Development and Validation of Deep-Learning Algorithm for Electrocardiography-Based Heart Failure Identification

Abstract

Background and Objectives Screening and early diagnosis for heart failure (HF) are critical. However, conventional screening diagnostic methods have limitations, and electrocardiography (ECG)-based HF identification may be helpful. This study aimed to develop and validate a deep-learning algorithm for ECG-based HF identification (DEHF). Methods The study involved 2 hospitals and 55,163 ECGs of 22,765 patients who performed echocardiography within 4 weeks were study subjects. ECGs were divided into derivation and validation data. Demographic and ECG features were used as predictive variables. The primary endpoint was detection of HF with reduced ejection fraction (HFrEF; ejection fraction [EF]≤40%), and the secondary endpoint was HF with mid-range to reduced EF (≤50%). We developed the DEHF using derivation data and the algorithm representing the risk of HF between 0 and 1. We confirmed accuracy and compared logistic regression (LR) and random forest (RF) analyses using validation data. Results The area under the receiver operating characteristic curves (AUROCs) of DEHF for identification of HFrEF were 0.843 (95% confidence interval, 0.840–0.845) and 0.889 (0.887–0.891) for internal and external validation, respectively, and these results significantly outperformed those of LR (0.800 [0.797–0.803], 0.847 [0.844–0.850]) and RF (0.807 [0.804–0.810], 0.853 [0.850–0.855]) analyses. The AUROCs of deep learning for identification of the secondary endpoint was 0.821 (0.819–0.823) and 0.850 (0.848–0.852) for internal and external validation, respectively, and these results significantly outperformed those of LR and RF. Conclusions The deep-learning algorithm accurately identified HF using ECG features and outperformed other machine-learning methods.

Artificial intelligence algorithm for detecting myocardial infarction using six-lead electrocardiography

“…Attia et al developed deep-learning algorithms for screening cardiac contractile dysfunction, predicting the occurrence of atrial fibrillation during sinus rhythm, approximating age and sex, and detecting hyperkalemia using raw ECG data and demonstrated its feasibility 11,12,24,25 . Our study showed that a deep-learning-based algorithm using ECG could outperform cardiologists in diagnosing left ventricular hypertrophy and valvular heart disease 14,15,26,27 . We used a sensitivity map to visualize the regions of the ECGs that were used for decision-making by the DLA.…”

A Prototype Photoplethysmography Electronic Device that Distinguishes Congestive Heart Failure from Healthy Individuals by Applying Natural Time Analysis

“…Previous ECG-based methods for automated computer aided detection (CAD) of HF using various methodologies have achieved encouraging results. The duration of ECG signal recording was variable and could be as short as 2 s [10,[54][55][56][57][58]. Simple clinical prediction rules (CPR) for HF detection performed less well compared to ECG based methods [59].…”

Feasibility of fully automated classification of whole slide images based on deep learning

“…In contrast, feature extraction and model learning take place in a unified step in deep learning [9]. Deep learning-based approaches have become very successful in a wide range of biomedical analysis tasks [10], including the analysis of retinal fundus images [11], radiologic images [12], pathologic tissue images [13], electrocardiograms [14] and electroencephalograms [15]. However, deep learning-based methods generally require large annotated datasets compared to traditional machine learning [16].…”