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Subramanian S, Tseng B, Del Carmen M, Goodman A, Dahl DM, Barbieri R, Brown EN. Monitoring surgical nociception using multisensor physiological models. Proc Natl Acad Sci U S A 2024; 121:e2319316121. [PMID: 39316050 DOI: 10.1073/pnas.2319316121] [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: 11/22/2023] [Accepted: 06/30/2024] [Indexed: 09/25/2024] Open
Abstract
Monitoring nociception, the flow of information associated with harmful stimuli through the nervous system even during unconsciousness, is critical for proper anesthesia care during surgery. Currently, this is done by tracking heart rate and blood pressure by eye. Monitoring objectively a patient's nociceptive state remains a challenge, causing drugs to often be over- or underdosed intraoperatively. Inefficient management of surgical nociception may lead to more complex postoperative pain management and side effects such as postoperative cognitive dysfunction, particularly in elderly patients. We collected a comprehensive and multisensor prospective observational dataset focused on surgical nociception (101 surgeries, 18,582 min, and 49,878 nociceptive stimuli), including annotations of all nociceptive stimuli occurring during surgery and medications administered. Using this dataset, we developed indices of autonomic nervous system activity based on physiologically and statistically rigorous point process representations of cardiac action potentials and sweat gland activity. Next, we constructed highly interpretable supervised and unsupervised models with appropriate inductive biases that quantify surgical nociception throughout surgery. Our models track nociceptive stimuli more accurately than existing nociception monitors. We also demonstrate that the characterizing signature of nociception learned by our models resembles the known physiology of the response to pain. Our work represents an important step toward objective multisensor physiology-based markers of surgical nociception. These markers are derived from an in-depth characterization of nociception as measured during surgery itself rather than using other experimental models as surrogates for surgical nociception.
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Affiliation(s)
- Sandya Subramanian
- Harvard-Massachusetts Institute of Technology Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA 02139
| | - Bryan Tseng
- Picower Institute of Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA 02139
| | | | | | | | - Riccardo Barbieri
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy 20133
| | - Emery N Brown
- Harvard-Massachusetts Institute of Technology Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA 02139
- Picower Institute of Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA 02139
- Massachusetts General Hospital, Boston, MA 02114
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Xiang Q, Reddy R, Faghih RT. Marked Point Process Secretory Events Statistically Characterize Leptin Pulsatile Dynamics. J Endocr Soc 2024; 8:bvae149. [PMID: 39281006 PMCID: PMC11398913 DOI: 10.1210/jendso/bvae149] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/11/2024] [Indexed: 09/18/2024] Open
Abstract
Recent studies have highlighted leptin, a key hormone that regulates energy intake and induces satiety, due to the worldwide prevalence of obesity. In this study, we analyzed plasma leptin measurements from 18 women with premenopausal obesity before and after bromocriptine treatment. By using underlying pulses recovered through deconvolution, we modeled the leptin secretory pulses as marked point processes and applied statistical distributions to evaluate the dynamics of leptin, including the interpulse intervals and amplitudes of the secretion. We fit the generalized inverse Gaussian and lognormal distributions to the intervals and the Gaussian, lognormal, and gamma distributions to the amplitudes of pulses. We evaluated the models' goodness of fit using statistical metrics including Akaike's information criterion, Kolmogorov-Smirnov plots, and quantile-quantile plots. Our evaluation results revealed the effectiveness of these statistical distributions in modeling leptin secretion. Although the lognormal and gamma distributions performed the best based on the metrics, we found all distributions capable of accurately modeling the timing of secretory events, leading us to a better understanding of the physiology of leptin secretion and providing a basis for leptin monitoring. In terms of pulse amplitude, the evaluation metrics indicated the gamma distribution as the most accurate statistical representation. We found no statistically significant effect of bromocriptine intake on the model parameters except for one distribution model.
