Lin Gen-Min, Liu Kiang
1Department of Preventive MedicineNorthwestern University Feinberg School of MedicineChicagoIL60611USA.
2Department of MedicineHualien Armed Forces General HospitalHualien97144Taiwan.
IEEE J Transl Eng Health Med. 2020 Apr 24;8:1800111. doi: 10.1109/JTEHM.2020.2990073. eCollection 2020.
The prevalence of physiological and pathological left ventricular hypertrophy (LVH) among young adults is about 5%. A use of electrocardiographic (ECG) voltage criteria and machine learning for the ECG parameters to identify the presence of LVH is estimated only 20-30% in the general population. The aim of this study is to develop an ECG system with anthropometric data using machine learning to increase the accuracy and sensitivity for a screen of LVH. In a large sample of 2,196 males, aged 17-45 years, the support vector machine (SVM) classifier is used as the machine learning method for 31 characteristics including age, body height and body weight in addition to 28 ECG parameters such as axes, intervals and voltages to link the output of LVH. The diagnosis of LVH is based on the echocardiographic criteria for young males to be 116 gram/meter (left ventricular mass (LVM)/body surface area) or 49 gram/meter (LVM/body height). On the purpose of increasing sensitivity, the specificity is adjusted around 70-75% and all data tested in proposed model reveal high sensitivity to 86.7%. The area under curve (AUC) of the Precision-Recall (PR) curve is 0.308 in the proposed model which is better than 0.109 and 0.077 using Cornell and Sokolow-Lyon voltage criteria for LVH, respectively. Our system provides a novel screening tool using age, body height, body weight and ECG data to identify most of the LVH among young adults. It provides a fast, accurate and practical diagnosis tool to identify LVH.
年轻成年人中心理性和病理性左心室肥厚(LVH)的患病率约为5%。使用心电图(ECG)电压标准和机器学习对ECG参数进行分析以识别LVH的存在,在普通人群中的估计准确率仅为20%-30%。本研究的目的是开发一种结合人体测量数据的ECG系统,利用机器学习提高LVH筛查的准确性和敏感性。在一个由2196名年龄在17-45岁之间的男性组成的大样本中,支持向量机(SVM)分类器被用作机器学习方法,用于分析包括年龄、身高和体重在内的31个特征,以及28个ECG参数,如轴、间期和电压,以关联LVH的输出结果。LVH的诊断基于针对年轻男性的超声心动图标准,即左心室质量(LVM)/体表面积为116克/平方米或LVM/身高为49克/平方米。为了提高敏感性,将特异性调整到70%-75%左右,在所提出模型中测试的所有数据显示出高达86.7%的高敏感性。在所提出模型中,精确召回率(PR)曲线的曲线下面积(AUC)为0.308,分别优于使用康奈尔和索科洛夫-里昂LVH电压标准时的0.109和0.077。我们的系统提供了一种新颖的筛查工具,利用年龄、身高、体重和ECG数据来识别大多数年轻成年人中的LVH。它提供了一种快速、准确且实用的LVH诊断工具。