Automatic feature extraction using generalised autoregressive conditional heteroscedasticity model: an application to electroencephalogram classification

Mihandoost, Sara; Amirani, Mehdi Chehel; Mazlaghani, M.; Mihandoost, A.

doi:10.1049/iet-spr.2011.0338

Cited by 16 publications

(10 citation statements)

References 19 publications

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“…[4] CHB-MIT 95.7 Samiee (2017) [9] CHB-MIT Sensitivity (91.13) Guo et al (2011) [17] A, E 95. 2 Mihandoost et al (2012) [12] A, D, E 98.87 Fu et al (2014) [2] A, E 99.13 Sharma and Pachori (2015) [7] C, D, E 98.67…”

Section: Resultsmentioning

confidence: 99%

“…A recent survey on the topic can be cited in [10]. The proposed approaches used different types of features such as wavelet transform [11], [12]; Fourier transform [13]; or other feature extraction methods [2]- [9] . Elmahdy et al proposed the use of two types of features; viz.…”

Section: Literature Surveymentioning

confidence: 99%

“…The authors reported 97.4 and 93.5% as sensitivity and specificity, respectively, and a 95% accuracy. Mihandoost et al used markov random fields to select features from discrete wavelet transform (DWT) [12]. They used multilayer perceptron (MLP) neural network with three hidden layers.…”

Section: Literature Surveymentioning

confidence: 99%

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Automatic Detection of Epilepsy Using EEG Energy and Frequency Bands

Bellegdi

Arafat

2017

ijamec

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This paper demonstrates the effectiveness of information fusion at the feature vectors level for automatic detection of epilepsy. Experiments used features ranging from separate EEG frequency band waves to combinations of band waves, in addition to signal energy. We used three classifiers with the feature vectors: TreeBoost, Random Forests, and support vector machines. We carried out experiments using a real life EEG signals data set that is available from the University of Bonn Hospital in Germany. This paper shows the effect of combining together signal energy with different EEG frequency band waves in order to classify epilepsy, and that this combination has computed 97.5% accuracy over using feature vectors with fewer band wave transformations (84-95.5% accuracy), using the TreeBoost algorithm and 10 folds cross validation. This combination computed 99% specificity and 95.5% sensitivity. Furthermore, the paper demonstrates and analyses the effectiveness of using ensemble based tree learning.

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Section: Resultsmentioning

confidence: 99%

Section: Literature Surveymentioning

confidence: 99%

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Automatic Detection of Epilepsy Using EEG Energy and Frequency Bands

Bellegdi

Arafat

2017

ijamec

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“…magnitude differences) of high‐frequency DCT coefficient can be a good feature to distinguish ictal signal from interictal signals. Note that EEG signal has non‐stationary nature [22]. If we use recorded data for a time window and use DCT coefficient characteristics, we can avoid the effect of non‐stationary characteristics of EEG signal analysis.…”

Section: Proposed Methodsmentioning

confidence: 99%

Epileptic seizure detection by exploiting temporal correlation of electroencephalogram signals

Parvez

Paul

2015

IET signal process.

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Electroencephalogram (EEG) has a great potential for diagnosis and treatment of brain disorders like epileptic seizure. Feature extraction and classification of EEG signals is the crucial task to detect the stages of ictal and interictal signals for treatment and precaution of epileptic patients. However, existing seizure and non-seizure feature extraction techniques are not good enough for the classification of ictal and interictal EEG signals considering the non-abruptness phenomena and inconsistency in different brain locations. In this study, the authors present a new approach for feature extraction and classification by exploiting temporal correlation within EEG signals for better seizure detection as any abruptness in the temporal correlation within a signal represents the transition of a phenomenon. In the proposed methods, they divide an EEG signal into a number of epochs and arrange them into two-dimensional matrix and then apply different transformation/decomposition to extract a number of statistical features. These features are then used as an input into LS-SVM to classify them. Experimental results show that the proposed methods outperform the existing state-of-the-art method for better classification in terms of sensitivity, specificity and accuracy of ictal and interictal period of epilepsy for benchmark datasets and different brain locations.

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“…Combining SVM with IA and feature weights, immune feature weighted SVM (IFWSVM) is used to multiclassify five different mental tasks. In [3] the authors have developed an automatic seizure detection system that diagnoses epilepsy. The proposed detection system is based on generalized autoregressive conditional heteroscedasticity (GARCH) model.…”

Section: Related Workmentioning

confidence: 99%

Epileptic Seizure Classification of EEG Image Using SVM

Pazhanirajan¹

2014

IJIRSET

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ABSTRACT:In recent years humans suffer from various neurological disorders such as headache, dementia, traumatic brain injuries, strokes and epilepsy. Nearly 50 million people of the world population in all ages suffer from epilepsy. To diagnose epilepsy an automatic seizure detection system is an important tool. In this paper we present a new approach for classification of Electroencephalogram (EEG) signals into two categories namely epilepsy and non epilepsy. The features of the EEG images are extracted using Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT). The extracted features are used in the model generation. The pattern classification model of SVM observes the distribution of the EEG features of classes. The experiment on various EEG image illustrate that the results of SVM are significant and effective.

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Automatic feature extraction using generalised autoregressive conditional heteroscedasticity model: an application to electroencephalogram classification

Cited by 16 publications

References 19 publications

Automatic Detection of Epilepsy Using EEG Energy and Frequency Bands

Automatic Detection of Epilepsy Using EEG Energy and Frequency Bands

Epileptic seizure detection by exploiting temporal correlation of electroencephalogram signals

Epileptic Seizure Classification of EEG Image Using SVM

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