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Yu X, Lu J, Liu W, Cheng Z, Xiao G. Exploring physiological stress response evoked by passive translational acceleration in healthy adults: a pilot study utilizing electrodermal activity and heart rate variability measurements. Sci Rep 2024; 14:11349. [PMID: 38762532 PMCID: PMC11102551 DOI: 10.1038/s41598-024-61656-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2024] [Accepted: 05/08/2024] [Indexed: 05/20/2024] Open
Abstract
Passive translational acceleration (PTA) has been demonstrated to induce the stress response and regulation of autonomic balance in healthy individuals. Electrodermal activity (EDA) and heart rate variability (HRV) measurements are reliable indicators of the autonomic nervous system (ANS) and can be used to assess stress levels. The objective of this study was to investigate the potential of combining EDA and HRV measurements in assessing the physiological stress response induced by PTA. Fourteen healthy subjects were randomly assigned to two groups of equal size. The experimental group underwent five trials of elevator rides, while the control group received a sham treatment. EDA and HRV indices were obtained via ultra-short-term analysis and compared between the two groups to track changes in the ANS. In addition, the complexity of the EDA time series was compared between the 4 s before and the 2-6 s after the onset of PTA to assess changes in the subjects' stress levels in the experimental group. The results revealed a significant increase in the skin conductance response (SCR) frequency and a decrease in the root mean square of successive differences (RMSSD) and high frequency (HF) components of HRV. In terms of stress assessment, the results showed an increase in the complexity of the EDA time series 2-6 s after the onset of PTA. These results indicate an elevation in sympathetic tone when healthy subjects were exposed to a translational transport scenario. Furthermore, evidence was provided for the ability of EDA complexity to differentiate stress states in individual trials of translational acceleration.
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Affiliation(s)
- Xiaoru Yu
- College of Metrology and Measurement Engineering, China Jiliang University, Hangzhou, Zhejiang, China
| | - JiaWei Lu
- College of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou, Zhejiang, China
| | - Wenchao Liu
- Xizi Elevator Co., Ltd., Hangzhou, Zhejiang, China
| | - Zhenbo Cheng
- Zhejiang University of Technology, Hangzhou, Zhejiang, China
| | - Gang Xiao
- College of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou, Zhejiang, China.
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2
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Alam S, Khazaei S, Faghih RT. Unveiling productivity: The interplay of cognitive arousal and expressive typing in remote work. PLoS One 2024; 19:e0300786. [PMID: 38748663 PMCID: PMC11095729 DOI: 10.1371/journal.pone.0300786] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Accepted: 03/05/2024] [Indexed: 05/19/2024] Open
Abstract
Cognitive Arousal, frequently elicited by environmental stressors that exceed personal coping resources, manifests in measurable physiological markers, notably in galvanic skin responses. This effect is prominent in cognitive tasks such as composition, where fluctuations in these biomarkers correlate with individual expressiveness. It is crucial to understand the nexus between cognitive arousal and expressiveness. However, there has not been a concrete study that investigates this inter-relation concurrently. Addressing this, we introduce an innovative methodology for simultaneous monitoring of these elements. Our strategy employs Bayesian analysis in a multi-state filtering format to dissect psychomotor performance (captured through typing speed), galvanic skin response or skin conductance (SC), and heart rate variability (HRV). This integrative analysis facilitates the quantification of expressive behavior and arousal states. At the core, we deploy a state-space model connecting one latent psychological arousal condition to neural activities impacting sweating (inferred through SC responses) and another latent state to expressive behavior during typing. These states are concurrently evaluated with model parameters using an expectation-maximization algorithms approach. Assessments using both computer-simulated data and experimental data substantiate the validity of our approach. Outcomes display distinguishable latent state patterns in expressive typing and arousal across different computer software used in office management, offering profound implications for Human-Computer Interaction (HCI) and productivity analysis. This research marks a significant advancement in decoding human productivity dynamics, with extensive repercussions for optimizing performance in telecommuting scenarios.
