Abstract
Background
Deep-learning algorithms to annotate electrocardiograms (ECGs) and classify different
types of cardiac arrhythmias with the use of a single-lead ECG input data set have
been developed. It remains to be determined whether these algorithms can be generalized
to 12-lead ECG-based rhythm classification.
Methods
We used a long short-term memory (LSTM) model to detect 12 heart rhythm classes with
the use of 65,932 digital 12-lead ECG signals from 38,899 patients, using annotations
obtained by consensus of 3 board-certified electrophysiologists as the criterion standard.
Results
The accuracy of the LSTM model for the classification of each of the 12 heart rhythms
was ≥ 0.982 (range 0.982-1.0), with an area under the receiver operating characteristic
curve of ≥ 0.987 (range 0.987-1.0). The precision and recall ranged from 0.692 to
1 and from 0.625 to 1, respectively, with an F1 score of ≥ 0.777 (range 0.777-1.0). The accuracy of the model (0.90) was superior
to the mean accuracies of internists (0.55), emergency physicians (0.73), and cardiologists
(0.83).
Conclusions
We demonstrated the feasibility and effectiveness of the deep-learning LSTM model
for interpreting 12 common heart rhythms according to 12-lead ECG signals. The findings
may have clinical relevance for the early diagnosis of cardiac rhythm disorders.
Résumé
Contexte
Des algorithmes d’apprentissage profond conçus pour annoter les électrocardiogrammes
(ECG) et classifier différents types d’arythmie cardiaque à partir des données d’un
ECG à une seule dérivation ont été mis au point. Nous avons tenté de déterminer si
ces algorithmes peuvent être généralisés pour obtenir une classification à partir
des données d’un ECG à 12 dérivations.
Méthodologie
Nous avons utilisé un modèle LSTM (Long Short-Term Memory) pour reconnaître 12 catégories de rythmes cardiaques à partir de 65 932 signaux
numériques d’ECG à 12 dérivations obtenus auprès de 38 899 patients; nous avons utilisé
les annotations consensuelles établies par trois électrophysiologistes spécialisés
comme critères de référence.
Résultats
L’exactitude du modèle LSTM utilisé pour classifier les rythmes cardiaques de chacune
des 12 catégories s’établissait à ≥ 0,982 (plage : de 0,982 à 1,0), l’aire sous la
courbe caractéristique de la performance du test étant de ≥ 0,987 (plage : de 0,987
à 1,0). La précision et le rappel allaient de 0,692 à 1 et de 0,625 à 1, respectivement,
le score F1 s’établissant à ≥ 0,777 (plage : de 0,777 à 1,0). L’exactitude du modèle (0,90) était
supérieure à l’exactitude moyenne des internistes (0,55), des urgentologues (0,73)
et des cardiologues (0,83).
Conclusions
Nous avons démontré la faisabilité et l’efficacité de l’emploi du modèle d’apprentissage
profond LSTM pour l’interprétation de 12 rythmes cardiaques courants à partir des
signaux d’un ECG à 12 dérivations. Ces résultats pourraient être utiles sur le plan
clinique aux fins du diagnostic précoce des arythmies.
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Article Info
Publication History
Published online: March 05, 2020
Accepted:
February 26,
2020
Received:
November 6,
2019
Footnotes
See editorial by Zhou et al., pages 17–18 of this issue.
See page 10 for disclosure information.
Identification
Copyright
© 2020 Canadian Cardiovascular Society. Published by Elsevier Inc. All rights reserved.
