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Li J, Zhou M, Zhang J, Zhang J, Zhang L, Shan H, Zhang J, Zhang H. Sleep-aiding music therapy for insomnia: Exploring EEG functional connectivity of sleep-related attentional bias. Sleep Med 2024; 122:149-162. [PMID: 39173211 DOI: 10.1016/j.sleep.2024.08.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/09/2024] [Revised: 07/12/2024] [Accepted: 08/14/2024] [Indexed: 08/24/2024]
Abstract
STUDY OBJECTIVES This study aimed to investigate the relationship between sleep-aiding music and sleep-related attentional bias based on electroencephalography (EEG) functional connectivity (FC) in patients with insomnia disorder (ID), to evaluate the effectiveness of music in aiding sleep. METHOD This study included 30 participants, comprising 15 patients with ID and 15 healthy controls (HCs). Six types of music were selected for sleep aid, and a dot-probe task based on sleep-related attentional bias was utilized to collect behavioral and EEG data. Vigilance bias and disengagement bias were measured using reaction time and EEG FC. Differences in sleep-related attentional bias before and after the intervention of music were explored to evaluate the sleep-aiding effects and identify EEG biomarkers. RESULTS Compared with HCs, patients with ID showed decreased sleep-related attentional bias of EEG FC between occipital-central and temporal-frontal lobes. Among the six types of music, International Standard Sleep Aid and Lullaby had a greater impact on decreasing vigilance bias in the ID group. Additionally, the International Standard Sleep Aid and Nature Sound were more effective in decreasing disengagement bias in the ID group. This study also examined the resting-state EEG FC of patients with ID before and after the intervention of music. The results showed that the FC in the temporal, frontal, and occipital lobes significantly differed before and after the intervention of music, especially with the use of International Standard Sleep Aid, Lullaby, and Alpha Sound Wave. However, it is worth noting that these three types of music showed no similarities in EEG FC, in contrast to the result of sleep-related attentional bias of EEG FC. CONCLUSION This study found that the sleep-related attentional bias of EEG FC has more distinct characteristics when compared to resting-state EEG FC. The results suggest that the sleep-related attentional bias of EEG FC could be a potential biomarker for assessing the sleep-aiding effect of music interventions. International Standard Sleep Aid was the most effective for patients with ID among six types of sleep-aiding music. These findings could facilitate the development of personalized therapies for patients with ID. CLINICAL TRIALS REGISTRATION Chinese Clinical Trial Register, http://www.chictr.org.cn, ID: ChiCTR2400081608.
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Affiliation(s)
- Jin Li
- School of Design, Hunan University, Changsha, China
| | - Meiling Zhou
- School of Design, Hunan University, Changsha, China
| | - Jiabo Zhang
- Faculty of Engineering & Applied Science, University of Regina, Canada
| | - Jiashuo Zhang
- Faculty of Engineering & Applied Science, University of Regina, Canada
| | - Lei Zhang
- Faculty of Engineering & Applied Science, University of Regina, Canada
| | - Huafeng Shan
- Keeson Technology Corporation Limited, Jiaxing, China
| | - Jianwei Zhang
- Keeson Technology Corporation Limited, Jiaxing, China
| | - Hanling Zhang
- School of Design, Hunan University, Changsha, China.
