基于脑电图的急性疼痛分类：机器学习模型比较与实时临床可行性
EEG-Based Acute Pain Classification: Machine Learning Model Comparison and Real-Time Clinical Feasibility
AI总结：
本研究比较九种机器学习模型分类激光诱发疼痛的脑电数据，支持向量机离线准确率88.9%，实时XGBoost延迟4毫秒、准确率94.2%，证明脑电疼痛监测临床可行。
摘要：
当前医院内的疼痛评估通常依赖于患者自我报告或非特异性的心电图生命体征。这种体系使得危重、镇静及认知受损的患者容易遭受疼痛治疗不足和阿片类药物过度使用的问题。脑电图（EEG）提供了一种无创测量大脑活动的方法。该技术有望作为一种辅助工具，用于突出伤害性处理过程，从而缓解这一问题。在本研究中，我们比较了机器学习模型在分类高疼痛与低/无疼痛脑电时段上的表现，数据来自五十二名健康成年人，他们暴露于三种强度（低、中、高）的激光诱发疼痛。每个四秒时段被转换为一个包含537个特征的向量，涵盖频谱功率、频带比率、Hjorth参数、熵度量、相干性、小波能量和峰值频率指标。我们使用留一参与者交叉验证评估了九种传统机器学习模型。采用径向基函数核的支持向量机在离线性能上表现最佳，准确率达88.9%，推理时间低于毫秒级（1.02毫秒）。我们的特征重要性分析与当前公认的疼痛生理学一致，显示对侧α波抑制、中线θ/α波增强以及额叶γ波爆发。实时XGBoost模型保持了约4毫秒的端到端延迟和94.2%的准确率，表明基于脑电图的疼痛监测器在临床环境中技术上可行，并为临床验证提供了途径。
英文摘要：
Current pain assessment within hospitals often relies on self-reporting or non-specific EKG vital signs. This system leaves critically ill, sedated, and cognitively impaired patients vulnerable to undertreated pain and opioid overuse. Electroencephalography (EEG) offers a noninvasive method of measuring brain activity. This technology could potentially be applied as an assistive tool to highlight nociceptive processing in order to mitigate this issue. In this study, we compared machine learning models for classifying high-pain versus low/no-pain EEG epochs using data from fifty-two healthy adults exposed to laser-evoked pain at three intensities (low, medium, high). Each four-second epoch was transformed into a 537-feature vector spanning spectral power, band ratios, Hjorth parameters, entropy measures, coherence, wavelet energies, and peak-frequency metrics. Nine traditional machine learning models were evaluated with leave-one-participant-out cross-validation. A support vector machine with radial basis function kernel achieved the best offline performance with 88.9% accuracy and sub-millisecond inference time (1.02 ms). Our Feature importance analysis was consistent with current canonical pain physiology, showing contralateral alpha suppression, midline theta/alpha enhancement, and frontal gamma bursts. The real-time XGBoost model maintained an end-to-end latency of about 4 ms and 94.2% accuracy, demonstrating that an EEG-based pain monitor is technically feasible within a clinical setting and provides a pathway towards clinical validation.
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