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.
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.
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DOI：
10.3390/s23187705
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发表时间：
2023-09-06
期刊：
影响因子：
--
通讯作者：
Clifton DA
中科院分区：
文献类型：
--
作者：
Lu P;Creagh AP;Lu HY;Hai HB;Vital Consortium;Thwaites L;Clifton DA
破伤风是一种威胁生命的细菌感染，通常在低收入和中等收入国家(LMIC)流行，包括越南。破伤风会影响神经系统，导致肌肉僵硬和痉挛。此外，严重破伤风与自主神经系统(ANS)功能障碍有关。为了确保ANS功能障碍的早期发现和有效处理，患者需要使用床边监护仪持续监测生命体征。可穿戴式心电传感器为床边监护仪提供了一种更具成本效益和用户友好的替代方案。基于机器学习的心电分析可以作为破伤风严重程度分类的宝贵资源；然而，使用现有的心电信号分析过于耗时。由于传统卷积神经网络(CNN)使用固定大小的核过滤器，它们捕获全局上下文信息的能力受到限制。在这项工作中，我们提出了一种2D-WinSpatt-Net，这是一种新颖的视觉转换器，它同时包含了局部空间窗口自我注意和全局空间自我注意机制。2D-WinSpatt-Net使用可穿戴的心电传感器提高了LMIC重症监护环境中破伤风严重程度的分类。时间序列成像--连续小波变换--从一维心电信号变换并输入到所提出的2D-WinSpatt网络。在破伤风严重程度的分类方面，2D-WinSpatt-Net在性能和准确性方面超过了最先进的方法。F1值为0.88±0.00，精确度为0.92±0.02，召回率为0.85±0.01，特异度为0.96±0.01，准确率为0.93±0.02，AUC为0.90±0.00。
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.
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影响因子：
--
作者：
Cygankiewicz, Iwona;Zareba, Wojciech
通讯作者：
Zareba, Wojciech
影响因子：
3.3
作者：
Ha Thi Hai Duong;Tadesse, Girmaw Abebe;Thwaites, Louise
通讯作者：
Thwaites, Louise
DOI：
10.3390/s22103866
发表时间：
2022-05-19
期刊：
Sensors (Basel, Switzerland)
影响因子：
--
作者：
通讯作者：
--
影响因子：
8
作者：
Bradley, AP
通讯作者：
Bradley, AP
影响因子：
3.3
作者：
Duong Bich Thuy;Campbell, James I.;Thwaites, C. Louise
通讯作者：
Thwaites, C. Louise