University of Twente Student Theses


Detection of dysfunctional breathing and uncontrolled asthma in children using respiratory sound recordings

Massa, M. (2021) Detection of dysfunctional breathing and uncontrolled asthma in children using respiratory sound recordings.

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Abstract:Dysfunctional breathing (DB) is a common respiratory condition in children which greatly affects a child's quality of life. Symptoms of DB are often similar to symptoms encountered with exercise-induced bronchoconstriction, which is indicative of uncontrolled asthma. As these two conditions require different treatment approaches, differentiation is indispensable. At this moment, DB is only identified by exclusion of uncontrolled asthma and other possible causes and this procedure requires demanding repetitive forced breathing manoeuvres. In this study, the potential of respiratory sounds recorded during an exercise challenge test (ECT) to detect DB and uncontrolled asthma was investigated. Literature research was performed and four clinicians assessed sound recordings made with a directional microphone during ECTs to determine relevant characteristics in respiratory sounds for detecting DB and uncontrolled asthma. Test measurements with healthy individuals and pediatric patients were analyzed to assess the quality of the sound recordings and to discover what information can be extracted from the recordings. The microphone settings were changed and a microphone was added to the measurement setup to improve the signal acquisition. Machine learning algorithms were applied to 28 sound recordings in order to develop a classification algorithm. The 28 recordings were from children with either DB, uncontrolled asthma or no established respiratory diagnosis. 32% of the sound recordings was correctly classified by clinicians. According to literature and clinicians, the nature of adventitious sounds and the moment at which symptoms occur in a sound recording were the most important characteristics for detecting DB and uncontrolled asthma. The test measurements resulted into signals with much ambient noise in which the discernability of a respiratory pattern varied amongst recordings. The adjustments in the measurement setup did not improve the quality of recordings. Recordings made after an ECT did show clear respiratory patterns. The machine learning approach did not result in a proper classification algorithm. The results of the study imply that at this point, sound recordings made during an ECT cannot be used to detect DB and uncontrolled asthma in children. Future research should focus on further improving the measurement setup to minimize disturbing sounds and extending the existing knowledge on relevant characteristics in sounds recordings. Higher quality sounds and this extended knowledge may provide objective detection of pediatric DB and uncontrolled asthma in the future.
Item Type:Essay (Master)
Faculty:EEMCS: Electrical Engineering, Mathematics and Computer Science
Subject:44 medicine, 50 technical science in general, 54 computer science
Programme:Biomedical Engineering MSc (66226)
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