Neural Networks for Atrial Late Potentials Features Detection
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Abstract
The development of information technologies enables to improve tools and diagnostic methods of the cardiovascular system. The new method of electrocardiography is the method of high-resolution electrocardiography ( HR ECG), which allows to detect late potentials (LP) that are low-amplitude components of electrocardiosignal and are invisible on the standard ECG.
In this paper a comprehensive method of processing HR ECG for the classification of electrocardiograms for the presence of atrial late potentials, which are markers of atrial arrhythmias, was proposed. The method is based on the combination of a neural networks algorithm and the classical methods of analysis of cardiac signals: in time domain, spectral and wavelet analysis.
Also, the methods for diagnostic features formation that based on the classical methods of analysis ECG for the training samples construction for neural networks were considered.
The estimation and analysis of the classification of ECG results with presence and absence of abnormalities were made.
Reference 9, figures 8.
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References
Abakumov V.G., Rybin O.I., Svatosh J. (2001), “Biomedical signals. Genesis, treatment, monitoring”. Nora-print. P.526. (Rus)
Akhmed N., Rao K.R., Fomenko N.B. (1980), “Orthogonal transformation of digital signals pro-cessing”. Moskva, Svyaz. P. 248. (Rus)
Bokeriya A.L., Golukhova E.Z., Ivanickyi A.V. (2002), “High resolution electrocardiography / Function-al diagnostics in cardiology”. Vol.1, Moskva, NCSSH Bakuleva RAMN. P. 427. (Rus)
Ivanov G.G., Grachev S.V., Syrkin A.L. (2003), “High resolution electrocardiography”. Moskva, Triada-X. P. 304. (Rus)
Ivanov G.G. (1999), “HR ECG”. http:// www.ecg.ru/books/
Ivanushkina N.G., Fesechko V.O. (2007), “Technologies in high resolution electrocardiography”. Kyiv, NTUU “KPI”. P. 116. (Rus)
Ivanko K.O., Ivanushkina N.G. (2010), “A comprehensive method for atrial late potentials detection”. Naukovi visti, Vol.6. pp. 11-18. (Rus)
Rezishvili A.S., Bokeriya L.A., Golykhova E.Z., Ivanickyi A.V. (2002), “Classification and mechanisms of arrhythmias / Functional diagnostics in cardiology”. Moskva, NCSSH Bakuleva RAMN, Vol. 1. P. 427. (Rus)
Tu J.T., Gonsales R.K. (1978), “Principles of Pattern Recognition”. Moskva, Mir. P. 411. (Rus)
