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Severity detection tool for patients with infectious disease

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Severity detection tool for patients with infectious disease

Content Provider	IET Digital Library
Author	Tadesse, Girmaw Abebe Zhu, Tingting Thanh, Nhan Le Nguyen Hung, Nguyen Thanh Duong, Ha Thi Hai Khanh, Truong Huu Quang, Pham Van Tran, Duc Duong Yen, Lam Minh Doorn, Rogier Van Hao, Nguyen Van Prince, John Javed, Hamza Kiyasseh, Dani Tan, Le Van Thwaites, Louise Clifton, David A.
Abstract	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.
Starting Page	45
Ending Page	50
Page Count	6
Volume Number	7
e-ISSN	20533713
Issue Number	Issue 2, Apr (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/htl/7/2
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/htl.2019.0030
Journal	Healthcare Technology Letters
Publisher	The Institution of Engineering and Technology
Publisher Date	2020-04-14
Access Restriction	Open
Rights License	Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/)
Subject Keyword	ANSD Level Autonomic Nervous System Dysfunction Bioelectric Signal Biology And Medical Computing Biomedical Measurement And Imaging Cardiology Classifying ANSD Levels Difficult Problem Digital Signal Processing Diseases Electrocardiogram Electrocardiography Electrodiagnostics And Other Electrical Measurement Technique Enormous Healthcare ReSource Feature Extraction Frequency Domain Healthcare HFMD High Mortality Rate Infectious Disease Knowledge Engineering Technique Learning in AI Low-cost Wearable Sonsor Medical Computing Medical Signal Processing Middle-income Countries Neurophysiology Patient Care Patient Care And Treatment Patient Diagnosis Patient Diagnostic Method And Instrumentation Patient Treatment Photoplethysmogram Waveform Physiological Patient Data Proof-of-principle Resource-demanding Serious Infectious Diseases Severity Detection Tool Standard Heart Rate Variability Analysis Statistics Support Vector Machine Tetanus Patients Young Children
Content Type	Text
Resource Type	Article

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