Using Time-series Breathing Patterns in Machine Learning Models to Classify Respiratory Diseases: An Initial Study

Gurpreet Singh; Aza Taha; Thanawin Trakoolwilaiwan; Augustine Tee; Joanne Yoong; Joanne Yoong; Malini Olivo

doi:10.1364/BODA.2021.JTu4A.12

Biophotonics Congress 2021
OSA Technical Digest (Optica Publishing Group, 2021),
paper JTu4A.12
•https://doi.org/10.1364/BODA.2021.JTu4A.12

Using Time-series Breathing Patterns in Machine Learning Models to Classify Respiratory Diseases: An Initial Study

Gurpreet Singh, Aza Taha, Thanawin Trakoolwilaiwan, Augustine Tee, Joanne Yoong, and Malini Olivo

Optics and the Brain 2021

Washington, DC United States
12–16 April 2021
ISBN: 978-1-943580-85-9

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Joint Poster Session I (JTu4A)

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G. Singh, A. Taha, T. Trakoolwilaiwan, A. Tee, J. Yoong, and M. Olivo, "Using Time-series Breathing Patterns in Machine Learning Models to Classify Respiratory Diseases: An Initial Study," in Biophotonics Congress 2021, C. Boudoux, K. Maitland, C. Hendon, M. Wojtkowski, K. Quinn, M. Schanne-Klein, N. Durr, D. Elson, F. Cichos, L. Oddershede, V. Emiliani, O. Maragò, S. Nic Chormaic, N. Pégard, S. Gibbs, S. Vinogradov, M. Niedre, K. Samkoe, A. Devor, D. Peterka, P. Blinder, and E. Buckley, eds., OSA Technical Digest (Optica Publishing Group, 2021), paper JTu4A.12.

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Abstract

Lung function classifies respiratory diseases. However, obtaining them with spirometry is difficult. We present an easy method that combines breathing patterns and machine learning to classify healthy from respiratory conditions at accuracy of 97.7%.

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