Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 57

Search results for: PTZ-induced seizure

57 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: EEG, epilepsy, phase correlation, seizure

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56 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: Artificial Neural Network (ANN), Discrete Wavelet Transform (DWT), Epilepsy Detection , Seizure.

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55 Automatic Seizure Detection Using Weighted Permutation Entropy and Support Vector Machine

Authors: Noha Seddik, Sherine Youssef, Mohamed Kholeif

Abstract:

The automated epileptic seizure detection research field has emerged in the recent years; this involves analyzing the Electroencephalogram (EEG) signals instead of the traditional visual inspection performed by expert neurologists. In this study, a Support Vector Machine (SVM) that uses Weighted Permutation Entropy (WPE) as the input feature is proposed for classifying normal and seizure EEG records. WPE is a modified statistical parameter of the permutation entropy (PE) that measures the complexity and irregularity of a time series. It incorporates both the mapped ordinal pattern of the time series and the information contained in the amplitude of its sample points. The proposed system utilizes the fact that entropy based measures for the EEG segments during epileptic seizure are lower than in normal EEG.

Keywords: electroencephalogram (EEG), epileptic seizure detection, weighted permutation entropy (WPE), support vector machine (SVM)

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54 Preliminary Results on a Maximum Mean Discrepancy Approach for Seizure Detection

Authors: Boumediene Hamzi, Turky N. AlOtaiby, Saleh AlShebeili, Arwa AlAnqary

Abstract:

We introduce a data-driven method for seizure detection drawing on recent progress in Machine Learning. The method is based on embedding probability measures in a high (or infinite) dimensional reproducing kernel Hilbert space (RKHS) where the Maximum Mean Discrepancy (MMD) is computed. The MMD is metric between probability measures that are computed as the difference between the means of probability measures after being embedded in an RKHS. Working in RKHS provides a convenient, general functional-analytical framework for theoretical understanding of data. We apply this approach to the problem of seizure detection.

Keywords: kernel methods, maximum mean discrepancy, seizure detection, machine learning

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53 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, seizure, phase correlation, fluctuation, deviation.

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52 Managing Psychogenic Non-Epileptic Seizure Disorder: The Benefits of Collaboration between Psychiatry and Neurology

Authors: Donald Kushon, Jyoti Pillai

Abstract:

Psychogenic Non-epileptic Seizure Disorder (PNES) is a challenging clinical problem for the neurologist. This study explores the benefits of on-site collaboration between psychiatry and neurology in the management of PNES. A 3 month period at a university hospital seizure clinic is described detailing specific management approaches taken as a result of this collaboration. This study describes four areas of interest: (1. After the video EEG results confirm the diagnosis of PNES, the presentation of the diagnosis of PNES to the patient. (2. The identification of co-morbid psychiatric illness (3. Treatment with specific psychotherapeutic interventions (including Cognitive Behavioral Therapy) and psychopharmacologic interventions (primarily SSRIs) and (4. Preliminary treatment outcomes.

Keywords: cognitive behavioral therapy (CBT), psychogenic non-epileptic seizure disorder (PNES), selective serotonin reuptake inhibitors (SSRIs), video electroencephalogram (VEEG)

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51 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: FP devices, seismic reliability, seismic robustness, seizure

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50 Analysis of Epileptic Electroencephalogram Using Detrended Fluctuation and Recurrence Plots

Authors: Mrinalini Ranjan, Sudheesh Chethil

Abstract:

