scholarly journals Crackle Pitch and Rate Do Not Vary Significantly During a Single Automated-Auscultation Session in Patients With Pneumonia, Congestive Heart Failure, or Interstitial Pulmonary Fibrosis

2011 ◽  
Vol 56 (6) ◽  
pp. 806-817 ◽  
Author(s):  
A. Vyshedskiy ◽  
S. Ishikawa ◽  
R. L. Murphy
Keyword(s):  
Heart Failure ◽  
Congestive Heart Failure ◽  
Pulmonary Fibrosis ◽  
Interstitial Pulmonary Fibrosis ◽  
Automated Auscultation

Transmission of Crackles in Patients With Interstitial Pulmonary Fibrosis, Congestive Heart Failure, and Pneumonia

CHEST Journal ◽  
2005 ◽  
Vol 128 (3) ◽  
pp. 1468-1474 ◽  
Author(s):  
Andrey Vyshedskiy ◽  
Francisco Bezares ◽  
Rozanne Paciej ◽  
Margo Ebril ◽  
John Shane ◽  
...  
Keyword(s):  
Heart Failure ◽  
Congestive Heart Failure ◽  
Pulmonary Fibrosis ◽  
Interstitial Pulmonary Fibrosis

scholarly journals Automated Analysis of Crackles in Patients with Interstitial Pulmonary Fibrosis

2011 ◽  
Vol 2011 ◽  
pp. 1-7 ◽  
Author(s):  
B. Flietstra ◽  
N. Markuzon ◽  
A. Vyshedskiy ◽  
R. Murphy
Keyword(s):  
Heart Failure ◽  
Pulmonary Fibrosis ◽  
Automated Analysis ◽  
Support Vector ◽  
Inappropriate Therapy ◽  
Lung Sound ◽  
Distinctive Features ◽  
Interstitial Pulmonary Fibrosis ◽  
Machine Learning Methods ◽  
Vector Machines

Background. The crackles in patients with interstitial pulmonary fibrosis (IPF) can be difficult to distinguish from those heard in patients with congestive heart failure (CHF) and pneumonia (PN). Misinterpretation of these crackles can lead to inappropriate therapy. The purpose of this study was to determine whether the crackles in patients with IPF differ from those in patients with CHF and PN.Methods. We studied 39 patients with IPF, 95 with CHF and 123 with PN using a 16-channel lung sound analyzer. Crackle features were analyzed using machine learning methods including neural networks and support vector machines.Results. The IPF crackles had distinctive features that allowed them to be separated from those in patients with PN with a sensitivity of 0.82, a specificity of 0.88 and an accuracy of 0.86. They were separated from those of CHF patients with a sensitivity of 0.77, a specificity of 0.85 and an accuracy of 0.82.Conclusion. Distinctive features are present in the crackles of IPF that help separate them from the crackles of CHF and PN. Computer analysis of crackles at the bedside has the potential of aiding clinicians in diagnosing IPF more easily and thus helping to avoid medication errors.

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