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

Steven Thompson Computer Science Liverpool John Moores University Liverpool, Merseyside S.R.Thompson@LJMU.AC.UK Denis Reilly Computer Science Liverpool John Moores University Liverpool, Merseyside D.Reilly@LJMU.AC.UK Paul Fergus Computer Science Liverpool John Moores University Liverpool, Merseysde P.Fergus@LJMU.AC.UK Carl Chalmers Computer Science Liverpool John Moores University Liverpool, Merseyside C.Chalmers@LJMU.AC.UK Abstract --The study in this paper presents a one-dimensional convolutional neural network (1DCNN) model, designed for the automated detection of obstructive Sleep Apnoea (OSA) captured from single-channel electrocardiogram (ECG) signals. The system provides mechanisms in clinical practice that help diagnose patients suffering with OSA. Using the state-of-the-art in 1DCNNs, a model is constructed using convolutional, max pooling layers and a fully connected Multilayer Perceptron (MLP) consisting of a hidden layer and SoftMax output for classification. The 1DCNN extracts prominent features, which are used to train an MLP. The model is trained using segmented ECG signals grouped into 5 unique datasets of set window sizes. A total of 6514 minutes of Apnoea was recorded. 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). This demonstrates the model can identify the presence of Apnoea with a high degree of accuracy.