Epilepsy is a common neurological disorder characterised by the recurrence of seizures. Electroencephalogram (EEG) signals are complex biomedical signals which exhibit nonlinear and nonstationary behavior. We use two methods 1) Detrended Fluctuation Analysis (DFA) and 2) Recurrence Plots (RP) to capture this complex behavior of EEG signals. DFA considers fluctuation from local linear trends. Scale invariance of these signals is well captured in the multifractal characterisation using detrended fluctuation analysis (DFA). Analysis of long-range correlations is vital for understanding the dynamics of EEG signals. Correlation properties in the EEG signal are quantified by the calculation of a scaling exponent. We report the existence of two scaling behaviours in the epileptic EEG signals which quantify short and long-range correlations. To illustrate this, we perform DFA on extant ictal (seizure) and interictal (seizure free) datasets of different patients in different channels. We compute the short term and long scaling exponents and report a decrease in short range scaling exponent during seizure as compared to pre-seizure and a subsequent increase during post-seizure period, while the long-term scaling exponent shows an increase during seizure activity. Our calculation of long-term scaling exponent yields a value between 0.5 and 1, thus pointing to power law behaviour of long-range temporal correlations (LRTC). We perform this analysis for multiple channels and report similar behaviour. We find an increase in the long-term scaling exponent during seizure in all channels, which we attribute to an increase in persistent LRTC during seizure. The magnitude of the scaling exponent and its distribution in different channels can help in better identification of areas in brain most affected during seizure activity. The nature of epileptic seizures varies from patient-to-patient. To illustrate this, we report an increase in long-term scaling exponent for some patients which is also complemented by the recurrence plots (RP). RP is a graph that shows the time index of recurrence of a dynamical state. We perform Recurrence Quantitative analysis (RQA) and calculate RQA parameters like diagonal length, entropy, recurrence, determinism, etc. for ictal and interictal datasets. We find that the RQA parameters increase during seizure activity, indicating a transition. We observe that RQA parameters are higher during seizure period as compared to post seizure values, whereas for some patients post seizure values exceeded those during seizure. We attribute this to varying nature of seizure in different patients indicating a different route or mechanism during the transition. Our results can help in better understanding of the characterisation of epileptic EEG signals from a nonlinear analysis.

Keywords: detrended fluctuation, epilepsy, long range correlations, recurrence plots

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49 5-[Aryloxypyridyl (or Nitrophenyl)]-4H-1,2,4-Triazoles as Flexible Benzodiazepine Analogs: Synthesis, Receptor Binding Affinity and the Lipophilicity-Dependent Anti-Seizure Onset of Action

Authors: Latifeh Navidpour, Shabnam Shabani, Alireza Heidari, Manouchehr Bashiri, Azadeh Ebrahim-Habibi, Soraya Shahhosseini, Hamed Shafaroodi, Sayyed Abbas Tabatabai, Mahsa Toolabi

Abstract:

A new series of 5-(2-aryloxy-4-nitrophenyl)-4H-1,2,4-triazoles and 5-(2-aryloxy-3-pyridyl)-4H-1,2,4-triazoles, possessing C-3 thio or alkylthio substituents, was synthesized and evaluated for their benzodiazepine receptor affinity and anti-seizure activity. These analogues revealed similar to significantly superior affinity to GABAA/ benzodiazepine receptor complex (IC50 values of 0.04–4.1 nM), relative to diazepam as the reference drug (IC50 value of 2.4 nM). To determine the onset of anti-seizure activity, the time-dependent effectiveness of i.p. administration of compounds on pentylenetetrazole induced seizure threshold was studied and a very good relationship was observed between the lipophilicity (cLogP) and onset of action of studied analogues (r2 = 0.964). The minimum effective dose of the compounds, determined at the time the analogues showed their highest activity, was demonstrated to be 0.025–0.1 mg/kg, relative to diazepam (0.025 mg/kg).

Keywords: 1, 2, 4-triazole, flexible benzodiazepines, GABAA/bezodiazepine receptor complex, onset of action, PTZ induced seizure threshold

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48 Investigation of the EEG Signal Parameters during Epileptic Seizure Phases in Consequence to the Application of External Healing Therapy on Subjects

Authors: Karan Sharma, Ajay Kumar

Abstract:

