Developing objective pain assessment techniques using physiological signals and machine learning methods addresses the unmet demand for accurate pain evaluation in clinical practice.
Can physiological signals and machine learning models objectively assess and quantify different types of pain?
Physiological signals such as ECG, EDA, PPG, and EEG, combined with machine learning techniques, show promise for developing objective pain assessment tools to overcome the limitations of subjective self-reporting.
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Abstract Pain is a complex multidimensional experience that integrates sensory and emotional components, presenting significant challenges for accurate assessment in clinical practice. Traditional methods of pain evaluation rely on subjective self-reporting and each individual’s ability to communicate their pain experience. In light of the effect of pain on the Autonomic Nervous System, researchers are interested in developing objective assessment techniques using physiological signals. This paper outlines the latest advances in pain biomarkers and machine learning methods for assessing pain using physiological signals, highlighting the growing interest and unmet demand in this area. A comprehensive literature review was conducted, covering studies between 2014 and 2024. The discussion is organised into two areas: first, an analysis of the variations in signal feature behaviour across different pain types, and second, a review of the current state-of-the-art models for pain assessment developed using classical machine learning and deep learning techniques.
Camacho-Navas et al. (Thu,) reported a other. Developing objective pain assessment techniques using physiological signals and machine learning methods addresses the unmet demand for accurate pain evaluation in clinical practice.