Detection of normal and epileptic EEG signals using by lifting based HAAR wavelet transform and artificial neural network

2021 ◽  
Author(s):  
Vani S ◽  
ChandraSekhar P ◽  
Ramanarayan Sankriti ◽  
Aparna G
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Wavelet Transform ◽  
Haar Wavelet ◽  
Eeg Signals ◽  
Haar Wavelet Transform ◽  
Artificial Neural

EEG Signal Analysis for Automated Epilepsy Seizure Detection Using Wavelet Transform and Artificial Neural Network

2019 ◽  
Vol 9 (6) ◽  
pp. 1301-1306 ◽  
Author(s):  
S. Vani ◽  
G. R. Suresh ◽  
T. Balakumaran ◽  
Cross T. Ashawise
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Wavelet Transform ◽  
Seizure Detection ◽  
Haar Wavelet ◽  
Patient Specific ◽  
Eeg Signal ◽  
Haar Wavelet Transform ◽  
Artificial Neural ◽  
The Brain

Electroencephalogram (EEG) measures electrical activity of the brain and proffers valuable insight of the brain dynamics. Accurate and careful analysis of EEG signal plays a prominent role in the diagnosis of brain diseases like epilepsy, brain tumor. EEG is the most significant method used for epilepsy monitoring, diagnosis and rehabilitation. A patient-specific seizure detection model has been developed using Haar wavelet and Artificial Neural Network. HAAR Wavelet decomposition of multi-channel EEG with five scales is made and three frequency bands of EEG selected for the consequent process. The conventional Haar wavelet transform (HWT) is replaced by a modified Haar wavelet transform whereas the number of multiplications and additions are reduced. The Haar wavelet reduces computational complexity from the existing Haar wavelet structure which consumes only 1–3 ms based on the decomposition level to detect epilepsy.

Classification of epileptic EEG signals using PSO based artificial neural network and tunable-Q wavelet transform

2020 ◽  
Vol 40 (2) ◽  
pp. 709-728 ◽  
Author(s):  
S. Thomas George ◽  
M.S.P. Subathra ◽  
N.J. Sairamya ◽  
L. Susmitha ◽  
M. Joel Premkumar
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Wavelet Transform ◽  
Eeg Signals ◽  
Artificial Neural

scholarly journals A Hybrid Approach to Gender Classification using Speech Signal

2019 ◽  
pp. 17-24
Author(s):  
M. Yasin Pir ◽  
Mohamad Idris Wani
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Wavelet Transform ◽  
Speech Signal ◽  
Hybrid Approach ◽  
Gender Classification ◽  
Power Spectral ◽  
Reconstructed Signal ◽  
Feed Forward Network ◽  
Artificial Neural

Speech forms a significant means of communication and the variation in pitch of a speech signal of a gender is commonly used to classify gender as male or female. In this study, we propose a system for gender classification from speech by combining hybrid model of 1-D Stationary Wavelet Transform (SWT) and artificial neural network. Features such as power spectral density, frequency, and amplitude of human voice samples were used to classify the gender. We use Daubechies wavelet transform at different levels for decomposition and reconstruction of the signal. The reconstructed signal is fed to artificial neural network using feed forward network for classification of gender. This study uses 400 voice samples of both the genders from Michigan University database which has been sampled at 16000 Hz. The experimental results show that the proposed method has more than 94% classification efficiency for both training and testing datasets.

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