Automatic Detection of Epilepsy Based on Entropy Feature Fusion and Convolutional Neural Network

Abstract

Epilepsy is a neurological disorder, caused by various genetic and acquired factors. Electroencephalogram (EEG) is an important means of diagnosis for epilepsy. Aiming at the low efficiency of clinical artificial diagnosis of epilepsy signals, this paper proposes an automatic detection algorithm for epilepsy based on multifeature fusion and convolutional neural network. Firstly, in order to retain the spatial information between multiple adjacent channels, a two-dimensional Eigen matrix is constructed from one-dimensional eigenvectors according to the electrode distribution diagram. According to the feature matrix, sample entropy SE, permutation entropy PE, and fuzzy entropy FE were used for feature extraction. The combined entropy feature is taken as the input information of three-dimensional convolutional neural network, and the automatic detection of epilepsy is realized by convolutional neural network algorithm. Epilepsy detection experiments were performed in CHB-MIT and TUH datasets, respectively. Experimental results show that the performance of the algorithm based on spatial multifeature fusion and convolutional neural network achieves excellent results.

Epileptic seizure detection using CHB-MIT dataset: The overlooked perspectives

“…The results have shown the perfect Sen scores for most subjects except for a few (2–7) subjects out of 24. A tremendous performance improvement was observed in some studies if these subjects were excluded [ 44 , 45 , 47 ]. It is very challenging to achieve outstanding performance in this study setting for the following reasons, which coincide with the main contributions of this study:…”

“…The majority of the previous studies discarded the records with no seizure events in them, even though those records are part of the continuous EEG signal. Some studies even considered a portion of the data or excluded some based on their choice for analysis [44][45][46][47][48]. When the data are balanced for testing or portions of data are left out of consideration in the analytical process, this can greatly impact the performance score.…”

Retracted: Automatic Detection of Epilepsy Based on Entropy Feature Fusion and Convolutional Neural Network

“…This article has been retracted by Hindawi following an investigation undertaken by the publisher [ 1 ]. This investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process: Discrepancies in scope Discrepancies in the description of the research reported Discrepancies between the availability of data and the research described Inappropriate citations Incoherent, meaningless and/or irrelevant content included in the article Manipulated or compromised peer review …”

“…This article has been retracted by Hindawi following an investigation undertaken by the publisher [1]. This investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process:…”