scholarly journals Detecting Epileptic Seizure from Scalp EEG Using Lyapunov Spectrum

2012 ◽  
Vol 2012 ◽  
pp. 1-11 ◽  
Author(s):  
Truong Quang Dang Khoa ◽  
Nguyen Thi Minh Huong ◽  
Vo Van Toi
Keyword(s):  
Chaotic Behavior ◽  
Lyapunov Spectrum ◽  
Largest Lyapunov Exponent ◽  
Eeg Signal ◽  
Medical Doctors ◽  
Eeg Signals ◽  
Scalp Eeg ◽  
Eeg Recordings ◽  
Filter Noise ◽  
Eeg Signal Processing

One of the inherent weaknesses of the EEG signal processing is noises and artifacts. To overcome it, some methods for prediction of epilepsy recently reported in the literature are based on the evaluation of chaotic behavior of intracranial electroencephalographic (EEG) recordings. These methods reduced noises, but they were hazardous to patients. In this study, we propose using Lyapunov spectrum to filter noise and detect epilepsy on scalp EEG signals only. We determined that the Lyapunov spectrum can be considered as the most expected method to evaluate chaotic behavior of scalp EEG recordings and to be robust within noises. Obtained results are compared to the independent component analysis (ICA) and largest Lyapunov exponent. The results of detecting epilepsy are compared to diagnosis from medical doctors in case of typical general epilepsy.

VIGILANCE ANALYSIS BASED ON EEG SIGNALS: SEEKING FOR SUITABLE FEATURES

2010 ◽  
Vol 18 (spec01) ◽  
pp. 81-99
Author(s):  
TIAN OUYANG ◽  
HONG-TAO LU ◽  
BAOLIANG LU
Keyword(s):  
Wavelet Transform ◽  
Classification Accuracy ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Large Set ◽  
Eeg Signal ◽  
Simulation Environment ◽  
Eeg Signals ◽  
Eeg Recordings ◽  
Eeg Signal Processing

Electroencephalography (EEG) is considered a reliable indicator of a person's vigilance level. In this paper, we use EEG recordings to discriminate three vigilance states of a person, namely alert, drowsy, and sleep, while driving a car in a simulation environment. EEG signals are recorded and divided into five-second long trials. From these EEG trials, we extract feature vectors containing a large set of features. Random forest is used to rank the plenty of features and select the most important ones for later classification. After dimension reduction, sample vectors are trained and classified by Support Vector Machine (SVM). The proposed framework explores different methods of EEG signal processing to discover the most suitable features for a real-time vigilance monitoring system. We investigate and compare three different kinds of features which are based on Continuous Wavelet Transform (CWT), Discrete Wavelet Transform (DWT), and Fractal Dimension (FD), respectively. On datasets acquired from 5 subjects, our result shows the CWT-based features reveal the highest classification accuracy (may reach over 96%). The DWT and FD-based features are less time-consuming in computation, and also reveal good result of classification accuracy (over 90%).

scholarly journals Detection of Hypoglycemia Using Measures of EEG Complexity in Type 1 Diabetes Patients

Entropy ◽  
2020 ◽  
Vol 22 (1) ◽  
pp. 81 ◽  
Author(s):  
Maria Rubega ◽  
Fabio Scarpa ◽  
Debora Teodori ◽  
Anne-Sophie Sejling ◽  
Christian S. Frandsen ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Single Channel ◽  
Permutation Entropy ◽  
Eeg Signals ◽  
Scalp Eeg ◽  
Power Spectral ◽  
Eeg Changes ◽  
Eeg Recordings ◽  
Clamp Study

Previous literature has demonstrated that hypoglycemic events in patients with type 1 diabetes (T1D) are associated with measurable scalp electroencephalography (EEG) changes in power spectral density. In the present study, we used a dataset of 19-channel scalp EEG recordings in 34 patients with T1D who underwent a hyperinsulinemic–hypoglycemic clamp study. We found that hypoglycemic events are also characterized by EEG complexity changes that are quantifiable at the single-channel level through empirical conditional and permutation entropy and fractal dimension indices, i.e., the Higuchi index, residuals, and tortuosity. Moreover, we demonstrated that the EEG complexity indices computed in parallel in more than one channel can be used as the input for a neural network aimed at identifying hypoglycemia and euglycemia. The accuracy was about 90%, suggesting that nonlinear indices applied to EEG signals might be useful in revealing hypoglycemic events from EEG recordings in patients with T1D.

scholarly journals Wavelet Design for Automatic Real-Time Eye Blink Detection and Recognition in EEG Signals

2019 ◽  
Vol 14 (3) ◽  
pp. 375-387
Author(s):  
Michael Gabriel Miranda ◽  
Renato Alberto Salinas ◽  
Ulrich Raff ◽  
Oscar Magna
Keyword(s):  
Real Time ◽  
Single Channel ◽  
Specific Pattern ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Time Operation ◽  
Real Time Operation ◽  
Blink Detection ◽  
Eeg Recordings ◽  
Detection And Localization

