Epileptic seizure detection using DWT based fuzzy approximate entropy and support vector machine

Kumar, Yatindra; Dewal, M. L.; Anand, R. S.

doi:10.1016/j.neucom.2013.11.009

Cited by 278 publications

(125 citation statements)

References 34 publications

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“…Although there has been previous research using SampEn and PE to study the Bonn data, the results were not as good as we achieved using FuzzyEn. Even when Kumar et al (2014) used FuzzyEn to classify the Bonn data, the accuracy was only 95; however, in our research, the accuracy was 100%. This result further demonstrates that a study using a classification algorithm and FuzzyEn as the classification index is more reasonable.…”

Section: Discussioncontrasting

confidence: 61%

The detection of epileptic seizure signals based on fuzzy entropy

Xiang

et al. 2015

Journal of Neuroscience Methods

203

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Section: Discussioncontrasting

confidence: 61%

The detection of epileptic seizure signals based on fuzzy entropy

Xiang

et al. 2015

Journal of Neuroscience Methods

203

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“…The common methods of dealing message are independent vector based method, Fourier transforms method, and wavelet transforms method and so on [6][7][8][9][10][11][12][13][14][15]. Given the instantaneity of developed system, we use FFT to transform the EEG signal from time domain to frequency domain, and extract the corresponding features in frequency domain.…”

Section: Extraction Of Brainwave Controlmentioning

confidence: 99%

Remote controller using steady state visual evoked potential

Song¹,

Wang²,

Wang³

et al. 2017

2nd Annual International Conference on Energy, Environmental &Amp; Sustainable Ecosystem Development (EESED 2016)

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This paper develops a new air-conditioning remote ccontrol system which based on brain waves and the Android system. First of all, the desired brain waves in EEG signals in the form of steady-state visual evoked is collected. By extracting and analysis the feature hidden in EEG signal, the system can identify the operation intention of volunteer to control air-conditioning machine, such as open or close and addition or subtraction about temperature. In addition, the control coding and wireless connections are implemented based on Bluetooth technology. Finally, we realize the Android mobile phones as the core of whole remote control. The eexperimental results show that brain wave control system of air conditioner remote is capable of stable and rapid detection of brain signals to control commands. Compared to commonly used buttons remote control, our designed system can realize the goal of non-contact control for air-conditioning.

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“…Epileptic seizure detection techniques for finding the modification of EEG-based indexes can be divided into four categories: time domain, frequency domain, time-frequency domain, and nonlinear methods [8][9][10][11]. The time domain method searches for periodic, rhythmic patterns in EEG for the seizure state and provides a measure for rhythmicity [12].…”

Section: Introductionmentioning

confidence: 99%

Automatic detection of absence seizures with compressive sensing EEG

et al. 2016

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Automatic detection of absence seizures with compressive sensing EEG, Neurocomputing, http://dx. AbstractAbsence epilepsy, a neurological disorder, is characterized by the recurrence of seizures, which have serious impact on the sufferers' daily life. The seizure detection has a great importance in the diagnosis and therapy of epileptic patients. Visual inspection of the electroencephalogram (EEG) signals for detection of interictal, pre-ictal and ictal activities is a strenuous and time-consuming task due to the huge volumes of EEG segments that have to be studied. In this study, we proposed a novel automatic detection method based on the altered compressibility of EEG during the three states with compressive sensing. To evaluate the proposed method, segments of interictal, pre-ictal and ictal EEG segments (100 segments in each state) were used. Two entropies, namely the Sample Entropy (SE) and the permutation Entropy (PE), and Hurst Index (HI) were extracted respectively from the segments to compare with the proposed method. Significant features were selected using the ANOVA test. After evaluating the performance of the selected features by four classifiers (Decision Tree, K-Nearest Neighbor, Discriminant Analysis, Support Vector Machine) respectively, the results show that the proposed method can achieve the highest accuracy of 76.7%, which is higher than HI (55.3%), sample entropy (71%), and permutation entropy (73%). Hence, the altered compressibility of EEG with CS can act as a good biomarker for distinguish seizure-free, per-seizure and seizure state. In addition, compressive sensing requires less energy but has competitive compression ratio compared to traditional compression techniques, which enables our method to tele-monitoring of epilepsy patients using wireless body-area networks in personalized medicine.

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Epileptic seizure detection using DWT based fuzzy approximate entropy and support vector machine

Cited by 278 publications

References 34 publications

The detection of epileptic seizure signals based on fuzzy entropy

The detection of epileptic seizure signals based on fuzzy entropy

Remote controller using steady state visual evoked potential

Automatic detection of absence seizures with compressive sensing EEG

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