Wepilet, optimal orthogonal wavelets for epileptic seizure prediction with one single surface channel

Bandarabadi, Mojtaba; Teixeira, César; Sales, Francisco; Dourado, António

doi:10.1109/iembs.2011.6091784

Cited by 5 publications

(5 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Bandarabadi et al . [ 24 ] recently developed Seizure-specific wavelets for seizure prediction with promising results. However, designing patient-specific wavelets is a very time-consuming task, and beyond the objectives intended for this work.…”

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%

See 1 more Smart Citation

Epileptic seizure prediction based on ratio and differential linear univariate features

et al. 2015

View full text Add to dashboard Cite

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.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Epileptic seizure prediction based on ratio and differential linear univariate features

et al. 2015

View full text Add to dashboard Cite

show abstract

“…The Daubiches-4 (db4) has shown good localization properties of EEG signal in the time and frequency domains (Petrosian et al, 2000) and is used in this work. More recently special wavelets have been developed for seizure prediction, showing promising results (Bandarabadi et al, 2011).…”

Section: Wavelet Coefficientsmentioning

confidence: 99%

Preprocessing effects of 22 linear univariate features on the performance of seizure prediction methods

Rasekhi

Mollaei

Bandarabadi

et al. 2013

Journal of Neuroscience Methods

118

View full text Add to dashboard Cite

Combining multiple linear univariate features in one feature space and classifying the feature space using machine learning methods could predict epileptic seizures in patients suffering from refractory epilepsy. For each patient, a set of twenty-two linear univariate features were extracted from 6 electroencephalogram (EEG) signals to make a 132 dimensional feature space. Preprocessing and normalization methods of the features, which affect the output of the seizure prediction algorithm, were studied in terms of alarm sensitivity and false prediction rate (FPR). The problem of choosing an optimal preictal time was tackled using 4 distinct values of 10, 20, 30, and 40 min. The seizure prediction problem has traditionally been considered a two-class classification problem, which is also exercised here. These studies have been conducted on the features obtained from 10 patients. For each patient, 48 different combinations of methods are compared to find the best configuration. Normalization by dividing by the maximum and smoothing are found to be the best configuration in most of the patients. The results also indicate that applying machine learning methods on a multidimensional feature space of 22 univariate features predicted seizure onsets with high performance. On average, the seizures were predicted in 73.9% of the cases (34 out of 46 in 737.9h of test data), with a FPR of 0.15 h(-1).

show abstract

“…In the literature, there is a wide range of proposals for the identification of general epileptic seizures [4][5][6][7][8][9][10][11][12][13], mostly based on the machine-learning approach, which support the doctors in the time-consuming manual labelling [4]. However, very few contributions specifically dealing with NFLE are present, and they reach lower performance indices.…”

Section: Introductionmentioning

confidence: 99%

Convolutional Neural Network for Seizure Detection of Nocturnal Frontal Lobe Epilepsy

et al. 2020

View full text Add to dashboard Cite

The Nocturnal Frontal Lobe Epilepsy (NFLE) is a form of epilepsy in which seizures occur predominantly during sleep. In other forms of epilepsy, the commonly used clinical approach mainly involves manual inspection of encephalography (EEG) signals, a laborious and time-consuming process which often requires the contribution of more than one experienced neurologist. In the last decades, numerous approaches to automate this detection have been proposed and, more recently, machine learning has shown very promising performance. In this paper, an original Convolutional Neural Network (CNN) architecture is proposed to develop patient-specific seizure detection models for three patients affected by NFLE. The performances, in terms of accuracy, sensitivity, and specificity, exceed by several percentage points those in the most recent literature. The capability of the patient-specific models has been also tested to compare the obtained seizure onset times with those provided by the neurologists, with encouraging results. Moreover, the same CNN architecture has been used to develop a cross-patient seizure detection system, resorting to the transfer-learning paradigm. Starting from a patient-specific model, few data from a new patient are enough to customize his model. This contribution aims to alleviate the task of neurologists, who may have a robust indication to corroborate their clinical conclusions.

show abstract

Wepilet, optimal orthogonal wavelets for epileptic seizure prediction with one single surface channel

Cited by 5 publications

References 13 publications

Epileptic seizure prediction based on ratio and differential linear univariate features

Epileptic seizure prediction based on ratio and differential linear univariate features

Preprocessing effects of 22 linear univariate features on the performance of seizure prediction methods

Convolutional Neural Network for Seizure Detection of Nocturnal Frontal Lobe Epilepsy

Contact Info

Product

Resources

About