Deep Learning Technologies for Time Series Anomaly Detection in Healthcare: A Review-Reference-Cited by-同舟云学术

Deep Learning Technologies for Time Series Anomaly Detection in Healthcare: A Review

Published:2023 Issue: Volume:11 Page:117788-117799
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Yang Xue¹^ORCID,Qi Xuejun²,Zhou Xiaobo³^ORCID

Affiliation:

1. Department of Pancreatic Surgery, Sichuan University, Chengdu, China

2. School of Architecture and Civil Engineering, Xihua University, Chengdu, China

3. Center for Computational Systems Medicine, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA

Funder

Center of Excellence-International Collaboration Initiative Grant, West China Hospital, Sichuan University

the 1.3.5 Project for Disciplines of Excellence–Clinical Research Incubation Project, West China Hospital, Sichuan University

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/10005208/10287948.pdf?arnumber=10287948

Reference73 articles.

1. Neurological abnormality detection from electroencephalography data: a review

2. A survey on semi-supervised learning

3. Anomaly detection in electroencephalography signal using deep learning model;tahura;Proc Int Conf Trends Comput Cognit Eng (TCCE),2021

4. CLECG: A Novel Contrastive Learning Framework for Electrocardiogram Arrhythmia Classification

5. EEG Pathology Detection Based on Deep Learning

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