Automatic Epileptic Seizures Detection and EEG Signals Classification Based on Multi-domain Feature Extraction and Multiscale Entropy Analysis

2020 ◽  
pp. 315-334
Author(s):  
Md. Abu Sayem ◽  
Md. Sohel Rana Sarker ◽  
Md Atiqur Rahman Ahad ◽  
Mosabber Uddin Ahmed
Keyword(s):  
Feature Extraction ◽  
Epileptic Seizures ◽  
Multiscale Entropy ◽  
Entropy Analysis ◽  
Eeg Signals

MULTISCALE ENTROPY ANALYSIS OF EEG RECORDINGS IN EPILEPTIC RATS

2009 ◽  
Vol 21 (03) ◽  
pp. 169-176 ◽  
Author(s):  
Gaoxiang Ouyang ◽  
Chuangyin Dang ◽  
Xiaoli Li
Keyword(s):  
Entropy Method ◽  
Multiscale Entropy ◽  
Test Results ◽  
Entropy Analysis ◽  
Eeg Signals ◽  
Linear Discriminant ◽  
Seizure State ◽  
Single Scale ◽  
Eeg Recordings ◽  
Electroencephalogram Eeg

In this study, we investigate multiscale entropy (MSE) as a tool to evaluate the dynamic characteristics of electroencephalogram (EEG) during seizure-free, pre-seizure and seizure state, respectively, in epileptic rats. The results show that MSE method is able to reveal that EEG signals are more complex in seizure-free state than in seizure state, and can successfully distinguish among different seizure states. The classification ability of the MSE measures is tested using the linear discriminant analysis (LDA). Test results confirm that the classification accuracy of MSE method is superior to traditional single-scale entropy method. MSE method has potential in classifying the epileptic EEG signals.

scholarly journals Feature Extraction and Enhanced Convolutional Neural Network (ECNN) for Detection and Diagnosis of Seizure using EEG Signals

2019 ◽  
Vol 9 (1) ◽  
pp. 1369-1376
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Convolutional Neural Network ◽  
Epileptic Seizures ◽  
Classification Performance ◽  
Experimental Result ◽  
Linear Filter ◽  
Least Mean Square ◽  
Eeg Signals ◽  
Time Frequency

Seizure detection in non-stationary electroencephalography (EEG) is perplexing and difficult task. The human examination for detecting the seizure activities in EEG signals is liable to errors. Apart from the errors, it is a time driven task and also the detection is not precise. In order to detect epileptic seizures more precisely various automatic systems have been emerged to assist neurophysiologists by researchers in various attempts. There are various limitations such as time-consuming, technical artifact issues, result variation with respect to reader expertise level, abnormalities identification. Enhanced Convolutional Neural Network (ECNN) is a technique proposed to mitigate the above mentioned limitations and to categorize more accurate epileptic seizures results. A novel automatic method to sense epileptic seizures using feature extraction and detection is proposed in this research. Linear filter is helpful in reducing the noise along with artifacts when the EEG signals are preprocessed. The noise can be still removed by applying Least Mean Square algorithm. In this proposed research the features are extracted via analytic time frequency with Cascaded wavelet transform and fractal dimension (FD) in order to detect epileptic seizures. Lastly, to analyze the EEG signal for better classification performance of the given dataset, ECNN is adopted. During this research to classify normal, preictal, and seizure classes, a 13-layer deep ECNN algorithm is implemented. This research has special characteristics such that the model yields promising classification accuracy. The experimental result demonstrates that the proposed ECNN is superior in terms of higher sensitivity, specificity, accuracy and lower time complexity rather than the existing methods.

