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Su CH, Ko LW, Jung TP, Onton J, Tzou SC, Juang JC, Hsu CY. Extracting Stress-Related EEG Patterns From Pre-Sleep EEG for Forecasting Slow-Wave Sleep Deficiency. IEEE Trans Neural Syst Rehabil Eng 2024; 32:1817-1827. [PMID: 38683718 DOI: 10.1109/tnsre.2024.3394471] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/02/2024]
Abstract
Sleep is vital to our daily activity. Lack of proper sleep can impair functionality and overall health. While stress is known for its detrimental impact on sleep quality, the precise effect of pre-sleep stress on subsequent sleep structure remains unknown. This study introduced a novel approach to study the pre-sleep stress effect on sleep structure, specifically slow-wave sleep (SWS) deficiency. To achieve this, we selected forehead resting EEG immediately before and upon sleep onset to extract stress-related neurological markers through power spectra and entropy analysis. These markers include beta/delta correlation, alpha asymmetry, fuzzy entropy (FuzzEn) and spectral entropy (SpEn). Fifteen subjects were included in this study. Our results showed that subjects lacking SWS often exhibited signs of stress in EEG, such as an increased beta/delta correlation, higher alpha asymmetry, and increased FuzzEn in frontal EEG. Conversely, individuals with ample SWS displayed a weak beta/delta correlation and reduced FuzzEn. Finally, we employed several supervised learning models and found that the selected neurological markers can predict subsequent SWS deficiency. Our investigation demonstrated that the classifiers could effectively predict varying levels of slow-wave sleep (SWS) from pre-sleep EEG segments, achieving a mean balanced accuracy surpassing 0.75. The SMOTE-Tomek resampling method could improve the performance to 0.77. This study suggests that stress-related neurological markers derived from pre-sleep EEG can effectively predict SWS deficiency. Such information can be integrated with existing sleep-improving techniques to provide a personalized sleep forecasting and improvement solution.
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Zavala-Yoé R, Iqbal HMN, Parra-Saldívar R, Ramírez-Mendoza RA. Stress Response Analysis via Dynamic Entropy in EEG: Caregivers in View. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:ijerph20105913. [PMID: 37239638 DOI: 10.3390/ijerph20105913] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Revised: 02/21/2023] [Accepted: 03/16/2023] [Indexed: 05/28/2023]
Abstract
According to the World Health Organization (WHO), stress can be defined as any type of alteration that causes physical, emotional, or psychological tension. A very important concept that is sometimes confused with stress is anxiety. The difference between stress and anxiety is that stress usually has an existing cause. Once that activator has passed, stress typically eases. In this respect, according to the American Psychiatric Association, anxiety is a normal response to stress and can even be advantageous in some circumstances. By contrast, anxiety disorders differ from temporary feelings of anxiousness or nervousness with more intense feelings of fear or anxiety. The Diagnostic and Statistical Manual (DSM-5) explicitly describes anxiety as exorbitant concern and fearful expectations, occurring on most days for at least 6 months, about a series of events. Stress can be measured by some standardized questionnaires; however, these resources are characterized by some major disadvantages, the main one being the time consumed to interpret them; i.e., qualitative information must be transformed to quantitative data. Conversely, a physiological recourse has the advantage that it provides quantitative spatiotemporal information directly from brain areas and it processes data faster than qualitative supplies. A typical option for this is an electroencephalographic record (EEG). We propose, as a novelty, the application of time series (TS) entropies developed by us to inspect collections of EEGs obtained during stress situations. We investigated this database related to 23 persons, with 1920 samples (15 s) captured in 14 channels for 12 stressful events. Our parameters reflected that out of 12 events, event 2 (Family/financial instability/maltreatment) and 10 (Fear of disease and missing an important event) created more tension than the others. In addition, the most active lobes reflected by the EEG channels were frontal and temporal. The former is in charge of performing higher functions, self-control, self monitoring, and the latter is in charge of auditory processing, but also emotional handling. Thus, events E2 and E10 triggering frontal and temporal channels revealed the actual state of participants under stressful situations. The coefficient of variation revealed that E7 (Fear of getting cheated/losing someone) and E11 (Fear of suffering a serious illness) were the events with more changes among participants. In the same sense, AF4, FC5, and F7 (mainly frontal lobe channels) were the most irregular on average for all participants. In summary, by means of dynamic entropy analysis, the goal is to process the EEG dataset in order to elucidate which event and brain regions are key for all participants. The latter will allow us to easily determine which was the most stressful and on which brain zone. This study can be applied to other caregivers datasets. All this is a novelty.
