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Lim KYT, Nguyen Thien MT, Nguyen Duc MA, Posada-Quintero HF. Application of DIY Electrodermal Activity Wristband in Detecting Stress and Affective Responses of Students. Bioengineering (Basel) 2024; 11:291. [PMID: 38534565 DOI: 10.3390/bioengineering11030291] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Revised: 03/10/2024] [Accepted: 03/17/2024] [Indexed: 03/28/2024] Open
Abstract
This paper describes the analysis of electrodermal activity (EDA) in the context of students' scholastic activity. Taking a multidisciplinary, citizen science and maker-centric approach, low-cost, bespoken wearables, such as a mini weather station and biometric wristband, were built. To investigate both physical health as well as stress, the instruments were first validated against research grade devices. Following this, a research experiment was created and conducted in the context of students' scholastic activity. Data from this experiment were used to train machine learning models, which were then applied to interpret the relationships between the environment, health, and stress. It is hoped that analyses of EDA data will further strengthen the emerging model describing the intersections between local microclimate and physiological and neurological stress. The results suggest that temperature and air quality play an important role in students' physiological well-being, thus demonstrating the feasibility of understanding the extent of the effects of various microclimatic factors. This highlights the importance of thermal comfort and air ventilation in real-life applications to improve students' well-being. We envision our work making a significant impact by showcasing the effectiveness and feasibility of inexpensive, self-designed wearable devices for tracking microclimate and electrodermal activity (EDA). The affordability of these wearables holds promising implications for scalability and encourages crowd-sourced citizen science in the relatively unexplored domain of microclimate's influence on well-being. Embracing citizen science can then democratize learning and expedite rapid research advancements.
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Affiliation(s)
- Kenneth Y T Lim
- National Institute of Education, Nanyang Technological University, Singapore 637616, Singapore
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Baghestani F, Kong Y, D'Angelo W, Chon KH. Analysis of sympathetic responses to cognitive stress and pain through skin sympathetic nerve activity and electrodermal activity. Comput Biol Med 2024; 170:108070. [PMID: 38330822 DOI: 10.1016/j.compbiomed.2024.108070] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Revised: 12/28/2023] [Accepted: 01/27/2024] [Indexed: 02/10/2024]
Abstract
We explored the non-invasive evaluation of the sympathetic nervous system (SNS) by employing two distinct physiological signals: skin sympathetic nerve activity (SKNA), extracted from electrocardiogram (ECG) signals, and electrodermal activity (EDA), a well-studied marker in the context of the SNS assessment. Our investigation focused on cognitive stress and pain; two conditions closely associated with the SNS. We sought to determine if the information and dynamics of EDA could be derived from the novel SKNA signal. To this end, ECG and EDA signals were recorded simultaneously during three experiments aimed at sympathetic stimulation, Valsalva maneuver (VM), Stroop test, and thermal-grill pain test. We calculated the integral area under the rectified SKNA signal (iSKNA) and decomposed the EDA signal to its phasic component (EDAphasic). An average delay of more than 4.6 s was observed in the onset of EDAphasic bursts compared to their corresponding iSKNA bursts. After shifting the EDAphasic segments by the extent of this delay and smoothing the corresponding iSKNA bursts, our results revealed a strong average correlation coefficient of 0.85±0.14 between the iSKNA and EDAphasic bursts, indicating a noteworthy similarity between the two signals. We also reconstructed the EDA signals with time-varying sympathetic (TVSymp) and modified TVSymp (MTVSymp) methods. Then we extracted the following features from iSKNA, EDAphasic, TVSymp, and MTVSymp signals: peak amplitude, average amplitude (aSKNA), standard deviation (vSKNA), and the cumulative duration during which the signals had higher amplitudes than a specified threshold (HaSKNA). A strong average correlation of 0.89±0.18 was found between vSKNA and subjects' self-rated pain levels during the pain test. Our statistical analysis also included applying Linear Mixed-Effects Models to check if there were significant differences in features across baseline and different levels of SNS stimulation. We then assessed the discriminating power of the features using Area Under the Receiver Operating Characteristic Curve (AUROC) and Fisher's Ratio. Finally, using all the four EDA features, a multi-layer perceptron (MLP) classifier reached the classification accuracies 95.56%, 89.29%, and 67.88% for the VM, Stroop, and thermal-grill pain control and stimulation classes. On the other hand, the highest classification accuracies based on SKNA features were achieved using K-nearest neighbors (KNN) (98.89%), KNN (89.29%), and MLP (95.11%) classifiers for the same experiments. Our comparative analysis showed the feasibility of SKNA as a novel tool for assessing the SNS with accurate classification capability, with a faster onset of amplitude increase in response to SNS activity, compared to EDA.
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Affiliation(s)
- Farnoush Baghestani
- Biomedical Engineering Department, University of Connecticut, United States of America
| | - Youngsun Kong
- Biomedical Engineering Department, University of Connecticut, United States of America
| | - William D'Angelo
- Biomedical Systems Engineering and Evaluation Department, Naval Medical Research Unit Department, San Antonio, TX, United States of America
| | - Ki H Chon
- Biomedical Engineering Department, University of Connecticut, United States of America.
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Yu H, Xu M, Xiao X, Xu F, Ming D. Detection of dynamic changes of electrodermal activity to predict the classroom performance of college students. Cogn Neurodyn 2024; 18:173-184. [PMID: 38406194 PMCID: PMC10881450 DOI: 10.1007/s11571-023-09930-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Revised: 12/02/2022] [Accepted: 01/09/2023] [Indexed: 02/20/2023] Open
Abstract
It is emphasized in the Self-regulated learning (SRL) framework that self-monitoring of learning state is vital for students to keep effective in studying. However, it's still challenging to get an accurate and timely understanding of their learning states during classes. In this study, we propose to use electrodermal activity (EDA) signals which are deemed to be associated with physiological arousal state to predict the college student's classroom performance. Twenty college students were recruited to attend eight lectures in the classroom, during which their EDA signals were recorded simultaneously. For each lecture, the students should complete pre- and after-class tests, and a self-reported scale (SRS) on their learning experience. EDA indices were extracted from both time and frequency domains, and they were furtherly mapped to the student's learning efficiency. As a result, the indices relevant to the dynamic changes of EDA had significant positive correlations with the learning efficiency. Furthermore, compared with only using SRS, a combination with EDA indices had significantly higher accuracy in predicting the learning efficiency. In conclusion, our findings demonstrate that the EDA dynamics are sensitive to the changes in learning efficiency, suggesting a promising approach to predicting the classroom performance of college students.
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Affiliation(s)
- Haiqing Yu
- Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, China
| | - Minpeng Xu
- Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, China
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
| | - Xiaolin Xiao
- Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, China
| | - Fangzhou Xu
- Department of Physics, School of Electronic and Information Engineering, Qilu University of Technology, Jinan, Shandong China
| | - Dong Ming
- Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, China
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
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Johansen AO, Mølgaard J, Rasmussen SS, Gu Y, Grønbæk KK, Sørensen HBD, Aasvang EK, Meyhoff CS. Deviations in continuously monitored electrodermal activity before severe clinical complications: a clinical prospective observational explorative cohort study. J Clin Monit Comput 2023; 37:1573-1584. [PMID: 37195623 PMCID: PMC10651525 DOI: 10.1007/s10877-023-01030-4] [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: 02/19/2022] [Accepted: 05/03/2023] [Indexed: 05/18/2023]
Abstract
Monitoring of high-risk patients in hospital wards is crucial in identifying and preventing clinical deterioration. Sympathetic nervous system activity measured continuously and non-invasively by Electrodermal activity (EDA) may relate to complications, but the clinical use remains untested. The aim of this study was to explore associations between deviations of EDA and subsequent serious adverse events (SAE). Patients admitted to general wards after major abdominal cancer surgery or with acute exacerbation of chronic obstructive pulmonary disease were continuously EDA-monitored for up to 5 days. We used time-perspectives consisting of 1, 3, 6, and 12 h of data prior to first SAE or from start of monitoring. We constructed 648 different EDA-derived features to assess EDA. The primary outcome was any SAE and secondary outcomes were respiratory, infectious, and cardiovascular SAEs. Associations were evaluated using logistic regressions with adjustment for relevant confounders. We included 714 patients and found a total of 192 statistically significant associations between EDA-derived features and clinical outcomes. 79% of these associations were EDA-derived features of absolute and relative increases in EDA and 14% were EDA-derived features with normalized EDA above a threshold. The highest F1-scores for primary outcome with the four time-perspectives were 20.7-32.8%, with precision ranging 34.9-38.6%, recall 14.7-29.4%, and specificity 83.1-91.4%. We identified statistically significant associations between specific deviations of EDA and subsequent SAE, and patterns of EDA may be developed to be considered indicators of upcoming clinical deterioration in high-risk patients.
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Affiliation(s)
- Andreas Ohrt Johansen
- Department of Anaesthesia and Intensive Care, Copenhagen University Hospital-Bispebjerg and Frederiksberg, Copenhagen, Denmark.
| | - Jesper Mølgaard
- Department of Anaesthesiology, Centre for Cancer and Organ Dysfunction, Copenhagen University Hospital-Rigshospitalet, Copenhagen, Denmark
| | | | - Ying Gu
- Department of Health Technology, Technical University of Denmark, Kgs. Lyngby, Denmark
| | - Katja Kjær Grønbæk
- Department of Anaesthesia and Intensive Care, Copenhagen University Hospital-Bispebjerg and Frederiksberg, Copenhagen, Denmark
| | - Helge B D Sørensen
- Department of Health Technology, Technical University of Denmark, Kgs. Lyngby, Denmark
| | - Eske Kvanner Aasvang
- Department of Anaesthesiology, Centre for Cancer and Organ Dysfunction, Copenhagen University Hospital-Rigshospitalet, Copenhagen, Denmark
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Christian Sylvest Meyhoff
- Department of Anaesthesia and Intensive Care, Copenhagen University Hospital-Bispebjerg and Frederiksberg, Copenhagen, Denmark
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
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Castro Ribeiro T, Sobregrau Sangrà P, García Pagès E, Badiella L, López-Barbeito B, Aguiló S, Aguiló J. Assessing effectiveness of heart rate variability biofeedback to mitigate mental health symptoms: a pilot study. Front Physiol 2023; 14:1147260. [PMID: 37234414 PMCID: PMC10206049 DOI: 10.3389/fphys.2023.1147260] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Accepted: 04/24/2023] [Indexed: 05/28/2023] Open
Abstract
Introduction: The increasing burden on mental health has become a worldwide concern especially due to its substantial negative social and economic impact. The implementation of prevention actions and psychological interventions is crucial to mitigate these consequences, and evidence supporting its effectiveness would facilitate a more assertive response. Heart rate variability biofeedback (HRV-BF) has been proposed as a potential intervention to improve mental wellbeing through mechanisms in autonomic functioning. The aim of this study is to propose and evaluate the validity of an objective procedure to assess the effectiveness of a HRV-BF protocol in mitigating mental health symptoms in a sample of frontline HCWs (healthcare workers) who worked in the COVID-19 pandemic. Methods: A prospective experimental study applying a HRV-BF protocol was conducted with 21 frontline healthcare workers in 5 weekly sessions. For PRE-POST intervention comparisons, two different approaches were used to evaluate mental health status: applying (a) gold-standard psychometric questionnaires and (b) electrophysiological multiparametric models for chronic and acute stress assessment. Results: After HRV-BF intervention, psychometric questionnaires showed a reduction in mental health symptoms and stress perception. The electrophysiological multiparametric also showed a reduction in chronic stress levels, while the acute stress levels were similar in PRE and POST conditions. A significant reduction in respiratory rate and an increase in some heart rate variability parameters, such as SDNN, LFn, and LF/HF ratio, were also observed after intervention. Conclusion: Our findings suggest that a 5-session HRV-BF protocol is an effective intervention for reducing stress and other mental health symptoms among frontline HCWs who worked during the COVID-19 pandemic. The electrophysiological multiparametric models provide relevant information about the current mental health state, being useful for objectively evaluating the effectiveness of stress-reducing interventions. Further research could replicate the proposed procedure to confirm its feasibility for different samples and specific interventions.
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Affiliation(s)
- Thais Castro Ribeiro
- Biomedical Research Network Center in Biogineering, Biomaterial and Nanomedicine (CIBER-BBN), Madrid, Spain
- Department of Microelectronics and Electronic Systems, Autonomous University of Barcelona, Barcelona, Spain
| | - Pau Sobregrau Sangrà
- Clínic Foundation for Biomedical Research, Hospital Clínic of Barcelona, Barcelona, Spain
| | - Esther García Pagès
- Department of Microelectronics and Electronic Systems, Autonomous University of Barcelona, Barcelona, Spain
| | - Llorenç Badiella
- Applied Statistics Service, Autonomous University of Barcelona, Barcelona, Spain
| | | | - Sira Aguiló
- Emergency Department, Hospital Clínic of Barcelona, Barcelona, Spain
| | - Jordi Aguiló
- Biomedical Research Network Center in Biogineering, Biomaterial and Nanomedicine (CIBER-BBN), Madrid, Spain
- Department of Microelectronics and Electronic Systems, Autonomous University of Barcelona, Barcelona, Spain
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Kong Y, Posada-Quintero HF, Tran H, Talati A, Acquista TJ, Chen IP, Chon KH. Differentiating between stress- and EPT-induced electrodermal activity during dental examination. Comput Biol Med 2023; 155:106695. [PMID: 36805230 PMCID: PMC10062482 DOI: 10.1016/j.compbiomed.2023.106695] [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: 10/27/2022] [Revised: 12/20/2022] [Accepted: 02/14/2023] [Indexed: 02/17/2023]
Abstract
Dental pain invokes the sympathetic nervous system, which can be measured by electrodermal activity (EDA). In the dental clinic, accurate quantification of pain is needed because it could enable optimized drug-dose treatments, thereby potentially reducing drug addiction. However, a confounding factor is that during pain there is also lingering residual stress, hence, both contribute to the EDA response. Therefore, we investigated whether EDA can differentiate stress from pain during dental examination. The use of electrical pulp test (EPT) is an ideal approach to tease out the dynamics of stress and mimic pain with lingering residual stress. Once the electrical sensation is felt and reaches a critical current threshold, the subject removes the probe from their tooth, hence, this stage of data represents largely EPT stimulus and the residual stress-induced EDA response is smaller. EPT was performed on necrotic and vital teeth in fifty-one subjects. We defined four different data groups of reactions based on each individual's EPT intensity level expectation based on the visual analog scale (VAS) of their baseline trial, as follows: mild stress, mild stress + EPT, strong stress, and strong stress + EPT. EDA-derived features exhibited significant difference between residual lingering stress + EPT groups and stress groups. We obtained 84.6% accuracy with 76.2% sensitivity and 86.8% specificity with multilayer perceptron in differentiating between pure-stress groups vs. stress + EPT groups. Moreover, EPT induced much greater EDA amplitude and faster response than stress. Our finding suggests that our machine learning approach can discriminate between stress and EPT stimulation in EDA signals.