Epileptic seizure is a type of disease due to which electrical charge in the brain flows abruptly resulting in abnormal activity by the subject. One percent of total world population gets epileptic seizure attacks.Due to abrupt flow of charge, EEG (Electroencephalogram) waveforms change. On the display appear a lot of spikes and sharp waves in the EEG signals. Detection of epileptic seizure by using conventional methods is time-consuming. Many methods have been evolved that detect it automatically. The initial part of this paper provides the review of techniques used to detect epileptic seizure automatically. The automatic detection is based on the feature extraction and classification patterns. For better accuracy decomposition of the signal is required before feature extraction. A number of parameters are calculated by the researchers using different techniques e.g. approximate entropy, sample entropy, Fuzzy approximate entropy, intrinsic mode function, cross-correlation etc. to discriminate between a normal signal & an epileptic seizure signal.The main objective of this review paper is to present the variations in the EEG signals at both stages (i) Interictal (recording between the epileptic seizure attacks). (ii) Ictal (recording during the epileptic seizure), using most appropriate methods of analysis to provide better healthcare diagnosis. This research paper then investigates the effects of a noninvasive healing therapy on the subjects by studying the EEG signals using latest signal processing techniques. The study has been conducted with Reiki as a healing technique, beneficial for restoring balance in cases of body mind alterations associated with an epileptic seizure. Reiki is practiced around the world and is recommended for different health services as a treatment approach. Reiki is an energy medicine, specifically a biofield therapy developed in Japan in the early 20th century. It is a system involving the laying on of hands, to stimulate the body’s natural energetic system. Earlier studies have shown an apparent connection between Reiki and the autonomous nervous system. The Reiki sessions are applied by an experienced therapist. EEG signals are measured at baseline, during session and post intervention to bring about effective epileptic seizure control or its elimination altogether.

Keywords: EEG signal, Reiki, time consuming, epileptic seizure

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47 Schiff Bases of Isatin and Admantane-1-Carbohydrazide: Synthesis, Characterization, and Anticonvulsant Activity

Authors: Hind O. Osman, Tilal Elsaman, Bashir A. Yousef, Esraa Elhadi, Aimun A. E. Ahmed, Eyman Mohamed Eltayib, Malik Suliman Mohamed, Magdi Awadalla Mohamed

Abstract:

Epilepsy is the most common neurological condition and cause of substantial morbidity and mortality. In the present study, the molecular hybridization tool was adopted to obtain six Schiff bases of isatin and adamantane-1-carbohydrazide (18–23). Then, their anticonvulsant activity was evaluated using a pentylenetetrazole- (PTZ-) induced seizure model using phenobarbitone as a positive control. Our findings showed that compounds 18–23 provided significant protection against PTZ-induced seizure, and maximum activities were associated with compound 23. Moreover, all investigated compounds increased the latency of induced convulsion and reduced the duration of epilepsy, with compound 23 being the best. Interestingly, most of the synthesized molecules showed a reduction in neurological symptoms and severity of the seizure. Molecular docking studies suggest GABA-A receptor as a potential target, and in silico ADME screening revealed that the pharmaceutical properties of compound 23 are within the specified limit. Thus, compound 23 was identified as a promising candidate that warrants further drug discovery processes.

Keywords: isatin and adamantane, anticonvulsant activity, PTZ-induced seizure, molecular docking

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46 Epileptic Seizure Onset Detection via Energy and Neural Synchronization Decision Fusion

Authors: Marwa Qaraqe, Muhammad Ismail, Erchin Serpedin

Abstract:

This paper presents a novel architecture for a patient-specific epileptic seizure onset detector using scalp electroencephalography (EEG). The proposed architecture is based on the decision fusion calculated from energy and neural synchronization related features. Specifically, one level of the detector calculates the condition number (CN) of an EEG matrix to evaluate the amount of neural synchronization present within the EEG channels. On a parallel level, the detector evaluates the energy contained in four EEG frequency subbands. The information is then fed into two independent (parallel) classification units based on support vector machines to determine the onset of a seizure event. The decisions from the two classifiers are then combined together according to two fusion techniques to determine a global decision. Experimental results demonstrate that the detector based on the AND fusion technique outperforms existing detectors with a sensitivity of 100%, detection latency of 3 seconds, while it achieves a 2:76 false alarm rate per hour. The OR fusion technique achieves a sensitivity of 100%, and significantly improves delay latency (0:17 seconds), yet it achieves 12 false alarms per hour.

