Tetanus Severity Classification in Low-Middle Income Countries through ECG Wearable Sensors and a 1D-Vision Transformer
Tetanus Severity Classification in Low-Middle Income Countries through ECG Wearable Sensors and a 1D-Vision Transformer
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DOI：
10.3390/biomedinformatics4010016
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发表时间：
2024-01
期刊：
影响因子：
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通讯作者：
Ping Lu;Zihao Wang;Hai Duong Ha Thi;Ho Bich Hai;Louise Thwaites;David A. Clifton
中科院分区：
文献类型：
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作者：
Ping Lu;Zihao Wang;Hai Duong Ha Thi;Ho Bich Hai;Louise Thwaites;David A. Clifton
破伤风是一种威胁生命的细菌感染，在越南等低收入和中等收入国家普遍存在，会影响神经系统，导致肌肉僵硬和痉挛。严重的破伤风通常涉及自主神经系统（ANS）功能障碍。及时检测和有效的ANS功能障碍管理需要连续的生命体征监测，传统上使用床边监护仪进行。然而，可穿戴心电图（ECG）传感器提供了一种更具成本效益和用户友好的替代方案。虽然基于机器学习的ECG分析可以帮助破伤风严重程度分类，但现有方法过于耗时。我们以前的研究已经调查了改善破伤风严重程度分类使用心电图时间序列成像。在这项研究中，我们的目的是探索一种替代方法，使用ECG数据，而不依赖于时间序列成像作为输入，目的是实现可比或改进的性能。为了解决这个问题，我们提出了一种新的方法，使用一维视觉Transformer，一种开创性的方法，通过提取关键的全球信息，从一维心电图信号分类破伤风的严重程度。与1D-CNN，2D-CNN和2D-CNN + Dual Attention相比，我们的模型取得了更好的结果，F1得分为0.77 ± 0.06，精度为0.70 ± 0。回忆率为0.89 ± 0.13，特异性为0.78 ± 0.12，准确性为0.82 ± 0.06，AUC为0.84 ± 0.05。
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.