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21 - Electrodermal Activity: Applications and Challenges

from Part V - Physiological Measures

Published online by Cambridge University Press:  12 December 2024

By
Md-Billal Hossain ,
Youngsun Kong ,
Hugo F. Posada-Quintero  and
Ki H. Chon
Edited by
John E. Edlund  and
Austin Lee Nichols
John E. Edlund
Affiliation:
Rochester Institute of Technology, New York
Austin Lee Nichols
Affiliation:
Central European University, Vienna
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Summary

Electrodermal activity (EDA) is a conductance measure that can be used to assess the sympathetic nervous system arousal and for the diagnosis of stress, pain, sleepiness, seizure prediction, neuropathies, depression, and other states. EDA has potential for ambulatory research applications, as it can be collected using wearable devices, but motion artifacts are an issue. While EDA was discovered in 1879 by Vigouroux, the signal was traditionally observed in most of the studies as the mean value of the signal in response to a given stimulus, which provides static information but does not account for time-varying dynamics of the signal. The new technologies for EDA collection and the development of novel and robust signal processing algorithms have increased the interest in EDA for many new and emerging fields, including affective computing, seizure prediction, and pain monitoring. We aim to summarize the characteristics of EDA, describe current and future applications, and outline challenges when using EDA.

Type
Chapter
Information
The Cambridge Handbook of Research Methods and Statistics for the Social and Behavioral Sciences
Volume 2: Performing Research
 , pp. 475 - 496
Publisher: Cambridge University Press
Print publication year: 2024

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References

Amin, Md. R., & Faghih, R. T. (2021). Identification of sympathetic nervous system activation from skin conductance: A sparse decomposition approach with physiological priors. IEEE Transactions on Biomedical Engineering, 68(5), 1726–1736. https://doi.org/10.1109/TBME.2020.3034632CrossRefGoogle ScholarPubMed
Amin, Md. R., & Faghih, R. T. (2022). Physiological characterization of electrodermal activity enables scalable near real-time autonomic nervous system activation inference. PLOS Computational Biology, 18(7), e1010275. https://doi.org/10.1371/journal.pcbi.1010275CrossRefGoogle ScholarPubMed
Bach, D. R., Daunizeau, J., Kuelzow, N., Friston, K. J., & Dolan, R. J. (2011). Dynamic causal modeling of spontaneous fluctuations in skin conductance. Psychophysiology, 48(2), 252–257. https://doi.org/10.1111/j.1469-8986.2010.01052.xCrossRefGoogle ScholarPubMed
Beauchaine, T. P., Neuhaus, E., Gatzke-Kopp, L. M., Reid, M. J., Chipman, J., Brekke, A., et al. (2015). Electrodermal responding predicts responses to, and may be altered by, preschool intervention for ADHD. Journal of Consulting and Clinical Psychology, 83, 293–303. https://doi.org/10.1037/a0038405CrossRefGoogle ScholarPubMed
Boucsein, W. (2012). Electrodermal Activity, 2nd ed. Springer. https://doi.org/10.1007/978-1-4614-1126-0CrossRefGoogle Scholar
Boucsein, W., Fowles, D. C., Grimnes, S., Ben-Shakhar, G., Roth, W. T., Dawson, M. E., et al. (2012). Publication recommendations for electrodermal measurements. Psychophysiology, 49(8), 1017–1034. https://doi.org/10.1111/j.1469-8986.2012.01384.xGoogle ScholarPubMed
Braithwaite, J., Watson, D., Robert, J., & Mickey, R. (2015). A Guide for Analysing Electrodermal Activity (EDA) & Skin Conductance Responses (SCRs) for Psychological Experiments, rev. ed. Technical Report, Selective Attention & Awareness Laboratory, Behavioural Brain Sciences Centre, University of Birmingham. www.birmingham.ac.uk/documents/college-les/psych/saal/guide-electrodermal-activity.pdfGoogle Scholar
