Clinical classification of psychogenic non-epileptic seizures based on video-EEG analysis and automatic clustering

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.

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Clinical classification of hyperuricemia in patients with chronic kidney disease

International Urology and Nephrology ◽

10.1007/s11255-020-02754-x ◽

2021 ◽

Author(s):

Fengqin Li ◽

Hui Guo ◽

Jianan Zou ◽

Chensheng Fu ◽

Song Liu ◽

...

Keyword(s):

Chronic Kidney Disease ◽

Kidney Disease ◽

Clinical Classification

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Classification of Epileptic Seizures using Artificial Neural Network with Adaptive Momentum

2020 International Conference on Data Analytics for Business and Industry: Way Towards a Sustainable Economy (ICDABI) ◽

10.1109/icdabi51230.2020.9325688 ◽

2020 ◽

Author(s):

Alaa Ali Hameed ◽

Akhtar Jamil ◽

Naim Ajlouni ◽

Jawad Rasheed ◽

Adem Ozyavas ◽

...

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Epileptic Seizures ◽

Artificial Neural ◽

Adaptive Momentum

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Analysis of statistical coefficients and autoregressive parameters over intrinsic mode functions (IMFs) for epileptic seizure detection

Biomedical Engineering / Biomedizinische Technik ◽

10.1515/bmt-2019-0233 ◽

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.

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