ECG database of short fragments with arrhythmias classification according to the degree of danger to the patient’s life-Reference-Cited by-同舟云学术

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ECG database of short fragments with arrhythmias classification according to the degree of danger to the patient’s life

Published:2022-11-11 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE International Multi-Conference on Engineering, Computer and Information Sciences (SIBIRCON)
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Author:

Manilo Liudmila A.¹,Nemirko Anatoly P.¹,Evdakova Ekaterina G.¹,Tatarinova Anna A.²,Alekseev Boris E.¹

Affiliation:

1. Saint Petersburg Electrotechnical University "LETI",Department of Bioengineering Systems,Saint Petersburg,Russia

2. Almazov National Medical Research Centre,Electrocardiology Research Laboratory,Saint Petersburg,Russia

Funder

Russian Foundation for Basic Research

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10016894/10016776/10017055.pdf?arnumber=10017055

Reference12 articles.

1. Automated identification of shockable and non-shockable life-threatening ventricular arrhythmias using convolutional neural network

2. ECG Fragment Database for the Exploration of Dangerous Arrhythmia (version 1.0.0);nemirko;PhysioNet,2022

3. Approaches to Sudden Death from Coronary Heart Disease

4. Recognition of biomedical signals based on their spectral description data analysis

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Unsupervised Anomaly Detection to handle Imbalanced Datasets using Auto encoders for ECG signal Classification;2023 Third International Conference on Secure Cyber Computing and Communication (ICSCCC);2023-05-26

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