Thompson, S, Fergus, P, Chalmers, C and Reilly, D Detection of Obstructive Sleep Apnoea Using Features Extracted from Segmented Time-Series ECG Signals Using a One Dimensional Convolutional Neural Network. In: IEEE Explore . (IEEE World Congress on Computational Intelligence (WCCI) 2020, 19-24th July 2020, Virtual Online Conference). (Accepted)

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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. 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.

Item Type:	Conference or Workshop Item (Paper)
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science R Medicine > R Medicine (General) T Technology > T Technology (General)
Divisions:	Computer Science & Mathematics Engineering
Publisher:	IEEE
Date Deposited:	26 Jun 2020 08:48
Last Modified:	13 Apr 2022 15:18
URI:	https://researchonline.ljmu.ac.uk/id/eprint/13188

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