Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 6

seizure Related Abstracts

6 A Randomised, Single-Dose, Two-Period, Cross-Over Phase I Pharmacokinetic Study to Compare TDS®-Diazepam with Rectal Diazepam in Healthy Adult Subjects

Authors: Faisal O. Al-Otaibi, Arthur T. Tucker, Richard M. Langford, Stuart Ratcliffe, Atholl Johnston, Terry D. Lee, Kenneth B. Kirby, Chandan A. Alam

Abstract:

The Transdermal Delivery System (TDS®) is a proprietary liquid formulation that can be applied to intact skin via a metered pump spray to facilitate drug delivery to the circulation. The aim of this study was to assess the ability of the TDS preparation to deliver diazepam systemically, and to characterize the pharmacokinetic profile of the drug in healthy adult subjects. We conducted a randomized, single-dose, two-period, crossover phase I (pharmacokinetic) comparative study in twelve healthy volunteers. All volunteers received both 10 mg TDS-diazepam topically to the upper chest and 10 mg of the rectal diazepam preparation (Diastat®, 10 mg diazepam gel), with a minimum washout of 14 days between dosing episodes. Both formulations were well tolerated in all volunteers. Following topical application of TDS-diazepam, the mean AUC0-72h was 1241 ng/mL.h and the Cmax 34 ng/mL. The values for rectal Diastat were 4109 ng/mL.h and 300 ng/mL respectively. This proof of concept study demonstrates that the TDS preparation successfully delivered diazepam systemically to adults. As expected, the concentration of diazepam following the TDS application was lower and not bioequivalent to rectal gel. Future development of this unique system is required.

Keywords: Bioequivalence, transdermal delivery system, diazepam, seizure, pharmacokinetic

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5 Performance Evaluation of Contemporary Classifiers for Automatic Detection of Epileptic EEG

Authors: K. E. Ch. Vidyasagar, M. Moghavvemi, T. S. S. T. Prabhat

Abstract:

Epilepsy is a global problem, and with seizures eluding even the smartest of diagnoses a requirement for automatic detection of the same using electroencephalogram (EEG) would have a huge impact in diagnosis of the disorder. Among a multitude of methods for automatic epilepsy detection, one should find the best method out, based on accuracy, for classification. This paper reasons out, and rationalizes, the best methods for classification. Accuracy is based on the classifier, and thus this paper discusses classifiers like quadratic discriminant analysis (QDA), classification and regression tree (CART), support vector machine (SVM), naive Bayes classifier (NBC), linear discriminant analysis (LDA), K-nearest neighbor (KNN) and artificial neural networks (ANN). Results show that ANN is the most accurate of all the above stated classifiers with 97.7% accuracy, 97.25% specificity and 98.28% sensitivity in its merit. This is followed closely by SVM with 1% variation in result. These results would certainly help researchers choose the best classifier for detection of epilepsy.

Keywords: Epilepsy, classification, ANN, SVM, LDA, kNN, seizure

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4 Epileptic Seizure Prediction by Exploiting Signal Transitions Phenomena

Authors: Mohammad Zavid Parvez, Manoranjan Paul

Abstract:

A seizure prediction method is proposed by extracting global features using phase correlation between adjacent epochs for detecting relative changes and local features using fluctuation/deviation within an epoch for determining fine changes of different EEG signals. A classifier and a regularization technique are applied for the reduction of false alarms and improvement of the overall prediction accuracy. The experiments show that the proposed method outperforms the state-of-the-art methods and provides high prediction accuracy (i.e., 97.70%) with low false alarm using EEG signals in different brain locations from a benchmark data set.

Keywords: Epilepsy, phase correlation, seizure, fluctuation, deviation

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3 Epileptic Seizure Prediction Focusing on Relative Change in Consecutive Segments of EEG Signal

Authors: Mohammad Zavid Parvez, Manoranjan Paul

Abstract:

Epilepsy is a common neurological disorders characterized by sudden recurrent seizures. Electroencephalogram (EEG) is widely used to diagnose possible epileptic seizure. Many research works have been devoted to predict epileptic seizure by analyzing EEG signal. Seizure prediction by analyzing EEG signals are challenging task due to variations of brain signals of different patients. In this paper, we propose a new approach for feature extraction based on phase correlation in EEG signals. In phase correlation, we calculate relative change between two consecutive segments of an EEG signal and then combine the changes with neighboring signals to extract features. These features are then used to classify preictal/ictal and interictal EEG signals for seizure prediction. Experiment results show that the proposed method carries good prediction rate with greater consistence for the benchmark data set in different brain locations compared to the existing state-of-the-art methods.

Keywords: Epilepsy, eeg, phase correlation, seizure

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2 Seizure Effects of FP Bearings on the Seismic Reliability of Base-Isolated Systems

Authors: Paolo Castaldo, Bruno Palazzo, Laura Lodato

Abstract:

This study deals with the seizure effects of friction pendulum (FP) bearings on the seismic reliability of a 3D base-isolated nonlinear structural system, designed according to Italian seismic code (NTC08). The isolated system consists in a 3D reinforced concrete superstructure, a r.c. substructure and the FP devices, described by employing a velocity dependent model. The seismic input uncertainty is considered as a random variable relevant to the problem, by employing a set of natural seismic records selected in compliance with L’Aquila (Italy) seismic hazard as provided from NTC08. Several non-linear dynamic analyses considering the three components of each ground motion have been performed with the aim to evaluate the seismic reliability of the superstructure, substructure, and isolation level, also taking into account the seizure event of the isolation devices. Finally, a design solution aimed at increasing the seismic robustness of the base-isolated systems with FPS is analyzed.

Keywords: Seismic Reliability, seizure, FP devices, seismic robustness

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1 Feedforward Neural Network with Backpropagation for Epilepsy Seizure Detection

Authors: Natalia Espinosa, Arthur Amorim, Rudolf Huebner

Abstract:

Epilepsy is a chronic neural disease and around 50 million people in the world suffer from this disease, however, in many cases, the individual acquires resistance to the medication, which is known as drug-resistant epilepsy, where a detection system is necessary. This paper showed the development of an automatic system for seizure detection based on artificial neural networks (ANN), which are common techniques of machine learning. Discrete Wavelet Transform (DWT) is used for decomposing electroencephalogram (EEG) signal into main brain waves, with these frequency bands is extracted features for training a feedforward neural network with backpropagation, finally made a pattern classification, seizure or non-seizure. Obtaining 95% accuracy in epileptic EEG and 100% in normal EEG.

Keywords: seizure, artificial neural network (ANN), Discrete Wavelet Transform (DWT), Epilepsy Detection

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