  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. 35 ECG signal recordings were selected from an
annotated database containing 70 night-time ECG recordings. (Group A = a01 to
a20 (Apnoea breathing), Group B = b01 to b05 (moderate), and Group C = c01 to
c10 (normal). 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.
