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Dimensionality Reduction Techniques for Processing Epileptic Encephalographic Signals
| Content Provider | Semantic Scholar |
|---|---|
| Author | Harikumar, R. Kumar, Peruvala Sunil |
| Copyright Year | 2015 |
| Abstract | Epilepsy is a chronic neurological disorder of the brain, approximately 1% of the world population suffers from epilepsy. Epilepsy is characterized by recurrent seizures that cause rapid but revertible changes in the brain functions. Temporary electrical interference of the brain roots epileptic seizures. The occurrence of an epileptic seizure appears unpredictable. Various methods have been proposed for dimensionality reduction and feature extraction, such as Principal Component Analysis (PCA), Independent Component Analysis (ICA), and Singular Value Decomposition (SVD). The advantages of the regularization dimension are that it is more precise than other approximation methods and it is easy to derive an estimator in the presence of noise due to the fully analytical definition. This paper gives an overall review of the dimensionality reduction techniques suitable for the application of electroencephalography signals from epileptic patients. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://www.biomedpharmajournal.org/dnload/R-Harikumar-and-P-Sunil-Kumar/BPJV08I01P103-106.pdf |
| Language | English |
| Access Restriction | Open |
| Subject Keyword | Approximation Dimensionality reduction Electroencephalography Epilepsy Feature extraction Independent Computing Architecture Independent component analysis Interference (communication) Mental Suffering Patients Plant Roots Principal component analysis Seizures Singular value decomposition nervous system disorder |
| Content Type | Text |
| Resource Type | Article |