Chen, W., Jaques, N., Taylor, S., Sano, A., Fedor, S., & Picard, R. W. (2015). Wavelet-based motion artifact removal for electrodermal activity. In 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (pp. 6223–6226). IEEE. https://doi.org/10.1109/EMBC.2015.7319814Google Scholar
Chiang, H.-T., Hsieh, Y.-Y., Fu, S.-W., Hung, K.-H., Tsao, Y., & Chien, S.-Y. (2019). Noise reduction in ECG signals using fully convolutional denoising autoencoders. IEEE Access, 7, 60806–60813. https://doi.org/10.1109/ACCESS.2019.2912036CrossRefGoogle Scholar
Choi, J., Ahmed, B., & Gutierrez-Osuna, R. (2012). Development and evaluation of an ambulatory stress monitor based on wearable sensors. IEEE Transactions on Information Technology in Biomedicine, 16(2), 279–286. https://doi.org/10.1109/TITB.2011.2169804CrossRefGoogle ScholarPubMed
Das, P., Khasnobish, A., & Tibarewala, D. N. (2016). Emotion recognition employing ECG and GSR signals as markers of ANS. In 2016 Conference on Advances in Signal Processing (CASP) (pp. 37–42). IEEE. https://doi.org/10.1109/CASP.2016.7746134Google Scholar
Dubé, A.-A., Duquette, M., Roy, M., Lepore, F., Duncan, G., & Rainville, P. (2009). Brain activity associated with the electrodermal reactivity to acute heat pain. NeuroImage, 45(1), 169–180. https://doi.org/10.1016/j.neuroimage.2008.10.024CrossRefGoogle ScholarPubMed
Dupuy, F. E., Clarke, A. R., Barry, R. J., Selikowitz, M., & McCarthy, R. (2014). EEG and electrodermal activity in girls with Attention-Deficit/Hyperactivity Disorder. Clinical Neurophysiology, 125(3), 491–499. https://doi.org/10.1016/j.clinph.2013.09.007CrossRefGoogle ScholarPubMed
Dutta, S., Mishra, B. K., Mitra, A., & Chakraborty, A. (2022). An analysis of emotion recognition based on GSR signal. ECS Transactions, 107(1), 12535. https://doi.org/10.1149/10701.12535ecstCrossRefGoogle Scholar
Elvebakk, O., Tronstad, C., Birkeland, K. I., Jenssen, T. G., Bjørgaas, M. R., Frøslie, K. F., et al. (2018). Evaluation of hypoglycaemia with non-invasive sensors in people with Type 1 diabetes and impaired awareness of hypoglycaemia. Scientific Reports, 8(1). https://doi.org/10.1038/s41598-018-33189-1CrossRefGoogle ScholarPubMed
Feng, X., Zhang, Y., & Glass, J. (2014). Speech feature denoising and dereverberation via deep autoencoders for noisy reverberant speech recognition. In 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 1759–1763). IEEE. https://doi.org/10.1109/ICASSP.2014.6853900CrossRefGoogle Scholar
Freixa i Baqué, E. (1983). Reliability of spontaneous electrodermal activity in humans as a function of sleep stages. Biological Psychology, 17(2), 137–143. https://doi.org/10.1016/0301-0511(83)90014-5CrossRefGoogle ScholarPubMed
Frewin, D. B., & Downey, J. A. (1976). Sweating – physiology and pathophysiology. Australasian Journal of Dermatology, 17(3), 82–86. https://doi.org/10.1111/j.1440-0960.1976.tb00794.xCrossRefGoogle ScholarPubMed
Ganapathy, N., Veeranki, Y. R., Kumar, H., & Swaminathan, R. (2021). Emotion recognition using electrodermal activity signals and multiscale deep convolutional neural network. Journal of Medical Systems, 45(4), 49. https://doi.org/10.1007/s10916-020-01676-6CrossRefGoogle ScholarPubMed
Gjoreski, M., Gjoreski, H., Luštrek, M., & Gams, M. (2016). Continuous stress detection using a wrist device: In laboratory and real life. In Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct (pp. 1185–1193). ACM. https://doi.org/10.1145/2968219.2968306CrossRefGoogle Scholar
Greco, A., Valenza, G., Lanata, A., Scilingo, E. P., & Citi, L. (2016). cvxEDA: A convex optimization approach to electrodermal activity processing. IEEE Transactions on Biomedical Engineering, 63(4), 797–804. https://doi.org/10.1109/TBME.2015.2474131Google ScholarPubMed
Harker, M. (2013). Psychological sweating: A systematic review focused on aetiology and cutaneous response. Skin Pharmacology and Physiology, 26(2), 92–100. https://doi.org/10.1159/000346930CrossRefGoogle ScholarPubMed
