Computerized Diagnosis of Respira tory Disorders
Methods of Information in Medicine, 2014•thieme-connect.com
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 …
“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 …
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.
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