Please use this identifier to cite or link to this item:
Full metadata record
|dc.contributor.author||João Pedro Teixeira||en|
|dc.contributor.author||Luís Filipe Teixeira||en|
|dc.description.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.||en|
|dc.title||Lung function classification of smartphone recordings comparison of signal processing and machine learning combination sets||en|
|Appears in Collections:||CTM - Articles in International Conferences|
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.