Keywords: epilepsy, EEG, seizure onset, electroencephalography, neuron, detection

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45 From Electroencephalogram to Epileptic Seizures Detection by Using Artificial Neural Networks

Authors: Gaetano Zazzaro, Angelo Martone, Roberto V. Montaquila, Luigi Pavone

Abstract:

Seizure is the main factor that affects the quality of life of epileptic patients. The diagnosis of epilepsy, and hence the identification of epileptogenic zone, is commonly made by using continuous Electroencephalogram (EEG) signal monitoring. Seizure identification on EEG signals is made manually by epileptologists and this process is usually very long and error prone. The aim of this paper is to describe an automated method able to detect seizures in EEG signals, using knowledge discovery in database process and data mining methods and algorithms, which can support physicians during the seizure detection process. Our detection method is based on Artificial Neural Network classifier, trained by applying the multilayer perceptron algorithm, and by using a software application, called Training Builder that has been developed for the massive extraction of features from EEG signals. This tool is able to cover all the data preparation steps ranging from signal processing to data analysis techniques, including the sliding window paradigm, the dimensionality reduction algorithms, information theory, and feature selection measures. The final model shows excellent performances, reaching an accuracy of over 99% during tests on data of a single patient retrieved from a publicly available EEG dataset.

Keywords: artificial neural network, data mining, electroencephalogram, epilepsy, feature extraction, seizure detection, signal processing

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44 Auricular Electroacupuncture Rescued Epilepsy Seizure by Attenuating TLR-2 Inflammatory Pathway in the Kainic Acid-Induced Rats

Authors: I-Han Hsiao, Chun-Ping Huang, Ching-Liang Hsieh, Yi-Wen Lin

Abstract:

Epilepsy is chronic brain disorder that results in the sporadic occurrence of spontaneous seizures in the temporal lobe, cerebral cortex, and hippocampus. Clinical antiepileptic medicines are often ineffective or little benefits in the small amount of patients and usually initiate severe side effects. This inflammation contributes to enhanced neuronal excitability and the onset of epilepsy. Auricular electric-stimulation (AES) can increase parasympathetic activity and stimulate the solitary tract nucleus to induce the cholinergic anti-inflammatory pathway. Furthermore, it may be a therapeutic strategy for the treatment of epilepsy. In the present study, we want to investigate the effects of AES on inflammatory mediators in kainic acid (KA)-induced epileptic seizure rats. Experimental KA injection increased expression of TLR-2 pathway associated inflammatory mediators, were further reduced by either 2Hz or 15 Hz AES in the prefrontal cortex, hippocampus, and somatosensory cortex. We suggest that AES can successfully control the epileptic seizure by down-regulation of inflammation signaling pathway.

Keywords: auricular electric-stimulation, epileptic seizures, anti-inflammation

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43 ARIMA-GARCH, A Statistical Modeling for Epileptic Seizure Prediction

Authors: Salman Mohamadi, Seyed Mohammad Ali Tayaranian Hosseini, Hamidreza Amindavar

Abstract:

In this paper, we provide a procedure to analyze and model EEG (electroencephalogram) signal as a time series using ARIMA-GARCH to predict an epileptic attack. The heteroskedasticity of EEG signal is examined through the ARCH or GARCH, (Autore- gressive conditional heteroskedasticity, Generalized autoregressive conditional heteroskedasticity) test. The best ARIMA-GARCH model in AIC sense is utilized to measure the volatility of the EEG from epileptic canine subjects, to forecast the future values of EEG. ARIMA-only model can perform prediction, but the ARCH or GARCH model acting on the residuals of ARIMA attains a con- siderable improved forecast horizon. First, we estimate the best ARIMA model, then different orders of ARCH and GARCH modelings are surveyed to determine the best heteroskedastic model of the residuals of the mentioned ARIMA. Using the simulated conditional variance of selected ARCH or GARCH model, we suggest the procedure to predict the oncoming seizures. The results indicate that GARCH modeling determines the dynamic changes of variance well before the onset of seizure. It can be inferred that the prediction capability comes from the ability of the combined ARIMA-GARCH modeling to cover the heteroskedastic nature of EEG signal changes.