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Affiliation(s)
- Qing Xiang
- Department of Biomedical Engineering, Tandon School of Engineering, New York University, New York, NY 11201, USA
| | - Revanth Reddy
- Department of Biomedical Engineering, Tandon School of Engineering, New York University, New York, NY 11201, USA
| | - Rose T Faghih
- Department of Biomedical Engineering, Tandon School of Engineering, New York University, New York, NY 11201, USA
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Andrikopoulos D, Vassiliou G, Fatouros P, Tsirmpas C, Pehlivanidis A, Papageorgiou C. Machine learning-enabled detection of attention-deficit/hyperactivity disorder with multimodal physiological data: a case-control study. BMC Psychiatry 2024; 24:547. [PMID: 39103819 DOI: 10.1186/s12888-024-05987-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/09/2024] [Accepted: 07/25/2024] [Indexed: 08/07/2024] Open
Abstract
BACKGROUND Attention-Deficit/Hyperactivity Disorder (ADHD) is a multifaceted neurodevelopmental psychiatric condition that typically emerges during childhood but often persists into adulthood, significantly impacting individuals' functioning, relationships, productivity, and overall quality of life. However, the current diagnostic process exhibits limitations that can significantly affect its overall effectiveness. Notably, its face-to-face and time-consuming nature, coupled with the reliance on subjective recall of historical information and clinician subjectivity, stand out as key challenges. To address these limitations, objective measures such as neuropsychological evaluations, imaging techniques and physiological monitoring of the Autonomic Nervous System functioning, have been explored. METHODS The main aim of this study was to investigate whether physiological data (i.e., Electrodermal Activity, Heart Rate Variability, and Skin Temperature) can serve as meaningful indicators of ADHD, evaluating its utility in distinguishing adult ADHD patients. This observational, case-control study included a total of 76 adult participants (32 ADHD patients and 44 healthy controls) who underwent a series of Stroop tests, while their physiological data was passively collected using a multi-sensor wearable device. Univariate feature analysis was employed to identify the tests that triggered significant signal responses, while the Informative k-Nearest Neighbors (KNN) algorithm was used to filter out less informative data points. Finally, a machine-learning decision pipeline incorporating various classification algorithms, including Logistic Regression, KNN, Random Forests, and Support Vector Machines (SVM), was utilized for ADHD patient detection. RESULTS Results indicate that the SVM-based model yielded the optimal performance, achieving 81.6% accuracy, maintaining a balance between the experimental and control groups, with sensitivity and specificity of 81.4% and 81.9%, respectively. Additionally, integration of data from all physiological signals yielded the best results, suggesting that each modality captures unique aspects of ADHD. CONCLUSIONS This study underscores the potential of physiological signals as valuable diagnostic indicators of adult ADHD. For the first time, to the best of our knowledge, our findings demonstrate that multimodal physiological data collected via wearable devices can complement traditional diagnostic approaches. Further research is warranted to explore the clinical applications and long-term implications of utilizing physiological markers in ADHD diagnosis and management.
