| Autor: |
Tadesse GA; Institute of Biomedical Engineering, University of Oxford, Oxford, UK.; IBM Research | Africa, Nairobi, Kenya., Zhu T; Institute of Biomedical Engineering, University of Oxford, Oxford, UK., Le Nguyen Thanh N; Children's Hospital Number 1, Ho Chi Minh City, Vietnam., Hung NT; Children's Hospital Number 1, Ho Chi Minh City, Vietnam., Duong HTH; Hospital for Tropical Diseases, Ho Chi Minh City, Vietnam., Khanh TH; Children's Hospital Number 1, Ho Chi Minh City, Vietnam., Quang PV; Children's Hospital Number 1, Ho Chi Minh City, Vietnam., Tran DD; Hospital for Tropical Diseases, Ho Chi Minh City, Vietnam., Yen LM; Oxford Clinical Research Unit, Ho Chi Minh City, Vietnam., Doorn RV; Oxford University Clinical Research Unit, Hanoi, Vietnam.; Centre for Tropical Medicine and Global Health, Oxford University, UK., Hao NV; Hospital for Tropical Diseases, Ho Chi Minh City, Vietnam., Prince J; Institute of Biomedical Engineering, University of Oxford, Oxford, UK., Javed H; Institute of Biomedical Engineering, University of Oxford, Oxford, UK., Kiyasseh D; Institute of Biomedical Engineering, University of Oxford, Oxford, UK., Tan LV; Oxford Clinical Research Unit, Ho Chi Minh City, Vietnam., Thwaites L; Oxford Clinical Research Unit, Ho Chi Minh City, Vietnam.; Centre for Tropical Medicine and Global Health, Oxford University, UK., Clifton DA; Institute of Biomedical Engineering, University of Oxford, Oxford, UK. |
| Abstrakt: |
Hand foot and mouth disease (HFMD) and tetanus are serious infectious diseases in low- and middle-income countries. Tetanus, in particular, has a high mortality rate and its treatment is resource-demanding. Furthermore, HFMD often affects a large number of infants and young children. As a result, its treatment consumes enormous healthcare resources, especially when outbreaks occur. Autonomic nervous system dysfunction (ANSD) is the main cause of death for both HFMD and tetanus patients. However, early detection of ANSD is a difficult and challenging problem. The authors aim to provide a proof-of-principle to detect the ANSD level automatically by applying machine learning techniques to physiological patient data, such as electrocardiogram waveforms, which can be collected using low-cost wearable sensors. Efficient features are extracted that encode variations in the waveforms in the time and frequency domains. The proposed approach is validated on multiple datasets of HFMD and tetanus patients in Vietnam. Results show that encouraging performance is achieved. Moreover, the proposed features are simple, more generalisable and outperformed the standard heart rate variability analysis. The proposed approach would facilitate both the diagnosis and treatment of infectious diseases in low- and middle-income countries, and thereby improve patient care. |
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