Classification of the Epileptic Seizures and the Epilepsies and Their Differential Diagnosis

1996 ◽  
Vol 10 ◽  
pp. 3-9
Author(s):  
Fritz E. Dreifuss
Keyword(s):  
Differential Diagnosis ◽  
Epileptic Seizures

Can conversation analytic findings help with differential diagnosis in routine seizure clinic interactions?

2016 ◽  
Vol 12 (1) ◽  
pp. 13-24 ◽  
Author(s):  
Katie Ekberg ◽  
Markus Reuber
Keyword(s):  
Differential Diagnosis ◽  
Correct Diagnosis ◽  
Diagnostic Errors ◽  
Epileptic Seizures ◽  
Routine Clinical Setting ◽  
Choice Of Treatment ◽  
Transient Loss ◽  
Factual Content ◽  
Patient’S History ◽  
Linguistic Methods

There are many areas in medicine in which the diagnosis poses significant difficulties and depends essentially on the clinician’s ability to take and interpret the patient’s history. The differential diagnosis of transient loss of consciousness (TLOC) is one such example, in particular the distinction between epilepsy and ‘psychogenic’ non-epileptic seizures (NES) is often difficult. A correct diagnosis is crucial because it determines the choice of treatment. Diagnosis is typically reliant on patients’ (and witnesses’) descriptions; however, conventional methods of history-taking focusing on the factual content of these descriptions are associated with relatively high rates of diagnostic errors. The use of linguistic methods (particularly conversation analysis) in research settings has demonstrated that these approaches can provide hints likely to be useful in the differentiation of epileptic and non-epileptic seizures. This paper explores to what extent (and under which conditions) the findings of these previous studies could be transposed from a research into a routine clinical setting.

Classification of papillary thyroid carcinoma histological images based on deep learning

2021 ◽  
pp. 1-11
Author(s):  
Yaning Liu ◽  
Lin Han ◽  
Hexiang Wang ◽  
Bo Yin
Keyword(s):  
Neural Network ◽  
Differential Diagnosis ◽  
Deep Learning ◽  
Papillary Thyroid Carcinoma ◽  
Thyroid Carcinoma ◽  
Image Features ◽  
Papillary Thyroid ◽  
Histological Image ◽  
Histological Images

Papillary thyroid carcinoma (PTC) is a common carcinoma in thyroid. As many benign thyroid nodules have the papillary structure which could easily be confused with PTC in morphology. Thus, pathologists have to take a lot of time on differential diagnosis of PTC besides personal diagnostic experience and there is no doubt that it is subjective and difficult to obtain consistency among observers. To address this issue, we applied deep learning to the differential diagnosis of PTC and proposed a histological image classification method for PTC based on the Inception Residual convolutional neural network (IRCNN) and support vector machine (SVM). First, in order to expand the dataset and solve the problem of histological image color inconsistency, a pre-processing module was constructed that included color transfer and mirror transform. Then, to alleviate overfitting of the deep learning model, we optimized the convolution neural network by combining Inception Network and Residual Network to extract image features. Finally, the SVM was trained via image features extracted by IRCNN to perform the classification task. Experimental results show effectiveness of the proposed method in the classification of PTC histological images.

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

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

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%).

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