Healey, J., Nachman, L., Subramanian, S., Shahabdeen, J., & Morris, M. (2010). Out of the lab and into the fray: Towards modeling emotion in everyday life. In Floréen, P., Krüger, A., & Spasojevic, M. (eds.), Pervasive Computing (pp. 156–173). Springer. https://doi.org/10.1007/978-3-642-12654-3_10CrossRefGoogle Scholar
Herlan, A., Ottenbacher, J., Schneider, J., Riemann, D., & Feige, B. (2019). Electrodermal activity patterns in sleep stages and their utility for sleep versus wake classification. Journal of Sleep Research, 28(2), e12694. https://doi.org/10.1111/jsr.12694CrossRefGoogle ScholarPubMed
Hernandez, J., Morris, R. R., & Picard, R. W. (2011). Call center stress recognition with person-specific models. In D’Mello, S., Graesser, A., Schuller, B., & Martin, J.-C. (eds.), Affective Computing and Intelligent Interaction (pp. 125–134). Springer. https://doi.org/10.1007/978-3-642-24600-5_16CrossRefGoogle Scholar
Hernando-Gallego, F., Luengo, D., & Artés-Rodríguez, A. (2018). Feature extraction of galvanic skin responses by nonnegative sparse deconvolution. IEEE Journal of Biomedical and Health Informatics, 22(5), 1385–1394. https://doi.org/10.1109/JBHI.2017.2780252CrossRefGoogle ScholarPubMed
Hossain, M.-B., Kong, Y., Posada-Quintero, H. F., & Chon, K. H. (2022). Comparison of electrodermal activity from multiple body locations based on standard EDA indices’ quality and robustness against motion artifact. Sensors, 22(9). https://doi.org/10.3390/s22093177CrossRefGoogle ScholarPubMed
Hossain, M. B., Posada-Quintero, H., & Chon, K. (2022a). A deep convolutional autoencoder for automatic motion artifact removal in electrodermal activity. IEEE Transactions on Biomedical Engineering, 69(12), 3601–3611. https://doi.org/10.1109/TBME.2022.3174509CrossRefGoogle ScholarPubMed
Hossain, M. B., Posada-Quintero, H., & Chon, K. (2022b). A deep convolutional autoencoder for motion artifact removal in electrodermal activity signals: A preliminary study. IEEE Transactions on Biomedical Engineering, 69(12), 3601–3611.CrossRefGoogle Scholar
Hossain, M. B., Posada-Quintero, H. F., Kong, Y., McNaboe, R., & Chon, K. (2021). A preliminary study on automatic motion artifacts detection in electrodermal activity data using machine learning. In 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (pp. 6920–6923). IEEE. https://doi.org/10.1109/EMBC46164.2021.9629513Google Scholar
Hossain, M. B., Posada-Quintero, H. F., Kong, Y., McNaboe, R., & Chon, K. (2022). Automatic motion artifact detection in electrodermal activity data using machine learning. Biomedical Signal Processing and Control, 74, 103483. https://doi.org/10.1016/j.bspc.2022.103483CrossRefGoogle Scholar
Jang, E.-H., Park, B.-J., Park, M.-S., Kim, S.-H., & Sohn, J.-H. (2015). Analysis of physiological signals for recognition of boredom, pain, and surprise emotions. Journal of Physiological Anthropology, 34(1). https://doi.org/10.1186/s40101-015-0063-5CrossRefGoogle ScholarPubMed
Jaques, N., Taylor, S., Azaria, A., Ghandeharioun, A., Sano, A., & Picard, R. (2015). Predicting students’ happiness from physiology, phone, mobility, and behavioral data. In 2015 International Conference on Affective Computing and Intelligent Interaction (ACII) (pp. 222–228). IEEE. https://doi.org/10.1109/ACII.2015.7344575CrossRefGoogle Scholar
Kasos, K., Kekecs, Z., Csirmaz, L., Zimonyi, S., Vikor, F., Kasos, E., et al. (2020). Bilateral comparison of traditional and alternate electrodermal measurement sites. Psychophysiology, 57(11), e13645. https://doi.org/10.1111/psyp.13645CrossRefGoogle ScholarPubMed
Kasos, K., Kekecs, Z., Kasos, E., Szekely, A., & Varga, K. (2018). Bilateral electrodermal activity in the active-alert hypnotic induction. International Journal of Clinical and Experimental Hypnosis, 66(3), 282–297. https://doi.org/10.1080/00207144.2018.1460551CrossRefGoogle ScholarPubMed
