A novel approach for classification of epileptic seizures using matrix determinant

Raghu, S.; Sriraam, N.; Hegde, Alangar S.; Kubben, Pieter L.

doi:10.1016/j.eswa.2019.03.021

Cited by 85 publications

(43 citation statements)

References 56 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Several methods have been presented for the detection and classification of seizure and seizure-free EEG segments by using time and frequency domain features such as energy [7], exponential energy [8], matrix determinant [2], spectral power of Hjorth's mobility components [9], cross-correlation, power spectral density [10], subband spectral powers [11], average value, maximum value, and minimum value [5]. Furthermore, several studies may be found in the literature using the wavelet transform and its derivative approaches [6,12].…”

Section: Related Studiesmentioning

confidence: 99%

“…Epilepsy is one of the neurological disorders associated with disruption of brain activity that affects approximately 50 million people of the world's population [1,2]. Detection of epileptic seizures is performed by neurologists by a visual examination of long-term electroencephalogram (EEG) signals.…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, epileptic seizures are initiated in different brain lobes of different individuals, so it is not possible to determine a standard focus center for the studies. Therefore long-term EEG recordings are needed to detect epileptic seizures and determine focus center [2][3][4][5].…”

Section: Introductionmentioning

confidence: 99%

“…Since visual examination of long-term EEG data makes it difficult to diagnose the disease, automatic seizure detection has become a very popular research area and various signal processing methods have been applied to solve this problem [2,5,6]. Many types of seizure detection and classification algorithms have been proposed in the literature [5].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Epileptic seizure classifications using empirical mode decomposition and its derivative

et al. 2020

View full text Add to dashboard Cite

Background: Epilepsy is one of the most common neurological disorders associated with disruption of brain activity. In the classification and detection of epileptic seizures, electroencephalography (EEG) measurements, which record the electrical activities of the brain, are frequently used. Empirical mode decomposition (EMD) and its derivative, ensemble EMD (EEMD) are recently developed methods used to decompose nonstationary and nonlinear signals such as EEG into a finite number of oscillations called intrinsic mode functions (IMFs). Our main objective in this study is to present a hybrid IMF selection method combining four different approaches (energy, correlation, power spectral distance, and statistical significance measures), and investigate the effect of selected IMFs extracted by EMD and EEMD on the classification. We have applied the proposed IMF selection approach on the classification of EEG signals recorded from epilepsy patients who are under treatment at our collaborator hospital. Multichannel EEG signals collected from epilepsy patients are decomposed into IMFs, and then IMF selection was performed. Finally, time-and spectral-domain, and nonlinear features are extracted and feature sets are created for the classification. Results: The maximum classification accuracies obtained using various combinations of IMFs were 94.56%, 95.63%, 96.8%, and 96.25% for SVM, KNN, naive Bayes, and logistic regression classifiers, respectively, by using EMD analysis; whereas, the EEMD approach has provided maximum classification accuracies of 96.06%, 97%, 97%, and 96.25% for SVM, KNN, naive Bayes, and logistic regression, respectively. Classification performance with the same features obtained using direct EEG signals instead of the decomposed IMFs was worse than the aforementioned 2 approaches for every combination. Conclusion: Simulation results demonstrate that the proposed IMF selection approach affects the classification results. Also, EEMD provides a robust method for feature extraction from EEG signals in order to classify pre-seizure and seizure segments.

show abstract

Section: Related Studiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Epileptic seizure classifications using empirical mode decomposition and its derivative

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Buettner et al (2019) extracts higher-order features for EEG analysis. Raghu et al (2019) realizes the signal recognition of epileptic seizure based on matrix terminator. Hossain et al (2019) establishes a deep learning network to visualize brain imaging.…”

Section: Introductionmentioning

confidence: 99%

Epilepsy EEG Signal Classification Algorithm Based on Improved RBF

Zhou

2020

Front. Neurosci.

View full text Add to dashboard Cite

Epilepsy is a chronic recurrent transient brain dysfunction syndrome. It is characterized by recurrent epilepsy caused by abnormal discharge of brain neurons. Epilepsy is one of the common diseases in nervous system. The analysis of EEG signals is a hot topic in current research. In order to solve the problem of epileptic EEG signals classification accurately, we carry out in-depth research on epileptic EEG signals, analyze features from linear and non-linear perspectives, input them into the improved RBF model to dynamically extract effective features, and introduce one against one strategy classifier to reduce the probability of error classification. Experiments show that the proposed algorithm has strong robustness and high epileptic signal recognition rate.

show abstract

A medium‐weight deep convolutional neural network‐based approach for onset epileptic seizures classification in EEG signals

Nemati

Meshgini

2022

Brain and Behavior

View full text Add to dashboard Cite

Introduction Epileptic condition can be detected in EEG data seconds before it occurs, according to evidence. To overcome the related long‐term mortality and morbidity from epileptic seizures, it is critical to make an initial diagnosis, uncover underlying causes, and avoid applicable risk factors. Progress in diagnosing onset epileptic seizures can ensure that seizures and destroyed damages are detectable at the time of manifestation. Previous seizure detection models had problems with the presence of multiple features, the lack of an appropriate signal descriptor, and the time‐consuming analysis, all of which led to uncertainty and different interpretations. Deep learning has recently made tremendous progress in categorizing and detecting epilepsy. Method This work proposes an effective classification strategy in response to these issues. The discrete wavelet transform (DWT) is used to breakdown the EEG signal, and a deep convolutional neural network (DCNN) is used to diagnose epileptic seizures in the first phase. Using a medium‐weight DCNN (mw‐DCNN) architecture, we use a preprocess phase to improve the decision‐maker method. The proposed approach was tested on the CHEG‐MIT Scalp EEG database's collected EEG signals. Result The results of the studies reveal that the mw‐DCNN algorithm produces proper classification results under various conditions. To solve the uncertainty challenge, K‐fold cross‐validation was used to assess the algorithm's repeatability at the test level, and the accuracies were evaluated in the range of 99%–100%. Conclusion The suggested structure can assist medical specialistsin analyzing epileptic seizures' EEG signals more precisely.

show abstract

A novel approach for classification of epileptic seizures using matrix determinant

Cited by 85 publications

References 56 publications

Epileptic seizure classifications using empirical mode decomposition and its derivative

Epileptic seizure classifications using empirical mode decomposition and its derivative

Epilepsy EEG Signal Classification Algorithm Based on Improved RBF

A medium‐weight deep convolutional neural network‐based approach for onset epileptic seizures classification in EEG signals

Contact Info

Product

Resources

About