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.
ABSTRACT
Since January 2018, over 53,000 hospitalisations and six deaths due to hand, foot and mouth disease (HFMD) have occurred across Vietnam with most cases from September onward. In a large tertiary referral hospital, Ho Chi Minh City, enterovirus A71 subgenogroup C4 was predominant, while B5 was only sporadically detected. The re-emergence of C4 after causing a severe HFMD outbreak with > 200 deaths in 2011-12 among susceptible young children raises concern of another impending severe outbreak.
