Summary
Introduction: This article is part of the Focus Theme of Methods of Information in Medicine on “Biosignal Interpretation: Advanced Methods for Studying Cardiovascular and Respiratory
Systems”.
Objectives: This work proposes an algorithm for diagnostic classification of multi-channel respiratory
sounds.
Methods: 14-channel respiratory sounds are modeled assuming a 250-point second order vector
autoregressive (VAR) process, and the estimated model parameters are used to feed
a support vector machine (SVM) classifier. Both a three-class classifier (healthy,
bronchi ectasis and interstitial pulmonary disease) and a binary classifier (healthy
versus pathological) are considered.
Results: In the binary scheme, the sensitivity and specificity for both classes are 85% ±
8.2%. In the three-class classification scheme, the healthy recall (95% ± 5%) and
the interstitial pulmonary disease recall and precision (100% ± 0% both) are rather
high. However, bronchiectasis recall is very low (30% ± 15.3%), resulting in poor
healthy and bronchiectasis precision rates (76% ± 8.7% and 75% ± 25%, respectively).
The main reason behind these poor rates is that the bronchiectasis is confused with
the healthy case.
Conclusions: The proposed method is promising, nevertheless, it should be improved such that other
mathematical models, additional features, and/or other classifiers are to be experimented
in future studies.
Keywords
Respiratory sounds - vector autoregressive (VAR) model - support vector machine (SVM)
classifier - diagnostic classification