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2
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Kim D, Park JY, Song YW, Kim E, Kim S, Joo EY. Machine-learning-based classification of obstructive sleep apnea using 19-channel sleep EEG data. Sleep Med 2024; 124:323-330. [PMID: 39368159 DOI: 10.1016/j.sleep.2024.09.041] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/06/2024] [Revised: 09/14/2024] [Accepted: 09/28/2024] [Indexed: 10/07/2024]
Abstract
OBJECTIVE This study aimed to investigate the neurophysiological effects of obstructive sleep apnea (OSA) using multi-channel sleep electroencephalography (EEG) through machine learning methods encompassing various analysis methodologies including power spectral analysis, network analysis, and microstate analysis. METHODS Twenty participants with apnea-hypopnea index (AHI) ≥ 15 and 18 participants with AHI <15 were recruited. Overnight polysomnography was conducted concurrently with 19-channel EEG. Preprocessed EEG data underwent computation of relative spectral power. A weighted network based on graph theory was generated; and indices of strength, path length, eigenvector centrality, and clustering coefficient were calculated. Microstate analysis was conducted to derive four topographic maps. Machine learning techniques were employed to assess EEG features capable of differentiating two groups. RESULTS Among 71 features that showed significant differences between the two groups, seven exhibited good classification performance, achieving 88.3 % accuracy, 92 % sensitivity, and 84 % specificity. These features were power at C4 theta, P3 theta, P4 theta, and F8 gamma during NREM1 sleep and at Pz gamma during REM sleep from power spectral analysis; eigenvector centrality at F7 gamma during REM sleep from network analysis; and duration of microstate 4 during NREM2 sleep from microstate analysis. These seven EEG features were significantly correlated with polysomnographic parameters reflecting the severity of OSA. CONCLUSIONS The application of machine learning techniques and various EEG analytical methods resulted in a model that showed good performance in classifying moderate to severe OSA and highlights the potential of EEG to serve as a biomarker of functional changes in OSA.
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Affiliation(s)
- Dongyeop Kim
- Department of Neurology, Ewha Womans University Seoul Hospital, Ewha Womans University College of Medicine, Seoul, Republic of Korea
| | - Ji Yong Park
- Department of Applied Artificial Intelligence, Hanyang University, Ansan, Republic of Korea
| | - Young Wook Song
- Department of Applied Artificial Intelligence, Hanyang University, Ansan, Republic of Korea
| | - Euijin Kim
- Department of Human-Computer Interaction, Hanyang University, Ansan, Republic of Korea
| | - Sungkean Kim
- Department of Applied Artificial Intelligence, Hanyang University, Ansan, Republic of Korea; Department of Human-Computer Interaction, Hanyang University, Ansan, Republic of Korea.
| | - Eun Yeon Joo
- Department of Neurology, Neuroscience Center, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.
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Luo X, Zhou B, Shi J, Li G, Zhu Y. Effects of gender and age on sleep EEG functional connectivity differences in subjects with mild difficulty falling asleep. Front Psychiatry 2024; 15:1433316. [PMID: 39045546 PMCID: PMC11264056 DOI: 10.3389/fpsyt.2024.1433316] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/15/2024] [Accepted: 06/17/2024] [Indexed: 07/25/2024] Open
Abstract
Introduction Difficulty falling asleep place an increasing burden on society. EEG-based sleep staging is fundamental to the diagnosis of sleep disorder, and the selection of features for each sleep stage is a key step in the sleep analysis. However, the differences of sleep EEG features in gender and age are not clear enough. Methods This study aimed to investigate the effects of age and gender on sleep EEG functional connectivity through statistical analysis of brain functional connectivity and machine learning validation. The two-overnight sleep EEG data of 78 subjects with mild difficulty falling asleep were categorized into five sleep stages using markers and segments from the "sleep-EDF" public database. First, the 78 subjects were finely grouped, and the mutual information of the six sleep EEG rhythms of δ, θ, α, β, spindle, and sawtooth wave was extracted as a functional connectivity measure. Then, one-way analysis of variance (ANOVA) was used to extract significant differences in functional connectivity of sleep rhythm waves across sleep stages with respect to age and gender. Finally, machine learning algorithms were used to investigate the effects of fine grouping of age and gender on sleep staging. Results and discussion The results showed that: (1) The functional connectivity of each sleep rhythm wave differed significantly across sleep stages, with delta and beta functional connectivity differing significantly across sleep stages. (2) Significant differences in functional connections among young and middle-aged groups, and among young and elderly groups, but no significant difference between middle-aged and elderly groups. (3) Female functional connectivity strength is generally higher than male at the high-frequency band of EEG, but no significant difference in the low-frequency. (4) Finer group divisions based on gender and age can indeed improve the accuracy of sleep staging, with an increase of about 3.58% by using the random forest algorithm. Our results further reveal the electrophysiological neural mechanisms of each sleep stage, and find that sleep functional connectivity differs significantly in both gender and age, providing valuable theoretical guidance for the establishment of automated sleep stage models.
