Emad Mahmood Ahmed Taleb
Post-graduate Student, Department of Biomedical and Electronic systems and technology,
Vladimir State University named after A.G. and N.G. Stoletovs
E-mail: noor2@mail.ru
L.T. Sushkova
D.Sc. (Eng.), Professor, Head of Department of Biomedical and Electronic systems and technology,
Vladimir State University named after A.G. and N.G. Stoletovs
E-mail: ludm@vlsu.ru
Walid Ahmed Al-Haidri
Ph.D. (Eng.), Assistant, Department of Biomedical and Electronic systems and technology,
Vladimir State University named after A.G. and N.G. Stoletovs
E-mail: fawaz_tariq@mail.ru
R.V. Isakov
Ph.D.(Eng.), Associate Professor, Department of Biomedical and Electronic systems and technology,
Vladimir State University named after A.G. and N.G. Stoletovs
E-mail: Isakov-RV@mail.ru

The wide prevalence and great social significance of ischemic heart disease (IHD) causes the need for timely and maximum reliable diagnosis of the disease. Despite significant success in resolving the issues of prognosis, therapy and prevention of cardiovascular diseases, the mortality and disability of persons of working age from this pathology is growing.

To date, there are many methods of diagnosing IHD, various in its reliability and informativity, including echocardiography, bicycle ergometry, stress tests, perfusion scintigraphy of myocardium, computed tomography (CT), magnetic resonance imaging (MRI), CT angiography, MP- angiography and electron beam tomography, each of which has its advantages disadvantages, such as radiation load in the case of CT, physical activity (cardiac stress test), high time and financial costs in MRI, and the rests. Of the above methods, electrocardiography  )ECG( is the most common, affordable and cheap method of objective examination of the heart, as well as absolutely safe, painless and without contraindications to the clinical diagnosis of IHD.

In this paper, we consider the possibility of using artificial neural networks (ANN). for the analysis of electrocardiographic signals recorded by a single-channel ECG in the first lead for the purpose of identifying IHD in the early stages. This approach is based on the use of averaged cardiac cycles (ACC) obtained with the help of a remote cardiomonitoring system (CardioQVARK) and annotated by specialist physicians with confirmation of the diagnosis by standard methods in clinical settings. ANN acts as a classifier of the ACC for the presence or absence of ischemic heart disease in this signal for subsequent recognition and decision-making. As the ANN input data, the first lead leads with the duration of 0.71 s with a sampling frequency of 1000 Hz. During this work, databases were created in the Scilab software environment. On the basis of this data, two samples corresponding to the "Norma" and "Pathology" classes were formed. ANN was trained in the MatLab environment.

The obtained results showed the effectiveness of the proposed approach. The advantages of this approach include the possibility of its use in outpatient settings and low time and financial costs in screening studies, which allows to reduce mortality and disability of able-bodied people due to early identification of IHD and to apply more effective methods of treatment. This technique also allows you to lead a mass population study in free conditions.

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