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Affiliation(s)
| | - Georgia Vassiliou
- First Department of Psychiatry, Eginition Hospital, Medical School National and Kapodistrian University of Athens, Athens, Greece
| | | | | | - Artemios Pehlivanidis
- First Department of Psychiatry, Eginition Hospital, Medical School National and Kapodistrian University of Athens, Athens, Greece
| | - Charalabos Papageorgiou
- First Department of Psychiatry, Eginition Hospital, Medical School National and Kapodistrian University of Athens, Athens, Greece
- Neurosciences and Precision Medicine Research Institute "Costas Stefanis", University Mental Health, Athens, Greece
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Lyu H, Huang H, He J, Zhu S, Hong W, Lai J, Gao T, Shao J, Zhu J, Li Y, Hu S. Task-state skin potential abnormalities can distinguish major depressive disorder and bipolar depression from healthy controls. Transl Psychiatry 2024; 14:110. [PMID: 38395985 PMCID: PMC10891315 DOI: 10.1038/s41398-024-02828-9] [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: 07/21/2023] [Revised: 02/07/2024] [Accepted: 02/13/2024] [Indexed: 02/25/2024] Open
Abstract
Early detection of bipolar depression (BPD) and major depressive disorder (MDD) has been challenging due to the lack of reliable and easily measurable biological markers. This study aimed to investigate the accuracy of discriminating patients with mood disorders from healthy controls based on task state skin potential characteristics and their correlation with individual indicators of oxidative stress. A total of 77 patients with BPD, 53 patients with MDD, and 79 healthy controls were recruited. A custom-made device, previously shown to be sufficiently accurate, was used to collect skin potential data during six emotion-inducing tasks involving video, pictorial, or textual stimuli. Blood indicators reflecting individual levels of oxidative stress were collected. A discriminant model based on the support vector machine (SVM) algorithm was constructed for discriminant analysis. MDD and BPD patients were found to have abnormal skin potential characteristics on most tasks. The accuracy of the SVM model built with SP features to discriminate MDD patients from healthy controls was 78% (sensitivity 78%, specificity 82%). The SVM model gave an accuracy of 59% (sensitivity 59%, specificity 79%) in classifying BPD patients, MDD patients, and healthy controls into three groups. Significant correlations were also found between oxidative stress indicators in the blood of patients and certain SP features. Patients with depression and bipolar depression have abnormalities in task-state skin potential that partially reflect the pathological mechanism of the illness, and the abnormalities are potential biological markers of affective disorders.
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Affiliation(s)
- Hailong Lyu
- Department of Psychiatry, The First Affiliated Hospital, Zhejiang University School of Medicine; Key Laboratory of Mental Disorder's Management of Zhejiang Province, Hangzhou, 310003, China
- Brain Research Institute of Zhejiang University, Hangzhou, 310003, China
- Zhejiang Engineering Center for Mathematical Mental Health, Hangzhou, 310003, China
| | - Huimin Huang
- The Third Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325200, China
- Ruian People's Hospital, Wenzhou, 325200, China
| | - Jiadong He
- College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, 310027, China
| | - Sheng Zhu
- Department of Psychiatry, The Ruian Fifth People's Hospital, Wenzhou, 325200, China
| | - Wanchu Hong
- Department of Psychiatry, The Ruian Fifth People's Hospital, Wenzhou, 325200, China
| | - Jianbo Lai
- Department of Psychiatry, The First Affiliated Hospital, Zhejiang University School of Medicine; Key Laboratory of Mental Disorder's Management of Zhejiang Province, Hangzhou, 310003, China
- Brain Research Institute of Zhejiang University, Hangzhou, 310003, China
- Zhejiang Engineering Center for Mathematical Mental Health, Hangzhou, 310003, China
| | | | - Jiamin Shao
- Department of Psychiatry, The First Affiliated Hospital, Zhejiang University School of Medicine; Key Laboratory of Mental Disorder's Management of Zhejiang Province, Hangzhou, 310003, China
- Brain Research Institute of Zhejiang University, Hangzhou, 310003, China
- Zhejiang Engineering Center for Mathematical Mental Health, Hangzhou, 310003, China
| | - Jianfeng Zhu
- Department of Psychiatry, The Ruian Fifth People's Hospital, Wenzhou, 325200, China
| | - Yubo Li
- College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, 310027, China.
| | - Shaohua Hu
- Department of Psychiatry, The First Affiliated Hospital, Zhejiang University School of Medicine; Key Laboratory of Mental Disorder's Management of Zhejiang Province, Hangzhou, 310003, China.
- Brain Research Institute of Zhejiang University, Hangzhou, 310003, China.
- Zhejiang Engineering Center for Mathematical Mental Health, Hangzhou, 310003, China.
- Ruian People's Hospital, Wenzhou, 325200, China.