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Affiliation(s)
- Samiul Alam
- Department of ECE, University of Houston, Houston, Texas, United States of America
| | - Saman Khazaei
- Department of Biomedical Engineering, New York University, New York City, New York, United States of America
| | - Rose T. Faghih
- Department of ECE, University of Houston, Houston, Texas, United States of America
- Department of Biomedical Engineering, New York University, New York City, New York, United States of America
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3
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Tan G, Adams J, Donovan K, Demarest P, Willie JT, Brunner P, Gorlewicz JL, Leuthardt EC. Does Vibrotactile Stimulation of the Auricular Vagus Nerve Enhance Working Memory? A Behavioral and Physiological Investigation. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.24.586365. [PMID: 38585960 PMCID: PMC10996508 DOI: 10.1101/2024.03.24.586365] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/09/2024]
Abstract
Background Working memory is essential to a wide range of cognitive functions and activities. Transcutaneous auricular VNS (taVNS) is a promising method to improve working memory performance. However, the feasibility and scalability of electrical stimulation are constrained by several limitations, such as auricular discomfort and inconsistent electrical contact. Objective We aimed to develop a novel and practical method, vibrotactile taVNS, to improve working memory. Further, we investigated its effects on arousal, measured by skin conductance and pupil diameter. Method This study included 20 healthy participants. Behavioral response, skin conductance, and eye tracking data were concurrently recorded while the participants performed N-back tasks under three conditions: vibrotactile taVNS delivered to the cymba concha, earlobe (sham control), and no stimulation (baseline control). Results In 4-back tasks, which demand maximal working memory capacity, active vibrotactile taVNS significantly improved the performance metric d ' compared to the baseline but not to the sham. Moreover, we found that the reduction rate of d ' with increasing task difficulty was significantly smaller during vibrotactile taVNS sessions than in both baseline and sham conditions. Arousal, measured as skin conductance and pupil diameter, declined over the course of the tasks. Vibrotactile taVNS rescued this arousal decline, leading to arousal levels corresponding to optimal working memory levels. Moreover, pupil diameter and skin conductance level were higher during high-cognitive-load tasks when vibrotactile taVNS was delivered to the concha compared to baseline and sham. Conclusion Our findings suggest that vibrotactile taVNS modulates the arousal pathway and could be a potential intervention for enhancing working memory. Highlights Vibrotactile stimulation of the auricular vagus nerve increases general arousal.Vibrotactile stimulation of the auricular vagus nerve mitigates arousal decreases as subjects continuously perform working memory tasks.6 Hz Vibrotactile auricular vagus nerve stimulation is a potential intervention for enhancing working memory performance.
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Alam S, Amin MR, Faghih RT. Sparse Multichannel Decomposition of Electrodermal Activity With Physiological Priors. IEEE OPEN JOURNAL OF ENGINEERING IN MEDICINE AND BIOLOGY 2023; 4:234-250. [PMID: 38196978 PMCID: PMC10776104 DOI: 10.1109/ojemb.2023.3332839] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Revised: 08/21/2023] [Accepted: 11/07/2023] [Indexed: 01/11/2024] Open
Abstract
Goal: Inferring autonomous nervous system (ANS) activity is a challenging issue and has critical applications in stress regulation. Sweat secretions caused by ANS activity influence the electrical conductance of the skin. Therefore, the variations in skin conductance (SC) measurements reflect the sudomotor nerve activity (SMNA) and can be used to infer the underlying ANS activity. These variations are strongly correlated with emotional arousal as well as thermoregulation. However, accurately recovering ANS activity and the corresponding state-space system from a single channel signal is difficult due to artifacts introduced by measurement noise. To minimize the impact of noise on inferring ANS activity, we utilize multiple channels of SC data. Methods: We model skin conductance using a second-order differential equation incorporating a time-shifted sparse impulse train input in combination with independent cubic basis spline functions. Finally, we develop a block coordinate descent method for SC signal decomposition by employing a generalized cross-validation sparse recovery approach while including physiological priors. Results: We analyze the experimental data to validate the performance of the proposed algorithm. We demonstrate its capacity to recover the ANS activations, the underlying physiological system parameters, and both tonic and phasic components. Finally, we present an overview of the algorithm's comparative performance under varying conditions and configurations to substantiate its ability to accurately model ANS activity. Our results show that our algorithm performs better in terms of multiple metrics like noise performance, AUC score, the goodness of fit of reconstructed signal, and lower missing impulses compared with the single channel decomposition approach. Conclusion: In this study, we highlight the challenges and benefits of concurrent decomposition and deconvolution of multichannel SC signals.