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Affiliation(s)
- Youngsun Kong
- Biomedical Engineering, University of Connecticut, Storrs, CT, 06269, USA.
| | | | - Hanh Tran
- Department of Oral Health and Diagnostic Sciences, University of Connecticut Health, Farmington, CT, 06032, USA
| | - Ankur Talati
- Department of Oral Health and Diagnostic Sciences, University of Connecticut Health, Farmington, CT, 06032, USA
| | - Thomas J Acquista
- Biomedical Engineering, University of Connecticut, Storrs, CT, 06269, USA
| | - I-Ping Chen
- Department of Oral Health and Diagnostic Sciences, University of Connecticut Health, Farmington, CT, 06032, USA
| | - Ki H Chon
- Biomedical Engineering, University of Connecticut, Storrs, CT, 06269, USA
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Tran HT, Kong Y, Talati A, Posada-Quintero H, Chon KH, Chen IP. The use of electrodermal activity in pulpal diagnosis and dental pain assessment. Int Endod J 2023; 56:356-368. [PMID: 36367715 PMCID: PMC10044487 DOI: 10.1111/iej.13868] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Revised: 11/07/2022] [Accepted: 11/08/2022] [Indexed: 11/13/2022]
Abstract
AIMS To explore whether electrodermal activity (EDA) can serve as a complementary tool for pulpal diagnosis (Aim 1) and an objective metric to assess dental pain before and after local anaesthesia (Aim 2). METHODOLOGY A total of 53 subjects (189 teeth) and 14 subjects (14 teeth) were recruited for Aim 1 and Aim 2, respectively. We recorded EDA using commercially available devices, PowerLab and Galvanic Skin Response (GSR) Amplifier, in conjunction with cold and electric pulp testing (EPT). Participants rated their level of sensation on a 0-10 visual analogue scale (VAS) after each test. We recorded EPT-stimulated EDA activity before and after the administration of local anaesthesia for participants who required root canal treatment (RCT) due to painful pulpitis. The raw data were converted to the time-varying index of sympathetic activity (TVSymp), a sensitive and specific parameter of EDA. Statistical analysis was performed using Python 3.6 and its Scikit-post hoc library. RESULTS Electrodermal activity was upregulated by the stimuli of cold and EPT testing in the normal pulp. TVSymp signals were significantly increased in vital pulp compared to necrotic pulp by both cold test and EPT. Teeth that exhibited intensive sensitivity to cold with or without lingering pain had increased peak numbers of TVSymp than teeth with mild sensation to cold. Pre- and post-anaesthesia EDA activity and VAS scores were recorded in patients with painful pulpitis. Post-anaesthesia EDA signals were significantly lower compared to pre-anaesthesia levels. Approximately 71% of patients (10 of 14 patients) experienced no pain during treatment and reported VAS score of 0 or 1. The majority of patients (10 of 14) showed a reduction of TVSymp after the administration of anaesthesia. Two of three patients who experienced increased pain during RCT (post-treatment VAS > pre-treatment VAS) exhibited increased post-anaesthesia TVSymp. CONCLUSIONS Our data show promising results for using EDA in pulpal diagnosis and for assessing dental pain. Whilst our testing was limited to subjects who had adequate communication skills, our future goal is to be able to use this technology to aid in the endodontic diagnosis of patients who have limited communication ability.
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Affiliation(s)
- Hanh T Tran
- Department of Oral Health and Diagnostic Sciences, School of Dental Medicine, University of Connecticut Health, Farmington, Connecticut, USA
| | - Youngsun Kong
- Department of Biomedical Engineering, School of Engineering, University of Connecticut, Storrs, Connecticut, USA
| | - Ankur Talati
- Department of Oral Health and Diagnostic Sciences, School of Dental Medicine, University of Connecticut Health, Farmington, Connecticut, USA
| | - Hugo Posada-Quintero
- Department of Biomedical Engineering, School of Engineering, University of Connecticut, Storrs, Connecticut, USA
| | - Ki H Chon
- Department of Biomedical Engineering, School of Engineering, University of Connecticut, Storrs, Connecticut, USA
| | - I-Ping Chen
- Department of Oral Health and Diagnostic Sciences, School of Dental Medicine, University of Connecticut Health, Farmington, Connecticut, USA
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Arasteh E, Mirian MS, Verchere WD, Surathi P, Nene D, Allahdadian S, Doo M, Park KW, Ray S, McKeown MJ. An Individualized Multi-Modal Approach for Detection of Medication "Off" Episodes in Parkinson's Disease via Wearable Sensors. J Pers Med 2023; 13:jpm13020265. [PMID: 36836501 PMCID: PMC9962500 DOI: 10.3390/jpm13020265] [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: 12/15/2022] [Revised: 01/20/2023] [Accepted: 01/27/2023] [Indexed: 02/04/2023] Open
Abstract
The primary treatment for Parkinson's disease (PD) is supplementation of levodopa (L-dopa). With disease progression, people may experience motor and non-motor fluctuations, whereby the PD symptoms return before the next dose of medication. Paradoxically, in order to prevent wearing-off, one must take the next dose while still feeling well, as the upcoming off episodes can be unpredictable. Waiting until feeling wearing-off and then taking the next dose of medication is a sub-optimal strategy, as the medication can take up to an hour to be absorbed. Ultimately, early detection of wearing-off before people are consciously aware would be ideal. Towards this goal, we examined whether or not a wearable sensor recording autonomic nervous system (ANS) activity could be used to predict wearing-off in people on L-dopa. We had PD subjects on L-dopa record a diary of their on/off status over 24 hours while wearing a wearable sensor (E4 wristband®) that recorded ANS dynamics, including electrodermal activity (EDA), heart rate (HR), blood volume pulse (BVP), and skin temperature (TEMP). A joint empirical mode decomposition (EMD) / regression analysis was used to predict wearing-off (WO) time. When we used individually specific models assessed with cross-validation, we obtained > 90% correlation between the original OFF state logged by the patients and the reconstructed signal. However, a pooled model using the same combination of ASR measures across subjects was not statistically significant. This proof-of-principle study suggests that ANS dynamics can be used to assess the on/off phenomenon in people with PD taking L-dopa, but must be individually calibrated. More work is required to determine if individual wearing-off detection can take place before people become consciously aware of it.
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Affiliation(s)
- Emad Arasteh
- Department of Neonatology, Wilhelmina Children’s Hospital, University Medical Center Utrecht, 3585 EA Utrecht, The Netherlands
- Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, B-3001 Leuven, Belgium
| | - Maryam S. Mirian
- Pacific Parkinson’s Research Centre, Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, BC V6T 2B5, Canada
| | - Wyatt D. Verchere
- Pacific Parkinson’s Research Centre, Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, BC V6T 2B5, Canada
| | - Pratibha Surathi
- Clinical Fellow-Neurophysiology, Columbia New York Presbyterian, New York, NY 1032, USA
| | - Devavrat Nene
- Department of Medicine, Division of Neurology, The University of Ottawa, Ottawa, ON K1Y 4E9, Canada
| | - Sepideh Allahdadian
- Pacific Parkinson’s Research Centre, Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, BC V6T 2B5, Canada
- Department of Neurology, Penn State Milton S. Hershey Medical Center, Hershey, PA 17033, USA
| | - Michelle Doo
- Pacific Parkinson’s Research Centre, Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, BC V6T 2B5, Canada
| | - Kye Won Park
- Pacific Parkinson’s Research Centre, Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, BC V6T 2B5, Canada
| | - Somdattaa Ray
- Pacific Parkinson’s Research Centre, Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, BC V6T 2B5, Canada
| | - Martin J. McKeown
- Pacific Parkinson’s Research Centre, Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, BC V6T 2B5, Canada
- Faculty of Medicine (Neurology), University of British Columbia, Vancouver, BC V6T 2B5, Canada
- Correspondence:
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Golzari K, Kong Y, Reed SA, Posada-Quintero HF. Sympathetic Arousal Detection in Horses Using Electrodermal Activity. Animals (Basel) 2023; 13:ani13020229. [PMID: 36670768 PMCID: PMC9855141 DOI: 10.3390/ani13020229] [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: 11/29/2022] [Revised: 01/04/2023] [Accepted: 01/05/2023] [Indexed: 01/10/2023] Open
Abstract
The continuous monitoring of stress, pain, and discomfort is key to providing a good quality of life for horses. The available tools based on observation are subjective and do not allow continuous monitoring. Given the link between emotions and sympathetic autonomic arousal, heart rate and heart rate variability are widely used for the non-invasive assessment of stress and pain in humans and horses. However, recent advances in pain and stress monitoring are increasingly using electrodermal activity (EDA), as it is a more sensitive and specific measure of sympathetic arousal than heart rate variability. In this study, for the first time, we have collected EDA signals from horses and tested the feasibility of the technique for the assessment of sympathetic arousal. Fifteen horses (six geldings, nine mares, aged 13.11 ± 5.4 years) underwent a long-lasting stimulus (Feeding test) and a short-lasting stimulus (umbrella Startle test) to elicit sympathetic arousal. The protocol was approved by the University of Connecticut. We found that EDA was sensitive to both stimuli. Our results show that EDA can capture sympathetic activation in horses and is a promising tool for non-invasive continuous monitoring of stress, pain, and discomfort in horses.
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Affiliation(s)
- Kia Golzari
- Department of Biomedical Engineering, University of Connecticut, Storrs, CT 06269, USA
| | - Youngsun Kong
- Department of Biomedical Engineering, University of Connecticut, Storrs, CT 06269, USA
| | - Sarah A. Reed
- Department of Animal Science, University of Connecticut, Storrs, CT 06269, USA
| | - Hugo F. Posada-Quintero
- Department of Biomedical Engineering, University of Connecticut, Storrs, CT 06269, USA
- Correspondence: ; Tel.: +1-(860)-486-1556
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Coppersmith DDL, Wang SB, Kleiman EM, Maimone JS, Fedor S, Bentley KH, Millner AJ, Fortgang RG, Picard RW, Beck S, Huffman JC, Nock MK. Real-time digital monitoring of a suicide attempt by a hospital patient. Gen Hosp Psychiatry 2023; 80:35-39. [PMID: 36566615 PMCID: PMC9884520 DOI: 10.1016/j.genhosppsych.2022.12.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Revised: 12/19/2022] [Accepted: 12/20/2022] [Indexed: 12/24/2022]
Abstract
Suicide is among the most devastating problems facing clinicians, who currently have limited tools to predict and prevent suicidal behavior. Here we report on real-time, continuous smartphone and sensor data collected before, during, and after a suicide attempt made by a patient during a psychiatric inpatient hospitalization. We observed elevated and persistent sympathetic nervous system arousal and suicidal thinking leading up to the suicide attempt. This case provides the highest resolution data to date on the psychological, psychophysiological, and behavioral markers of imminent suicidal behavior and highlights new directions for prediction and prevention efforts.
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Affiliation(s)
| | - Shirley B Wang
- Harvard University, Department of Psychology, United States of America
| | - Evan M Kleiman
- Rutgers University, Department of Psychology, United States of America
| | - Joseph S Maimone
- Massachusetts General Hospital, Department of Psychiatry, United States of America
| | - Szymon Fedor
- Massachusetts Institute of Technology, Media Lab, United States of America
| | - Kate H Bentley
- Harvard University, Department of Psychology, United States of America; Massachusetts General Hospital, Department of Psychiatry, United States of America
| | - Alexander J Millner
- Harvard University, Department of Psychology, United States of America; Franciscan Children's Hospital, Mental Health Research, United States of America
| | - Rebecca G Fortgang
- Harvard University, Department of Psychology, United States of America; Massachusetts General Hospital, Department of Psychiatry, United States of America
| | - Rosalind W Picard
- Massachusetts Institute of Technology, Media Lab, United States of America
| | - Stuart Beck
- Massachusetts General Hospital, Department of Psychiatry, United States of America
| | - Jeff C Huffman
- Massachusetts General Hospital, Department of Psychiatry, United States of America
| | - Matthew K Nock
- Harvard University, Department of Psychology, United States of America; Massachusetts General Hospital, Department of Psychiatry, United States of America; Franciscan Children's Hospital, Mental Health Research, United States of America
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Sánchez-Reolid R, López de la Rosa F, Sánchez-Reolid D, López MT, Fernández-Caballero A. Machine Learning Techniques for Arousal Classification from Electrodermal Activity: A Systematic Review. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22228886. [PMID: 36433482 PMCID: PMC9695360 DOI: 10.3390/s22228886] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Revised: 11/14/2022] [Accepted: 11/14/2022] [Indexed: 05/14/2023]
Abstract
This article introduces a systematic review on arousal classification based on electrodermal activity (EDA) and machine learning (ML). From a first set of 284 articles searched for in six scientific databases, fifty-nine were finally selected according to various criteria established. The systematic review has made it possible to analyse all the steps to which the EDA signals are subjected: acquisition, pre-processing, processing and feature extraction. Finally, all ML techniques applied to the features of these signals for arousal classification have been studied. It has been found that support vector machines and artificial neural networks stand out within the supervised learning methods given their high-performance values. In contrast, it has been shown that unsupervised learning is not present in the detection of arousal through EDA. This systematic review concludes that the use of EDA for the detection of arousal is widely spread, with particularly good results in classification with the ML methods found.