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Fernandez Rojas R, Hirachan N, Brown N, Waddington G, Murtagh L, Seymour B, Goecke R. Multimodal physiological sensing for the assessment of acute pain. FRONTIERS IN PAIN RESEARCH 2023; 4:1150264. [PMID: 37415829 PMCID: PMC10321707 DOI: 10.3389/fpain.2023.1150264] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Accepted: 05/29/2023] [Indexed: 07/08/2023] Open
Abstract
Pain assessment is a challenging task encountered by clinicians. In clinical settings, patients' self-report is considered the gold standard in pain assessment. However, patients who are unable to self-report pain are at a higher risk of undiagnosed pain. In the present study, we explore the use of multiple sensing technologies to monitor physiological changes that can be used as a proxy for objective measurement of acute pain. Electrodermal activity (EDA), photoplethysmography (PPG), and respiration (RESP) signals were collected from 22 participants under two pain intensities (low and high) and on two different anatomical locations (forearm and hand). Three machine learning models were implemented, including support vector machines (SVM), decision trees (DT), and linear discriminant analysis (LDA) for the identification of pain. Various pain scenarios were investigated, identification of pain (no pain, pain), multiclass (no pain, low pain, high pain), and identification of pain location (forearm, hand). Reference classification results from individual sensors and from all sensors together were obtained. After feature selection, results showed that EDA was the most informative sensor in the three pain conditions, 93.2±8% in identification of pain, 68.9±10% in the multiclass problem, and 56.0±8% for the identification of pain location. These results identify EDA as the superior sensor in our experimental conditions. Future work is required to validate the obtained features to improve its feasibility in more realistic scenarios. Finally, this study proposes EDA as a candidate to design a tool that can assist clinicians in the assessment of acute pain of nonverbal patients.
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Affiliation(s)
- Raul Fernandez Rojas
- Human-Centred Technology Research Centre, Faculty of Science and Technology, University of Canberra, Canberra, ACT, Australia
| | - Niraj Hirachan
- Human-Centred Technology Research Centre, Faculty of Science and Technology, University of Canberra, Canberra, ACT, Australia
| | - Nicholas Brown
- Faculty of Health, Queensland University of Technology, Brisbane, QLD, Australia
| | - Gordon Waddington
- Australian Institute of Sport, Canberra, ACT, Australia
- University of Canberra Research Institute for Sport and Exercise (UCRISE), University of Canberra, Canberra, ACT, Australia
| | - Luke Murtagh
- Department of Anaesthesia, Pain and Perioperative Medicine, The Canberra Hospital, Canberra, ACT, Australia
| | - Ben Seymour
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, John Radcliffe Hospital, Headington, UK
- Oxford Institute for Biomedical Engineering, University of Oxford, Headington, UK
| | - Roland Goecke
- Human-Centred Technology Research Centre, Faculty of Science and Technology, University of Canberra, Canberra, ACT, Australia
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Wang XY, Nie ZY, Yu QQ, Chen W, ZHANG XN, Wan HY, Su YS, He W, Li RY, Jing XH. Acupuncture Enhances Signals at Sensitized Acupoints to Elevate Pressure Pain Threshold in Knee Osteoarthritis Patients. Chin J Integr Med 2022; 28:1105-1110. [DOI: 10.1007/s11655-022-3588-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/22/2022] [Indexed: 11/24/2022]
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Peel JS, McNarry MA, Heffernan SM, Nevola VR, Kilduff LP, Waldron M. Measurement of thermal sweating at rest and steady-state exercise in healthy adults: Inter-day reliability and relationships with components of partitional calorimetry. PLoS One 2022; 17:e0278652. [PMID: 36455061 PMCID: PMC9714830 DOI: 10.1371/journal.pone.0278652] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Accepted: 11/21/2022] [Indexed: 12/03/2022] Open
Abstract
