID: PMC6639586 DOI: 10.3389/fphys.2019.00870] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2019] [Accepted: 06/21/2019] [Indexed: 11/13/2022] Open
Abstract
In this paper, we apply novel techniques for characterizing leg muscle activation patterns via electromyograms (EMGs) and for relating them to changes in electroencephalogram (EEG) activity during gait experiments. Specifically, we investigate changes of leg-muscle EMG amplitudes and EMG frequencies during walking, intentional stops, and unintended freezing-of-gait (FOG) episodes. FOG is a frequent paroxysmal gait disturbance occurring in many patients suffering from Parkinson's disease (PD). We find that EMG amplitudes and frequencies do not change significantly during FOG episodes with respect to walking, while drastic changes occur during intentional stops. Phase synchronization between EMG signals is most pronounced during walking in controls and reduced in PD patients. By analyzing cross-correlations between changes in EMG patterns and brain-wave amplitudes (from EEGs), we find an increase in EEG-EMG coupling at the beginning of stop and FOG episodes. Our results may help to better understand the enigmatic pathophysiology of FOG, to differentiate between FOG events and other gait disturbances, and ultimately to improve diagnostic procedures for patients suffering from PD.
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Affiliation(s)
- Moritz Günther
- Institute of Physics, Martin-Luther-University Halle-Wittenberg, Halle, Germany
- Department of Physics, Bar-Ilan University, Ramat Gan, Israel
| | | | - Yael Miron-Shahar
- Center of Advanced Technologies in Rehabilitation, Sheba Medical Center, Tel Hashomer, Israel
- Neuroscience Department, Sackler Faculty of Medicine, School of Graduate Studies, Tel-Aviv University, Tel Aviv, Israel
| | - Sharon Hassin-Baer
- Sagol Neuroscience Center and Department of Neurology, Sheba Medical Center, Movement Disorders Institute, Tel-Hashomer, Israel
- Sackler Faculty of Medicine, Tel-Aviv University, Tel Aviv, Israel
| | - Rivka Inzelberg
- Department of Neurology and Neurosurgery, Sackler Faculty of Medicine, Tel-Aviv University, Tel Aviv, Israel
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
- Department of Applied Mathematics and Computer Science, The Weizmann Institute of Science, Rehovot, Israel
| | - Jürgen Kurths
- Potsdam Institute for Climate Impact Research, Potsdam, Germany
- Department of Physics, Humboldt University of Berlin, Berlin, Germany
- Saratov State University, Saratov, Russia
| | - Meir Plotnik
- Center of Advanced Technologies in Rehabilitation, Sheba Medical Center, Tel Hashomer, Israel
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
- Department of Physiology and Pharmacology, Sackler Faculty of Medicine, Tel-Aviv University, Tel-Aviv, Israel
- Gonda Brain Research Center, Bar Ilan University, Ramat-Gan, Israel
| | - Jan W. Kantelhardt
- Institute of Physics, Martin-Luther-University Halle-Wittenberg, Halle, Germany
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31
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Walter LM, Tamanyan K, Weichard AJ, Biggs SN, Davey MJ, Nixon GM, Horne RSC. Age and autonomic control, but not cerebral oxygenation, are significant determinants of EEG spectral power in children. Sleep 2019; 42:5513436. [DOI: 10.1093/sleep/zsz118] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2018] [Revised: 03/05/2019] [Indexed: 01/28/2023] Open
Abstract
AbstractStudy ObjectivesSleep disordered breathing (SDB) in children has significant effects on daytime functioning and cardiovascular control; attributed to sleep fragmentation and repetitive hypoxia. Associations between electroencephalograph (EEG) spectral power, autonomic cardiovascular control and cerebral oxygenation have been identified in adults with SDB. To date, there have been no studies in children. We aimed to assess associations between EEG spectral power and heart rate variability as a measure of autonomic control, with cerebral oxygenation in children with SDB.MethodsOne hundred sixteen children (3–12 years) with SDB and 42 controls underwent overnight polysomnography including measurement of cerebral oxygenation. Power spectral analysis of the EEG derived from C4-M1 and F4-M1, quantified delta, theta, alpha, and beta waveforms during sleep. Multiple regression tested whether age, SDB severity, heart rate (HR), HR variability (HRV), and cerebral oxygenation were determinants of EEG spectral power.ResultsThere were no differences in EEG spectral power derived from either central or frontal regions for any frequency between children with different severities of SDB so these were combined. Age, HR, and HRV low frequency power were significant determinants of EEG spectral power depending on brain region and sleep stage.ConclusionThe significant findings of this study were that age and autonomic control, rather than cerebral oxygenation and SDB severity, were predictive of EEG spectral power in children. Further research is needed to elucidate how the physiology that underlies the relationship between autonomic control and EEG impacts on the cardiovascular sequelae in children with SDB.
