Optimized feature subsets for epileptic seizure prediction studies

Direito, Bruno; Ventura, Francisco; Teixeira, César; Dourado, António

doi:10.1109/iembs.2011.6090472

Cited by 16 publications

(8 citation statements)

References 15 publications

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“…Direito et al . [ 23 ] by studying 22 univariate features, and employing feature reduction techniques, emphasized the performance of wavelet coefficients in comparison to other features. The Daubechies-4 (db4) mother wavelet possesses good localization properties for EEG signals both in time and frequency domains.…”

Section: Methodsmentioning

confidence: 99%

“…Most seizure prediction methods extract some features from a time moving window of electroencephalogram (EEG) signals and study their behavior during the preictal time compared with the other times. The linear univariate features of statistical moments,[ 19 ] spectral power,[ 20 21 22 ] Hjorth parameters of mobility and complexity,[ 19 ] decorrelation time,[ 19 ] wavelet coefficients[ 23 24 ] have been investigated in seizure prediction studies.…”

Section: Introductionmentioning

confidence: 99%

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Epileptic seizure prediction based on ratio and differential linear univariate features

et al. 2015

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Bivariate features, obtained from multichannel electroencephalogram recordings, quantify the relation between different brain regions. Studies based on bivariate features have shown optimistic results for tackling epileptic seizure prediction problem in patients suffering from refractory epilepsy. A new bivariate approach using univariate features is proposed here. Differences and ratios of 22 linear univariate features were calculated using pairwise combination of 6 electroencephalograms channels, to create 330 differential, and 330 relative features. The feature subsets were classified using support vector machines separately, as one of the two classes of preictal and nonpreictal. Furthermore, minimum Redundancy Maximum Relevance feature reduction method is employed to improve the predictions and reduce the number of false alarms. The studies were carried out on features obtained from 10 patients. For reduced subset of 30 features and using differential approach, the seizures were on average predicted in 60.9% of the cases (28 out of 46 in 737.9 h of test data), with a low false prediction rate of 0.11 h−1. Results of bivariate approaches were compared with those achieved from original linear univariate features, extracted from 6 channels. The advantage of proposed bivariate features is the smaller number of false predictions in comparison to the original 22 univariate features. In addition, reduction in feature dimension could provide a less complex and the more cost-effective algorithm. Results indicate that applying machine learning methods on a multidimensional feature space resulting from relative/differential pairwise combination of 22 univariate features could predict seizure onsets with high performance.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Epileptic seizure prediction based on ratio and differential linear univariate features

et al. 2015

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“…Features can be extracted in two ways: one is to extract hand-crafted features and other is automated feature extraction using deep learning methods. Handcrafted features include univariate [60] and multivariate features [61] in both time as well as in frequency domain. Temporal features include statistical moments [62] mean [63], variance [64], skewness [65] and kurtosis [60], entropy [66], approximate entropy [25], Hjorth parameters [67], PCA [68], [69] and Lyapunov exponent [70].…”

Section: B Feature Extractionmentioning

confidence: 99%

Epileptic Seizures Prediction Using Deep Learning Techniques

2020

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Epilepsy is a very common neurological disease that has affected more than 65 million people worldwide. In more than 30 % of the cases, people affected by this disease cannot be cured with medicines or surgery. However, predicting a seizure before it actually occurs can help in its prevention; through therapeutic intervention. Studies have observed that abnormal activity inside the brain begins a few minutes before the start of a seizure, which is known as preictal state. Many researchers have tried to find a way for predicting this preictal state of a seizure but an effective prediction in terms of high sensitivity and specificity still remains a challenge. The current study, proposes a seizure prediction system that employs deep learning methods. This method includes preprocessing of scalp EEG signals, automated features extraction; using convolution neural network and classification with the support of vector machines. The proposed method has been applied on 24 subjects of scalp EEG dataset of CHBMIT resulting in successfully achieving an average sensitivity and specificity of 92.7% and 90.8% respectively.

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“…The disease cells can spread to the circulatory system and lymph hubs. They can likewise head out to the mind and spinal line (the focal sensory system) and different pieces of the body [1][2][3][4]. incorporate smoking, ionizing radiation, a few synthetic concoctions, (for example, benzene), earlier chemotherapy, and Down disorder.…”

Section: Introductionmentioning

confidence: 99%

Automatic Detection and Classification Techniques of Acute Myelogenous Leukemia (AML) using SVM and Generic Algorithm

2019

ijrte

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Majority of youngsters’ having connected online through internet either through computers or by smart phones. After the entry of Jio in the field of internet, the competition began and the cost of internet service became much cheaper andnoweveryone can afford the cost. Latest “Times of India” statistics shows around 59% of internet users are college students/young men. The trend of going to the physical stores to buy the product is in the decline stage where as the trend of surfing product specification as well as its cost and alternates through online marketing sites is increasing among youngsters. Since, it is more convenient for them to shop anywhere and anytime. Shopping can be done 24 x 7 and before buying; review of product performance through social media and compare its price through varies alternate sites. There is no compulsion to buy the product while surfingoreven if visited the siteforany number of times. The payment can be made through online transition and products will be delivered to doorstep.Hence shopping through online become a joyful experience and preferred by youngsters.

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Optimized feature subsets for epileptic seizure prediction studies

Cited by 16 publications

References 15 publications

Epileptic seizure prediction based on ratio and differential linear univariate features

Epileptic seizure prediction based on ratio and differential linear univariate features

Epileptic Seizures Prediction Using Deep Learning Techniques

Automatic Detection and Classification Techniques of Acute Myelogenous Leukemia (AML) using SVM and Generic Algorithm

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