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Capabilities application of artificial neural networks for classification phonocardiogram signals


W.M. Ayedh – Post-graduate Student, Department «Biomedical and Electronic Systems and Technology», Vladimir State University named after A.&N. Stoletovs E-mail: W.A. Al-Haidri – Post-graduate Student, Department «Biomedical and Electronic Systems and Technology», Vladimir State University named after A.&N. Stoletovs E-mail: R.V. Isakov – Ph. D. (Eng.), Associate Professor, Department «Biomedical and Electronic Systems and Technology», Vladimir State University named after A.&N. Stoletovs E-mail: L.T. Sushkova – Dr. Sc. (Eng.), Professor, Head of Department «Biomedical and Electronic Systems and Technology», Vladimir State University named after A.&N. Stoletovs E-mail:

The human body is a complex dynamic system, comprising a number of subsystems and processes. Several physiological processes can be active at the same. Each of them generates a plurality of signals of different types. The appearance of the signals from the processes and systems that are not currently objects of study can be regarded as a physiological disturbance. This raises the problem of improving the quality and reliability of the information content of functional diagnostics in medicine. Cardiovascular diseases (CVD) are in the first place among the causes of death in the world. Therefore, it is very highly topical scientific and practical justification and development of effective methods of treatment, rehabilitation and prevention and, above all, accurate early diagnosis of CVD in the presence of minimal symptoms (complaints or feeling sick). There are various methods of detecting CVD. The most common electrocardiography (ECG) and phonocardiography (PCG) in connection with high efficiency, safety and ease of hardware implementation of these methods. Method of PCG is based on the registration and analysis the sounds that arising during contraction and relaxation of the heart. This method is affordable and relatively easy way to diagnose the functional state of cardiovascular systems. This work is associated with the study of the possibility of capabilities application of artificial neural networks (ANN) for classification phonocardiogram signals. propose an algorithm for classification of functional status on the phonocardiogram (PCG) signal based on ANN. The proposed algorithm seems more promising due to its versatility, simplicity and clarity. Considered the issue of segmentation accuracy PCG and correctness the preparation of informative data for the ANN. Classification of functional states is held by the type of «Norma» or «pathology». The results obtained in this paper of classification PCG have shown high efficiency on this approach, which is confirmed by the values generally, accepted criteria: sensitivity (90.06%) and specificity (87.89%). It testifies high efficiency assessment of heart sounds by PCG. The algorithm provides a qualitative tool doctor for primary diagnosis using simple techniques phonocardiography.


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