PhD project by Alexander N. Olesen

Name: Alexander N. Olesen
Project Title: Deep Learning Methods for Clinical Sleep Analysis
Group: Biomedical Signal Processing and AI
Supervisor: Helge B.D. Sørensen
Co-Supervisors: Poul Jørgen Jennum and Emmanuel Mignot

Project Description:
Sleep disorders are common in the general population and have major implications for personal health and mortality including increased risk of cardiovascular, metabolic, and psychiatric complications, while also being a major economic burden on society.

The current standard for sleep disorder diagnosis is based on visual analysis of brain activity (EEG), eye movements (EOG), muscle activity (EMG) and cardiorespiratory variables recorded during sleep. This process is expensive, timeconsuming, and prone to subjective interpretation of analysis guidelines. Introducing automated analysis methods in the clinics has the potential to overcome these issues by virtue of being fast, consistent, and objective.

This thesis proposes new automatic methods for clinical sleep analysis based on artificial intelligence algorithms. First, two models for automatic sleep stage classification based on deep neural networks and EEG, EOG, and EMG data are proposed. Such models are crucial, as the dynamics of sleep stages can be indicative of certain sleep disorders. Second, a model for detection of sleep microevents using raw EEG, EOG, EMG, respiratory variables, and state-of-the-art computer vision algorithms is presented, which can precisely annotate several different types of events. Excessive amounts of sleep micro-events can also be indicative of certain sleep disorders, making such a model valuable for clinical use. Third, a model is presented for fast and accurate classification of narcolepsy, a sleep disorder where the basic mechanisms regulating sleep and wake are disrupted. This model was able to identify patients with narcolepsy as well as current gold standard methods, while being faster and cheaper in use.

In conclusion, this thesis presents new automatic methods for clinical sleep analysis based on artificial intelligence. Compared to current methods, the proposed models have the advantage of being faster and more reliable, allowing clinicians an increased focus on patient care.