Fast Monitoring of Epileptic Seizures Based on Recurrence Time Analysis of EEGs

2011 ◽  
Author(s):  
Jianbo Gao ◽  
Jing Hu
Keyword(s):  
Visual Inspection ◽  
Epileptic Seizures ◽  
Sampling Frequency ◽  
Recurrence Time ◽  
Time Analysis ◽  
Nonlinear Methods ◽  
Cpu Time ◽  
Eeg Data ◽  
Excellent Candidate ◽  
The Brain

Epilepsy is one of the most common disorders of the brain. Currently, studies of epileptic seizures often involve tedious and time-consuming visual inspection of multi-channel long EEG data by medical experts. To better monitor seizures and make medications more effective, we propose a recurrence time based approach to characterize brain electrical activity. Unlike many other nonlinear methods, the proposed approach does not require that the EEG data be chaotic and/or stationary. It only contains a few parameters that are largely signal-independent, and hence, is very easy to use. The method detects epileptic seizures with accuracy close to 100% (when subclinical seizures are not counted) and false alarm rate per hour close to 0. Most critically, the method is very fast: with an ordinary PC (CPU speed less than 2 GHz), computation of the recurrence time from one channel EEG data of duration one hour with sampling frequency of 200 Hz takes about 1 minute CPU time. Therefore, with an ordinary PC, the method is able to process all 28 channels of 1-hour EEG data in about half an hour, and thus faster than the data being continuously collected. The method can also effectively monitor propagation of seizures in the brain. Therefore, it has the potential to be an excellent candidate for real-time monitoring of epileptic seizures in a clinical setting.

scholarly journals Hyperspherical Manifold for EEG Signals of Epileptic Seizures

2012 ◽  
Vol 2012 ◽  
pp. 1-22 ◽  
Author(s):  
Tahir Ahmad ◽  
Vinod Ramachandran
Keyword(s):  
Measure Theory ◽  
Epileptic Seizures ◽  
Physical Symptoms ◽  
Measure Data ◽  
Dimensional Manifold ◽  
Eeg Signals ◽  
Discrete Probability ◽  
Eeg Data ◽  
Manifold Theory ◽  
The Brain

The mathematical modelling of EEG signals of epileptic seizures presents a challenge as seizure data is erratic, often with no visible trend. Limitations in existing models indicate a need for a generalized model that can be used to analyze seizures without the need for apriori information, whilst minimizing the loss of signal data due to smoothing. This paper utilizes measure theory to design a discrete probability measure that reformats EEG data without altering its geometric structure. An analysis of EEG data from three patients experiencing epileptic seizures is made using the developed measure, resulting in successful identification of increased potential difference in portions of the brain that correspond to physical symptoms demonstrated by the patients. A mapping then is devised to transport the measure data onto the surface of a high-dimensional manifold, enabling the analysis of seizures using directional statistics and manifold theory. The subset of seizure signals on the manifold is shown to be a topological space, verifying Ahmad's approach to use topological modelling.

scholarly journals Spatiotemporal evolution of epileptic seizure based on mutual information and dynamic brain network

2021 ◽  
Vol 21 (S2) ◽  
Author(s):  
Mengnan Ma ◽  
Xiaoyan Wei ◽  
Yinlin Cheng ◽  
Ziyi Chen ◽  
Yi Zhou
Keyword(s):  
Mutual Information ◽  
Complex Network ◽  
Epileptic Seizures ◽  
Brain Network ◽  
The State ◽  
Propagation Path ◽  
Network Function ◽  
Eeg Data ◽  
The Brain ◽  
Abnormal Brain

Abstract Background Epilepsy was defined as an abnormal brain network model disease in the latest definition. From a microscopic perspective, it is also particularly important to observe the Mutual Information (MI) of the whole brain network based on different lead positions. Methods In this study, we selected EEG data from representative temporal lobe and frontal lobe epilepsy patients. Based on Phase Space Reconstruction and the calculation of MI indicator, we used Complex Network technology to construct a dynamic brain network function model of epilepsy seizure. At the same time, about the analysis of our network, we described the index changes and propagation paths of epilepsy discharge in different periods, and spatially monitors the seizure change process based on the analysis of the parameter characteristics of the complex network. Results Our model portrayed the functional synergy between the various regions of the brain and the state transition during the seizure process. We also characterized the EEG synchronous propagation path and core nodes during seizures. The results shown the full node change path and the distribution of important indicators during the seizure process, which makes the state change of the seizure process more clearly. Conclusion In this study, we have demonstrated that synchronization-based brain networks change with time and space. The EEG synchronous propagation path and core nodes during epileptic seizures can provide a reference for finding the focus area.

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.

