[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120790-en":3,"doc-seo-120790-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120790,8796095360427,"Lucas Martin","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Comparative Analysis of Electrodermal Activity Decomposition Methods in Emotion Detection Using Machine Learning - paper","Electrodermal activity (EDA) captures sympathetic nervous system activity through skin conductance changes, enabling emotion-related analysis. Decomposition separates EDA into tonic and phasic components, and this work compares two decomposition algorithms for recognizing emotions including amusing, boring, relaxing, and scary. Using the CASE dataset, EDA signals were pre-processed and deconvolved via cvxEDA and BayesianEDA, followed by extraction of 12 phasic time-domain features. Logistic regression (LR) and support vector machine (SVM) then evaluated performance, showing BayesianEDA superiority over cvxEDA with strong statistical discrimination and SVM outperforming LR.","Caring is Sharing – Exploiting the Value in Data for Health and Innovation 73  \nM. Hägglund et al. (Eds.)  \n© 2023 European Federation for Medical Informatics (EFMI) andIOS Press.  \nThis article is published online with Open Access by IOS Press and distributed under the terms of the Creative Commons Attribution Non-Commercial License 4.0 (CC BY-NC 4.0).  \ndoi:10.3233/SHTI230067  \nComparative Analysis of Electrodermal Activity Decomposition Methods in Emotion Detection Using Machine Learning  \nSriram Kumar Pa,1, Praveen Kumar GOVARTHAN a, Nagarajan GANAPATHY b and Jac Fredo AGASTINOSE RONICKOM a  \na School of Biomedical Engineering, Indian Institute of Technology (BHU), Varanasi,  \nUttar Pradesh, India-221005  \nb Department of Biomedical Engineering, Indian Institute of Technology Hyderabad,  \nKandi, Telangana, India  \nORCiD ID: Sriram Kumar P [https://orcid.org/](https://orcid.org/)[https://orcid.org/0000-0002-8604-2329](https://orcid.org/0000-0002-8604-2329)  \nAbstract. Electrodermal activity (EDA) reflects sympathetic nervous system activity through sweating-related changes in skin conductance. Decomposition analysis is used to deconvolve the EDA into slow and fast varying tonic and phasic activity, respectively. In this study, we used machine learning models to compare the performance of two EDA decomposition algorithms to detect emotions such as amusing, boring, relaxing, and scary. The EDA data considered in this study were obtained from the publicly available Continuously Annotated Signals of Emotion (CASE) dataset. Initially, we pre-processed and deconvolved the EDA data into tonic and phasic components using decomposition methods such as cvxEDA and BayesianEDA. Further, 12 time-domain features were extracted from the phasic component of EDA data. Finally, we applied machine learning algorithms such as logistic regression (LR) and support vector machine (SVM), to evaluate the performance of the decomposition method. Our results imply that the BayesianEDA decomposition method outperforms the cvxEDA. The mean of the first derivative feature discriminated all the considered emotional pairs with high statistical significance (p\u003C0.05) . SVM was able to detect emotions better than the LR classifier. We achieved a 10-fold average classification accuracy, sensitivity, specificity, precision, and f1-score of 88.2%, 76.25%, 92.08%, 76.16%, and 76.15% respectively, using BayesianEDA and SVM classifiers. The proposed framework can be utilized to detect emotional states for the early diagnosis of  \npsychological conditions.  \nKeywords. Emotion detection, Electrodermal activity, Deconvolution, Time  \ndomain features, Machine learning.  \n1. Introduction  \nElectrodermal activity (EDA) is a physiological measure of changes in the sympathetic system, reflecting emotional and cognitive states. It tracks the changing electrical conductance of the skin due to the activity of sweat glands and is composed of tonic  \n1 Corresponding Author: Sriram Kumar P, [E-mail: psrirmakumar.rs.bme21@iitbhu.ac.in](E-mail: psrirmakumar.rs.bme21@iitbhu.ac.in).  \n74 Sriram Kumar P et al. / Comparative Analysis of Electrodermal Activity Decomposition Methods and phasic components [1] . The tonic component is a slowly varying or low-frequency signal of EDA. It is influenced by the thermoregulation of the body as well as the surrounding air’s humidity and temperature. Additionally, the tonic component includes information on an individual’s degree of overall arousal. On the other hand, the phasic component reflects neural stimulation from the sympathetic nervous system and is a fast-varying or high-frequency component of EDA [2] . The performance of emotion detection highly relies on decomposition methods, so it’s essential to find a reliable decomposition technique that will improve the human emotion monitoring system. Researchers have proposed a variety of EDA decomposition methods such as non-negative deconvolution, dynamic causal modelling, cubic-spline-based","cbCain5dQV0psYJX","https://ap.wps.com/l/cbCain5dQV0psYJX","pdf",594970,1,5,"English","en",105,"# Introduction\n# Materials and Methods\n## Dataset and preprocessing\n## Decomposition methods\n## Feature extraction and classification","[{\"question\":\"What does electrodermal activity (EDA) represent in emotion detection?\",\"answer\":\"EDA reflects sympathetic nervous system activity through sweating-related changes in skin conductance, and it contains tonic and phasic components linked to arousal and neural stimulation.\"},{\"question\":\"Which EDA decomposition methods are compared in this study?\",\"answer\":\"The study compares cvxEDA and BayesianEDA to deconvolve EDA into tonic and phasic activity.\"},{\"question\":\"How are emotions recognized from the EDA signals?\",\"answer\":\"After decomposition, the method extracts 12 time-domain features from the phasic component and applies machine learning classifiers including logistic regression and support vector machine.\"}]","Comparative Analysis of Electrodermal Activity Decomposition Methods in Emotion Detection Using Machine Learning - 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