Abstract:
Cardiovascular diseases (CVDs) are one of the major causes of deaths in the world. CVDs
generally describe the conditions of the heart that involve narrowed or blocked blood vessels,
causing heart attack, angina or stroke. Human heart beats continuously by contraction and
relaxation of heart muscles. During the contraction state (termed as systole) the ventricles
contract, forcing blood through vessels into the lungs and the body. During the relaxation
(termed as diastole) the ventricles are filled with the blood coming from the right and left atria.
Sounds produced by the heart due to snap shut of the valves are the heart sounds which can be
heard and detected through stethoscope and phonocardiograph. The phonocardiogram (PCG)
signals provide valuable information about the heart condition for exact and timely detection of
heart diseases. Digital stethoscopes have the ability to record the PCG signal and transmit it for
further analysis and identification of abnormal sounds. PCG signal consists of various events like
S1 sound, S2 sound, S3 sound, S4 sound and murmurs. S1 and S2 are the main events considered
for the heart sound segmentation which are followed by the systolic and diastolic activities of the
heart. This research work proposes a heart sound segmentation and classification algorithm
which diagnose the abnormal symptoms of the heart in time. The proposed algorithm first
performs segmentation of heart sound into S1, systole, S2 and diastole with accuracy of 91%
using proposed multi-threshold and spectrograms, afterwards it extract features from these four
states to perform classification of PCG as normal or abnormal using SVM and incorporating
spectrogram with accuracy of 93.3%. The proposed algorithm is implemented on the PhysioNet
heart sound challenge dataset using Support Vector Machine and Convolutional Neural Network
for classification of heart beats into S1 and S2 based on extracted features. For classification of
normal and abnormal heart sound Support Vector Machine, K-Nearest Neighbor and Neural
Network are employed. The developed algorithm is suitable for the detection of normal and
abnormal heart sounds for cardiovascular disease detection.