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Affiliation(s)
- Lisa M Walter
- The Ritchie Centre, Hudson Institute of Medical Research, Melbourne, Australia
- Department of Paediatrics, Monash University, Melbourne, Australia
| | - Knarik Tamanyan
- The Ritchie Centre, Hudson Institute of Medical Research, Melbourne, Australia
- Department of Paediatrics, Monash University, Melbourne, Australia
| | - Aidan J Weichard
- The Ritchie Centre, Hudson Institute of Medical Research, Melbourne, Australia
- Department of Paediatrics, Monash University, Melbourne, Australia
| | - Sarah N Biggs
- The Ritchie Centre, Hudson Institute of Medical Research, Melbourne, Australia
- Department of Paediatrics, Monash University, Melbourne, Australia
| | - Margot J Davey
- The Ritchie Centre, Hudson Institute of Medical Research, Melbourne, Australia
- Department of Paediatrics, Monash University, Melbourne, Australia
- Melbourne Children’s Sleep Centre, Monash Children’s Hospital, Melbourne, Australia
| | - Gillian M Nixon
- The Ritchie Centre, Hudson Institute of Medical Research, Melbourne, Australia
- Department of Paediatrics, Monash University, Melbourne, Australia
- Melbourne Children’s Sleep Centre, Monash Children’s Hospital, Melbourne, Australia
| | - Rosemary S C Horne
- The Ritchie Centre, Hudson Institute of Medical Research, Melbourne, Australia
- Department of Paediatrics, Monash University, Melbourne, Australia
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32
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Periocular skin warming elevates the distal skin temperature without affecting the proximal or core body temperature. Sci Rep 2019; 9:5743. [PMID: 30952920 PMCID: PMC6450979 DOI: 10.1038/s41598-019-42116-x] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2018] [Accepted: 03/25/2019] [Indexed: 11/15/2022] Open
Abstract
Periocular skin warming reportedly improves the objective and subjective sleep quality in adults with mild difficulty in falling asleep. To clarify the effects of periocular warming, we examined the distal skin temperatures (hands and feet), proximal skin temperature (infraclavicular region) and core body temperature as well as the distal-proximal skin temperature gradient (DPG). Nineteen healthy males underwent two experimental sessions, wherein they used a warming or sham eye mask under a semi-constant routine protocol in a crossover manner. Participants were instructed to maintain wakefulness with their eyes closed for 60 minutes after wearing the eye mask. The warming eye mask increased the periocular skin temperature to 38–40 °C for the first 20 minutes, whereas the temperature remained unchanged with the sham mask. Compared to that of the sham eye mask, the warming eye mask significantly increased the temperatures of the hands and feet and the DPG, whereas the proximal skin and core body temperatures were unaffected. Subjective sleepiness and pleasantness were significantly increased by the warming eye mask. These results represent physiological heat loss associated with sleep initiation without affecting the proximal skin or core body temperatures, suggesting that thermal stimulation in certain areas can provoke similar changes in remote areas of the body.
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Padfield N, Zabalza J, Zhao H, Masero V, Ren J. EEG-Based Brain-Computer Interfaces Using Motor-Imagery: Techniques and Challenges. SENSORS 2019; 19:s19061423. [PMID: 30909489 PMCID: PMC6471241 DOI: 10.3390/s19061423] [Citation(s) in RCA: 153] [Impact Index Per Article: 30.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/30/2019] [Revised: 03/10/2019] [Accepted: 03/19/2019] [Indexed: 12/11/2022]
Abstract
Electroencephalography (EEG)-based brain-computer interfaces (BCIs), particularly those using motor-imagery (MI) data, have the potential to become groundbreaking technologies in both clinical and entertainment settings. MI data is generated when a subject imagines the movement of a limb. This paper reviews state-of-the-art signal processing techniques for MI EEG-based BCIs, with a particular focus on the feature extraction, feature selection and classification techniques used. It also summarizes the main applications of EEG-based BCIs, particularly those based on MI data, and finally presents a detailed discussion of the most prevalent challenges impeding the development and commercialization of EEG-based BCIs.
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Affiliation(s)
- Natasha Padfield
- Centre for Signal and Image Processing, University of Strathclyde, Glasgow G1 1XW, UK.
| | - Jaime Zabalza
- Centre for Signal and Image Processing, University of Strathclyde, Glasgow G1 1XW, UK.
| | - Huimin Zhao
- School of Computer Sciences, Guangdong Polytechnic Normal University, Guangzhou 510665, China.
- The Guangzhou Key Laboratory of Digital Content Processing and Security Technologies, Guangzhou 510665, China.
| | - Valentin Masero
- Department of Computer Systems and Telematics Engineering, Universidad de Extremadura, 06007 Badajoz, Spain.
| | - Jinchang Ren
- Centre for Signal and Image Processing, University of Strathclyde, Glasgow G1 1XW, UK.
- School of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan 030024, China.
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Yoon H, Choi SH, Kim SK, Kwon HB, Oh SM, Choi JW, Lee YJ, Jeong DU, Park KS. Human Heart Rhythms Synchronize While Co-sleeping. Front Physiol 2019; 10:190. [PMID: 30914965 PMCID: PMC6421336 DOI: 10.3389/fphys.2019.00190] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2018] [Accepted: 02/14/2019] [Indexed: 11/13/2022] Open
Abstract
Human physiological systems have a major role in maintenance of internal stability. Previous studies have found that these systems are regulated by various types of interactions associated with physiological homeostasis. However, whether there is any interaction between these systems in different individuals is not well-understood. The aim of this research was to determine whether or not there is any interaction between the physiological systems of independent individuals in an environment where they are connected with one another. We investigated the heart rhythms of co-sleeping individuals and found evidence that in co-sleepers, not only do independent heart rhythms appear in the same relative phase for prolonged periods, but also that their occurrence has a bidirectional causal relationship. Under controlled experimental conditions, this finding may be attributed to weak cardiac vibration delivered from one individual to the other via a mechanical bed connection. Our experimental approach could help in understanding how sharing behaviors or social relationships between individuals are associated with interactions of physiological systems.