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Affiliation(s)
- Samiul Alam
- Department of Electrical and Computer EngineeringUniversity of HoustonHoustonTX77004USA
| | - Md. Rafiul Amin
- Department of Electrical and Computer EngineeringUniversity of HoustonHoustonTX77004USA
| | - Rose T. Faghih
- Department of Electrical and Computer EngineeringUniversity of HoustonHoustonTX77004USA
- Department of Biomedical EngineeringNew York UniversityNew YorkNY10010USA
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5
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Nebe S, Reutter M, Baker DH, Bölte J, Domes G, Gamer M, Gärtner A, Gießing C, Gurr C, Hilger K, Jawinski P, Kulke L, Lischke A, Markett S, Meier M, Merz CJ, Popov T, Puhlmann LMC, Quintana DS, Schäfer T, Schubert AL, Sperl MFJ, Vehlen A, Lonsdorf TB, Feld GB. Enhancing precision in human neuroscience. eLife 2023; 12:e85980. [PMID: 37555830 PMCID: PMC10411974 DOI: 10.7554/elife.85980] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Accepted: 07/23/2023] [Indexed: 08/10/2023] Open
Abstract
Human neuroscience has always been pushing the boundary of what is measurable. During the last decade, concerns about statistical power and replicability - in science in general, but also specifically in human neuroscience - have fueled an extensive debate. One important insight from this discourse is the need for larger samples, which naturally increases statistical power. An alternative is to increase the precision of measurements, which is the focus of this review. This option is often overlooked, even though statistical power benefits from increasing precision as much as from increasing sample size. Nonetheless, precision has always been at the heart of good scientific practice in human neuroscience, with researchers relying on lab traditions or rules of thumb to ensure sufficient precision for their studies. In this review, we encourage a more systematic approach to precision. We start by introducing measurement precision and its importance for well-powered studies in human neuroscience. Then, determinants for precision in a range of neuroscientific methods (MRI, M/EEG, EDA, Eye-Tracking, and Endocrinology) are elaborated. We end by discussing how a more systematic evaluation of precision and the application of respective insights can lead to an increase in reproducibility in human neuroscience.
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Affiliation(s)
- Stephan Nebe
- Zurich Center for Neuroeconomics, Department of Economics, University of ZurichZurichSwitzerland
| | - Mario Reutter
- Department of Psychology, Julius-Maximilians-UniversityWürzburgGermany
| | - Daniel H Baker
- Department of Psychology and York Biomedical Research Institute, University of YorkYorkUnited Kingdom
| | - Jens Bölte
- Institute for Psychology, University of Münster, Otto-Creuzfeldt Center for Cognitive and Behavioral NeuroscienceMünsterGermany
| | - Gregor Domes
- Department of Biological and Clinical Psychology, University of TrierTrierGermany
- Institute for Cognitive and Affective NeuroscienceTrierGermany
| | - Matthias Gamer
- Department of Psychology, Julius-Maximilians-UniversityWürzburgGermany
| | - Anne Gärtner
- Faculty of Psychology, Technische Universität DresdenDresdenGermany
| | - Carsten Gießing
- Biological Psychology, Department of Psychology, School of Medicine and Health Sciences, Carl von Ossietzky University of OldenburgOldenburgGermany
| | - Caroline Gurr
- Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, University Hospital, Goethe UniversityFrankfurtGermany
- Brain Imaging Center, Goethe UniversityFrankfurtGermany
| | - Kirsten Hilger
- Department of Psychology, Julius-Maximilians-UniversityWürzburgGermany
- Department of Psychology, Psychological Diagnostics and Intervention, Catholic University of Eichstätt-IngolstadtEichstättGermany
| | - Philippe Jawinski
- Department of Psychology, Humboldt-Universität zu BerlinBerlinGermany
| | - Louisa Kulke
- Department of Developmental with Educational Psychology, University of BremenBremenGermany
| | - Alexander Lischke
- Department of Psychology, Medical School HamburgHamburgGermany
- Institute of Clinical Psychology and Psychotherapy, Medical School HamburgHamburgGermany
| | - Sebastian Markett
- Department of Psychology, Humboldt-Universität zu BerlinBerlinGermany
| | - Maria Meier
- Department of Psychology, University of KonstanzKonstanzGermany
- University Psychiatric Hospitals, Child and Adolescent Psychiatric Research Department (UPKKJ), University of BaselBaselSwitzerland
| | - Christian J Merz
- Department of Cognitive Psychology, Institute of Cognitive Neuroscience, Faculty of Psychology, Ruhr University BochumBochumGermany
