Department of Neurosurgery, School of Medicine, Eulji University, Daejeon, Republic of Korea.
Institute for Basic Science (IBS) Center for Cognition and Sociality, Daejeon, Republic of Korea.
Sci Rep. 2024 Aug 30;14(1):20188. doi: 10.1038/s41598-024-71219-3.
Increasing evidence is present to enable pain measurement by using frontal channel EEG-based signals with spectral analysis and phase-amplitude coupling. To identify frontal channel EEG-based biomarkers for quantifying pain severity, we investigated band-power features to more complex features and employed various machine learning algorithms to assess the viability of these features. We utilized a public EEG dataset obtained from 36 patients with chronic pain during an eyes-open resting state and performed correlation analysis between clinically labelled pain scores and EEG features from Fp1 and Fp2 channels (EEG band-powers, phase-amplitude couplings (PAC), and its asymmetry features). We also conducted regression analysis with various machine learning models to predict patients' pain intensity. All the possible feature sets combined with five machine learning models (Linear Regression, random forest and support vector regression with linear, non-linear and polynomial kernels) were intensively checked, and regression performances were measured by adjusted R-squared value. We found significant correlations between beta power asymmetry (r = -0.375), gamma power asymmetry (r = -0.433) and low beta to low gamma coupling (r = -0.397) with pain scores while band power features did not show meaningful results. In the regression analysis, Support Vector Regression with a polynomial kernel showed the best performance (R squared value = 0.655), enabling the regression of pain intensity within a clinically usable error range. We identified the four most selected features (gamma power asymmetry, PAC asymmetry of theta to low gamma, low beta to low/high gamma). This study addressed the importance of complex features such as asymmetry and phase-amplitude coupling in pain research and demonstrated the feasibility of objectively observing pain intensity using the frontal channel-based EEG, that are clinically crucial for early intervention.
越来越多的证据表明，可以使用基于额叶通道 EEG 的信号进行疼痛测量，这些信号具有谱分析和相位-幅度耦合功能。为了确定基于额叶通道 EEG 的生物标志物来量化疼痛严重程度，我们研究了带宽特征，以及更复杂的特征，并采用了各种机器学习算法来评估这些特征的可行性。我们利用来自 36 名慢性疼痛患者的公开 EEG 数据集，这些患者在睁眼静息状态下进行了研究，并对临床标记的疼痛评分与 Fp1 和 Fp2 通道（EEG 频带功率、相位-幅度耦合（PAC）及其不对称特征）的 EEG 特征之间进行了相关性分析。我们还使用各种机器学习模型进行了回归分析，以预测患者的疼痛强度。所有可能的特征集与五个机器学习模型（线性回归、随机森林和支持向量回归，其线性、非线性和多项式核）相结合，进行了深入检查，并通过调整后的 R 方值来衡量回归性能。我们发现β波不对称（r=-0.375）、γ波不对称（r=-0.433）和低β波到低γ波耦合（r=-0.397）与疼痛评分之间存在显著相关性，而频带功率特征没有显示出有意义的结果。在回归分析中，具有多项式核的支持向量回归显示出最佳性能（R 方值=0.655），能够在临床可接受的误差范围内回归疼痛强度。我们确定了四个最被选择的特征（γ波不对称、θ波到低γ波的 PAC 不对称、低β波到低/高γ波）。本研究强调了不对称和相位-幅度耦合等复杂特征在疼痛研究中的重要性，并证明了使用基于额叶通道的 EEG 客观观察疼痛强度的可行性，这对于早期干预至关重要。
