Seizure prediction using low frequency EEG wavesfrom WAG/Rij rats
| Autor: | Amalesh Samanta, Amiyangshu De, Amit Konar, Piyali Basak, Souvik Biswas |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
medicine.diagnostic_test
business.industry Spectral density Wavelet transform Pattern recognition Electroencephalography Low frequency medicine.disease 03 medical and health sciences Epilepsy 0302 clinical medicine Wavelet Frontal lobe Range (statistics) medicine 030212 general & internal medicine Artificial intelligence business 030217 neurology & neurosurgery Mathematics |
| Zdroj: | 2017 2nd International Conference for Convergence in Technology (I2CT). |
| DOI: | 10.1109/i2ct.2017.8226129 |
| Popis: | Frontal lobe epilepsy is the second most common form of epilepsy which initiates during sleep and causes death. Early detection is the solitary measure to control seizure. Electroencephalography (EEG)is the only confirmatory test for seizure. However, epileptic research still depends on studies based on animal models. In this research, our main objective is to study the significance of low frequency brainwaves (which is dominant during sleep) in the prognosis of seizure from WAG/Rij rat data. Wavelet based decomposition coefficients and power spectral density (PSD) are selected as features to take care ofnon-stationary nature of brain waves. A comparative study of classifiers' performance is formulated between low frequency wave range (0.5–13 Hz) and the total frequency range (0.5–40 Hz) where RBF-SVM provides the maximum classification accuracy. The average classification accuracy of RBF-SVM for low frequency wave range is found to be 92.50%, which lies within a range of |
| Databáze: | OpenAIRE |
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