| | - Tzvetan Popov
- Department of Psychology, Methods of Plasticity Research, University of ZurichZurichSwitzerland
| | - Lara MC Puhlmann
- Leibniz Institute for Resilience ResearchMainzGermany
- Max Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
| | - Daniel S Quintana
- Max Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
- NevSom, Department of Rare Disorders & Disabilities, Oslo University HospitalOsloNorway
- KG Jebsen Centre for Neurodevelopmental Disorders, University of OsloOsloNorway
- Norwegian Centre for Mental Disorders Research (NORMENT), University of OsloOsloNorway
| | - Tim Schäfer
- Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, University Hospital, Goethe UniversityFrankfurtGermany
- Brain Imaging Center, Goethe UniversityFrankfurtGermany
| | | | - Matthias FJ Sperl
- Department of Clinical Psychology and Psychotherapy, University of GiessenGiessenGermany
- Center for Mind, Brain and Behavior, Universities of Marburg and GiessenGiessenGermany
| | - Antonia Vehlen
- Department of Biological and Clinical Psychology, University of TrierTrierGermany
| | - Tina B Lonsdorf
- Department of Systems Neuroscience, University Medical Center Hamburg-EppendorfHamburgGermany
- Department of Psychology, Biological Psychology and Cognitive Neuroscience, University of BielefeldBielefeldGermany
| | - Gordon B Feld
- Department of Clinical Psychology, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg UniversityMannheimGermany
- Department of Psychology, Heidelberg UniversityHeidelbergGermany
- Department of Addiction Behavior and Addiction Medicine, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg UniversityMannheimGermany
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg UniversityMannheimGermany
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6
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Fekri Azgomi H, F Branco LR, Amin MR, Khazaei S, Faghih RT. Regulation of brain cognitive states through auditory, gustatory, and olfactory stimulation with wearable monitoring. Sci Rep 2023; 13:12399. [PMID: 37553409 PMCID: PMC10409795 DOI: 10.1038/s41598-023-37829-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Accepted: 06/28/2023] [Indexed: 08/10/2023] Open
Abstract
Inspired by advances in wearable technologies, we design and perform human-subject experiments. We aim to investigate the effects of applying safe actuation (i.e., auditory, gustatory, and olfactory) for the purpose of regulating cognitive arousal and enhancing the performance states. In two proposed experiments, subjects are asked to perform a working memory experiment called n-back tasks. Next, we incorporate listening to different types of music, drinking coffee, and smelling perfume as safe actuators. We employ signal processing methods to seamlessly infer participants' brain cognitive states. The results demonstrate the effectiveness of the proposed safe actuation in regulating the arousal state and enhancing performance levels. Employing only wearable devices for human monitoring and using safe actuation intervention are the key components of the proposed experiments. Our dataset fills the existing gap of the lack of publicly available datasets for the self-management of internal brain states using wearable devices and safe everyday actuators. This dataset enables further machine learning and system identification investigations to facilitate future smart work environments. This would lead us to the ultimate idea of developing practical automated personalized closed-loop architectures for managing internal brain states and enhancing the quality of life.
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Affiliation(s)
- Hamid Fekri Azgomi
- Electrical and Computer Engineering Department, University of Houston, Houston, TX, 77004, USA
- Department of Neurological Surgery, University of California San Francisco, San Francisco, CA, 94143, USA
| | - Luciano R F Branco
- Electrical and Computer Engineering Department, University of Houston, Houston, TX, 77004, USA
- Biomedical Engineering Department, University of Houston, Houston, TX, 77004, USA
| | - Md Rafiul Amin
- Electrical and Computer Engineering Department, University of Houston, Houston, TX, 77004, USA
| | - Saman Khazaei
- Electrical and Computer Engineering Department, University of Houston, Houston, TX, 77004, USA
- Department of Biomedical Engineering, New York University, New York, New York, 10003, USA
| | - Rose T Faghih
- Electrical and Computer Engineering Department, University of Houston, Houston, TX, 77004, USA.
- Department of Biomedical Engineering, New York University, New York, New York, 10003, USA.
