Improved feature exctraction process to detect seizure using CHBMIT-dataset
| Autor: | C. K. Narayanappa, T H Raveendra Kumar |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
CHBMIT-dataset
General Computer Science Computer science business.industry statistical moments Feature extraction Process (computing) FDS Pattern recognition medicine.disease Graph Correlation Epilepsy Feature (computer vision) medicine Graph (abstract data type) Fraction (mathematics) Artificial intelligence Electrical and Electronic Engineering business TDS |
| Popis: | One of the most dangerous neurological disease, which is occupying worldwide, is epilepsy. Fraction of second nerves in the brain starts impulsion i.e. electrical discharge, which is higher than the normal pulsing. So many researches have done the investigation and proposed the numerous methodology. However, our methodology will give effective result in feature extraction. Moreover, we used numerous number of statistical moments features. Existing approaches are implemented on few statistical moments with respect to time and frequency. Our proposed system will give the way to find out the seizure-effected part of the brain very easily using TDS, FDS, Correlation and Graph presentation. The resultant value will give the huge difference between normal and seizure effected brain. It also explore the hidden features of the brain. |
| Databáze: | OpenAIRE |
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