驾驶员的身体状况与交通安全息息相关，尤其是驾驶员的心脑血管健康状况。实时监测驾驶员的健康情况，有助于驾驶员及时了解自己的身体状况，减少因突发疾病导致的交通事故。文中对657份来自广西壮族自治区桂林市人民医院的PPG脉搏波数据集通过Chebyshev Ⅱ 滤波器降噪和快速傅里叶法提取时域特征、频域特征和小波包特征后，将脑血管疾病进行二分类数值化，再将数值标签化后的脑血管疾病类型作为输出参数，以构建驾驶员脑血管疾病数据集。针对实际数据集样本的分类不均衡问题，通过SMOTE算法进行过采样补充，构建基于PPG特征值的驾驶员脑血管疾病分类模型SSA-DELM，并利用实际数据集进行训练和实验，发现所提出的方法对脑血管疾病的预测精确率达83%，查准率达80%，查全率达76.6%、F₁分数为0.79，平均查准率均值达0.80，表明该分类模型能够为患有脑梗或脑血管疾病驾驶员提供较为准确的预警。文中研究成果可为基于PPG信号的驾驶员动态健康监测系统提供理论模型基础和技术支持，在新能源汽车行业的软件服务和智能医疗中具有较大的应用空间，这与新能源车企“终端+软件+服务”的全产业链销售模式相契合，也与现代人注重环保、家庭健康和智能交通的理念相契合。

The physical condition of drivers is closely related to traffic safety, especially the driver’s cardiovascular health condition. Real-time monitoring of drivers’ health can help drivers understand their physical condition in time and reduce traffic accidents caused by sudden illnesses. In this study, firstly, 657 PPG (Photoplethysmography Signal) pulse wave datasets from Guilin People’s Hospital, Guangxi Zhuang Autonomous Region, China, were dichotomized numerically for cerebrovascular diseases after the noise reduction by Chebyshev Ⅱ filter and the extraction of time domain features, frequency domain features and wavelet packet features by fast Fourier method. Then, the numerically labeled cerebrovascular disease types were used as output parameters to construct driver cerebrovascular disease dataset. To solve the problem of unbalanced classification of samples in actual dataset, an oversampling supplement was performed by the SMOTE algorithm and a driver cerebrovascular disease classification model, namely SSA-DELM, was constructed based on PPG feature values, followed with model training and testing on actual datasets. The results show that the proposed classification model can provide comparatively accurate early warning for drivers suffering from cerebral infarction or cerebrovascular disease, with an accuracy of 83%, an average precision of 80%, a completeness of 76.6%, an F₁ score of 0.79, and a mean average precision of 0.80. This research can provide theoretical model basis and technical support for drivers’ dynamic health monitoring system based on PPG signal. The proposed model has a large application space in the software service and intelligent medical care of the new energy automobile industry, which is in line with the sales mode of the whole industry chain of “terminal + software + service” of new energy automobile enterprises, and is also in line with modern people’s attention to environmental protection, family health and intelligent transportation.