Title: Biotechnical neural network system for predicting cardiovascular health state using processing of bio-signals

Authors: Sergey Filist; Riad Taha Al-Kasasbeh; Olga Vladimirovna Shatalova; Mohammad Hjouj Btoush; Manafaddin Namazov; Ashraf Adel Shaqadan; Mahdi Salman Alshamasin; Nikolay Korenevskiy; Saleh Aloqeili; Maxim Borisovich Myasnyankin

Addresses: Southwest State University (SWSU), 305040, St. 50 let Oktyabrya, 94, Kursk, Russia ' Department of Mechatronics Engineering, Faculty of Engineering, The University of University, Aljubeiha, Amman, Jordan ' Southwest State University (SWSU), 305040, St. 50 let Oktyabrya,94, Kursk, Russia ' Department of Computer Science, Prince Abdullah bin Ghazi Faculty of Information and Communication Technology, Al-Balqa Applied University, Jordan ' Department of Automation and Electric Power Engineering, Faculty of Engineering, Baku Engineering University, Azerbaijan ' Civil Engineering Department, Engineering Technology Faculty, Zarqa University, Zarqa, Jordan ' Department of Mechatronics, Faculty of Engineering Technology, Al-Balqa Applied University, Amman, Jordan ' Southwest State University (SWSU), 305040, St. 50 let Oktyabrya, 94, Kursk, Russia ' Al-Balqa Applied University, Amman, Jordan ' Southwest State University (SWSU), 305040, St. 50 let Oktyabrya, 94, Kursk, Russia

Abstract: In this study, for the early diagnosis of cardiovascular diseases, a multimodal classifier is built, in which three groups of heterogeneous data are used. The data is classified by autonomous intelligent agents with subsequent aggregation of their solutions at the next hierarchical level of classification. As one of the lower-level classifiers, a classifier is used, built on descriptors obtained on the basis of monitoring and analysing the evolution of the amplitudes of the harmonics of the 0.1 Hz systemic rhythm. The presented architecture of the multimodal classifier showed an increase in the accuracy of the diagnostic efficiency by 11%.

Keywords: system rhythms; signal demodulation; electrocardiosignal; spectral analysis; neural networks.

DOI: 10.1504/IJMEI.2024.139884

International Journal of Medical Engineering and Informatics, 2024 Vol.16 No.4, pp.324 - 349

Received: 27 Jul 2021
Accepted: 11 Jan 2022
Published online: 09 Jul 2024 *

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