Korean Circ J.  2019 Jul;49(7):629-639. 10.4070/kcj.2018.0446.

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

• Kwon JM
• Kim KH
• Jeon KH
• Kim HM
• Kim MJ
• Lim SM
• Song PS
• Park J
• Choi RK
• Oh BH

• KMID: 2450395
• DOI: http://doi.org/10.4070/kcj.2018.0446

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.

Keyword

Deep learning; Heart failure; Electrocardiography; Machine learning; Artificial intelligence

MeSH Terms

Artificial Intelligence
Early Diagnosis
Echocardiography
Electrocardiography
Forests
Heart Failure*
Heart*
Humans
Learning
Logistic Models
Machine Learning
Mass Screening
ROC Curve

Figure

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  Figure 1 Study flow chart. DEHF = deep-learning algorithm for electrocardiography-based heart failure identification; ECG = electrocardiography.

• 

  Figure 2 Development and validation of DEHF. DEHF = deep-learning algorithm for electrocardiography-based heart failure identification; ECG = electrocardiography; HF = heart failure.

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  Figure 3 AUROC of each algorithm for identification of HF. AUROC = area under the receiver operating characteristic curve; EF = ejection fraction; HF = heart failure; HFrEF = heart failure with reduced ejection fraction; HFmrEF = heart failure with mid-range ejection fraction.

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