TY - GEN
T1 - Automatic Premature Ventricular Contractions Detection for Multi-Lead Electrocardiogram Signal
AU - Rahhal, Mohamad Mahmoud Al
AU - Ajlan, Naif Al
AU - Bazi, Yakoub
AU - Hichri, Haikel Al
AU - Rabczuk, Timon
N1 - 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.
PY - 2018/10/18
Y1 - 2018/10/18
N2 - 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).
AB - 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).
UR - http://www.scopus.com/inward/record.url?scp=85057131453&partnerID=8YFLogxK
U2 - 10.1109/EIT.2018.8500197
DO - 10.1109/EIT.2018.8500197
M3 - Conference contribution
AN - SCOPUS:85057131453
T3 - IEEE International Conference on Electro Information Technology
SP - 169
EP - 173
BT - 2018 IEEE International Conference on Electro/Information Technology, EIT 2018
PB - IEEE Computer Society
T2 - 2018 IEEE International Conference on Electro/Information Technology, EIT 2018
Y2 - 3 May 2018 through 5 May 2018
ER -