Machine learning detection of obstructive hypertrophic cardiomyopathy using a wearable biosensor
Machine learning detection of obstructive hypertrophic cardiomyopathy using a wearable biosensor
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DOI：
10.1038/s41746-019-0130-0
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发表时间：
2019-06-24
影响因子：
15.2
通讯作者：
Semigran, Marc J.
中科院分区：
文献类型：
--
作者：
Green, Eric M.;van Mourik, Reinier;Semigran, Marc J.
肥厚性心肌病(HCM)是一种遗传性心肌疾病，即使在无症状的患者中也会增加心力衰竭、中风和猝死的风险。由于目前只有10%-20%的受影响人群被确诊，在临床环境之外对有效的筛查工具的需求尚未得到满足。光体积描记术使用商业智能手表中集成的非侵入性光学传感器来检测皮肤表面的血流量变化。本文对19例肥厚型心肌病左室流出道梗阻患者和健康志愿者进行了光体积描记和超声心动图检查。自动化分析显示oHCM患者38/42的形态脉搏波特征有显著差异，包括收缩射血时间、收缩期间上升速率和呼吸变异的测量。我们开发了一个机器学习分类器，实现了oHCM检测的C统计量0.99(95%CI：0.99-1.0)。随着进一步的发展，该方法可以为梗阻性肥厚性心肌病提供一种无创和广泛使用的筛查工具。
Hypertrophic cardiomyopathy (HCM) is a heritable disease of heart muscle that increases the risk for heart failure, stroke, and sudden death, even in asymptomatic patients. With only 10-20% of affected people currently diagnosed, there is an unmet need for an effective screening tool outside of the clinical setting. Photoplethysmography uses a noninvasive optical sensor incorporated in commercial smart watches to detect blood volume changes at the skin surface. In this study, we obtained photoplethysmography recordings and echocardiograms from 19 HCM patients with left ventricular outflow tract obstruction (oHCM) and a control cohort of 64 healthy volunteers. Automated analysis showed a significant difference in oHCM patients for 38/42 morphometric pulse wave features, including measures of systolic ejection time, rate of rise during systole, and respiratory variation. We developed a machine learning classifier that achieved a C-statistic for oHCM detection of 0.99 (95% CI: 0.99-1.0). With further development, this approach could provide a noninvasive and widely available screening tool for obstructive HCM.