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Affiliation(s)
- Roberto Sánchez-Reolid
- Departamento de Sistemas Informáticos, Universidad de Castilla-La Mancha, 02071 Albacete, Spain
- Neurocognition and Emotion Unit, Instituto de Investigación en Informática, 02071 Albacete, Spain
| | | | - Daniel Sánchez-Reolid
- Neurocognition and Emotion Unit, Instituto de Investigación en Informática, 02071 Albacete, Spain
| | - María T. López
- Departamento de Sistemas Informáticos, Universidad de Castilla-La Mancha, 02071 Albacete, Spain
- Neurocognition and Emotion Unit, Instituto de Investigación en Informática, 02071 Albacete, Spain
| | - Antonio Fernández-Caballero
- Departamento de Sistemas Informáticos, Universidad de Castilla-La Mancha, 02071 Albacete, Spain
- Neurocognition and Emotion Unit, Instituto de Investigación en Informática, 02071 Albacete, Spain
- CIBERSAM-ISCIII (Biomedical Research Networking Center in Mental Health, Instituto de Salud Carlos III), 28016 Madrid, Spain
- Correspondence:
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McNaboe R, Beardslee L, Kong Y, Smith BN, Chen IP, Posada-Quintero HF, Chon KH. Design and Validation of a Multimodal Wearable Device for Simultaneous Collection of Electrocardiogram, Electromyogram, and Electrodermal Activity. SENSORS (BASEL, SWITZERLAND) 2022; 22:8851. [PMID: 36433449 PMCID: PMC9695854 DOI: 10.3390/s22228851] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/17/2022] [Revised: 11/04/2022] [Accepted: 11/14/2022] [Indexed: 06/16/2023]
Abstract
Bio-signals are being increasingly used for the assessment of pathophysiological conditions including pain, stress, fatigue, and anxiety. For some approaches, a single signal is not sufficient to provide a comprehensive diagnosis; however, there is a growing consensus that multimodal approaches allow higher sensitivity and specificity. For instance, in visceral pain subjects, the autonomic activation can be inferred using electrodermal activity (EDA) and heart rate variability derived from the electrocardiogram (ECG), but including the muscle activation detected from the surface electromyogram (sEMG) can better differentiate the disease that causes the pain. There is no wearable device commercially capable of collecting these three signals simultaneously. This paper presents the validation of a novel multimodal low profile wearable data acquisition device for the simultaneous collection of EDA, ECG, and sEMG signals. The device was validated by comparing its performance to laboratory-scale reference devices. N = 20 healthy subjects were recruited to participate in a four-stage study that exposed them to an array of cognitive, orthostatic, and muscular stimuli, ensuring the device is sensitive to a range of stressors. Time and frequency domain analyses for all three signals showed significant similarities between our device and the reference devices. Correlation of sEMG metrics ranged from 0.81 to 0.95 and EDA/ECG metrics showed few instances of significant difference in trends between our device and the references. With only minor observed differences, we demonstrated the ability of our device to collect EDA, sEMG, and ECG signals. This device will enable future practical and impactful advances in the field of chronic pain and stress measurement and can confidently be implemented in related studies.
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Affiliation(s)
- Riley McNaboe
- Biomedical Engineering Department, University of Connecticut, Storrs, CT 06269, USA
| | - Luke Beardslee
- Institute for Electronics and Nanotechnology, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - Youngsun Kong
- Biomedical Engineering Department, University of Connecticut, Storrs, CT 06269, USA
| | - Brittany N. Smith
- Biomedical Engineering Department, University of Connecticut, Storrs, CT 06269, USA
| | - I-Ping Chen
- Department of Oral Health and Diagnostic Sciences, School of Dental Medicine, University of Connecticut Health, Farmington, CT 06030, USA
| | | | - Ki H. Chon
- Biomedical Engineering Department, University of Connecticut, Storrs, CT 06269, USA
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Kong Y, Posada-Quintero HF, Chon KH. Multi-level Pain Quantification using a Smartphone and Electrodermal Activity. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:2475-2478. [PMID: 36085748 DOI: 10.1109/embc48229.2022.9871228] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Appropriate prescription of pain medication is challenging because pain is difficult to quantify due to the subjectiveness of pain assessment. Currently, clinicians must entirely rely on pain scales based on patients' assessments. This has been alleged to be one of the causes of drug overdose and addiction, and a contributor to the opioid crisis. Therefore, there is an urgent unmet need for objective pain assessment. Furthermore, as pain can occur anytime and anywhere, ambulatory pain monitoring would be welcomed in practice. In our previous study, we developed electrodermal activity (EDA)-derived indices and implemented them in a smartphone application that can communicate via Bluetooth to an EDA wearable device. While we previously showed high accuracy for high-level pain detection, multi-level pain detection has not been demonstrated. In this paper, we tested our smartphone application with a multi-level pain-induced dataset. The dataset was collected from fifteen subjects who underwent four levels of pain-inducing electrical pulse (EP) stimuli. We then performed statistical analyses and machine-learning techniques to classify multiple pain levels. Significant differences were observed in our EDA-derived indices among no-pain, low-pain, and high-pain segments. A random forest classifier showed 62.6% for the balanced accuracy, and a random forest regressor exhibited 0.441 for the coefficient of determination. Clinical Relevance - This is one of the first studies to present a smartphone application for detecting multiple levels of pain in real time using an EDA wearable device. This work shows the feasibility of ambulatory pain monitoring which can potentially be useful for chronic pain management.
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Constable PA, Marmolejo-Ramos F, Gauthier M, Lee IO, Skuse DH, Thompson DA. Discrete Wavelet Transform Analysis of the Electroretinogram in Autism Spectrum Disorder and Attention Deficit Hyperactivity Disorder. Front Neurosci 2022; 16:890461. [PMID: 35733935 PMCID: PMC9207322 DOI: 10.3389/fnins.2022.890461] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2022] [Accepted: 05/09/2022] [Indexed: 12/30/2022] Open
Abstract
Background To evaluate the electroretinogram waveform in autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD) using a discrete wavelet transform (DWT) approach. Methods A total of 55 ASD, 15 ADHD and 156 control individuals took part in this study. Full field light-adapted electroretinograms (ERGs) were recorded using a Troland protocol, accounting for pupil size, with five flash strengths ranging from –0.12 to 1.20 log photopic cd.s.m–2. A DWT analysis was performed using the Haar wavelet on the waveforms to examine the energy within the time windows of the a- and b-waves and the oscillatory potentials (OPs) which yielded six DWT coefficients related to these parameters. The central frequency bands were from 20–160 Hz relating to the a-wave, b-wave and OPs represented by the coefficients: a20, a40, b20, b40, op80, and op160, respectively. In addition, the b-wave amplitude and percentage energy contribution of the OPs (%OPs) in the total ERG broadband energy was evaluated. Results There were significant group differences (p < 0.001) in the coefficients corresponding to energies in the b-wave (b20, b40) and OPs (op80 and op160) as well as the b-wave amplitude. Notable differences between the ADHD and control groups were found in the b20 and b40 coefficients. In contrast, the greatest differences between the ASD and control group were found in the op80 and op160 coefficients. The b-wave amplitude showed both ASD and ADHD significant group differences from the control participants, for flash strengths greater than 0.4 log photopic cd.s.m–2 (p < 0.001). Conclusion This methodological approach may provide insights about neuronal activity in studies investigating group differences where retinal signaling may be altered through neurodevelopment or neurodegenerative conditions. However, further work will be required to determine if retinal signal analysis can offer a classification model for neurodevelopmental conditions in which there is a co-occurrence such as ASD and ADHD.
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Affiliation(s)
- Paul A. Constable
- College of Nursing and Health Sciences, Caring Futures Institute, Flinders University, Adelaide, SA, Australia
- *Correspondence: Paul A. Constable,
| | - Fernando Marmolejo-Ramos
- Centre for Change and Complexity in Learning, The University of South Australia, Adelaide, SA, Australia
| | - Mercedes Gauthier
- Department of Ophthalmology & Visual Sciences, Faculty of Medicine and Health Sciences, McGill University, Montréal, QC, Canada
| | - Irene O. Lee
- Behavioural and Brain Sciences Unit, Population, Policy and Practice Programme, UCL Great Ormond Street Institute of Child Health, University College London, London, United Kingdom
| | - David H. Skuse
- Behavioural and Brain Sciences Unit, Population, Policy and Practice Programme, UCL Great Ormond Street Institute of Child Health, University College London, London, United Kingdom
| | - Dorothy A. Thompson
- The Tony Kriss Visual Electrophysiology Unit, Clinical and Academic Department of Ophthalmology, Great Ormond Street Hospital for Children NHS Trust, London, United Kingdom
- UCL Great Ormond Street Institute of Child Health, University College London, London, United Kingdom
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Xing Y, Zhang Y, Xiao Z, Yang C, Li J, Cui C, Wang J, Chen H, Li J, Liu C. An Artifact-Resistant Feature SKNAER for Quantifying the Burst of Skin Sympathetic Nerve Activity Signal. BIOSENSORS 2022; 12:355. [PMID: 35624656 PMCID: PMC9138869 DOI: 10.3390/bios12050355] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Revised: 05/15/2022] [Accepted: 05/18/2022] [Indexed: 06/15/2023]
Abstract
Evaluation of sympathetic nerve activity (SNA) using skin sympathetic nerve activity (SKNA) signal has attracted interest in recent studies. However, signal noises may obstruct the accurate location for the burst of SKNA, leading to the quantification error of the signal. In this study, we use the Teager−Kaiser energy (TKE) operator to preprocess the SKNA signal, and then candidates of burst areas were segmented by an envelope-based method. Since the burst of SKNA can also be discriminated by the high-frequency component in QRS complexes of electrocardiogram (ECG), a strategy was designed to reject their influence. Finally, a feature of the SKNA energy ratio (SKNAER) was proposed for quantifying the SKNA. The method was verified by both sympathetic nerve stimulation and hemodialysis experiments compared with traditional heart rate variability (HRV) and a recently developed integral skin sympathetic nerve activity (iSKNA) method. The results showed that SKNAER correlated well with HRV features (r = 0.60 with the standard deviation of NN intervals, 0.67 with low frequency/high frequency, 0.47 with very low frequency) and the average of iSKNA (r = 0.67). SKNAER improved the detection accuracy for the burst of SKNA, with 98.2% for detection rate and 91.9% for precision, inducing increases of 3.7% and 29.1% compared with iSKNA (detection rate: 94.5% (p < 0.01), precision: 62.8% (p < 0.001)). The results from the hemodialysis experiment showed that SKNAER had more significant differences than aSKNA in the long-term SNA evaluation (p < 0.001 vs. p = 0.07 in the fourth period, p < 0.01 vs. p = 0.11 in the sixth period). The newly developed feature may play an important role in continuously monitoring SNA and keeping potential for further clinical tests.
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Affiliation(s)
- Yantao Xing
- School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China; (Y.X.); (Z.X.); (C.Y.); (J.L.)
| | - Yike Zhang
- Division of Cardiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210096, China; (Y.Z.); (C.C.); (H.C.)
| | - Zhijun Xiao
- School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China; (Y.X.); (Z.X.); (C.Y.); (J.L.)
| | - Chenxi Yang
- School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China; (Y.X.); (Z.X.); (C.Y.); (J.L.)
| | - Jiayi Li
- School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China; (Y.X.); (Z.X.); (C.Y.); (J.L.)
| | - Chang Cui
- Division of Cardiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210096, China; (Y.Z.); (C.C.); (H.C.)
| | - Jing Wang
- Division of Nephrology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210096, China;
| | - Hongwu Chen
- Division of Cardiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210096, China; (Y.Z.); (C.C.); (H.C.)
| | - Jianqing Li
- School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China; (Y.X.); (Z.X.); (C.Y.); (J.L.)
| | - Chengyu Liu
- School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China; (Y.X.); (Z.X.); (C.Y.); (J.L.)
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16
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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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17
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Daley MS, Diaz K, Posada-Quintero HF, Kong Y, Chon K, Bolkhovsky JB. Archetypal physiological responses to prolonged wakefulness. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103529] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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18
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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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19
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Taylor MK, Barczak-Scarboro NE, Laver DC, Hernández LM. Combat and blast exposure blunt sympathetic response to acute exercise stress in specialised military men. Stress Health 2022; 38:31-37. [PMID: 34021693 DOI: 10.1002/smi.3069] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/19/2021] [Revised: 05/07/2021] [Accepted: 05/17/2021] [Indexed: 11/06/2022]
Abstract
Electrodermal activity (EDA)-a measure of electrical skin conductance reflecting (exclusive) sympathetic control of the eccrine sweat gland-holds promise as an indicator of central sympathetic activation. The aim of this study was to determine whether combat and blast exposure modulate the EDA response to acute exercise stress in specialised military men. Fifty-one men (age M = 36.1, SD = 6.5) participated in this study as part of the Explosive Ordnance Disposal Operational Health Surveillance System. The EDA complex (i.e., tonic + phasic conductance) was continuously measured throughout a maximal effort, graded exercise test. As expected, exercise stress resulted in measurable, stepwise increases in EDA before tapering at higher exercise intensities. Individuals with more substantial combat exposure and those with blast exposure demonstrated blunted EDA patterns in comparison to their low/nonexposed counterparts. This blunted pattern might imply sub-optimal sympathetic nervous system function in the exposed cohorts and enhances our knowledge of factors influencing resilience in these men.
