Your browser doesn't support javascript.
loading
Development and Validation of Deep-Learning Algorithm for Electrocardiography-Based Heart Failure Identification
Korean Circulation Journal ; : 629-639, 2019.
Article Dans Anglais | WPRIM | ID: wpr-759445
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.
Sujets)

Texte intégral: Disponible Indice: WPRIM (Pacifique occidental) Sujet Principal: Intelligence artificielle / Échocardiographie / Modèles logistiques / Forêts / Dépistage de masse / Courbe ROC / Diagnostic précoce / Électrocardiographie / Apprentissage machine / Coeur Type d'étude: Etude diagnostique / Étude pronostique / Facteurs de risque / Étude de dépistage Limites du sujet: Humains langue: Anglais Texte intégral: Korean Circulation Journal Année: 2019 Type: Article

Documents relatifs à ce sujet

MEDLINE

...
LILACS

LIS

Texte intégral: Disponible Indice: WPRIM (Pacifique occidental) Sujet Principal: Intelligence artificielle / Échocardiographie / Modèles logistiques / Forêts / Dépistage de masse / Courbe ROC / Diagnostic précoce / Électrocardiographie / Apprentissage machine / Coeur Type d'étude: Etude diagnostique / Étude pronostique / Facteurs de risque / Étude de dépistage Limites du sujet: Humains langue: Anglais Texte intégral: Korean Circulation Journal Année: 2019 Type: Article