Paper Info
Title
Towards Passive Assessment of Pulmonary Function from Natural Speech Recorded Using a Mobile Phone
Abstract
Chronic obstructive pulmonary disease (COPD) and asthma are the most common respiratory diseases that impact millions of people worldwide annually. With advances in mobile computing and machine learning techniques, there has been increased interest in using mobile devices to monitor pulmonary diseases. Nevertheless, the current state-of-the-art technology requires active involvement and high-effort input from the users, impeding continuous monitoring of pulmonary conditions. In this work, two algorithms are proposed for passive assessment of pulmonary condition: one for detection of obstructive pulmonary disease and the other for estimation of the pulmonary function in terms of FEV <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> /FVC ratio, which is an established clinical metric. The algorithms were developed and validated using the data sets from two studies: research study (healthy=40, pathological=91) and in-clinic study (healthy=10, pathological=60). From the cross-study validation where a classifier was trained on the research data set and tested on the in-clinic data set, the detection accuracy of the pathological class was obtained as 73.7% and the F1 score was 84.5% (87.2% precision and 82.0% recall). In our regression analysis, the FEV <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> /FVC ratio was predicted with a mean absolute error of 8.6%. Our analysis shows promising results and this work presents a meaningful milestone towards the passive assessment of pulmonary functions from spontaneous speech collected from a mobile phone.
Year
DOI
Venue
2020
10.1109/PerCom45495.2020.9127380
2020 IEEE International Conference on Pervasive Computing and Communications (PerCom)
Keywords
DocType
ISSN
Mobile Computing,Pulmonary Assessment,COPD,Asthma,Natural Speech,Inspiratory Sound
Conference
2474-2503
ISBN
Citations 
PageRank 
978-1-7281-4657-7
0
0.34
References 
Authors
0
7
Name
Order
Citations
PageRank
Keum San Chun100.34
Viswam Nathan222.45
Korosh Vatanparvar313416.20
Ebrahim Nemati48415.30
Md. Mahmudur Rahman51716.00
Erin Blackstock600.34
Jilong Kuang73817.00