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Affiliation(s)
| | - Nikki E Barczak-Scarboro
- Naval Health Research Center, San Diego, California, USA.,Innovative Employee Solutions, San Diego, California, USA
| | - D Christine Laver
- Naval Health Research Center, San Diego, California, USA.,Innovative Employee Solutions, San Diego, California, USA
| | - Lisa M Hernández
- Naval Health Research Center, San Diego, California, USA.,Leidos, San Diego, California, USA
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A Real-Time PPG Peak Detection Method for Accurate Determination of Heart Rate during Sinus Rhythm and Cardiac Arrhythmia. BIOSENSORS 2022; 12:bios12020082. [PMID: 35200342 PMCID: PMC8869811 DOI: 10.3390/bios12020082] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/06/2022] [Revised: 01/25/2022] [Accepted: 01/27/2022] [Indexed: 01/20/2023]
Abstract
Objective: We have developed a peak detection algorithm for accurate determination of heart rate, using photoplethysmographic (PPG) signals from a smartwatch, even in the presence of various cardiac rhythms, including normal sinus rhythm (NSR), premature atrial contraction (PAC), premature ventricle contraction (PVC), and atrial fibrillation (AF). Given the clinical need for accurate heart rate estimation in patients with AF, we developed a novel approach that reduces heart rate estimation errors when compared to peak detection algorithms designed for NSR. Methods: Our peak detection method is composed of a sequential series of algorithms that are combined to discriminate the various arrhythmias described above. Moreover, a novel Poincaré plot scheme is used to discriminate between basal heart rate AF and rapid ventricular response (RVR) AF, and to differentiate PAC/PVC from NSR and AF. Training of the algorithm was performed only with Samsung Simband smartwatch data, whereas independent testing data which had more samples than did the training data were obtained from Samsung’s Gear S3 and Galaxy Watch 3. Results: The new PPG peak detection algorithm provides significantly lower average heart rate and interbeat interval beat-to-beat estimation errors—30% and 66% lower—and mean heart rate and mean interbeat interval estimation errors—60% and 77% lower—when compared to the best of the seven other traditional peak detection algorithms that are known to be accurate for NSR. Our new PPG peak detection algorithm was the overall best performers for other arrhythmias. Conclusion: The proposed method for PPG peak detection automatically detects and discriminates between various arrhythmias among different waveforms of PPG data, delivers significantly lower heart rate estimation errors for participants with AF, and reduces the number of false negative peaks. Significance: By enabling accurate determination of heart rate despite the presence of AF with rapid ventricular response or PAC/PVCs, we enable clinicians to make more accurate recommendations for heart rate control from PPG data.
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Posada-Quintero HF, Landon CS, Stavitzski NM, Dean JB, Chon KH. Seizures Caused by Exposure to Hyperbaric Oxygen in Rats Can Be Predicted by Early Changes in Electrodermal Activity. Front Physiol 2022; 12:767386. [PMID: 35069238 PMCID: PMC8767060 DOI: 10.3389/fphys.2021.767386] [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: 09/21/2021] [Accepted: 11/26/2021] [Indexed: 11/13/2022] Open
Abstract
Hyperbaric oxygen (HBO2) is breathed during undersea operations and in hyperbaric medicine. However, breathing HBO2 by divers and patients increases the risk of central nervous system oxygen toxicity (CNS-OT), which ultimately manifests as sympathetic stimulation producing tachycardia and hypertension, hyperventilation, and ultimately generalized seizures and cardiogenic pulmonary edema. In this study, we have tested the hypothesis that changes in electrodermal activity (EDA), a measure of sympathetic nervous system activation, precedes seizures in rats breathing 5 atmospheres absolute (ATA) HBO2. Radio telemetry and a rodent tether apparatus were adapted for use inside a sealed hyperbaric chamber. The tethered rat was free to move inside a ventilated animal chamber that was flushed with air or 100% O2. The animal chamber and hyperbaric chamber (air) were pressurized in parallel at ~1 atmosphere/min. EDA activity was recorded simultaneously with cortical electroencephalogram (EEG) activity, core body temperature, and ambient pressure. We have captured the dynamics of EDA using time-varying spectral analysis of raw EDA (TVSymp), previously developed as a tool for sympathetic tone assessment in humans, adjusted to detect the dynamic changes of EDA in rats that occur prior to onset of CNS-OT seizures. The results show that a significant increase in the amplitude of TVSymp values derived from EDA recordings occurs on average (±SD) 1.9 ± 1.6 min before HBO2-induced seizures. These results, if corroborated in humans, support the use of changes in TVSymp activity as an early "physio-marker" of impending and potentially fatal seizures in divers and patients.
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Affiliation(s)
- Hugo F Posada-Quintero
- Department of Biomedical Engineering, University of Connecticut, Storrs, CT, United States
| | - Carol S Landon
- Department of Molecular Pharmacology and Physiology, Morsani College of Medicine, University of South Florida, Tampa, FL, United States
| | - Nicole M Stavitzski
- Department of Molecular Pharmacology and Physiology, Morsani College of Medicine, University of South Florida, Tampa, FL, United States
| | - Jay B Dean
- Department of Molecular Pharmacology and Physiology, Morsani College of Medicine, University of South Florida, Tampa, FL, United States
| | - Ki H Chon
- Department of Biomedical Engineering, University of Connecticut, Storrs, CT, United States
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22
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Horvers A, Tombeng N, Bosse T, Lazonder AW, Molenaar I. Detecting Emotions through Electrodermal Activity in Learning Contexts: A Systematic Review. SENSORS 2021; 21:s21237869. [PMID: 34883870 PMCID: PMC8659871 DOI: 10.3390/s21237869] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Revised: 11/16/2021] [Accepted: 11/19/2021] [Indexed: 01/10/2023]
Abstract
There is a strong increase in the use of devices that measure physiological arousal through electrodermal activity (EDA). Although there is a long tradition of studying emotions during learning, researchers have only recently started to use EDA to measure emotions in the context of education and learning. This systematic review aimed to provide insight into how EDA is currently used in these settings. The review aimed to investigate the methodological aspects of EDA measures in educational research and synthesize existing empirical evidence on the relation of physiological arousal, as measured by EDA, with learning outcomes and learning processes. The methodological results pointed to considerable variation in the usage of EDA in educational research and indicated that few implicit standards exist. Results regarding learning revealed inconsistent associations between physiological arousal and learning outcomes, which seem mainly due to underlying methodological differences. Furthermore, EDA frequently fluctuated during different stages of the learning process. Compared to this unimodal approach, multimodal designs provide the potential to better understand these fluctuations at critical moments. Overall, this review signals a clear need for explicit guidelines and standards for EDA processing in educational research in order to build a more profound understanding of the role of physiological arousal during learning.
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Posada-Quintero HF, Derrick BJ, Winstead-Derlega C, Gonzalez SI, Claire Ellis M, Freiberger JJ, Chon KH. Time-varying Spectral Index of Electrodermal Activity to Predict Central Nervous System Oxygen Toxicity Symptoms in Divers: Preliminary results. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:1242-1245. [PMID: 34891512 DOI: 10.1109/embc46164.2021.9629924] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
The most effective method to mitigate decompression sickness in divers is hyperbaric oxygen (HBO2) pre-breathing. However, divers breathing HBO2 are at risk for developing central nervous system oxygen toxicity (CNS-OT), which can manifest as symptoms that might impair a diver's performance, or cause more serious symptoms like seizures. In this study, we have collected electrodermal activity (EDA) signals in fifteen subjects at elevated oxygen partial pressures (2.06 ATA, 35 FSW) in the "foxtrot" chamber pool at the Duke University Hyperbaric Center, while performing a cognitive stress test for up to 120 minutes. Specifically, we have computed the time-varying spectral analysis of EDA (TVSymp) as a tool for sympathetic tone assessment and evaluated its feasibility for the prediction of symptoms of CNS-OT in divers. The preliminary results show large increase in the amplitude TVSymp values derived from EDA recordings ~2 minutes prior to expert human adjudication of symptoms related to oxygen toxicity. An early detection based on TVSymp might allow the diver to take countermeasures against the dire consequences of CNS-OT which can lead to drowning.Clinical Relevance-This study provides a sensitive analysis method which indicates a significant increase in the electrodermal activity prior to human expert adjudication of symptoms related to CNS-OT.
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Hossain MB, Posada-Quintero HF, Kong Y, McNaboe R, Chon KH. A Preliminary Study on Automatic Motion Artifact Detection in Electrodermal Activity Data Using Machine Learning. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:6920-6923. [PMID: 34892695 DOI: 10.1109/embc46164.2021.9629513] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
The electrodermal activity (EDA) signal is a sensitive and non-invasive surrogate measure of sympathetic function. Use of EDA has increased in popularity in recent years for such applications as emotion and stress recognition; assessment of pain, fatigue, and sleepiness; diagnosis of depression and epilepsy; and other uses. Recently, there have been several studies using ambulatory EDA recordings, which are often quite useful for analysis of many physiological conditions. Because ambulatory monitoring uses wearable devices, EDA signals are often affected by noise and motion artifacts. An automated noise and motion artifact detection algorithm is therefore of utmost importance for accurate analysis and evaluation of EDA signals. In this paper, we present machine learning-based algorithms for motion artifact detection in EDA signals. With ten subjects, we collected two simultaneous EDA signals from the right and left hands, while instructing the subjects to move only the right hand. Using these data, we proposed a cross-correlation-based approach for non-biased labeling of EDA data segments. A set of statistical, spectral and model-based features were calculated which were then subjected to a feature selection algorithm. Finally, we trained and validated several machine learning methods using a leave-one-subject-out approach. The classification accuracy of the developed model was 83.85% with a standard deviation of 4.91%, which was better than a recent standard method that we considered for comparison to our algorithm.
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Kong Y, Posada-Quintero HF, Chon KH. Female-male Differences Should be Considered in Physical Pain Quantification based on Electrodermal Activity: Preliminary Study. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:6941-6944. [PMID: 34892700 DOI: 10.1109/embc46164.2021.9630637] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Objective pain quantification is an important but difficult goal. Electrodermal activity (EDA) has been widely explored for this purpose, given its reported sensitivity to pain. However, cognitive stress can hinder successful estimation of physical pain when using EDA signals. We collected EDA signals from ten subjects (5 male and 5 female) undergoing pain stimulation, and calculated phasic, tonic, and frequency-domain features. Each subject experienced pain with and without stress. Three low and three high pain sessions were induced using two thermal grills (low-level for visual analog scale [VAS] 4 or 5 and high-level for VAS 7 or more). The Stroop test was performed for inducing cognitive stress. Significant differences between EDA features of painless and pain segments were observed. Significant differences between no pain and stress were also observed. Furthermore, we compared differences in EDA features between females and males under pain and cognitive stress. Frequency-domain EDA features of pain increased with stress for both females and males. Frequency-domain features derived from females also showed higher standard deviation than did those derived from males. We performed machine learning analysis and evaluated the models using leave-one-subject-out cross-validation. We obtained balanced accuracies of 63.5%, 72.4%, and 53.2% (combined, male, and female) when using training data of the same sex and 47.6%, 57.4%, and 42.7% (combined, male, and female) when using different sex for training.Clinical Relevance-Our preliminary results suggest that sex of patients should be considered to increase the accuracy of pain quantification based on EDA in the presence of cognitive stress.
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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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Bashar SK, Ding EY, Walkey AJ, McManus DD, Chon KH. Atrial Fibrillation Prediction from Critically Ill Sepsis Patients. BIOSENSORS 2021; 11:269. [PMID: 34436071 PMCID: PMC8391773 DOI: 10.3390/bios11080269] [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: 07/16/2021] [Revised: 08/05/2021] [Accepted: 08/06/2021] [Indexed: 02/01/2023]
Abstract
Sepsis is defined by life-threatening organ dysfunction during infection and is the leading cause of death in hospitals. During sepsis, there is a high risk that new onset of atrial fibrillation (AF) can occur, which is associated with significant morbidity and mortality. Consequently, early prediction of AF during sepsis would allow testing of interventions in the intensive care unit (ICU) to prevent AF and its severe complications. In this paper, we present a novel automated AF prediction algorithm for critically ill sepsis patients using electrocardiogram (ECG) signals. From the heart rate signal collected from 5-min ECG, feature extraction is performed using the traditional time, frequency, and nonlinear domain methods. Moreover, variable frequency complex demodulation and tunable Q-factor wavelet-transform-based time-frequency methods are applied to extract novel features from the heart rate signal. Using a selected feature subset, several machine learning classifiers, including support vector machine (SVM) and random forest (RF), were trained using only the 2001 Computers in Cardiology data set. For testing the proposed method, 50 critically ill ICU subjects from the Medical Information Mart for Intensive Care (MIMIC) III database were used in this study. Using distinct and independent testing data from MIMIC III, the SVM achieved 80% sensitivity, 100% specificity, 90% accuracy, 100% positive predictive value, and 83.33% negative predictive value for predicting AF immediately prior to the onset of AF, while the RF achieved 88% AF prediction accuracy. When we analyzed how much in advance we can predict AF events in critically ill sepsis patients, the algorithm achieved 80% accuracy for predicting AF events 10 min early. Our algorithm outperformed a state-of-the-art method for predicting AF in ICU patients, further demonstrating the efficacy of our proposed method. The annotations of patients' AF transition information will be made publicly available for other investigators. Our algorithm to predict AF onset is applicable for any ECG modality including patch electrodes and wearables, including Holter, loop recorder, and implantable devices.
