Affiliations 

  • 1 School of Mechatronic Engineering, Universiti Malaysia Perlis (UniMAP), 02600, Arau, Perlis, Malaysia. Electronic address: engr.fizza@yahoo.com
  • 2 Centre for Telecommunication Research & Innovation (CeTRI), Fakulti Kejuruteraan Elektronik & Kejuruteraan Komputer (FKEKK), Universiti Teknikal Malaysia Melaka (UTeM), 76100, Durian Tunggal, Melaka, Malaysia. Electronic address: kenneth@utem.edu.my
  • 3 School of Mechatronic Engineering, Universiti Malaysia Perlis (UniMAP), 02600, Arau, Perlis, Malaysia. Electronic address: lckiang@unimap.edu.my
  • 4 College of Engineering, AMA International University, 8041, Salamabad, Bahrain. Electronic address: prkmect@gmail.com
Comput Biol Med, 2019 01;104:52-61.
PMID: 30439599 DOI: 10.1016/j.compbiomed.2018.10.035

Abstract

OBJECTIVE: This study aimed to investigate and classify wheeze sounds of asthmatic patients according to their severity level (mild, moderate and severe) using spectral integrated (SI) features.

METHOD: Segmented and validated wheeze sounds were obtained from auscultation recordings of the trachea and lower lung base of 55 asthmatic patients during tidal breathing manoeuvres. The segments were multi-labelled into 9 groups based on the auscultation location and/or breath phases. Bandwidths were selected based on the physiology, and a corresponding SI feature was computed for each segment. Univariate and multivariate statistical analyses were then performed to investigate the discriminatory behaviour of the features with respect to the severity levels in the various groups. The asthmatic severity levels in the groups were then classified using the ensemble (ENS), support vector machine (SVM) and k-nearest neighbour (KNN) methods.

RESULTS AND CONCLUSION: All statistical comparisons exhibited a significant difference (p 

* Title and MeSH Headings from MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.