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DC Field | Value | Language |
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dc.contributor.author | Mathunjwa, BM | - |
dc.contributor.author | Lin, Y-T | - |
dc.contributor.author | Lin, C-H | - |
dc.contributor.author | Abbod, MF | - |
dc.contributor.author | Sadrawi, M | - |
dc.contributor.author | Shieh, J-S | - |
dc.date.accessioned | 2023-08-22T17:25:13Z | - |
dc.date.available | 2023-08-22T17:25:13Z | - |
dc.date.issued | 2023-06-04 | - |
dc.identifier | ORFCID iD: Maysam F. Abbod https://orcid.org/0000-0002-8515-7933 | - |
dc.identifier | 105070 | - |
dc.identifier.citation | Mathunjwa, B.M. et al. (2023) 'Automatic IHR-based sleep stage detection using features of residual neural network', Biomedical Signal Processing and Control, 85, 105070, pp. 1 - 10. doi: 10.1016/j.bspc.2023.105070. | en_US |
dc.identifier.issn | 1746-8094 | - |
dc.identifier.uri | https://bura.brunel.ac.uk/handle/2438/27029 | - |
dc.description | Data availability; The authors do not have permission to share data. | en_US |
dc.description.abstract | Untreated sleep disorders can harm bodily functions, and a sleep study and monitoring of sleep stages are the first steps in diagnosing these disorders. Using Polysomnography (PSG), signal scoring for sleep stage determination has become a familiar investigation in recent years. Despite its effectiveness, the procedure is time-consuming and costly. This study presents a cost-effective method for sleep classification based on Electrocardiogram (ECG) input signals. We proposed a multi-ethnic study of the Atherosclerosis dataset, including 1700 PSG, to develop a Residual Neural Network (RNN) classifier to stage sleep from Instantaneous Heart Rate (IHR) extracted from the ECG signals. The proposed system follows the following steps: ECG collection, signal preprocessing (including ECG normalization and segmentation, instant heart rate calculation and normalization, resampling, and filtering), and classification using an RNN. A Convolutional Neural Network (CNN) is used to detect sleep stages using preprocessed segments of the IHR time series of 240 samples centered on 30-s epochs as inputs. The proposed algorithm in the five-fold cross-validation achieved an accuracy of 85.32%, a kappa of 77.11%, a Sensitivity of 81.14%, a Specificity of 82.68%, and an F-1 score of 81.87%. The results show that ECG data provide valuable information about sleep stages for a large population. | en_US |
dc.description.sponsorship | Ministry of Science and Technology, Taiwan (Grant number: MOST 107-2221-E-155-009-MY2). | en_US |
dc.format.extent | 1 - 10 | - |
dc.format.medium | Print-Electronic | - |
dc.language | English | - |
dc.language.iso | en_US | en_US |
dc.publisher | Elsevier | en_US |
dc.rights | Copyright © 2023 Elsevier. All rights reserved. This is the accepted manuscript version of an article which has been published in final form at https://doi.org/10.1016/j.bspc.2023.105070, made available on this repository under a Creative Commons CC BY-NC-ND attribution licence (https://creativecommons.org/licenses/by-nc-nd/4.0/). | - |
dc.rights.uri | https://creativecommons.org/licenses/by-nc-nd/4.0/ | - |
dc.subject | electrocardiography | en_US |
dc.subject | instantaneous heart rate | en_US |
dc.subject | residual neural network | en_US |
dc.subject | sleep stages classification | en_US |
dc.title | Automatic IHR-based sleep stage detection using features of residual neural network | en_US |
dc.type | Article | en_US |
dc.identifier.doi | https://doi.org/10.1016/j.bspc.2023.105070 | - |
dc.relation.isPartOf | Biomedical Signal Processing and Control | - |
pubs.publication-status | Published | - |
pubs.volume | 85 | - |
dc.identifier.eissn | 1746-8108 | - |
dc.rights.holder | Elsevier | - |
Appears in Collections: | Dept of Electronic and Electrical Engineering Embargoed Research Papers |
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