Seizure Classification From EEG Signals Using an Online Selective Transfer TSK Fuzzy Classifier With Joint Distribution Adaption and Manifold Regularization

Emotion plays a significant role in human daily activities, and it can be effectively recognized from EEG signals. However, individual variability limits the generalization of emotion classifiers across subjects. Domain adaptation (DA) is a reliable method to solve the issue. Due to the nonstationarity of EEG, the inferior-quality source domain data bring negative transfer in DA procedures. To solve this problem, an auto-augmentation joint distribution adaptation (AA-JDA) method and a burden-lightened and source-preferred JDA (BLSP-JDA) approach are proposed in this paper. The methods are based on a novel transfer idea, learning the specific knowledge of the target domain from the samples that are appropriate for transfer, which reduces the difficulty of transfer between two domains. On multiple emotion databases, our model shows state-of-the-art performance.

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Epileptic seizure classification using novel entropy features applied on maximal overlap discrete wavelet packet transform of EEG signals

2017 7th International Conference on Computer and Knowledge Engineering (ICCKE) ◽

10.1109/iccke.2017.8167910 ◽

2017 ◽

Cited By ~ 2

Author(s):

Amirmasoud Ahmadi ◽

Shiva Tafakori ◽

Vahid Shalchyan ◽

Mohammad Reza Daliri

Keyword(s):

Epileptic Seizure ◽

Wavelet Packet ◽

Wavelet Packet Transform ◽

Discrete Wavelet ◽

Eeg Signals ◽

Discrete Wavelet Packet Transform ◽

Seizure Classification

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Automated Seizure Classification Using Deep Neural Network Based on Autoencoder

Handbook of Research on Advancements of Artificial Intelligence in Healthcare Engineering - Advances in Healthcare Information Systems and Administration ◽

10.4018/978-1-7998-2120-5.ch001 ◽

2020 ◽

pp. 1-19 ◽

Cited By ~ 1

Author(s):

Rahul Sharma ◽

Pradip Sircar ◽

Ram Bilas Pachori

Keyword(s):

Neural Network ◽

Classification Accuracy ◽

Deep Neural Network ◽

Eeg Signals ◽

Linear Dynamics ◽

Softmax Classifier ◽

Domain Expertise ◽

Electroencephalogram Eeg ◽

The Brain ◽

Seizure Classification

A neurological abnormality in the brain that manifests as a seizure is the prime risk of epilepsy. The earlier and accurate detection of the epileptic seizure is the foremost task for the diagnosis of epilepsy. In this chapter, a nonlinear deep neural network is used for seizure classification. The proposed network is based on the autoencoder that significantly explores the non-linear dynamics of the electroencephalogram (EEG) signals. It involves the traditional deep neural domain expertise to extract the features from the raw data in order to fit a deep neural network-based learning model and predicts the class of the unknown seizures. The EEG signals are subjected to an autoencoder-based neural network that unintendedly extracts the significant attributes that are applied to the softmax classifier. The achieved classification accuracy is up to 100% on different publicly available Bonn University database classes. The proposed algorithm is suitable for real-time implementation.

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Epileptic seizure classification in EEG signals using second-order difference plot of intrinsic mode functions

Computer Methods and Programs in Biomedicine ◽

10.1016/j.cmpb.2013.11.014 ◽

2014 ◽

Vol 113 (2) ◽

pp. 494-502 ◽

Cited By ~ 158

Author(s):

Ram Bilas Pachori ◽

Shivnarayan Patidar

Keyword(s):

Epileptic Seizure ◽

Second Order ◽

Intrinsic Mode Functions ◽

Eeg Signals ◽

Order Difference ◽

Seizure Classification ◽

Difference Plot

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Epileptic Seizure Classification of EEG signals using XGBoost Classifier

Journal of Xidian University ◽

10.37896/jxu14.5/612 ◽

2020 ◽

Vol 14 (5) ◽

Keyword(s):

