Detection of Epileptic Seizures using a Five-level Db4 Wavelet, Scalogram and CNN

Rieke Syochrani Zaef; Anisa Dwizarah; Lysa Islami; Roza Susanti; Andika Fitra; Zikra Hafiza

doi:10.59890/ijatss.v4i5.237

Authors

Rieke Syochrani Zaef Politeknik Negeri Padang Maret, Padang, Indonesia
Anisa Dwizarah Politeknik Negeri Padang Maret, Padang, Indonesia
Lysa Islami Politeknik Negeri Padang Maret, Padang, Indonesia
Roza Susanti Politeknik Negeri Padang Maret, Padang, Indonesia
Andika Fitra Politeknik Negeri Padang Maret, Padang, Indonesia
Zikra Hafiza Politeknik Negeri Padang Maret, Padang, Indonesia

DOI:

https://doi.org/10.59890/ijatss.v4i5.237

Keywords:

Epilepsy, EEG, Db4 Wavelet, Scalogram, CNN

Abstract

This Epilepsy is a serious neurological disorder after stroke that affects 50 million individuals. People with epilepsy face various challenges, with around 20-30% unable to fully control seizures. Due to this issue, automated detection based on EEG signals is necessary to support epilepsy studies with appropriate methodological approaches. This paper's epilepsy detection is conducted based on EEG raw signals categorized into normal and epileptic seizures, the programming language used is Python. The signals are decomposed into five-level by Daubechies4 Discrete Wavelet Transform (DWT) to extract both approximation and detail signals. This system utilizes Python programming. A scalogram is used to visualize how transient signal activity changes with changes in time scale. DWT processthe with original sampling frequency is 128 Hz, and the number of levels is 5. The frequency range of the output data after DWT at level 5 is 4-7 Hz. Finally, an Convolutional Neural Network (CNN) classifies all the extracted features. With an accuracy of 97%

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