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Affiliation(s)
- Heenam Yoon
- Interdisciplinary Program in Bioengineering, Seoul National University, Seoul, South Korea
| | - Sang Ho Choi
- Interdisciplinary Program in Bioengineering, Seoul National University, Seoul, South Korea
| | - Sang Kyong Kim
- Interdisciplinary Program in Bioengineering, Seoul National University, Seoul, South Korea
| | - Hyun Bin Kwon
- Interdisciplinary Program in Bioengineering, Seoul National University, Seoul, South Korea
| | - Seong Min Oh
- Department of Neuropsychiatry and Center for Sleep and Chronobiology, Seoul National University Hospital, Seoul, South Korea
| | - Jae-Won Choi
- Department of Neuropsychiatry, Eulji University School of Medicine, Eulji General Hospital, Seoul, South Korea
| | - Yu Jin Lee
- Department of Neuropsychiatry and Center for Sleep and Chronobiology, Seoul National University Hospital, Seoul, South Korea
| | - Do-Un Jeong
- Department of Neuropsychiatry and Center for Sleep and Chronobiology, Seoul National University Hospital, Seoul, South Korea
| | - Kwang Suk Park
- Department of Biomedical Engineering, College of Medicine, Seoul National University, Seoul, South Korea
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Gorlova S, Ichiba T, Nishimaru H, Takamura Y, Matsumoto J, Hori E, Nagashima Y, Tatsuse T, Ono T, Nishijo H. Non-restorative Sleep Caused by Autonomic and Electroencephalography Parameter Dysfunction Leads to Subjective Fatigue at Wake Time in Shift Workers. Front Neurol 2019; 10:66. [PMID: 30804882 PMCID: PMC6370690 DOI: 10.3389/fneur.2019.00066] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2018] [Accepted: 01/17/2019] [Indexed: 01/06/2023] Open
Abstract
Sleep is a physiological state that plays important role in the recovery of fatigue. However, the relationship between the physiological status of sleep and subjective fatigue remains unknown. In the present study, we hypothesized that the non-recovery of fatigue at wake time due to non-restorative sleep might be ascribed to changes in specific parameters of electroencephalography (EEG) and heart rate variability (HRV) in poor sleepers. Twenty healthy female shift-working nurses participated in the study. Subjective fatigue was assessed using the visual analog scale (VAS) at bedtime and wake time. During sleep on the night between 2 consecutive day shifts, the EEG powers at the frontal pole, HRV based on electrocardiograms, and distal-proximal gradient of skin temperature were recorded and analyzed. The results indicated that the subjects with high fatigue on the VAS at wake time exhibited (1) a decrease in deep non-rapid eye movement (NREM) (stageN3) sleep duration in the first sleep cycle; (2) a decrease in REM latency; (3) a decrease in ultra-slow and delta EEG powers, particularly from 30 to 65 min after sleep onset; (4) a decrease in the total power of HRV, particularly from 0 to 30 min after sleep onset; (5) an increase in the very low frequency component of HRV; and (6) a smaller increase in the distal-proximal gradient of skin temperature, than those of the subjects with low fatigue levels. The correlational and structural equation modeling analyses of these parameters suggested that an initial decrease in the total power of HRV from 0 to 30 min after sleep onset might inhibit the recovery from fatigue during sleep (i.e., increase the VAS score at wake time) via its effects on the ultra-slow and delta powers from 30 to 65 min after sleep onset, stageN3 duration in the first sleep cycle, REM latency, and distal-proximal gradient of skin temperature. These findings suggest an important role of these physiological factors in recovery from fatigue during sleep, and that interventions to modify these physiological factors might ameliorate fatigue at wake time.
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Affiliation(s)
- Sofya Gorlova
- System Emotional Science, Graduate School of Medicine and Pharmaceutical Sciences, University of Toyama, Toyama, Japan
| | | | - Hiroshi Nishimaru
- System Emotional Science, Graduate School of Medicine and Pharmaceutical Sciences, University of Toyama, Toyama, Japan
| | - Yusaku Takamura
- System Emotional Science, Graduate School of Medicine and Pharmaceutical Sciences, University of Toyama, Toyama, Japan
| | - Jumpei Matsumoto
- System Emotional Science, Graduate School of Medicine and Pharmaceutical Sciences, University of Toyama, Toyama, Japan
| | - Etsuro Hori
- System Emotional Science, Graduate School of Medicine and Pharmaceutical Sciences, University of Toyama, Toyama, Japan
| | | | - Tsuyoshi Tatsuse
- Department of Epidemiology and Health Policy, Graduate School of Medicine and Pharmaceutical Sciences, University of Toyama, Toyama, Japan
| | - Taketoshi Ono
- System Emotional Science, Graduate School of Medicine and Pharmaceutical Sciences, University of Toyama, Toyama, Japan
| | - Hisao Nishijo
- System Emotional Science, Graduate School of Medicine and Pharmaceutical Sciences, University of Toyama, Toyama, Japan
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SKORNYAKOV E, GADDAMEEDHI S, PAECH GM, SPARROW AR, SATTERFIELD BC, SHATTUCK NL, LAYTON ME, KARATSOREOS I, VAN DONGEN HPA. Cardiac autonomic activity during simulated shift work. INDUSTRIAL HEALTH 2019; 57:118-132. [PMID: 30089765 PMCID: PMC6363578 DOI: 10.2486/indhealth.2018-0044] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/22/2018] [Accepted: 07/03/2018] [Indexed: 06/08/2023]
Abstract
Shift work leads to adverse health outcomes including increased risk of cardiovascular disease. Heart rate (HR) and heart rate variability (HRV) are measures of cardiac autonomic activity and markers of cardiovascular disease and mortality. To investigate the effects of shift work on cardiac autonomic activity, we assessed the influence of simulated night work on HR and HRV, and dissociated the direct effects of circadian misalignment from those of sleep displacement and altered physical activity patterns. A total of 29 subjects each participated in one of two in-laboratory, simulated shift work studies. In both studies, EKG was continuously monitored via Holter monitors to measure HR and the high frequency (HF) component of HRV (HF-HRV). We found endogenous circadian rhythmicity in HR and HF-HRV. Sleep and waking physical activity, both displaced during simulated night work, had more substantial, and opposite, effects on HR and HF-HRV. Our findings show systematic but complex, interacting effects of time of day, sleep/wake state, and physical activity on cardiac autonomic activity. These effects need to be taken into account when evaluating HR and HRV in shift work settings and when interpreting these measures of cardiac autonomic activity as markers of cardiovascular disease.
