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Multisensor data fusion for sleep apnea monitoring and classification

University of British Columbia

Sleep apnea is the most common type of sleep disorder that is related to breathing, amongst the adult population. Although laboratory polysomnography is the gold standard for the detection of apnea, wearable sleep monitoring devices are preferred due to many reasons such as comfort, monitoring in a familiar sleep environment, accessibility without delay, and low cost. It is important then to extract suitable features for the classification of apneic events as suitable for a wearable sleep monitoring device while maintaining the same accuracy levels as in laboratory polysomnography. This thesis first identifies suitable preprocessing and feature extraction techniques for standard biomedical signals monitored in laboratory polysomnography. Then it develops a feature-extraction technique and designs and implements a neural network for sleep apnea detection and classification of sleep stages. Composite Multiscale Sample Entropy (CMSE) is used as the feature extraction technique, in view of its desirable characteristics. The performance of the developed methodology is evaluated using true clinical data of sleep monitoring. The designed neural networks in the present work is found to have the ability to process and classify apneic events and sleep stages while maintaining the accuracy levels of sleep scoring in clinical polysomnography, which is the existing gold standard of sleep monitoring and scoring. The neural network used for classification of sleep stages may be subsequently incorporated as the input to a neural network for classifying apneic events. In addition, the thesis demonstrates the extent to which each individual signal that is monitored in polysomnography has the ability to independently detect apneic events. This would be useful in the implementation of a portable wearable device with clinical capability for sleep monitoring, which is the end objective of the current project.

Text

2019-07-31

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/66533

Mechanical Engineering

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/