Detection of Obstructive Sleep Apnoea Using Features Extracted from Segmented Time-Series ECG Signals Using a One Dimensional Convolutional Neural Network

no code implementations • 3 Feb 2020 • Steven Thompson, Paul Fergus, Carl Chalmers, Denis Reilly

Evaluation of the model is performed using a set of standard metrics which show the proposed model achieves high classification results in both training and validation using our windowing strategy, particularly W=500 (Sensitivity 0. 9705, Specificity 0. 9725, F1 Score 0. 9717, Kappa Score 0. 9430, Log Loss 0. 0836, ROCAUC 0. 9945).

Specificity Time Series +1