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. 2022 Nov 17;22(22):8886.
doi: 10.3390/s22228886.

Machine Learning Techniques for Arousal Classification from Electrodermal Activity: A Systematic Review

Roberto Sánchez-Reolid  1   2 , Francisco López de la Rosa  2 , Daniel Sánchez-Reolid  2 , María T López  1   2 , Antonio Fernández-Caballero  1   2   3
Affiliations

Machine Learning Techniques for Arousal Classification from Electrodermal Activity: A Systematic Review

Roberto Sánchez-Reolid et al. Sensors (Basel). .

Abstract

This article introduces a systematic review on arousal classification based on electrodermal activity (EDA) and machine learning (ML). From a first set of 284 articles searched for in six scientific databases, fifty-nine were finally selected according to various criteria established. The systematic review has made it possible to analyse all the steps to which the EDA signals are subjected: acquisition, pre-processing, processing and feature extraction. Finally, all ML techniques applied to the features of these signals for arousal classification have been studied. It has been found that support vector machines and artificial neural networks stand out within the supervised learning methods given their high-performance values. In contrast, it has been shown that unsupervised learning is not present in the detection of arousal through EDA. This systematic review concludes that the use of EDA for the detection of arousal is widely spread, with particularly good results in classification with the ML methods found.

Keywords: arousal; electrodermal activity; machine learning; systematic review.

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Conflict of interest statement

The authors declare that they have no conflict of interest.

Figures

Figure 1
Figure 1
Search strategy.
Figure 2
Figure 2
Paper grouping.
Figure 3
Figure 3
Usual stages in signal acquisition, pre-processing and processing.
Figure 4
Figure 4
Flowchart of the experimental design during raw signal acquisition.
Figure 5
Figure 5
Contemporary labelling of electrodermal activity, inspired in [105].
Figure 6
Figure 6
Flowchart of the deconvolution process.
See this image and copyright information in PMC

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References

    1. Le Fevre M., Matheny J., Kolt G.S. Eustress, distress and interpretation in occupational stress. J. Manag. Psychol. 2003;18:726–744. doi: 10.1108/02683940310502412. - DOI
    1. Russell J.A. A circumplex model of affect. J. Personal. Soc. Psychol. 1980;39:1161. doi: 10.1037/h0077714. - DOI
    1. Halim Z., Kalsoom R., Bashir S., Abbas G. Artificial Intelligence techniques for driving safety and vehicle crash prediction. Artif. Intell. Rev. 2016;46:351–387. doi: 10.1007/s10462-016-9467-9. - DOI
    1. Al-Shwaheen T.I., Moghbel M., Hau Y.W., Ooi C.Y. Use of learning approaches to predict clinical deterioration in patients based on various variables: A review of the literature. Artif. Intell. Rev. 2021;55:1055–1084. doi: 10.1007/s10462-021-09982-2. - DOI
    1. Said S., Karar A.S., Beyrouthy T., Alkork S., Nait-ali A. Biometrics Verification Modality Using Multi-Channel sEMG Wearable Bracelet. Appl. Sci. 2020;10:6960. doi: 10.3390/app10196960. - DOI

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