Automated System for Epileptic Seizures Prediction based on Multi-Channel Recordings of Electrical Brain Activity

2018 ◽  
pp. 115-122 ◽  
Author(s):  
V. A. Maksimenko ◽  
A. A. Harchenko ◽  
A. Lüttjohann
Keyword(s):  
Epileptic Seizure ◽  
Brain Activity ◽  
Epileptic Seizures ◽  
Automated System ◽  
Brain Computer Interfaces ◽  
Electrical Brain Activity ◽  
Computer Interfaces ◽  
Prediction And Prevention ◽  
The Brain

Introduction: Now the great interest in studying the brain activity based on detection of oscillatory patterns on the recorded data of electrical neuronal activity (electroencephalograms) is associated with the possibility of developing brain-computer interfaces. Braincomputer interfaces are based on the real-time detection of characteristic patterns on electroencephalograms and their transformation  into commands for controlling external devices. One of the important areas of the brain-computer interfaces application is the control of the pathological activity of the brain. This is in demand for epilepsy patients, who do not respond to drug treatment.Purpose: A technique for detecting the characteristic patterns of neural activity preceding the occurrence of epileptic seizures.Results:Using multi-channel electroencephalograms, we consider the dynamics of thalamo-cortical brain network, preceded the occurrence of an epileptic seizure. We have developed technique which allows to predict the occurrence of an epileptic seizure. The technique has been implemented in a brain-computer interface, which has been tested in-vivo on the animal model of absence epilepsy.Practical relevance:The results of our study demonstrate the possibility of epileptic seizures prediction based on multichannel electroencephalograms. The obtained results can be used in the development of neurointerfaces for the prediction and prevention of seizures of various types of epilepsy in humans. 

Epilepsy and Related Disorders

2018 ◽  
Author(s):  
Barbara Dworetzky ◽  
Jong Woo Lee
Keyword(s):  
Differential Diagnosis ◽  
Generic Drugs ◽  
Sudden Change ◽  
Epileptic Seizures ◽  
Quality Measures ◽  
The Elderly ◽  
Childhood And Adolescence ◽  
Nonepileptic Seizures ◽  
Infancy And Early Childhood ◽  
The Brain

Epilepsy is a chronic disorder of the brain characterized by recurrent unprovoked seizures. A seizure is a sudden change in behavior that is accompanied by electrical discharges in the brain. Many patients presenting with a first-ever seizure are surprised to find that it is a very common event. A reversible or avoidable seizure precipitant, such as alcohol, argues against underlying epilepsy and therefore against treatment with medication. This chapter discusses the epidemiology, etiology, and classification of epilepsy and provides detailed descriptions of neonatal syndromes, syndromes of infancy and early childhood, and syndromes of late childhood and adolescence. The pathophysiology, diagnosis, and differential diagnosis are described, as are syncope, migraine, and psychogenic nonepileptic seizures. Two case histories are provided, as are sections on treatment (polytherapy, brand-name versus generic drugs, surgery, stimulation therapy, dietary treatments), complications of epilepsy and related disorders, prognosis, and quality measures. Special topics discussed are women?s issues and the elderly. Figures illustrate a left midtemporal epileptic discharge, wave activity during drowsiness, cortical dysplasias, convulsive syncope, rhythmic theta activity, right hippocamal sclerosis, and right temporal hypometabolism. Tables describe international classifications of epileptic seizures and of epilepsies, epilepsy syndromes and related seizure disorders, differential diagnosis of seizure, differentiating epileptic versus nonepileptic seizures, antiepileptic drugs, status epilepticus protocol for treatment, when to consider referral to a specialist, and quality measures in epilepsy.  This review contains 7 figures, 10 tables, and 33 references. Key Words: Seizures, focal (partial)seizure, generalized seizures, Myoclonic seizures, Atonic seizures, Concurrent electromyographyTonic-clonic (grand mal) seizures

A fuzzy sensitivity analysis approach to estimate brain effective connectivity and its application to epileptic seizure detection

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Nader Moharamzadeh ◽  
Ali Motie Nasrabadi
Keyword(s):  
Time Series ◽  
Sensitivity Analysis ◽  
Effective Connectivity ◽  
Multivariate Time Series ◽  
Inference System ◽  
Eeg Data ◽  
Adaptive Neurofuzzy Inference System ◽  
Intracranial Electroencephalography ◽  
Fuzzy Network ◽  
The Brain

Abstract The brain is considered to be the most complicated organ in human body. Inferring and quantification of effective (causal) connectivity among regions of the brain is an important step in characterization of its complicated functions. The proposed method is comprised of modeling multivariate time series with Adaptive Neurofuzzy Inference System (ANFIS) and carrying out a sensitivity analysis using Fuzzy network parameters as a new approach to introduce a connectivity measure for detecting causal interactions between interactive input time series. The results of simulations indicate that this method is successful in detecting causal connectivity. After validating the performance of the proposed method on synthetic linear and nonlinear interconnected time series, it is applied to epileptic intracranial Electroencephalography (EEG) signals. The result of applying the proposed method on Freiburg epileptic intracranial EEG data recorded during seizure shows that the proposed method is capable of discriminating between the seizure and non-seizure states of the brain.

scholarly journals Neuropsychological aspects of psychogenic nonepileptic seizures

