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Feature selection of voluntary cough patterns for detecting lung diseases.

Authors
Abaza-AA; Mahmoud-AM; Day-JB; Goldsmith-WT; Afshari-AA; Reynolds-JS; Frazer-DG
Source
Proceedings of the 25th Southern Biomedical Engineering Conference, May 15-17, 2009, Miami, Florida. IFMBE Proceedings 24. McGoron AJ, Li C-Z, Lin W-C, eds., Berlin, Germany: Springer Verlag, 2009 May; 24:323-328
NIOSHTIC No.
20035559
Abstract
Cough is a classic symptom of respiratory disease. Airflow patterns produced during a cough represent a portion of the maximum expiratory flow-volume curve which has often been used to diagnose lung disorders. We have previously described a system for detecting lung disease that was based on both the airflow and the acoustic properties of a voluntary cough. The system used 26 representative features of the cough airflow measurements and 111 of the cough sound pressure wave. Redundancy within the feature set was eliminated using principle component analysis (PCA). A classifier was developed based on the projections of the principle components. The objective of this study was to determine the effect of eliminating irrelevant features of the cough prior to the PCA classifier to maintain, or even improve, overall system accuracy. Four types of feature selection methods were examined. They included forward sequential selection (SFS), backward sequential selection (SBS), sequential plus l-take away r (SLR), and genetic algorithm (GA) techniques. Three coughs from 112 individual with and without lung disease were classified using this system, and the results were compared with the diagnosis of pulmonary physicians. The overall classification accuracy was 94% when no attempt was made to optimize the feature set. This can be compared with the results of the genetic algorithm which used only 59 out of 137 features and increased the average classifier accuracy to 97.6%. The accuracy (number of features) using the above-mentioned algorithms was; 97.32% (35) for the SFS; 96.71% (111) for the SBS; 97.08 % (42) for the LRS; and 97.62% (59) for the GA. In conclusion, all feature selection methods improved the classification accuracy while simultaneously reducing the number of features.
Keywords
Airborne-particles; Air-sampling-techniques; Analytical-methods; Breathing; Genetics; Inhalation-studies; Lung; Lung-disorders; Lung-irritants; Mathematical-models; Pulmonary-congestion; Pulmonary-disorders; Pulmonary-function; Pulmonary-system; Pulmonary-system-disorders; Respiratory-infections; Respiratory-irritants; Respiratory-system-disorders; Statistical-analysis; Author Keywords: Voluntary Cough; Cough Analysis; Feature Selection; Pattern Classification
Contact
HELD, National Institute for Occupational Safety and Health, 1095 Willowdale Road, Morgantown, WV 26505, USA
Publication Date
20090515
Document Type
Conference/Symposia Proceedings
Editors
McGoron-AJ; Li-C-Z; Lin-W-C
Fiscal Year
2009
NTIS Accession No.
NTIS Price
ISBN No.
9783642016967
ISSN
1680-0737
NIOSH Division
HELD
Priority Area
Healthcare and Social Assistance
Source Name
Proceedings of the 25th Southern Biomedical Engineering Conference, May 15-17, 2009, Miami, Florida
State
WV
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