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Statistical sampling and feature selection for epilepsy pattern recognition
Epilepsy is one of the most common neurological diseases that has broad spectrum of debilitating medical and social consequences. The automatic forecasting and detecting systems are vitally important, since they allow patients to avoid dangerous activities in advance of the seizure. We present som...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Видавничий дім "Академперіодика" НАН України
2020
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| Series: | Доповіді НАН України |
| Subjects: | |
| Online Access: | http://dspace.nbuv.gov.ua/handle/123456789/170409 |
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| Summary: | Epilepsy is one of the most common neurological diseases that has broad spectrum of debilitating medical and social
consequences. The automatic forecasting and detecting systems are vitally important, since they allow patients to
avoid dangerous activities in advance of the seizure. We present some methods of feature extraction and selection
for detecting the epileptiform activity in electroencephalography signals, based on the processing of a non-stationary
signal. The proposed approach is based on the application of the Discrete Wavelet Transform (DWT) and signal
processing techniques in order to create the feature vector. Afterwards, the principal component analysis and support
vector machine techniques are used in order to reduce the dimensionality of the feature vector. |
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