Personalized Deep Bi-LSTM RNN Based Model for Pain Intensity Classification Using EDA Signal

• Published in: Sensors (Basel, Switzerland) Vol. 22; no. 21; p. 8087
• Main Authors: Pouromran, Fatemeh, Lin, Yingzi, Kamarthi, Sagar
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 22.10.2022; MDPI
• Subjects: Algorithms; Automation; Classification; Datasets; Deep learning; Diagnosis; EDA signal; Electrocardiography; Electroencephalography; Feature selection; Humans; Knowledge; Machine Learning; Methods; Neural networks; Neural Networks, Computer; Pain; pain intensity classification; Pain Measurement; Physiology; recurrent neural networks; Respiration; Sensors; Signal processing; Skin; Time series; Wearable computers; United States; deep learning; EDA signal; recurrent neural networks; machine learning; pain intensity classification
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s22218087

Automatic pain intensity assessment from physiological signals has become an appealing approach, but it remains a largely unexplored research topic. Most studies have used machine learning approaches built on carefully designed features based on the domain knowledge available in the literature on the time series of physiological signals. However, a deep learning framework can automate the feature engineering step, enabling the model to directly deal with the raw input signals for real-time pain monitoring. We investigated a personalized Bidirectional Long short-term memory Recurrent Neural Networks (BiLSTM RNN), and an ensemble of BiLSTM RNN and Extreme Gradient Boosting Decision Trees (XGB) for four-category pain intensity classification. We recorded Electrodermal Activity (EDA) signals from 29 subjects during the cold pressor test. We decomposed EDA signals into tonic and phasic components and augmented them to original signals. The BiLSTM-XGB model outperformed the BiLSTM classification performance and achieved an average F1-score of 0.81 and an Area Under the Receiver Operating Characteristic curve (AUROC) of 0.93 over four pain states: no pain, low pain, medium pain, and high pain. We also explored a concatenation of the deep-learning feature representations and a set of fourteen knowledge-based features extracted from EDA signals. The XGB model trained on this fused feature set showed better performance than when it was trained on component feature sets individually. This study showed that deep learning could let us go beyond expert knowledge and benefit from the generated deep representations of physiological signals for pain assessment.