Kim, A. Y., Jang, E. H., Kim, S., Choi, K. W., Jeon, H. J., Yu, H. Y., & Byun, S. (2018). Automatic detection of major depressive disorder using electrodermal activity. Scientific Reports, 8(1). https://doi.org/10.1038/s41598-018-35147-3CrossRefGoogle ScholarPubMed
Kim, H., Kwon, S., Kwon, Y.-T., & Yeo, W.-H. (2021). Soft wireless bioelectronics and differential electrodermal activity for home sleep monitoring. Sensors, 21(2). https://doi.org/10.3390/s21020354Google ScholarPubMed
Kleckner, I. R., Jones, R. M., Wilder-Smith, O., Wormwood, J. B., Akcakaya, M., Quigley, K. S., et al. (2018). Simple, transparent, and flexible automated quality assessment procedures for ambulatory electrodermal activity data. IEEE Transactions on Biomedical Engineering, 65(7), 1460–1467. https://doi.org/10.1109/TBME.2017.2758643CrossRefGoogle ScholarPubMed
Koelstra, S., Muhl, C., Soleymani, M., Lee, J.-S., Yazdani, A., Ebrahimi, T., et al. (2012). DEAP: A database for emotion analysis; using physiological signals. IEEE Transactions on Affective Computing, 3(1), 18–31. https://doi.org/10.1109/T-AFFC.2011.15CrossRefGoogle Scholar
Kong, Y., Posada-Quintero, H. F., & Chon, K. H. (2020). Pain Detection using a Smartphone in Real Time*. 2020 42nd Annual International Conference of the IEEE Engineering in Medicine Biology Society (EMBC), 4526–4529. https://doi.org/10.1109/EMBC44109.2020.9176077CrossRefGoogle Scholar
Kong, Y., Posada-Quintero, H. F., & Chon, K. H. (2021a). Sensitive physiological indices of pain based on differential characteristics of electrodermal activity. IEEE Transactions on Biomedical Engineering, 3122–3130. https://doi.org/10.1109/TBME.2021.3065218CrossRefGoogle Scholar
Kong, Y., Posada-Quintero, H. F., & Chon, K. H. (2021b). Real-time high-level acute pain detection using a smartphone and a wrist-worn electrodermal activity sensor. Sensors, 21(12). https://doi.org/10.3390/s21123956CrossRefGoogle Scholar
Koumans, A. J. R., Tursky, B., & Solomon, P. (1968). Electrodermal levels and fluctuations during normal sleep. Psychophysiology, 5(3), 300–306. https://doi.org/10.1111/j.1469-8986.1968.tb02826.xCrossRefGoogle ScholarPubMed
Lee, J., Sun, S., Yang, S. M., Sohn, J. J., Park, J., Lee, S., & Kim, H. C. (2019). Bidirectional recurrent auto-encoder for photoplethysmogram denoising. IEEE Journal of Biomedical and Health Informatics, 23(6), 2375–2385. https://doi.org/10.1109/JBHI.2018.2885139CrossRefGoogle ScholarPubMed
Li, J., Struzik, Z., Zhang, L., & Cichocki, A. (2015). Feature learning from incomplete EEG with denoising autoencoder. Neurocomputing, 165, 23–31. https://doi.org/10.1016/j.neucom.2014.08.092CrossRefGoogle Scholar
Liu, J. C. J., Verhulst, S., Massar, S. A. A., & Chee, M. W. L. (2015). Sleep deprived and sweating it out: The effects of total sleep deprivation on skin conductance reactivity to psychosocial stress. Sleep, 38(1), 155–159. https://doi.org/10.5665/sleep.4346CrossRefGoogle ScholarPubMed
Liu, Y., & Du, S. (2018). Psychological stress level detection based on electrodermal activity. Behavioural Brain Research, 341, 50–53. https://doi.org/10.1016/j.bbr.2017.12.021CrossRefGoogle ScholarPubMed
Llanes-Jurado, J., Carrasco-Ribelles, L. A., Alcañiz, M., & Marín-Morales, J. (2021). Automatic artifact recognition and correction for electrodermal activity in uncontrolled environments [preprint].CrossRefGoogle Scholar
Loddenkemper, T., Kellinghaus, C., Gandjour, J., Nair, D. R., Najm, I. M., Bingaman, W., & Lüders, H. O. (2004). Localising and lateralising value of ictal piloerection. Journal of Neurology, Neurosurgery & Psychiatry, 75(6), 879–883. https://doi.org/10.1136/jnnp.2003.023333CrossRefGoogle ScholarPubMed
Meisel, C., El Atrache, R., Jackson, M., Schubach, S., Ufongene, C., & Loddenkemper, T. (2020). Machine learning from wristband sensor data for wearable, noninvasive seizure forecasting. Epilepsia, 61(12), 2653–2666. https://doi.org/10.1111/epi.16719CrossRefGoogle ScholarPubMed