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Affiliation(s)
- Elena SKORNYAKOV
- Sleep and Performance Research Center, Washington State
University, USA
- Department of Physical Therapy, Eastern Washington
University, USA
| | - Shobhan GADDAMEEDHI
- Sleep and Performance Research Center, Washington State
University, USA
- Department of Pharmaceutical Sciences, College of Pharmacy
and Pharmaceutical Sciences, Washington State University, USA
| | - Gemma M. PAECH
- Sleep and Performance Research Center, Washington State
University, USA
| | - Amy R. SPARROW
- Sleep and Performance Research Center, Washington State
University, USA
- Elson S. Floyd College of Medicine, Washington State
University, USA
| | - Brieann C. SATTERFIELD
- Sleep and Performance Research Center, Washington State
University, USA
- Social, Cognitive, and Affective Neuroscience Laboratory,
Department of Psychiatry, College of Medicine, University of Arizona, USA
| | | | - Matthew E. LAYTON
- Sleep and Performance Research Center, Washington State
University, USA
- Elson S. Floyd College of Medicine, Washington State
University, USA
| | - Ilia KARATSOREOS
- Sleep and Performance Research Center, Washington State
University, USA
- Department of Integrative Physiology and Neuroscience,
Washington State University, USA
| | - Hans P. A. VAN DONGEN
- Sleep and Performance Research Center, Washington State
University, USA
- Elson S. Floyd College of Medicine, Washington State
University, USA
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Yang H, Haack M, Dang R, Gautam S, Simpson NS, Mullington JM. Heart rate variability rebound following exposure to persistent and repetitive sleep restriction. Sleep 2019; 42:5185653. [PMID: 30476269 PMCID: PMC6369727 DOI: 10.1093/sleep/zsy226] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2018] [Revised: 10/19/2018] [Accepted: 11/14/2018] [Indexed: 01/20/2023] Open
Abstract
While it is well established that slow-wave sleep electroencephalography (EEG) rebounds following sleep deprivation, very little research has investigated autonomic nervous system recovery. We examined heart rate variability (HRV) and cardiovagal baroreflex sensitivity (BRS) during four blocks of repetitive sleep restriction and sequential nights of recovery sleep. Twenty-one healthy participants completed the 22-day in-hospital protocol. Following three nights of 8-hr sleep, they were assigned to a repetitive sleep restriction condition. Participants had two additional 8-hr recovery sleep periods at the end of the protocol. Sleep EEG, HRV, and BRS were compared for the baseline, the four blocks of sleep restriction, and the second (R2) and third (R3) nocturnal recovery sleep periods following the last sleep restriction block. Within the first hour of each sleep period, vagal activation, as indexed by increase in high frequency (HF; HRV spectrum analysis), showed a rapid increase, reaching its 24-hr peak. HF was more pronounced (rebound) in R2 than during baseline (p < 0.001). The BRS increased within the first hour of sleep and was higher across all sleep restriction blocks and recovery nights (p = 0.039). Rebound rapid eye movement sleep was observed during both R2 and R3 (p = 0.004), whereas slow-wave sleep did not differ between baseline and recovery nights (p > 0.05). Our results indicate that the restoration of autonomic homeostasis requires a time course that includes at least three nights, following an exposure to multiple nights of sleep curtailed to about half the normal nightly amount.
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Affiliation(s)
- Huan Yang
- Department of Neurology, Beth Israel Deaconess Medical Center/Harvard Medical School, Boston, MA
| | - Monika Haack
- Department of Neurology, Beth Israel Deaconess Medical Center/Harvard Medical School, Boston, MA
| | - Rammy Dang
- Department of Neurology, Beth Israel Deaconess Medical Center/Harvard Medical School, Boston, MA
| | - Shiva Gautam
- Department of Medicine, University of Florida College of Medicine–Jacksonville, Jacksonville, FL
| | - Norah S Simpson
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA
| | - Janet M Mullington
- Department of Neurology, Beth Israel Deaconess Medical Center/Harvard Medical School, Boston, MA
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De Pascalis V, Scacchia P. The influence of reward sensitivity, heart rate dynamics and EEG-delta activity on placebo analgesia. Behav Brain Res 2018; 359:320-332. [PMID: 30439452 DOI: 10.1016/j.bbr.2018.11.014] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2018] [Revised: 10/10/2018] [Accepted: 11/08/2018] [Indexed: 02/07/2023]
Abstract
Personality traits have been shown to interact with environmental cues to modulate biological responses including treatment responses, and potentially having a role in the formation of placebo effects. Here we used the Reinforcement Sensitivity Theory Personality Questionnaire (RST-PQ) to identify personality traits that predict placebo analgesic responding. Cardiac inter-beat (RR) time series and electroencephalographic (EEG) band oscillations were recorded from healthy women in a cold-pain (Pain) and placebo analgesia (PA) condition. The measures of Hypnotizability, and self-reported ratings of Hypnotic Depth, Motivation, Pain Expectation, Involuntariness in PA responding, Pain and Distress intensity were obtained. Separate principal components factor analyses with varimax rotation were performed on summarized heart rate variability (HRV) measures of time, frequency, nonlinear Complexity, and EEG-band activity. Both analyses yielded a similar three-factor solution including Frequency HRV (factor-1), Complexity HRV dynamics (factor-2), and time HRV & EEG-delta activity (factor-3). Reward Interest sub-trait of the Behavioral Approach System (BAS-RI), Pain Expectation, Involuntariness in PA responding, and Hypnotic Depth were positively associated, whereas negative changes in time-HRV & EEG-delta scores were associated with Pain Reduction. Multiple mediation analyses disclosed that BAS-RI, potentially served by the dopaminergic system, through Involuntariness in PA responding can alter placebo responding to laboratory pain. Our results also show that a linear compound of HR slowing and higher EEG delta activity during PA explains a substantial proportion of the variance in placebo analgesic responses. Future studies should examine the potential role that these individual difference measures may play in patient responsiveness to treatments for clinical pain.
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Affiliation(s)
- V De Pascalis
- Department of Psychology "La Sapienza" University of Rome, Italy.
| | - P Scacchia
- Department of Psychology "La Sapienza" University of Rome, Italy
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Kamata K, Fujiwara K, Kinoshita T, Kano M. Missing RRI Interpolation Algorithm based on Locally Weighted Partial Least Squares for Precise Heart Rate Variability Analysis. SENSORS 2018; 18:s18113870. [PMID: 30423835 PMCID: PMC6263608 DOI: 10.3390/s18113870] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/07/2018] [Revised: 11/07/2018] [Accepted: 11/09/2018] [Indexed: 11/29/2022]
Abstract
The R-R interval (RRI) fluctuation in electrocardiogram (ECG) is called heart rate variability (HRV), which reflects activities of the autonomic nervous system (ANS) and has been used for various health monitoring services. Accurate R wave detection is crucial for success in HRV-based health monitoring services; however, ECG artifacts often cause missing R waves and deteriorate the accuracy of HRV analysis. The present work proposes a new missing RRI interpolation technique based on Just-In-Time (JIT) modeling. In the JIT modeling framework, a local regression model is built by weighing samples stored in the database according to the distance from a query and output is estimated only when an estimate is requested. The proposed method builds a local model and estimates missing RRI only when an RRI detection error is detected. Locally weighted partial least squares (LWPLS) is adopted for local model construction. The proposed method is referred to as LWPLS-based RRI interpolation (LWPLS-RI). The performance of the proposed LWPLS-RI was evaluated through its application to RRI data with artificial missing RRIs. We used the MIT-BIH Normal Sinus Rhythm Database for nominal RRI dataset construction. Missing RRIs were artificially introduced and they were interpolated by the proposed LWPLS-RI. In addition, MEAN that replaces the missing RRI by a mean of the past RRI data was compared as a conventional method. The result showed that the proposed LWPLS-RI improved root mean squared error (RMSE) of RRI by about 70% in comparison with MEAN. In addition, the proposed method realized precise HRV analysis. The proposed method will contribute to the realization of precise HRV-based health monitoring services.