The blinking of an eye can be detected in electroencephalographic (EEG) recordings and can be understood as a useful control signal in some information processing tasks. The detection of a specific pattern associated with the blinking of an eye in real time using EEG signals of a single channel has been analyzed. This study considers both theoretical and practical principles enabling the design and implementation of a system capable of precise real-time detection of eye blinks within the EEG signal. This signal or pattern is subject to considerable scale changes and multiple incidences. In our proposed approach, a new wavelet was designed to improve the detection and localization of the eye blinking signal. The detection of multiple occurrences of the blinking perturbation in the recordings performed in real-time operation is achieved with a window giving a time-limited projection of an ongoing analysis of the sampled EEG signal.

scholarly journals EEG Signal Discrimination using Non-linear Dynamics in the EMD Domain S. M. Shafiul Alam,S. M. Shafiul Alam,Aurangozeb, and Syed TarekShahriar Abstract—An EMD-chaos based approach is proposed todiscriminate EEG signals corresponding to healthy persons,and epileptic patients during seizure-free intervals and seizureattacks. An electroencephalogram (EEG) is first empiricallydecomposed to intrinsic mode functions (IMFs). The nonlineardynamics of these IMFs are quantified in terms of the largestLyapunov exponent (LLE) and correlation dimension (CD).This chaotic analysis in EMD domain is applied to a large groupof EEG signals corresponding to healthy persons as well asepileptic patients (both with and without seizure attacks). It isshown that the values of the obtained LLE and CD exhibitfeatures by which EEG for seizure attacks can be clearlydistinguished from other EEG signals in the EMD domain.Thus, the proposed approach may aid researchers in developingeffective techniques to predict seizure activities. Index Terms—Electroencephalogram (EEG), empiricalmode decomposition (EMD), largest Lyapunov exponent (LLE),correlation dimension (CD), epileptic seizures. The Authors are with the Electrical and Electronic EngineeringDepartment, Bangladesh University of Engineering and Technology,Dhaka-1000, Bangladesh (e-mail: [email protected]) [PDF] Cite: S. M. Shafiul Alam,S. M. Shafiul Alam,Aurangozeb, and Syed Tarek Shahriar, "EEG Signal Discrimination using Non-linear Dynamics in the EMD Domain," International Journal of Computer and Electrical Engineering vol. 4, no. 3, pp. 326-330, 2012. PREVIOUS PAPER Perception of Emotions Using Constructive Learningthrough Speech NEXT PAPER Physical Layer Impairments Aware OVPN Connection Selection Mechanisms Copyright © 2008-2013. International Association of Computer Science and Information Technology Press (IACSIT Press)

2012 ◽  
pp. 326-330 ◽  
Author(s):  
S. M. Shafiul Alam ◽  
S. M. Shafiul Alam ◽  
Aurangozeb ◽  
Syed TarekShahriar
Keyword(s):  
Correlation Dimension ◽  
Largest Lyapunov Exponent ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Linear Dynamics ◽  
Signal Discrimination ◽  
Non Linear ◽  
Electroencephalogram Eeg ◽  
Seizure Attacks ◽  
Healthy Persons

scholarly journals Four-sphere head model for EEG signals revisited

2017 ◽  
Author(s):  
Solveig Næss ◽  
Chaitanya Chintaluri ◽  
Torbjørn V. Ness ◽  
Anders M. Dale ◽  
Gaute T. Einevoll ◽  
...  
Keyword(s):  
Analytical Formula ◽  
Mathematical Expression ◽  
Head Model ◽  
The Finite Element Method ◽  
Sphere Model ◽  
Eeg Signals ◽  
Scalp Eeg ◽  
Spherical Head ◽  
Analytical Formulas ◽  
Eeg Recordings

AbstractElectric potential recorded at the scalp (EEG) is dominated by contributions from current dipoles set by active neurons in the cortex. Estimation of these currents, called ’inverse modeling’, requires a ’forward’ model, which gives the potential when the positions, sizes, and directions of the current dipoles are known. Different models of varying complexity and realism are used in the field. An important analytical example is the four-sphere model which assumes a four-layered spherical head where the layers represent brain tissue, cerebrospinal fluid (CSF), skull, and scalp, respectively. This model has been used extensively in the analysis of EEG recordings. Since it is analytical, it can also serve as a benchmark against which numerical schemes, such as the Finite Element Method (FEM), can be tested. While conceptually clear, the mathematical expression for the scalp potentials in the four-sphere model is quite cumbersome, and we observed the formulas presented in the literature to contain errors. We here derive and present the correct analytical formulas for future reference. They are compared with the results of FEM simulations of four-sphere model. We also provide scripts for computing EEG potentials in this model with the correct analytical formula and using FEM.

scholarly journals Nonlinear analysis of scalp EEGs from normal and brain tumour subjects

