The Role of Facial Expression Analysis and Electrodermal Activity as an Objective Evaluation of Persistent Idiopathic Facial Pain

Rokicki, Jan Pavel; Ivanauskas, A.; Janužis, Gintaras

doi:10.1097/scs.0000000000007847

Cited by 1 publication

(1 citation statement)

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“…Physical expressions of pain encompass visual cues (facial expressions and body movements), vocalization cues (verbally and non-verbally), and physiological cues (electrocardiography (ECG), electromyography (EMG), Electrodermal Activity (EDA), and brain activity) [ 3 , 4 , 5 ]; these cues play a significant role in assessing pain in individuals [ 5 ]. The extracted features from EDA and facial expression modalities indicate the spontaneous pain expression, stress, and anxiety caused by different pain levels; both modalities are good measures for pain assessment [ 6 , 7 ]. This study presents the findings obtained from analyzing two important modalities regarding classification and regression.…”

Section: Introductionmentioning

confidence: 99%

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

Othman,

Werner,

Saxen

et al. 2023

Life

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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.

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