Epileptic seizure prediction based on ratio and differential linear univariate features

Abstract

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.

Improved Patient-Independent System for Detection of Electrical Onset of Seizures

“…Previous studies used different preictal durations of 5, 10, 20, 30, 40, 120, and 240 minutes to predict the electrical onset of seizure and the mean prediction time varied from a few seconds to minutes. 6,7,40,41 Using the algorithm named Advanced Seizure Prediction via Pre-Ictal Relabeling (ASPPR), the best intervention time proposed for the prediction of the electrographic onset of seizures was 1 minute. 42 A seizure usually lasts for 30 seconds to 2 minutes and is followed by a postictal phase lasting several minutes.…”

“…42 A seizure usually lasts for 30 seconds to 2 minutes and is followed by a postictal phase lasting several minutes. 3,41 According to the length of available EEG, the data were split into preictal and ictal phases. In this work, we defined the preictal phase as a 3-minute-long EEG sample immediately before the clinical onset, whereas the ictal phase was defined as a 2-minute EEG recording clipped immediately after the clinical onset (Fig.…”

Concept-Drifts Adaptation For Machine Learning EEG Epilepsy Seizure Prediction

“…See the Supplemental Material in the Feature Description section to understand each featureŠs a priori expected perceived quality. It is also crucial to remember that a variety of nonlinear and bi/multivariate features would also be interesting to explore 19,28,31,32 . However, this would signiĄcantly increase the computational effort of our work.…”

“…We only make the comparison with studies from the same database as ours, as it is a more reliable comparison because in seizure prediction the performances vary considerably depending on the database and the type of data. There are some studies 8,27,31 who also used the EPILEPSIAE database, which cannot be directly compared to our methodology, as model selection was based on the test performance, which is a priori unknown, maybe resulting in an overestimation of the performance.…”

An efficient approach for driver drowsiness detection at moderate drowsiness level based on electroencephalography signal and vehicle dynamics data

“…to classify epileptic patients' EEG data into preictal and non-preictal seizure classes. [ 19 ] In order to detect motor imagery signals, Wang and Zhang used the Naive Bayes classifier. [ 20 ] In Prasad et al .…”