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Affiliation(s)
- Syed Khairul Bashar
- Biomedical Engineering Department, University of Connecticut, Storrs, CT 06269, USA;
| | - Eric Y. Ding
- Division of Cardiology, University of Massachusetts Medical School, Worcester, MA 01655, USA; (E.Y.D.); (D.D.M.)
| | - Allan J. Walkey
- Department of Medicine, Boston University School of Medicine, Boston, MA 02118, USA;
| | - David D. McManus
- Division of Cardiology, University of Massachusetts Medical School, Worcester, MA 01655, USA; (E.Y.D.); (D.D.M.)
| | - Ki H. Chon
- Biomedical Engineering Department, University of Connecticut, Storrs, CT 06269, USA;
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Sciaraffa N, Liu J, Aricò P, Flumeri GD, Inguscio BMS, Borghini G, Babiloni F. Multivariate model for cooperation: bridging social physiological compliance and hyperscanning. Soc Cogn Affect Neurosci 2021; 16:193-209. [PMID: 32860692 PMCID: PMC7812636 DOI: 10.1093/scan/nsaa119] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2020] [Revised: 06/30/2020] [Accepted: 08/26/2020] [Indexed: 01/06/2023] Open
Abstract
The neurophysiological analysis of cooperation has evolved over the past 20 years, moving towards the research of common patterns in neurophysiological signals of people interacting. Social physiological compliance (SPC) and hyperscanning represent two frameworks for the joint analysis of autonomic and brain signals, respectively. Each of the two approaches allows to know about a single layer of cooperation according to the nature of these signals: SPC provides information mainly related to emotions, and hyperscanning that related to cognitive aspects. In this work, after the analysis of the state of the art of SPC and hyperscanning, we explored the possibility to unify the two approaches creating a complete neurophysiological model for cooperation considering both affective and cognitive mechanisms We synchronously recorded electrodermal activity, cardiac and brain signals of 14 cooperative dyads. Time series from these signals were extracted, and multivariate Granger causality was computed. The results showed that only when subjects in a dyad cooperate there is a statistically significant causality between the multivariate variables representing each subject. Moreover, the entity of this statistical relationship correlates with the dyad’s performance. Finally, given the novelty of this approach and its exploratory nature, we provided its strengths and limitations.
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Affiliation(s)
- Nicolina Sciaraffa
- Department of Molecular Medicine, Sapienza University of Rome, Rome, Italy.,BrainSigns srl, Rome, Italy
| | - Jieqiong Liu
- School of Psychology and Cognitive Science, Shanghai Changning-ECNU Mental Health Center, East China Normal University, Shanghai, China
| | - Pietro Aricò
- Department of Molecular Medicine, Sapienza University of Rome, Rome, Italy.,BrainSigns srl, Rome, Italy.,IRCCS Fondazione Santa Lucia, Neuroelectrical Imaging and BCI Lab, Rome, Italy
| | - Gianluca Di Flumeri
- Department of Molecular Medicine, Sapienza University of Rome, Rome, Italy.,BrainSigns srl, Rome, Italy.,IRCCS Fondazione Santa Lucia, Neuroelectrical Imaging and BCI Lab, Rome, Italy
| | - Bianca M S Inguscio
- BrainSigns srl, Rome, Italy.,Department of Sense Organs, Sapienza University of Rome, Rome, Italy
| | - Gianluca Borghini
- Department of Molecular Medicine, Sapienza University of Rome, Rome, Italy.,BrainSigns srl, Rome, Italy.,IRCCS Fondazione Santa Lucia, Neuroelectrical Imaging and BCI Lab, Rome, Italy
| | - Fabio Babiloni
- Department of Molecular Medicine, Sapienza University of Rome, Rome, Italy.,BrainSigns srl, Rome, Italy.,College of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou Zhejiang Province, People's Republic of China
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Subramanian S, Purdon PL, Barbieri R, Brown EN. Elementary integrate-and-fire process underlies pulse amplitudes in Electrodermal activity. PLoS Comput Biol 2021; 17:e1009099. [PMID: 34232965 PMCID: PMC8289084 DOI: 10.1371/journal.pcbi.1009099] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Revised: 07/19/2021] [Accepted: 05/21/2021] [Indexed: 11/19/2022] Open
Abstract
Electrodermal activity (EDA) is a direct read-out of sweat-induced changes in the skin’s electrical conductance. Sympathetically-mediated pulsatile changes in skin sweat measured as EDA resemble an integrate-and-fire process, which yields an inverse Gaussian model as the inter-pulse interval distribution. We have previously showed that the inter-pulse intervals in EDA follow an inverse Gaussian distribution. However, the statistical structure of EDA pulse amplitudes has not yet been characterized based on the physiology. Expanding upon the integrate-and-fire nature of sweat glands, we hypothesized that the amplitude of an EDA pulse is proportional to the excess volume of sweat produced compared to what is required to just reach the surface of the skin. We modeled this as the difference of two inverse Gaussian models for each pulse, one which represents the time required to produce just enough sweat to rise to the surface of the skin and one which represents the time requires to produce the actual volume of sweat. We proposed and tested a series of four simplifications of our hypothesis, ranging from a single difference of inverse Gaussians to a single simple inverse Gaussian. We also tested four additional models for comparison, including the lognormal and gamma distributions. All models were tested on EDA data from two subject cohorts, 11 healthy volunteers during 1 hour of quiet wakefulness and a different set of 11 healthy volunteers during approximately 3 hours of controlled propofol sedation. All four models which represent simplifications of our hypothesis outperformed other models across all 22 subjects, as measured by Akaike’s Information Criterion (AIC), as well as mean and maximum distance from the diagonal on a quantile-quantile plot. Our broader model set of four simplifications offered a useful framework to enhance further statistical descriptions of EDA pulse amplitudes. Some of the simplifications prioritize fit near the mode of the distribution, while others prioritize fit near the tail. With this new insight, we can summarize the physiologically-relevant amplitude information in EDA with at most four parameters. Our findings establish that physiologically based probability models provide parsimonious and accurate description of temporal and amplitude characteristics in EDA. Electrodermal activity (EDA) is an indirect read-out of the body’s sympathetic nervous system, or fight-or-flight response, measured as sweat-induced changes in the electrical conductance properties of the skin. Interest is growing in using EDA to track physiological conditions such as stress levels, sleep quality, and emotional states. Our previous worked showed that the times in between EDA pulses obeyed a specific statistical distribution, the inverse Gaussian, that arises from the physiology of EDA production. In this work, we build on that insight to analyze the amplitudes of EDA pulses. In an analysis of EDA data recorded in 11 healthy volunteers during quiet wakefulness and 11 different healthy volunteers during controlled propofol sedation, we establish that the amplitudes of EDA pulses also have specific statistical structure, as the differences of inverse Gaussians, that arises from the physiology of sweat production. We capture that structure using a series of progressively simpler models that each prioritize different parts of the pulse amplitude distribution. Our findings show that a physiologically-based statistical model provides a parsimonious and accurate description of EDA. This enables increased reliability and robustness in analyzing EDA data collected under any circumstance.
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Affiliation(s)
- Sandya Subramanian
- Harvard-Massachusetts Institute of Technology Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
- Department of Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital, Boston, Massachusetts, United States of America
- Institute of Medical Engineering and Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
- * E-mail:
| | - Patrick L. Purdon
- Department of Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital, Boston, Massachusetts, United States of America
| | - Riccardo Barbieri
- Department of Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital, Boston, Massachusetts, United States of America
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
- Department of Electronics, Information, and Bioengineering, Politecnico di Milano, Milan, Italy
| | - Emery N. Brown
- Harvard-Massachusetts Institute of Technology Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
- Department of Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital, Boston, Massachusetts, United States of America
- Institute of Medical Engineering and Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
- Picower Institute of Learning and Memory, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
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Posada-Quintero HF, Kong Y, Chon KH. Objective pain stimulation intensity and pain sensation assessment using machine learning classification and regression based on electrodermal activity. Am J Physiol Regul Integr Comp Physiol 2021; 321:R186-R196. [PMID: 34133246 DOI: 10.1152/ajpregu.00094.2021] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
An objective measure of pain remains an unmet need of people with chronic pain, estimated to be 1/3 of the adult population in the United States. The current gold standard to quantify pain is highly subjective, based upon self-reporting with numerical or visual analog scale (VAS). This subjectivity complicates pain management and exacerbates the epidemic of opioid abuse. We have tested classification and regression machine learning models to objectively estimate pain sensation in healthy subjects using electrodermal activity (EDA). Twenty-three volunteers underwent pain stimulation using thermal grills. Three different "pain stimulation intensities" were induced for each subject, who reported the "pain sensation" right after each stimulus using a VAS (0-10). EDA data were collected throughout the experiment. For machine learning, we computed validated features of EDA based on time-domain decomposition, spectral analysis, and differential features. Models for estimation of pain stimulation intensity and pain sensation achieved maximum macroaveraged geometric mean scores of 69.7% and 69.2%, respectively, when three classes were considered ("No," "Low," and "High"). Regression of levels of stimulation intensity and pain sensation achieved R2 values of 0.357 and 0.47, respectively. Overall, the high variance and inconsistency of VAS scores led to lower performance of pain sensation classification, but regression was better for pain sensation than stimulation intensity. Our results provide that three levels of pain can be quantified with good accuracy and physiological evidence that sympathetic responses recorded by EDA are more correlated to the applied stimuli's intensity than to the pain sensation reported by the subject.
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Affiliation(s)
| | - Youngsun Kong
- Department of Biomedical Engineering, University of Connecticut, Storrs, Connecticut
| | - Ki H Chon
- Department of Biomedical Engineering, University of Connecticut, Storrs, Connecticut
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Kong Y, Posada-Quintero HF, Chon KH. Real-Time High-Level Acute Pain Detection Using a Smartphone and a Wrist-Worn Electrodermal Activity Sensor. SENSORS (BASEL, SWITZERLAND) 2021; 21:3956. [PMID: 34201268 PMCID: PMC8227650 DOI: 10.3390/s21123956] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Revised: 06/02/2021] [Accepted: 06/03/2021] [Indexed: 01/02/2023]
Abstract
The subjectiveness of pain can lead to inaccurate prescribing of pain medication, which can exacerbate drug addiction and overdose. Given that pain is often experienced in patients' homes, there is an urgent need for ambulatory devices that can quantify pain in real-time. We implemented three time- and frequency-domain electrodermal activity (EDA) indices in our smartphone application that collects EDA signals using a wrist-worn device. We then evaluated our computational algorithms using thermal grill data from ten subjects. The thermal grill delivered a level of pain that was calibrated for each subject to be 8 out of 10 on a visual analog scale (VAS). Furthermore, we simulated the real-time processing of the smartphone application using a dataset pre-collected from another group of fifteen subjects who underwent pain stimulation using electrical pulses, which elicited a VAS pain score level 7 out of 10. All EDA features showed significant difference between painless and pain segments, termed for the 5-s segments before and after each pain stimulus. Random forest showed the highest accuracy in detecting pain, 81.5%, with 78.9% sensitivity and 84.2% specificity with leave-one-subject-out cross-validation approach. Our results show the potential of a smartphone application to provide near real-time objective pain detection.
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Affiliation(s)
| | | | - Ki H. Chon
- Biomedical Engineering Department, University of Connecticut, Storrs, CT 06269, USA; (Y.K.); (H.F.P.-Q.)
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32
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Ghiasi S, Greco A, Faes L, Javorka M, Barbieri R, Scilingo EP, Valenza G. Quantifying multidimensional control mechanisms of cardiovascular dynamics during multiple concurrent stressors. Med Biol Eng Comput 2021; 59:775-785. [PMID: 33665768 DOI: 10.1007/s11517-020-02311-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2020] [Accepted: 12/30/2020] [Indexed: 10/22/2022]
Abstract
Heartbeat regulation is achieved through different routes originating from central autonomic network sources, as well as peripheral control mechanisms. While previous studies successfully characterized cardiovascular regulatory mechanisms during a single stressor, to the best of our knowledge, a combination of multiple concurrent elicitations leading to the activation of different autonomic regulatory routes has not been investigated yet. Therefore, in this study, we propose a novel modeling framework for the quantification of heartbeat regulatory mechanisms driven by different neural routes. The framework is evaluated using two heartbeat datasets gathered from healthy subjects undergoing physical and mental stressors, as well as their concurrent administration. Experimental results indicate that more than 70% of the heartbeat regulatory dynamics is driven by the physical stressor when combining physical and cognitive/emotional stressors. The proposed framework provides quantitative insights and novel perspectives for neural activity on cardiac control dynamics, likely highlighting new biomarkers in the psychophysiology and physiopathology fields. A Matlab implementation of the proposed tool is available online.
