• Users Online: 110
  • Home
  • Print this page
  • Email this page
Home About us Editorial board Ahead of print Current issue Search Archives Submit article Instructions Subscribe Contacts Login 
ORIGINAL ARTICLE
Year : 2018  |  Volume : 3  |  Issue : 2  |  Page : 55-59

Analysis of 12-lead electrocardiogram signal based on deep learning


1 Department of Cardiology, Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University; Guangdong Province Key Laboratory of Arrhythmia and Electrophysiology, Guangzhou, Guangdong Province, China
2 Zhujiang Hospital, Southern Medical University; Department of Bioinformatics, Guangzhou Gencoding Lab, Guangzhou, Guangdong Province, China
3 Department of Anesthesiology, Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong Province, China
4 Department of Bioinformatics, Guangzhou Gencoding Lab, Guangzhou, Guangdong Province, China
5 Department of Cardiology, Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University, Guangzhou, Guangdong Province, China
6 Department of Tumor Chemotherapy, Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University, Guangzhou, Guangdong Province, China

Correspondence Address:
Prof. Jingfeng Wang
Department of Cardiology, Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University, Guangzhou 510120, Guangdong Province
China
Login to access the Email id

Source of Support: None, Conflict of Interest: None


DOI: 10.4103/IJHR.IJHR_4_18

Rights and Permissions

Background: In this work, a deep learning method is proposed to identify the types of arrhythmia. Methods: The 12-lead electrocardiogram signal is first denoised by filters to eliminate the baseline drift and the myoelectric interference. Then, the filtered signal is sliced into beats and sent to a deep neural network, which contains four convolutional layers, two gated recurrent unit layers, and one full-connected layer. Features in both the spatial domain and the time-frequency domain can be extracted implicitly by the deep neural network, instead of being extracted manually. Results: On the test split of the dataset, our neural network model achieves an accuracy of 98.15%. Among the accuracies for the four types of arrhythmia, respectively, the lowest one is 96% and the highest is 99%. Our model is must better than a baseline support vector machines classifier, with a test accuracy of 73.54%. Conclusion: The results give a supportive evidence to make our model clinically applicable to assist physicians in diagnosing certain diseases.


[FULL TEXT] [PDF]*
Print this article     Email this article
 Next article
 Previous article
 Table of Contents

 Similar in PUBMED
   Search Pubmed for
   Search in Google Scholar for
 Related articles
 Citation Manager
 Access Statistics
 Reader Comments
 Email Alert *
 Add to My List *
 * Requires registration (Free)
 

 Article Access Statistics
    Viewed907    
    Printed130    
    Emailed0    
    PDF Downloaded179    
    Comments [Add]    

Recommend this journal