Abstract
The detection of arousal is very important given its great implication on daily well-being. In this regards, the use of artificial neural networks and other classifiers applied to physiological signals has increased considerably. Different architectures for arousal detection using electrodermal activity are presented in this paper. Moreover, two different strategies are analysed and compared. The first one is based on the collection of 21 features (temporal, morphological, statistical and frequential), whereas the second used the processed EDA data (phasic component data) directly on different machine learning algorithms. The first approach offers F1-scores 92.02% and 90.95% for a multilayer perceptron and a one-dimensional convolutional network, respectively. For the second scenario, it has been found that the best F1-scores are 91.02% and 88.12% for bilateral long short-term memory and long short-term memory, respectively.
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Acknowledgements
This work has been partially supported by Spanish Ministerio de Ciencia e Innovación, Agencia Estatal de Investigación (AEI)/European Regional Development Fund (FEDER, UE) under EQC2019-006063-P and PID2020-115220RB-C21 grants, and by CIBERSAM of the Instituto de Salud Carlos III. Roberto Sánchez-Reolid holds BES-2017-081958 scholarship from Spanish Ministerio de Educación y Formación Profesional.
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Sánchez-Reolid, R., de la Rosa, F.L., Sánchez-Reolid, D., López, M.T., Fernández-Caballero, A. (2021). Feature and Time Series Extraction in Artificial Neural Networks for Arousal Detection from Electrodermal Activity. In: Rojas, I., Joya, G., Català, A. (eds) Advances in Computational Intelligence. IWANN 2021. Lecture Notes in Computer Science(), vol 12861. Springer, Cham. https://doi.org/10.1007/978-3-030-85030-2_22
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