Wavelet transform based neural network model to detect and characterise ECG and EEG signals simultaneously

2015 ◽  
Author(s):  
Vedavathi B. S. ◽  
S. G. Hiremath ◽  
Shilpa Biradar ◽  
Thippeswamy G.
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Network Model ◽  
Neural Network Model ◽  
Eeg Signals

Image Intelligent Detection Based on the Gabor Wavelet and the Neural Network Combined Neural Network

2016 ◽  
Vol 13 (10) ◽  
pp. 7074-7079
Author(s):  
Yajun Xu ◽  
Fengmei Liang ◽  
Gang Zhang ◽  
Huifang Xu
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Network Model ◽  
Neural Network Model ◽  
Gabor Wavelet ◽  
Two Dimensional ◽  
Human Face ◽  
Gabor Wavelet Transform ◽  
Gabor Function ◽  
The Neural Network

This paper first analyzes the one-dimensional Gabor function and expands it to a two-dimensional one. The two-dimensional Gabor function generates the two-dimensional Gabor wavelet through measure stretching and rotation. At last, the two-dimensional Gabor wavelet transform is employed to extract the image feature information. Based on the BP neural network model, the image intelligent test model based on the Gabor wavelet and the neural network model is built. The human face image detection is adopted as an example. Results suggest that, when the method combining Gabor wavelet transform and the neural network is used to test the human face, it will not influence the detection results despite of complex textures and illumination variations on face images. Besides, when ORL human face database is used to test the model, the human face detection accuracy can reach above 0.93.

Research of Classification Methods of EEG Signal Based on Wavelet Packet Transform and LVQ Neural

2014 ◽  
Vol 1049-1050 ◽  
pp. 1626-1630
Author(s):  
Xue Yan Pang ◽  
Shi Nin Yin ◽  
Hong Zhou Li ◽  
Jian Ming Zhu ◽  
Zhen Cheng Chen
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Wavelet Packet ◽  
Recognition Rate ◽  
Signal Recognition ◽  
Eeg Signals ◽  
Specific Frequency ◽  
Correct Recognition Rate ◽  
Electroencephalogram Eeg

In order to improve the correct recognition rate of EEG(Electroencephalogram,EEG) signals to meet the needs of Brain-Computer Interface system,this paper put forward a new method of signal recognition which combines wavelet packet decomposition and LVQ neural network.First,using the method of wavelet packet to analyze the signal,and then extract the specific frequency band’s energy of wavelet packet as characteristics.Then using the LVQ neural network model to study the distinguishing between the two EEG datas of Motor Imagery.The simulation experiment uses Matlab software to design LVQ neural network model to judge the two kinds of Motor Imagery task.In the process of judgment,respecti-vely to classify the data by using BP neural network and LVQ neural network.Experimental results show that the LVQ neural network can have a higher correct accuracy to recognize the motor imaginary task than BP neural.

A Radial Basis Function Neural Network Model for Classification of Epilepsy Using EEG Signals

2008 ◽  
Vol 32 (5) ◽  
pp. 403-408 ◽  
Author(s):  
Kezban Aslan ◽  
Hacer Bozdemir ◽  
Cenk Şahin ◽  
Seyfettin Noyan Oğulata ◽  
Rızvan Erol
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Network Model ◽  
Neural Network Model ◽  
Basis Function ◽  
Eeg Signals ◽  
Radial Basis
Sign in / Sign up

Export Citation Format

Share Document