Melander, C. A., Kikhia, B., Olsson, M., Wälivaara, B.-M., & Sävenstedt, S. (2018). The impact of using measurements of electrodermal activity in the assessment of problematic behaviour in dementia. Dementia and Geriatric Cognitive Disorders Extra, 8(3), 333–347. https://doi.org/10.1159/000493339CrossRefGoogle ScholarPubMed
Miranda-Correa, J. A., Abadi, M. K., Sebe, N., & Patras, I. (2021). AMIGOS: A dataset for affect, personality and mood research on individuals and groups. IEEE Transactions on Affective Computing, 12(2), 479–493. https://doi.org/10.1109/TAFFC.2018.2884461CrossRefGoogle Scholar
Momin, A., Bhattacharya, S., Sanyal, S., & Chakraborty, P. (2020). Visual attention, mental stress and gender: A study using physiological signals. IEEE Access, 8, 165973–165988. https://doi.org/10.1109/ACCESS.2020.3022727CrossRefGoogle Scholar
Munsters, J., Wallström, L., Ågren, J., Norsted, T., & Sindelar, R. (2012). Skin conductance measurements as pain assessment in newborn infants born at 22–27weeks gestational age at different postnatal age. Early Human Development, 88(1), 21–26. https://doi.org/10.1016/j.earlhumdev.2011.06.010CrossRefGoogle Scholar
Murali, N. S., Svatikova, A., & Somers, V. K. (2003). Cardiovascular physiology and sleep. Frontiers in Bioscience-Landmark, 8(6). https://doi.org/10.2741/1105Google ScholarPubMed
Nahman-Averbuch, H., & Coghill, R. C. (2017). Pain-autonomic relationships: Implications for experimental design and the search for an “objective marker” for pain. PAIN, 158(11), 2064–2065. https://doi.org/10.1097/j.pain.0000000000001035CrossRefGoogle ScholarPubMed
Nasseri, M., Pal Attia, T., Joseph, B., Gregg, N. M., Nurse, E. S., Viana, P. F., et al. (2021). Ambulatory seizure forecasting with a wrist-worn device using long-short term memory deep learning. Scientific Reports, 11(1), 1–9.CrossRefGoogle ScholarPubMed
Nickel, M. M., May, E. S., Tiemann, L., Postorino, M., Ta Dinh, S., & Ploner, M. (2017). Autonomic responses to tonic pain are more closely related to stimulus intensity than to pain intensity. PAIN, 158(11), 2129–2136. https://doi.org/10.1097/j.pain.0000000000001010CrossRefGoogle ScholarPubMed
Perugia, G., Rodríguez-Martín, D., Díaz Boladeras, M., Mallofré, A. C., Barakova, E., & Rauterberg, M. (2017). Electrodermal activity: Explorations in the psychophysiology of engagement with social robots in dementia. In 26th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN) (pp. 1248–1254). IEEE. https://doi.org/10.1109/ROMAN.2017.8172464Google Scholar
Poh, M.-Z., Loddenkemper, T., Reinsberger, C., Swenson, N. C., Goyal, S., Sabtala, M. C., et al. (2012). Convulsive seizure detection using a wrist-worn electrodermal activity and accelerometry biosensor. Epilepsia, 53(5), e93–e97. https://doi.org/10.1111/j.1528-1167.2012.03444.xCrossRefGoogle ScholarPubMed
Poh, M.-Z., Loddenkemper, T., Swenson, N. C., Goyal, S., Madsen, J. R., & Picard, R. W. (2010). Continuous monitoring of electrodermal activity during epileptic seizures using a wearable sensor. In 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (pp. 4415–4418). IEEE. https://doi.org/10.1109/IEMBS.2010.5625988Google Scholar
Poh, M.-Z., Swenson, N. C., & Picard, R. W. (2010). A wearable sensor for unobtrusive, long-term assessment of electrodermal activity. IEEE Transactions on Biomedical Engineering, 57(5), 1243–1252. https://doi.org/10.1109/TBME.2009.2038487Google ScholarPubMed
Posada-Quintero, H. F., & Chon, K. H. (2020). Innovations in electrodermal activity data collection and signal processing: A systematic review. Sensors, 20(2). https://doi.org/10.3390/s20020479CrossRefGoogle ScholarPubMed
