Abstract
In this paper, we propose an electrocardiogram (ECG) technique for the automatic detection of Premature Ventricular Contractions (PVC) based on multi-lead signals and on a deep learning architecture which is built using Stacked Denoising Autoencoders (SDAEs) networks. The proposed method consists of two main stages; feature learning and classification. In the first stage, we learn a new feature representation from data using SDAEs. Regarding the classification, we add a softmax regression layer on the top of the resulting hidden representation layer yielding a deep neural network (DNN). The proposed method fuses the results of several ECG leads (up to 12) in order to increase the detection accuracy. In the experiments, we use INCART database to test the proposed DNN multi-lead method. The obtained results are 98.6%, 91.4%, and 97.7% respectively for overall accuracy (OA), average sensitivity (Se), and average positive productivity (Pp).
Original language | English |
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Title of host publication | 2018 IEEE International Conference on Electro/Information Technology, EIT 2018 |
Publisher | IEEE Computer Society |
Pages | 169-173 |
Number of pages | 5 |
ISBN (Electronic) | 9781538653982 |
DOIs | |
Publication status | Published - 2018 Oct 18 |
Event | 2018 IEEE International Conference on Electro/Information Technology, EIT 2018 - Rochester, United States Duration: 2018 May 3 → 2018 May 5 |
Publication series
Name | IEEE International Conference on Electro Information Technology |
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Volume | 2018-May |
ISSN (Print) | 2154-0357 |
ISSN (Electronic) | 2154-0373 |
Other
Other | 2018 IEEE International Conference on Electro/Information Technology, EIT 2018 |
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Country/Territory | United States |
City | Rochester |
Period | 18/5/3 → 18/5/5 |
Bibliographical note
Funding Information:The authors extend their appreciation to the Distinguished Scientist Fellowship Program (DSFP) at King Saud University for funding this work.
Publisher Copyright:
© 2018 IEEE.
ASJC Scopus subject areas
- Computer Science Applications
- Information Systems
- Control and Systems Engineering
- Electrical and Electronic Engineering