scholarly journals From theory to practice: Critical points in the 2017 ILAE classification of epileptic seizures and epilepsies

Epilepsia ◽  
2020 ◽  
Vol 61 (2) ◽  
pp. 350-353 ◽  
Author(s):  
André Palmini ◽  
Naoki Akamatsu ◽  
Thomas Bast ◽  
Sebastian Bauer ◽  
Christoph Baumgartner ◽  
...  
Keyword(s):  
Critical Points ◽  
Epileptic Seizures ◽  
Theory To Practice ◽  
Ilae Classification

scholarly journals Perception about Etiology of Epilepsy and Help-Seeking Behavior in Patients with Epilepsy

2021 ◽  
Author(s):  
Gautam Das ◽  
Samar Biswas ◽  
Souvik Dubey ◽  
Durjoy Lahiri ◽  
Biman Kanti Ray ◽  
...  
Keyword(s):  
Help Seeking ◽  
Tertiary Care ◽  
Epileptic Seizures ◽  
Treatment Seeking ◽  
Professional Help ◽  
Epileptic Patients ◽  
Seeking Behavior ◽  
Tertiary Care Centers ◽  
Patients With Epilepsy

Abstract Objectives Patients with epilepsy and their family have diverse beliefs about the cause of their illness that generally determine their treatment-seeking behavior. In this study, our aim was to find out different beliefs about epilepsy that lead to different help-seeking patterns, which act as barrier to the intended modern medical management of epilepsy. Materials and Methods One hundred and fifty consecutive consenting patients accompanied by a reliable informant/family member fulfilling the International Classification of Epileptic Seizures (ICES), simplified version, were included. Demographic and clinical data of all the eligible subjects was collected. Perceived cause of illness and help-seeking pattern were explored from patient/informant by administering proper instruments. Results Respondents identified varied causes of epilepsy and explored multiple help-seeking options before reaching tertiary care centers. We observed that, generally, epileptic patients/relatives who had belief in causes like supernatural causes sought help from nonprofessional personnel and those attributed their symptom to bodily pathology had professional help-seeking. Conclusions The belief in supernatural causes not being conformed to the biomedical models of the epileptic disorders increases the treatment gap.

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.

scholarly journals A Recent Investigation on Detection and Classification of Epileptic Seizure Techniques Using EEG Signal

2021 ◽  
Vol 11 (5) ◽  
pp. 668
Author(s):  
Sani Saminu ◽  
Guizhi Xu ◽  
Zhang Shuai ◽  
Isselmou Abd El Kader ◽  
Adamu Halilu Jabire ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Algorithms ◽  
Epileptic Seizures ◽  
Seizure Detection ◽  
Performance Comparison ◽  
Eeg Signal ◽  
Detection Algorithms ◽  
Advantages And Disadvantages ◽  
Monitoring And Diagnosis

The benefits of early detection and classification of epileptic seizures in analysis, monitoring and diagnosis for the realization and actualization of computer-aided devices and recent internet of medical things (IoMT) devices can never be overemphasized. The success of these applications largely depends on the accuracy of the detection and classification techniques employed. Several methods have been investigated, proposed and developed over the years. This paper investigates various seizure detection algorithms and classifications in the last decade, including conventional techniques and recent deep learning algorithms. It also discusses epileptiform detection as one of the steps towards advanced diagnoses of disorders of consciousness (DOCs) and their understanding. A performance comparison was carried out on the different algorithms investigated, and their advantages and disadvantages were explored. From our survey, much attention has recently been paid to exploring the efficacy of deep learning algorithms in seizure detection and classification, which are employed in other areas such as image processing and classification. Hybrid deep learning has also been explored, with CNN-RNN being the most popular.

4-17-20 Difficulties for classification of epileptic seizures (ILAE) in epidemiologic study

1997 ◽  
Vol 150 ◽  
pp. S236-S237
Author(s):  
M.-C. Picot ◽  
A. Crespel ◽  
J.-P. Daurès ◽  
M. Baldy-Moulinier
Keyword(s):  
Epidemiologic Study ◽  
Epileptic Seizures

Knowledge Management: FROM THEORY TO PRACTICE

2012 ◽  
Vol 01 (11) ◽  
pp. 22-30
Author(s):  
Kamran Nazari ◽  
Mostafa Emami
Keyword(s):  
Decision Making ◽  
Knowledge Management ◽  
Critical Points ◽  
Organization Performance ◽  
Management Process ◽  
Dynamic Learning ◽  
Collective Wisdom ◽  
Theory To Practice ◽  
Wide Range ◽  
Peter Drucker

Knowledge management is a process that helps organizations to find important information, select, organize and publish them; and it’s a proficiency that will be necessary for actions like solving problems, dynamic learning, decision making. Knowledge management can improve a wide range of organization performance properties by enabling company to more intelligent performance, but it’s not enough alone; because knowledge management to be useful needs undertaking staff to organization and their job, that accept the knowledge management process with spirit and heart and perform it (Wiig, 1999:14).Knowledge management is the leveraging of collective wisdom to increase responsiveness and innovation. It is important that you discern from this definition three critical points. This definition implies that three criteria must be met before information can be considered knowledge. » Knowledge is connected. It exists in a collection (collective wisdom) of multiple experiences and perspectives Knowledge management is a catalyst. It is an action – leveraging. Knowledge is always relevant to environmental conditions, and stimulates action in response to these conditions. Information that does not precipitate action of some kind is not knowledge. In the words of Peter Drucker, ‘‘Knowledge for the most part exists only in application.’’ » Knowledge is applicable in un-encountered environments. Information becomes knowledge when it is used to address novel situations for which no direct precedent exists. Information that is merely ‘‘plugged in’’ to a previously encountered model is not knowledge and lacks innovation.

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