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Affiliation(s)
- Ricardo Zavala-Yoé
- Tecnológico de Monterrey, Calzada del Puente, 222. Col. Ejidos de Huipulco, Mexico City 14380, Mexico
| | - Hafiz M N Iqbal
- Tecnológico de Monterrey, Eugenio Garza Sada 2501, Monterrey 64849, Mexico
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Lau ZJ, Pham T, Chen SHA, Makowski D. Brain entropy, fractal dimensions and predictability: A review of complexity measures for EEG in healthy and neuropsychiatric populations. Eur J Neurosci 2022; 56:5047-5069. [PMID: 35985344 PMCID: PMC9826422 DOI: 10.1111/ejn.15800] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Revised: 07/20/2022] [Accepted: 08/10/2022] [Indexed: 01/11/2023]
Abstract
There has been an increasing trend towards the use of complexity analysis in quantifying neural activity measured by electroencephalography (EEG) signals. On top of revealing complex neuronal processes of the brain that may not be possible with linear approaches, EEG complexity measures have also demonstrated their potential as biomarkers of psychopathology such as depression and schizophrenia. Unfortunately, the opacity of algorithms and descriptions originating from mathematical concepts have made it difficult to understand what complexity is and how to draw consistent conclusions when applied within psychology and neuropsychiatry research. In this review, we provide an overview and entry-level explanation of existing EEG complexity measures, which can be broadly categorized as measures of predictability and regularity. We then synthesize complexity findings across different areas of psychological science, namely, in consciousness research, mood and anxiety disorders, schizophrenia, neurodevelopmental and neurodegenerative disorders, as well as changes across the lifespan, while addressing some theoretical and methodological issues underlying the discrepancies in the data. Finally, we present important considerations when choosing and interpreting these metrics.
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Affiliation(s)
- Zen J. Lau
- School of Social SciencesNanyang Technological UniversitySingapore
| | - Tam Pham
- School of Social SciencesNanyang Technological UniversitySingapore
| | - S. H. Annabel Chen
- School of Social SciencesNanyang Technological UniversitySingapore,Centre for Research and Development in LearningNanyang Technological UniversitySingapore,Lee Kong Chian School of MedicineNanyang Technological UniversitySingapore,National Institute of EducationNanyang Technological UniversitySingapore
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Garcia-Martinez B, Fernandez-Caballero A, Alcaraz R, Martinez-Rodrigo A. Application of Dispersion Entropy for the Detection of Emotions With Electroencephalographic Signals. IEEE Trans Cogn Dev Syst 2022. [DOI: 10.1109/tcds.2021.3099344] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Beatriz Garcia-Martinez
- Departamento de Sistemas Informáticos, Escuela Técnica Superior de Ingenieros Industriales, Instituto de Investigación en Informática de Albacete, Universidad de Castilla-La Mancha, Albacete, Spain
| | - Antonio Fernandez-Caballero
- Departamento de Sistemas Informáticos, Escuela Técnica Superior de Ingenieros Industriales, Instituto de Investigación en Informática de Albacete, Universidad de Castilla-La Mancha, Albacete, Spain
| | - Raul Alcaraz
- Research Group in Electronic, Biomedical and Telecommunication Engineering, Escuela Politécnica de Cuenca, Universidad de Castilla-La Mancha, Cuenca, Spain
| | - Arturo Martinez-Rodrigo
- Research Group in Electronic, Biomedical and Telecommunication Engineering, Facultad de Comunicación, Instituto de Tecnologías Audiovisuales de Castilla-La Mancha, Universidad de Castilla-La Mancha, Cuenca, Spain
