Multi-label Anomaly Classification Based on Electrocardiogram

被引：0
作者：
Li, Chenyang
Sun, Le [1 ]
机构：
[1] Nanjing Univ Informat Sci & Technol, Engn Res Ctr Digital Forens, Minist Educ, Nanjing 210044, Peoples R China
来源：
HEALTH INFORMATION SCIENCE, HIS 2021 | 2021年 / 13079卷
基金：
中国国家自然科学基金;
关键词：
Multi-label classification; Electrocardiogram; Classification of arrhythmia;
D O I：
10.1007/978-3-030-90885-0_16
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
Under the background of 5G and AI, it is particularly important to use cloud computing, Internet of things and big data technology to analyze massive physiological signals of patients in real time. Arrhythmia can cause some major diseases, such as heart failure, atrial fibrillation and so on. It's difficult to analysis them quickly. In this paper, a deep learning model of multi-label classification based on optimized temporal convolution network is proposed to detect abnormal electrocardiogram. The experimental results show that the accuracy of the model is 0.960, and the Micro F1 score is 0.87.
引用
收藏
页码：171 / 178
页数：8
相关论文
共 31 条
[1]  
Almalchy MT, 2020, 2020 13TH INTERNATIONAL CONFERENCE ON COMMUNICATIONS (COMM), P197, DOI [10.1109/comm48946.2020.9141994, 10.1109/COMM48946.2020.9141994]
[2]   High Precision Digitization of Paper-Based ECG Records: A Step Toward Machine Learning [J].
Baydoun, Mohammed ;
Safatly, Lise ;
Abou Hassan, Ossama K. ;
Ghaziri, Hassan ;
El Hajj, Ali ;
Isma'eel, Hussain .
IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE, 2019, 7
[3]   CLASSIFICATION OF ECG ARRHYTHMIA WITH MACHINE LEARNING TECHNIQUES [J].
Bulbul, Halil Ibrahim ;
Usta, Nese ;
Yildiz, Musa .
2017 16TH IEEE INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS (ICMLA), 2017, :546-549
[4]   Multi-ECGNet for ECG Arrythmia Multi-Label Classification [J].
Cai, Junxian ;
Sun, Weiwei ;
Guan, Jianfeng ;
You, Ilsun .
IEEE ACCESS, 2020, 8 :110848-110858
[5]   Towards the classification of heart sounds based on convolutional deep neural network [J].
Demir, Fatih ;
Sengur, Abdulkadir ;
Bajaj, Varun ;
Polat, Kemal .
HEALTH INFORMATION SCIENCE AND SYSTEMS, 2019, 7 (01)
[6]  
Di Wang, 2019, 2019 7th International Conference on Information, Communication and Networks (ICICN), P171, DOI 10.1109/ICICN.2019.8834931
[7]   FM-ECG: A fine-grained multi-label framework for ECG image classification [J].
Du, Nan ;
Cao, Qing ;
Yu, Li ;
Liu, Nathan ;
Zhong, Erheng ;
Liu, Zizhu ;
Shen, Ying ;
Chen, Kang .
INFORMATION SCIENCES, 2021, 549 :164-177
[8]  
Feng Y., 2020, 2020 COMPUTING CARDI, P1
[9]   On the consistency of multi-label learning [J].
Gao, Wei ;
Zhou, Zhi-Hua .
ARTIFICIAL INTELLIGENCE, 2013, 199 :22-44
[10]   Automatic Classification of Arrhythmias by Residual Network and BiGRU With Attention Mechanism [J].
He, Runnan ;
Wang, Kuanquan ;
Zhao, Na ;
Sun, Qiang ;
Li, Yacong ;
Li, Qince ;
Zhang, Henggui .
2020 COMPUTING IN CARDIOLOGY, 2020,
1234