Ding Xiaodong, Cheng Feng, Morris Robert, Chen Cong, Wang Yiqin
Shanghai Key Laboratory of Health Identification and Assessment, Laboratory of Traditional Chinese Medicine Four Diagnostic Information, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
Department of Pharmaceutical Science, College of Pharmacy, University of South Florida, Tampa, FL, United States.
JMIR Med Inform. 2020 Jun 22;8(6):e18134. doi: 10.2196/18134.
The radial artery pulse wave is a widely used physiological signal for disease diagnosis and personal health monitoring because it provides insight into the overall health of the heart and blood vessels. Periodic radial artery pulse signals are subsequently decomposed into single pulse wave periods (segments) for physiological parameter evaluations. However, abnormal periods frequently arise due to external interference, the inherent imperfections of current segmentation methods, and the quality of the pulse wave signals.
The objective of this paper was to develop a machine learning model to detect abnormal pulse periods in real clinical data.
Various machine learning models, such as k-nearest neighbor, logistic regression, and support vector machines, were applied to classify the normal and abnormal periods in 8561 segments extracted from the radial pulse waves of 390 outpatients. The recursive feature elimination method was used to simplify the classifier.
It was found that a logistic regression model with only four input features can achieve a satisfactory result. The area under the receiver operating characteristic curve from the test set was 0.9920. In addition, these classifiers can be easily interpreted.
We expect that this model can be applied in smart sport watches and watchbands to accurately evaluate human health status.
桡动脉脉搏波是一种广泛用于疾病诊断和个人健康监测的生理信号，因为它能洞察心脏和血管的整体健康状况。随后，周期性桡动脉脉搏信号会被分解为单个脉搏波周期（段）以进行生理参数评估。然而，由于外部干扰、当前分割方法的固有缺陷以及脉搏波信号的质量，异常周期经常出现。
本文的目的是开发一种机器学习模型，以检测真实临床数据中的异常脉搏周期。
应用多种机器学习模型，如k近邻、逻辑回归和支持向量机，对从390名门诊患者的桡动脉脉搏波中提取的8561个段进行正常和异常周期分类。采用递归特征消除方法简化分类器。
发现仅具有四个输入特征的逻辑回归模型就能取得令人满意的结果。测试集的受试者工作特征曲线下面积为0.9920。此外，这些分类器易于解释。
我们期望该模型可应用于智能运动手表和表带，以准确评估人体健康状况。