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Affiliation(s)
- Xiaodong Luo
- Psychiatry Department, The Second Hospital of Jinhua, Jinhua, China
| | - Bin Zhou
- College of Mathematical Medicine, Zhejiang Normal University, Jinhua, China
| | - Jilong Shi
- College of Engineering, Zhejiang Normal University, Jinhua, China
| | - Gang Li
- College of Mathematical Medicine, Zhejiang Normal University, Jinhua, China
| | - Yixia Zhu
- Psychiatry Department, The Second Hospital of Jinhua, Jinhua, China
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Yu S, Shen Z, Xu H, Xia Z, Peng W, Hu Y, Feng F, Zeng F. Top-down and bottom-up alterations of connectivity patterns of the suprachiasmatic nucleus in chronic insomnia disorder. Eur Arch Psychiatry Clin Neurosci 2024; 274:245-254. [PMID: 36811711 DOI: 10.1007/s00406-022-01534-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Accepted: 12/16/2022] [Indexed: 02/24/2023]
Abstract
The importance of the suprachiasmatic nucleus (SCN, also called the master circadian clock) in regulating sleep and wakefulness has been confirmed by multiple animal research. However, human studies of SCN in vivo are still nascent. Recently, the development of resting-state functional magnetic resonance imaging (fMRI) has made it possible to study SCN-related connectivity changes in patients with chronic insomnia disorder (CID). Hence, this study aimed to explore whether sleep-wake circuitry (i.e., communication between the SCN and other brain regions) is disrupted in human insomnia. Forty-two patients with CID and 37 healthy controls (HCs) underwent fMRI scanning. Resting-state functional connectivity (rsFC) and Granger causality analysis (GCA) were performed to find abnormal functional and causal connectivity of the SCN in CID patients. In addition, correlation analyses were conducted to detect associations between features of disrupted connectivity and clinical symptoms. Compared to HCs, CID patients showed enhanced rsFC of the SCN-left dorsolateral prefrontal cortex (DLPFC), as well as reduced rsFC of the SCN-bilateral medial prefrontal cortex (MPFC); these altered cortical regions belong to the "top-down" circuit. Moreover, CID patients exhibited disrupted functional and causal connectivity between the SCN and the locus coeruleus (LC) and the raphe nucleus (RN); these altered subcortical regions constitute the "bottom-up" pathway. Importantly, the decreased causal connectivity from the LC-to-SCN was associated with the duration of disease in CID patients. These findings suggest that the disruption of the SCN-centered "top-down" cognitive process and "bottom-up" wake-promoting pathway may be intimately tied to the neuropathology of CID.
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Affiliation(s)
- Siyi Yu
- Department of Acupuncture and Tuina, Chengdu University of Traditional Chinese Medicine, Chengdu, China.
| | - Zhifu Shen
- Department of Traditional Chinese Medicine, the Affiliated Hospital of North Sichuan Medical College, Nanchong, China
- Department of Traditional Chinese and Western Medicine, North Sichuan Medical College, Nanchong, China
| | - Hao Xu
- Department of Acupuncture and Tuina, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Zihao Xia
- Department of Acupuncture and Tuina, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Wei Peng
- Department of Acupuncture and Tuina, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Youping Hu
- Department of Acupuncture and Tuina, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Fen Feng
- Affiliated Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Fang Zeng
- Department of Acupuncture and Tuina, Chengdu University of Traditional Chinese Medicine, Chengdu, China.
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Zhang H, Zhou QQ, Chen H, Hu XQ, Li WG, Bai Y, Han JX, Wang Y, Liang ZH, Chen D, Cong FY, Yan JQ, Li XL. The applied principles of EEG analysis methods in neuroscience and clinical neurology. Mil Med Res 2023; 10:67. [PMID: 38115158 PMCID: PMC10729551 DOI: 10.1186/s40779-023-00502-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Accepted: 11/23/2023] [Indexed: 12/21/2023] Open
Abstract
Electroencephalography (EEG) is a non-invasive measurement method for brain activity. Due to its safety, high resolution, and hypersensitivity to dynamic changes in brain neural signals, EEG has aroused much interest in scientific research and medical fields. This article reviews the types of EEG signals, multiple EEG signal analysis methods, and the application of relevant methods in the neuroscience field and for diagnosing neurological diseases. First, three types of EEG signals, including time-invariant EEG, accurate event-related EEG, and random event-related EEG, are introduced. Second, five main directions for the methods of EEG analysis, including power spectrum analysis, time-frequency analysis, connectivity analysis, source localization methods, and machine learning methods, are described in the main section, along with different sub-methods and effect evaluations for solving the same problem. Finally, the application scenarios of different EEG analysis methods are emphasized, and the advantages and disadvantages of similar methods are distinguished. This article is expected to assist researchers in selecting suitable EEG analysis methods based on their research objectives, provide references for subsequent research, and summarize current issues and prospects for the future.