Posada-Quintero, H. F., Derrick, B. J., Winstead-Derlega, C., Gonzalez, S. I., Claire Ellis, M., Freiberger, J. J., & Chon, K. H. (2021). Time-varying spectral index of electrodermal activity to predict central nervous system oxygen toxicity symptoms in divers: Preliminary results. In 43rd Annual International Conference of the IEEE Engineering in Medicine Biology Society (pp. 1242–1245). IEEE. https://doi.org/10.1109/EMBC46164.2021.9629924Google Scholar
Posada-Quintero, H. F., Florian, J. P., Orjuela-Cañón, A. D., Aljama-Corrales, T., Charleston-Villalobos, S., & Chon, K. H. (2016). Power spectral density analysis of electrodermal activity for sympathetic function assessment. Annals of Biomedical Engineering, 44(10), 3124–3135. https://doi.org/10.1007/s10439-016-1606-6CrossRefGoogle ScholarPubMed
Posada-Quintero, H. F., Florian, J. P., Orjuela-Cañón, A. D., & Chon, K. H. (2018). Electrodermal activity is sensitive to cognitive stress under water. Frontiers in Physiology, 8. https://doi.org/10.3389/fphys.2017.01128CrossRefGoogle ScholarPubMed
Posada-Quintero, H. F., Kong, Y., & Chon, K. H. (2021). Objective pain stimulation intensity and pain sensation assessment using machine learning classification and regression based on electrodermal activity. American Journal of Physiology – Regulatory, Integrative and Comparative Physiology, 321(2), R186–R196. https://doi.org/10.1152/ajpregu.00094.2021CrossRefGoogle ScholarPubMed
Posada-Quintero, H. F., Kong, Y., Nguyen, K., Tran, C., Beardslee, L., Chen, L., et al. (2020). Using electrodermal activity to validate multilevel pain stimulation in healthy volunteers evoked by thermal grills. American Journal of Physiology – Regulatory, Integrative and Comparative Physiology, 319(3), R366–R375. https://doi.org/10.1152/ajpregu.00102.2020CrossRefGoogle ScholarPubMed
Posada-Quintero, H. F., Landon, C. S., Stavitzski, N. M., Dean, J. B., & Chon, K. H. (2022). Seizures caused by exposure to hyperbaric oxygen in rats can be predicted by early changes in electrodermal activity. Frontiers in Physiology, 12. https://doi.org/10.3389/fphys.2021.767386CrossRefGoogle ScholarPubMed
Prince, E. B., Kim, E. S., Wall, C. A., Gisin, E., Goodwin, M. S., Simmons, E. S., et al. (2017). The relationship between autism symptoms and arousal level in toddlers with autism spectrum disorder, as measured by electrodermal activity. Autism, 21(4), 504–508. https://doi.org/10.1177/1362361316648816CrossRefGoogle ScholarPubMed
Reljin, N., Lazaro, J., Hossain, M. D., Noh, Y. S., Cho, C. H., & Chon, K. H. (2020). Using the redundant convolutional encoder–decoder to denoise QRS complexes in ECG signals recorded with an armband wearable device. Sensors, 20(16). https://doi.org/10.3390/s20164611CrossRefGoogle ScholarPubMed
Romine, W., Banerjee, T., & Goodman, G. (2019). Toward sensor-based sleep monitoring with electrodermal activity measures. Sensors, 19(6). https://doi.org/10.3390/s19061417CrossRefGoogle ScholarPubMed
Ruiz-Robledillo, N., & Moya-Albiol, L. (2015). Lower electrodermal activity to acute stress in caregivers of people with autism spectrum disorder: An adaptive habituation to stress. Journal of Autism and Developmental Disorders, 45(2), 576–588. https://doi.org/10.1007/s10803-013-1996-3CrossRefGoogle ScholarPubMed
Saga, K. (2002). Structure and function of human sweat glands studied with histochemistry and cytochemistry. Progress in Histochemistry and Cytochemistry, 37(4), 323–386. https://doi.org/10.1016/s0079-6336(02)80005-5CrossRefGoogle ScholarPubMed
Sano, A., Picard, R. W., & Stickgold, R. (2014). Quantitative analysis of wrist electrodermal activity during sleep. International Journal of Psychophysiology, 94(3), 382–389. https://doi.org/10.1016/j.ijpsycho.2014.09.011CrossRefGoogle ScholarPubMed
Sato, K., Kang, W. H., Saga, K., & Sato, K. T. (1989). Biology of sweat glands and their disorders. I. Normal sweat gland function. Journal of the American Academy of Dermatology, 20(4), 537–563. https://doi.org/10.1016/S0190-9622(89)70063-3CrossRefGoogle ScholarPubMed
