Towards Sleep Data Science: Objective Analysis of Sleep Disorders Using Machine Learning Techniques

In recent years there has been an expansion in the availability of technologies to monitor sleep, however, research into sleep has been restricted by techniques developed early in the sleep science field. Sleep studies are widely conducted, but there are key challenges arising from the differences in equipment, data collection and processing, the confidential nature of medical studies, and the subjective, manual and labour-intensive sleep scoring and measurement. This motivates the need to develop objective, data-driven machine learning methods to analyse sleep and sleep disorders. We developed automated and scalable methods, employing machine learning techniques, to provide an objective measurement of sleep, opening up new avenues for large-scale sleep analysis. We proposed and evaluated machine learning techniques for sleep disorder detection and phenotyping to further objective analysis in sleep science. We explored the two most common sleep disorders: sleep apnea and insomnia disorder. Sleep apnea is characterised by a reduction or a stop in breathing temporarily during sleep and involves the use of manual analysis of multiple Polysomnography (PSG) channels. We explored the use of a single respiratory channel, the nasal airflow, to detect sleep apnea events using Convolutional Neural Network (CNN) with two types of signals: the raw 1-D signal and with a 2-D wavelet spectrogram representation of the signal. We achieved a high accuracy with the 2-D CNN achieving a higher accuracy (79.8%) than the 1-D CNN (77.6%), improving over previous work on detection of sleep apnea events by applying an objective analysis of the nasal airflow channel. The investigation of insomnia disorder started with a cluster analysis for potential phenotyping using physiology-based Quantitative Electroencephalography (qEEG) parameters based on a neural-field brain model. Motivated by previous work into insomnia disorder, we developed a novel, data-driven approach to group people with insomnia from the Insomnia-100 dataset successfully, with three meaningful clusters found: insomnia with low beta iv ABSTRACT v frequency of peak power, insomnia with high delta peak power and insomnia with low delta peak power. We also discuss that the most informative features identified were from sleep stage 3: the peak power in the delta band for the O1, F3 and C3 channels, and then the peak frequency in the beta band in the O1, F3, and C3 channels, which is consistent with previous insomnia studies. One difficulty when investigating sleep disorders like insomnia disorder is the manual subjective discrete sleep staging, which can be inconsistent across various datasets. This poses difficulties for large-scale machine learning algorithms that classifying those sleep stages. We investigated the use of an alternative method to sleep staging with sleep trajectories that provide a continuous parameterised trajectory of sleep that is based on an existing neural-field brain model. We used a data-driven approach using a multi-class Conditional Deep Convolutional GAN (CDCGAN) to distinguish between people with insomnia and good sleepers, based on sleep trajectories. This was done by using the CDCGAN as a semi-supervised classifier on 20-minute subtrajectories of sleep to learn the characteristics of insomnia disorder and good sleepers using two datasets. We had promising results with the CDCGAN with an accuracy of 74.5% on a hold-out test set, substantially outperforming alternative methods used for comparison. While sleep trajectories are a promising method to quantify sleep, they are very time-consuming and computationally expensive and so are not suitable for large-scale datasets. Therefore, we proposed a deep neural network as a surrogate model to approximate the sleep trajectories in a much faster way in an individual model and ensemble model approach. To demonstrate the effectiveness of the surrogate model, we investigated the use of the trained surrogate ensemble to classify people with insomnia disorder, achieving Area Under the Receiver Operating Characteristics (AUROC) of 0.94 and an accuracy of 89.2%. This research provides methods for analysing sleep data, with specific demonstrations for the most common sleep disorders, through automated, scalable machine learning techniques. Furthermore, by solving fundamental limitations of more sophisticated models, new directions are opened up for analysis of sleep using a sleep trajectories representation