Automated Annotation of Polysomnogram Epochs for Apnoea and Non-apnoea Arousals.

A system for automated annotation of selected signals from the polysomnogram (PSG) for the presence of apnoea and non-apnoea arousals is presented. Fifty nine time- and frequency-domain features were derived from the PSG for each 15 second epoch and after combining features from adjacent epochs, the feature information was processed with a bank of feed-forward neural networks that provided a probability estimate that each epoch was associated with an apnoea or non-apnoea arousal, or no-arousal. Data from the Physionet Computing in Cardiology Challenge 2018 was used to develop and test the system. Performance of the system was assessed using volume under the receiver operator characteristic surface (VUROS) as well as no-arousal specificity and arousal sensitivities. Using a bank of ten feed-forward neural networks with each network processing ±4 epochs of features and each used a single hidden layer of 20 units, the system achieved a VUROS of 0.73 with a specificity of 70%, a sensitivity of 75% for the apnoea arousals, and a sensitivity of 70% for the non-apnoea arousals.