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Affiliation(s)
- Keisuke Kamata
- The Department of Systems Science, Kyoto University, Kyoto 615-8085, Japan; (K.K.); (T.K.); (M.K.)
| | - Koichi Fujiwara
- The Department of Systems Science, Kyoto University, Kyoto 615-8085, Japan; (K.K.); (T.K.); (M.K.)
- The Department of Material Process Engineering, Nagoya University, Nagoya 464-8601, Japan
- Correspondence: or
| | - Takafumi Kinoshita
- The Department of Systems Science, Kyoto University, Kyoto 615-8085, Japan; (K.K.); (T.K.); (M.K.)
| | - Manabu Kano
- The Department of Systems Science, Kyoto University, Kyoto 615-8085, Japan; (K.K.); (T.K.); (M.K.)
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Fujiwara K, Abe E, Kamata K, Nakayama C, Suzuki Y, Yamakawa T, Hiraoka T, Kano M, Sumi Y, Masuda F, Matsuo M, Kadotani H. Heart Rate Variability-Based Driver Drowsiness Detection and Its Validation With EEG. IEEE Trans Biomed Eng 2018; 66:1769-1778. [PMID: 30403616 DOI: 10.1109/tbme.2018.2879346] [Citation(s) in RCA: 37] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
OBJECTIVE Driver drowsiness detection is a key technology that can prevent fatal car accidents caused by drowsy driving. The present work proposes a driver drowsiness detection algorithm based on heart rate variability (HRV) analysis and validates the proposed method by comparing with electroencephalography (EEG)-based sleep scoring. METHODS Changes in sleep condition affect the autonomic nervous system and then HRV, which is defined as an RR interval (RRI) fluctuation on an electrocardiogram trace. Eight HRV features are monitored for detecting changes in HRV by using multivariate statistical process control, which is a well known anomaly detection method. RESULT The performance of the proposed algorithm was evaluated through an experiment using a driving simulator. In this experiment, RRI data were measured from 34 participants during driving, and their sleep onsets were determined based on the EEG data by a sleep specialist. The validation result of the experimental data with the EEG data showed that drowsiness was detected in 12 out of 13 pre-N1 episodes prior to the sleep onsets, and the false positive rate was 1.7 times per hour. CONCLUSION The present work also demonstrates the usefulness of the framework of HRV-based anomaly detection that was originally proposed for epileptic seizure prediction. SIGNIFICANCE The proposed method can contribute to preventing accidents caused by drowsy driving.
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Niizeki K, Saitoh T. Association Between Phase Coupling of Respiratory Sinus Arrhythmia and Slow Wave Brain Activity During Sleep. Front Physiol 2018; 9:1338. [PMID: 30319446 PMCID: PMC6167474 DOI: 10.3389/fphys.2018.01338] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2018] [Accepted: 09/05/2018] [Indexed: 11/23/2022] Open
Abstract
Phase coupling of respiratory sinus arrhythmia (RSA) has been proposed to be an alternative measure for evaluating autonomic nervous system (ANS) activity. The aim of this study was to analyze how phase coupling of RSA is altered during sleep, in order to explore whether this measure is a predictor of slow wave sleep (SWS). Overnight electroencephalograms (EEG), electrocardiograms (ECG), and breathing using inductance plethysmography were recorded from 30 healthy volunteers (six females, age range 21–64, 31.6 ± 14.7 years). Slow wave activity was evaluated by the envelope of the amplitude of the EEG δ-wave (0.5–4 Hz). The RSA was extracted from the change in the R-R interval (RRI) by band-pass filter, where pass band frequencies were determined from the profile of the power spectral density for respiration. The analytic signals of RSA and respiration were obtained by Hilbert transform, after which the amplitude of RSA (ARSA) and the degree of phase coupling (λ) were quantified. Additionally, the normalized high-frequency component (HFn) of the frequency-domain heart rate variability (HRV) was calculated. Using auto- and cross-correlation analyses, we found that overnight profiles of λ and δ-wave were correlated, with significant cross-correlation coefficients (0.461 ± 0.107). The δ-wave and HFn were also correlated (0.426 ± 0.115). These correlations were higher than that for the relationship between δ-wave and ARSA (0.212 ± 0.161). The variation of λ precedes the onset of the δ-wave by ~3 min, suggesting a vagal enhancement prior to the onset of SWS. Auto correlation analysis revealed that the periodicity of λ was quite similar to that of the δ-wave (88.3 ± 15.7 min vs. 88.6 ± 16.3 min, λ-cycle = 0.938 × δ-cycle + 5.77 min, r = 0.902). These results suggest that phase coupling analysis of RSA appears to be a marker for predicting SWS intervals, thereby complementing other noninvasive tools and diagnostic efforts.
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Affiliation(s)
- Kyuichi Niizeki
- Department of Biosystems Engineering, Graduate School of Science and Engineering, Yamagata University, Yamagata, Japan
| | - Tadashi Saitoh
- Department of Biosystems Engineering, Graduate School of Science and Engineering, Yamagata University, Yamagata, Japan
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Abstract
PURPOSE To investigate heart rate and EEG variability and their coupling in patients with sepsis and determine their relationship to sepsis severity and severity of sepsis-associated brain dysfunction. METHODS Fifty-two patients with sepsis were prospectively identified, categorized as comatose (N = 30) and noncomatose (N = 22), and compared with 11 control subjects. In a 30-minute EEG and electrocardiogram recording, heart rate variability and EEG variability (measured by the variability of relative power in a modified alpha band = RAP) and their coupled oscillations were quantified using linear (least-square periodogram and magnitude square coherence) and nonlinear (Shannon entropy and mutual information) measures. These measures were compared between the three groups and correlated with outcome, adjusting for severity of sepsis. RESULTS Several measures of heart rate variability and EEG variability and of their coupled oscillations were significantly lower in patients with sepsis compared with controls and correlated with outcome. This correlation was not independent when adjusting for severity of sepsis. CONCLUSIONS Sepsis is associated with lower variability of both heart rate and RAP on EEG and reduction of their coupled oscillations. This uncoupling is associated with the severity of encephalopathy. Combined EEG and electrocardiogram monitoring may be used to gain insight in underlying mechanisms of sepsis and quantify brainstem or thalamic dysfunction.