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Sanchez-Reolid R, Martinez-Saez MC, Garcia-Martinez B, Fernandez-Aguilar L, Segura LR, Latorre JM, Fernandez-Caballero A. Emotion Classification from EEG with a Low-Cost BCI Versus a High-End Equipment. Int J Neural Syst 2022; 32:2250041. [DOI: 10.1142/s0129065722500411] [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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García-Martínez B, Fernández-Caballero A, Martínez-Rodrigo A. Entropy and the Emotional Brain: Overview of a Research Field. ARTIF INTELL 2022. [DOI: 10.5772/intechopen.98342] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
During the last years, there has been a notable increase in the number of studies focused on the assessment of brain dynamics for the recognition of emotional states by means of nonlinear methodologies. More precisely, different entropy metrics have been applied for the analysis of electroencephalographic recordings for the detection of emotions. In this sense, regularity-based entropy metrics, symbolic predictability-based entropy indices, and different multiscale and multilag variants of the aforementioned methods have been successfully tested in a series of studies for emotion recognition from the EEG recording. This chapter aims to unify all those contributions to this scientific area, summarizing the main discoverings recently achieved in this research field.
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Mayor D, Panday D, Kandel HK, Steffert T, Banks D. CEPS: An Open Access MATLAB Graphical User Interface (GUI) for the Analysis of Complexity and Entropy in Physiological Signals. ENTROPY 2021; 23:e23030321. [PMID: 33800469 PMCID: PMC7998823 DOI: 10.3390/e23030321] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/31/2020] [Revised: 02/28/2021] [Accepted: 03/03/2021] [Indexed: 12/14/2022]
Abstract
BACKGROUND We developed CEPS as an open access MATLAB® GUI (graphical user interface) for the analysis of Complexity and Entropy in Physiological Signals (CEPS), and demonstrate its use with an example data set that shows the effects of paced breathing (PB) on variability of heart, pulse and respiration rates. CEPS is also sufficiently adaptable to be used for other time series physiological data such as EEG (electroencephalography), postural sway or temperature measurements. METHODS Data were collected from a convenience sample of nine healthy adults in a pilot for a larger study investigating the effects on vagal tone of breathing paced at various different rates, part of a development programme for a home training stress reduction system. RESULTS The current version of CEPS focuses on those complexity and entropy measures that appear most frequently in the literature, together with some recently introduced entropy measures which may have advantages over those that are more established. Ten methods of estimating data complexity are currently included, and some 28 entropy measures. The GUI also includes a section for data pre-processing and standard ancillary methods to enable parameter estimation of embedding dimension m and time delay τ ('tau') where required. The software is freely available under version 3 of the GNU Lesser General Public License (LGPLv3) for non-commercial users. CEPS can be downloaded from Bitbucket. In our illustration on PB, most complexity and entropy measures decreased significantly in response to breathing at 7 breaths per minute, differentiating more clearly than conventional linear, time- and frequency-domain measures between breathing states. In contrast, Higuchi fractal dimension increased during paced breathing. CONCLUSIONS We have developed CEPS software as a physiological data visualiser able to integrate state of the art techniques. The interface is designed for clinical research and has a structure designed for integrating new tools. The aim is to strengthen collaboration between clinicians and the biomedical community, as demonstrated here by using CEPS to analyse various physiological responses to paced breathing.
