Tetanus severity classification in low-middle income countries through ECG wearable sensors and a 1D-vision transformer
Tetanus, a life-threatening bacterial infection prevalent in low- and middle-income countries like Vietnam, impacts the nervous system, causing muscle stiffness and spasms. Severe tetanus often involves dysfunction of the autonomic nervous system (ANS). Timely detection and effective ANS dysfunction...
| Main Authors: | , , , , , |
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| Other Authors: | |
| Format: | Journal article |
| Language: | English |
| Published: |
MDPI
2024
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| _version_ | 1826312097546371072 |
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| author | Lu, P Wang, Z Ha Thi, HD Hai, HB Thwaites, L Clifton, DA |
| author2 | VITAL Consortium |
| author_facet | VITAL Consortium Lu, P Wang, Z Ha Thi, HD Hai, HB Thwaites, L Clifton, DA |
| author_sort | Lu, P |
| collection | OXFORD |
| description | Tetanus, a life-threatening bacterial infection prevalent in low- and middle-income countries like Vietnam, impacts the nervous system, causing muscle stiffness and spasms. Severe tetanus often involves dysfunction of the autonomic nervous system (ANS). Timely detection and effective ANS dysfunction management require continuous vital sign monitoring, traditionally performed using bedside monitors. However, wearable electrocardiogram (ECG) sensors offer a more cost-effective and user-friendly alternative. While machine learning-based ECG analysis can aid in tetanus severity classification, existing methods are excessively time-consuming. Our previous studies have investigated the improvement of tetanus severity classification using ECG time series imaging. In this study, our aim is to explore an alternative method using ECG data without relying on time series imaging as an input, with the aim of achieving comparable or improved performance. To address this, we propose a novel approach using a 1D-Vision Transformer, a pioneering method for classifying tetanus severity by extracting crucial global information from 1D ECG signals. Compared to 1D-CNN, 2D-CNN, and 2D-CNN + Dual Attention, our model achieves better results, boasting an F1 score of 0.77 ± 0.06, precision of 0.70 ± 0. 09, recall of 0.89 ± 0.13, specificity of 0.78 ± 0.12, accuracy of 0.82 ± 0.06 and AUC of 0.84 ± 0.05. |
| first_indexed | 2024-03-07T08:21:00Z |
| format | Journal article |
| id | oxford-uuid:fad9baf6-16b3-4fd6-9b5c-155fec3cf44c |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T08:21:00Z |
| publishDate | 2024 |
| publisher | MDPI |
| record_format | dspace |
| spelling | oxford-uuid:fad9baf6-16b3-4fd6-9b5c-155fec3cf44c2024-01-25T16:21:07ZTetanus severity classification in low-middle income countries through ECG wearable sensors and a 1D-vision transformerJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:fad9baf6-16b3-4fd6-9b5c-155fec3cf44cEnglishSymplectic ElementsMDPI2024Lu, PWang, ZHa Thi, HDHai, HBThwaites, LClifton, DAVITAL ConsortiumTetanus, a life-threatening bacterial infection prevalent in low- and middle-income countries like Vietnam, impacts the nervous system, causing muscle stiffness and spasms. Severe tetanus often involves dysfunction of the autonomic nervous system (ANS). Timely detection and effective ANS dysfunction management require continuous vital sign monitoring, traditionally performed using bedside monitors. However, wearable electrocardiogram (ECG) sensors offer a more cost-effective and user-friendly alternative. While machine learning-based ECG analysis can aid in tetanus severity classification, existing methods are excessively time-consuming. Our previous studies have investigated the improvement of tetanus severity classification using ECG time series imaging. In this study, our aim is to explore an alternative method using ECG data without relying on time series imaging as an input, with the aim of achieving comparable or improved performance. To address this, we propose a novel approach using a 1D-Vision Transformer, a pioneering method for classifying tetanus severity by extracting crucial global information from 1D ECG signals. Compared to 1D-CNN, 2D-CNN, and 2D-CNN + Dual Attention, our model achieves better results, boasting an F1 score of 0.77 ± 0.06, precision of 0.70 ± 0. 09, recall of 0.89 ± 0.13, specificity of 0.78 ± 0.12, accuracy of 0.82 ± 0.06 and AUC of 0.84 ± 0.05. |
| spellingShingle | Lu, P Wang, Z Ha Thi, HD Hai, HB Thwaites, L Clifton, DA Tetanus severity classification in low-middle income countries through ECG wearable sensors and a 1D-vision transformer |
| title | Tetanus severity classification in low-middle income countries through ECG wearable sensors and a 1D-vision transformer |
| title_full | Tetanus severity classification in low-middle income countries through ECG wearable sensors and a 1D-vision transformer |
| title_fullStr | Tetanus severity classification in low-middle income countries through ECG wearable sensors and a 1D-vision transformer |
| title_full_unstemmed | Tetanus severity classification in low-middle income countries through ECG wearable sensors and a 1D-vision transformer |
| title_short | Tetanus severity classification in low-middle income countries through ECG wearable sensors and a 1D-vision transformer |
| title_sort | tetanus severity classification in low middle income countries through ecg wearable sensors and a 1d vision transformer |
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