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de Zambotti M, Trinder J, Silvani A, Colrain IM, Baker FC. Dynamic coupling between the central and autonomic nervous systems during sleep: A review. Neurosci Biobehav Rev 2018; 90:84-103. [PMID: 29608990 PMCID: PMC5993613 DOI: 10.1016/j.neubiorev.2018.03.027] [Citation(s) in RCA: 99] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2017] [Revised: 02/16/2018] [Accepted: 03/24/2018] [Indexed: 12/19/2022]
Abstract
Sleep is characterized by coordinated cortical and cardiac oscillations reflecting communication between the central (CNS) and autonomic (ANS) nervous systems. Here, we review fluctuations in ANS activity in association with CNS-defined sleep stages and cycles, and with phasic cortical events during sleep (e.g., arousals, K-complexes). Recent novel analytic methods reveal a dynamic organization of integrated physiological networks during sleep and indicate how multiple factors (e.g., sleep structure, age, sleep disorders) affect "CNS-ANS coupling". However, these data are mostly correlational and there is a lack of clarity of the underlying physiology, making it challenging to interpret causality and direction of coupling. Experimental manipulations (e.g., evoking K-complexes or arousals) provide information on the precise temporal sequence of cortical-cardiac activity, and are useful for investigating physiological pathways underlying CNS-ANS coupling. With the emergence of new analytical approaches and a renewed interest in ANS and CNS communication during sleep, future work may reveal novel insights into sleep and cardiovascular interactions during health and disease, in which coupling could be adversely impacted.
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Affiliation(s)
| | - John Trinder
- Melbourne School of Psychological Sciences, University of Melbourne, Parkville, Victoria, Australia.
| | - Alessandro Silvani
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Italy.
| | - Ian M Colrain
- Center for Health Sciences, SRI International, Menlo Park, CA, USA; Melbourne School of Psychological Sciences, University of Melbourne, Parkville, Victoria, Australia.
| | - Fiona C Baker
- Center for Health Sciences, SRI International, Menlo Park, CA, USA; Brain Function Research Group, School of Physiology, University of the Witwatersrand, Johannesburg, South Africa.
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Bruce SA, Hall MH, Buysse DJ, Krafty RT. Conditional adaptive Bayesian spectral analysis of nonstationary biomedical time series. Biometrics 2018; 74:260-269. [PMID: 28482111 PMCID: PMC5677586 DOI: 10.1111/biom.12719] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2016] [Revised: 03/01/2017] [Accepted: 04/01/2017] [Indexed: 11/27/2022]
Abstract
Many studies of biomedical time series signals aim to measure the association between frequency-domain properties of time series and clinical and behavioral covariates. However, the time-varying dynamics of these associations are largely ignored due to a lack of methods that can assess the changing nature of the relationship through time. This article introduces a method for the simultaneous and automatic analysis of the association between the time-varying power spectrum and covariates, which we refer to as conditional adaptive Bayesian spectrum analysis (CABS). The procedure adaptively partitions the grid of time and covariate values into an unknown number of approximately stationary blocks and nonparametrically estimates local spectra within blocks through penalized splines. CABS is formulated in a fully Bayesian framework, in which the number and locations of partition points are random, and fit using reversible jump Markov chain Monte Carlo techniques. Estimation and inference averaged over the distribution of partitions allows for the accurate analysis of spectra with both smooth and abrupt changes. The proposed methodology is used to analyze the association between the time-varying spectrum of heart rate variability and self-reported sleep quality in a study of older adults serving as the primary caregiver for their ill spouse.
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Affiliation(s)
- Scott A. Bruce
- Department of Statistical Science, Temple University, Philadelphia, Pennsylvania, 19122, U.S.A
| | - Martica H. Hall
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, Pennsylvania, 15213, U.S.A
| | - Daniel J. Buysse
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, Pennsylvania, 15213, U.S.A
| | - Robert T. Krafty
- Department of Biostatistics, University of Pittsburgh, Pittsburgh, Pennsylvania, 15261, U.S.A
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Fujimoto K, Ura M, Yamazaki H, Uematsu A. Instability of parasympathetic nerve function evaluated by instantaneous time–frequency analysis in patients with obstructive sleep apnea. Sleep Biol Rhythms 2018. [DOI: 10.1007/s41105-018-0153-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Kozlowska K, Spooner CJ, Palmer DM, Harris A, Korgaonkar MS, Scher S, Williams LM. "Motoring in idle": The default mode and somatomotor networks are overactive in children and adolescents with functional neurological symptoms. Neuroimage Clin 2018; 18:730-743. [PMID: 29876262 PMCID: PMC5987846 DOI: 10.1016/j.nicl.2018.02.003] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2017] [Revised: 01/19/2018] [Accepted: 02/02/2018] [Indexed: 12/20/2022]
Abstract
Objective Children and adolescents with functional neurological symptom disorder (FND) present with diverse neurological symptoms not explained by a disease process. Functional neurological symptoms have been conceptualized as somatoform dissociation, a disruption of the brain's intrinsic organization and reversion to a more primitive level of function. We used EEG to investigate neural function and functional brain organization in children/adolescents with FND. Method EEG was recorded in the resting eyes-open condition in 57 patients (aged 8.5-18 years) and 57 age- and sex-matched healthy controls. Using a topographical map, EEG power data were quantified for regions of interest that define the default mode network (DMN), salience network, and somatomotor network. Source localization was examined using low-resolution brain electromagnetic tomography (LORETA). The contributions of chronic pain and arousal as moderators of differences in EEG power were also examined. Results Children/adolescents with FND had excessive theta and delta power in electrode clusters corresponding to the DMN-both anteriorly (dorsomedial prefrontal cortex [dmFPC]) and posteriorly (posterior cingulate cortex [PCC], precuneus, and lateral parietal cortex)-and in the premotor/supplementary motor area (SMA) region. There was a trend toward increased theta and delta power in the salience network. LORETA showed activation across all three networks in all power bands and localized neural sources to the dorsal anterior cingulate cortex/dmPFC, mid cingulate cortex, PCC/precuneus, and SMA. Pain and arousal contributed to slow wave power increases in all three networks. Conclusions These findings suggest that children and adolescents with FND are characterized by overactivation of intrinsic resting brain networks involved in threat detection, energy regulation, and preparation for action.
