Optimizing ECG to detect echocardiographic left ventricular hypertrophy with computer-based ECG data and machine learning.

ABSTRACT:

Background

Left ventricular hypertrophy detected by echocardiography (Echo-LVH) is an independent predictor of mortality. Integration of the Philips DXL-16 algorithm into the electrocardiogram (ECG) extensively analyses the electricity of the heart. Machine learning techniques such as the C5.0 could lead to a new decision tree criterion to detect Echo-LVH.

Objectives

To search for a new combination of ECG parameters predictive of Echo-LVH. The final model is called the Cardiac Hypertrophy Computer-based model (CHCM).

Methods

We extracted the 458 ECG parameters provided by the Philips DXL-16 algorithm in patients with Echo-LVH and controls. We used the C5.0 ML algorithm to train, test, and validate the CHCM. We compared its diagnostic performance to validate state-of-t

SUBMITTER: De la Garza Salazar F 

PROVIDER: S-EPMC8631676 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature