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large amount of low frequency components. This can
be solved by taking recording at positions with low
heart sound to respiratory sound amplitude ratio, or
preprocessing using a notch filter to suppress the ef-
fect of heartbeat. However, the algorithm is immune
to other ambient noises due to the wide spectrum oc-
cupied by the noises.
0 1 2 3 4 5 6 7 8
x 10
4
−0.4
−0.2
0
0.2
0.4
0.6
Samples
Normal Adult Tracheal Breath
0 0.5 1 1.5 2 2.5 3
x 10
4
−0.4
−0.2
0
0.2
0.4
0.6
Samples
Noraml Infant Tracheal Breath
3 3.5 4 4.5 5 5.5 6 6.5 7
x 10
4
−0.4
−0.2
0
0.2
0.4
0.6
Samples
Expiratory Mild Wheeze
0 1 2 3 4 5 6 7 8 9 10
x 10
4
−0.4
−0.2
0
0.2
0.4
0.6
Samples
Inspiratory Stridor and Expiratory Moderate Wheeze
0 1 2 3 4 5 6 7 8
x 10
4
−0.4
−0.2
0
0.2
0.4
0.6
Samples
Inspiratory Stridor and Expiratory Severe Wheeze
(a)
(b)
(c)
(d)
(e)
Figure 3: The segmentation results displayed with original
signal waveform for (a)-(b) normal tracheal breath of adult/
infant; (c) expiratory mild wheeze; (d)-(e) inspiratory stri-
dor and expiratory moderate/ severe wheeze.
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APPLICATION OF WALSH TRANSFORM BASED METHOD ON TRACHEAL BREATH SOUND SIGNAL
SEGEMENTATION
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