Catching Breath

Catching Breath: A model-based approach to non-invasive respiratory effort estimation during sleep

Finnsson, Eysteinn (TU Delft Mechanical, Maritime and Materials Engineering)

Hellendoorn, Hans (mentor)

Delft University of Technology

2017-09-19

Sleep is an immensely important biological function and dysregulated sleep can have severe detrimental effects on health and well-being. Over 80 sleep disorders have been classified and of those sleep apnoea is one of the most common, having a prevalence of at least 20 percent in the general population. Sleep apnoea belongs to a family of disorders called Sleep-Disordered Breathing (SDB) which are characterized by breathing difficulties that occur during sleep. The underlying causes for SDB can be difficult to pinpoint making it important to develop effective and efficient detection methods. Sleep disorders are detected in sleep studies using one of two standard setups, Polysomnography (PSG) or Polygraphy (PG). A sleep study involves measuring physiological signals such as ventilation, heart rate and brain activity over a night of sleep. Recent studies have suggested that respiratory effort and the associated increase in negative intrathoracic pressure is a key variable for diagnosing and determining the optimal treatment for SDB. This variable is difficult to measure non-invasively and is not a part of a standard PG or PSG sleep study. The purpose of this MSc thesis was to explore and develop methods to infer respiratory effort from the non-invasive sensors used in a standard sleep study.
A model-based approach was proposed to create a mapping between respiratory movements, measured by Respiratory Inductance Plethysmography (RIP) and the intrathoracic pressure. A grey-box model of the respiratory system was constructed based on the underlying physiology and the existing lung model literature. The model parameters were identified for each patient using a novel two step identification procedure, fitting the model to recorded sleep study data. The resulting model was used to simulate a system response where the desired intrathoracic pressure could be estimated. In addition to the model-based method two alternative methods were put forward, to explore the relationship between RIP and respiratory effort in the frequency and entropy domains.
The modelling method and the two secondary methods were applied to recorded PSG data. Respiratory effort was observed via esophageal pressure, measured with esophageal manometry. The maximum performance of the modelling-, frequency domain- and entropy-method, evaluated as the Spearman correlation to the measured respiratory effort, were 0.53, 0.61 and 0.43 respectively. The performance of different methods varied greatly between patients in the dataset and at this point no method can conclusively be said to outperform another. This MSc thesis constitutes preliminary work in the development of non-invasive respiratory effort estimation methods. The results seem to validate the premise that there is a connection between respiratory movements and intrathoracic pressure. The modelling method is the most complicated of the three but has the most potential for improvements.

Sleep-disordered breathing
Upper airway physiology
Control of ventilation
Personalised medicine
System modelling
System identification
Respiratory effort
Esophageal pressure
Respiratory inductance plethysmography
Sleep apnoea

http://resolver.tudelft.nl/uuid:4689f875-614a-4235-a04f-27b2b38d33f6

2020-10-06

Student theses

master thesis

© 2017 Eysteinn Finnsson