Performance Analysis of DWT-Based Epileptic Seizure Detection
Electroencephalogram. Discrete wavelet transform Support vector machine, Fast-Fourier transform, look-up table.
Epilepsy is a central nervous system disorder that is well defined by the startling and atypical behavior of seizures and causes loss of consciousness. The Electroencephalogram (EEG) signal is particularly good for assessing neurogenic activity and is often used in central nervous system interfaces in the human brain, and neuron disease diagnosis. Among the three modules, discrete wavelet transforms(DWT), feature extraction (FE), and the classification of the support vector machine(SVM), the FE unit matches the relevant neurobiological areas of the EEG data using the 9/7th DWT and extracts the required features. Feature extraction classifies the extracted EEG information into four basic sub-bands namely, alpha, beta, gamma, and theta. Feature Extraction utilizes signal analysis methods and computer technologies to extract information from electroencephalography signals. The proposed design consists of medically converted EEG data, DWT, wavelet decomposition, higher-order statistics, a feature extraction (FE) module, and an SVM module unit. Among the various machine-learning techniques, the support vector machine differentiates between healthy and unhealthy illnesses such as epileptic seizures, SVM is deployed due to its high reliability & adaptation to the presence of arbitrary nonlinear decision limits. The posited designed system provides higher accuracy and reliability compared to the conventional methods designed before in time.
"Performance Analysis of DWT-Based Epileptic Seizure Detection", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.7, Issue 9, page no.926 - 932, September-2022, Available :https://ijsdr.org/papers/IJSDR2209148.pdf
Volume 7
Issue 9,
September-2022
Pages : 926 - 932
Paper Reg. ID: IJSDR_201889
Published Paper Id: IJSDR2209148
Downloads: 000347201
Research Area: Electronics & Communication Engg.
Country: bangalore, karnataka, India
ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: IJSDR(IJ Publication) Janvi Wave
