A new scheme for automatic classification of pathologic lung sounds
In this paper, a classification scheme has been proposed to classify crackles based on waveform features and frequency domain features. This purpose is very important in the analysis of respiratory disorders. In fact, morphological characters of crackles can be well represented by time amplitude distribution. Thus, they convey significant diagnostic information, for their precise timing in the respiratory cycle, their repeatability, and shape all mightily correlate with pulmonary diseases. The ability to analyze the acoustic patterns of these breathing-induced phenomena will enhance the expertise of the physiology and pathophysiology of respiratory disorders that can be very useful in clinical considerations.
Keywords: Lung sound, Classification, Crackles Fuzzy Logic, Fine, Coarse, Medium.
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ABOUT THE AUTHORS
Fatma Ayari
University of Tunis Elmanar, Electrical Engineering Department, National School of Engineers Tunis (ENIT), B.P.342, Le Belvedere, 1002 Tunis, Tunisia
Mekki Ksouri
University of Tunis Elmanar, Electrical Engineering Department, National School of Engineers Tunis (ENIT), B.P.342, Le Belvedere, 1002 Tunis, Tunisia
And Ali Alouani
Tennessee Technological University Electrical Engineering Department, , Po Box 5004, Cookeville, TN 38 505, USA,
Fatma Ayari
University of Tunis Elmanar, Electrical Engineering Department, National School of Engineers Tunis (ENIT), B.P.342, Le Belvedere, 1002 Tunis, Tunisia
Mekki Ksouri
University of Tunis Elmanar, Electrical Engineering Department, National School of Engineers Tunis (ENIT), B.P.342, Le Belvedere, 1002 Tunis, Tunisia
And Ali Alouani
Tennessee Technological University Electrical Engineering Department, , Po Box 5004, Cookeville, TN 38 505, USA,
