Abstract:
This paper proposes a robust method for wheeze
sound detection. The presented approach is based
on a time series regularity measure called sample
entropy of a time-frequency distribution (Gabor
Spectrogram). First, the respiratory sound signals
are segmented into their respective
inspiration/expiration phases for segment-wise
detection of wheeze sounds. Applying Gabor
Spectrogram to these extracted segments, timefrequency
representation of each segment is
obtained. From this representation, regularity of
each segment is determined using Sample Entropy.
A decision rule is then applied to sample entropy
sequences to determine whether wheeze or normal
sound. The accuracy of method is tested on wheeze
sounds with low and high intensity wheeze
inspirations/expirations segments of respiratory
sound signals. The experimental results reveal that
the overall detection accuracy is 86.25% for
inspiration and is 82.5% for expiration.
