A Comparison of SVM and GMM-Based Classifier Configurations for Diagnostic Classification of Pulmonary Sounds

• Published in: IEEE transactions on biomedical engineering Vol. 62; no. 7; pp. 1768 - 1776
• Main Authors: Sen, Ipek, Saraclar, Murat, Kahya, Yasemin P.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.07.2015
• Subjects: Data models; Diseases; Humans; Lung Diseases - diagnosis; Lung Diseases - physiopathology; Lungs; Normal Distribution; Pathology; pulmonary sounds; Reactive power; Respiratory Sounds - classification; Respiratory Sounds - physiopathology; Signal Processing, Computer-Assisted; Spirometry - methods; Support Vector Machine; Support vector machine (SVM); Support vector machines; vector autoregressive (VAR) model; Vectors; Diagnostic classification; Gaussian mixture model (GMM); pulmonary sounds; support vector machine (SVM); vector autoregressive (VAR) model
• ISSN: 0018-9294; 1558-2531; 1558-2531
• DOI: 10.1109/TBME.2015.2403616

Goal: The aim of this study is to find a useful methodology to classify multiple distinct pulmonary conditions including the healthy condition and various pathological types, using pulmonary sounds data. Methods: Fourteen-channel pulmonary sounds data of 40 subjects (healthy and pathological, where the pathologies are of obstructive and restrictive types) are modeled using a second order 250-point vector autoregressive model. The estimated model parameters are fed to support vector machine and Gaussian mixture model (GMM) classifiers which are used in various configurations, resulting in eight different methodologies in total. Results: Among the eight methodologies, the hierarchical GMM classifier yields the best performance with a total correct classification rate of 85%, where the term hierarchical refers here to first classifying the data into healthy and pathological classes, then the pathological class into obstructive and restrictive types. Both the sensitivity and specificity for the healthy versus pathological classification at the first stage of hierarchy are 90%. Conclusion: The newly proposed methodologies provide improved results compared to the previous study. The hierarchical framework is suggested for diagnostic classification of pulmonary sounds, although the algorithms are still open for further improvements. Significance: This study proposes new methodologies for diagnostic classification of pulmonary sounds, and suggests using a hierarchical framework for the first time.