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Affiliation(s)
- David Mayor
- School of Health and Social Work, University of Hertfordshire, Hatfield AL10 9AB, UK
- Correspondence:
| | - Deepak Panday
- School of Engineering and Computer Science, University of Hertfordshire, Hatfield AL10 9AB, UK;
| | - Hari Kala Kandel
- Department of Computing, Goldsmiths College, University of London, New Cross, London SE14 6NW, UK;
| | - Tony Steffert
- MindSpire, Napier House, 14-16 Mount Ephraim Rd, Tunbridge Wells TN1 1EE, UK;
- School of Life, Health and Chemical Sciences, Walton Hall, The Open University, Milton Keynes MK7 6AA, UK;
| | - Duncan Banks
- School of Life, Health and Chemical Sciences, Walton Hall, The Open University, Milton Keynes MK7 6AA, UK;
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Keshmiri S. Conditional Entropy: A Potential Digital Marker for Stress. ENTROPY (BASEL, SWITZERLAND) 2021; 23:286. [PMID: 33652891 PMCID: PMC7996836 DOI: 10.3390/e23030286] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Revised: 02/20/2021] [Accepted: 02/23/2021] [Indexed: 12/12/2022]
Abstract
Recent decades have witnessed a substantial progress in the utilization of brain activity for the identification of stress digital markers. In particular, the success of entropic measures for this purpose is very appealing, considering (1) their suitability for capturing both linear and non-linear characteristics of brain activity recordings and (2) their direct association with the brain signal variability. These findings rely on external stimuli to induce the brain stress response. On the other hand, research suggests that the use of different types of experimentally induced psychological and physical stressors could potentially yield differential impacts on the brain response to stress and therefore should be dissociated from more general patterns. The present study takes a step toward addressing this issue by introducing conditional entropy (CE) as a potential electroencephalography (EEG)-based resting-state digital marker of stress. For this purpose, we use the resting-state multi-channel EEG recordings of 20 individuals whose responses to stress-related questionnaires show significantly higher and lower level of stress. Through the application of representational similarity analysis (RSA) and K-nearest-neighbor (KNN) classification, we verify the potential that the use of CE can offer to the solution concept of finding an effective digital marker for stress.
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Affiliation(s)
- Soheil Keshmiri
- Advanced Telecommunications Research Institute International (ATR), Kyoto 619-0237, Japan
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Cross-sample entropy for the study of coordinated brain activity in calm and distress conditions with electroencephalographic recordings. Neural Comput Appl 2021. [DOI: 10.1007/s00521-021-05694-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
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García-Martínez B, Fernández-Caballero A, Zunino L, Martínez-Rodrigo A. Recognition of Emotional States from EEG Signals with Nonlinear Regularity- and Predictability-Based Entropy Metrics. Cognit Comput 2020. [DOI: 10.1007/s12559-020-09789-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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Gao Y, Wang X, Potter T, Zhang J, Zhang Y. Single-trial EEG emotion recognition using Granger Causality/Transfer Entropy analysis. J Neurosci Methods 2020; 346:108904. [PMID: 32898573 DOI: 10.1016/j.jneumeth.2020.108904] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2020] [Revised: 08/04/2020] [Accepted: 08/05/2020] [Indexed: 02/03/2023]
Abstract
BACKGROUND Emotion recognition has been studied for decades, but the classification accuracy needs to be improved. NEW METHOD In this study, a novel emotional classification approach is proposed by combining the Histogram of Oriented Gradient (HOG) method with the Granger Causality (GC) or Transfer Entropy (TE) methods. HOG extracts local valid information from the GC/TE relationship matrices and then the Support Vector Machine (SVM) is employed to classify the emotional states of stress and calm. RESULTS Compared with previous studies, the classification accuracy has been greatly improved. The results of this study show that the classification based on GC or TE with HOG offers an average accuracy 88.93 % and 95.21 %, respectively. The achieved accuracy is about 12 % higher than that achieved without using HOG feature extraction. COMPARISON WITH EXISTING METHOD(S) Numerous efforts have been devoted to classify emotional states by extracting EEG characteristics on a single channel basis, the method developed in this study utilizes information interaction between brain channels as a feature to classify emotional states. Furthermore, this study combines HOG and relation matrices for the first time and uses image processing to extract EEG features. CONCLUSION Our results demonstrate the feasibility of combining TE with HOG for emotion recognition with improved classification accuracy by taking advantage of both network and gradient features. More specific features can be selected to improve classification accuracy by taking advantage of information exchanges between EEG channels directly or the extracted property of the relationship matrix based on information interactions.
