  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. In this paper, we 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
(ECG) and photoplethysmogram (PPG) 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. A support vector
machine is employed to classify the ANSD levels. The proposed approach is
validated on multiple datasets of HFMD and tetanus patients in Vietnam. Results
show that encouraging performance is achieved in classifying ANSD levels.
Moreover, the proposed features are simple, more generalisable and outperformed
the standard heart rate variability (HRV) analysis. The proposed approach would
facilitate both the diagnosis and treatment of infectious diseases in low and
middle income countries, and thereby improve overall patient care.
