Improving Sleep Health with Deep Learning: Automated Classification of Sleep Stages and Detection of Sleep Disorders
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Date
2024-07-07
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Saudi Digital Library
Abstract
Sleep consumes roughly one-third of a person’s lifetime, and it is characterized by distinct stages within sleep
cycle. The sequence of these stages at night provides insights into the quality of sleep. Poor sleep quality can
have numerous consequences, including drowsiness, reduced concentration, and fatigue. Beyond sleep quality,
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.
Sleep stages are categorized into five distinct stages, namely Wake (W), non-rapid eye movement (NREM)
stages comprising N1, N2, and N3, and rapid eye movement (REM) stage. Throughout the duration of sleep,
individuals experience multiple cycles of sleep stages. Each cycle contains a standard allocation of each stage.
An unbalanced distribution of the stages can indicate the presence of sleep disorders. Previous studies primarily
classified sleep stages using a single channel of electroencephalography (EEG) signals. However, incorporating
a combination of signals from electromyography (EMG) and electrooculogram (EOG) alongside EEG data
provides additional features. These features extracted from muscle activity and eye movements during sleep,
thereby enhancing classification accuracy. In this thesis, a robust model called SSNet is proposed to accurately
classify sleep stages from a fusion of EEG, EMG, and EOG signals. This model combine convolutional neural
networks (CNNs) and long short-term memory (LSTM) networks to extract the salient features from various
physiological signals. The CNN architecture extracts spatial features from the input signals, while LSTM
architecture captures the temporal features present in signals. This study has obtained encouraging outcomes in
the classification of sleep stages through the fusion of physiological signals and deep learning techniques.
Secondly, this thesis aim to detect obstructive sleep apnoea (OSA) from electrocardiography (ECG) signals
using deep learning methods. Sleep disorder breathing (SDB) is categorized into three different types, which are
OSA, central sleep apnoea, and mixed sleep apnoea. OSA is the most common form of SDB that is characterized
by repeated interruptions in breathing during sleep, leading to fragmented sleep patterns and various health
complications. Previous studies developed feature engineering methods and machine learning models for the
detection of OSA. Feature engineering methods involve crafting relevant features to feed into machine learning
models. However, feature engineering is time-consuming and requires domain expertise. In contrast, deep
learning automatically extracts features from ECG signals for OSA detection, eliminating the need for manual
feature engineering methods. In this thesis, three deep learning architectures are proposed, including standalone
convolutional neural networks (CNN), CNN with long short-term memory (LSTM), and CNN with gated
recurrent unit (GRU). Through rigorous experimentation and evaluation, the combination of CNN and LSTM
architecture is the best-performing model for OSA detection. To further enhance the architecture’s performance,
the hyperparameters of the CNN and LSTM models were tuned and tested over a large dataset to validate their
effectiveness.
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. PLM is characterized by
involuntary leg movements during sleep. These movements can disrupt sleep and result in daytime sleepiness
with reduced quality of life. Detecting PLM and SDB events during NREM stage allows for quantifying the
severity of sleep disorders. Previous studies have focused on the development of signal-based models for detecting
PLM or SDB. However, the models lacked the ability to distinguish these events within specific sleep stages. To
address this problem, a novel deep learning architecture known as DeepSDBPLM is proposed. This architecture
aims to detect PLM and SDB events during the NREM stage. This architecture incorporates novel input features
called attention EMDRaw signals and utilizes a Residual Convolutional Neural Network (ResCNN) model.
This thesis presents experimental results using publicly available datasets to evaluate the performance of the
proposed deep learning models for classification of sleep stages, and detection of sleep disorders. The models
were evaluated standard metrics. It includes accuracy, sensitivity, specificity, and F1 score. The empirical results
establish the effectiveness of proposed approaches. The models can be a stepping stone towards more advanced
techniques.
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Keywords
Sleep, deep learning, classification, machine learning