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Affiliation(s)
- Hao Zhang
- School of Systems Science, Beijing Normal University, Beijing, 100875, China
| | - Qing-Qi Zhou
- College of Electrical and Control Engineering, North China University of Technology, Beijing, 100041, China
| | - He Chen
- School of Automation Science and Engineering, South China University of Technology, Guangzhou, 510641, China
| | - Xiao-Qing Hu
- Department of Psychology, the State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong SAR, 999077, China
- HKU-Shenzhen Institute of Research and Innovation, Shenzhen, 518057, Guangdong, China
| | - Wei-Guang Li
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, 999077, China
| | - Yang Bai
- Department of Rehabilitation Medicine, the First Affiliated Hospital of Nanchang University, Nanchang, 330006, China
- Rehabilitation Medicine Clinical Research Center of Jiangxi Province, Nanchang, 330006, China
| | - Jun-Xia Han
- Beijing Key Laboratory of Learning and Cognition, School of Psychology, Capital Normal University, Beijing, 100048, China
| | - Yao Wang
- School of Communication Science, Beijing Language and Culture University, Beijing, 100083, China
| | - Zhen-Hu Liang
- Institute of Electrical Engineering, Yanshan University, Qinhuangdao, 066004, Hebei, China.
| | - Dan Chen
- School of Computer Science, Wuhan University, Wuhan, 430072, China.
| | - Feng-Yu Cong
- School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, 116081, Liaoning, China.
| | - Jia-Qing Yan
- College of Electrical and Control Engineering, North China University of Technology, Beijing, 100041, China.
| | - Xiao-Li Li
- School of Automation Science and Engineering, South China University of Technology, Guangzhou, 510641, China.
- Guangdong Artificial Intelligence and Digital Economy Laboratory (Guangzhou), Guangzhou, 510335, China.
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Duce B, Kulkas A, Oksenberg A, Töyräs J, Hukins C. Power spectral analysis of the sleep electroencephalogram in positional obstructive sleep apnea. Sleep Med 2023; 104:83-89. [PMID: 36905777 DOI: 10.1016/j.sleep.2023.02.021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Revised: 02/14/2023] [Accepted: 02/24/2023] [Indexed: 02/27/2023]
Abstract
OBJECTIVE/BACKGROUND Previous studies have shown that obstructive sleep apnoea (OSA) is associated with reduced delta EEG and increased beta EEG power and increased EEG slowing ratio. There are however no studies that explore differences in sleep EEG between positional obstructive sleep apnoea (pOSA) and non-positional obstructive sleep apnoea (non-pOSA) patients. PATIENTS/METHODS 556 of 1036 consecutive patients (246 of 556 were female) undertaking polysomnography (PSG) for the suspicion of OSA met the inclusion criteria for this study. We calculated power spectra of each sleep epoch using Welch's method with ten, 4-s overlapping windows. Outcome measures such as Epworth Sleepiness Scale, SF-36 Quality of Life, Functional Outcomes of Sleep Questionnaire and Pyschomotor Vigilance Task were compared between the groups. RESULTS Patients with pOSA had greater delta EEG power in NREM and greater N3 proportions compared to their non-pOSA counterparts. There were no differences in theta (4-8Hz), alpha (8-12Hz), sigma (12-15Hz) or beta (15-25Hz) EEG power or EEG slowing ratio between the two groups. There were also no differences in the outcome measures between these two groups. The division of pOSA into spOSA and siOSA groups showed better sleep parameters in siOSA but with no difference in sleep power spectra. CONCLUSIONS This study partially supports our hypothesis in showing that pOSA, compared to non-pOSA, is associated with increased delta EEG power but did not show any variation to beta EEG power or EEG slowing ratio. This limited improvement in sleep quality did not translate to measurable changes to outcomes, suggesting beta EEG power or EEG slowing ratio may be key factors.