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Affiliation(s)
- Kasia Kozlowska
- The Children's Hospital at Westmead, Psychological Medicine, Locked Bag 4001, Westmead, NSW 2145, Australia; The Brain Dynamics Centre, Westmead Institute for Medical Research, 176 Hawkesbury Rd, Westmead, NSW 2145, Australia; The University of Sydney, Sydney, Australia.
| | | | - Donna M Palmer
- The Brain Dynamics Centre, Westmead Institute for Medical Research, 176 Hawkesbury Rd, Westmead, NSW 2145, Australia; The University of Sydney, Sydney, Australia.
| | - Anthony Harris
- The Brain Dynamics Centre, Westmead Institute for Medical Research, 176 Hawkesbury Rd, Westmead, NSW 2145, Australia; The University of Sydney, Sydney, Australia; Westmead Hospital Psychiatry Department, Darcy Rd, Westmead, NSW 2145, Australia.
| | - Mayuresh S Korgaonkar
- The Brain Dynamics Centre, Westmead Institute for Medical Research, 176 Hawkesbury Rd, Westmead, NSW 2145, Australia; The University of Sydney, Sydney, Australia.
| | - Stephen Scher
- The University of Sydney, Sydney, Australia; Department of Psychiatry, Harvard Medical School, McLean Hospital, Belmont, MA, USA.
| | - Leanne M Williams
- Psychiatry and Behavioral Sciences, Stanford University, VA Palo Alto (Sierra-Pacific MIRECC) 401 Quarry Rd, United States.
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Altered nocturnal blood pressure profiles in women with insomnia disorder in the menopausal transition. Menopause 2018; 24:278-287. [PMID: 27749736 DOI: 10.1097/gme.0000000000000754] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
OBJECTIVE Insomnia disorder is a risk factor for cardiovascular (CV) pathology. It is unknown whether insomnia that develops in the context of the menopausal transition (MT) impacts the CV system. We assessed nocturnal blood pressure (BP) and heart rate (HR) profiles in women with insomnia disorder in the MT. METHODS Twelve women meeting DSM-IV criteria for insomnia in the MT (age, mean ± SD: 50.5 ± 3.6 y) and 11 controls (age, mean ± SD: 49.0 ± 3.0 y) had polysomnographic recordings on one or two nights during which beat-to-beat BP and HR were assessed and analyzed hourly from lights-out across the first 6 hours of the night and according to sleep stage. Physiological hot flashes were identified from fluctuations in sternal skin conductance. RESULTS Women with insomnia and controls had similar distributions of sleep stages and awakenings/arousals across hours of the night, although insomnia participants tended to have more wakefulness overall. More women in the insomnia group (7 of 12) than in the control group (2 of 11) had at least one physiological hot flash at night (P < 0.05). Both groups showed a drop in BP in the first part of the night; however, systolic and diastolic BP patterns diverged later, remaining low in controls but increasing in insomnia participants 4 to 6 hours after lights-out (P < 0.05). Both groups showed a similar pattern of decline in HR across the night. CONCLUSIONS Our findings suggest altered regulatory control of BP during sleep in the MT insomnia. The causes and long-term consequences of this altered nocturnal BP profile remain to be determined.
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Satpute AB, Kragel PA, Barrett LF, Wager TD, Bianciardi M. Deconstructing arousal into wakeful, autonomic and affective varieties. Neurosci Lett 2018; 693:19-28. [PMID: 29378297 DOI: 10.1016/j.neulet.2018.01.042] [Citation(s) in RCA: 51] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2017] [Revised: 01/13/2018] [Accepted: 01/22/2018] [Indexed: 12/11/2022]
Abstract
Arousal plays a central role in a wide variety of phenomena, including wakefulness, autonomic function, affect and emotion. Despite its importance, it remains unclear as to how the neural mechanisms for arousal are organized across them. In this article, we review neuroscience findings for three of the most common origins of arousal: wakeful arousal, autonomic arousal, and affective arousal. Our review makes two overarching points. First, research conducted primarily in non-human animals underscores the importance of several subcortical nuclei that contribute to various sources of arousal, motivating the need for an integrative framework. Thus, we outline an integrative neural reference space as a key first step in developing a more systematic understanding of central nervous system contributions to arousal. Second, there is a translational gap between research on non-human animals, which emphasizes subcortical nuclei, and research on humans using non-invasive neuroimaging techniques, which focuses more on gross anatomical characterizations of cortical (e.g. network architectures including the default mode network) and subcortical structures. We forecast the importance of high-field neuroimaging in bridging this gap to examine how the various networks within the neural reference space for arousal operate across varieties of arousal-related phenomena.
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Affiliation(s)
- Ajay B Satpute
- Departments of Psychology and Neuroscience, Pomona College, Claremont, CA, USA; Department of Psychology, Northeastern University, Boston, MA, USA.
| | - Philip A Kragel
- Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, USA; The Institute of Cognitive Science, University of Colorado Boulder, Boulder, USA
| | - Lisa Feldman Barrett
- Department of Psychology, Northeastern University, Boston, MA, USA; Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, USA; Department of Radiology, Harvard Medical School, Boston, MA, USA; Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
| | - Tor D Wager
- Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, USA; The Institute of Cognitive Science, University of Colorado Boulder, Boulder, USA
| | - Marta Bianciardi
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, USA; Department of Radiology, Harvard Medical School, Boston, MA, USA.
