Date of Award
Doctor of Philosophy
Computer-aided classification of respiratory small airways dysfunction is not an easy task. There is a need to develop more robust classifiers, specifically for children as the classification studies performed to date have the following limitations: 1) they include features derived from tests that are not suitable for children and 2) they cannot distinguish between mild and severe small airway dysfunction.
This Dissertation describes the classification algorithms with high discriminative capacity to distinguish different levels of respiratory small airways function in children (Asthma, Small Airways Impairment, Possible Small Airways Impairment, and Normal lung function). This ability came from innovative feature selection, where features were derived from the child-friendly and reliable Impulse Oscillometry (IOS) technique. The feature selection process included deep statistical analyses and a proposed novel invariance-based pre-processing approach in the study of IOS features. The results are 100% accurate, sensitive and specific to classify normal lung function vs. small airways dysfunction; and 92%- 95% accurate, 73%-100% sensitive, and 80%-100% specific for classifying a specific type of small airways dysfunction. These results are better than any of the previous computer- aided classification of small airways dysfunction results.
Received from ProQuest
Nancy Selene Avila
Avila, Nancy Selene, "Computer-Aided Classification Of Impulse Oscillometric Measures Of Respiratory Small Airways Function In Children" (2019). Open Access Theses & Dissertations. 37.