Real-time discrimination of ventricular tachyarrhythmia with Fourier-transform neural network

1999 ◽  
Vol 46 (2) ◽  
pp. 179-185 ◽  
Author(s):  
K. Minami ◽  
H. Nakajima ◽  
T. Toyoshima
Keyword(s):  
Neural Network ◽  
Fourier Transform ◽  
Real Time ◽  
Ventricular Tachyarrhythmia ◽  
Time Discrimination

scholarly journals A Frequency Pattern Mining Model Based on Deep Neural Network for Real-Time Classification of Heart Conditions

Healthcare ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 234 ◽  
Author(s):  
Hyun Yoo ◽  
Soyoung Han ◽  
Kyungyong Chung
Keyword(s):  
Neural Network ◽  
Big Data ◽  
Fourier Transform ◽  
Fast Fourier Transform ◽  
Real Time ◽  
Normal Control ◽  
Input Data ◽  
Deep Neural Network ◽  
Pattern Mining ◽  
F Measure

Recently, a massive amount of big data of bioinformation is collected by sensor-based IoT devices. The collected data are also classified into different types of health big data in various techniques. A personalized analysis technique is a basis for judging the risk factors of personal cardiovascular disorders in real-time. The objective of this paper is to provide the model for the personalized heart condition classification in combination with the fast and effective preprocessing technique and deep neural network in order to process the real-time accumulated biosensor input data. The model can be useful to learn input data and develop an approximation function, and it can help users recognize risk situations. For the analysis of the pulse frequency, a fast Fourier transform is applied in preprocessing work. With the use of the frequency-by-frequency ratio data of the extracted power spectrum, data reduction is performed. To analyze the meanings of preprocessed data, a neural network algorithm is applied. In particular, a deep neural network is used to analyze and evaluate linear data. A deep neural network can make multiple layers and can establish an operation model of nodes with the use of gradient descent. The completed model was trained by classifying the ECG signals collected in advance into normal, control, and noise groups. Thereafter, the ECG signal input in real time through the trained deep neural network system was classified into normal, control, and noise. To evaluate the performance of the proposed model, this study utilized a ratio of data operation cost reduction and F-measure. As a result, with the use of fast Fourier transform and cumulative frequency percentage, the size of ECG reduced to 1:32. According to the analysis on the F-measure of the deep neural network, the model had 83.83% accuracy. Given the results, the modified deep neural network technique can reduce the size of big data in terms of computing work, and it is an effective system to reduce operation time.

The use of neural network and discrete Fourier transform for real-time evaluation of friction stir welding

2011 ◽  
Vol 11 (8) ◽  
pp. 4839-4846 ◽  
Author(s):  
Enkhsaikhan Boldsaikhan ◽  
Edward M. Corwin ◽  
Antonette M. Logar ◽  
William J. Arbegast
Keyword(s):  
Neural Network ◽  
Fourier Transform ◽  
Friction Stir Welding ◽  
Real Time ◽  
Discrete Fourier Transform ◽  
Friction Stir ◽  
Time Evaluation

Real Time Harmonic Load Identification Based on Fast Fourier Transform and Artificial Neural Network

2021 ◽  
Vol 16 (3) ◽  
pp. 220
Author(s):  
Dimas Okky Anggriawan ◽  
Aidin Amsyar ◽  
Aji Akbar Firdaus ◽  
Endro Wahjono ◽  
Indhana Sudiharto ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fourier Transform ◽  
Fast Fourier Transform ◽  
Real Time ◽  
Harmonic Load ◽  
Load Identification ◽  
Time Harmonic ◽  
Artificial Neural
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