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Affiliation(s)
- Brett Duce
- Sleep Disorders Centre, Department of Respiratory & Sleep Medicine, Princess Alexandra Hospital, Ipswich Rd, Woolloongabba, Qld, Australia; Institute for Health and Biomedical Innovation, Queensland University of Technology, Qld, Australia.
| | - Antti Kulkas
- Department of Clinical Neurophysiology, Seinäjoki Central Hospital, Seinäjoki, Finland; Department of Technical Physics, University of Eastern Finland, Kuopio, Finland
| | - Arie Oksenberg
- Sleep Disorders Unit, Loewenstein Hospital Rehabilitation Center, POB 3, Raanana, Israel
| | - Juha Töyräs
- Department of Technical Physics, University of Eastern Finland, Kuopio, Finland; Science Service Center, Kuopio University Hospital, Kuopio, Finland; School of Information Technology and Electrical Engineering, The University of Queensland, Qld, Australia
| | - Craig Hukins
- Sleep Disorders Centre, Department of Respiratory & Sleep Medicine, Princess Alexandra Hospital, Ipswich Rd, Woolloongabba, Qld, Australia
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7
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Kandhare PG, Ambalavanan N, Travers CP, Carlo WA, Sirakov NM, Nakhmani A. Comparison metrics for multi-step prediction of rare events in vital sign signals. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104371] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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8
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Distinct functional brain abnormalities in insomnia disorder and obstructive sleep apnea. Eur Arch Psychiatry Clin Neurosci 2022; 273:493-509. [PMID: 36094570 DOI: 10.1007/s00406-022-01485-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/24/2022] [Accepted: 08/29/2022] [Indexed: 11/03/2022]
Abstract
Insomnia disorder (ID) and obstructive sleep apnea (OSA) are the two most prevalent sleep disorders worldwide, but the pathological mechanism has not been fully understood. Functional neuroimaging findings indicated regional abnormal neural activities existed in both diseases, but the results were inconsistent. This meta-analysis aimed to explore concordant regional functional brain changes in ID and OSA, respectively. We conducted a coordinate-based meta-analysis (CBMA) of resting-state functional magnetic resonance imaging (rs-fMRI) studies using the anisotropic effect-size seed-based d mapping (AES-SDM) approach. Studies that applied regional homogeneity (ReHo), amplitude of low-frequency fluctuations (ALFF) or fractional ALFF (fALFF) to analyze regional spontaneous brain activities in ID or OSA were included. Meta-regressions were then applied to investigate potential associations between demographic variables and regional neural activity alterations. Significantly increased brain activities in the left superior temporal gyrus (STG.L) and right superior longitudinal fasciculus (SLF.R), as well as decreased brain activities in several right cerebral hemisphere areas were identified in ID patients. As for OSA patients, more distinct and complicated functional activation alterations were identified. Several neuroimaging alterations were functionally correlated with mean age, duration or illness severity in two patients groups revealed by meta-regressions. These functionally altered areas could be served as potential targets for non-invasive brain stimulation methods. This present meta-analysis distinguished distinct brain function changes in ID and OSA, improving our knowledge of the neuropathological mechanism of these two most common sleep disturbances, and also provided potential orientations for future clinical applications.Registration number: CRD42022301938.
