Improving sleep health with deep learning: automated classification of sleep stages and detection of sleep disorders

Haifa Almutairi

doi:10.26182/ex2c-3z62

Improving sleep health with deep learning: automated classification of sleep stages and detection of sleep disorders

Haifa Almutairi

School of Physics, Mathematics and Computing

Research output: Thesis › Doctoral Thesis

162 Downloads (Pure)

Abstract

An analysis of the sequence of sleep stages can uncover the presence of sleep disorders. This thesis aims to focus on three key research problems related to sleep. Firstly, it focuses on the classification of sleep stages using a combination of signals and deep learning models. Secondly, this thesis detects obstructive sleep apnoea (OSA) from electrocardiography (ECG) signals using deep learning methods. The third research problem addressed in this thesis is detection of periodic leg movements (PLM) and SDB from NREM stage by using a combination of signals and deep learning models.

Original language	English
Qualification	Doctor of Philosophy
Awarding Institution	The University of Western Australia
Supervisors/Advisors	Datta, Amitava, Supervisor Hassan, Mubashar, Supervisor
Award date	18 Jul 2024
DOIs	https://doi.org/10.26182/ex2c-3z62
Publication status	Unpublished - 2024

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10.26182/ex2c-3z62

THESIS - DOCTOR OF PHILOSOPHY - ALMUTAIRI Haifa Mohammed H - 2024
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2 Conference paper
1 Article
1 Review article

Detecting Sleep Disorders from NREM Using DeepSDBPLM
Almutairi, H., Hassan, G. M. & Datta, A., 2024, Proceedings of the 12th International Conference on Robotics, Vision, Signal Processing and Power Applications. Ahmad, N. S., Mohamad-Saleh, J. & Teh, J. (eds.). Singapore: Springer Science + Business Media, p. 459-467 9 p. (Lecture Notes in Electrical Engineering; vol. 1123 LNEE).
Research output: Chapter in Book/Conference paper › Conference paper › peer-review
Machine-Learning-Based-Approaches for Sleep Stage Classification Utilising a Combination of Physiological Signals: A Systematic Review
Almutairi, H., Hassan, M. & Datta, A., 15 Dec 2023, In: Applied Sciences (Switzerland). 13, 24, 24 p., 13280.
Research output: Contribution to journal › Review article › peer-review

Open Access
3 Citations (Scopus)
Classification of Obstructive Sleep Apnoea from single-lead ECG signals using convolutional neural and Long Short Term Memory networks
Almutairi, H., Hassan, G. M. & Datta, A., Aug 2021, In: Biomedical Signal Processing and Control. 69, 102906.
Research output: Contribution to journal › Article › peer-review
38 Citations (Scopus)

Cite this

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title = "Improving sleep health with deep learning: automated classification of sleep stages and detection of sleep disorders",

abstract = "An analysis of the sequence of sleep stages can uncover the presence of sleep disorders. This thesis aims to focus on three key research problems related to sleep. Firstly, it focuses on the classification of sleep stages using a combination of signals and deep learning models. Secondly, this thesis detects obstructive sleep apnoea (OSA) from electrocardiography (ECG) signals using deep learning methods. The third research problem addressed in this thesis is detection of periodic leg movements (PLM) and SDB from NREM stage by using a combination of signals and deep learning models.",

keywords = "Classification, sleep stages, sleep apnoea, periodic leg movement, detection, deep learning",

author = "Haifa Almutairi",

year = "2024",

doi = "10.26182/ex2c-3z62",

language = "English",

school = "The University of Western Australia",

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AB - An analysis of the sequence of sleep stages can uncover the presence of sleep disorders. This thesis aims to focus on three key research problems related to sleep. Firstly, it focuses on the classification of sleep stages using a combination of signals and deep learning models. Secondly, this thesis detects obstructive sleep apnoea (OSA) from electrocardiography (ECG) signals using deep learning methods. The third research problem addressed in this thesis is detection of periodic leg movements (PLM) and SDB from NREM stage by using a combination of signals and deep learning models.

KW - Classification

KW - sleep stages

KW - sleep apnoea

KW - periodic leg movement

KW - detection

KW - deep learning

U2 - 10.26182/ex2c-3z62

DO - 10.26182/ex2c-3z62

M3 - Doctoral Thesis

ER -

Improving sleep health with deep learning: automated classification of sleep stages and detection of sleep disorders

Abstract

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Research output

Detecting Sleep Disorders from NREM Using DeepSDBPLM

Machine-Learning-Based-Approaches for Sleep Stage Classification Utilising a Combination of Physiological Signals: A Systematic Review

Classification of Obstructive Sleep Apnoea from single-lead ECG signals using convolutional neural and Long Short Term Memory networks

Cite this