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Affiliation(s)
- Shadi Ghiasi
- Department of Information Engineering & Research Center E. Piaggio, University of Pisa, Pisa, Italy.
| | - Alberto Greco
- Department of Information Engineering & Research Center E. Piaggio, University of Pisa, Pisa, Italy
| | - Luca Faes
- Department of Engineering, University of Palermo, Palermo, Italy
| | - Michal Javorka
- Department of Physiology and the Biomedical Center Martin, Jessenius Faculty of Medicine, Comenius University in Bratislava, Martin, Slovakia
| | - Riccardo Barbieri
- Department of Electronics, Informatics and Bioengineering, Politecnico di Milano, Milano, Italy
| | - Enzo Pasquale Scilingo
- Department of Information Engineering & Research Center E. Piaggio, University of Pisa, Pisa, Italy
| | - Gaetano Valenza
- Department of Information Engineering & Research Center E. Piaggio, University of Pisa, Pisa, Italy
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Hossain MB, Bashar SK, Lazaro J, Reljin N, Noh Y, Chon KH. A robust ECG denoising technique using variable frequency complex demodulation. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 200:105856. [PMID: 33309076 PMCID: PMC7920915 DOI: 10.1016/j.cmpb.2020.105856] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/12/2020] [Accepted: 11/16/2020] [Indexed: 06/12/2023]
Abstract
BACKGROUND AND OBJECTIVE Electrocardiogram (ECG) is widely used for the detection and diagnosis of cardiac arrhythmias such as atrial fibrillation. Most of the computer-based automatic cardiac abnormality detection algorithms require accurate identification of ECG components such as QRS complexes in order to provide a reliable result. However, ECGs are often contaminated by noise and artifacts, especially if they are obtained using wearable sensors, therefore, identification of accurate QRS complexes often becomes challenging. Most of the existing denoising methods were validated using simulated noise added to a clean ECG signal and they did not consider authentically noisy ECG signals. Moreover, many of them are model-dependent and sampling-frequency dependent and require a large amount of computational time. METHODS This paper presents a novel ECG denoising technique using the variable frequency complex demodulation (VFCDM) algorithm, which considers noises from a variety of sources. We used the sub-band decomposition of the noise-contaminated ECG signals using VFCDM to remove the noise components so that better-quality ECGs could be reconstructed. An adaptive automated masking is proposed in order to preserve the QRS complexes while removing the unnecessary noise components. Finally, the ECG was reconstructed using a dynamic reconstruction rule based on automatic identification of the severity of the noise contamination. The ECG signal quality was further improved by removing baseline drift and smoothing via adaptive mean filtering. RESULTS Evaluation results on the standard MIT-BIH Arrhythmia database suggest that the proposed denoising technique provides superior denoising performance compared to studies in the literature. Moreover, the proposed method was validated using real-life noise sources collected from the noise stress test database (NSTDB) and data from an armband ECG device which contains significant muscle artifacts. Results from both the wearable armband ECG data and NSTDB data suggest that the proposed denoising method provides significantly better performance in terms of accurate QRS complex detection and signal to noise ratio (SNR) improvement when compared to some of the recent existing denoising algorithms. CONCLUSIONS The detailed qualitative and quantitative analysis demonstrated that the proposed denoising method has been robust in filtering varieties of noises present in the ECG. The QRS detection performance of the denoised armband ECG signals indicates that the proposed denoising method has the potential to increase the amount of usable armband ECG data, thus, the armband device with the proposed denoising method could be used for long term monitoring of atrial fibrillation.
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Affiliation(s)
- Md-Billal Hossain
- Department of Biomedical Engineering, University of Connecticut, 260 Glenbrook Road, Unit 3247 Storrs, CT 06269-3247, USA
| | - Syed Khairul Bashar
- Department of Biomedical Engineering, University of Connecticut, 260 Glenbrook Road, Unit 3247 Storrs, CT 06269-3247, USA
| | - Jesus Lazaro
- Aragon Institute for Engineering Research, University of Zaragoza, Spain
| | - Natasa Reljin
- Department of Biomedical Engineering, University of Connecticut, 260 Glenbrook Road, Unit 3247 Storrs, CT 06269-3247, USA
| | - Yeonsik Noh
- College of Nursing/Department of Electrical and Computer Engineering, University of Massachusetts Amherst, USA
| | - Ki H Chon
- Department of Biomedical Engineering, University of Connecticut, 260 Glenbrook Road, Unit 3247 Storrs, CT 06269-3247, USA.
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Parent M, Albuquerque I, Tiwari A, Cassani R, Gagnon JF, Lafond D, Tremblay S, Falk TH. PASS: A Multimodal Database of Physical Activity and Stress for Mobile Passive Body/ Brain-Computer Interface Research. Front Neurosci 2020; 14:542934. [PMID: 33363449 PMCID: PMC7753022 DOI: 10.3389/fnins.2020.542934] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2020] [Accepted: 11/16/2020] [Indexed: 12/27/2022] Open
Abstract
With the burgeoning of wearable devices and passive body/brain-computer interfaces (B/BCIs), automated stress monitoring in everyday settings has gained significant attention recently, with applications ranging from serious games to clinical monitoring. With mobile users, however, challenges arise due to other overlapping (and potentially confounding) physiological responses (e.g., due to physical activity) that may mask the effects of stress, as well as movement artifacts that can be introduced in the measured signals. For example, the classical increase in heart rate can no longer be attributed solely to stress and could be caused by the activity itself. This makes the development of mobile passive B/BCIs challenging. In this paper, we introduce PASS, a multimodal database of Physical Activity and StresS collected from 48 participants. Participants performed tasks of varying stress levels at three different activity levels and provided quantitative ratings of their perceived stress and fatigue levels. To manipulate stress, two video games (i.e., a calm exploration game and a survival game) were used. Peripheral physical activity (electrocardiography, electrodermal activity, breathing, skin temperature) as well as cerebral activity (electroencephalography) were measured throughout the experiment. A complete description of the experimental protocol is provided and preliminary analyses are performed to investigate the physiological reactions to stress in the presence of physical activity. The PASS database, including raw data and subjective ratings has been made available to the research community at http://musaelab.ca/pass-database/. It is hoped that this database will help advance mobile passive B/BCIs for use in everyday settings.
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Affiliation(s)
- Mark Parent
- INRS-EMT, Université du Québec, Montréal, QC, Canada
| | | | | | | | | | - Daniel Lafond
- Thales Research and Technology Canada, Quebec City, QC, Canada
| | | | - Tiago H Falk
- INRS-EMT, Université du Québec, Montréal, QC, Canada.,PERFORM Center, Concordia University, Montréal, QC, Canada
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Barquero-Pérez Ó, Cámara-Vázquez MA, Vadillo-Valderrama A, Goya-Esteban R. Autonomic Nervous System and Recall Modeling in Audiovisual Emotion-Mediated Advertising Using Partial Least Squares-Path Modeling. Front Psychol 2020; 11:576771. [PMID: 33192889 PMCID: PMC7662410 DOI: 10.3389/fpsyg.2020.576771] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2020] [Accepted: 09/02/2020] [Indexed: 11/13/2022] Open
Abstract
Interest in improving advertisement impact on potential consumers has increased recently. One well-known strategy is to use emotion-based advertisement. In this approach, an emotional link with consumers is created, aiming to enhance the memorization process. In recent years, Neuromarketing techniques have allowed us to obtain more objective information on this process. However, the role of the autonomic nervous system (ANS) in the memorization process using emotional advertisement still needs further research. In this work, we propose the use of two physiological signals, namely, an electrocardiogram (heart rate variability, HRV) and electrodermal activity (EDA), to obtain indices assessing the ANS. We measured these signals in 43 subjects during the observation of six different spots, each conveying a different emotion (rational, disgust, anger, surprise, and sadness). After observing the spots, subjects were asked to answer a questionnaire to measure the spontaneous and induced recall. We propose the use of a statistical data-driven model based on Partial Least Squares-Path Modeling (PSL-PM), which allows us to incorporate contextual knowledge by defining a relational graph of unobservable variables (latent variables, LV), which are, in turn, estimated by measured variables (indices of the ANS). We defined four LVs, namely, sympathetic, vagal, ANS, and recall. Sympathetic and vagal are connected to the ANS, the latter being a measure of recall, estimated from a questionnaire. The model is then fitted to the data. Results showed that vagal activity (described by HRV indices) is the most critical factor to describe ANS activity; they are inversely related except for the spot, which is mainly rational. The model captured a moderate-to-high variability of ANS behavior, ranging from 38% up to 64% of the explained variance of the ANS. However, it can explain at most 11% of the recall score of the subjects. The proposed approach allows for the easy inclusion of more physiological measurements and provides an easy-to-interpret model of the ANS response to emotional advertisement.
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Affiliation(s)
- Óscar Barquero-Pérez
- Signal Theory and Communications Department, University Rey Juan Carlos, Madrid, Spain
| | | | | | - Rebeca Goya-Esteban
- Signal Theory and Communications Department, University Rey Juan Carlos, Madrid, Spain
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Kong Y, Posada-Quintero HF, Chon KH. Pain Detection using a Smartphone in Real Time. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2020:4526-4529. [PMID: 33019000 DOI: 10.1109/embc44109.2020.9176077] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
We developed an objective real-time pain detection method using a smartphone and a wrist-worn wearable device to collect electrodermal activity (EDA) signals. Recently, various researchers have developed pain management applications. However, they rely on subjective self-reported pain scores or the video camera of a smartphone to detect pain, but the latter method's accuracy needs further improvement. In our work, we use a wrist-worn EDA device which transmits data via Bluetooth to a smartphone. A smartphone application was developed to analyze the EDA data so that near real-time processed pain detection information can be displayed. The analysis of EDA is based on estimating time-varying spectral power in the frequency range (0.08-0.24 Hz) associated with the sympathetic nervous system. This time-varying characterization of EDA is termed TVSymp. In this work, we also examined whether removing baseline EDA fluctuations from TVSymp would provide more accurate results. This was carried out by taking the moving average of the EDA response prior to stimulus and subtracting that value from the EDA response post stimulus. This approach is termed modified TVSymp (MTVSymp). Pain stimuli were induced in ten subjects using a thermal grill, which gives intense pain perception without damaging skin tissues. We compared both TVSymp and MTVSymp in detecting pain induced by the thermal grill using machine learning approaches. We found the accuracy of pain detection of TVSymp and MTVSymp to be 80% and 90%, respectively.
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Posada-Quintero HF, Kong Y, Nguyen K, Tran C, Beardslee L, Chen L, Guo T, Cong X, Feng B, Chon KH. Using electrodermal activity to validate multilevel pain stimulation in healthy volunteers evoked by thermal grills. Am J Physiol Regul Integr Comp Physiol 2020; 319:R366-R375. [PMID: 32726157 DOI: 10.1152/ajpregu.00102.2020] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
We have tested the feasibility of thermal grills, a harmless method to induce pain. The thermal grills consist of interlaced tubes that are set at cool or warm temperatures, creating a painful "illusion" (no tissue injury is caused) in the brain when the cool and warm stimuli are presented collectively. Advancement in objective pain assessment research is limited because the gold standard, the self-reporting pain scale, is highly subjective and only works for alert and cooperative patients. However, the main difficulty for pain studies is the potential harm caused to participants. We have recruited 23 subjects in whom we induced electric pulses and thermal grill (TG) stimulation. The TG effectively induced three different levels of pain, as evidenced by the visual analog scale (VAS) provided by the subjects after each stimulus. Furthermore, objective physiological measurements based on electrodermal activity showed a significant increase in levels as stimulation level increased. We found that VAS was highly correlated with the TG stimulation level. The TG stimulation safely elicited pain levels up to 9 out of 10. The TG stimulation allows for extending studies of pain to ranges of pain in which other stimuli are harmful.
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Affiliation(s)
| | | | | | - Cara Tran
- University of Connecticut, Storrs, Connecticut
| | - Luke Beardslee
- Emory University School of Medicine Department of Surgery, Atlanta, Georgia
| | - Longtu Chen
- University of Connecticut, Storrs, Connecticut
| | | | | | - Bin Feng
- University of Connecticut, Storrs, Connecticut
| | - Ki H Chon
- University of Connecticut, Storrs, Connecticut
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Ghiasi S, Greco A, Barbieri R, Scilingo EP, Valenza G. Assessing Autonomic Function from Electrodermal Activity and Heart Rate Variability During Cold-Pressor Test and Emotional Challenge. Sci Rep 2020; 10:5406. [PMID: 32214158 PMCID: PMC7096472 DOI: 10.1038/s41598-020-62225-2] [Citation(s) in RCA: 45] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2019] [Accepted: 02/28/2020] [Indexed: 12/11/2022] Open
Abstract
Standard functional assessment of autonomic nervous system (ANS) activity on cardiovascular control relies on spectral analysis of heart rate variability (HRV) series. However, difficulties in obtaining a reliable measure of sympathetic activity from HRV spectra limits the exploitation of sympatho-vagal metrics. On the other hand, measures of electrodermal activity (EDA) have been demonstrated to provide a reliable quantifier of sympathetic dynamics. In this study we propose novel indices of phasic autonomic regulation mechanisms by combining HRV and EDA correlates and thoroughly investigating their time-varying dynamics. HRV and EDA series were gathered from 26 healthy subjects during a cold-pressor test and emotional stimuli. Instantaneous linear and nonlinear (bispectral) estimates of vagal dynamics were obtained from HRV through inhomogeneous point-process models, and combined with a sensitive maker of sympathetic tone from EDA spectral power. A wavelet decomposition analysis was applied to estimate phasic components of the proposed sympatho-vagal indices. Results show significant statistical differences for the proposed indices between the cold-pressor elicitation and previous resting state. Furthermore, an accuracy of 73.08% was achieved for the automatic emotional valence recognition. The proposed nonlinear processing of phasic ANS markers brings novel insights on autonomic functioning that can be exploited in the field of affective computing and psychophysiology.
