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Computational Intelligence to Assess the Existence of Pain, Based on the Use of Electrophysiological Signals

10.24050/reia.v20i40.1683
Published 2023-12-19
Articles

Abstract

Pain is a health problem that affects people physically and emotionally. To determine the
pain experimented, a survey is carried out, which implies self-evaluation, honesty, and
verbal or facial communication capability. In this paper, we present a comparison of two
computational algorithms for two classifiers: the first classifier discriminates between
pain and no pain, and the second one classifies three levels of pain. The algorithms
employed were the support vector machine (SVM) and a quadratic discriminant analysis
method (QDA). Acute pain was induced in fifteen participants by electrostimulation,
during the experiment we assessed electromyography (EMG), electrocardiography (ECG),
electrodermal activity (EDA), and electroencephalography (EEG), as well we asked the
participants to report their pain perception using the visual analog scale. Subsequently, we
extracted features related to pain assessment from the acquired signals. Three analyses
were performed, binary classifications with multiple features, binary classifications with
one feature, and three-level classifications with various features. We compared the SVM and
the QDA algorithms using the confusion matrix, and the computational cost. For the binary
classification, the SVM algorithm accuracy was 88.02% and the QDA algorithm accuracy was
70.78%, with a computational cost of 9.587 s and 3.023 s, respectively.

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References

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