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Aydın S. Cross-validated Adaboost Classification of Emotion Regulation Strategies Identified by Spectral Coherence in Resting-State. Neuroinformatics 2022; 20:627-639. [PMID: 34536200 DOI: 10.1007/s12021-021-09542-7] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/18/2021] [Indexed: 12/31/2022]
Abstract
In the present study, quantitative relations between Cognitive Emotion Regulation strategies (CERs) and EEG synchronization levels have been investigated for the first time. For this purpose, spectral coherence (COH), phase locking value and mutual information have been applied to short segments of 62-channel resting state eyes-opened EEG data collected from healthy adults who use contrasting emotion regulation strategies (frequently and rarely use of rumination&distraction, frequently and rarely use of suppression&reappraisal). In tests, the individuals are grouped depending on their self-responses to both emotion regulation questionnaire (ERQ) and cognitive ERQ. Experimental data are downloaded from publicly available data-base, LEMON. Regarding EEG electrode pairs that placed on right and left cortical regions, inter-hemispheric dependency measures are computed for non-overlapped short segments of 2 sec at 2 min duration trials. In addition to full-band EEG analysis, dependency metrics are also obtained for both alpha and beta sub-bands. The contrasting groups are discriminated from each other with respect to the corresponding features using cross-validated adaboost classifiers. High classification accuracies (CA) of 99.44% and 98.33% have been obtained through instant classification driven by full-band COH estimations. Considering regional features that provide the high CA, CERs are found to be highly relevant with associative memory functions and cognition. The new findings may indicate the close relation between neuroplasticity and cognitive skills.
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Affiliation(s)
- Serap Aydın
- Biophysics Department, Medical Faculty, Hacettepe University, Ankara, Turkey.
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Cao J, Zhao Y, Shan X, Wei H, Guo Y, Chen L, Erkoyuncu JA, Sarrigiannis PG. Brain functional and effective connectivity based on electroencephalography recordings: A review. Hum Brain Mapp 2022; 43:860-879. [PMID: 34668603 PMCID: PMC8720201 DOI: 10.1002/hbm.25683] [Citation(s) in RCA: 57] [Impact Index Per Article: 28.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Revised: 09/10/2021] [Accepted: 09/27/2021] [Indexed: 12/02/2022] Open
Abstract
Functional connectivity and effective connectivity of the human brain, representing statistical dependence and directed information flow between cortical regions, significantly contribute to the study of the intrinsic brain network and its functional mechanism. Many recent studies on electroencephalography (EEG) have been focusing on modeling and estimating brain connectivity due to increasing evidence that it can help better understand various brain neurological conditions. However, there is a lack of a comprehensive updated review on studies of EEG-based brain connectivity, particularly on visualization options and associated machine learning applications, aiming to translate those techniques into useful clinical tools. This article reviews EEG-based functional and effective connectivity studies undertaken over the last few years, in terms of estimation, visualization, and applications associated with machine learning classifiers. Methods are explored and discussed from various dimensions, such as either linear or nonlinear, parametric or nonparametric, time-based, and frequency-based or time-frequency-based. Then it is followed by a novel review of brain connectivity visualization methods, grouped by Heat Map, data statistics, and Head Map, aiming to explore the variation of connectivity across different brain regions. Finally, the current challenges of related research and a roadmap for future related research are presented.
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Affiliation(s)
- Jun Cao
- School of Aerospace, Transport and ManufacturingCranfield UniversityCranfield
| | - Yifan Zhao
- School of Aerospace, Transport and ManufacturingCranfield UniversityCranfield
| | - Xiaocai Shan
- School of Aerospace, Transport and ManufacturingCranfield UniversityCranfield
- Institute of Geology and Geophysics, Chinese Academy of SciencesBeijingChina
| | - Hua‐liang Wei
- Department of Automatic Control and Systems EngineeringUniversity of SheffieldSheffieldUK
| | - Yuzhu Guo
- School of Automation Science and Electrical EngineeringBeihang UniversityBeijingChina
| | - Liangyu Chen
- Department of NeurosurgeryShengjing Hospital of China Medical UniversityShenyangChina
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Huang H, Zhang J, Zhu L, Tang J, Lin G, Kong W, Lei X, Zhu L. EEG-Based Sleep Staging Analysis with Functional Connectivity. SENSORS (BASEL, SWITZERLAND) 2021; 21:1988. [PMID: 33799850 PMCID: PMC7999974 DOI: 10.3390/s21061988] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/29/2020] [Revised: 02/26/2021] [Accepted: 03/08/2021] [Indexed: 12/20/2022]
Abstract
Sleep staging is important in sleep research since it is the basis for sleep evaluation and disease diagnosis. Related works have acquired many desirable outcomes. However, most of current studies focus on time-domain or frequency-domain measures as classification features using single or very few channels, which only obtain the local features but ignore the global information exchanging between different brain regions. Meanwhile, brain functional connectivity is considered to be closely related to brain activity and can be used to study the interaction relationship between brain areas. To explore the electroencephalography (EEG)-based brain mechanisms of sleep stages through functional connectivity, especially from different frequency bands, we applied phase-locked value (PLV) to build the functional connectivity network and analyze the brain interaction during sleep stages for different frequency bands. Then, we performed the feature-level, decision-level and hybrid fusion methods to discuss the performance of different frequency bands for sleep stages. The results show that (1) PLV increases in the lower frequency band (delta and alpha bands) and vice versa during different stages of non-rapid eye movement (NREM); (2) alpha band shows a better discriminative ability for sleeping stages; (3) the classification accuracy of feature-level fusion (six frequency bands) reaches 96.91% and 96.14% for intra-subject and inter-subjects respectively, which outperforms decision-level and hybrid fusion methods.
