Clinical Report: Physiological Indicators in Pain Assessment Across Pain Types

Overview

This scoping review synthesizes evidence from 89 studies on the use of physiological signals and machine learning for objective pain assessment across acute, chronic, and perioperative/postoperative pain. Electrocardiography (ECG), electrodermal activity (EDA), photoplethysmography (PPG), and electroencephalography (EEG) were the most frequently investigated signals, with growing research interest over the past decade.

Background

Pain is a complex sensory and emotional experience involving neural and psychological components, making objective assessment challenging. Traditional clinical pain evaluation relies on self-report scales, which are limited by subjective and psychosocial influences. Physiological signals reflecting autonomic nervous system changes offer a promising avenue for objective pain measurement. Despite advances, no physiological-based pain assessment tool has yet been widely adopted in clinical practice.

Data Highlights

Key Findings

• Physiological signals such as ECG, EDA, PPG, and EEG are the most studied modalities for objective pain assessment.
• Heart rate variability (HRV) features derived from ECG are widely discussed as pain-related markers.
• Machine learning techniques were employed in 36 of the 89 studies to enhance pain detection and classification.
• There is an increasing trend in research publications on physiological pain assessment from 2014 to 2024.
• Two commercial devices, Medasense PMD-200 and NIPE Monitor, are currently used for clinical pain monitoring based on physiological signals.
• Studies often combine multiple physiological signals to improve pain assessment accuracy.

Clinical Implications

Objective pain assessment using physiological indicators can complement traditional self-report tools, especially in patients unable to communicate effectively. Incorporating machine learning models with multimodal physiological data may enhance pain detection accuracy and support personalized analgesic management. Clinicians should be aware of emerging commercial devices that utilize these technologies for intraoperative and neonatal pain monitoring.

Conclusion

Physiological signals provide valuable insights into pain states across different pain types, with growing evidence supporting their integration into clinical pain assessment. Continued research and technological development are needed to establish standardized, objective pain measurement tools for routine clinical use.

References

1. International Association for the Study of Pain (IASP) -- Pain Definition and Classification
2. Medasense PMD-200 System -- Clinical Use in Analgesic Dosage Monitoring
3. Newborn Infant Parasympathetic Evaluation (NIPE) Monitor -- Neonatal Pain Assessment