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Affiliation(s)
- Shadi Ghiasi
- Department of Information Engineering & Bioengineering and Robotics Research Center E. Piaggio, School of Engineering, University of Pisa, Pisa, Italy.
| | - Alberto Greco
- Department of Information Engineering & Bioengineering and Robotics Research Center E. Piaggio, School of Engineering, University of Pisa, Pisa, Italy
| | - Riccardo Barbieri
- Department of Electronics, Informatics and Bioengineering, Politecnico di Milano, Milano, Italy
| | - Enzo Pasquale Scilingo
- Department of Information Engineering & Bioengineering and Robotics Research Center E. Piaggio, School of Engineering, University of Pisa, Pisa, Italy
| | - Gaetano Valenza
- Department of Information Engineering & Bioengineering and Robotics Research Center E. Piaggio, School of Engineering, University of Pisa, Pisa, Italy
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Daviaux Y, Bonhomme E, Ivers H, de Sevin É, Micoulaud-Franchi JA, Bioulac S, Morin CM, Philip P, Altena E. Event-Related Electrodermal Response to Stress: Results From a Realistic Driving Simulator Scenario. HUMAN FACTORS 2020; 62:138-151. [PMID: 31050918 DOI: 10.1177/0018720819842779] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
OBJECTIVE The study goal was to test whether induced stress during driving could be measured at the event level through electrodermal activity responses. BACKGROUND Stress measured in simulation scenarios could thus far show an overall change in the stress state, but not be well attributed to acute stressful events. Driving simulator scenarios that induce stress measurable at the event level in realistic situations are thus warranted. As such, acute stress reactions can be measured in the context of changing situational factors such as fatigue, substance abuse, or medical conditions. METHOD Twelve healthy female participants drove the same route numerous times in a driving simulator, each time with different random traffic events occurring throughout. During one of the scenarios, unknown to the participants, 10 programmed neutral traffic events occurred, whereas in another scenario, at the same location, 10 stressful events occurred. RESULTS Electrodermal response results showed both effects of scenario type and of events. The amplitude of the electrodermal response was significantly correlated with subjective stress experience. CONCLUSION We conclude that our developed ecological driving simulation scenarios can be used to induce and measure stress at the event level. APPLICATION The developed simulator scenarios enable us to measure stress reactions in driving situations at the time when the event actually happens. With these scenarios, we can measure how situational factors, such as fatigue or substance abuse, can change immediate stress reactions when driving. We can further measure more specifically how induced driving stress can affect physical and mental functioning afterward.
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Posada-Quintero HF, Chon KH. Innovations in Electrodermal Activity Data Collection and Signal Processing: A Systematic Review. SENSORS (BASEL, SWITZERLAND) 2020; 20:E479. [PMID: 31952141 PMCID: PMC7014446 DOI: 10.3390/s20020479] [Citation(s) in RCA: 128] [Impact Index Per Article: 32.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/25/2019] [Revised: 01/06/2020] [Accepted: 01/11/2020] [Indexed: 02/05/2023]
Abstract
The electrodermal activity (EDA) signal is an electrical manifestation of the sympathetic innervation of the sweat glands. EDA has a history in psychophysiological (including emotional or cognitive stress) research since 1879, but it was not until recent years that researchers began using EDA for pathophysiological applications like the assessment of fatigue, pain, sleepiness, exercise recovery, diagnosis of epilepsy, neuropathies, depression, and so forth. The advent of new devices and applications for EDA has increased the development of novel signal processing techniques, creating a growing pool of measures derived mathematically from the EDA. For many years, simply computing the mean of EDA values over a period was used to assess arousal. Much later, researchers found that EDA contains information not only in the slow changes (tonic component) that the mean value represents, but also in the rapid or phasic changes of the signal. The techniques that have ensued have intended to provide a more sophisticated analysis of EDA, beyond the traditional tonic/phasic decomposition of the signal. With many researchers from the social sciences, engineering, medicine, and other areas recently working with EDA, it is timely to summarize and review the recent developments and provide an updated and synthesized framework for all researchers interested in incorporating EDA into their research.
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Affiliation(s)
| | - Ki H. Chon
- Department of Biomedical Engineering, University of Connecticut, Storrs, CT 06269, USA;
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Posada-Quintero HF, Reljin N, Moutran A, Georgopalis D, Lee ECH, Giersch GEW, Casa DJ, Chon KH. Mild Dehydration Identification Using Machine Learning to Assess Autonomic Responses to Cognitive Stress. Nutrients 2019; 12:nu12010042. [PMID: 31877912 PMCID: PMC7019291 DOI: 10.3390/nu12010042] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2019] [Accepted: 12/19/2019] [Indexed: 12/17/2022] Open
Abstract
The feasibility of detecting mild dehydration by using autonomic responses to cognitive stress was studied. To induce cognitive stress, subjects (n = 17) performed the Stroop task, which comprised four minutes of rest and four minutes of test. Nine indices of autonomic control based on electrodermal activity (EDA) and pulse rate variability (PRV) were obtained during both the rest and test stages of the Stroop task. Measurements were taken on three consecutive days in which subjects were "wet" (not dehydrated) and "dry" (experiencing mild dehydration caused by fluid restriction). Nine approaches were tested for classification of "wet" and "dry" conditions: (1) linear (LDA) and (2) quadratic discriminant analysis (QDA), (3) logistic regression, (4) support vector machines (SVM) with cubic, (5) fine Gaussian kernel, (6) medium Gaussian kernel, (7) a k-nearest neighbor (KNN) classifier, (8) decision trees, and (9) subspace ensemble of KNN classifiers (SE-KNN). The classification models were tested for all possible combinations of the nine indices of autonomic nervous system control, and their performance was assessed by using leave-one-subject-out cross-validation. An overall accuracy of mild dehydration detection was 91.2% when using the cubic SE-KNN and indices obtained only at rest, and the accuracy was 91.2% when using the cubic SVM classifiers and indices obtained only at test. Accuracy was 86.8% when rest-to-test increments in the autonomic indices were used along with the KNN and QDA classifiers. In summary, measures of autonomic function based on EDA and PRV are suitable for detecting mild dehydration and could potentially be used for the noninvasive testing of dehydration.
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Affiliation(s)
- Hugo F. Posada-Quintero
- Department of Biomedical Engineering, University of Connecticut, Storrs, CT 06269, USA; (N.R.); (A.M.); (D.G.); (K.H.C.)
- Correspondence:
| | - Natasa Reljin
- Department of Biomedical Engineering, University of Connecticut, Storrs, CT 06269, USA; (N.R.); (A.M.); (D.G.); (K.H.C.)
| | - Aurelie Moutran
- Department of Biomedical Engineering, University of Connecticut, Storrs, CT 06269, USA; (N.R.); (A.M.); (D.G.); (K.H.C.)
| | - Dimitrios Georgopalis
- Department of Biomedical Engineering, University of Connecticut, Storrs, CT 06269, USA; (N.R.); (A.M.); (D.G.); (K.H.C.)
| | - Elaine Choung-Hee Lee
- Department of Kinesiology, Human Performance Laboratory, University of Connecticut, Storrs, CT 06269, USA; (E.C.-H.L.); (G.E.W.G.); (D.J.C.)
| | - Gabrielle E. W. Giersch
- Department of Kinesiology, Human Performance Laboratory, University of Connecticut, Storrs, CT 06269, USA; (E.C.-H.L.); (G.E.W.G.); (D.J.C.)
| | - Douglas J. Casa
- Department of Kinesiology, Human Performance Laboratory, University of Connecticut, Storrs, CT 06269, USA; (E.C.-H.L.); (G.E.W.G.); (D.J.C.)
| | - Ki H. Chon
- Department of Biomedical Engineering, University of Connecticut, Storrs, CT 06269, USA; (N.R.); (A.M.); (D.G.); (K.H.C.)
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Wickramasuriya DS, Qi C, Faghih RT. A State-Space Approach for Detecting Stress from Electrodermal Activity. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2019; 2018:3562-3567. [PMID: 30441148 DOI: 10.1109/embc.2018.8512928] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
The human body responds to neurocognitive stress in multiple ways through its autonomic nervous system. Increases in heart rate, salivary cortisol and skin conductance level are often observed accompanying high levels of stress. Stress can also take on different forms including emotional, cognitive and motivational. While a precise definition for stress is lacking, a pertinent issue is to quantify the state of psychological stress manifested in the nervous system. State-space models have previously been applied to estimate an unobserved neural state (e.g. learning, consciousness) from physiological signal measurements and data collected during behavioral experiments. In this paper, we relate stress to the probability that a phasic driver impulse occurs in skin conductance signals. We apply state-space modeling to extracted binary measures to continuously track a stress level across episodes of cognitive and emotional stress as well as relaxation. Results demonstrate a promising approach for tracking stress through wearable devices.
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Bashar SK, Ding E, Walkey AJ, McManus DD, Chon KH. Noise Detection in Electrocardiogram Signals for Intensive Care Unit Patients. IEEE ACCESS : PRACTICAL INNOVATIONS, OPEN SOLUTIONS 2019; 7:88357-88368. [PMID: 33133877 PMCID: PMC7597656 DOI: 10.1109/access.2019.2926199] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Long term electrocardiogram (ECG) signals recorded in an intensive care unit (ICU) are often corrupted by severe motion and noise artifacts (MNA), which may lead to many false alarms including inaccurate detection of atrial fibrillation (AF). We developed an automated method to detect MNA from ECG recordings in the Medical Information Mart for Intensive Care (MIMIC) III database. Since AF detection is often based on identification of irregular RR intervals derived from the QRS complexes, the main design focus of our MNA detection algorithm was to identify corrupted QRS complexes of the ECG signals. The MNA in the MIMIC III database contain not only motion-induced noise, but also a plethora of non-ECG waveforms, which must also be automatically identified. Our algorithm is designed to first discriminate between ECG and non-ECG waveforms using both time and spectral-domain properties. For the segments of data containing ECG waveforms, a time-frequency spectrum and its sub-band decomposition approach were used to identify MNA, and high frequency noise ECG segments, respectively. The algorithm was tested on data from 35 subjects in normal sinus rhythm and 25 AF subjects. The proposed method is shown to accurately discriminate between segments that contained real ECG waveforms and those that did not, even though the latter were numerous in some subjects. In addition, we found a significant reduction (> 94%) in false positive detection of AF in normal subjects when our MNA detection algorithm was used. Without using it, we inaccurately detected AF owing to the MNA.
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Affiliation(s)
- Syed Khairul Bashar
- Department of Biomedical Engineering, University of Connecticut, Storrs, CT 06269, USA
| | - Eric Ding
- Division of Cardiology, University of Massachusetts Medical School, Worcester, MA 01655, USA
| | - Allan J. Walkey
- Department of Medicine, Boston University School of Medicine, Boston, MA 02118, USA
| | - David D. McManus
- Division of Cardiology, University of Massachusetts Medical School, Worcester, MA 01655, USA
| | - Ki H. Chon
- Department of Biomedical Engineering, University of Connecticut, Storrs, CT 06269, USA
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Posada-Quintero HF, Bolkhovsky JB. Machine Learning models for the Identification of Cognitive Tasks using Autonomic Reactions from Heart Rate Variability and Electrodermal Activity. Behav Sci (Basel) 2019; 9:bs9040045. [PMID: 31027251 PMCID: PMC6523197 DOI: 10.3390/bs9040045] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2019] [Revised: 04/19/2019] [Accepted: 04/23/2019] [Indexed: 11/28/2022] Open
Abstract
Indices of heart rate variability (HRV) and electrodermal activity (EDA), in conjunction with machine learning models, were used to identify one of three tasks a subject is performing based on autonomic response elicited by the specific task. Using non-invasive measures to identify the task performed by a subject can help to provide individual monitoring and guidance to avoid the consequences of reduced performance due to fatigue or other stressors. In the present study, sixteen subjects were enrolled to undergo three tasks: The psychomotor vigilance task (PVT), an auditory working memory task (the n-back paradigm), and a visual search (ship search, SS). Electrocardiogram (ECG) (for HRV analysis) and EDA data were collected during the tests. For task-classification, we tested four machine learning classification tools: k-nearest neighbor classifier (KNN), support vector machines (SVM), decision trees, and discriminant analysis (DA). Leave-one-subject-out cross-validation was used to evaluate the performance of the constructed models to prevent overfitting. The most accurate models were the KNN (66%), linear SVM (62%), and linear DA (62%). The results of this study showed that it is possible to identify the task a subject is performing based on the subject’s autonomic reactions (from HRV and EDA). This information can be used to monitor individuals within a larger group to assist in reducing errors caused by uncoordinated or poor performance by allowing for automated tracking of and communication between individuals.
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Kim AY, Jang EH, Choi KW, Jeon HJ, Byun S, Sim JY, Choi JH, Yu HY. Skin conductance responses in Major Depressive Disorder (MDD) under mental arithmetic stress. PLoS One 2019; 14:e0213140. [PMID: 30943195 PMCID: PMC6447153 DOI: 10.1371/journal.pone.0213140] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2018] [Accepted: 02/17/2019] [Indexed: 12/27/2022] Open
Abstract
Depressive symptoms are related to abnormalities in the autonomic nervous system (ANS), and physiological signals that can be used to measure and evaluate such abnormalities have previously been used as indicators for diagnosing mental disorder, such as major depressive disorder (MDD). In this study, we investigate the feasibility of developing an objective measure of depressive symptoms that is based on examining physiological abnormalities in individuals when they are experiencing mental stress. To perform this, we recruited 30 patients with MDD and 31 healthy controls. Then, skin conductance (SC) was measured during five 5-min experimental phases, comprising baseline, mental stress, recovery from the stress, relaxation, and recovery from the relaxation, respectively. For each phase, the mean amplitude of the skin conductance level (MSCL), standard deviations of the SCL (SDSCL), slope of the SCL (SSCL), mean amplitude of the non-specific skin conductance responses (MSCR), number of non-specific skin conductance responses (NSCR), and power spectral density (PSD) were evaluated from the SC signals, producing 30 parameters overall (six features for each phase). These features were used as input data for a support vector machine (SVM) algorithm designed to distinguish MDD patients from healthy controls based on their physiological responses. Statistical tests showed that the main effect of task was significant in all SC features, and the main effect of group was significant in MSCL, SDSCL, SSCL, and PSD. In addition, the proposed algorithm achieved 70% accuracy, 70% sensitivity, 71% specificity, 70% positive predictive value, 71% negative predictive value in classifying MDD patients and healthy controls. These results demonstrated that it is possible to extract meaningful features that reflect changes in ANS responses to various stimuli. Using these features, detection of MDD was feasible, suggesting that SC analysis has great potential for future diagnostics and prediction of depression based on objective interpretation of depressive states.
