Next Article in Journal
Development and Validation of Deep Learning-Based Algorithms for Predicting Lumbar Herniated Nucleus Pulposus Using Lumbar X-rays
Next Article in Special Issue
Association of a Common NOS1AP Variant with Attenuation of QTc Prolongation in Men with Heroin Dependence Undergoing Methadone Treatment
Previous Article in Journal
Deep Convolutional Gated Recurrent Unit Combined with Attention Mechanism to Classify Pre-Ictal from Interictal EEG with Minimized Number of Channels
Previous Article in Special Issue
The Sequential Change in Left Ventricular Function among Various Cardiovascular Diseases: A 12-Year Study
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
JPM
Volume 12
Issue 5
10.3390/jpm12050764
jpm-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

A High-Precision Deep Learning Algorithm to Localize Idiopathic Ventricular Arrhythmias

by
Ting-Yung Chang
1,2,3,4,
Ke-Wei Chen
5,
Chih-Min Liu
1,2,3,
Shih-Lin Chang
1,2,3,*,
Yenn-Jiang Lin
1,2,3,
Li-Wei Lo
1,2,3,
Yu-Feng Hu
1,2,3,
Fa-Po Chung
1,2,3,
Chin-Yu Lin
1,2,3,
Ling Kuo
1,2,3 and
Shih-Ann Chen
1,2,3,6
1
Heart Rhythm Center, Division of Cardiology, Department of Medicine, Taipei Veterans General Hospital, Taipei 11217, Taiwan
2
Institute of Cardiovascular Research, National Yang Ming Chiao Tung University, Taipei 11221, Taiwan
3
Institute of Clinical Medicine, National Yang Ming Chiao Tung University, Taipei 11221, Taiwan
4
Department of Nursing, National Taipei University of Nursing and Health Sciences, Taipei 112303, Taiwan
5
Department of BioMedical Engineering, National Cheng Kung University, Tainan City 701401, Taiwan
6
Cardiovascular Center, Taichung Veterans General Hospital, Taichung 40705, Taiwan
*
Author to whom correspondence should be addressed.
J. Pers. Med. 2022, 12(5), 764; https://doi.org/10.3390/jpm12050764
Submission received: 28 March 2022 / Revised: 30 April 2022 / Accepted: 6 May 2022 / Published: 9 May 2022
(This article belongs to the Special Issue The Challenges and Prospects in Cardiology)
Browse Figures
Versions Notes

Abstract

Background: An accurate prediction of ventricular arrhythmia (VA) origins can optimize the strategy of ablation, and facilitate the procedure. Objective: This study aimed to develop a machine learning model from surface ECG to predict VA origins. Methods: We obtained 3628 waves of ventricular premature complex (VPC) from 731 patients. We chose to include all signal information from 12 ECG leads for model input. A model is composed of two groups of convolutional neural network (CNN) layers. We chose around 13% of all the data for model testing and 10% for validation. Results: In the first step, we trained a model for binary classification of VA source from the left or right side of the chamber with an area under the curve (AUC) of 0.963. With a threshold of 0.739, the sensitivity and specification are 90.7% and 92.3% for identifying left side VA. Then, we obtained the second model for predicting VA from the LV summit with AUC is 0.998. With a threshold of 0.739, the sensitivity and specificity are 100% and 98% for the LV summit. Conclusions: Our machine learning algorithm of surface ECG facilitates the localization of VPC, especially for the LV summit, which might optimize the ablation strategy.
Keywords: machine learning; ventricular arrhythmia; localization; catheter ablation machine learning; ventricular arrhythmia; localization; catheter ablation

Share and Cite

MDPI and ACS Style

Chang, T.-Y.; Chen, K.-W.; Liu, C.-M.; Chang, S.-L.; Lin, Y.-J.; Lo, L.-W.; Hu, Y.-F.; Chung, F.-P.; Lin, C.-Y.; Kuo, L.; et al. A High-Precision Deep Learning Algorithm to Localize Idiopathic Ventricular Arrhythmias. J. Pers. Med. 2022, 12, 764. https://doi.org/10.3390/jpm12050764

AMA Style

Chang T-Y, Chen K-W, Liu C-M, Chang S-L, Lin Y-J, Lo L-W, Hu Y-F, Chung F-P, Lin C-Y, Kuo L, et al. A High-Precision Deep Learning Algorithm to Localize Idiopathic Ventricular Arrhythmias. Journal of Personalized Medicine. 2022; 12(5):764. https://doi.org/10.3390/jpm12050764

Chicago/Turabian Style

Chang, Ting-Yung, Ke-Wei Chen, Chih-Min Liu, Shih-Lin Chang, Yenn-Jiang Lin, Li-Wei Lo, Yu-Feng Hu, Fa-Po Chung, Chin-Yu Lin, Ling Kuo, and et al. 2022. "A High-Precision Deep Learning Algorithm to Localize Idiopathic Ventricular Arrhythmias" Journal of Personalized Medicine 12, no. 5: 764. https://doi.org/10.3390/jpm12050764

APA Style

Chang, T.-Y., Chen, K.-W., Liu, C.-M., Chang, S.-L., Lin, Y.-J., Lo, L.-W., Hu, Y.-F., Chung, F.-P., Lin, C.-Y., Kuo, L., & Chen, S.-A. (2022). A High-Precision Deep Learning Algorithm to Localize Idiopathic Ventricular Arrhythmias. Journal of Personalized Medicine, 12(5), 764. https://doi.org/10.3390/jpm12050764

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop