Identification of 27 abnormalities from multi-lead ECG signals: An ensembled SE_ResNet framework with Sign Loss function

Identification of 27 abnormalities from multi-lead ECG signals: An ensembled SE_ResNet framework with Sign Loss function

10.1088/1361-6579/ac08e6

Saved in:
Bibliographic Details
Main Authors: Zhu, Zhaowei, Lan, Xiang, Zhao, Tingting, Guo, Yangming, Kojodjojo, Pipin, Xu, Zhuoyang, Liu, Zhuo, Liu, Siqi, Wang, Han, Sun, Xingzhi, Feng, Mengling
Other Authors: SAW SWEE HOCK SCHOOL OF PUBLIC HEALTH
Format: Article
Published: IOP Publishing Ltd 2022
Subjects:
Deep neural network
Ecg abnormalities
Ecg signal
Ensemble modeling
Multi-label classification
Online Access:https://scholarbank.nus.edu.sg/handle/10635/232924
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: National University of Singapore

Internet

https://scholarbank.nus.edu.sg/handle/10635/232924

Similar Items