基于机器学习和心率变异性的经皮耳迷走神经电刺激疼痛阈值预测

经皮耳迷走神经电刺激（taVNS）作为新兴的精神和心血管疾病的治疗方法，其刺激强度设置需要将刺激电流调整至疼痛阈值后再降低其幅值。该设置方式不仅缺乏一致性，且影响治疗效果和舒适度。本文提出了一种结合心率变异性（HRV）特征和机器学习回归模型的新方法，实现了taVNS疼痛阈值的预测。基于实验采集的数据，系统的比较了将HRV特征作为各种机器学习模型输入的预测精度。结果表明，HRV特征与极端随机树的组合性能最优，使用遗传算法去除冗余特征能够有效改善模型预测性能，均方根误差在1.18到1.56之间，均方差在0.77到0.96之间。该方法可用于不同个体的taVNS刺激强度的预测，对受试者在taVNS期间的治疗效果有积极作用。

Prediction of pain threshold for transcutaneous auricular vagal nerve electrical stimulation based on machine learning and heart rate variability

Transcutaneous auricular vagus nerve stimulation (taVNS) is an emerging treatment method for psychiatric and cardiovascular diseases, and its stimulation intensity setting needs to adjust the stimulation current to the pain threshold and then reduce its amplitude. This approach not only lacks uniformity, but it also has an impact on treatment efficacy and comfort. To estimate taVNS pain thresholds, this research provides a novel technique that combines HRV characteristics and machine learning regression models. Based on the experimentally collected data, the prediction accuracy of HRV characteristics as input to various machine learning models was systematically compared. The results show that the combination of HRV characteristics and extra trees regression has the best performance, and the use of genetic algorithm to remove redundant features can effectively improve the model prediction performance. The root-mean-square error ranges from 1.18 to 1.56, while the mean-square error ranges from 0.77 to 0.96. This method can be utilized to predict taVNS stimulation intensity and has a positive effect on the treatment effect of subjects during taVNS.