A new method for epileptic seizure classification in EEG using adapted wavelet packets

Ahmadi, Amirmasoud; Shalchyan, Vahid; Daliri, Mohammad Reza

doi:10.1109/ebbt.2017.7956756

Cited by 19 publications

(2 citation statements)

References 40 publications

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“…Sudden unexpected death in epilepsy (SUDEP) is likely the most common disease-related cause of death in epilepsy. It is not frequent, but it is a genuine problem, and people need to be aware of its risk [3]- [6].…”

Section: Introductionmentioning

confidence: 99%

Deep C-LSTM Neural Network for Epileptic Seizure and Tumor Detection Using High-Dimension EEG Signals

et al. 2020

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Electroencephalography (EEG) is a common and significant tool for aiding in the diagnosis of epilepsy and studying the human brain electrical activity. Previously, the traditional machine learning (ML)-based classifier are used to identify the seizure by extracting features from the EEG signals manually. Although the effectiveness of these contributions have already been proved, they cannot achieve multiple class classification with automatic feature extraction. Meanwhile, the identifiable EEG segment is too long to limit the capability of real-time epileptic seizure detection. In this paper, a novel deep convolutional long short-term memory (C-LSTM) model is proposed for detecting seizure and tumor in human brain and identifying two eyes statuses (open and close). It achieves to predict a result in every 0.006 seconds with a short detection duration (one second). By comparing with other two types deep learning approaches (DCNN and LSTM), the presented deep C-LSTM obtains the best performance for classifying these five classes. All of the obtained total accuracy are over 98.80%. INDEX TERMS Deep learning, C-LSTM, epileptic seizure, high-dimension electroencephalogram (EEG).

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Section: Introductionmentioning

confidence: 99%

Deep C-LSTM Neural Network for Epileptic Seizure and Tumor Detection Using High-Dimension EEG Signals

et al. 2020

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“…Current approaches to solve this problem use very sophisticated techniques, like the combination of wavelet transform with classical machine learning classification approaches [1,2,3] or instead with a deep convolutional neural network [4].…”

Section: Introductionmentioning

confidence: 99%

Dynamical Component Analysis (DYCA) and Its Application on Epileptic EEG

Korn

Seifert

Uhl

2019

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Dynamical Component Analysis (DyCA) is a recentlyproposed method to detect projection vectors to reduce the dimensionality of multi-variate deterministic datasets. It is based on the solution of a generalized eigenvalue problem and therefore straight forward to implement. DyCA is introduced and applied to EEG data of epileptic seizures. The obtained eigenvectors are used to project the signal and the corresponding trajectories in phase space are compared with PCA and ICA-projections. The eigenvalues of DyCA are utilized for seizure detection and the obtained results in terms of specificity, false discovery rate and miss rate are compared to other seizure detection algorithms.

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