Networking Property During Epileptic Seizure with Multi-channel EEG Recordings

2006 ◽  
pp. 573-578 ◽  
Author(s):  
Huihua Wu ◽  
Xiaoli Li ◽  
Xinping Guan
Keyword(s):  
Epileptic Seizure ◽  
Eeg Recordings

scholarly journals ScoreNet: A neural network-based post-processing model for identifying epileptic seizure onset and offset in EEGs

2020 ◽  
Author(s):  
Poomipat Boonyakitanont ◽  
Apiwat Lek-uthai ◽  
Jitkomut Songsiri
Keyword(s):  
Neural Network ◽  
Epileptic Seizure ◽  
False Positive Rate ◽  
Imbalanced Data ◽  
Detection Algorithm ◽  
Processing Technique ◽  
Post Processing ◽  
Scalp Eeg ◽  
Positive Rate ◽  
Eeg Recordings

AbstractThis article aims to design an automatic detection algorithm of epileptic seizure onsets and offsets in scalp EEGs. A proposed scheme consists of two sequential steps: the detection of seizure episodes, and the determination of seizure onsets and offsets in long EEG recordings. We introduce a neural network-based model called ScoreNet as a post-processing technique to determine the seizure onsets and offsets in EEGs. A cost function called a log-dice loss that has an analogous meaning to F1 is proposed to handle an imbalanced data problem. In combination with several classifiers including random forest, CNN, and logistic regression, the ScoreNet is then verified on the CHB-MIT Scalp EEG database. As a result, in seizure detection, the ScoreNet can significantly improve F1 to 70.15% and can considerably reduce false positive rate per hour to 0.05 on average. In addition, we propose detection delay metric, an effective latency index as a summation of the exponential of delays, that includes undetected events into account. The index can provide a better insight into onset and offset detection than conventional time-based metrics.

Epileptic Seizure Detection in Long-Term EEG Recordings by Using Wavelet-Based Directed Transfer Function

2018 ◽  
Vol 65 (11) ◽  
pp. 2591-2599 ◽  
Author(s):  
Dong Wang ◽  
Doutian Ren ◽  
Kuo Li ◽  
Yiming Feng ◽  
Dan Ma ◽  
...  
Keyword(s):  
Transfer Function ◽  
Epileptic Seizure ◽  
Seizure Detection ◽  
Directed Transfer Function ◽  
Epileptic Seizure Detection ◽  
Eeg Recordings

Epileptic seizure recognition using EEG wavelet decomposition based on nonlinear and statistical features with support vector machine classification

2020 ◽  
Vol 65 (2) ◽  
pp. 133-148 ◽  
Author(s):  
Dib Nabil ◽  
Radhwane Benali ◽  
Fethi Bereksi Reguig
Keyword(s):  
Support Vector Machine ◽  
Epileptic Seizure ◽  
Approximate Entropy ◽  
Brain Dysfunction ◽  
Research Literature ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Statistical Parameters ◽  
Difficult Time ◽  
Eeg Recordings

AbstractEpileptic seizure (ES) is a neurological brain dysfunction. ES can be detected using the electroencephalogram (EEG) signal. However, visual inspection of ES using long-time EEG recordings is a difficult, time-consuming and a costly procedure. Thus, automatic epilepsy recognition is of primary importance. In this paper, a new method is proposed for automatic ES recognition using short-time EEG recordings. The method is based on first decomposing the EEG signals on sub-signals using discrete wavelet transform. Then, from the obtained sub-signals, different non-linear parameters such as approximate entropy (ApEn), largest Lyapunov exponents (LLE) and statistical parameters are determined. These parameters along with phase entropies, calculated through higher order spectrum analysis, are used as an input vector of a multi-class support vector machine (MSVM) for ES recognition. The proposed method is evaluated using the standard EEG database developed by the Department of Epileptology, University of Bonn, Germany. The evaluation is carried out through a large number of classification experiments. Different statistical metrics namely Sensitivity (Se), Specificity (Sp) and classification accuracy (Ac) are calculated and compared to those obtained in the scientific research literature. The obtained results show that the proposed method achieves high accuracies, which are as good as the best existing state-of-the-art methods studied using the same EEG database.