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Affiliation(s)
- Hui Huang
- School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, China; (H.H.); (J.Z.); (J.T.); (G.L.)
- Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province, Hangzhou Dianzi University, Hangzhou 310018, China;
| | - Jianhai Zhang
- School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, China; (H.H.); (J.Z.); (J.T.); (G.L.)
- Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province, Hangzhou Dianzi University, Hangzhou 310018, China;
| | - Li Zhu
- School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, China; (H.H.); (J.Z.); (J.T.); (G.L.)
- Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province, Hangzhou Dianzi University, Hangzhou 310018, China;
| | - Jiajia Tang
- School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, China; (H.H.); (J.Z.); (J.T.); (G.L.)
- Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province, Hangzhou Dianzi University, Hangzhou 310018, China;
| | - Guang Lin
- School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, China; (H.H.); (J.Z.); (J.T.); (G.L.)
- Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province, Hangzhou Dianzi University, Hangzhou 310018, China;
| | - Wanzeng Kong
- Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province, Hangzhou Dianzi University, Hangzhou 310018, China;
| | - Xu Lei
- Sleep and NeuroImaging Center, Faculty of Psychology, Southwest University, Chongqing 400715, China;
- Key Laboratory of Cognition and Personality, Ministry of Education, Chongqing 400715, China
| | - Lei Zhu
- School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China;
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12
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Quantitative sleep EEG synchronization analysis for automatic arousals detection. Biomed Signal Process Control 2020. [DOI: 10.1016/j.bspc.2020.101895] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
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Diykh M, Li Y, Abdulla S. EEG sleep stages identification based on weighted undirected complex networks. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2020; 184:105116. [PMID: 31629158 DOI: 10.1016/j.cmpb.2019.105116] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/10/2018] [Revised: 09/14/2019] [Accepted: 10/02/2019] [Indexed: 06/10/2023]
Abstract
BACKGROUND AND OBJECTIVE Sleep scoring is important in sleep research because any errors in the scoring of the patient's sleep electroencephalography (EEG) recordings can cause serious problems such as incorrect diagnosis, medication errors, and misinterpretations of patient's EEG recordings. The aim of this research is to develop a new automatic method for EEG sleep stages classification based on a statistical model and weighted brain networks. METHODS Each EEG segment is partitioned into a number of blocks using a sliding window technique. A set of statistical features are extracted from each block. As a result, a vector of features is obtained to represent each EEG segment. Then, the vector of features is mapped into a weighted undirected network. Different structural and spectral attributes of the networks are extracted and forwarded to a least square support vector machine (LS-SVM) classifier. At the same time the network's attributes are also thoroughly investigated. It is found that the network's characteristics vary with their sleep stages. Each sleep stage is best represented using the key features of their networks. RESULTS In this paper, the proposed method is evaluated using two datasets acquired from different channels of EEG (Pz-Oz and C3-A2) according to the R&K and the AASM without pre-processing the original EEG data. The obtained results by the LS-SVM are compared with those by Naïve, k-nearest and a multi-class-SVM. The proposed method is also compared with other benchmark sleep stages classification methods. The comparison results demonstrate that the proposed method has an advantage in scoring sleep stages based on single channel EEG signals. CONCLUSIONS An average accuracy of 96.74% is obtained with the C3-A2 channel according to the AASM standard, and 96% with the Pz-Oz channel based on the R&K standard.
