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http://bura.brunel.ac.uk/handle/2438/27029
Title: | Automatic IHR-based sleep stage detection using features of residual neural network |
Authors: | Mathunjwa, BM Lin, Y-T Lin, C-H Abbod, MF Sadrawi, M Shieh, J-S |
Keywords: | electrocardiography;instantaneous heart rate;residual neural network;sleep stages classification |
Issue Date: | 4-Jun-2023 |
Publisher: | Elsevier |
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. |
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. |
Description: | Data availability; The authors do not have permission to share data. |
URI: | https://bura.brunel.ac.uk/handle/2438/27029 |
DOI: | https://doi.org/10.1016/j.bspc.2023.105070 |
ISSN: | 1746-8094 |
Other Identifiers: | ORFCID iD: Maysam F. Abbod https://orcid.org/0000-0002-8515-7933 105070 |
Appears in Collections: | Dept of Electronic and Electrical Engineering Embargoed Research Papers |
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