A neural network classification system using a symlet wavelet filter with five coefficients achieved an accuracy of 87.5% in recognizing electrocardiogram signals for atrial fibrillation.
An LSTM-based neural network with symlet wavelet preprocessing achieved 87.5% accuracy in detecting atrial fibrillation from ECG signals.
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.
Lyakhov et al. (Thu,) conducted a other in Atrial fibrillation. Neural network classification system based on LSTM with symlet wavelet filter preprocessing was evaluated on Accuracy in recognizing electrocardiogram signals. A neural network classification system using a symlet wavelet filter with five coefficients achieved an accuracy of 87.5% in recognizing electrocardiogram signals for atrial fibrillation.