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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Bibliographic Details
Published in:Procedia computer science Vol. 184; pp. 99 - 106
Main Authors: Hossein Aqajari, Seyed Amir, Naeini, Emad Kasaeyan, Mehrabadi, Milad Asgari, Labbaf, Sina, Dutt, Nikil, Rahmani, Amir M.
Format: Journal Article
Language:English
Published: Elsevier B.V 2021
Subjects:
Autoencoders
Convolutional Neural Networks
Electrodermal Activity
Galvanic Skin Response
Health Monitoring
Internet of Things
Machine Learning
Open-Source
Physiological signals
Toolkit
Wearable Electronics
Health Monitoring
Galvanic Skin Response
Electrodermal Activity
Physiological signals
Autoencoders
Toolkit
Convolutional Neural Networks
Internet of Things
Machine Learning
Open-Source
Wearable Electronics
ISSN:1877-0509, 1877-0509
Online Access:Get full text
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Summary: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.
ISSN:1877-0509
1877-0509
DOI:10.1016/j.procs.2021.03.021