TFFormer: A Time–Frequency Information Fusion-Based CNN-Transformer Model for OSA Detection With Single-Lead ECG-Reference-Cited by-同舟云学术

TFFormer: A Time–Frequency Information Fusion-Based CNN-Transformer Model for OSA Detection With Single-Lead ECG

Published:2023 Issue: Volume:72 Page:1-17
ISSN:0018-9456
Container-title:IEEE Transactions on Instrumentation and Measurement
language:
Short-container-title:IEEE Trans. Instrum. Meas.

Author:

Li Chengjian¹^ORCID,Shi Zhenghao¹^ORCID,Zhou Liang¹^ORCID,Zhang Zhijun¹^ORCID,Wu Chenwei¹^ORCID,Ren Xiaoyong²^ORCID,Hei Xinhong¹^ORCID,Zhao Minghua¹^ORCID,Zhang Yitong²^ORCID,Liu Haiqin²^ORCID,You Zhenzhen¹^ORCID,He Lifeng³^ORCID

Affiliation:

1. School of Computer Science and Engineering, Xi’an University of Technology, Xi’an, China

2. Department of Otolaryngology, The Second Affiliated Hospital, Xi’an Jiaotong University, Xi’an, China

3. School of Information Science and Technology, Aichi Prefectural University, Nagakute, Japan

Funder

National Natural Science Foundation of China

Key Natural Science Foundation of Shaanxi Province of China

Natural Science Foundation of Shaanxi Province of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Instrumentation

Link

http://xplorestaging.ieee.org/ielx7/19/10012124/10242151.pdf?arnumber=10242151

Reference52 articles.

1. AIOSA: An approach to the automatic identification of obstructive sleep apnea events based on deep learning

2. Detection of Sleep Apnea from Single-Lead ECG: Comparison of Deep Learning Algorithms

3. A dual-model deep learning method for sleep apnea detection based on representation learning and temporal dependence

4. Deep Residual Shrinkage Networks for Fault Diagnosis

5. The impact of the MIT-BIH Arrhythmia Database

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