PMID- 31946432
OWN - NLM
STAT- MEDLINE
DCOM- 20200422
LR  - 20200928
IS  - 2694-0604 (Electronic)
IS  - 2375-7477 (Linking)
VI  - 2019
DP  - 2019 Jul
TI  - Myocardial Infarction Detection Based on Multi-lead Ensemble Neural Network.
PG  - 2614-2617
LID - 10.1109/EMBC.2019.8856392 [doi]
AB  - Automatic myocardial infarction (MI) detection using an electrocardiogram (ECG) 
      is of great significance for improving the survival rate of patients. In this 
      study, we propose a multi-lead ensemble neural network (MENN) to distinguish 
      anterior myocardial infarction (AMI) and inferior myocardial infarction (IMI) 
      from healthy control (HC) respectively. In the study, three kinds of sub-networks 
      and multi-lead ECG signals are combined, which fully explores the information of 
      ECG signals and improves the classification performance. The algorithm is 
      evaluated on the PTB database by 5-fold inter-subject cross-validation and the 
      sensitivity (Se), specificity (Sp) and area under the curve (AUC) of AMI 
      detection are 98.35%, 97.49%, 97.92%; The Se, Sp, and AUC of IMI detection are 
      93.17%, 92.02%, 92.60%. The proposed method achieves the state of the art results 
      on both tasks and outperforms the baseline methods. Hence, the proposed method is 
      potential for automatic MI diagnosis.
FAU - Wang, H M
AU  - Wang HM
FAU - Zhao, W
AU  - Zhao W
FAU - Jia, D Y
AU  - Jia DY
FAU - Hu, J
AU  - Hu J
FAU - Li, Z Q
AU  - Li ZQ
FAU - Yan, C
AU  - Yan C
FAU - You, T Y
AU  - You TY
LA  - eng
PT  - Journal Article
PL  - United States
TA  - Annu Int Conf IEEE Eng Med Biol Soc
JT  - Annual International Conference of the IEEE Engineering in Medicine and Biology 
      Society. IEEE Engineering in Medicine and Biology Society. Annual International 
      Conference
JID - 101763872
SB  - IM
MH  - Algorithms
MH  - *Diagnosis, Computer-Assisted
MH  - *Electrocardiography
MH  - Humans
MH  - Myocardial Infarction/*diagnosis
MH  - *Neural Networks, Computer
MH  - Sensitivity and Specificity
EDAT- 2020/01/18 06:00
MHDA- 2020/04/23 06:00
CRDT- 2020/01/18 06:00
PHST- 2020/01/18 06:00 [entrez]
PHST- 2020/01/18 06:00 [pubmed]
PHST- 2020/04/23 06:00 [medline]
AID - 10.1109/EMBC.2019.8856392 [doi]
PST - ppublish
SO  - Annu Int Conf IEEE Eng Med Biol Soc. 2019 Jul;2019:2614-2617. doi: 
      10.1109/EMBC.2019.8856392.