OBJECTIVE Inter-day reliability of sweat measurements, including the absorbent patch and modified iodine-paper techniques, at rest and exercise were evaluated. We further evaluated the effect of iodine paper size and the method of establishing sweat gland activation (sweat gland counting or surface area covered) on reliability. Furthermore, the relationships between all measurement techniques and metabolic heat production [Ḣprod] and evaporative requirement for heat balance [Ėreq] were determined. METHOD Twelve participants were assessed for whole-body sweat loss (WBSL), local sweat rate (LSR; absorbent patch) and sweat gland activation (SGA; iodine-paper) during rest and sub-maximal cycling at ~200, ~250 and ~300 W/m2 Ḣprod in the heat. Variations in iodine paper (1 x 1 cm-9 x 9 cm) were used to quantify SGA by counting sweat glands or surface area covered. The 'optimal' area of SGA was also determined based on the highest density of recruited glands. RESULTS All measures of the sweating response were positively related with Ḣprod and Ėreq (r = 0.53-0.84), with the 9 x 9 cm and 6 x 6 cm iodine paper sizes being the strongest (r = 0.66-0.84) for SGA. Superior inter-day reliability was found for all measures during exercise (CV% = 6-33.2) compared to rest (CV% = 33.5-77.9). The iodine-paper technique was most reliable at 9 x 9 cm (CV% = 15.9) or when the 1 x 1 cm (CV% = 17.6) and 3 x 3 cm (CV% = 15.5) optimal SGA was determined, particularly when measuring the sweat gland number. SIGNIFICANCE WBSL, LSR and SGA measurement techniques are sufficiently reliable to detect changes in thermal sweating typically reported. We recommend 9 x 9 cm paper sizes or 1 x 1 cm-3 x 3 cm optimal areas, using either gland counting or surface area to determine SGA.
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Affiliation(s)
- Jennifer S. Peel
- Faculty of Science and Engineering, A-STEM Centre, Swansea University, Swansea, United Kingdom
- * E-mail:
| | - Melitta A. McNarry
- Faculty of Science and Engineering, A-STEM Centre, Swansea University, Swansea, United Kingdom
| | - Shane M. Heffernan
- Faculty of Science and Engineering, A-STEM Centre, Swansea University, Swansea, United Kingdom
| | - Venturino R. Nevola
- Faculty of Science and Engineering, A-STEM Centre, Swansea University, Swansea, United Kingdom
- Defence Science and Technology Laboratory (Dstl), Fareham, Hampshire, United Kingdom
| | - Liam P. Kilduff
- Faculty of Science and Engineering, A-STEM Centre, Swansea University, Swansea, United Kingdom
- Welsh Institute of Performance Science, Swansea University, Swansea, United Kingdom
| | - Mark Waldron
- Faculty of Science and Engineering, A-STEM Centre, Swansea University, Swansea, United Kingdom
- Welsh Institute of Performance Science, Swansea University, Swansea, United Kingdom
- School of Health and Behavioural Sciences, University of the Sunshine Coast, Queensland, Australia
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Subramanian S, Tseng B, Barbieri R, Brown EN. An unsupervised automated paradigm for artifact removal from electrodermal activity in an uncontrolled clinical setting. Physiol Meas 2022; 43. [PMID: 36113446 DOI: 10.1088/1361-6579/ac92bd] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Accepted: 09/16/2022] [Indexed: 02/07/2023]
Abstract
Objective. Electrodermal activity (EDA) reflects sympathetic nervous system activity through sweating-related changes in skin conductance and could be used in clinical settings in which patients cannot self-report pain, such as during surgery or when in a coma. To enable EDA data to be used robustly in clinical settings, we need to develop artifact detection and removal frameworks that can handle the types of interference experienced in clinical settings while salvaging as much useful information as possible.Approach. In this study, we collected EDA data from 70 subjects while they were undergoing surgery in the operating room. We then built a fully automated artifact removal framework to remove the heavy artifacts that resulted from the use of surgical electrocautery during the surgery and compared it to two existing state-of-the-art methods for artifact removal from EDA data. This automated framework consisted of first utilizing three unsupervised machine learning methods for anomaly detection, and then customizing the threshold to separate artifact for each data instance by taking advantage of the statistical properties of the artifact in that data instance. We also created simulated surgical data by introducing artifacts into cleaned surgical data and measured the performance of all three methods in removing it.Main results. Our method achieved the highest overall accuracy and precision and lowest overall error on simulated data. One of the other methods prioritized high sensitivity while sacrificing specificity and precision, while the other had low sensitivity, high error, and left behind several artifacts. These results were qualitatively similar between the simulated data instances and operating room data instances.Significance. Our framework allows for robust removal of heavy artifact from EDA data in clinical settings such as surgery, which is the first step to enable clinical integration of EDA as part of standard monitoring.
