Results Suggestive of the Brain Trying to Re-Organizee Itself and Recover During the Early Stages of an Epileptic Seizure?

2018 ◽  
Author(s):  
Richard Robertson
Keyword(s):  
Epileptic Seizure ◽  
Early Stages ◽  
The Brain

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. 

scholarly journals Alternative Form of Ordinary Differential Equation of Electroencephalography Signals During an Epileptic Seizure

2021 ◽  
Vol 17 (2) ◽  
pp. 109-113
Author(s):  
Ameen Omar Barja
Keyword(s):  
Differential Equation ◽  
Ordinary Differential Equation ◽  
Epileptic Seizure ◽  
Essential Role ◽  
Clinical Neurophysiology ◽  
Alternative Form ◽  
Eeg Signals ◽  
Seizure Disorders ◽  
Electroencephalogram Eeg ◽  
The Brain

One of the most important fields in clinical neurophysiology is an electroencephalogram (EEG). It is a test used to detect problems related to the brain electrical activity, and it can track and records patterns of brain waves. EEG continues to play an essential role in diagnosis and management of patients with epileptic seizure disorders. Nevertheless, the outcome of EEG as a tool for evaluating epileptic seizure is often interpreted as a noise rather than an ordered pattern. The mathematical modelling of EEG signals provides valuable data to neurologists, and is heavily utilized in the diagnosis and treatment of epilepsy. EEG signals during the seizure can be modeled as ordinary differential equation (ODE). In this study we will present an alternative form of ODE of EEG signals through the seizure.

Epilepsy in later childhood and adulthood

2010 ◽  
pp. 4810-4826
Author(s):  
G.D. Perkin ◽  
M.R. Johnson
Keyword(s):  
Brain Imaging ◽  
Neuronal Activity ◽  
Case History ◽  
Epileptic Seizure ◽  
Neurological Disease ◽  
Epileptic Seizures ◽  
Social Consequences ◽  
Definition Of ◽  
The Brain ◽  
Structural Brain

Case History—A 33 yr old woman, known to have epilepsy, now presenting with odd behaviour. An epileptic seizure is a transient occurrence of signs and/or symptoms due to abnormal excessive or synchronous neuronal activity in the brain. Epilepsy is defined as a disorder of the brain characterized by an enduring predisposition to generate epileptic seizures and by the neurobiological, cognitive, psychological, and social consequences of this condition. The definition of epilepsy requires the occurrence of at least one epileptic seizure and evidence for an enduring alteration in the brain that increases the likelihood of future seizures such as an ‘epileptiform’ EEG abnormality, an appropriate lesion on structural brain imaging (CT or MRI), or the presence of recurrent (two or more) seizures. Epilepsy is a common, serious neurological disease, with prevalence 1% and a cumulative lifetime risk of 5%....

Epileptic Seizure Detection From EEG Signals Using Bagged Ensemble Approach

2020 ◽  
pp. 67-79
Author(s):  
Pradeep Singh ◽  
Sujith Kumar Appikatla
Keyword(s):  
Epileptic Seizure ◽  
Seizure Detection ◽  
Ensemble Classifier ◽  
High Accuracy ◽  
Machine Learning Algorithms ◽  
Eeg Signals ◽  
Epileptic Seizure Detection ◽  
Electrical Pulses ◽  
Two Phases ◽  
The Brain

Seizures are caused by irregular electrical pulses in the brain. Epileptic seizure detection on EEG signals is a long process, which is done manually by epileptologists. The aim of the study is automatically detecting the seizures of the brain, given the electroencephalogram signals by feature extraction and processing through different machine learning algorithms. Machines can be trained to do this type of observation and predict the output with high accuracy. In this chapter, the classification study of individual and ensemble classifier is performed for epileptic seizure detection. The proposed method consists of two phases: extraction of data from EEG signals and development of an individual and ensemble models. Bagging ensemble is developed to achieve better results. The development of the ensemble using various classification algorithms contributes towards increasing the diversity of the ensemble. An extensive comparative study with existing benchmark algorithm is performed for epileptic seizure detection.

scholarly journals S-nitrosation of proteins relevant to Alzheimer’s disease during early stages of neurodegeneration

2016 ◽  
Vol 113 (15) ◽  
pp. 4152-4157 ◽  
Author(s):  
Uthpala Seneviratne ◽  
Alexi Nott ◽  
Vadiraja B. Bhat ◽  
Kodihalli C. Ravindra ◽  
John S. Wishnok ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Protein Function ◽  
Amyloid Precursor Protein Processing ◽  
Amyloid Formation ◽  
Early Stages ◽  
Model Of Alzheimer’S Disease ◽  
Liquid Chromatography Tandem Mass ◽  
Cysteine Thiols ◽  
The Brain