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Salai Selvam V ◽  
Shenbaga Devi S
Keyword(s):  
Brain Tumour ◽  
Nonlinear Dynamic ◽  
Normal Subjects ◽  
Eeg Signal ◽  
Dynamic Features ◽  
Eeg Signals ◽  
Scalp Eeg ◽  
Pathological Conditions ◽  
Tumour Group ◽  
The Brain

AbstractMeasurement of features from the chaos theory or as popularly known, the concept of nonlinear dynamics, as indicatives of several pathological conditions and cognition states using the electroencephalography (EEG) signal is very popular. In this paper, the analysis of scalp EEG signals of normal subjects and brain tumour patients using the nonlinear dynamic features has been presented. The nonlinear dynamic features that represent the dimensional and waveform complexities of the signal being analyzed have been considered. The statistical analysis of the selected nonlinear dynamic features has been presented. The results show that the nonlinear dynamic features significantly discriminate the brain tumour group from the normal group.

DETECTION OF THE ONSET OF EPILEPTIC SEIZURE SIGNAL FROM SCALP EEG USING BLIND SIGNAL SEPARATION

2009 ◽  
Vol 21 (04) ◽  
pp. 287-290 ◽  
Author(s):  
M. Moghavvemi ◽  
S. Mehrkanoon
Keyword(s):  
Signal Separation ◽  
Blind Signal Separation ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Blind Signal ◽  
Scalp Eeg ◽  
Mapping Process ◽  
Independent Features ◽  
Electroencephalogram Eeg ◽  
The Brain

Investigation of epileptic electroencephalogram (EEG) signal is one of the major areas of study in the field of signal processing. The ability to detect the seizure signal and its origin within the brain is of prime importance. This paper proposes a sequential blind signal separation (BSS) based system to extract the seizure signal from scalp EEG and to pinpoint the main location of seizure signal within the brain. BSS algorithm is used to demix the EEG signal into signals with independent features. Scalp time-mapping process is applied to determine the main location of the extracted seizure signal within the brain. The algorithm has been tested on epileptic EEG signals recorded from patients for detection of the onset of seizure waves and their origin within the brain.

EEG Signal Processing Based on Genetic Algorithm for Extracting Mixed Features

2019 ◽  
Vol 33 (06) ◽  
pp. 1958008 ◽  
Author(s):  
Yanping Li ◽  
Linyan Wu ◽  
Tao Wang ◽  
Nuo Gao ◽  
Qi Wang
Keyword(s):  
Genetic Algorithm ◽  
Laboratory Data ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Hilbert Huang Transform ◽  
Online Test ◽  
Mixed Features ◽  
Data Credibility ◽  
Eeg Signal Processing

In order to improve the classification of motor imagery EEG accuracy, this paper proposes a method based on Genetic Algorithm (GA) EEG signal classification method to extract mixed characteristics. This method uses wavelet analysis and Hilbert–Huang Transform (HHT) to analyze EEG signals and optimizes the characteristics through Common Spatial Patterns (CSP). Finally, the 14 sub features are optimized by GA, and the weights and data credibility of different sub features are obtained. The experiment was tested with 2003BCI competition data and the EEG signal collected by the laboratory. The accuracy rate of competition data was increased from about 75% before weighting to more than 80% after weighting, and the laboratory data increased from about 65% before weighting to about 75% after weighting. Experimental results show that this method can effectively improve the classification accuracy of EEG signals, and the most useful EEG signals can be extracted from large amounts of data for feature extraction and classification. Finally, the online test is carried out to further verify the feasibility of the method.

scholarly journals Enhanced Epileptic Seizure Detection using Imbalanced Classification

2020 ◽  
Vol 9 (1) ◽  
pp. 2412-2420
Keyword(s):  
Visual Analytics ◽  
Principal Component ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Imbalanced Classification ◽  
Machine Learning Classifiers ◽  
Epileptic Patients ◽  
Eeg Recordings ◽  
Artificial Neural Network Ann ◽  
Electroencephalogram Eeg

Epilepsy is the second most persistent neurological condition, endangering the lives of patients. Though there have been many advancements in neurological imaging approaches, the Electroencephalogram (EEG) still remains to be the most effective tool for testing and diagnosing epileptic patients. The visual analytics of EEG signals is a very prolonged process and always open to the subjective judgment of the physicians. The main goal of our study is to build an automatic classifier that can analyze and detect epilepsy from EEG recordings obtained from epileptic and healthy patients, thus helping the neurosurgeons to diagnose epilepsy in a better way. Synthetic minority oversampling technique (SMOTE) has been used for balancing the EEG dataset and the Principal component analysis (PCA) technique is applied further, for reducing the EEG signal dimensionality. For data classification, seven machine learning classifiers have been used and after comparing the results the authors conclude that Artificial Neural Network (ANN), outperforms the other classifiers by providing an accuracy of 97.82%.

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.

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