Extraction of EEG Signal Recording Features using Discrete Wavelet Transform (DWT) Method For Classification Of Ictal Epilepsy: Ekstraksi Fitur Rekaman Sinyal EEG menggunakan Metode Discrete Wavelet Transform (DWT) Untuk Klasifikasi Iktal Epilepsi
- EEG,
- Iktal Epilepsi,
- DWT,
- SVM
Copyright (c) 2022 Ade Eviyanti, Arif Senja Fitrani, Umi khoirun Nisak
This work is licensed under a Creative Commons Attribution 4.0 International License.
Abstract
Epilepsy is the most common neurological disorder in humans characterized by recurrent ictal (convulsions). Ictalism is defined as a sudden change in the electrical function of the brain, resulting in behavioral changes, such as loss of consciousness, jerky movements, loss of breath and temporary memory. Epilepsy is a chronic, non-communicable brain disease that affects about 50 million people worldwide. Electroencephalogram (EEG) signals contain important details regarding the electrical actions performed by the brain. EEG signal analysis is important for detecting diseases, one of which is epilepsy. However, these signals can be complex and require human expertise. The random, non-stationary behavior of the EEG signal makes ictal prediction difficult. So ictal detection and prediction is a very important issue. Various signal processing methods along with feature extraction are adapted to categorize EEG signal segments to obtain specific characteristics of the signal. The purpose of this paper is to improve the accuracy of the classification of epileptic ictal, then the EEG signal feature extraction is carried out so that the specific characteristics of the signal needed in the trial process are obtained. The study data used EEG signals from 24 patients from the CHB-MIT EEG public dataset from Children's Hospital, Boston. Signals were recorded from 23 epileptic children (of which 2 cases were obtained from the same child at 1.5 year intervals). The approach presented in this paper for feature extraction uses the Discrete Wavelet Transform (DWT) method to obtain the characteristics of the EEG signal, while for the classification process using the SVM method. It is hoped that the proposed method can detect epileptic ictal using EEG signal recording.
References
[2] Arnaz and Muhammad Taufiq Regia, “Hubungan Gambaran Elektroensefalografi Interiktal Terhadap Respons Terapi Antiepilepsi pada Penderita Epilepsi,” pp. 1–158, 2020.
[3] G. Saverius Siregar and Y. Rahayu, “Sistem Pendeteksi Gelombang Otak berbasis Electroencephalogram (EEG) pada Studi Kasus Anak Penderita Autisme,” Jom FTEKNIK, vol. 6, no. 0, pp. 1–8, 2020.
[4] A. Bragin, I. Mody, C. L. Wilson, and J. Engel, “Local generation of fast ripples in epileptic brain,” J. Neurosci., vol. 22, no. 5, pp. 2012–2021, 2002, doi: 10.1523/jneurosci.22-05-02012.2002.
[5] A. K. Jaiswal and H. Banka, “Epileptic seizure detection in EEG signal using machine learning techniques,” Australas. Phys. Eng. Sci. Med., vol. 41, no. 1, pp. 81–94, 2018, doi: 10.1007/s13246-017-0610-y.
[6] S. Lahmiri, “Accurate Classification of Seizure and Seizure-Free Intervals of Intracranial EEG Signals From Epileptic Patients,” IEEE Trans. Instrum. Meas., vol. PP, pp. 1–6, 2018, doi: 10.1109/TIM.2018.2855518.
[7] A. H. Osman and A. A. Alzahrani, “New Approach for Automated Epileptic Disease Diagnosis Using an Integrated Self-Organization Map and Radial Basis Function Neural Network Algorithm,” IEEE Access, vol. 7, pp. 4741–4747, 2019, doi: 10.1109/ACCESS.2018.2886608.
[8] S. Raghu, N. Sriraam, Y. Temel, S. Vasudeva, A. Satyaranjandas, and P. L. Kubben, “Performance evaluation of DWT based sigmoid entropy in time and frequency domains for automated detection of epileptic seizures using SVM classi fi er,” Comput. Biol. Med., vol. 110, no. December 2018, pp. 127–143, 2019, doi: 10.1016/j.compbiomed.2019.05.016.
[9] I. Mustiadi, T. S. Widodo, and I. Soesanti, “Analisis Ekstraksi Ciri Sinyal Emgmenggunakan Wavelet Discrete Transform,” vol. 2012, no. semnasIF, pp. 41–47, 2012.
[10] R. Akut, “Wavelet based deep learning approach for epilepsy detection,” Heal. Inf. Sci. Syst., vol. 7, no. 1, pp. 1–9, 2019, doi: 10.1007/s13755-019-0069-1.
[11] S. Saminu et al., “brain sciences A Recent Investigation on Detection and Classification of Epileptic Seizure Techniques Using EEG Signal,” 2021.