2007 ◽  
Vol 13 (4 suppl 1) ◽  
pp. 24-27
Author(s):  
Mirna Wetters Portuguez ◽  
Danielle Irigoyen da Costa ◽  
Sabine Possa Marroni ◽  
Vanessa Pagliarini ◽  
Karin Vieira
Keyword(s):  
Sexual Abuse ◽  
Physical Abuse ◽  
Multidisciplinary Team ◽  
Emotional Abuse ◽  
Epileptic Seizures ◽  
Mental Processes ◽  
Psychogenic Nonepileptic Seizures ◽  
Therapeutic Procedures ◽  
Nonepileptic Seizures ◽  
The Brain

Psychogenic nonepileptic seizures (PNES) may be defined as paroxysmal changes in behavior that are similar to epileptic seizures but are not associated with quantifiable alterations in the electrical activity of the brain. At the Epilepsy Surgery Program (ESP) of the São Lucas Hospital at PUCRS (HSL-PUCRS), we studied 52 individuals (37 females and 15 males) with a diagnosis of PNES, associated (57%) or not (23%) with refractory epileptic seizures. We found emotional abuse (100%), physical abuse (80%), emotional neglect (80%), physical negligence (70%) and sexual abuse (30%), mood (40%) and anxiety disorders (50%), as the main psychological components in such population. Although the medical and psychosocial impact of PNES can be estimated as significant, the absence of specialized services for its treatment is striking. Multiple diagnostic and therapeutic procedures and the participation of a specialized multidisciplinary team – where neuropsychology functions as a link between the mental processes/psychopathologies and the brain – are required to ensure proper management of such cases.

scholarly journals The role of thickness inhomogeneities in hierarchical cortical folding

2020 ◽  
Author(s):  
Lucas da Costa Campos ◽  
Raphael Hornung ◽  
Gerhard Gompper ◽  
Jens Elgeti ◽  
Svenja Caspers
Keyword(s):  
Fetal Brain ◽  
Epileptic Seizures ◽  
Quantitative Model ◽  
Higher Order ◽  
Mammalian Brain ◽  
Cortical Folding ◽  
Differential Growth ◽  
The Brain ◽  
Short Life Expectancy

AbstractThe morphology of the mammalian brain cortex is highly folded. For long it has been known that specific patterns of folding are necessary for an optimally functioning brain. On the extremes, lissencephaly, a lack of folds in humans, and polymicrogyria, an overly folded brain, can lead to severe mental retardation, short life expectancy, epileptic seizures, and tetraplegia. The construction of a quantitative model on how and why these folds appear during the development of the brain is the first step in understanding the cause of these conditions. In recent years, there have been various attempts to understand and model the mechanisms of brain folding. Previous works have shown that mechanical instabilities play a crucial role in the formation of brain folds, and that the geometry of the fetal brain is one of the main factors in dictating the folding characteristics. However, modeling higher-order folding, one of the main characteristics of the highly gyrencephalic brain, has not been fully tackled. The effects of thickness inhomogeneity in the gyrogenesis of the mammalian brain are studied in silico. Finite-element simulations of rectangular slabs are performed. The slabs are divided into two distinct regions, where the outer layer mimics the gray matter, and the inner layer the underlying white matter. Differential growth is introduced by growing the top layer tangentially, while keeping the underlying layer untouched. The brain tissue is modeled as a neo-Hookean hyperelastic material. Simulations are performed with both, homogeneous and inhomogeneous cortical thickness. The homogeneous cortex is shown to fold into a single wavelength, as is common for bilayered materials, while the inhomogeneous cortex folds into more complex conformations. In the early stages of development of the inhomogeneous cortex, structures reminiscent of the deep sulci in the brain are obtained. As the cortex continues to develop, secondary undulations, which are shallower and more variable than the structures obtained in earlier gyrification stage emerge, reproducing well-known characteristics of higher-order folding in the mammalian, and particularly the human, brain.

scholarly journals Binaural summation of amplitude modulation involves weak interaural suppression

2018 ◽  
Author(s):  
D.H. Baker ◽  
G. Vilidaite ◽  
E. McClarnon ◽  
E. Valkova ◽  
A. Bruno ◽  
...  
Keyword(s):  
Amplitude Modulation ◽  
Modulation Depth ◽  
Visual Signal ◽  
Data Sets ◽  
Single Object ◽  
Ecological Constraints ◽  
Binaural Summation ◽  
Eeg Data ◽  
Signal Combination ◽  
The Brain

AbstractThe brain combines sounds from the two ears, but what is the algorithm used to achieve this summation of signals? Here we combine psychophysical amplitude modulation discrimination and steady-state electroencephalography (EEG) data to investigate the architecture of binaural combination for amplitude-modulated tones. Discrimination thresholds followed a ‘dipper’ shaped function of pedestal modulation depth, and were consistently lower for binaural than monaural presentation of modulated tones. The EEG responses were greater for binaural than monaural presentation of modulated tones, and when a masker was presented to one ear, it produced only weak suppression of the response to a signal presented to the other ear. Both data sets were well-fit by a computational model originally derived for visual signal combination, but with suppression between the two channels (ears) being much weaker than in binocular vision. We suggest that the distinct ecological constraints on vision and hearing can explain this difference, if it is assumed that the brain avoids over-representing sensory signals originating from a single object. These findings position our understanding of binaural summation in a broader context of work on sensory signal combination in the brain, and delineate the similarities and differences between vision and hearing.

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