Please use this identifier to cite or link to this item: https://dspace.ncfu.ru/handle/20.500.12258/18067
Title: System for neural network determination of atrial fibrillation on ecg signals with wavelet-based preprocessing
Authors: Lyakhov, P. A.
Ляхов, П. А.
Kiladze, M. R.
Киладзе, М. Р.
Lyakhova, U. A.
Ляхова, У. А.
Keywords: Spectral entropy;Symlet wavelet;Digital filter;Electrocardiogram;Instantaneous frequency;LSTM;Signal denoising
Issue Date: 2021
Publisher: MDPI AG
Citation: Lyakhov, P. A.; Kiladze, M. R.; Lyakhova, U. A. System for neural network determination of atrial fibrillation on ecg signals with wavelet-based preprocessing // Applied Sciences (Switzerland). - 2021. - Том 11. - Выпуск 162. - Номер статьи 7213
Series/Report no.: Applied Sciences (Switzerland)
Abstract: Today, cardiovascular disease is the leading cause of death in developed countries. The most common arrhythmia is atrial fibrillation, which increases the risk of ischemic stroke. An electrocardiogram is one of the best methods for diagnosing cardiac arrhythmias. Often, the signals of the electrocardiogram are distorted by noises of varying nature. In this paper, we propose a neural network classification system for electrocardiogram signals based on the Long Short-Term Memory neural network architecture with a preprocessing stage. Signal preprocessing was carried out using a symlet wavelet filter with further application of the instantaneous frequency and spectral entropy functions. For the experimental part of the article, electrocardiogram signals were selected from the open database PhysioNet Computing in Cardiology Challenge 2017 (CinC Challenge). The simulation was carried out using the MatLab 2020b software package for solving technical calculations. The best simulation result was obtained using a symlet with five coefficients and made it possible to achieve an accuracy of 87.5% in recognizing electrocardiogram signals
URI: http://hdl.handle.net/20.500.12258/18067
Appears in Collections:Статьи, проиндексированные в SCOPUS, WOS

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