Processing of EEG Signal for Classification of Epilepsy
| Autor: | S Ishaasamyuktha, B. Geethanjali, Mahesh Veezhinathan, Bhuvaneshwari Rajendran, R Ananya, Vaishali Harimani |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
021110 strategic
defence & security studies education.field_of_study medicine.diagnostic_test business.industry Computer science Population Feature extraction 0211 other engineering and technologies Pattern recognition 02 engineering and technology Electroencephalography medicine.disease Hjorth parameters Daubechies wavelet Epilepsy Moving average medicine Ictal Artificial intelligence education business |
| Zdroj: | 2020 International Conference on Communication and Signal Processing (ICCSP). |
| DOI: | 10.1109/iccsp48568.2020.9182271 |
| Popis: | This paper aims at analysing the electroencephalogram of the local, epileptic population of Chennai. The subjects used for this study suffer from different types of epilepsy. Their EEG was recorded at a sampling rate of 256 Hz. 10 second durations of these signals were exported and preprocessed using a moving average filter to remove the noise, and filtered with a band pass filter to pass the required frequency range. Wavelet denoising using Daubechies wavelet removed the artifacts in the signal, and the denoised signal was used for feature extraction. The features used are the Hjorth parameters, which characterize the power and frequency of the signal. These parameters were estimated for pre-interictal, interictal and post-interictal states. The correlation between the Hjorth parameters were plotted for all the patients, electrode wise, in order to understand the dominant electrode for the population sampled, in case of an epileptic event. The correlation proved that general epilepsy was the most common type of epilepsy existing in the sample population. It was inferred after calculating the p-values, that the Hjorth parameters characterize the epileptic signal excellently. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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