Automatic Detection of Epileptic Seizures in EEG Using Sparse CSP and Fisher Linear Discrimination Analysis Algorithm

Fu, Rongrong; Tian, Yue; Shi, Pengfei; Bao, Tiantian

doi:10.1007/s10916-019-1504-1

Cited by 30 publications

(15 citation statements)

References 35 publications

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“…In the literature [ 19 ], the original EEG signals were convolved with convolutional neural network in one dimension to predict epileptic seizures. In [ 35 , 36 ], the original signal is transformed into the frequency domain through the Fourier transform, and then the convolutional neural network is used for classification.…”

Section: Related Workmentioning

confidence: 99%

Automatic Detection of Epilepsy Based on Entropy Feature Fusion and Convolutional Neural Network

Sun

Chen

2022

Oxidative Medicine and Cellular Longevity

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Epilepsy is a neurological disorder, caused by various genetic and acquired factors. Electroencephalogram (EEG) is an important means of diagnosis for epilepsy. Aiming at the low efficiency of clinical artificial diagnosis of epilepsy signals, this paper proposes an automatic detection algorithm for epilepsy based on multifeature fusion and convolutional neural network. Firstly, in order to retain the spatial information between multiple adjacent channels, a two-dimensional Eigen matrix is constructed from one-dimensional eigenvectors according to the electrode distribution diagram. According to the feature matrix, sample entropy SE, permutation entropy PE, and fuzzy entropy FE were used for feature extraction. The combined entropy feature is taken as the input information of three-dimensional convolutional neural network, and the automatic detection of epilepsy is realized by convolutional neural network algorithm. Epilepsy detection experiments were performed in CHB-MIT and TUH datasets, respectively. Experimental results show that the performance of the algorithm based on spatial multifeature fusion and convolutional neural network achieves excellent results.

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Section: Related Workmentioning

confidence: 99%

Automatic Detection of Epilepsy Based on Entropy Feature Fusion and Convolutional Neural Network

Sun

Chen

2022

Oxidative Medicine and Cellular Longevity

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“…Feature extraction plays a key role in BCIs: Principal component analysis (PCA), 13,14 short‐time Fourier transform (STFT), 15 canonical correlation analysis (CCA), 16 empirical mode decomposition (EMD), 17 power spectral entropy (PSE), 18 and other feature extraction techniques have all been studied so far for MI‐based EEG signals. The feature translation procedure has also been completed utilizing many traditional machine learning algorithms, including linear discriminant analysis (LDA), 19 support vector machine (SVM), 20 K‐nearest neighbors (K‐NN), 21 fisher discriminant analysis (FDA), 22 and Bayesian classifiers 23 . The back‐propagation (BP) neural network outperformed the self‐organizing feature map (SOFM) neural network and LDA classifier in a study by Wang et al, employing the feature extraction method based on spectrum analysis and wavelet packet analysis (WPA) for the classification of EEG signals of MI movements 24 .…”

Section: Introductionmentioning

confidence: 99%

“…The feature translation procedure has also been completed utilizing many traditional machine learning algorithms, including linear discriminant analysis (LDA), 19 support vector machine (SVM), 20 K-nearest neighbors (K-NN), 21 fisher discriminant analysis (FDA), 22 and Bayesian classifiers. 23 The back-propagation (BP) neural network outperformed the self-organizing feature map (SOFM) neural network and LDA classifier in a study by Wang et al, employing the feature extraction method based on spectrum analysis and wavelet packet analysis (WPA) for the classification of EEG signals of MI movements.…”

Section: Introductionmentioning

confidence: 99%

Deep CNN‐based classification of motor imagery tasks from EEG signals using 2D wavelet transformed images of adaptively reconstructed signals from MVMD decomposed modes

Alizadeh

Afrakhteh

Mosavi

2023

Int J Imaging Syst Tech

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Brain–computer interfaces (BCI) have begun to revolutionize many aspects of human life, ranging from health to smart living and communication devices. Therefore, BCI technologies require accurate recognition and classification systems of the brain responses to a variety of motor imagery (MI) movements through electroencephalogram (EEG) signals. Nowadays, deep learning models have received a lot of interest for learning features and classifying numerous kinds of data. However, it has not been thoroughly investigated for EEG signal classification. In this study, to reach an improved performance in MI‐EEG signal classification, we proposed a new strategy based on continuous wavelet transform (CWT)‐based time‐frequency maps to generate two‐dimensional (2D) EEG images of adaptively reconstructed signals from the extracted multivariate mode decomposition (MVMD) modes. In the next step, the extracted signals are projected in the new space using the proposed multiclass common spatial pattern (MCCSP) filtering. The resulting images are then fed to the convolutional neural network (CNN) architectures (AlexNet and LeNet). The proposed framework has the benefit of achieving high classification accuracy to be attained even with a significant amount of input data. LeNet and AlexNet reach the best average accuracy rates of 95.33% and 93.66% on dataset 1 from BCI competition IV and the results on dataset 2a from competition IV are more promising than the current state of the arts. Our results depict that MI‐EEG task recognition using image classification approaches, along with CNNs, is as comparable or even superior to other existing traditional approaches and provides high potential for upcoming research.

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“…There exists a wide variety of methods to accurately detect seizures and their patterns in EEG. Most of these methods are based on supervised machine learning techniques, such as Support Vector Machine [7], logistic regression [8], decision trees [9], k-Nearest Neighbor, Random Forest [10], or discriminant analysis [11]. They mainly differ according to their feature extraction and classification approaches.…”

Section: Introductionmentioning

confidence: 99%

Statistical Model-Based Classification to Detect Patient-Specific Spike-and-Wave in EEG Signals

et al. 2020

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Spike-and-wave discharge (SWD) pattern detection in electroencephalography (EEG) is a crucial signal processing problem in epilepsy applications. It is particularly important for overcoming time-consuming, difficult, and error-prone manual analysis of long-term EEG recordings. This paper presents a new method to detect SWD, with a low computational complexity making it easily trained with data from standard medical protocols. Precisely, EEG signals are divided into time segments for which the continuous Morlet 1-D wavelet decomposition is computed. The generalized Gaussian distribution (GGD) is fitted to the resulting coefficients and their variance and median are calculated. Next, a k-nearest neighbors (k-NN) classifier is trained to detect the spike-and-wave patterns, using the scale parameter of the GGD in addition to the variance and the median. Experiments were conducted using EEG signals from six human patients. Precisely, 106 spike-and-wave and 106 non-spike-and-wave signals were used for training, and 96 other segments for testing. The proposed SWD classification method achieved 95% sensitivity (True positive rate), 87% specificity (True Negative Rate), and 92% accuracy. These promising results set the path for new research to study the causes underlying the so-called absence epilepsy in long-term EEG recordings.

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Automatic Detection of Epileptic Seizures in EEG Using Sparse CSP and Fisher Linear Discrimination Analysis Algorithm

Cited by 30 publications

References 35 publications

Automatic Detection of Epilepsy Based on Entropy Feature Fusion and Convolutional Neural Network

Automatic Detection of Epilepsy Based on Entropy Feature Fusion and Convolutional Neural Network

Deep CNN‐based classification of motor imagery tasks from EEG signals using 2D wavelet transformed images of adaptively reconstructed signals from MVMD decomposed modes

Statistical Model-Based Classification to Detect Patient-Specific Spike-and-Wave in EEG Signals

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