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Affiliation(s)
- Sandya Subramanian
- Department of Bioengineering, Stanford University, Stanford, CA, United States of America
| | - Bryan Tseng
- Picower Institute for Learning and Memory, Cambridge, MA, United States of America
| | - Riccardo Barbieri
- Department of Electronics, Informatics and Engineering, Politecnico di Milano, Milano, Italy.,Department of Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital, Boston, MA, United States of America
| | - Emery N Brown
- Picower Institute for Learning and Memory, Cambridge, MA, United States of America.,Department of Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital, Boston, MA, United States of America.,Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, United States of America.,Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, United States of America
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Tseng B, Subramanian S, Barbieri R, Brown EN. Tonic Electrodermal Activity is a Robust Marker of Psychological and Physiological Changes during Induction of Anesthesia. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:418-421. [PMID: 36086567 DOI: 10.1109/embc48229.2022.9871080] [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
Electrodermal activity (EDA), which tracks sweat gland activity as a proxy for sympathetic activation, has the potential to be a biomarker of physiological and psychological changes in the clinic. To show this, in this study, we demonstrate that the tonic component of EDA responds consistently and robustly during induction of anesthesia in the operating room in 8 subjects during surgery. This response is seen bilaterally. The response shows a significant increase in EDA in anticipation of induction and then a gradual decrease in response to the administration of medication, which agrees with both the expected psychological effects of stress and anxiety and the physiological effects of anesthetic medication on sweat glands. The results also show a slightly faster response to drug in the arm directly receiving the medication intravenously compared to the opposite, though the magnitude of the effect evens out over time. Clinical Relevance- EDA can serve as a robust non-invasive biomarker in the clinic to track both psychologically and physiologically induced autonomic changes.
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Amin R, Faghih RT. Physiological characterization of electrodermal activity enables scalable near real-time autonomic nervous system activation inference. PLoS Comput Biol 2022; 18:e1010275. [PMID: 35900988 PMCID: PMC9333288 DOI: 10.1371/journal.pcbi.1010275] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Accepted: 06/02/2022] [Indexed: 12/01/2022] Open
Abstract
Electrodermal activities (EDA) are any electrical phxenomena observed on the skin. Skin conductance (SC), a measure of EDA, shows fluctuations due to autonomic nervous system (ANS) activation induced sweat secretion. Since it can capture psychophysiological information, there is a significant rise in the research work for tracking mental and physiological health with EDA. However, the current state-of-the-art lacks a physiologically motivated approach for real-time inference of ANS activation from EDA. Therefore, firstly, we propose a comprehensive model for the SC dynamics. The proposed model is a 3D state-space representation of the direct secretion of sweat via pore opening and diffusion followed by corresponding evaporation and reabsorption. As the input to the model, we consider a sparse signal representing the ANS activation that causes the sweat glands to produce sweat. Secondly, we derive a scalable fixed-interval smoother-based sparse recovery approach utilizing the proposed comprehensive model to infer the ANS activation enabling edge computation. We incorporate a generalized-cross-validation to tune the sparsity level. Finally, we propose an Expectation-Maximization based deconvolution approach for learning the model parameters during the ANS activation inference. For evaluation, we utilize a dataset with 26 participants, and the results show that our comprehensive state-space model can successfully describe the SC variations with high scalability, showing the feasibility of real-time applications. Results validate that our physiology-motivated state-space model can comprehensively explain the EDA and outperforms all previous approaches. Our findings introduce a whole new perspective and have a broader impact on the standard practices of EDA analysis.
