A Neonatal Apnoea Monitor for Resource-Constrained Environments

Journal article

A prototype Android application was designed to monitor for apnoea in neonates using a smartphone. The application receives data from a wireless pulse oximeter and uses machine learning techniques to detect apnoea. Distribution of the system requires only the pulse oximeter and a current mid-range smartphone. This work builds on previous research, but with a particular focus on classifying events accurately using a reduced set of information appropriate to a resource-constrained environment. This information consists only of the photoplethysmogram (PPG) and a set of PPG-derived physiological variables including heart rate and respiration rate. Various methods using the Support Vector Machine (SVM) were assessed using data from 27 annotated stays in a neonatal intensive care unit, divided approximately in half into training and test data. The best approach was found to be a combination of a feature selection method based on mutual information and an SVM with a radial basis function kernel, producing a classifier with a sensitivity of 98.7%, a specificity of 62.2% and a balanced accuracy of 80.5% on a training set of 796 events, and a sensitivity of 76.9%, a specificity of 52.0% and a balanced accuracy of 64.4% on a test set of 663 events. © 2012 CCAL.

Authors: Daly, J; Monasterio, V; Clifford, G

• Publication status: Published
• Journal: 2012 COMPUTING IN CARDIOLOGY (CINC), VOL 39
• Volume: 39
• Pages: 321-324
• Publication date: 2012-01-01
• EISSN: 2325-887X
• ISSN: 0276-6574
• Language: English