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Affiliation(s)
- Mohammed Diykh
- School of Agricultural, Computational and Environmental Sciences, University of Southern Queensland, Australia; College of Education for Pure Science, University of Thi-Qar, Iraq.
| | - Yan Li
- School of Agricultural, Computational and Environmental Sciences, University of Southern Queensland, Australia.
| | - Shahab Abdulla
- Open Access College, University of Southern Queensland, Australia.
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Zhang P, Wang X, Chen J, You W. Feature Weight Driven Interactive Mutual Information Modeling for Heterogeneous Bio-Signal Fusion to Estimate Mental Workload. SENSORS 2017; 17:s17102315. [PMID: 29023364 PMCID: PMC5677372 DOI: 10.3390/s17102315] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/01/2017] [Revised: 10/02/2017] [Accepted: 10/03/2017] [Indexed: 12/11/2022]
Abstract
Many people suffer from high mental workload which may threaten human health and cause serious accidents. Mental workload estimation is especially important for particular people such as pilots, soldiers, crew and surgeons to guarantee the safety and security. Different physiological signals have been used to estimate mental workload based on the n-back task which is capable of inducing different mental workload levels. This paper explores a feature weight driven signal fusion method and proposes interactive mutual information modeling (IMIM) to increase the mental workload classification accuracy. We used EEG and ECG signals to validate the effectiveness of the proposed method for heterogeneous bio-signal fusion. The experiment of mental workload estimation consisted of signal recording, artifact removal, feature extraction, feature weight calculation, and classification. Ten subjects were invited to take part in easy, medium and hard tasks for the collection of EEG and ECG signals in different mental workload levels. Therefore, heterogeneous physiological signals of different mental workload states were available for classification. Experiments reveal that ECG can be utilized as a supplement of EEG to optimize the fusion model and improve mental workload estimation. Classification results show that the proposed bio-signal fusion method IMIM can increase the classification accuracy in both feature level and classifier level fusion. This study indicates that multi-modal signal fusion is promising to identify the mental workload levels and the fusion strategy has potential application of mental workload estimation in cognitive activities during daily life.
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Affiliation(s)
- Pengbo Zhang
- State Key Laboratory of Precision Measurement Technology and Instruments, Department of Precision Instrument, Tsinghua University, Beijing 100084, China.
| | - Xue Wang
- State Key Laboratory of Precision Measurement Technology and Instruments, Department of Precision Instrument, Tsinghua University, Beijing 100084, China.
| | - Junfeng Chen
- State Key Laboratory of Precision Measurement Technology and Instruments, Department of Precision Instrument, Tsinghua University, Beijing 100084, China.
| | - Wei You
- State Key Laboratory of Precision Measurement Technology and Instruments, Department of Precision Instrument, Tsinghua University, Beijing 100084, China.
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Detection of Shockable Ventricular Arrhythmia using Variational Mode Decomposition. J Med Syst 2016; 40:79. [PMID: 26798076 DOI: 10.1007/s10916-016-0441-5] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2015] [Accepted: 01/11/2016] [Indexed: 10/22/2022]
Abstract
Ventricular tachycardia (VT) and ventricular fibrillation (VF) are shockable ventricular cardiac ailments. Detection of VT/VF is one of the important step in both automated external defibrillator (AED) and implantable cardioverter defibrillator (ICD) therapy. In this paper, we propose a new method for detection and classification of shockable ventricular arrhythmia (VT/VF) and non-shockable ventricular arrhythmia (normal sinus rhythm, ventricular bigeminy, ventricular ectopic beats, and ventricular escape rhythm) episodes from Electrocardiogram (ECG) signal. The variational mode decomposition (VMD) is used to decompose the ECG signal into number of modes or sub-signals. The energy, the renyi entropy and the permutation entropy of first three modes are evaluated and these values are used as diagnostic features. The mutual information based feature scoring is employed to select optimal set of diagnostic features. The performance of the diagnostic features is evaluated using random forest (RF) classifier. Experimental results reveal that, the feature subset derived from mutual information based scoring and the RF classifier produces accuracy, sensitivity and specificity values of 97.23 %, 96.54 %, and 97.97 %, respectively. The proposed method is compared with some of the existing techniques for detection of shockable ventricular arrhythmia episodes from ECG.
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