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Affiliation(s)
- Rafiul Amin
- Department of Electrical and Computer Engineering, University of Houston, Houston, Texas, United States of America
| | - Rose T. Faghih
- Department of Electrical and Computer Engineering, University of Houston, Houston, Texas, United States of America
- Department of Biomedical Engineering, New York University, New York City, New York, United States of America
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11
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Huang TY, Lim HL. Electrogenic Staphylococcus warneri in lactate-rich skin. Biochem Biophys Res Commun 2022; 618:67-72. [PMID: 35716597 DOI: 10.1016/j.bbrc.2022.06.020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2022] [Revised: 06/01/2022] [Accepted: 06/08/2022] [Indexed: 11/02/2022]
Abstract
The electrogenicity of environmental bacteria has been thoroughly explored and has been known to have the unique capability of decomposing hazardous chemicals for environmental remediation. However, electrogenic bacteria in human skin in regards to their electrical properties and locations have not yet been determined. Here, electrodermal activities and metabolite compositions at different locations of arm skin were assessed. Compared to the uppermost part of arm, we found that the forearm elicited high electrodermal activity and carried abundant lactate and alpha-ketoglutarate, two components commonly present in sweat. Upon culturing bacteria from the forearm, an iron-resistant strain of Staphylococcus warneri (S. warneri) was identified through 16S ribosomal RNA sequencing. Voltage changes induced by S. warneri in the presence of glucose were detected by two voltmeters of different electrode materials, demonstrating the electrogenicity of skin bacteria. Furthermore, we discovered that S. warneri has the ability to metabolize lactate to generate electricity. The results of this study reveal changes in skin conductance caused by bacterial electricity that are mediated by skin endogenous molecules and may provide a novel method of monitoring environmental skin insults.
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Tronstad C, Amini M, Bach DR, Martinsen OG. Current trends and opportunities in the methodology of electrodermal activity measurement. Physiol Meas 2022; 43. [PMID: 35090148 DOI: 10.1088/1361-6579/ac5007] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Accepted: 01/28/2022] [Indexed: 11/12/2022]
Abstract
Electrodermal activity (EDA) has been measured in the laboratory since the late 1800s. Although the influence of sudomotor nerve activity and the sympathetic nervous system on EDA is well established, the mechanisms underlying EDA signal generation are not completely understood. Owing to simplicity of instrumentation and modern electronics, these measurements have recently seen a transfer from the laboratory to wearable devices, sparking numerous novel applications while bringing along both challenges and new opportunities. In addition to developments in electronics and miniaturization, current trends in material technology and manufacturing have sparked innovations in electrode technologies, and trends in data science such as machine learning and sensor fusion are expanding the ways that measurement data can be processed and utilized. Although challenges remain for the quality of wearable EDA measurement, ongoing research and developments may shorten the quality gap between wearable EDA and standardized recordings in the laboratory. In this topical review, we provide an overview of the basics of EDA measurement, discuss the challenges and opportunities of wearable EDA, and review recent developments in instrumentation, material technology, signal processing, modeling and data science tools that may advance the field of EDA research and applications over the coming years.
