pyEDA: An Open-Source Python Toolkit for Pre-processing and Feature Extraction of Electrodermal Activity
Physiological response is an automatic reaction that triggers a physical response to a stimulus such as stress, emotion, pain, etc. Examples include changes in heart rate, respiration, perspiration, and eye pupil dilation. Electrodermal Activity (EDA), also known as Galvanic Skin Response (GSR), mea...
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| Veröffentlicht in: | Procedia computer science Jg. 184; S. 99 - 106 |
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2021
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| Abstract | Physiological response is an automatic reaction that triggers a physical response to a stimulus such as stress, emotion, pain, etc. Examples include changes in heart rate, respiration, perspiration, and eye pupil dilation. Electrodermal Activity (EDA), also known as Galvanic Skin Response (GSR), measures changes in perspiration by detecting the changes in electrical conductivity of skin. Previous studies have already shown that EDA is one of the leading indicators for a stimulus. However, the EDA signal itself is not trivial to analyze. To detect different stimuli in human subjects, variety of features are extracted from EDA signals such as the number of peaks, max peak amplitude, to name a few, showing the prevalence of this signal in bio-medical as well as ubiquitous and wearable computing research. In this paper, we present an open-source Python toolkit for EDA signal preprocessing and statistical and automatic feature extraction. To the best of our knowledge, this is the first effort for developing a versatile and generic tool to extract any number of automatic features from EDA signals. We evaluate our toolkit using different machine learning algorithms applied to the Wearable Stress and Affect Detection (WESAD) dataset. Our results show higher validation accuracy for a stress detection task using the the features automatically extracted by pyEDA. |
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| AbstractList | Physiological response is an automatic reaction that triggers a physical response to a stimulus such as stress, emotion, pain, etc. Examples include changes in heart rate, respiration, perspiration, and eye pupil dilation. Electrodermal Activity (EDA), also known as Galvanic Skin Response (GSR), measures changes in perspiration by detecting the changes in electrical conductivity of skin. Previous studies have already shown that EDA is one of the leading indicators for a stimulus. However, the EDA signal itself is not trivial to analyze. To detect different stimuli in human subjects, variety of features are extracted from EDA signals such as the number of peaks, max peak amplitude, to name a few, showing the prevalence of this signal in bio-medical as well as ubiquitous and wearable computing research. In this paper, we present an open-source Python toolkit for EDA signal preprocessing and statistical and automatic feature extraction. To the best of our knowledge, this is the first effort for developing a versatile and generic tool to extract any number of automatic features from EDA signals. We evaluate our toolkit using different machine learning algorithms applied to the Wearable Stress and Affect Detection (WESAD) dataset. Our results show higher validation accuracy for a stress detection task using the the features automatically extracted by pyEDA. |
| Author | Hossein Aqajari, Seyed Amir Naeini, Emad Kasaeyan Dutt, Nikil Mehrabadi, Milad Asgari Rahmani, Amir M. Labbaf, Sina |
| Author_xml | – sequence: 1 givenname: Seyed Amir surname: Hossein Aqajari fullname: Hossein Aqajari, Seyed Amir email: saqajari@uci.edu organization: University of California, Irvine, Department of Electrical Engineering and Computer Science, Irvine, California, USA – sequence: 2 givenname: Emad Kasaeyan surname: Naeini fullname: Naeini, Emad Kasaeyan organization: University of California, Irvine, Department of Computer Science, Irvine, California, USA – sequence: 3 givenname: Milad Asgari surname: Mehrabadi fullname: Mehrabadi, Milad Asgari organization: University of California, Irvine, Department of Electrical Engineering and Computer Science, Irvine, California, USA – sequence: 4 givenname: Sina surname: Labbaf fullname: Labbaf, Sina organization: University of California, Irvine, Department of Computer Science, Irvine, California, USA – sequence: 5 givenname: Nikil surname: Dutt fullname: Dutt, Nikil organization: University of California, Irvine, Department of Electrical Engineering and Computer Science, Irvine, California, USA – sequence: 6 givenname: Amir M. surname: Rahmani fullname: Rahmani, Amir M. organization: University of California, Irvine, Department of Electrical Engineering and Computer Science, Irvine, California, USA |
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| Cites_doi | 10.3390/s20040969 10.1023/A:1010933404324 10.1145/3290607.3312875 10.3758/s13428-015-0665-1 10.1016/j.neunet.2014.09.003 10.1080/00031305.1992.10475879 10.1007/11941354_44 10.1109/EMBC.2018.8512389 10.3390/s18072074 10.1080/03091902.2020.1759707 10.3389/fict.2017.00001 10.1109/EMBC46164.2021.9629794 10.1007/s11042-017-5069-z 10.23919/EUSIPCO.2019.8902631 10.1109/TCAD.2020.3013074 10.1145/3380446.3430628 10.1145/3394171.3414691 10.2196/preprints.20465 10.3390/info11040212 10.1145/3242969.3242985 10.1023/A:1022627411411 |
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| Keywords | Health Monitoring Galvanic Skin Response Electrodermal Activity Physiological signals Autoencoders Toolkit Convolutional Neural Networks Internet of Things Machine Learning Open-Source Wearable Electronics |
| Language | English |
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| SubjectTerms | Autoencoders Convolutional Neural Networks Electrodermal Activity Galvanic Skin Response Health Monitoring Internet of Things Machine Learning Open-Source Physiological signals Toolkit Wearable Electronics |
| Title | pyEDA: An Open-Source Python Toolkit for Pre-processing and Feature Extraction of Electrodermal Activity |
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