基于心电-脉搏波的心血管疾病识别研究

为实现心血管疾病的早期筛查，降低心血管疾病临床检测的成本。本研究基于上肢脉搏波传导速度（PWV）及脉搏波相关血液动力学基础理论，采集了总计51人的脉搏波与心电信号数据，提取了包括3种PWV和脉搏波特征参数总计16个特征参数，将不同的PWV与脉搏波特征组成3个样本特征数据集，分别建立了基于K近邻学习（KNN）和支持向量机（SVM）的心血管疾病识别模型。KNN模型分类准确率为66.28%，SVM模型分类准确率为84.3%，并通过对比不同PWV对模型性能的影响，确定了用于血管评估的最优脉搏波传导速度pwvm。研究表明基于SVM建立的分类模型对心血管疾病识别有一定可靠性，为低成本的心血管疾病早期筛查提供了新思路，也为穿戴式心血管系统监测提供了基础。

Cardiovascular disease recognition based on electrocardiogram data and pulse wave

Abstract: To achieve an early screening of cardiovascular diseases and reduce the cost of clinical detection of cardiovascular disease, a research based on pulse wave velocity (PWV) in the upper extremities and the hemodynamic theory related to pulse waves is performed. The pulse waves and electrocardiogram data of 51 volunteers were collected, and 16 feature parameters including 3 types of PWV and pulse wave features were extracted. Three sample feature data sets which are composed of different PWV and pulse wave features are used to establish two different cardiovascular diseases recognition models based on K-nearest neighbor (KNN) or support vector machine (SVM). The clssification accuracy of KNN and SVM models is 66.28% and 84.3%, respectively. By comparing the effects of different PWV on the performance of models, the optimal pwvm for vascular assessment is determined. The research results show that the SVM model is reliable in the cardiovascular disease recognition, providing a new idea for the low-cost and early screening of cardiovascular diseases and providing a basis for wearable cardiovascular system monitoring.