Optimising the Apnoea Classification Performance of a Neural Network Classifier Processing ECG-Oximetry Signals.

In this paper we investigate using principal components analysis to optimize the performance of a neural network system processing simultaneously acquired electrocardiogram (ECG) and oximetry signals. The algorithm identifies epochs of normal breathing, central apnoea (CA), and obstructive apnoea (OA) by processing a pooled feature set containing information capturing the desaturations from the oximeter sensor as well as time and spectral features from the ECG. Training and testing of the system was facilitated with a dataset of 125 scored polysomnogram recordings with accompanying respiratory event annotations. When classifying the three epoch types, our system achieved a specificity of 91%, a sensitivity to CA of 28% and sensitivity to OA of 63%. A sensitivity of 81% was achieved when the CA and OA epochs were combined into one class.