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7
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Ghita M, Birs IR, Copot D, Muresan CI, Ionescu CM. Bioelectrical impedance analysis of thermal-induced cutaneous nociception. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104678] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/17/2023]
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8
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Navarro-Santana MJ, Valera-Calero JA, Romanos-Castillo G, Hernández-González VC, Fernández-de-las-Peñas C, López-de-Uralde-Villanueva I, Plaza-Manzano G. Immediate Effects of Dry Needling on Central Pain Processing and Skin Conductance in Patients with Chronic Nonspecific Neck Pain: A Randomized Controlled Trial. J Clin Med 2022; 11:jcm11226616. [PMID: 36431093 PMCID: PMC9694175 DOI: 10.3390/jcm11226616] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Revised: 11/01/2022] [Accepted: 11/07/2022] [Indexed: 11/11/2022] Open
Abstract
Although current evidence supports the use of dry needling for improving some clinical outcomes in people with neck pain, no previous research explored the effects of dry needling on the central processing of pain and autonomic nervous system in this population. Therefore, this clinical trial aimed to compare the effects of real and sham dry needling on autonomic nervous system function, pain processing as well as clinical and psychological variables in patients with chronic nonspecific neck pain. A double-blinded randomized clinical trial including 60 patients with neck pain was conducted. Patients were randomized to the real needling (n = 30) or sham needling (n = 30) group. Skin conductance (SC), pressure pain thresholds (PPTs), temporal summation (TS), conditioned pain modulation (CPM) as well as pain intensity, related-disability, catastrophism, and kinesiophobia levels were assessed by an assessor blinded to the allocation intervention. The results did not find significant group * time interactions for most outcomes, except for the global percentage of change of SC values (mean: F = 35.90, p < 0.001, ηp2 = 0.459; minimum: F = 33.99, p = 0.839, ηp2 = 0.371; maximum: F = 24.71, p < 0.001, ηp2 = 0.037) and PPTs at C5-C6 joint in the same side of needling (F = 9.982; p = 0.003; = 0.147), in favor of the dry needling group. Although the proportion of subjects experiencing moderate to large self-perceived improvement after the intervention was significantly higher (X2 = 8.297; p = 0.004) within the dry needling group (n = 18, 60%) than in the sham needling group (n = 7, 23.3%), both groups experienced similar improvements in clinical and psychological variables. Our results suggested that dry needling applied to patients with chronic nonspecific neck pain produced an immediate decrease in mechanical hyperalgesia at local sites and produced an increase in skin conductance as compared with sham needling. No changes in central pain processing were observed. A single session of sham or real dry needling was similarly effective for decreasing related disability, pain intensity, catastrophism, and kinesiophobia levels. Further studies are needed to better understand the clinical implications of autonomic nervous system activation on central sensitization and pain processing in the long-term after the application of dry needling.
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Affiliation(s)
| | - Juan Antonio Valera-Calero
- VALTRADOFI Research Group, Department of Physiotherapy, Faculty of Health, Universidad Camilo José Cela, Villanueva de la Cañada, Villanueva de la Cañada, 28692 Madrid, Spain
- Department of Physiotherapy, Faculty of Health, Universidad Camilo José Cela, Villanueva de la Cañada, 28692 Madrid, Spain
- Correspondence:
| | - Guillermo Romanos-Castillo
- Department of Physiotherapy, Faculty of Health, Universidad Camilo José Cela, Villanueva de la Cañada, 28692 Madrid, Spain
| | - Victor C. Hernández-González
- Department of Physiotherapy, Faculty of Health, Universidad Camilo José Cela, Villanueva de la Cañada, 28692 Madrid, Spain
| | - César Fernández-de-las-Peñas
- Department of Physical Therapy, Occupational Therapy, Physical Medicine and Rehabilitation, Universidad Rey Juan Carlos, 28922 Alcorcón, Spain
- Cátedra Institucional en Docencia, Clínica e Investigación en Fisioterapia: Terapia Manual, Punción Seca y Ejercicio Terapéutico, Universidad Rey Juan Carlos, 28922 Alcorcón, Spain
| | | | - Gustavo Plaza-Manzano
- Department of Radiology, Rehabilitation and Physiotherapy, Universidad Complutense de Madrid, 28040 Madrid, Spain
- Grupo InPhysio, Instituto de Investigación Sanitaria San Carlos (IdISSC), 28040 Madrid, Spain
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9
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Amin R, Faghih RT. Physiological characterization of electrodermal activity enables scalable near real-time autonomic nervous system activation inference. PLoS Comput Biol 2022; 18:e1010275. [PMID: 35900988 PMCID: PMC9333288 DOI: 10.1371/journal.pcbi.1010275] [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: 09/28/2021] [Accepted: 06/02/2022] [Indexed: 12/01/2022] Open
Abstract
Electrodermal activities (EDA) are any electrical phxenomena observed on the skin. Skin conductance (SC), a measure of EDA, shows fluctuations due to autonomic nervous system (ANS) activation induced sweat secretion. Since it can capture psychophysiological information, there is a significant rise in the research work for tracking mental and physiological health with EDA. However, the current state-of-the-art lacks a physiologically motivated approach for real-time inference of ANS activation from EDA. Therefore, firstly, we propose a comprehensive model for the SC dynamics. The proposed model is a 3D state-space representation of the direct secretion of sweat via pore opening and diffusion followed by corresponding evaporation and reabsorption. As the input to the model, we consider a sparse signal representing the ANS activation that causes the sweat glands to produce sweat. Secondly, we derive a scalable fixed-interval smoother-based sparse recovery approach utilizing the proposed comprehensive model to infer the ANS activation enabling edge computation. We incorporate a generalized-cross-validation to tune the sparsity level. Finally, we propose an Expectation-Maximization based deconvolution approach for learning the model parameters during the ANS activation inference. For