Classification of segmented heart sounds with Artificial Neural Networks

Yıl 2018, Cilt: 6 Sayı: 4, 39 - 44, 31.12.2018

Ömer Deperliğlu

Öz

Nowadays
heart diseases are the first cause of human deaths. For this reason, many
studies have been carried out to reduce early diagnosis and death of heart
diseases. These studies are mostly about developing computer-aided diagnosis
systems by utilizing the developing technology. Some computer aided systems are
clinical decision support systems developed to more easily detect heart
diseases from heart sounds. These systems are used in the automatic analysis of
heart sounds based on the classification of heart sounds in general. Much of
the work done to diagnose heart diseases is to increase the success of
classification. Segmentation of heart sound signals is also one of the
frequently used methods to increase classification performance. In this study,
S1-S2 sounds were segmented using the resampled energy method and the
contribution to segmentation performance of the segment was examined. In
practice PASCAL Btraining data set which is widely used for heart diseases
application is used. The PASCAL Btraining data set contains three different
heart sounds such as normal, murmur, and extrasystole. Artificial Neural
Networks (ANN) were used to classify these sounds. For the comparison of the
obtained results, two classifications were made for the segmented and the
non-segmented sounds. As a result of the classification studies, the average
all accuracy of classification 84% was achieved in the non-segmented ANN study,
and the average all accuracy of classification 88.6% was obtained in the
segmented S1-S2 sounds ANN study. Thus, segmentation of heart sounds increased
the accuracy of classification by about 4.6%.

Anahtar Kelimeler

Heart sounds classification, artificial neural network, heart sounds segmentation, re-sampled signal energy, heart sounds.

Kaynakça

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