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Herzig D, Eser P, Omlin X, Riener R, Wilhelm M, Achermann P. Reproducibility of Heart Rate Variability Is Parameter and Sleep Stage Dependent. Front Physiol 2018; 8:1100. [PMID: 29367845 PMCID: PMC5767731 DOI: 10.3389/fphys.2017.01100] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2017] [Accepted: 12/13/2017] [Indexed: 12/31/2022] Open
Abstract
Objective: Measurements of heart rate variability (HRV) during sleep have become increasingly popular as sleep could provide an optimal state for HRV assessments. While sleep stages have been reported to affect HRV, the effect of sleep stages on the variance of HRV parameters were hardly investigated. We aimed to assess the variance of HRV parameters during the different sleep stages. Further, we tested the accuracy of an algorithm using HRV to identify a 5-min segment within an episode of slow wave sleep (SWS, deep sleep). Methods: Polysomnographic (PSG) sleep recordings of 3 nights of 15 healthy young males were analyzed. Sleep was scored according to conventional criteria. HRV parameters of consecutive 5-min segments were analyzed within the different sleep stages. The total variance of HRV parameters was partitioned into between-subjects variance, between-nights variance, and between-segments variance and compared between the different sleep stages. Intra-class correlation coefficients of all HRV parameters were calculated for all sleep stages. To identify an SWS segment based on HRV, Pearson correlation coefficients of consecutive R-R intervals (rRR) of moving 5-min windows (20-s steps). The linear trend was removed from the rRR time series and the first segment with rRR values 0.1 units below the mean rRR for at least 10 min was identified. A 5-min segment was placed in the middle of such an identified segment and the corresponding sleep stage was used to assess the accuracy of the algorithm. Results: Good reproducibility within and across nights was found for heart rate in all sleep stages and for high frequency (HF) power in SWS. Reproducibility of low frequency (LF) power and of LF/HF was poor in all sleep stages. Of all the 5-min segments selected based on HRV data, 87% were accurately located within SWS. Conclusions: SWS, a stable state that, in contrast to waking, is unaffected by internal and external factors, is a reproducible state that allows reliable determination of heart rate, and HF power, and can satisfactorily be detected based on R-R intervals, without the need of full PSG. Sleep may not be an optimal condition to assess LF power and LF/HF power ratio.
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Affiliation(s)
- David Herzig
- Preventive Cardiology and Sports Medicine, University Clinic for Cardiology, Bern University Hospital (Inselspital), University of Bern, Bern, Switzerland
| | - Prisca Eser
- Preventive Cardiology and Sports Medicine, University Clinic for Cardiology, Bern University Hospital (Inselspital), University of Bern, Bern, Switzerland
| | - Ximena Omlin
- Sensory-Motor Systems Lab, Institute of Robotics and Intelligent Systems, ETH Zurich, Zurich, Switzerland
| | - Robert Riener
- Sensory-Motor Systems Lab, Institute of Robotics and Intelligent Systems, ETH Zurich, Zurich, Switzerland.,Medical Faculty, University of Zurich, Zurich, Switzerland
| | - Matthias Wilhelm
- Preventive Cardiology and Sports Medicine, University Clinic for Cardiology, Bern University Hospital (Inselspital), University of Bern, Bern, Switzerland
| | - Peter Achermann
- Institute of Pharmacology and Toxicology, Zurich Center for Interdisciplinary Sleep Research and Zurich Center for Integrative Human Physiology, University of Zurich, Zurich, Switzerland
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Fonseca P, Weysen T, Goelema MS, Møst EIS, Radha M, Lunsingh Scheurleer C, van den Heuvel L, Aarts RM. Validation of Photoplethysmography-Based Sleep Staging Compared With Polysomnography in Healthy Middle-Aged Adults. Sleep 2017; 40:3868868. [PMID: 28838130 DOI: 10.1093/sleep/zsx097] [Citation(s) in RCA: 82] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Study Objectives To compare the accuracy of automatic sleep staging based on heart rate variability measured from photoplethysmography (PPG) combined with body movements measured with an accelerometer, with polysomnography (PSG) and actigraphy. Methods Using wrist-worn PPG to analyze heart rate variability and an accelerometer to measure body movements, sleep stages and sleep statistics were automatically computed from overnight recordings. Sleep-wake, 4-class (wake/N1 + N2/N3/REM) and 3-class (wake/NREM/REM) classifiers were trained on 135 simultaneously recorded PSG and PPG recordings of 101 healthy participants and validated on 80 recordings of 51 healthy middle-aged adults. Epoch-by-epoch agreement and sleep statistics were compared with actigraphy for a subset of the validation set. Results The sleep-wake classifier obtained an epoch-by-epoch Cohen's κ between PPG and PSG sleep stages of 0.55 ± 0.14, sensitivity to wake of 58.2 ± 17.3%, and accuracy of 91.5 ± 5.1%. κ and sensitivity were significantly higher than with actigraphy (0.40 ± 0.15 and 45.5 ± 19.3%, respectively). The 3-class classifier achieved a κ of 0.46 ± 0.15 and accuracy of 72.9 ± 8.3%, and the 4-class classifier, a κ of 0.42 ± 0.12 and accuracy of 59.3 ± 8.5%. Conclusions The moderate epoch-by-epoch agreement and, in particular, the good agreement in terms of sleep statistics suggest that this technique is promising for long-term sleep monitoring, although more evidence is needed to understand whether it can complement PSG in clinical practice. It also offers an improvement in sleep/wake detection over actigraphy for healthy individuals, although this must be confirmed on a larger, clinical population.
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Affiliation(s)
- Pedro Fonseca
- Philips Group Innovation Research, Eindhoven, The Netherlands.,Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Tim Weysen
- Philips Group Innovation Research, Eindhoven, The Netherlands
| | - Maaike S Goelema
- Philips Group Innovation Research, Eindhoven, The Netherlands.,Department of Industrial Design, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Els I S Møst
- Philips Group Innovation Research, Eindhoven, The Netherlands
| | - Mustafa Radha
- Philips Group Innovation Research, Eindhoven, The Netherlands.,Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Charlotte Lunsingh Scheurleer
- Philips Group Innovation Research, Eindhoven, The Netherlands.,Department of Industrial Design, Eindhoven University of Technology, Eindhoven, The Netherlands
| | | | - Ronald M Aarts
- Philips Group Innovation Research, Eindhoven, The Netherlands.,Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
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