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

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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Bibliographic Details
Main Authors: Steven Thompson, Denis Reilly, Paul Fergus, Carl Chalmers
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Access
Subjects:
Apnoea–Hypopnoea index (AHI)
electrocardiography (ECG)
obstructed sleep Apnoea (OSA)
one dimensional convolutional neural network (1DCNN)
machine learning (ML)
deep learning (DL)
Online Access:https://ieeexplore.ieee.org/document/10373019/

Internet

https://ieeexplore.ieee.org/document/10373019/

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