Authors:
João Teixeira
;
Luís Teixeira
;
João Fonseca
and
Tiago Jacinto
Affiliation:
University of Porto, Portugal
Keyword(s):
Asthma, Breath, COPD, Machine Learning, Signal Processing, Smartphone, Spirometry.
Related
Ontology
Subjects/Areas/Topics:
Biomedical Engineering
;
Cloud Computing
;
Decision Support Systems
;
Design and Development Methodologies for Healthcare IT
;
Distributed and Mobile Software Systems
;
e-Health
;
Health Engineering and Technology Applications
;
Health Information Systems
;
Mobile Technologies
;
Mobile Technologies for Healthcare Applications
;
Neural Rehabilitation
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition and Machine Learning
;
Platforms and Applications
;
Software Engineering
Abstract:
Worldwide, over 250 million people are affected by chronic lung conditions such as Asthma and COPD.
These can cause breathlessness, a harsh decrease in quality of life and, if not detected and duly managed, even
death. In this paper, we aim to find the best and most efficient combination of signal processing and machine
learning approaches to produce a smartphone application that could accurately classify lung function, using
microphone recordings as the only input. A total of 61 patients performed the forced expiration maneuver
providing a dataset of 101 recordings. The signal processing comparison experiments were conducted in
a backward selection approach, reducing from 54 to 12 final envelopes, per recording. The classification
experiments focused first on differentiating Normal from Abnormal lung function, and second in multiple
lung function patterns. The results from this project encourage further development of the system.