Keywords: epileptic seizure prediction , ARIMA, ARCH and GARCH modeling, heteroskedasticity, EEG

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42 Epilepsy Seizure Prediction by Effective Connectivity Estimation Using Granger Causality and Directed Transfer Function Analysis of Multi-Channel Electroencephalogram

Authors: Mona Hejazi, Ali Motie Nasrabadi

Abstract:

Epilepsy is a persistent neurological disorder that affects more than 50 million people worldwide. Hence, there is a necessity to introduce an efficient prediction model for making a correct diagnosis of the epileptic seizure and accurate prediction of its type. In this study we consider how the Effective Connectivity (EC) patterns obtained from intracranial Electroencephalographic (EEG) recordings reveal information about the dynamics of the epileptic brain and can be used to predict imminent seizures, as this will enable the patients (and caregivers) to take appropriate precautions. We use this definition because we believe that effective connectivity near seizures begin to change, so we can predict seizures according to this feature. Results are reported on the standard Freiburg EEG dataset which contains data from 21 patients suffering from medically intractable focal epilepsy. Six channels of EEG from each patients are considered and effective connectivity using Directed Transfer Function (DTF) and Granger Causality (GC) methods is estimated. We concentrate on effective connectivity standard deviation over time and feature changes in five brain frequency sub-bands (Alpha, Beta, Theta, Delta, and Gamma) are compared. The performance obtained for the proposed scheme in predicting seizures is: average prediction time is 50 minutes before seizure onset, the maximum sensitivity is approximate ~80% and the false positive rate is 0.33 FP/h. DTF method is more acceptable to predict epileptic seizures and generally we can observe that the greater results are in gamma and beta sub-bands. The research of this paper is significantly helpful for clinical applications, especially for the exploitation of online portable devices.

Keywords: effective connectivity, Granger causality, directed transfer function, epilepsy seizure prediction, EEG

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41 Current and Emerging Pharmacological Treatment for Status Epilepticus in Adults

Authors: Mathew Tran, Deepa Patel, Breann Prophete, Irandokht Khaki Najafabadi

Abstract:

Status epilepticus is a neurological disorder requiring emergent control with medical therapy. Based on guideline recommendations for adults with status epilepticus, the first-line treatment is to start a benzodiazepine, as they are quick at seizure control. The second step is to initiate a non-benzodiazepine anti-epileptic drug to prevent refractory seizures. Studies show that the anti-epileptic drugs are approximately equivalent in status epilepticus control once a benzodiazepine has been given. This review provides a brief overview of the management of status epilepticus based on evidence from the literature and evidence-based guidelines.

Keywords: neurological disorder, seizure, status epilepticus, benzo diazepines, antiepileptic agents

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40 Covid Encephalopathy and New-Onset Seizures in the Context of a Prior Brain Abnormality: A Case Report

Authors: Omar Sorour, Michael Leahy, Thomas Irvine, Vladimir Koren

Abstract:

Introduction: Covid encephalitis is a rare yet dangerous complication, particularly affecting the older and immunocompromised. Symptoms range from confusion to delirium, coma, and seizures. Although neurological manifestations have become more well-characterized in COVID patients, little is known about whether priorneurological abnormalities may predispose patients to COVID encephalopathy. Case Description: A 73 y.o. male with a CT and MRI-confirmed stable, prior 9 mm cavernoma in the right frontal lobe and no past history of seizures was hospitalized with generalized weakness, abdominal pain, nausea, and shortness of breath with subsequent COVID pneumonia. Three days after the initial presentation, the patient developed a spontaneous generalized tonic-clonic seizure consistent with presumed COVID encephalitis, along with somnolence and confusion. A day later, the patient had two other seizure episodes. Follow-up EEG suggested an inter-ictal epileptic focus with sharp waves corresponding to roughly the same location as the patient’s pre-existing cavernoma. The patient’s seizures stopped shortly thereafter, while his encephalopathy continued for days. Conclusion: We illustrate that a pre-existing anatomic cortical abnormality may act as a potential nidus for new-onset seizure activity in the context of suggested COVID encephalopathy. Future studies may further demonstrate that manifestations of COVIDencephalopathy in certain patients may be more predictable than initially assumed.

