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

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.

scholarly journals Etiology of epilepsy a prospective study of 210 cases

1991 ◽  
Vol 49 (3) ◽  
pp. 251-254 ◽  
Author(s):  
Walter Oleschko Arruda
Keyword(s):  
Ct Scan ◽  
Neurological Examination ◽  
Epileptic Seizures ◽  
Partial Seizures ◽  
Complex Partial Seizures ◽  
Blood Tests ◽  
Generalized Seizures ◽  
A Prospective Study ◽  
Csf Examination

The objective of this study was to establish the etiology of epilepsy in 210 chronic epileptics (110 female, 100 male), aged 14-82 years (34.2±13.3). Patients less than 10 years-old and alcoholism were excluded. All underwent neurological examination, routine blood tests, EEG and CT-scan. Twenty patients (10.5%) were submitted to spinal tap for CSF examination. Neurological examination was abnormal in 26 (12.4%), the EEG in 68 (45.5%), and CT-scan in 93 (44.3%). According to the International Classification of Epileptic Seizures (1981), 101 (48.1%) have generalized seizures, 66 (31.4%) partial seizures secondarily generalized, 25 (11.8%) simple partial and complex partial seizures, and 14 (6.6%) generalized and partial seizures. Four patients (2.0%) could not be classified. In 125 (59.5%) patients the etiology was unknown. Neurocysticercosis accounted for 57 (27.1%) of cases, followed by cerebrovascular disease 8 (3.8%), perinatal damage 5 (2.4%), familial epilepsy 4 (1.9%), head injury 4 (1.9%), infective 1 (0.5%), and miscelanea 6 (2.8%).

scholarly journals Proposed Diagnostic Scheme for the Classification of Epileptic Seizures and Epilepsies (ILAE, 2001): Proposal from Japan Epilepsy Society

Epilepsia ◽  
2006 ◽  
Vol 47 (9) ◽  
pp. 1588-1589 ◽  
Author(s):  
Kazuie Iinuma ◽  
Kiyoshi Morimoto ◽  
Tomoyuki Akiyama ◽  
Akio Ikeda ◽  
Mana Kurihara
Keyword(s):  
Epileptic Seizures

Classification of seizures and epilepsy

2019 ◽  
pp. 935-944
Author(s):  
Andrew McEvoy ◽  
Tim Wehner ◽  
Victoria Wykes
Keyword(s):  
Focal Epilepsy ◽  
Focal Cortical Dysplasia ◽  
Hippocampal Sclerosis ◽  
Epileptic Seizures ◽  
Recurrence Risk ◽  
Medical Emergency ◽  
Unprovoked Seizure ◽  
Generalized Seizures ◽  
The Brain

Epileptic seizures are transient neurologic alterations due to abnormal excessive or synchronous neuronal cerebral activity. They may cause subjective symptoms (aura), and objective autonomic, behavioural, or cognitive alterations in any combination. Focal seizures are initially generated in one circumscribed area in the brain, whereas generalized seizures involve bihemispheric neuronal networks from the seizure onset. Epilepsy is a brain disease defined by the occurrence of two unprovoked seizures more than 24 h apart or one unprovoked seizure with underlying pathological or genetic factors resulting in a similar recurrence risk. Focal epilepsy syndromes are best classified by aetiology or anatomical area of origin. A seizure that does not self-terminate results in status epilepticus, and constitutes a medical emergency that requires immediate treatment. Focal cortical dysplasia and hippocampal sclerosis are the commonest aetiologies of epilepsy amenable to surgical treatment and are reviewed here. The limbic pathway may be involved in seizure propagation, and the anatomy is described.

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