E.M.A. Taleb – Post-graduate Student, Department of Biomedical and Electronic Systems and Technology,  Vladimir State University n.a. Alexander and Nicolay 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 n.a. Alexander and Nicolay Stoletovs E-mail: ludm@vlsu.ru

R.V. Isakov – Ph.D.(Eng.), Associate Professor, Department of Biomedical and Electronic Systems and Technology,  Vladimir State University n.a. Alexander and Nicolay Stoletovs

Email: Isakov-RV@mail.ru

W.A. Al-Haidri – Ph.D. (Eng.), Senior Lecturer, Department of Biomedical and Electronic Systems and Technology,  Vladimir State University n.a. Alexander and Nicolay Stoletovs 

Email: fawaz_tariq@mail.ru 

A.R.A. Abdulraqeb – Ph.D. (Eng.), Assistant, Department of Biomedical and Electronic Systems and Technology, Vladimir State University n.a. Alexander and Nicolay Stoletovs

Email: atef_alsanawy@mail.ru

The wide prevalence and high social significance of coronary heart disease (CHD) make the timely and most reliable diagnosis of this disease very necessary. To date, there are many methods for diagnosing CHD, however, the most common, absolutely safe, painless and without contraindications method is Electrocardiography (ECG).

In this work a comparative analysis of the processing methods of ECG-signal has been made. Furthermore, an extraction of ECG- signal informative (diagnostic) features, for early detection of CHD based on single-channel electrocardiographic signals recorded in the first lead, has been carried out.

As materials of this study, an averaged cardiocycles (ACC) with duration of 0.71s and sampling frequency of 1000 Hz obtained by the remote cardiac monitoring system (CardioQVARK) and annotated by medical specialists (Doctors) with diagnosis confirmation by standard methods in the clinical setting have been used.

In this article, the informative capabilities of modern approaches for the processing and analysis of the ECG signal, namely: Fourier transform (FT), wavelet transform (WT) (Morlet, Daubechies) and singular decomposition (SVD) were investigated.

To evaluate the information content of each of the listed methods, a neural network classifier (multilayer perceptron) was used.

The feature spaces of the studied methods were used for training and testing the neural network.

INN performs the role of ACC classifier to determine the presence or absence of CHD in a given signal for further recognition and decisionmaking. The input data of the INN are four databases: a database of ACC spectral estimates, two databases of ACC wavelet transforms (one by using the Morlet basis, and the other be the Daubechies) and a database which was obtained as a result of SVD.

The study results of processing and analyzing methods of ECG signal were compared with each other. The results showed the effectiveness of using the SVD when compared to informative features, obtained by using FT and WT.

The advantages of this approach include the possibility of its use in the outpatient setting and low time and low financial costs during the screening tests that can reduce mortality and disability of working age persons due to the early detection of CHD and to apply more effective treatments. This technique also allows conducting mass tests to the population in free conditions.

1. Еstestvennoe dvizhenie naseleniya v razreze sub"ektov rossijskoj federacii za yanvar'-dekabr' 2018 goda. http://www.gks.ru/free_doc/2018/demo/t3_3.xls.
2. World health statistics 2014 (Mirovaya statistika zdravoohraneniya 2014 g.). Vsemirnaya organizaciya zdravoohraneniya. 2014 g. 178 s.
3. Litvinova M.A. Analiz informativnosti razlichnyh metodov diagnostiki ishemicheskoj bolezni serdca // Zdorov'e i obrazovanie v XXI veke. 2016. № 1. S. 241–245.
4. Aravin O.I. Primenenie iskusstvennyh nejronnyh setej dlya analiza patologij v krovenosnyh sosudah // Rossijskij zhurnal biomekhaniki. 2011. № 3. S. 45–51. 
5. Kardiomonitor CardioQVARK kardiogramma s pomoshch'yu telefona. URL: http://www.cardioqvark.ru/ 
6. Dubrovin V.I., SHCHedrina T.A. Avtomatizirovannaya sistema analiza elektrokardiogramm na osnove vejvlet-tekhnologij // Iskusstvennyj intellekt. 2010. № 4. S. 190–194.
7. Latfullin I.A., Kim Z.F., Teptin G.M., Mamedova L.E. EKG vysokogo razresheniya: ot dejstvitel'nogo k vozmozhnomu // Rossijskij kardiologicheskij zhurnal. 2010. № 2. S. 29–34. 
8. Grigor'ev D.S., Spicyn V.G. Primenenie nejronnoj seti i diskretnogo vejvlet-preobrazovaniya dlya analiza i klassifikacii elektrokardiogramm // Izv. Tomskogo politekhnicheskogo universiteta. Inzhiniring georesursov. 2012. № 5. S. 57–61.
9. Dubrovin V.I., Tverdohleb YU.V. Usovershenstvovanie metodov analiza EKG-signalov na osnove vejvletpreobrazovaniya v sisteme elektrokardiografii vysokogo razresheniya // Radioelektronika, informatika, upravlinnya. 2011. № 1. S. 91–98.
10. Kyzdarbekova A.C., Kacymbekova K.B., Dutbajeva D.M., Kyzdarbek U.C. Algoritm izvlecheniya priznakov i udaleniya shuma elektrokardiosignala na osnove vejvlet-preobrazovaniya // Problemy sovremennoj nauki i obrazovaniya. 2017. № 7. S. 112–116. 
11. Kashkin V.B., Rubleva T.V. Primenenie singulyarnogo spektral'nogo analiza dlya vydeleniya slabo vyrazhennyh trendov // Izv. Tomskogo politekhnicheskogo universiteta. Inzhiniring georesursov. 2007. № 5. S. 116–119. 
12. Voronov I.V., Politov Е.A., Еfremenko V.M. Obzor tipov iskusstvennyh nejronnyh setej i metodov ih obucheniya // Vestnik Kuzbasskogo gosudarstvennogo tekhnicheskogo universiteta. 2007. № 3. S. 38–42. 
13. Salekh M.A., Isakov R.V. Ocenka effektivnosti primeneniya iskusstvennyh nejronnyh setej dlya analiza segmentirovannyh elektrokardiokompleksov // Biomedicinskaya radioelektronika. 2012. № 6. S. 21–27.
14. Ivanushkina N.G., Ivan'ko Е.O., Matveeva N.A. Nejronnye seti dlya raspoznavaniya obrazov pozdnih potencialov predserdij // Electronics and communications. 2013. № 5. S. 72–80.
15. Al'-Hajdri V.A., Isakov R.V., Sushkova L.T. Issledovanie vliyaniya vybora funkcij aktivacii na effektivnost' raboty mnogoslojnogo perseptrona // Nejrokomp'yutery: razrabotka i primenenie. 2015. № 7. S. 60–66.
16. Taleb Е.M., Sushkova L.T., Al'-Hajdri V.A., Isakov R.V. Primenenie iskusstvennyh nejronnyh setej dlya vyyavleniya ishemicheskoj bolezni serdca po odnokanal'noj EKG // Biomedicinskaya radioelektronika. 2018. № 9. S. 3–9.