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.
Highlights
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
This work is of scientific interest, due to the different methodology and resources used, it cannot be compared with the proposed system for neural network determination of atrial fibrillation on ECG signals with wavelet-based preprocessing
Noise is removed from the ECG signals using a discrete wavelet transform
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Since the P wave is not detected during atrial fibrillation, the interval between QRS complexes increases and there is no possibility to calculate the PQ and QT intervals. Low-pass filters in the range of 0.5–1 Hz can reduce tion waves, different R-R intervals, the heart-rate (HR) is constant or accelerated, and th the effect of the floating contour while introducing distortions in the shape of the ST. A neural network classification effect of the floating contour while introducing distortions in the shape of the ST segmen system for ECG signals is proposed for determining atrial fibrillation with a preprocessing [4]. Automatic detection of atrial fibrillation from ECG signals will allow doctors to determine if a patient needs cardiac care. Presents a method for detecting atrial fibrillation doctors to determine if a patient needs cardiac care
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