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Canadian Journal of Cardiology

Usefulness of Machine Learning-Based Detection and Classification of Cardiac Arrhythmias With 12-Lead Electrocardiograms

Published:March 05, 2020DOI:https://doi.org/10.1016/j.cjca.2020.02.096

      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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