SynthesisSensors (Basel, Switzerland)2022
Machine Learning Techniques for Arousal Classification from Electrodermal Activity: A Systematic Review.
Synthesis in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
10 citing papers in PubMed.
- Hypnosis reshapes multilevel stress response and enhances executive performance in stressed medical students.Scientific reports · 2026Trial
- Psychophysiological responses to tactile contact with hairy versus hairless plant leaves.Scientific reports · 2026Article
- Artificial intelligence in clinical physiology: System-wise applications in diagnostics, monitoring, and medical education.Journal of family medicine and primary care · 2026Article
- Associations of soundscape naturalness and natural-element complexity with short-term psychophysiological restoration in campus green spaces: a virtual reality study.Frontiers in psychology · 2026Article
- Augmented State-Space Modeling and Control of Latent Arousal States Under Inhibitory and Excitatory Conditions.IEEE open journal of engineering in medicine and biology · 2026Article
- Towards In-Vehicle Non-Contact Estimation of EDA-Based Arousal with LiDAR.Sensors (Basel, Switzerland) · 2025Article
- Photoplethysmography-based HRV analysis and machine learning for real-time stress quantification in mental health applications.APL bioengineering · 2025Article
- An AI recognition method for children's clinical operative pain by skin potential (SP) signal.Heliyon · 2025Article
- Detecting Emotional Arousal and Aggressive Driving Using Neural Networks: A Pilot Study Involving Young Drivers in Duluth.Sensors (Basel, Switzerland) · 2024Article
- Driver Drowsiness Detection: A Machine Learning Approach on Skin Conductance.Sensors (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
5 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.