Complexity Measures of Brain Electrophysiological Activity

2010 ◽  
Vol 24 (2) ◽  
pp. 131-135 ◽  
Author(s):  
Włodzimierz Klonowski ◽  
Pawel Stepien ◽  
Robert Stepien
Keyword(s):  
Brain Activity ◽  
Deterministic Chaos ◽  
Epileptic Seizures ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Complexity Measures ◽  
Electrophysiological Activity ◽  
Signal Complexity ◽  
Physiological Sleep ◽  
The Brain

Over 20 years ago, Watt and Hameroff (1987 ) suggested that consciousness may be described as a manifestation of deterministic chaos in the brain/mind. To analyze EEG-signal complexity, we used Higuchi’s fractal dimension in time domain and symbolic analysis methods. Our results of analysis of EEG-signals under anesthesia, during physiological sleep, and during epileptic seizures lead to a conclusion similar to that of Watt and Hameroff: Brain activity, measured by complexity of the EEG-signal, diminishes (becomes less chaotic) when consciousness is being “switched off”. So, consciousness may be described as a manifestation of deterministic chaos in the brain/mind.

scholarly journals Using Bidimensional Multiscale Entropy Analysis of Ultrasound Images to Assess the Effect of Various Walking Intensities on Plantar Soft Tissues

Entropy ◽  
2021 ◽  
Vol 23 (3) ◽  
pp. 264
Author(s):  
Ben-Yi Liau ◽  
Fu-Lien Wu ◽  
Keying Zhang ◽  
Chi-Wen Lung ◽  
Chunmei Cao ◽  
...  
Keyword(s):  
Soft Tissues ◽  
Linear Analysis ◽  
Multiscale Entropy ◽  
Ultrasound Images ◽  
Entropy Analysis ◽  
Nonlinear Complexity ◽  
Human Movements ◽  
Before And After ◽  
Walking Performance ◽  
Linear And Nonlinear

Walking performance is usually assessed by linear analysis of walking outcome measures. However, human movements consist of both linear and nonlinear complexity components. The purpose of this study was to use bidimensional multiscale entropy analysis of ultrasound images to evaluate the effects of various walking intensities on plantar soft tissues. Twelve participants were recruited to perform six walking protocols, consisting of three speeds (slow at 1.8 mph, moderate at 3.6 mph, and fast at 5.4 mph) for two durations (10 and 20 min). A B-mode ultrasound was used to assess plantar soft tissues before and after six walking protocols. Bidimensional multiscale entropy (MSE2D) and the Complexity Index (CI) were used to quantify the changes in irregularity of the ultrasound images of the plantar soft tissues. The results showed that the CI of ultrasound images after 20 min walking increased when compared to before walking (CI4: 0.39 vs. 0.35; CI5: 0.48 vs. 0.43, p < 0.05). When comparing 20 and 10 min walking protocols at 3.6 mph, the CI was higher after 20 min walking than after 10 min walking (CI4: 0.39 vs. 0.36, p < 0.05; and CI5: 0.48 vs. 0.44, p < 0.05). This is the first study to use bidimensional multiscale entropy analysis of ultrasound images to assess plantar soft tissues after various walking intensities.

Analysis of statistical coefficients and autoregressive parameters over intrinsic mode functions (IMFs) for epileptic seizure detection

2020 ◽  
Vol 65 (6) ◽  
pp. 693-704
Author(s):  
Rafik Djemili
Keyword(s):  
Involuntary Movement ◽  
Epileptic Seizures ◽  
Intrinsic Mode Functions ◽  
Eeg Signals ◽  
Time Segment ◽  
Feature Extraction Method ◽  
Mode Decomposition ◽  
Finite Set ◽  
Student’S T

AbstractEpilepsy is a persistent neurological disorder impacting over 50 million people around the world. It is characterized by repeated seizures defined as brief episodes of involuntary movement that might entail the human body. Electroencephalography (EEG) signals are usually used for the detection of epileptic seizures. This paper introduces a new feature extraction method for the classification of seizure and seizure-free EEG time segments. The proposed method relies on the empirical mode decomposition (EMD), statistics and autoregressive (AR) parameters. The EMD method decomposes an EEG time segment into a finite set of intrinsic mode functions (IMFs) from which statistical coefficients and autoregressive parameters are computed. Nevertheless, the calculated features could be of high dimension as the number of IMFs increases, the Student’s t-test and the Mann–Whitney U test were thus employed for features ranking in order to withdraw lower significant features. The obtained features have been used for the classification of seizure and seizure-free EEG signals by the application of a feed-forward multilayer perceptron neural network (MLPNN) classifier. Experimental results carried out on the EEG database provided by the University of Bonn, Germany, demonstrated the effectiveness of the proposed method which performance assessed by the classification accuracy (CA) is compared to other existing performances reported in the literature.

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