An Automatic System for Continuous Pain Intensity Monitoring Based on Analyzing Data from Uni-, Bi-, and Multi-Modality

Abstract

Pain is a reliable indicator of health issues; it affects patients’ quality of life when not well managed. The current methods in the clinical application undergo biases and errors; moreover, such methods do not facilitate continuous pain monitoring. For this purpose, the recent methodologies in automatic pain assessment were introduced, which demonstrated the possibility for objectively and robustly measuring and monitoring pain when using behavioral cues and physiological signals. This paper focuses on introducing a reliable automatic system for continuous monitoring of pain intensity by analyzing behavioral cues, such as facial expressions and audio, and physiological signals, such as electrocardiogram (ECG), electromyogram (EMG), and electrodermal activity (EDA) from the X-ITE Pain Dataset. Several experiments were conducted with 11 datasets regarding classification and regression; these datasets were obtained from the database to reduce the impact of the imbalanced database problem. With each single modality (Uni-modality) experiment, we used a Random Forest [RF] baseline method, a Long Short-Term Memory (LSTM) method, and a LSTM using a sample weighting method (called LSTM-SW). Further, LSTM and LSTM-SW were used with fused modalities (two modalities = Bi-modality and all modalities = Multi-modality) experiments. Sample weighting was used to downweight misclassified samples during training to improve the performance. The experiments’ results confirmed that regression is better than classification with imbalanced datasets, EDA is the best single modality, and fused modalities improved the performance significantly over the single modality in 10 out of 11 datasets.

Multimodal physiological sensing for the assessment of acute pain

“…In another experiment using EDA, ECG, and EMG, it was found that EDA was the most information rich sensor for continuous pain intensity prediction ( 35 ). Similarly, EDA was found to be the best single modality in both classification and regression using EDA, ECG, and EMG sensors ( 36 ). In our study, the EDA-based features represents a more compact set of features that can produce less complex learning models.…”

PainMeter: Automatic Assessment of Pain Intensity Levels From Multiple Physiological Signals Using Machine Learning

“…The study's results proved that EMG and EDA signals are the most effective features for multimodal performance. Furthermore, multimodels using all features demonstrated better performance, especially handling imbalance datasets [51].…”

“…Othman et al [51] introduced a multimodal approach for automatic pain assessment, integrating facial expressions, audio, and physiological signals (ECG, EMG, EDA). Researchers highlighted the challenge of database imbalance and outliers, leading to model failure in identifying minority classes and handling noise.…”

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

“…This study introduces RFc and RFr as baseline methods for continuously monitoring pain intensity using the X-ITE Pain Database. In our recent study [ 42 ], we presented the results of investigations involving multiple modalities (frontal RGB camera, audio, ECG, EMG, and EDA). This work represents a further investigation into analyzing only the two most informative modalities (facial expression and EDA signal), which are then fused using Decision Fusion (DF) on both balanced and imbalanced datasets.…”