Abstract:
The segmentation of heart sounds plays a major role in the automatic classification and analysis of a Phonocardiogram (PCG). Segmentation is the
processes by which a heart cycle of PCG is segmented into its four sounds
(S1, Systole, S2 and Diastole). In this project we aim to build a framework
for classification and segmentation of PCG sounds. Objective was to employ
neural network based approaches for sequence modeling and prediction such
as RNNs and LSTMs for this purpose. The proposed algorithm was long
short term memory network that consist forget gate input gate and output
gate. We used the long short term memory algorithm to find the emission
probability matrix this network was combined with the hidden Markov model
for the segmentation of the heart sound and we get 80 % of F1 score. The
second experiment was to use the LSTM network solely for the whole seg mentation processes, for that we have used the 792 audios of different lengths
from 5 sec to 90 sec. By LSTM we get the 80% average F1 score for S1 sound
and 82% average score for S2 sound. The proposed method is useful for the
practical applications because it has been trained on the both noisy and clean
data. That shows that if the noise is added while recording the heart sounds
the algorithm can give good segmentation results as compare to the previous
segmentation methods
