Cargando…
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
Tetanus is a life-threatening bacterial infection that is often prevalent in low- and middle-income countries (LMIC), Vietnam included. Tetanus affects the nervous system, leading to muscle stiffness and spasms. Moreover, severe tetanus is associated with autonomic nervous system (ANS) dysfunction....
Autores principales: | , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10535235/ https://www.ncbi.nlm.nih.gov/pubmed/37765761 http://dx.doi.org/10.3390/s23187705 |
_version_ | 1785112582489636864 |
---|---|
author | Lu, Ping Creagh, Andrew P. Lu, Huiqi Y. Hai, Ho Bich Thwaites, Louise Clifton, David A. |
author_facet | Lu, Ping Creagh, Andrew P. Lu, Huiqi Y. Hai, Ho Bich Thwaites, Louise Clifton, David A. |
author_sort | Lu, Ping |
collection | PubMed |
description | Tetanus is a life-threatening bacterial infection that is often prevalent in low- and middle-income countries (LMIC), Vietnam included. Tetanus affects the nervous system, leading to muscle stiffness and spasms. Moreover, severe tetanus is associated with autonomic nervous system (ANS) dysfunction. To ensure early detection and effective management of ANS dysfunction, patients require continuous monitoring of vital signs using bedside monitors. Wearable electrocardiogram (ECG) sensors offer a more cost-effective and user-friendly alternative to bedside monitors. Machine learning-based ECG analysis can be a valuable resource for classifying tetanus severity; however, using existing ECG signal analysis is excessively time-consuming. Due to the fixed-sized kernel filters used in traditional convolutional neural networks (CNNs), they are limited in their ability to capture global context information. In this work, we propose a 2D-WinSpatt-Net, which is a novel Vision Transformer that contains both local spatial window self-attention and global spatial self-attention mechanisms. The 2D-WinSpatt-Net boosts the classification of tetanus severity in intensive-care settings for LMIC using wearable ECG sensors. The time series imaging—continuous wavelet transforms—is transformed from a one-dimensional ECG signal and input to the proposed 2D-WinSpatt-Net. In the classification of tetanus severity levels, 2D-WinSpatt-Net surpasses state-of-the-art methods in terms of performance and accuracy. It achieves remarkable results with an F1 score of 0.88 ± 0.00, precision of 0.92 ± 0.02, recall of 0.85 ± 0.01, specificity of 0.96 ± 0.01, accuracy of 0.93 ± 0.02 and AUC of 0.90 ± 0.00. |
format | Online Article Text |
id | pubmed-10535235 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-105352352023-09-29 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 Lu, Ping Creagh, Andrew P. Lu, Huiqi Y. Hai, Ho Bich Thwaites, Louise Clifton, David A. Sensors (Basel) Article Tetanus is a life-threatening bacterial infection that is often prevalent in low- and middle-income countries (LMIC), Vietnam included. Tetanus affects the nervous system, leading to muscle stiffness and spasms. Moreover, severe tetanus is associated with autonomic nervous system (ANS) dysfunction. To ensure early detection and effective management of ANS dysfunction, patients require continuous monitoring of vital signs using bedside monitors. Wearable electrocardiogram (ECG) sensors offer a more cost-effective and user-friendly alternative to bedside monitors. Machine learning-based ECG analysis can be a valuable resource for classifying tetanus severity; however, using existing ECG signal analysis is excessively time-consuming. Due to the fixed-sized kernel filters used in traditional convolutional neural networks (CNNs), they are limited in their ability to capture global context information. In this work, we propose a 2D-WinSpatt-Net, which is a novel Vision Transformer that contains both local spatial window self-attention and global spatial self-attention mechanisms. The 2D-WinSpatt-Net boosts the classification of tetanus severity in intensive-care settings for LMIC using wearable ECG sensors. The time series imaging—continuous wavelet transforms—is transformed from a one-dimensional ECG signal and input to the proposed 2D-WinSpatt-Net. In the classification of tetanus severity levels, 2D-WinSpatt-Net surpasses state-of-the-art methods in terms of performance and accuracy. It achieves remarkable results with an F1 score of 0.88 ± 0.00, precision of 0.92 ± 0.02, recall of 0.85 ± 0.01, specificity of 0.96 ± 0.01, accuracy of 0.93 ± 0.02 and AUC of 0.90 ± 0.00. MDPI 2023-09-06 /pmc/articles/PMC10535235/ /pubmed/37765761 http://dx.doi.org/10.3390/s23187705 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Lu, Ping Creagh, Andrew P. Lu, Huiqi Y. Hai, Ho Bich Thwaites, Louise Clifton, David A. 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 |
title | 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 |
title_full | 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 |
title_fullStr | 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 |
title_full_unstemmed | 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 |
title_short | 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 |
title_sort | 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 |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10535235/ https://www.ncbi.nlm.nih.gov/pubmed/37765761 http://dx.doi.org/10.3390/s23187705 |
work_keys_str_mv | AT luping 2dwinspattnetadualspatialselfattentionvisiontransformerboostsclassificationoftetanusseverityforpatientswearingecgsensorsinlowandmiddleincomecountries AT creaghandrewp 2dwinspattnetadualspatialselfattentionvisiontransformerboostsclassificationoftetanusseverityforpatientswearingecgsensorsinlowandmiddleincomecountries AT luhuiqiy 2dwinspattnetadualspatialselfattentionvisiontransformerboostsclassificationoftetanusseverityforpatientswearingecgsensorsinlowandmiddleincomecountries AT haihobich 2dwinspattnetadualspatialselfattentionvisiontransformerboostsclassificationoftetanusseverityforpatientswearingecgsensorsinlowandmiddleincomecountries AT 2dwinspattnetadualspatialselfattentionvisiontransformerboostsclassificationoftetanusseverityforpatientswearingecgsensorsinlowandmiddleincomecountries AT thwaiteslouise 2dwinspattnetadualspatialselfattentionvisiontransformerboostsclassificationoftetanusseverityforpatientswearingecgsensorsinlowandmiddleincomecountries AT cliftondavida 2dwinspattnetadualspatialselfattentionvisiontransformerboostsclassificationoftetanusseverityforpatientswearingecgsensorsinlowandmiddleincomecountries |