IEEE Trans Biomed Eng. 2017 Dec;64(12):2988-2996. doi: 10.1109/TBME.2017.2756870. Epub 2017 Sep 25.
Effective pain assessment and management strategies are needed to better manage pain. In addition to self-report, an objective pain assessment system can provide a more complete picture of the neurophysiological basis for pain. In this study, a robust and accurate machine learning approach is developed to quantify tonic thermal pain across healthy subjects into a maximum of ten distinct classes.
A random forest model was trained to predict pain scores using time-frequency wavelet representations of independent components obtained from electroencephalography (EEG) data, and the relative importance of each frequency band to pain quantification is assessed.
The mean classification accuracy for predicting pain on an independent test subject for a range of 1-10 is 89.45%, highest among existing state of the art quantification algorithms for EEG. The gamma band is the most important to both intersubject and intrasubject classification accuracy.
The robustness and generalizability of the classifier are demonstrated.
Our results demonstrate the potential of this tool to be used clinically to help us to improve chronic pain treatment and establish spectral biomarkers for future pain-related studies using EEG.
需要有效的疼痛评估和管理策略来更好地管理疼痛。除了自我报告，客观的疼痛评估系统可以更全面地了解疼痛的神经生理基础。在这项研究中，开发了一种强大而准确的机器学习方法，将健康受试者的持续热痛量化为最多十个不同的类别。
使用从脑电图 (EEG) 数据中获得的独立分量的时频小波表示，训练随机森林模型来预测疼痛评分，并评估每个频带对疼痛量化的相对重要性。
对于预测 1-10 范围内的独立测试对象的疼痛，平均分类准确率为 89.45%，在现有的 EEG 定量算法中最高。伽马波段对个体间和个体内分类准确性都很重要。
证明了分类器的稳健性和通用性。
我们的结果表明，该工具具有在临床上使用的潜力，有助于改善慢性疼痛治疗，并为未来使用 EEG 的与疼痛相关的研究建立光谱生物标志物。
