Khan Muhammad Umar, Aziz Sumair, Murtagh Luke, Chetty Girija, Goecke Roland, Fernandez Rojas Raul
Faculty of Science and Technology, University of Canberra, Canberra, ACT, 2617, Australia.
Faculty of Science and Technology, University of Canberra, Canberra, ACT, 2617, Australia.
Comput Biol Med. 2025 Jun;192(Pt B):110300. doi: 10.1016/j.compbiomed.2025.110300. Epub 2025 May 14.
The accurate assessment of pain in clinical settings is challenging due to its subjective nature. In this study, we used functional near-infrared spectroscopy (fNIRS) to measure brain activity by detecting changes in blood oxygenation. Leveraging the AI4Pain Grand Challenge dataset, we aimed to classify pain levels into No Pain (NP), Low Pain (LP), and High Pain (HP) categories using both binary (NP vs. HP) and multiclass (NP vs. LP vs. HP) approaches. This involved collecting a comprehensive dataset of fNIRS data from 65 subjects, with recordings from 24 channels. We proposed novel Empirically Transformed Energy Patterns to extract meaningful bio-information related to pain conditions. An optimised Ensemble Classifier, evaluated using leave-one-subject-out cross-validation, was employed to classify pain levels. Our results demonstrated that this approach outperformed traditional classifiers, achieving 91.41% accuracy in the binary task and 68.20% accuracy in the multiclass task, with high sensitivity and specificity. This study highlights the effectiveness of using optimised machine learning models with fNIRS data for precise pain level classification, which holds significant potential for improving pain management in clinical settings.
由于疼痛具有主观性，在临床环境中对其进行准确评估具有挑战性。在本研究中，我们使用功能近红外光谱（fNIRS）通过检测血液氧合变化来测量大脑活动。利用AI4Pain重大挑战数据集，我们旨在使用二元（无疼痛[NP]与高疼痛[HP]）和多类别（NP与低疼痛[LP]与HP）方法将疼痛水平分类为无疼痛（NP）、低疼痛（LP）和高疼痛（HP）类别。这涉及从65名受试者收集一个全面的fNIRS数据数据集，记录来自24个通道。我们提出了新颖的经验转换能量模式来提取与疼痛状况相关的有意义的生物信息。使用留一法交叉验证评估的优化集成分类器用于对疼痛水平进行分类。我们的结果表明，这种方法优于传统分类器，在二元任务中准确率达到91.41%，在多类别任务中准确率达到68.20%，具有高敏感性和特异性。本研究强调了使用优化的机器学习模型和fNIRS数据进行精确疼痛水平分类的有效性，这在改善临床环境中的疼痛管理方面具有巨大潜力。
