High-Performance Seizure Detection System Using a Wavelet-Approximate Entropy-fSVM Cascade With Clinical Validation

Abstract

The classification of electroencephalography (EEG) signals is one of the most important methods for seizure detection. However, verification of an atypical epileptic seizure often can only be done through long-term EEG monitoring for 24 hours or longer. Hence, automatic EEG signal analysis for clinical screening is necessary for the diagnosis of epilepsy. We propose an EEG analysis system of seizure detection, based on a cascade of wavelet-approximate entropy for feature selection, Fisher scores for adaptive feature selection, and support vector machine for feature classification. Performance of the system was tested on open source data, and the overall accuracy reached 99.97%. We further tested the performance of the system on clinical EEG obtained from a clinical EEG laboratory and bedside EEG recordings. The results showed an overall accuracy of 98.73% for routine EEG, and 94.32% for bedside EEG, which verified the high performance and usefulness of such a cascade system for seizure detection. Also, the prediction model, trained by routine EEG, can be successfully generalized to bedside EEG of independent patients.

Medical Big Data: Neurological Diseases Diagnosis Through Medical Data Analysis

“…2, the abnormal pattern of the signals significantly appears in the seizure period. More recently, Shen et al [23] introduced a method based on a cascade of wavelet-approximate entropy for feature extraction in the epileptic EEG signal classification. They tested three existing methods for classification: support vector machine (SVM), k-nearest neighbour (kNN) and radial basis function neural network (RBFNN), to determine which has the best performance in such as cascaded EEG analysis system.…”

Automatic Detection of Epilepsy and Seizure Using Multiclass Sparse Extreme Learning Machine Classification

“…Although the Bonn datasets have been used by many studies to test their EEG analysis algorithms, they have some limitations, one of which is that the Bonn datasets about epilepsy patients are obtained by using intracranial electrodes [27]. Considering that the intracranial recordings is not always available in the clinic [27, 32], the open CHB-MIT scalp EEG database [40] is also used to verify the effectiveness of classification algorithms in some studies.…”

“…Considering that the intracranial recordings is not always available in the clinic [27, 32], the open CHB-MIT scalp EEG database [40] is also used to verify the effectiveness of classification algorithms in some studies. As is known, the CHB-MIT scalp EEG database is collected from epilepsy patients and therefore only includes the seizure class and the seizure-free class.…”

“…Although some previous feature selection methods can increase the detection performance [27, 28], they suffer from the high dimensionality of features, and the complexity makes hardware implementation difficult and expensive. In this work, three-level LDWT is used to decompose the EEG signals into delta, theta, alpha, and beta subbands; the feature values of each subband are computed to create multidimensional feature vectors.…”

Ultra-fast Epileptic seizure detection using EMD based on multichannel electroencephalogram

“…Our team had proposed some outcome with wavelet transform and approximate entropy using support vector machine [15] [16].…”