Automatic wheeze detection based on auditory modelling.
Automatic wheeze detection has several potential benefits compared with reliance on human auscultation: it is experience independent, an automated historical record can easily be kept, and it allows quantification of wheeze severity. Previous attempts to detect wheezes automatically have had partial success but have not been reliable enough to become widely accepted as a useful tool. In this paper an improved algorithm for automatic wheeze detection based on auditory modelling is developed, called the frequency- and duration-dependent threshold algorithm. The mean frequency and duration of each wheeze component are obtained automatically. The detected wheezes are marked on a spectrogram. In the new algorithm, the concept of a frequency- and duration-dependent threshold for wheeze detection is introduced. Another departure from previous work is that the threshold is based not on global power but on power corresponding to a particular frequency range. The algorithm has been tested on 36 subjects, 11 of whom exhibited characteristics of wheeze. The results show a marked improvement in the accuracy of wheeze detection when compared with previous algorithms.
Qiu
Y
Y
Department of Mechanical Engineering, University of Glasgow, Glasgow, UK.
Whittaker
A R
AR
Lucas
M
M
Anderson
K
K
D000465:Algorithms; D001185:Artificial Intelligence; D001307:Auditory Perception / Q000502:physiology*; D001314:Auscultation / Q000379:methods*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D006801:Humans; D008954:Models, Biological; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012135:Respiratory Sounds / Q000145:classification / Q000503:physiopathology*; D012680:Sensitivity and Specificity; D013018:Sound Spectrography / Q000379:methods*