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Affiliation(s)
- Yunyuan Gao
- Intelligent Control and Robotics Institute, College of Automation, Hangzhou Dianzi University, Hangzhou, China; Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province, Hangzhou, China.
| | - Xiangkun Wang
- Intelligent Control and Robotics Institute, College of Automation, Hangzhou Dianzi University, Hangzhou, China
| | - Thomas Potter
- Department of Biomedical Engineering, University of Houston, Houston, USA
| | - Jianhai Zhang
- Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province, Hangzhou, China
| | - Yingchun Zhang
- Department of Biomedical Engineering, University of Houston, Houston, USA.
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Sipahi R, Porfiri M. Improving on transfer entropy-based network reconstruction using time-delays: Approach and validation. CHAOS (WOODBURY, N.Y.) 2020; 30:023125. [PMID: 32113235 DOI: 10.1063/1.5115510] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/18/2019] [Accepted: 01/23/2020] [Indexed: 06/10/2023]
Abstract
Transfer entropy constitutes a viable model-free tool to infer causal relationships between two dynamical systems from their time-series. In an information-theoretic sense, transfer entropy associates a cause-and-effect relationship with directed information transfer, such that one may improve the prediction of the future of a dynamical system from the history of another system. Recent studies have proposed the use of transfer entropy to reconstruct networks, but the inherent dyadic nature of this metric challenges the development of a robust approach that can discriminate direct from indirect interactions between nodes. In this paper, we seek to fill this methodological gap through the cogent integration of time-delays in the transfer entropy computation. By recognizing that information transfer in the network is bound by a finite speed, we relate the value of the time-delayed transfer entropy between two nodes to the number of walks between them. Upon this premise, we lay out the foundation of an alternative framework for network reconstruction, which we illustrate through closed-form results on three-node networks and numerically validate on larger networks, using examples of Boolean models and chaotic maps.
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Affiliation(s)
- Rifat Sipahi
- Department of Mechanical and Industrial Engineering, Northeastern University, Boston, Massachusetts 02115, USA
| | - Maurizio Porfiri
- Department of Mechanical and Aerospace Engineering and Department of Biomedical Engineering, New York University Tandon School of Engineering, 6 MetroTech Center, Brooklyn, New York 11201, USA
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Al-Qazzaz NK, Sabir MK, Ali SHBM, Ahmad SA, Grammer K. Electroencephalogram Profiles for Emotion Identification over the Brain Regions Using Spectral, Entropy and Temporal Biomarkers. SENSORS (BASEL, SWITZERLAND) 2019; 20:E59. [PMID: 31861913 PMCID: PMC6982965 DOI: 10.3390/s20010059] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/24/2019] [Revised: 11/28/2019] [Accepted: 12/03/2019] [Indexed: 12/17/2022]
Abstract
Identifying emotions has become essential for comprehending varied human behavior during our daily lives. The electroencephalogram (EEG) has been adopted for eliciting information in terms of waveform distribution over the scalp. The rationale behind this work is twofold. First, it aims to propose spectral, entropy and temporal biomarkers for emotion identification. Second, it aims to integrate the spectral, entropy and temporal biomarkers as a means of developing spectro-spatial ( S S ) , entropy-spatial ( E S ) and temporo-spatial ( T S ) emotional profiles over the brain regions. The EEGs of 40 healthy volunteer students from the University of Vienna were recorded while they viewed seven brief emotional video clips. Features using spectral analysis, entropy method and temporal feature were computed. Three stages of two-way analysis of variance (ANOVA) were undertaken so as to identify the emotional biomarkers and Pearson's correlations were employed to determine the optimal explanatory profiles for emotional detection. The results evidence that the combination of applied spectral, entropy and temporal sets of features may provide and convey reliable biomarkers for identifying S S , E S and T S profiles relating to different emotional states over the brain areas. EEG biomarkers and profiles enable more comprehensive insights into various human behavior effects as an intervention on the brain.
