Detection of Obstructive Sleep Apnoea Using Features Extracted From Segmented Time-Series ECG Signals With a One Dimensional Convolutional Neural Network
This paper reports on ongoing research, which aims to prove that features of Obstructed Sleep Apnoea (OSA) can be automatically identified from single-lead electrocardiogram (ECG) signals using a One-Dimensional Convolutional Neural Network (1DCNN) model. The 1DCNN is also compared against other mac...
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IEEE
2024-01-01
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Online Access: | https://ieeexplore.ieee.org/document/10373019/ |
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author | Steven Thompson Denis Reilly Paul Fergus Carl Chalmers |
author_facet | Steven Thompson Denis Reilly Paul Fergus Carl Chalmers |
author_sort | Steven Thompson |
collection | DOAJ |
description | This paper reports on ongoing research, which aims to prove that features of Obstructed Sleep Apnoea (OSA) can be automatically identified from single-lead electrocardiogram (ECG) signals using a One-Dimensional Convolutional Neural Network (1DCNN) model. The 1DCNN is also compared against other machine learning (ML) classifier models, namely Support Vector Machine (SVM) and Random Forest Classifier (RFC). The 1DCNN architecture consists of 4 major parts, a Convolutional Layer, a Flattened Dense Layer, a Max Pooling Layer and a Fully Connected Multilayer Perceptron (MLP), with 1 Hidden Layer and a SoftMax output. The model repeatedly learns how to better extract prominent features from one-dimensional data and map it to the MLP for increased prediction. Training and validation are achieved using pre-processed time-series ECG signals captured from 35 ECG recordings. Using our unique windowing strategy, the data is shaped into 5 datasets of different window sizes. A total of 15 models (5 for each group, 1DCNNs, RFCs, SVMs) were evaluated using various metrics, with each being run over numerous experiments. Results show the 1DCNN-500 model delivered the greatest degree of accuracy and rapidity in comparison to the best producing RFC and SVM classifiers. 1DCNN-500 (Sensitivity 0.9743, Specificity 0.9708, Accuracy 0.9699); RFC-500 (Sensitivity/Recall (0) 0.90 / (1) 0.94, Precision (0) 0.94 / (1) 0.90, Accuracy 0.91); SVM-500 (Sensitivity (0) 0.94 / (1) 0.50, Precision (0) 0.65 / (1) 0.90, Accuracy 0.72). The model presents a novel approach that could provide support mechanisms in clinical practice to promptly diagnose patients suffering from OSA. |
first_indexed | 2024-03-08T16:57:10Z |
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institution | Directory Open Access Journal |
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language | English |
last_indexed | 2024-03-08T16:57:10Z |
publishDate | 2024-01-01 |
publisher | IEEE |
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series | IEEE Access |
spelling | doaj.art-b717adb2e6d3423a8a23deaf71cea4ff2024-01-05T00:02:59ZengIEEEIEEE Access2169-35362024-01-01121076109110.1109/ACCESS.2023.334668910373019Detection of Obstructive Sleep Apnoea Using Features Extracted From Segmented Time-Series ECG Signals With a One Dimensional Convolutional Neural NetworkSteven Thompson0https://orcid.org/0009-0004-6796-3028Denis Reilly1https://orcid.org/0000-0002-8161-9010Paul Fergus2https://orcid.org/0000-0002-7070-4447Carl Chalmers3https://orcid.org/0000-0003-0822-1150Department of Computer Science, Liverpool John Moores University, Liverpool, U.K.Department of Computer Science, Liverpool John Moores University, Liverpool, U.K.Department of Computer Science, Liverpool John Moores University, Liverpool, U.K.Department of Computer Science, Liverpool John Moores University, Liverpool, U.K.This paper reports on ongoing research, which aims to prove that features of Obstructed Sleep Apnoea (OSA) can be automatically identified from single-lead electrocardiogram (ECG) signals using a One-Dimensional Convolutional Neural Network (1DCNN) model. The 1DCNN is also compared against other machine learning (ML) classifier models, namely Support Vector Machine (SVM) and Random Forest Classifier (RFC). The 1DCNN architecture consists of 4 major parts, a Convolutional Layer, a Flattened Dense Layer, a Max Pooling Layer and a Fully Connected Multilayer Perceptron (MLP), with 1 Hidden Layer and a SoftMax output. The model repeatedly learns how to better extract prominent features from one-dimensional data and map it to the MLP for increased prediction. Training and validation are achieved using pre-processed time-series ECG signals captured from 35 ECG recordings. Using our unique windowing strategy, the data is shaped into 5 datasets of different window sizes. A total of 15 models (5 for each group, 1DCNNs, RFCs, SVMs) were evaluated using various metrics, with each being run over numerous experiments. Results show the 1DCNN-500 model delivered the greatest degree of accuracy and rapidity in comparison to the best producing RFC and SVM classifiers. 1DCNN-500 (Sensitivity 0.9743, Specificity 0.9708, Accuracy 0.9699); RFC-500 (Sensitivity/Recall (0) 0.90 / (1) 0.94, Precision (0) 0.94 / (1) 0.90, Accuracy 0.91); SVM-500 (Sensitivity (0) 0.94 / (1) 0.50, Precision (0) 0.65 / (1) 0.90, Accuracy 0.72). The model presents a novel approach that could provide support mechanisms in clinical practice to promptly diagnose patients suffering from OSA.https://ieeexplore.ieee.org/document/10373019/Apnoea–Hypopnoea index (AHI)electrocardiography (ECG)obstructed sleep Apnoea (OSA)one dimensional convolutional neural network (1DCNN)machine learning (ML)deep learning (DL) |
spellingShingle | Steven Thompson Denis Reilly Paul Fergus Carl Chalmers Detection of Obstructive Sleep Apnoea Using Features Extracted From Segmented Time-Series ECG Signals With a One Dimensional Convolutional Neural Network IEEE Access Apnoea–Hypopnoea index (AHI) electrocardiography (ECG) obstructed sleep Apnoea (OSA) one dimensional convolutional neural network (1DCNN) machine learning (ML) deep learning (DL) |
title | Detection of Obstructive Sleep Apnoea Using Features Extracted From Segmented Time-Series ECG Signals With a One Dimensional Convolutional Neural Network |
title_full | Detection of Obstructive Sleep Apnoea Using Features Extracted From Segmented Time-Series ECG Signals With a One Dimensional Convolutional Neural Network |
title_fullStr | Detection of Obstructive Sleep Apnoea Using Features Extracted From Segmented Time-Series ECG Signals With a One Dimensional Convolutional Neural Network |
title_full_unstemmed | Detection of Obstructive Sleep Apnoea Using Features Extracted From Segmented Time-Series ECG Signals With a One Dimensional Convolutional Neural Network |
title_short | Detection of Obstructive Sleep Apnoea Using Features Extracted From Segmented Time-Series ECG Signals With a One Dimensional Convolutional Neural Network |
title_sort | detection of obstructive sleep apnoea using features extracted from segmented time series ecg signals with a one dimensional convolutional neural network |
topic | Apnoea–Hypopnoea index (AHI) electrocardiography (ECG) obstructed sleep Apnoea (OSA) one dimensional convolutional neural network (1DCNN) machine learning (ML) deep learning (DL) |
url | https://ieeexplore.ieee.org/document/10373019/ |
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