Abnormal breath sounds like wheezes, crackles and stridor at times manifest similar morphologies and pathological features of lung airways obstruction. This may pose problems to proper diagnosis and evaluation of the underlying respiratory condition by human auscultation. In this study, the authors experimented with Time-Frequency threshold-dependent (TFTD) algorithm for detection and classification of breath sounds based on Smartphone. The TFTD algorithm computes important and distinct features of each breath sound using spectro-temporal analysis of recorded lung sounds which can enhance qualitative measurement and quantitative indexing of different respiratory sounds. Several algorithms which run exclusively on desktop computers have been developed for detecting and analyzing specific lung sounds such as wheezes. However, few attempts have been made to perform such analysis on portable devices like mobile phones due to computational complexities and high power consumption associated with the analyses. Our experimental results demonstrate that recent smartphones with improved computational capacity are able to provide comparative performance on analysis of respiratory signals. Furthermore, these phones can serve as convenient tools for measuring and detecting early signs of pulmonary disorders particularly at home and during ambulatory care services where conventional and specialized medical devices may not be accessible.

Respiratory sounds smartphone auscultation detection abnormal algorithm analysis computational capacity.