evaluation, we utilize a dataset with 26 participants, and the results show that our comprehensive state-space model can successfully describe the SC variations with high scalability, showing the feasibility of real-time applications. Results validate that our physiology-motivated state-space model can comprehensively explain the EDA and outperforms all previous approaches. Our findings introduce a whole new perspective and have a broader impact on the standard practices of EDA analysis. The current state-of-the-art lacks physiology-motivated models for electrodermal activities (EDA) that have the power to comprehensively describe the variations in skin conductance (SC)–a measure of EDA. In this study, we propose a physiology-motivated state-space model to address previous challenges. On the other hand, there is also an absence of a scalable autonomic nervous system (ANS) activation inference method that simultaneously solve for the physiological system parameters. Furthermore, we develop a scalable ANS activation inference approach based on the proposed model with a goal for real-time edge computation. We utilize a dataset with 26 participants to validate the new model and the scalable method. Results demonstrate that our physiology-motivated state-space model can comprehensively explain the EDA. Our findings introduce a whole new perspective and have a broader impact on standard practices of EDA analysis.
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Affiliation(s)
- Rafiul Amin
- Department of Electrical and Computer Engineering, University of Houston, Houston, Texas, United States of America
| | - Rose T. Faghih
- Department of Electrical and Computer Engineering, University of Houston, Houston, Texas, United States of America
- Department of Biomedical Engineering, New York University, New York City, New York, United States of America
- * E-mail:
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10
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Hossain MB, Kong Y, Posada-Quintero HF, Chon KH. Comparison of Electrodermal Activity from Multiple Body Locations Based on Standard EDA Indices' Quality and Robustness against Motion Artifact. SENSORS (BASEL, SWITZERLAND) 2022; 22:3177. [PMID: 35590866 PMCID: PMC9104297 DOI: 10.3390/s22093177] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Revised: 04/14/2022] [Accepted: 04/19/2022] [Indexed: 06/15/2023]
Abstract
The most traditional sites for electrodermal activity (EDA) data collection, palmar locations such as fingers or palms, are not usually recommended for ambulatory monitoring given that subjects have to use their hands regularly during their daily activities, and therefore, alternative sites are often sought for EDA data collection. In this study, we collected EDA signals (n = 23 subjects, 19 male) from four measurement sites (forehead, back of neck, finger, and inner edge of foot) during cognitive stress and induction of mild motion artifacts by walking and one-handed weightlifting. Furthermore, we computed several EDA indices from the EDA signals obtained from different sites and evaluated their efficiency to classify cognitive stress from the baseline state. We found a high within-subject correlation between the EDA signals obtained from the finger and the feet. Consistently high correlation was also found between the finger and the foot EDA in both the phasic and tonic components. Statistically significant differences were obtained between the baseline and cognitive stress stage only for the EDA indices computed from the finger and the foot EDA. Moreover, the receiver operating characteristic curve for cognitive stress detection showed a higher area-under-the-curve for the EDA indices computed from the finger and foot EDA. We also evaluated the robustness of the different body sites against motion artifacts and found that the foot EDA location was the best alternative to other sites.
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11
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Hossain MB, Posada-Quintero HF, Kong Y, McNaboe R, Chon KH. Automatic motion artifact detection in electrodermal activity data using machine learning. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103483] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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12
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Branco LRF, Ehteshami A, Azgomi HF, Faghih RT. Closed-Loop Tracking and Regulation of Emotional Valence State From Facial Electromyogram Measurements. Front Comput Neurosci 2022; 16:747735. [PMID: 35399915 PMCID: PMC8990324 DOI: 10.3389/fncom.2022.747735] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Accepted: 02/21/2022] [Indexed: 11/25/2022] Open
Abstract
Affective studies provide essential insights to address emotion recognition and tracking. In traditional open-loop structures, a lack of knowledge about the internal emotional state makes the system incapable of adjusting stimuli parameters and automatically responding to changes in the brain. To address this issue, we propose to use facial electromyogram measurements as biomarkers to infer the internal hidden brain state as feedback to close the loop. In this research, we develop a systematic way to track and control emotional valence, which codes emotions as being pleasant or obstructive. Hence, we conduct a simulation study by modeling and tracking the subject's emotional valence dynamics using state-space approaches. We employ Bayesian filtering to estimate the person-specific model parameters along with the hidden valence state, using continuous and binary features extracted from experimental electromyogram measurements. Moreover, we utilize a mixed-filter estimator to infer the secluded brain state in a real-time simulation environment. We close the loop with a fuzzy logic controller in two categories of regulation: inhibition and excitation. By designing a control action, we aim to automatically reflect any required adjustments within the simulation and reach the desired emotional state levels. Final results demonstrate that, by making use of physiological data, the proposed controller could effectively regulate the estimated valence state. Ultimately, we envision future outcomes of this research to support alternative forms of self-therapy by using wearable machine interface architectures capable of mitigating periods of pervasive emotions and maintaining daily well-being and welfare.