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Affiliation(s)
- Ah Young Kim
- Bio-Medical IT Convergence Research Department, Electronics and Telecommunications Research Institute(ETRI), Gajeong-Ro, Yoseong-Gu, Daejeon, Rep. of Korea
- * E-mail:
| | - Eun Hye Jang
- Bio-Medical IT Convergence Research Department, Electronics and Telecommunications Research Institute(ETRI), Gajeong-Ro, Yoseong-Gu, Daejeon, Rep. of Korea
| | - Kwan Woo Choi
- Department of Psychiatry, Depression Center, Samsung Medical Center, Sungkyunkwan University School of Medicine, Irwon-ro, Gangnam-gu, Seoul, Rep. of Korea
| | - Hong Jin Jeon
- Department of Psychiatry, Depression Center, Samsung Medical Center, Sungkyunkwan University School of Medicine, Irwon-ro, Gangnam-gu, Seoul, Rep. of Korea
| | - Sangwon Byun
- Department of Electronics Engineering, Incheon National University, Incheon, Korea
| | - Joo Yong Sim
- Bio-Medical IT Convergence Research Department, Electronics and Telecommunications Research Institute(ETRI), Gajeong-Ro, Yoseong-Gu, Daejeon, Rep. of Korea
| | - Jae Hun Choi
- Bio-Medical IT Convergence Research Department, Electronics and Telecommunications Research Institute(ETRI), Gajeong-Ro, Yoseong-Gu, Daejeon, Rep. of Korea
| | - Han Young Yu
- Bio-Medical IT Convergence Research Department, Electronics and Telecommunications Research Institute(ETRI), Gajeong-Ro, Yoseong-Gu, Daejeon, Rep. of Korea
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Bashar SK, Walkey AJ, McManus DD, Chon KH. VERB: VFCDM-Based Electrocardiogram Reconstruction and Beat Detection Algorithm. IEEE ACCESS : PRACTICAL INNOVATIONS, OPEN SOLUTIONS 2019; 7:13856-13866. [PMID: 31741809 PMCID: PMC6860377 DOI: 10.1109/access.2019.2894092] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
We have developed a novel method to accurately detect QRS complex peaks using the variable frequency complex demodulation (VFCDM) method. The approach's novelty stems from reconstructing an ECG signal using only the frequency components associated with the QRS waveforms by VFCDM decomposition. After signal reconstruction, both top and bottom sides of the signal are used for peak detection, after which we compare locations of the peaks detected from both sides to ensure false peaks are minimized. Finally, we impose position-dependent adaptive thresholds to remove any remaining false peaks from the prior step. We applied the proposed method to the widely benchmarked MIT-BIH arrhythmia dataset, and obtained among the best results compared to many of the recently published methods. Our approach resulted in 99.94% sensitivity, 99.95% positive predictive value and a 0.11% detection error rate. Three other datasets-the MIMIC III database, University of Massachusetts atrial fibrillation data, and SCUBA diving in salt water ECG data-were used to further test the robustness of our proposed algorithm. For all these three datasets, our method retained consistently higher accuracy when compared to the BioSig Matlab toolbox, which is publicly available and known to be reliable for ECG peak detection.
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Affiliation(s)
| | - Allan J. Walkey
- Department of Medicine, Boston University School of Medicine, Boston, MA, USA
| | - David D. McManus
- Division of Cardiology, University of Massachusetts Medical School, Worcester, MA, USA
| | - Ki H. Chon
- University of Connecticut, Storrs, CT, USA
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47
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Posada-Quintero HF, Reljin N, Mills C, Mills I, Florian JP, VanHeest JL, Chon KH. Time-varying analysis of electrodermal activity during exercise. PLoS One 2018; 13:e0198328. [PMID: 29856815 PMCID: PMC5983430 DOI: 10.1371/journal.pone.0198328] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2018] [Accepted: 05/17/2018] [Indexed: 11/28/2022] Open
Abstract
The electrodermal activity (EDA) is a useful tool for assessing skin sympathetic nervous activity. Using spectral analysis of EDA data at rest, we have previously found that the spectral band which is the most sensitive to central sympathetic control is largely confined to 0.045 to 0.25 Hz. However, the frequency band associated with sympathetic control in EDA has not been studied for exercise conditions. Establishing the band limits more precisely is important to ensure the accuracy and sensitivity of the technique. As exercise intensity increases, it is intuitive that the frequencies associated with the autonomic dynamics should also increase accordingly. Hence, the aim of this study was to examine the appropriate frequency band associated with the sympathetic nervous system in the EDA signal during exercise. Eighteen healthy subjects underwent a sub-maximal exercise test, including a resting period, walking, and running, until achieving 85% of maximum heart rate. Both EDA and ECG data were measured simultaneously for all subjects. The ECG was used to monitor subjects' instantaneous heart rate, which was used to set the experiment's end point. We found that the upper bound of the frequency band (Fmax) containing the EDA spectral power significantly shifted to higher frequencies when subjects underwent prolonged low-intensity (Fmax ~ 0.28) and vigorous-intensity exercise (Fmax ~ 0.37 Hz) when compared to the resting condition. In summary, we have found shifting of the sympathetic dynamics to higher frequencies in the EDA signal when subjects undergo physical activity.
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Affiliation(s)
| | - Natasa Reljin
- University of Connecticut, Storrs, CT, United States of America
| | - Craig Mills
- University of Connecticut, Storrs, CT, United States of America
| | - Ian Mills
- University of Connecticut, Storrs, CT, United States of America
| | - John P. Florian
- Navy Experimental Diving Unit, Panama City, FL, United States of America
| | | | - Ki H. Chon
- University of Connecticut, Storrs, CT, United States of America
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48
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Gilfriche P, Arsac LM, Daviaux Y, Diaz-Pineda J, Miard B, Morellec O, André JM. Highly sensitive index of cardiac autonomic control based on time-varying respiration derived from ECG. Am J Physiol Regul Integr Comp Physiol 2018; 315:R469-R478. [PMID: 29741930 DOI: 10.1152/ajpregu.00057.2018] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Frequency-domain indices of heart rate variability (HRV) have been used as markers of sympathovagal balance. However, they have been shown to be degraded by interindividual or task-dependent variability, and especially variations in breathing frequency. The study introduces a method to analyze respiration-(vagally) mediated HRV, to better assess subtle variations in sympathovagal balance using ECG recordings. The method enhances HRV analysis by focusing the quantification of respiratory sinus arrhythmia (RSA) gain on the respiratory frequency. To this end, instantaneous respiratory frequency was obtained with ECG-derived respiration (EDR) and was used for variable frequency complex demodulation (VFCDM) of R-R intervals to extract RSA. The ability to detect cognitive stress in 27 subjects (athletes and nonathletes) was taken as a quality criterion to compare our method to other HRV analyses: Root mean square of successive differences, Fourier transform, wavelet transform, and scaling exponent. Three computer-based tasks from MATB-II were used to induce cognitive stress. Sympathovagal index (HFnu) computed with our method better discriminates cognitive tasks from baseline, as indicated by P values and receiver operating characteristic curves. Here, transient decreases in respiratory frequency have shown to bias classical HRV indices, while only EDR-VFCDM consistently exhibits the expected decrease in the HFnu index with cognitive stress in both groups and all cognitive tasks. We conclude that EDR-VFCDM is robust against atypical respiratory profiles, which seems relevant to assess variations in mental demand. Given the variety of individual respiratory profiles reported especially in highly trained athletes and patients with chronic respiratory conditions, EDR-VFCDM could better perform in a wide range of applications.
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Affiliation(s)
- Pierre Gilfriche
- Université de Bordeaux, Centre National de la Recherche Scientifique, Laboratoire-de l'Intégration du Matériau au Système, UMR 5218, Talence , France.,Centre Aquitain des Technologies de l'Information et Electroniques , Talence , France
| | - Laurent M Arsac
- Université de Bordeaux, Centre National de la Recherche Scientifique, Laboratoire-de l'Intégration du Matériau au Système, UMR 5218, Talence , France
| | | | - Jaime Diaz-Pineda
- Centre Aquitain des Technologies de l'Information et Electroniques , Talence , France
| | - Brice Miard
- Centre Aquitain des Technologies de l'Information et Electroniques , Talence , France
| | | | - Jean-Marc André
- Bordeaux INP-L'Ecole Nationale Supérieure de Cognitique, Centre National de la Recherche Scientifique, Laboratoire de l'Intégration du Matériau au Système, UMR 5218, Talence, France
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49
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Posada-Quintero HF, Florian JP, Orjuela-Cañón AD, Chon KH. Electrodermal Activity Is Sensitive to Cognitive Stress under Water. Front Physiol 2018; 8:1128. [PMID: 29387015 PMCID: PMC5776121 DOI: 10.3389/fphys.2017.01128] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2017] [Accepted: 12/20/2017] [Indexed: 11/13/2022] Open
Abstract
When divers are at depth in water, the high pressure and low temperature alone can cause severe stress, challenging the human physiological control systems. The addition of cognitive stress, for example during a military mission, exacerbates the challenge. In these conditions, humans are more susceptible to autonomic imbalance. Reliable tools for the assessment of the autonomic nervous system (ANS) could be used as indicators of the relative degree of stress a diver is experiencing, which could reveal heightened risk during a mission. Electrodermal activity (EDA), a measure of the changes in conductance at the skin surface due to sweat production, is considered a promising alternative for the non-invasive assessment of sympathetic control of the ANS. EDA is sensitive to stress of many kinds. Therefore, as a first step, we tested the sensitivity of EDA, in the time and frequency domains, specifically to cognitive stress during water immersion of the subject (albeit with their measurement finger dry for safety). The data from 14 volunteer subjects were used from the experiment. After a 4-min adjustment and baseline period after being immersed in water, subjects underwent the Stroop task, which is known to induce cognitive stress. The time-domain indices of EDA, skin conductance level (SCL) and non-specific skin conductance responses (NS.SCRs), did not change during cognitive stress, compared to baseline measurements. Frequency-domain indices of EDA, EDASymp (based on power spectral analysis) and TVSymp (based on time-frequency analysis), did significantly change during cognitive stress. This leads to the conclusion that EDA, assessed by spectral analysis, is sensitive to cognitive stress in water-immersed subjects, and can potentially be used to detect cognitive stress in divers.
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Affiliation(s)
- Hugo F Posada-Quintero
- Department of Biomedical Engineering, University of Connecticut, Storrs, CT, United States
| | - John P Florian
- Navy Experimental Diving Unit, Panama City, FL, United States
| | - Alvaro D Orjuela-Cañón
- Faculty of Electronics and Biomedical Engineering, Universidad Antonio Nariño, Bogotá, Colombia
| | - Ki H Chon
- Department of Biomedical Engineering, University of Connecticut, Storrs, CT, United States
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Posada-Quintero HF, Bolkhovsky JB, Reljin N, Chon KH. Sleep Deprivation in Young and Healthy Subjects Is More Sensitively Identified by Higher Frequencies of Electrodermal Activity than by Skin Conductance Level Evaluated in the Time Domain. Front Physiol 2017; 8:409. [PMID: 28676763 PMCID: PMC5476732 DOI: 10.3389/fphys.2017.00409] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2016] [Accepted: 05/29/2017] [Indexed: 11/17/2022] Open
Abstract
We analyzed multiple measures of the autonomic nervous system (ANS) based on electrodermal activity (EDA) and heart rate variability (HRV) for young healthy subjects undergoing 24-h sleep deprivation. In this study, we have utilized the error awareness test (EAT) every 2 h (13 runs total), to evaluate the deterioration of performance. EAT consists of trials where the subject is presented words representing colors. Subjects are instructed to press a button (“Go” trials) or withhold the response if the word presented and the color of the word mismatch (“Stroop No-Go” trial), or the screen is repeated (“Repeat No-Go” trials). We measured subjects' (N = 10) reaction time to the “Go” trials, and accuracy to the “Stroop No-Go” and “Repeat No-Go” trials. Simultaneously, changes in EDA and HRV indices were evaluated. Furthermore, the relationship between reactiveness and vigilance measures and indices of sympathetic control based on HRV were analyzed. We found the performance improved to a stable level from 6 through 16 h of deprivation, with a subsequently sustained impairment after 18 h. Indices of higher frequencies of EDA related more to vigilance measures, whereas lower frequencies index (skin conductance leve, SCL) measured the reactiveness of the subject. We conclude that indices of EDA, including those of the higher frequencies, termed TVSymp, EDASymp, and NSSCRs, provide information to better understand the effect of sleep deprivation on subjects' autonomic response and performance.
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Affiliation(s)
| | - Jeffrey B Bolkhovsky
- Biomedical Engineering Department, University of ConnecticutStorrs, CT, United States
| | - Natasa Reljin
- Biomedical Engineering Department, University of ConnecticutStorrs, CT, United States
| | - Ki H Chon
- Biomedical Engineering Department, University of ConnecticutStorrs, CT, United States
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