Quantifying and Characterizing Tonic Thermal Pain Across Subjects From EEG Data Using Random Forest Models.
Quantifying and Characterizing Tonic Thermal Pain Across Subjects From EEG Data Using Random Forest Models.
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DOI：
10.1109/tbme.2017.2756870
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发表时间：
2017-12
期刊：
影响因子：
--
通讯作者：
He B
中科院分区：
文献类型：
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作者：
Vijayakumar V;Case M;Shirinpour S;He B
为了更好地管理疼痛，需要有效的疼痛评估和管理策略。除了自我报告外，客观的疼痛评估系统可以更全面地了解疼痛的神经生理学基础。在这项研究中，开发了一种稳健和准确的机器学习方法，将健康受试者的强直性热痛量化为最多十个不同的类别。利用从脑电(EEG)数据中获得的独立分量的时频小波表示，对随机森林模型进行训练以预测疼痛评分，并评估每个频段对疼痛量化的相对重要性。对于一个独立的测试对象，在1-10的范围内预测疼痛的平均分类准确率为89.45%，是现有最先进的脑电量化算法中最高的。伽马波段对于对象间和对象内的分类精度都是最重要的。证明了该分类器的健壮性和泛化能力。我们的结果证明了这种工具在临床上用于帮助改善慢性疼痛治疗的潜力，并为未来使用脑电进行疼痛相关研究建立光谱生物标记物。
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 inter-subject and intra-subject classification accuracy. The robustness and generalizability of the classifier is demonstrated. Our results demonstrate the potential of this tool to be used clinically to help improve chronic pain treatment, and establish spectral biomarkers for future pain-related studies using EEG.