Scarpina, F., & Tagini, S. (2017). The Stroop color and word test. Frontiers in Psychology, 8, 557. https://doi.org/10.3389/fpsyg.2017.00557CrossRefGoogle ScholarPubMed
Scharmüller, W., Wabnegger, A., & Schienle, A. (2015). Functional brain connectivity during fear of pain: A comparison between dental phobics and controls. Brain Connectivity, 5(3), 187–191. https://doi.org/10.1089/brain.2014.0297CrossRefGoogle Scholar
Schupak, B. M., Parasher, R. K., & Zipp, G. P. (2016). Reliability of electrodermal activity: Quantifying sensory processing in children with autism. American Journal of Occupational Therapy, 70(6), 1–6. https://doi.org/10.5014/ajot.2016.018291CrossRefGoogle ScholarPubMed
Setz, C., Arnrich, B., Schumm, J., Marca, R. L., Tröster, G., & Ehlert, U. (2010). Discriminating stress from cognitive load using a wearable EDA device. IEEE Transactions on Information Technology in Biomedicine, 14(2), 410–417. https://doi.org/10.1109/TITB.2009.2036164CrossRefGoogle ScholarPubMed
Shaffer, F., Combatalade, D., Peper, E., & Meehan, Z. M. (2016). A guide to cleaner electrodermal activity measurements. Biofeedback, 44(2), 90–100. https://doi.org/10.5298/1081-5937-44.2.01CrossRefGoogle Scholar
Shu, L., Xie, J., Yang, M., Li, Z., Li, Z., Liao, D., et al. (2018). A review of emotion recognition using physiological signals. Sensors, 18(7). https://doi.org/10.3390/s18072074CrossRefGoogle ScholarPubMed
Shukla, J., Barreda-Angeles, M., Oliver, J., Nandi, G. C., & Puig, D. (2019). Feature extraction and selection for emotion recognition from electrodermal activity. IEEE Transactions on Affective Computing, 857–869. https://doi.org/10.1109/TAFFC.2019.2901673Google Scholar
Silvani, A., & Dampney, R. A. L. (2013). Central control of cardiovascular function during sleep. American Journal of Physiology– Heart and Circulatory Physiology, 305(12), H1683–H1692. https://doi.org/10.1152/ajpheart.00554.2013CrossRefGoogle ScholarPubMed
Somers, V. K., Dyken, M. E., Mark, A. L., & Abboud, F. M. (1993). Sympathetic-nerve activity during sleep in normal subjects. New England Journal of Medicine, 328(5), 303–307. https://doi.org/10.1056/NEJM199302043280502CrossRefGoogle ScholarPubMed
Subramanian, S., Tseng, B., Barbieri, R., & Brown, E. N. (2021). Unsupervised machine learning methods for artifact removal in electrodermal activity. In 43rd Annual International Conference of the IEEE Engineering in Medicine Biology Society (pp. 399–402). IEEE. https://doi.org/10.1109/EMBC46164.2021.9630535Google Scholar
Subramanian, S., Tseng, B., Barbieri, R., & Brown, E. N. (2022). An unsupervised automated paradigm for artifact removal from electrodermal activity in an uncontrolled clinical setting. Physiological Measurement, 43(11) https://doi.org/10.1088/1361-6579/ac92bdCrossRefGoogle Scholar
Sugimine, S., Saito, S., & Takazawa, T. (2020). Normalized skin conductance level could differentiate physical pain stimuli from other sympathetic stimuli. Scientific Reports, 10(1). https://doi.org/10.1038/s41598-020-67936-0CrossRefGoogle ScholarPubMed
Susam, B. T., Akcakaya, M., Nezamfar, H., Diaz, D., Xu, X., de Sa, V. R., et al. (2018). Automated pain assessment using electrodermal activity data and machine learning. In 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (pp. 372–375). IEEE. https://doi.org/10.1109/EMBC.2018.8512389Google Scholar
Taylor, S., Jaques, N., Chen, W., Fedor, S., Sano, A., & Picard, R. (2015). Automatic identification of artifacts in electrodermal activity data. In 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (pp. 1934–1937). IEEE. https://doi.org/10.1109/EMBC.2015.7318762Google Scholar
Theodoros, A. (2014). Electrodermal activity: Applications in perioperative care. International Journal of Medical Research & Health Sciences, 3(3).CrossRefGoogle Scholar
Topoglu, Y., Watson, J., Suri, R., & Ayaz, H. (2020). Electrodermal activity in ambulatory settings: A narrative review of literature. In Ayaz, H. (ed.), Advances in Neuroergonomics and Cognitive Engineering (pp. 91–102). Springer. https://doi.org/10.1007/978-3-030-20473-0_10CrossRefGoogle Scholar