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Affiliation(s)
- Luciano R. F. Branco
- Department of Electrical and Computer Engineering, University of Houston, Houston, TX, United States
| | - Arian Ehteshami
- Department of Electrical and Computer Engineering, University of Houston, Houston, TX, United States
| | - Hamid Fekri Azgomi
- Department of Electrical and Computer Engineering, University of Houston, Houston, TX, United States
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, United States
| | - Rose T. Faghih
- Department of Electrical and Computer Engineering, University of Houston, Houston, TX, United States
- Department of Biomedical Engineering, New York University, New York, NY, United States
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13
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Tronstad C, Amini M, Bach DR, Martinsen OG. Current trends and opportunities in the methodology of electrodermal activity measurement. Physiol Meas 2022; 43. [PMID: 35090148 DOI: 10.1088/1361-6579/ac5007] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Accepted: 01/28/2022] [Indexed: 11/12/2022]
Abstract
Electrodermal activity (EDA) has been measured in the laboratory since the late 1800s. Although the influence of sudomotor nerve activity and the sympathetic nervous system on EDA is well established, the mechanisms underlying EDA signal generation are not completely understood. Owing to simplicity of instrumentation and modern electronics, these measurements have recently seen a transfer from the laboratory to wearable devices, sparking numerous novel applications while bringing along both challenges and new opportunities. In addition to developments in electronics and miniaturization, current trends in material technology and manufacturing have sparked innovations in electrode technologies, and trends in data science such as machine learning and sensor fusion are expanding the ways that measurement data can be processed and utilized. Although challenges remain for the quality of wearable EDA measurement, ongoing research and developments may shorten the quality gap between wearable EDA and standardized recordings in the laboratory. In this topical review, we provide an overview of the basics of EDA measurement, discuss the challenges and opportunities of wearable EDA, and review recent developments in instrumentation, material technology, signal processing, modeling and data science tools that may advance the field of EDA research and applications over the coming years.
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Affiliation(s)
- Christian Tronstad
- Department of Clinical and Biomedical Engineering, Oslo University Hospital, Sognsvannsveien 20, Oslo, 0372, NORWAY
| | - Maryam Amini
- Physics, University of Oslo Faculty of Mathematics and Natural Sciences, Sem Sælands vei 24, Oslo, 0371, NORWAY
| | - Dominik R Bach
- Wellcome Centre for Human Neuroimaging, University College London, 12 Queen Square, London, London, WC1N 3AZ, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND
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14
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Azgomi HF, Faghih RT. Enhancement of Closed-Loop Cognitive Stress Regulation using Supervised Control Architectures. IEEE OPEN JOURNAL OF ENGINEERING IN MEDICINE AND BIOLOGY 2022; 3:7-17. [PMID: 35399789 PMCID: PMC8979622 DOI: 10.1109/ojemb.2022.3143686] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 11/06/2021] [Accepted: 12/13/2021] [Indexed: 11/15/2022] Open
Abstract
Goal: We propose novel supervised control architectures to regulate the cognitive stress state and close the loop. Methods: We take information present in underlying neural impulses of skin conductance signals and employ model-based control techniques to close the loop in a state-space framework. For performance enhancement, we establish a supervised knowledge-based layer to update control system in real time. In the supervised architecture, the controller parameters are being updated in real-time. Results: Statistical analyses demonstrate the efficiency of supervised control architectures in improving the closed-loop results while maintaining stress levels within a desired range with more optimized control efforts. The model-based approaches would guarantee the control system-perspective criteria such as stability and optimality, and the proposed supervised knowledge-based layer would further enhance their efficiency. Conclusion: Outcomes in this in silico study verify the proficiency of the proposed supervised architectures to be implemented in the real world.
