Abstract:
Cardiac auscultation is a method used to listen heart sound. Condition of the heart can be
predicted with cardiac auscultation because heart generates a specific rhythm of sound and any
changes in the rhythm of the heart sound may be due to abnormalities of heart. Auscultation is
an easy way to diagnose heart abnormalities; however, it needs training and years of
physician’s experience to diagnose heart and identify any heart abnormalities. With years of
experience it is still difficult to analyse heart sound. The ability to automatically identify
abnormalities or at least support physician decision is relevant to ease the reach of medical
diagnosis using mobile or Digi-scope. The phonocardiogram PCG signal are collected with the
help of mobile or electronic stethoscope. Heart beat detection is very important in these signals
for segmentation of fundamental heart sound. Finding heart rhythm in PCG signals is a
challenging task due to the presence of noise i.e. external environmental noise or internal body
noise. Another challenging task is segmentation of S1 and S2 heart sound. This thesis presents
a novel approach for segmentation of S1 and S2 heart sounds by using some of heart sounds
temporal and spectral features. Total of four features are extracted from these signals, in which
two features are temporal feature and two are its spectral feature. K-mean clustering algorithm
is used for segmentation of S1 and S2 on the bases of these features. PASCAL PCG heart sound
dataset is used for testing our algorithm. Our method differentiates between S1 and S2 heart
sounds to great extent and also improves the results.
