Abstract:
Nowadays, cardiovascular diseases have been a major cause of death in the world. The heart
sound is still used as a primary tool for screening and diagnosing many pathological conditions of the human
heart. The abnormality in the heart sounds starts appearing much earlier than the symptoms of the disease.
Methods: In this paper, the Phonocardiography signal has been studied and classified into three classes,
namely normal signal, murmur signal and extra sound signal. A total of 15 features from different domains
have been extracted and then reduced to 7 features. The features have been selected on the basis of
correlation based feature selection (CFS) technique. The selected features are used to classify the signal
into the predefined classes using multi-class support vector machine (SVM) classifier. The performance of
the proposed denoising algorithm is evaluated using the signal to noise ratio (SNR), percentage root means
square difference, and root mean square error.
Results: The experimental result shows that the 4th level of decomposition for the Db10 wavelets gives
the highest SNR values when using the soft and hard thresholding. The overall accuracy, sensitivity and
specificity of the developed algorithm is 97.96%, 97.92% and 98.0% respectively.
Conclusions: The algorithms presented in this research require only electronic stethoscope as input signal
unlike other methods which require electrocardiogram (ECG) gating and the proposed method delivered a
considerable improved result for detection heart valve-related diseases