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Affiliation(s)
- Hamid Fekri Azgomi
- Department of Electrical and Computer EngineeringUniversity of Houston Houston TX 77004 USA
- Department of Neurological SurgeryUniversity of California San Francisco San Francisco CA 94143 USA
| | - Rose T Faghih
- Department of Biomedical EngineeringNew York University New York NY 10010 USA
- Department of Electrical and Computer EngineeringUniversity of Houston Houston TX 77004 USA
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15
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Amin MR, Pednekar DD, Azgomi HF, van Wietmarschen H, Aschbacher K, Faghih RT. Sparse System Identification of Leptin Dynamics in Women With Obesity. Front Endocrinol (Lausanne) 2022; 13:769951. [PMID: 35480480 PMCID: PMC9037068 DOI: 10.3389/fendo.2022.769951] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Accepted: 01/24/2022] [Indexed: 01/03/2023] Open
Abstract
The prevalence of obesity is increasing around the world at an alarming rate. The interplay of the hormone leptin with the hypothalamus-pituitary-adrenal axis plays an important role in regulating energy balance, thereby contributing to obesity. This study presents a mathematical model, which describes hormonal behavior leading to an energy abnormal equilibrium that contributes to obesity. To this end, we analyze the behavior of two neuroendocrine hormones, leptin and cortisol, in a cohort of women with obesity, with simplified minimal state-space modeling. Using a system theoretic approach, coordinate descent method, and sparse recovery, we deconvolved the serum leptin-cortisol levels. Accordingly, we estimate the secretion patterns, timings, amplitudes, number of underlying pulses, infusion, and clearance rates of hormones in eighteen premenopausal women with obesity. Our results show that minimal state-space model was able to successfully capture the leptin and cortisol sparse dynamics with the multiple correlation coefficients greater than 0.83 and 0.87, respectively. Furthermore, the Granger causality test demonstrated a negative prospective predictive relationship between leptin and cortisol, 14 of 18 women. These results indicate that increases in cortisol are prospectively associated with reductions in leptin and vice versa, suggesting a bidirectional negative inhibitory relationship. As dysregulation of leptin may result in an abnormality in satiety and thereby associated to obesity, the investigation of leptin-cortisol sparse dynamics may offer a better diagnostic methodology to improve better treatments plans for individuals with obesity.
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Affiliation(s)
- Md Rafiul Amin
- Department of Electrical and Computer Engineering, University of Houston, Houston, TX, United States
| | - Divesh Deepak Pednekar
- Department of Electrical and Computer Engineering, University of Houston, Houston, TX, United States
| | - Hamid Fekri Azgomi
- Department of Electrical and Computer Engineering, University of Houston, Houston, TX, United States
| | | | - Kirstin Aschbacher
- Department of Psychiatry, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - Rose T Faghih
- Department of Electrical and Computer Engineering, University of Houston, Houston, TX, United States
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16
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Kong Y, Posada-Quintero HF, Chon KH. Sensitive Physiological Indices of Pain Based on Differential Characteristics of Electrodermal Activity. IEEE Trans Biomed Eng 2021; 68:3122-3130. [PMID: 33705307 DOI: 10.1109/tbme.2021.3065218] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
OBJECTIVE Electrodermal activity (EDA) has been widely used to assess human response to stressful stimuli, including pain. Recently, spectral analysis of EDA has been found to be more sensitive and reproducible for assessment of sympathetic arousal than traditional indices (e.g., tonic and phasic components). However, none of the aforementioned analyses incorporate the differential characteristics of EDA, which could be more sensitive to capturing fast-changing dynamics associated with pain responses. METHODS We have tested the feasibility of using the derivative of phasic EDA and the modified time-varying spectral analysis of EDA. Sixteen subjects underwent four levels of pain stimulation using electric stimulation. Five-second segments of EDA were used for each level of stimulation, and pre-stimulation segments were considered stimulation level 0. We used support vector machines with the radial basis function kernel and multi-layer perceptron for three different scenarios of stimulation-level classification tasks: five stimulation levels (four levels of stimulation plus no stimulation); low, medium, and high pain stimulation (stimulation levels 0-1, 2, and 3-4, respectively); and high stimulation levels (stimulation levels 3-4) vs. no stimulation. RESULTS The maximum balanced accuracies were 44% (five stimulation levels), 63% (for low, medium, and high pain stimulation), and 87% (sensitivity 83% and specificity 89%, for high stimulation vs. no stimulation). CONCLUSION The differential characteristics of EDA contributed highly to the accuracy of pain stimulation level detection of the classifiers. The external validity dataset was not considered in the study. SIGNIFICANCE Our approach has the potential for accurate pain quantification using EDA.
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