دورية أكاديمية
Tetanus Severity Classification in Low-Middle Income Countries through ECG Wearable Sensors and a 1D-Vision Transformer
| العنوان: | Tetanus Severity Classification in Low-Middle Income Countries through ECG Wearable Sensors and a 1D-Vision Transformer |
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| المؤلفون: | Ping Lu, Zihao Wang, Hai Duong Ha Thi, Ho Bich Hai, VITAL Consortium, Louise Thwaites, David A. Clifton |
| المصدر: | BioMedInformatics, Vol 4, Iss 1, Pp 285-294 (2024) |
| بيانات النشر: | MDPI AG, 2024. |
| سنة النشر: | 2024 |
| المجموعة: | LCC:Neurosciences. Biological psychiatry. Neuropsychiatry LCC:Computer applications to medicine. Medical informatics |
| مصطلحات موضوعية: | tetanus, electrocardiogram, classification, Transformer, Vision Transformer, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571, Computer applications to medicine. Medical informatics, R858-859.7 |
| الوصف: | 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. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 2673-7426 |
| Relation: | https://www.mdpi.com/2673-7426/4/1/16; https://doaj.org/toc/2673-7426 |
| DOI: | 10.3390/biomedinformatics4010016 |
| URL الوصول: | https://doaj.org/article/d038fb3c93ac4b51808b1b9c275dad42 |
| رقم الأكسشن: | edsdoj.038fb3c93ac4b51808b1b9c275dad42 |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 26737426 |
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| DOI: | 10.3390/biomedinformatics4010016 |