Improving Classification of Tetanus Severity for Patients in Low-Middle Income Countries Wearing ECG Sensors by Using a CNN-Transformer Network
Author:
Affiliation:
1. Department of Engineering Science, University of Oxford, Oxford, U.K.
2. Department of Engineering Science, University of Oxford, U.K.
3. Oxford University Clinical Research Unit, Vietnam
Funder
Wellcome Trust
NIHR Oxford Biomedical Research Centre
InnoHK Hong Kong Centre for Cerebro-cardiovascular Health Engineering
Pandemic Sciences Institute at the University of Oxford
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Biomedical Engineering
Link
http://xplorestaging.ieee.org/ielx7/10/10078065/09926154.pdf?arnumber=9926154
Reference54 articles.
1. Classification of Tetanus Severity in Intensive-Care Settings for Low-Income Countries Using Wearable Sensing
2. Wearable sensors to improve detection of patient deterioration
3. Heart rate variability
4. Heart Rate Variability as an Indicator of Autonomic Nervous System Disturbance in Tetanus
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1. Tetanus Severity Classification in Low-Middle Income Countries through ECG Wearable Sensors and a 1D-Vision Transformer;BioMedInformatics;2024-01-19
2. 2D-WinSpatt-Net: A Dual Spatial Self-Attention Vision Transformer Boosts Classification of Tetanus Severity for Patients Wearing ECG Sensors in Low- and Middle-Income Countries;Sensors;2023-09-06
3. Advancing ECG Biometrics Through Vision Transformers: A Confidence-Driven Approach;IEEE Access;2023
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