Protein S-nitrosation (SNO-protein), the nitric oxide-mediated posttranslational modification of cysteine thiols, is an important regulatory mechanism of protein function in both physiological and pathological pathways. A key first step toward elucidating the mechanism by which S-nitrosation modulates a protein’s function is identification of the targeted cysteine residues. Here, we present a strategy for the simultaneous identification of SNO-cysteine sites and their cognate proteins to profile the brain of the CK-p25–inducible mouse model of Alzheimer’s disease-like neurodegeneration. The approach—SNOTRAP (SNO trapping by triaryl phosphine)—is a direct tagging strategy that uses phosphine-based chemical probes, allowing enrichment of SNO-peptides and their identification by liquid chromatography tandem mass spectrometry. SNOTRAP identified 313 endogenous SNO-sites in 251 proteins in the mouse brain, of which 135 SNO-proteins were detected only during neurodegeneration. S-nitrosation in the brain shows regional differences and becomes elevated during early stages of neurodegeneration in the CK-p25 mouse. The SNO-proteome during early neurodegeneration identified increased S-nitrosation of proteins important for synapse function, metabolism, and Alzheimer’s disease pathology. In the latter case, proteins related to amyloid precursor protein processing and secretion are S-nitrosated, correlating with increased amyloid formation. Sequence analysis of SNO-cysteine sites identified potential linear motifs that are altered under pathological conditions. Collectively, SNOTRAP is a direct tagging tool for global elucidation of the SNO-proteome, providing functional insights of endogenous SNO proteins in the brain and its dysregulation during neurodegeneration.

scholarly journals A Review of Epileptic Seizure Prediction: Physiological Mechanism and Data Based Attempts

2021 ◽  
pp. 1-13
Author(s):  
Agboola HA ◽  
◽  
Susu AA ◽  
Keyword(s):  
Epileptic Seizure ◽  
Physiological Mechanism ◽  
Prediction Algorithm ◽  
Seizure Prediction ◽  
Brain Disorder ◽  
Treatment Protocols ◽  
Epileptic Patients ◽  
Epileptic Seizure Prediction ◽  
The Brain ◽  
Modelling Methods

Epilepsy is a chronic brain disorder and epileptic patients encounter recurrent seizures caused by abnormally synchronous electrical activity in parts of the brain. Over 50 million people spread across the world have epilepsy amongst whom approximately 30% suffer from refractory epilepsy which cannot be controlled by existing treatment protocols. For all epileptic sufferers, the thought that their next seizure could come at any time is agonizing and traumatic. However, if seizures could be predicted reliably, associated dangers and inconveniences will be greatly mitigated. Although the epileptic seizure prediction challenge has been tackled headlong by researchers through different modelling methods the problem of prediction has not yet been satisfactorily solved. In this paper, a systematic literature review of prominent epileptic seizure prediction attempts was carried out. We focus majorly on the two predominant classes of modelling attempts used: physiological mechanism and data based. The review underscores the richness and utility of the diverse modeling strategies as well as the gainful contribution of researchers in the field of epilepsy. It shows that meaningful progress has been made towards discovering the exact mechanism of seizure generation and realization of reliable and consistent seizure prediction algorithm

scholarly journals Toxic Models of Parkinson's Disease: History and Prospects

2021 ◽  
Vol 6 (3) ◽  
pp. 78-84
Author(s):  
D. S. Yaroshenko ◽  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Basal Ganglia ◽  
Early Diagnosis ◽  
Brain Stem ◽  
Genetic Predisposition ◽  
Common Disease ◽  
Extrapyramidal System ◽  
Early Stages ◽  
The Brain

The review article presents data on the history of research of extrapyramidal system dysfunctions, modern ideas about the etiology and diagnosis of Parkinson's disease, as the most common disease of the group of extrapyramidal disorders. Currently, no concept of effective therapy for patients with extrapyramidal system dysfunction has been developed, but it has been proven that the probability of developing the disease largely depends on the genetic predisposition and the level of environmental pollution. In the early stages, the disease is slow and asymptomatic, but gradually more than half of patients with Parkinson's disease die, and others need outside care. According to experts, in the near future, Parkinson's disease will become a problem for a significant part of people, because today it affects more and more people of working age. Under such conditions, reliable and early diagnosis of the disease is of great importance, which guarantees timely and most effective treatment. Modern therapies fail to stop the progressive death of the dopaminergic neurons of the substantia nigra, but traditional treatment can achieve symptomatic relief. Currently, it is known that the probability of developing Parkinson's disease depends on the genetic predisposition and the level of man-made environmental stress. The researchers consider that the pathological development of Parkinson's disease in the brain begins in the lower structures of the brain stem with the involvement of the caudal-Rostral nuclei, as well as the involvement of the cortico-basal ganglia-cerebellar pathways. The pathological process affects the ascending pathways and gradually passes to the midbrain, directly to the black substance, spreads from there and weakens the mesocortex and neocortex. Injuries in the brain stem lead to disorganization of the cortico-basal ganglia and cerebellar pathways, followed by the formation of alternative pathways to compensate for the initial disorders in the early stages of the disease. In addition, in Parkinson's disease, intracellular Lewy bodies and neurites formed by the protein alpha-synuclein are created, which are found in the autopsy material of most patients. Poor results of diagnostic evaluation and treatment of Parkinson's disease are usually associated with a lack of understanding of the pathogenesis of Parkinson's disease. The study of the biological basis and pathogenesis of Parkinson's disease is an important task of a whole complex of scientific studies of extrapyramidal system dysfunction. Conclusion. The article discusses the creation of toxic models of Parkinson's disease in vivo and in vitro, which help to recreate the pathogenesis of the disease for early diagnosis and the development of new ways to treat neurodegenerative diseases. In toxic models of Parkinsonism, not only deficits of motor functions such as bradykinesia, tremor, and posture disorders are actively studied, but also non-motor symptoms such as sleep disorders, neuropsychiatric and cognitive abnormalities

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