Development of a Visualization Deep Learning Model for Classifying Origins of Ventricular Arrhythmias.
Circ J
; 86(8): 1273-1280, 2022 07 25.
Article
in En
| MEDLINE
| ID: mdl-35387940
ABSTRACT
BACKGROUND:
Several algorithms have been proposed for differentiating the right and left outflow tracts (RVOT/LVOT) arrhythmia origins from 12-lead electrocardiograms (ECGs); however, the procedure is complicated. A deep learning (DL) model, a form of artificial intelligence, can directly use ECGs and depict the importance of the leads and waveforms. This study aimed to create a visualized DL model that could classify arrhythmia origins more accurately.MethodsâandâResults:
This study enrolled 80 patients who underwent catheter ablation. A convolutional neural network-based model that could classify arrhythmia origins with 12-lead ECGs and visualize the leads that contributed to the diagnosis using a gradient-weighted class activation mapping method was developed. The average prediction results of the origins by the DL model were 89.4% (88.2-90.6) for accuracy and 95.2% (94.3-96.2) for recall, which were significantly better than when a conventional algorithm is used. The ratio of the contribution to the prediction differed between RVOT and LVOT origins. Although leads V1 to V3 and the limb leads had a focused balance in the LVOT group, the contribution ratio of leads aVR, aVL, and aVF was higher in the RVOT group.CONCLUSIONS:
This study diagnosed the arrhythmia origins more accurately than the conventional algorithm, and clarified which part of the 12-lead waveforms contributed to the diagnosis. The visualized DL model was convincing and may play a role in understanding the pathogenesis of arrhythmias.Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Tachycardia, Ventricular
/
Catheter Ablation
/
Ventricular Premature Complexes
/
Deep Learning
Type of study:
Diagnostic_studies
/
Prognostic_studies
Limits:
Humans
Language:
En
Journal:
Circ J
Journal subject:
ANGIOLOGIA
/
CARDIOLOGIA
Year:
2022
Document type:
Article