scholarly journals Automatic Detection of Atrial Fibrillation Based on Continuous Wavelet Transform and 2D Convolutional Neural Networks

2018 ◽  
Vol 9 ◽  
Author(s):  
Runnan He ◽  
Kuanquan Wang ◽  
Na Zhao ◽  
Yang Liu ◽  
Yongfeng Yuan ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Neural Networks ◽  
Wavelet Transform ◽  
Convolutional Neural Networks ◽  
Continuous Wavelet Transform ◽  
Automatic Detection ◽  
Continuous Wavelet

scholarly journals Polynomial, Neural Network, and Spline Wavelet Models for Continuous Wavelet Transform of Signals

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6416
Author(s):  
Andrey Stepanov
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Wavelet Transform ◽  
Continuous Wavelet Transform ◽  
Approximation Error ◽  
Continuous Wavelet ◽  
Spline Models ◽  
Polynomial Neural Network ◽  
Artificial Neural

In this paper a modified wavelet synthesis algorithm for continuous wavelet transform is proposed, allowing one to obtain a guaranteed approximation of the maternal wavelet to the sample of the analyzed signal (overlap match) and, at the same time, a formalized representation of the wavelet. What distinguishes this method from similar ones? During the procedure of wavelets’ synthesis for continuous wavelet transform it is proposed to use splines and artificial neural networks. The paper also suggests a comparative analysis of polynomial, neural network, and wavelet spline models. It also deals with feasibility of using these models in the synthesis of wavelets during such studies like fine structure of signals, as well as in analysis of large parts of signals whose shape is variable. A number of studies have shown that during the wavelets’ synthesis, the use of artificial neural networks (based on radial basis functions) and cubic splines enables the possibility of obtaining guaranteed accuracy in approaching the maternal wavelet to the signal’s sample (with no approximation error). It also allows for its formalized representation, which is especially important during software implementation of the algorithm for calculating the continuous conversions at digital signal processors and microcontrollers. This paper demonstrates the possibility of using synthesized wavelet, obtained based on polynomial, neural network, and spline models, during the performance of an inverse continuous wavelet transform.

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