Automated Electrodermal Activity and Facial Expression Analysis for Continuous Pain Intensity Monitoring on the X-ITE Pain Database

Abstract

This study focuses on improving healthcare quality by introducing an automated system that continuously monitors patient pain intensity. The system analyzes the Electrodermal Activity (EDA) sensor modality modality, compares the results obtained from both EDA and facial expressions modalities, and late fuses EDA and facial expressions modalities. This work extends our previous studies of pain intensity monitoring via an expanded analysis of the two informative methods. The EDA sensor modality and facial expression analysis play a prominent role in pain recognition; the extracted features reflect the patient’s responses to different pain levels. Three different approaches were applied: Random Forest (RF) baseline methods, Long-Short Term Memory Network (LSTM), and LSTM with the sample-weighting method (LSTM-SW). Evaluation metrics included Micro average F1-score for classification and Mean Squared Error (MSE) and intraclass correlation coefficient (ICC [3, 1]) for both classification and regression. The results highlight the effectiveness of late fusion for EDA and facial expressions, particularly in almost balanced datasets (Micro average F1-score around 61%, ICC about 0.35). EDA regression models, particularly LSTM and LSTM-SW, showed superiority in imbalanced datasets and outperformed guessing (where the majority of votes indicate no pain) and baseline methods (RF indicates Random Forest classifier (RFc) and Random Forest regression (RFr)). In conclusion, by integrating both modalities or utilizing EDA, they can provide medical centers with reliable and valuable insights into patients’ pain experiences and responses.

Digital technology and mental health during the COVID-19 pandemic: a narrative review with a focus on depression, anxiety, stress, and trauma

“…• Pain exhibits a particularly pronounced facial reflection. Thus, the evidence (52,56,57) shows that the proposed camerabased facial diagnostic system can diagnose these health conditions. A wide application for recognition of health status from the human face using camera-based pattern recognition apps combined with AI is made possible as a result.…”

“…Similar to pain therapy, in addition to assessing measurable but nonspecific physiological parameters, the examiner relies on facial expression, gestures, facial color, sweat production, and stimulusrelated reactions of the patient's face. With the work of (56,57), it is possible to relate the health conditions of patients and their changes to corresponding facial changes and investigate camera-based automatic analysis methods for their practicality. The focus is on pain.…”