Developing neural network model for predicting cardiac and cardiovascular health using bioelectrical signal processing
Filist S. Al-kasasbeh R.T. Shatalova O. Aikeyeva A. Korenevskiy N. Shaqadan A. Trifonov A. Ilyash M.
2022Taylor and Francis Ltd.
Computer Methods in Biomechanics and Biomedical Engineering
2022#25Issue 8908 - 921 pp.
Coronary vascular disease (CHD) is one of the most fatal diseases worldwide. Cardio vascular diseases are not easily diagnosed in early disease stages. Early diagnosis is important for effective treatment, however, medical diagnoses are based on physicians personal experiences of the disease which increase time and testing cost to reach diagnosis. Physicians assess patients condition based on electrocardiography, sonography and blood test results. In this research we develop classification model of the functional state of the cardiovascular system based on the monitoring of the evolution of the amplitudes of the first and second harmonics of the system rhythm of 0.1 Hz. We separate the signal to three streams; the first stream works with natural electro cardio signal, the other two streams are obtained as a result of frequency analysis of the amplitude- and frequency-detected electro cardio signal. We use sliding window of a demodulated electro cardio signal by means of amplitude and frequency detectors. The developed NN model showed an increase in accuracy of diagnostic efficiency by 11%. The neural network model can be trained to give accurate early detection of disease class.
electrocardiosignal , neural networks , signal demodulation , spectral analysis , System rhythms
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Department of Biomedical Engineering, Southwest State University, Kursk, Russian Federation
Electrical Energy Department, Balqa Applied University, Amman, Jordan
Gumilyov Eurasian National University, Faculty of Transport and Energy, Electric power industry Department, Astana, Kazakhstan
Civil Engineering Department, Zarqa University, Zarqa, Jordan
Mechanics and Optics, Saint-Petersburg National Research University of Information Technologies, Sankt Peterburg, Russian Federation
Department of Biomedical Engineering
Electrical Energy Department
Gumilyov Eurasian National University
Civil Engineering Department
Mechanics and Optics
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