用于心律失常检测的智能手表光电容积脉搏波描记术的数字图像处理特征
Digital Image Processing Features of Smartwatch Photoplethysmography for Cardiac Arrhythmia Detection.
作者信息
Han Dong, Bashar Syed K, Zieneddin Fearass, Ding Eric, Whitcomb Cody, McManus David D, Chon Ki H
出版信息
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:4071-4074. doi: 10.1109/EMBC44109.2020.9176142.
The aim of our work is to design an algorithm to detect premature atrial contraction (PAC), premature ventricular contraction (PVC), and atrial fibrillation (AF) among normal sinus rhythm (NSR) using smartwatch photoplethysmographic (PPG) data. Novel image processing features and two machine learning methods are used to enhance the PAC/PVC detection results of the Poincaré plot method. Compared with support vector machine (SVM) methods, the Random Forests (RF) method performs better. It yields a 10-fold cross validation (CV) averaged sensitivity, specificity, positive predicted value (PPV), negative predicted value (NPV), and accuracy for PAC/PVC labels of 63%, 98%, 83%, 94%, and 93%, respectively, and a 10-fold CV averaged sensitivity, specificity, PPV, NPV, and accuracy for AF subjects of 92%, 96%, 85%, 98%, and 95%, respectively. This is one of the first studies to derive image processing features from Poincaré plots to further enhance the accuracy of PAC/PVC detection using PPG recordings from a smartwatch.
我们工作的目的是设计一种算法，利用智能手表光电容积脉搏波描记法（PPG）数据，在正常窦性心律（NSR）中检测房性早搏（PAC）、室性早搏（PVC）和房颤（AF）。采用新颖的图像处理特征和两种机器学习方法来提高庞加莱图法的PAC/PVC检测结果。与支持向量机（SVM）方法相比，随机森林（RF）方法表现更好。它对PAC/PVC标签的10倍交叉验证（CV）平均灵敏度、特异性、阳性预测值（PPV）、阴性预测值（NPV）和准确率分别为63%、98%、83%、94%和93%，对AF受试者的10倍CV平均灵敏度、特异性、PPV、NPV和准确率分别为92%、96%、85%、98%和95%。这是首批从庞加莱图中提取图像处理特征，以进一步提高使用智能手表PPG记录检测PAC/PVC准确性的研究之一。
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