scholarly journals Epileptic Seizure Detection using Deep Learning Approach

2019 ◽  
Vol 3 (2) ◽  
pp. 41 ◽  
Author(s):  
Sirwan Tofiq Jaafar ◽  
Mokhtar Mohammadi
Keyword(s):  
Deep Learning ◽  
Epileptic Seizure ◽  
Domain Knowledge ◽  
Short Term Memory ◽  
Time Frequency ◽  
Standard Tool ◽  
Machine Learning Applications ◽  
Memory Network ◽  
Eeg Recordings ◽  
Almost All

An epileptic seizure is a sign of abnormal activity in the human brain. Electroencephalogram (EEG) is a standard tool that has been used vastly for detection of seizure activities. Many methods have been developed to help the neurophysiologists to detect the seizure activities with high accuracy. Most of them rely on the features extracted in the time, frequency, or time-frequency domains. The performance of the proposed methods is related to the performance of the features extracted from EEG recordings. Deep neural networks enable learning directly on the data without the domain knowledge needed to construct a feature set. This approach has been hugely successful in almost all machine learning applications. We propose a new framework that also learns directly from the data, without extracting a feature set. We proposed an original deep-learning-based method to classify EEG recordings. The EEG signal is segmented into 4 s segments and used to train the long- and short-term memory network. The trained model is used to discriminate the EEG seizure from the background. The Freiburg EEG dataset is used to assess the performance of the classifier. The 5-fold cross-validation is selected for evaluating the performance of the proposed method. About 97.75% of the accuracy is achieved.

Epileptic Seizure Detection with Log-Euclidean Gaussian Kernel-Based Sparse Representation

2016 ◽  
Vol 26 (03) ◽  
pp. 1650011 ◽  
Author(s):  
Shasha Yuan ◽  
Weidong Zhou ◽  
Qi Wu ◽  
Yanli Zhang
Keyword(s):  
Riemannian Manifold ◽  
Sparse Representation ◽  
Epileptic Seizure ◽  
Reproducing Kernel ◽  
Reproducing Kernel Hilbert Space ◽  
Seizure Detection ◽  
Detection Methods ◽  
Gaussian Kernel ◽  
Epileptic Seizure Detection ◽  
Eeg Recordings

Epileptic seizure detection plays an important role in the diagnosis of epilepsy and reducing the massive workload of reviewing electroencephalography (EEG) recordings. In this work, a novel algorithm is developed to detect seizures employing log-Euclidean Gaussian kernel-based sparse representation (SR) in long-term EEG recordings. Unlike the traditional SR for vector data in Euclidean space, the log-Euclidean Gaussian kernel-based SR framework is proposed for seizure detection in the space of the symmetric positive definite (SPD) matrices, which form a Riemannian manifold. Since the Riemannian manifold is nonlinear, the log-Euclidean Gaussian kernel function is applied to embed it into a reproducing kernel Hilbert space (RKHS) for performing SR. The EEG signals of all channels are divided into epochs and the SPD matrices representing EEG epochs are generated by covariance descriptors. Then, the testing samples are sparsely coded over the dictionary composed by training samples utilizing log-Euclidean Gaussian kernel-based SR. The classification of testing samples is achieved by computing the minimal reconstructed residuals. The proposed method is evaluated on the Freiburg EEG dataset of 21 patients and shows its notable performance on both epoch-based and event-based assessments. Moreover, this method handles multiple channels of EEG recordings synchronously which is more speedy and efficient than traditional seizure detection methods.

scholarly journals An Efficient Method for Epileptic Seizure Detection in Long-Term EEG Recordings

2014 ◽  
Vol 07 (12) ◽  
pp. 963-972 ◽  
Author(s):  
Alaa Eldeen Mahmoud Helal ◽  
Ahmed Farag Seddik ◽  
Mohammed Ali Eldosoky ◽  
Ayat Allah Farouk Hussein
Keyword(s):  
Efficient Method ◽  
Epileptic Seizure ◽  
Seizure Detection ◽  
Epileptic Seizure Detection ◽  
Eeg Recordings

Automated Real-Time Epileptic Seizure Detection in Scalp EEG Recordings Using an Algorithm Based on Wavelet Packet Transform

2010 ◽  
Vol 57 (7) ◽  
pp. 1639-1651 ◽  
Author(s):  
Ali Shahidi Zandi ◽  
Manouchehr Javidan ◽  
Guy A Dumont ◽  
Reza Tafreshi
Keyword(s):  
Real Time ◽  
Epileptic Seizure ◽  
Wavelet Packet ◽  
Seizure Detection ◽  
Wavelet Packet Transform ◽  
Epileptic Seizure Detection ◽  
Scalp Eeg ◽  
Eeg Recordings
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