Automatic epileptic seizure detection algorithm based on dual density dual tree complex wavelet transform

• Published in: Sheng wu yi xue gong cheng xue za zhi Vol. 38; no. 6; p. 1035
• Main Authors: Kang, Tongzhou, Zuo, Rundong, Zhong, Lanfeng, Chen, Wenjing, Zhang, Heng, Liu, Hongxiu, Lai, Dakun
• Format: Journal Article
• Language: Chinese
• Published: China 25.12.2021
• Subjects: Algorithms; Electroencephalography; Epilepsy - diagnosis; Humans; Seizures - diagnosis; Signal Processing, Computer-Assisted; Support Vector Machine; Wavelet Analysis; epilepsy; wavelet entropy; intracranial electroencephalogram; epileptic seizure detection; dual density dual tree complex wavelet
• ISSN: 1001-5515
• DOI: 10.7507/1001-5515.202105075

It is very important for epilepsy treatment to distinguish epileptic seizure and non-seizure. In this study, an automatic seizure detection algorithm based on dual density dual tree complex wavelet transform (DD-DT CWT) for intracranial electroencephalogram (iEEG) was proposed. The experimental data were collected from 15 719 competition data set up by the National Institutes of Health (NINDS) in Kaggle. The processed database consisted of 55 023 seizure epochs and 501 990 non-seizure epochs. Each epoch was 1 second long and contained 174 sampling points. Firstly, the signal was resampled. Then, DD-DT CWT was used for EEG signal processing. Four kinds of features include wavelet entropy, variance, energy and mean value were extracted from the signal. Finally, these features were sent to least squares-support vector machine (LS-SVM) for learning and classification. The appropriate decomposition level was selected by comparing the experimental results under different wavelet decomposition levels. The experiment