Anderson Keri, Stein Sebastian, Suen Ho, Purcell Mariel, Belci Maurizio, McCaughey Euan, McLean Ronali, Khine Aye, Vuckovic Aleksandra
Biomedical Engineering Division, James Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, UK.
School of Computing Science, University of Glasgow, Glasgow G12 8QQ, UK.
Biomedicines. 2025 Jan 16;13(1):213. doi: 10.3390/biomedicines13010213.
The objective was to test the generalisability of electroencephalography (EEG) markers of future pain using two independent datasets. Datasets, A [N = 20] and B [N = 35], were collected from participants with subacute spinal cord injury who did not have neuropathic pain at the time of recording. In both datasets, some participants developed pain within six months, (PDP) will others did not (PNP). EEG features were extracted based on either band power or Higuchi fractal dimension (HFD). Three levels of generalisability were tested: (1) classification PDP vs. PNP in datasets A and B separately; (2) classification between groups in datasets A and B together; and (3) classification where one dataset (A or B) was used for training and testing, and the other for validation. A novel normalisation method was applied to HFD features. Training and testing of individual datasets achieved classification accuracies of >80% using either feature set, and classification of joint datasets (A and B) achieved a maximum accuracy of 86.4% (HFD, support vector machine (SVM)). With normalisation and feature reduction (principal components), the validation accuracy was 66.6%. An SVM classifier with HFD features showed the best robustness, and normalisation improved the accuracy of predicting future neuropathic pain well above the chance level.
目的是使用两个独立数据集来测试未来疼痛的脑电图（EEG）标记物的普遍性。数据集A [N = 20]和数据集B [N = 35]是从记录时没有神经性疼痛的亚急性脊髓损伤参与者中收集的。在这两个数据集中，一些参与者在六个月内出现了疼痛（疼痛发展参与者，PDP），而另一些则没有（无疼痛发展参与者，PNP）。基于频段功率或 Higuchi 分形维数（HFD）提取 EEG 特征。测试了三个层次的普遍性：（1）分别在数据集A和数据集中对PDP与PNP进行分类；（2）将数据集A和数据集B中的组一起进行分类；（3）使用一个数据集（A或B）进行训练和测试，另一个用于验证的分类。一种新颖的归一化方法应用于HFD特征。使用任一特征集对单个数据集进行训练和测试，分类准确率均超过80%，联合数据集（A和B）的分类最高准确率为86.4%（HFD，支持向量机（SVM））。通过归一化和特征约简（主成分），验证准确率为66.6%。具有HFD特征的SVM分类器显示出最佳的稳健性，并且归一化将预测未来神经性疼痛的准确率提高到远高于随机水平。
