A method to screen left ventricular dysfunction through ECG based on convolutional neural network.

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Abstract

This study aims to develop an artificial intelligence-based method to screen patients with left ventricular ejection fraction (LVEF) of ≤ 50% using ECG data alone.
Convolutional neural network (CNN) is a class of deep neural networks, which has been widely used in medical image recognition. We collected standard 12-lead electrocardiogram (ECG) and transthoracic echocardiogram (TTE) data including the LVEF value. Then, we paired the ECG and TTE data from the same individual. For multiple ECG-TTE pairs from a single individual, only the earliest data pair was included. All the ECG-TTE pairs were randomly divided into the training, validation, or testing dataset in a ratio of 9:1:1 to create or evaluate the CNN model. Finally, we assessed the screening performance by overall accuracy, sensitivity, specificity, positive predictive value, and negative predictive value.
We retrospectively enrolled a total of 26,786 ECG-TTE pairs and randomly divided them into training (n=21,732), validation (n=2,530), and testing dataset (n=2,530). In the testing set, the CNN algorithm showed an overall accuracy of 73.9%, sensitivity of 69.2%, specificity of 70.5%, positive predictive value of 70.1%, and negative predictive value of 69.9%.
Our results demonstrate that a well-trained CNN algorithm may be used as a low-cost and non-invasive method to identify patients with left ventricular dysfunction. This article is protected by copyright. All rights reserved.
This article is protected by copyright. All rights reserved.