Machine Learning of ECG Waveforms to Improve Selection for Testing for Asymptomatic Left Ventricular Dysfunction
Machine Learning of ECG Waveforms to Improve Selection for Testing for Asymptomatic Left Ventricular Dysfunction
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DOI：
10.1016/j.jcmg.2021.04.020
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发表时间：
2021-10-04
影响因子：
14
通讯作者：
Marwick, Thomas H.
中科院分区：
文献类型：
--
作者：
Potter, Elizabeth L.;Rodrigues, Carlos H. M.;Marwick, Thomas H.
本研究的目的是确定从连续波变换（CWT）的处理中进行机器学习以提供“能量波形”心电图（ewECG）是否可以与亚临床收缩和舒张左心室功能障碍（LVD）的超声心动图评估相结合。背景无症状LVD具有管理意义，但在有心力衰竭风险的受试者中不进行常规超声心动图。采用连续小波变换对体表心电图进行信号处理，可以识别异常心肌舒张功能。方法对398例有心力衰竭（HF）危险的患者进行EwECG和超声心动图检查。整体纵向应变降低（GLS 10或舒张受损）或LV肥大定义了LVD。EwECG特征选择和随机森林（RF）分类器的监督机器学习使用643个CWT衍生特征和ARIC（社区动脉粥样硬化风险）心力衰竭风险评分进行。结果ARIC评分和18个CWT特征被选择用于在训练数据集（n = 287;
OBJECTIVES The purpose of this study was to identify whether machine learning from processing of continuous wave transforms (CWTs) to provide an "energy waveform" electrocardiogram (ewECG) could be integrated with echocardiographic assessment of subclinical systolic and diastolic left ventricular dysfunction (LVD).BACKGROUND Asymptomatic LVD has management implications, but routine echocardiography is not undertaken in subjects at risk of heart failure. Signal processing of the surface ECG with the use of CWT can identify abnormal myocardial relaxation.METHODS EwECG and echocardiography were undertaken in 398 participants at risk of heart failure (HF). Reduced global longitudinal strain (GLS 10 or impaired relaxation) or LV hypertrophy defined LVD. EwECG feature selection and supervised machine-learning by random forest (RF) classifier was undertaken with 643 CWT-derived features and the ARIC (Atherosclerosis Risk In Communities) heart failure risk score.RESULTS The ARIC score and 18 CWT features were selected to build a RF predictive model for LVD in a training dataset (n = 287;