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Affiliation(s)
- Noor Kamal Al-Qazzaz
- Department of Biomedical Engineering, Al-Khwarizmi College of Engineering, University of Baghdad, Baghdad 47146, Iraq;
- Department of Electrical, Electronic & Systems Engineering, Faculty of Engineering & Built Environment, Universiti Kebangsaan Malaysia, UKM Bangi, Selangor 43600, Malaysia;
| | - Mohannad K. Sabir
- Department of Biomedical Engineering, Al-Khwarizmi College of Engineering, University of Baghdad, Baghdad 47146, Iraq;
| | - Sawal Hamid Bin Mohd Ali
- Department of Electrical, Electronic & Systems Engineering, Faculty of Engineering & Built Environment, Universiti Kebangsaan Malaysia, UKM Bangi, Selangor 43600, Malaysia;
| | - Siti Anom Ahmad
- Department of Electrical and Electronic Engineering, Faculty of Engineering, Universiti Putra Malaysia, UPM Serdang, Selangor 43400, Malaysia;
- Malaysian Research Institute of Ageing (MyAgeing), Universiti Putra Malaysia, Serdang, Selangor 43400, Malaysia
| | - Karl Grammer
- Department of Evolutionary Anthropology, University of Vienna, Althan strasse 14, A-1090 Vienna, Austria;
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Çotuk HB, Duru AD, Aktaş Ş. Monitoring Autonomic and Central Nervous System Activity by Permutation Entropy during Short Sojourn in Antarctica. ENTROPY 2019. [PMCID: PMC7515415 DOI: 10.3390/e21090893] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
The aim of this study was to monitor acute response patterns of autonomic and central nervous system activity during an encounter with Antarctica by synchronously recording heart rate variability (HRV) and electroencephalography (EEG). On three different time-points during the two-week sea journey, the EEG and HRV were recorded from nine male scientists who participated in “The First Turkish Antarctic Research Expedition”. The recordings were performed in a relaxed state with the eyes open, eyes closed, and during a space quantity perception test. For the EEG recordings, the wireless 14 channel EPOC-Emotiv device was used, and for the HRV recordings, a Polar heart rate monitor S810i was used. The HRV data were analyzed by time/frequency domain parameters and ordinal pattern statistics. For the EEG data, spectral band power in the conventional frequency bands, as well as permutation entropy values were calculated. Regarding HRV, neither conventional nor permutation entropy calculations produced significant differences for the different journey time-points, but only permutation entropy was able to differentiate between the testing conditions. During the cognitive test, permutation entropy values increased significantly, whereas the conventional HRV parameters did not show any significant differences. In the EEG analysis, the ordinal pattern statistics revealed significant transitions in the course of the sea voyage as permutation entropy values decreased, whereas spectral band power analysis could not detect any significant difference. Permutation entropy analysis was further able to differentiate between the three testing conditions as well between the brain regions. In the conventional spectral band power analysis, alpha band power could separate the three testing conditions and brain regions, and beta band power could only do so for the brain regions. This superiority of permutation entropy in discerning subtle differences in the autonomic and central nervous system’s responses to an overwhelming subjective experience renders it suitable as an analysis tool for biomonitoring in extreme environments.
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Affiliation(s)
- H. Birol Çotuk
- Department of Sport Health Sciences, Marmara University, 34810 İstanbul, Turkey;
- Correspondence:
| | - Adil Deniz Duru
- Department of Sport Health Sciences, Marmara University, 34810 İstanbul, Turkey;
| | - Şamil Aktaş
- Department of Underwater and Hyperbaric Medicine, İstanbul University, 34093 İstanbul, Turkey;
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