Personalized k-fold Cross-Validation Analysis with Transfer from Phasic to Tonic Pain Recognition on X-ITE Pain Database

Automatic pain recognition is currently one of the most interesting challenges in affective computing as it has great potential to improve pain management for patients in a clinical environment. In this work, we analyse automatic pain recognition using binary classification with personalized k-fold cross-validation analysis which is an approach that focuses on training on the labels of specific subjects and validating on the labels of other subjects in the Experimentally Induced Thermal and Electrical (X-ITE) Pain Database using both a random forest and a dense neural network model. The effectiveness of each approach is inspected on each of the phasic electro, phasic heat, tonic electro, and tonic heat datasets separately. Therefore, the analysis of subsets of the X-ITE dataset (phasic electro, tonic electro, phasic heat, and tonic heat) was made individually. Afterward, phasic to tonic transfer was made by training models on the phasic electro dataset and testing them on the tonic electro dataset. Our outcomes and evaluations indicate that electro datasets always perform better than heat datasets. Also, personalized scores had better performance than normal scores. Moreover, dense neural networks performed better than randoms forests in transfer from phasic electro to tonic electro and showed promising performance in the personalized transfer.