An Unsupervised Segmentation of Vocal Breath Sounds

Breathing is essential to human survival, which carries information about a person’s physiological and psychological state. Mostly breath sound boundaries are marked manually before being used for any task such as classification, spectral analysis, etc., which is very tedious. Various techniques have been proposed to segment breath sounds recorded at the chest, and trachea but vocal breath sounds (VBS) are under-explored. An unsupervised algorithm for VBS segmentation has been proposed in this work. Each breath phase in continuous breaths has been modeled using triangles, where the end points of triangles representing breath boundaries are estimated using dynamic programming. Data from 60 subjects (31 healthy, 29 asthmatic patients) having 307 breaths have been used. The proposed method’s performance was found to be comparable with the manually marked boundaries. Comparable asthmatic versus healthy subject mean(standard deviation) classification accuracy using manually marked and predicted boundaries are 75%(±11%) and 72%(±15%),respectively are found.