Al-Nafjan Abeer, Alshehri Hadeel, Aldayel Mashael
Computer Science Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia.
Information Technology Department, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia.
Biology (Basel). 2025 Feb 17;14(2):210. doi: 10.3390/biology14020210.
Objective pain measurements are essential in clinical settings for determining effective treatment strategies. This study aims to utilize brain-computer interface technology for reliable pain classification and detection. We developed an electroencephalography-based pain detection system comprising two main components: (1) pain/no-pain detection and (2) pain severity classification across three levels: low, moderate, and high. Deep learning models, including convolutional neural networks and recurrent neural networks, were employed to classify the wavelet features extracted through time-frequency domain analysis. Furthermore, we compared the performance of our system against conventional machine learning models, such as support vector machines and random forest classifiers. Our deep learning approach outperformed the baseline models, achieving accuracies of 91.84% for pain/no-pain detection and 87.94% for pain severity classification, respectively.
在临床环境中，客观的疼痛测量对于确定有效的治疗策略至关重要。本研究旨在利用脑机接口技术进行可靠的疼痛分类和检测。我们开发了一种基于脑电图的疼痛检测系统，该系统包括两个主要部分：（1）疼痛/无疼痛检测；（2）跨低、中、高三个水平的疼痛严重程度分类。采用包括卷积神经网络和循环神经网络在内的深度学习模型对通过时频域分析提取的小波特征进行分类。此外，我们将我们系统的性能与传统机器学习模型（如支持向量机和随机森林分类器）进行了比较。我们的深度学习方法优于基线模型，在疼痛/无疼痛检测和疼痛严重程度分类方面的准确率分别达到了91.84%和87.94%。
