3 research outputs found
Severity detection tool for patients with infectious disease
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