Epileptic Seizure ◽

Eeg Signals ◽

Seizure Classification

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Epilepsy Signal Recognition Using Online Transfer TSK Fuzzy Classifier Underlying Classification Error and Joint Distribution Consensus Regularization

IEEE/ACM Transactions on Computational Biology and Bioinformatics ◽

10.1109/tcbb.2020.3002562 ◽

2020 ◽

pp. 1-1 ◽

Cited By ~ 1

Author(s):

Yuanpeng Zhang ◽

Ziyuan Zhou ◽

Wenjie Pan ◽

Heming Bai ◽

Wei Liu ◽

...

Keyword(s):

Joint Distribution ◽

Classification Error ◽

Signal Recognition ◽

Fuzzy Classifier

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Multiclass epileptic seizure classification using time-frequency analysis of EEG signals

2012 7th International Conference on Electrical and Computer Engineering ◽

10.1109/icece.2012.6471535 ◽

2012 ◽

Cited By ~ 8

Author(s):

Partha Pratim Acharjee ◽

Celia Shahnaz

Keyword(s):

Frequency Analysis ◽

Epileptic Seizure ◽

Eeg Signals ◽

Time Frequency Analysis ◽

Time Frequency ◽

Seizure Classification

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APPLICATION OF NON-LINEAR AND WAVELET BASED FEATURES FOR THE AUTOMATED IDENTIFICATION OF EPILEPTIC EEG SIGNALS

International Journal of Neural Systems ◽

10.1142/s0129065712500025 ◽

2012 ◽

Vol 22 (02) ◽

pp. 1250002 ◽

Cited By ~ 187

Author(s):

U. RAJENDRA ACHARYA ◽

S. VINITHA SREE ◽

PENG CHUAN ALVIN ANG ◽

RATNA YANTI ◽

JASJIT S. SURI

Keyword(s):

Nearest Neighbor ◽

Probabilistic Neural Network ◽

Automatic Monitoring ◽

Gaussian Mixture ◽

Support Vector ◽

K Nearest Neighbor ◽

Eeg Signals ◽

Fuzzy Classifier ◽

Linear Features ◽

Non Linear

Epilepsy, a neurological disorder, is characterized by the recurrence of seizures. Electroencephalogram (EEG) signals, which are used to detect the presence of seizures, are non-linear and dynamic in nature. Visual inspection of the EEG signals for detection of normal, interictal, and ictal activities is a strenuous and time-consuming task due to the huge volumes of EEG segments that have to be studied. Therefore, non-linear methods are being widely used to study EEG signals for the automatic monitoring of epileptic activities. The aim of our work is to develop a Computer Aided Diagnostic (CAD) technique with minimal pre-processing steps that can classify all the three classes of EEG segments, namely normal, interictal, and ictal, using a small number of highly discriminating non-linear features in simple classifiers. To evaluate the technique, segments of normal, interictal, and ictal EEG segments (100 segments in each class) were used. Non-linear features based on the Higher Order Spectra (HOS), two entropies, namely the Approximation Entropy (ApEn) and the Sample Entropy (SampEn), and Fractal Dimension and Hurst Exponent were extracted from the segments. Significant features were selected using the ANOVA test. After evaluating the performance of six classifiers (Decision Tree, Fuzzy Sugeno Classifier, Gaussian Mixture Model, K-Nearest Neighbor, Support Vector Machine, and Radial Basis Probabilistic Neural Network) using a combination of the selected features, we found that using a set of all the selected six features in the Fuzzy classifier resulted in 99.7% classification accuracy. We have demonstrated that our technique is capable of achieving high accuracy using a small number of features that accurately capture the subtle differences in the three different types of EEG (normal, interictal, and ictal) segments. The technique can be easily written as a software application and used by medical professionals without any extensive training and cost. Such software can evolve into an automatic seizure monitoring application in the near future and can aid the doctors in providing better and timely care for the patients suffering from epilepsy.

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