Pain perception involves sensory (nociception) and emotional components mediated by nervous system and brain interactions; pain triggers autonomic nervous system changes measurable via physiological signals
Target Population
Patients experiencing various types of pain including acute, chronic, and perioperative/postoperative pain
Care Setting
Clinical settings including perioperative care and pain management contexts
Key Highlights
Pain is a complex sensory and emotional experience challenging to assess objectively.
Physiological signals such as ECG, EDA, PPG, and EEG are increasingly studied for objective pain assessment.
Machine learning techniques are applied in 36 of 89 reviewed studies to analyze physiological data for pain evaluation.
Guideline-Based Recommendations
Diagnosis
Current standard relies on self-assessment tools like VAS, NRS, and VRS for pain evaluation.
Objective assessment using physiological signals is promising but not yet integrated into standard practice.
Management
Commercial devices such as Medasense PMD-200 and NIPE Monitor assist in analgesic dosing and pain monitoring under anesthesia.
Monitoring & Follow-up
Physiological signals including heart rate variability (HRV), electrodermal activity (EDA), photoplethysmography (PPG), and EEG can be monitored to assess nociceptive responses.
Risks
Self-report pain scales may be influenced by psychological and social factors, limiting accuracy.
No objective physiological pain assessment method has yet been widely accepted clinically.
Patient & Prescribing Data
Patients undergoing general anesthesia and those with acute or chronic pain conditions
Physiological monitoring devices can guide analgesic dosing and improve pain management, though further validation is needed for broader clinical adoption.
Clinical Best Practices
Use validated self-report scales alongside physiological monitoring for comprehensive pain assessment.
Incorporate multiple physiological signals when feasible to enhance pain detection accuracy.
