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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
1

Evaluating acoustic variables with clinical assessments in patients with asthma and chronic obstructive pulmonary disease

Mendez-Lozano, Nancy 15 July 2020 (has links)
OBJECTIVE: The purpose of this study is to determine if there are any acoustic variables that can determine compromised lung function in patients with asthma and COPD. METHODS: This study involved using mobile and wearable technology to record voice and respiratory changes during various speaking and breathing tasks before and after administration of albuterol. Collaborators at Samsung Research America, Inc. used algorithms to measure pause time, pause frequency, respiratory rate, and inhale:exhale ratio. These variables were correlated with spirometry values before and after albuterol to assess clinical significance. RESULTS: We identified several acoustic markers that significantly correlate with lung function in patients with asthma and COPD. In particular, we found that the ratio of the one-second forced expiratory volume to forced vital capacity (FEV1/FVC) after administration of albuterol significantly correlated with the inhale:exhale ratio in asthma patients during the tidal breathing task. The post-albuterol FEV1/FVC significantly correlated with the inhale:exhale ratio in COPD patients during the supine breathing task. The pre-albuterol FVC significantly correlated with the pause frequency in asthma patients during the scripted speech task. CONCLUSION: The results in this study indicate that pause frequency and inhale:exhale ratio may be important biomarkers for identifying a respiratory illness, such as asthma and COPD. More research needs to be done using digital health to monitor disease symptoms with a larger sample size.

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