van Andel, J., Ungureanu, C., Arends, J., Tan, F., Van Dijk, J., Petkov, G., et al. (2017). Multimodal, automated detection of nocturnal motor seizures at home: Is a reliable seizure detector feasible? Epilepsia Open, 2(4), 424–431.CrossRefGoogle ScholarPubMed
van Dooren, M., de Vries, J. J. G., & Janssen, J. H. (2012). Emotional sweating across the body: Comparing 16 different skin conductance measurement locations. Physiology & Behavior, 106(2), 298–304. https://doi.org/10.1016/j.physbeh.2012.01.020CrossRefGoogle ScholarPubMed
Vandecasteele, K., De Cooman, T., Gu, Y., Cleeren, E., Claes, K., Van Paesschen, , et al. (2017). Automated epileptic seizure detection based on wearable ECG and PPG in a hospital environment. Sensors, 17(10).CrossRefGoogle Scholar
Veeranki, Y. R., Ganapathy, N., & Swaminathan, R. (2021). Electrodermal activity based emotion recognition using time-frequency methods and machine learning algorithms. Current Directions in Biomedical Engineering, 7(2), 863–866. https://doi.org/10.1515/cdbme-2021-2220CrossRefGoogle Scholar
Vincent, P., Larochelle, H., Lajoie, I., Bengio, Y., & Manzagol, P.-A. (2010). Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion. Journal of Machine Learning Research, 11, 3371–3408.Google Scholar
von Polier, G. G., Biskup, C. S., Kötting, W. F., Bubenzer, S., Helmbold, K., Eisert, A., et al.(2014). Change in electrodermal activity after acute tryptophan depletion associated with aggression in young people with attention deficit hyperactivity disorder (ADHD). Journal of Neural Transmission, 121(4), 451–455. https://doi.org/10.1007/s00702-013-1119-5CrossRefGoogle Scholar
Wang, Y., Yao, H., & Zhao, S. (2016). Auto-encoder based dimensionality reduction. Neurocomputing, 184, 232–242. https://doi.org/10.1016/j.neucom.2015.08.104CrossRefGoogle Scholar
Wannamaker, B. B. (1985). Autonomic nervous system and epilepsy. Epilepsia, 26(s1), S31–S39. https://doi.org/10.1111/j.1528-1157.1985.tb05722.xCrossRefGoogle ScholarPubMed
Wendt, J., Lotze, M., Weike, A. I., Hosten, N., & Hamm, A. O. (2008). Brain activation and defensive response mobilization during sustained exposure to phobia-related and other affective pictures in spider phobia. Psychophysiology, 45(2), 205–215. https://doi.org/10.1111/j.1469-8986.2007.00620.xCrossRefGoogle ScholarPubMed
Wickramasuriya, D. S., & Faghih, R. T. (2020). A marked point process filtering approach for tracking sympathetic arousal from skin conductance. IEEE Access, 8, 68499–68513. https://doi.org/10.1109/ACCESS.2020.2984508CrossRefGoogle Scholar
Wu, G., Liu, G., & Hao, M. (2010). The analysis of emotion recognition from GSR based on PSO. In 2010 International Symposium on Intelligence Information Processing and Trusted Computing (pp. 360–363). IEEE. https://doi.org/10.1109/IPTC.2010.60CrossRefGoogle Scholar
Xia, V., Jaques, N., Taylor, S., Fedor, S., & Picard, R. (2015). Active learning for electrodermal activity classification. In 2015 IEEE Signal Processing in Medicine and Biology Symposium (pp. 1–6). https://doi.org/10.1109/SPMB.2015.7405467CrossRefGoogle Scholar
Yang, T. T., Simmons, A. N., Matthews, S. C., Tapert, S. F., Bischoff-Grethe, A., Frank, G. K. W., et al. (2007). Increased amygdala activation is related to heart rate during emotion processing in adolescent subjects. Neuroscience Letters, 428(2), 109–114. https://doi.org/10.1016/j.neulet.2007.09.039CrossRefGoogle ScholarPubMed
Zhang, Y., Haghdan, M., & Xu, K. S. (2017). Unsupervised motion artifact detection in wrist-measured electrodermal activity data. In Proceedings of the 2017 ACM International Symposium on Wearable Computers (pp. 54–57). ACM. https://doi.org/10.1145/3123021.3123054CrossRefGoogle Scholar

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