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Affiliation(s)
- Christian Tronstad
- Department of Clinical and Biomedical Engineering, Oslo University Hospital, Sognsvannsveien 20, Oslo, 0372, NORWAY
| | - Maryam Amini
- Physics, University of Oslo Faculty of Mathematics and Natural Sciences, Sem Sælands vei 24, Oslo, 0371, NORWAY
| | - Dominik R Bach
- Wellcome Centre for Human Neuroimaging, University College London, 12 Queen Square, London, London, WC1N 3AZ, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND
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Subramanian S, Purdon PL, Barbieri R, Brown EN. A Model-Based Framework for Assessing the Physiologic Structure of Electrodermal Activity. IEEE Trans Biomed Eng 2021; 68:2833-2845. [PMID: 33822719 PMCID: PMC8425954 DOI: 10.1109/tbme.2021.3071366] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Objective: We present a statistical model for extracting physiologic
characteristics from electrodermal activity (EDA) data in observational
settings. Methods: We based our model on the integrate-and-fire physiology of sweat
gland bursts, which predicts inverse Gaussian (IG) inter-pulse interval
structure. At the core of our model-based paradigm is a subject-specific
amplitude threshold selection process for EDA pulses based on the
statistical properties of four right-skewed models including the IG. By
performing a sensitivity analysis across thresholds and fitting all four
models, we selected for IG-like structure and verified the pulse selection
with a goodness-of-fit analysis, maximizing capture of physiology at the
time scale of EDA responses. Results: We tested the model-based paradigm on simulated EDA time series and
data from two different experimental cohorts recorded during different
experimental conditions, using different equipment. In both the simulated
and experimental data, our model-based method robustly recovered pulses that
captured the IG-like structure predicted by physiology, despite large
differences in noise level. In contrast, established EDA analysis tools,
which attempted to estimate neural activity from slower EDA responses, did
not provide physiological validation and were susceptible to noise. Conclusion: We present a computationally efficient, statistically rigorous, and
physiology-informed paradigm for pulse selection from EDA data that is
robust across individuals and experimental conditions, yet adaptable to
varying noise level. Significance: The robustness of the model-based paradigm and its physiological
basis provide empirical support for the use of EDA as a clinical marker for
sympathetic activity in conditions such as pain, anxiety, depression, and
sleep states.
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Subramanian S, Purdon PL, Barbieri R, Brown EN. Quantitative assessment of the relationship between behavioral and autonomic dynamics during propofol-induced unconsciousness. PLoS One 2021; 16:e0254053. [PMID: 34379623 PMCID: PMC8357089 DOI: 10.1371/journal.pone.0254053] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2020] [Accepted: 06/19/2021] [Indexed: 12/30/2022] Open
Abstract
During general anesthesia, both behavioral and autonomic changes are caused by the administration of anesthetics such as propofol. Propofol produces unconsciousness by creating highly structured oscillations in brain circuits. The anesthetic also has autonomic effects due to its actions as a vasodilator and myocardial depressant. Understanding how autonomic dynamics change in relation to propofol-induced unconsciousness is an important scientific and clinical question since anesthesiologists often infer changes in level of unconsciousness from changes in autonomic dynamics. Therefore, we present a framework combining physiology-based statistical models that have been developed specifically for heart rate variability and electrodermal activity with a robust statistical tool to compare behavioral and multimodal autonomic changes before, during, and after propofol-induced unconsciousness. We tested this framework on physiological data recorded from nine healthy volunteers during computer-controlled administration of propofol. We studied how autonomic dynamics related to behavioral markers of unconsciousness: 1) overall, 2) during the transitions of loss and recovery of consciousness, and 3) before and after anesthesia as a whole. Our results show a strong relationship between behavioral state of consciousness and autonomic dynamics. All of our prediction models showed areas under the curve greater than 0.75 despite the presence of non-monotonic relationships among the variables during the transition periods. Our analysis highlighted the specific roles played by fast versus slow changes, parasympathetic vs sympathetic activity, heart rate variability vs electrodermal activity, and even pulse rate vs pulse amplitude information within electrodermal activity. Further advancement upon this work can quantify the complex and subject-specific relationship between behavioral changes and autonomic dynamics before, during, and after anesthesia. However, this work demonstrates the potential of a multimodal, physiologically-informed, statistical approach to characterize autonomic dynamics.
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Affiliation(s)
- Sandya Subramanian
- Harvard-Massachusetts Institute of Technology Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, United States of America
- Department of Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital, Boston, MA, United States of America
- Institute of Medical Engineering and Sciences, Massachusetts Institute of Technology, Cambridge, MA, United States of America
| | - Patrick L. Purdon
- Department of Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital, Boston, MA, United States of America
| | - Riccardo Barbieri
- Department of Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital, Boston, MA, United States of America
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, 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, MA, United States of America
- Department of Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital, Boston, MA, United States of America
- Institute of Medical Engineering and Sciences, Massachusetts Institute of Technology, Cambridge, MA, United States of America
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, United States of America
- Picower Institute of Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, United States of America
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15
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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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