Exploring Deep Physiological Models for Nociceptive Pain Recognition

Abstract

Standard feature engineering involves manually designing and assessing measurable descriptors based on some expert knowledge in the domain of application, followed by the selection of the best performing set of designed features in order to optimize an inference model. Several studies have shown that this whole manual process can be efficiently replaced by deep learning approaches which are characterized by the integration of feature engineering, feature selection and inference model optimization into a single learning process. Such techniques have proven to be very successful in the domain of image processing and have been able to attain state-of-the-art performances while significantly outperforming traditional approaches based on hand-crafted features. In the following work, we explore deep learning approaches for the analysis of physiological signals. More precisely, deep learning architectures are designed for the assessment of measurable physiological channels in order to perform an accurate classification of different levels of artificially induced nociceptive pain. Most of the previous works related to pain intensity classification based on physiological signals rely on a carefully designed set of hand-crafted features in order to achieve a relatively good classification performance. Therefore, the current work aims at building competitive pain intensity classification models without the need of domain specific expert knowledge for the generation of relevant features. The assessment of the designed deep learning architectures is based on the BioVid Heat Pain Database (Part A) and experimental validation demonstrates that the proposed uni-modal architecture for the electrodermal activity (EDA) and the deep fusion approaches significantly outperform previous classification methods reported in the literature, with respective average performances of 85.03% and 83.76% for the binary classification experiment consisting of the discrimination between the baseline level and the pain tolerance level (T 0 vs.T 4 ) in a Leave-One-Subject-Out (LOSO) cross-validation evaluation setting.

Role of Artificial Intelligence and Machine Learning in the prediction of the pain: A scoping systematic review

“…Lim et al 14 reported an accuracy of 82%. Thiam et al 15 reported average performances of 84.57% and 84.40%. Cava et al 16 had accuracy of 86.21% (83.62%– 87.93%), precision: 86.11% (83.78%–88.57%), recall: 91.18% (88.24%–91.18%), specificity: 79.17% (75%– 83.33%), AUC: 0.89 (0.87–0.90) and kappa index: 0.71 (0.66–0.75).…”

Tree-Based Models for Pain Detection from Biomedical Signals

“…In Table 4, we compare our results with previous studies on single signals of Part-A for classifying no pain and highest pain levels. For EDA signal, Thiam et al [26] applied CNN algorithm with an average accuracy of 84.57% and achieved the highest accuracy among previous work. For XGBoost, we achieved an accuracy of 85.23%.…”

“…In [26], the pain classification with EDA signals was much higher than other signals and the fusion of EDA and ECG signals. However in this work with our new model, we observed different results.…”

“…Thiam et al [25] proposed a multi-modal information aggregation approach based on Deep Denoising Convolutional Auto-Encoders for pain assessment on two different pain databases including BioVid Heat Pain Database. Thaim et al [26] designed Convolutional Neural Network (CNN) based on physiological signals (EDA, ECG, and EMG) for pain classification. Pouromran et al [27] computed features from EDA, ECG and EMG signals and trained machine learning models-including Linear Regression, Support Vector Regression, Neural Network and XGBoost-on these features for pain intensity estimation.…”

Deep Learning-Based Models for Pain Recognition: A Systematic Review

“…More recently, Thiam et al [16] explored several CNNs architectures based on the available BioVid Heat Pain Database (Part A). They used the three modalities of signals: EDA, ECG and EMG.…”