Log Energy Entropy-Based EEG Classification with Multilayer Neural Networks in Seizure

Abstract

In this study, normal EEG series recorded from healthy volunteers and epileptic EEG series recorded from patients within and without seizure are classified by using Multilayer Neural Network (MLNN) architectures with respect to several time domain entropy measures such as Shannon Entropy (ShanEn), Log Energy Entropy (LogEn), and Sample Entropy (Sampen). In tests, the MLNN is performed with several numbers of neurons for both one hidden layer and two hidden layers. The results show that segments in seizure have significantly lower entropy values than normal EEG series. This result indicates an important increase of EEG regularity in epilepsy patients. The LogEn approach, which has not been experienced in EEG classification yet, provides the most reliable features into the EEG classification with very low absolute error as 0.01. In particular, the MLNN can be proposed to distinguish the seizure activity from the seizure-free epileptic series where the LogEn values are considered as signal features that characterize the degree of EEG complexity. The highest classification accuracy is obtained for one hidden layer architecture.

Classification of epileptic seizures using wavelet packet log energy and norm entropies with recurrent Elman neural network classifier

“…2. Entropies based detection of an epileptic seizure using EEGs have been studied and well reported in the recent past (Srinivasan et al 2007;Pravin et al 2010;Aydin et al 2009;Wang et al 2011;Gopan et al 2015). Srinivasan et al (2007) have employed ApEn feature with REN network as a classifier and have achieved 100 % CA.…”

“…Significant features were selected using ANOVA test and 99 % classification accuracy was obtained using Gaussian mixture model (GMM) classifier with tenfold cross validation. Aydin et al (2009) make use of log energy entropy with a multilayer neural network for distinguishing epileptic EEGs from normal. Our work makes use of WPT followed by log energy entropy to explore the low-frequency components during epileptic EEG signal and recorded best results compared to work done by Aydin et al…”

“…Any EEG-based quantitative study towards epileptic seizure detection requires the appropriate application of filtering, segmentation, features selection and pattern classifier techniques. Although several attempts have been made by make use of entropy as a feature pattern to detect epileptic seizures from EEG recordings (Aydin et al 2009;Pravin et al 2010;Guo et al 2010;Wang et al 2013), there are still unsolved issues related to computational complexity and overall performance. The appropriate selection of feature and pattern classifier by considering its relative performance will confirm its suitability for the real time seizure detection.…”

Controlling a virtual forehand prosthesis using an adaptive and affective Human-Machine Interface

“…The filtered data from channel 3 was divided into non-overlapped 128-msec time slots. Then, the logarithm of energy entropy (hereafter, entropy or statistical entropy) for each time slot is calculated using the same method as described in [9]: ∑…”

Co-Adaptive and Affective Human-Machine Interface for Improving Training Performances of Virtual Myoelectric Forearm Prosthesis

“…Nonetheless, problems can arise when this effort is required for a long period. The physiological data to understand the effort from the operator are used in various studies (e.g., [31], [32], [33]). Specifically, electroencephalography signals have yielded a reliable description of the cognitive state [17], [34].…”