Keywords: cavernoma, covid, encephalopathy, seizures

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39 Effect of Amlodipine on Dichlorvos-Induced Seizure in Mice

Authors: Omid Ghollipoor Bashiri, Farzam Hatefi

Abstract:

Dichlorvos a synthetic organophosphate poisons are used as insecticide. These toxins can be used insecticides in agriculture and medicine for destruction and/or eradication of ectoparasites of animals. Studies have shown that Dichlorvos creation seizure effects in different animals. Amlodipine, dihydropyridine calcium channel blockers, widely used for treatment of cardiovascular diseases. Studies have shown that the calcium channel blockers are anticonvulsant effects in different animal models. The aim of this study was to determine the effect of Amlodipine on Dichlorvos-induced seizures in mice. In this experiment, the animals were received different doses of Amlodipine (2.5, 5, 10, 20 and 40 mg/ kg b.wt.) intraperitoneally 30 min before intraperitoneal injection of Dichlorvos (50 mg/kg b.wt). After Dichlorvos injection, clonic and tonic seizures, and finally was the fate was investigated. Results showed that Amlodipine dose-dependently reduced the severity of Dichlorvos-induced seizures, so that Amlodipine at a dose of 5mg (The lowest, p<0.05) and 40 mg/kg b.wt. (The highest, p<0.001) which had anticonvulsant effects. The anticonvulsant activity of Amlodipine suggests that possibly due to the antagonistic effect on voltage-dependent calcium channel.

Keywords: dichlorvos, amlodipine, seizures, mice

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38 Fast and Accurate Model to Detect Ictal Waveforms in Electroencephalogram Signals

Authors: Piyush Swami, Bijaya Ketan Panigrahi, Sneh Anand, Manvir Bhatia, Tapan Gandhi

Abstract:

Visual inspection of electroencephalogram (EEG) signals to detect epileptic signals is very challenging and time-consuming task even for any expert neurophysiologist. This problem is most challenging in under-developed and developing countries due to shortage of skilled neurophysiologists. In the past, notable research efforts have gone in trying to automate the seizure detection process. However, due to high false alarm detections and complexity of the models developed so far, have vastly delimited their practical implementation. In this paper, we present a novel scheme for epileptic seizure detection using empirical mode decomposition technique. The intrinsic mode functions obtained were then used to calculate the standard deviations. This was followed by probability density based classifier to discriminate between non-ictal and ictal patterns in EEG signals. The model presented here demonstrated very high classification rates ( > 97%) without compromising the statistical performance. The computation timings for each testing phase were also very low ( < 0.029 s) which makes this model ideal for practical applications.

Keywords: electroencephalogram (EEG), epilepsy, ictal patterns, empirical mode decomposition

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37 Case Report: Clinical Improvement of Forbrain Neurologic Signs in 3-Months Old Persian Mastiff Dog with Calvarial Hyperostosis Syndrome after Corticosteroid, Antiepileptic and Antibiotic Therapy

Authors: Hamidreza Jahani, Zahra Salehzadeh, Ehsan Amini, Mohsen Tohidifar

Abstract:

Calvarial Hyperostosis Syndrome( CHS) is a benign bone disease of the skull. It is a non-neoplastic and proliferative bone disease, and the main feature of the disease is progressive and asymmetrical bone involvement. CHS is mostly reported in young male and female bullmastiff dogs and less frequently in other breeds. The etiology of CHS is unknown. This is the first case report of CHS in Iran. A 3-month-old male Persian Mastiff was presented with chief complaints of multiple episodes of seizure, pacing, bizarre behavior, delayed growth, head pressing, and difficulty in opening the mouth. Central blindness and open fontanelles were observed in clinical examination. No abnormality was found in complete blood count and routine blood biochemical tests. CT scan findings include cortical thickening of frontal and parietal bones and enlargement of left retropharyngeal lymph node. For treatment, oral clindamycine for two weeks, prednisolone and phenobarbital for one month respectively, were administrated, and the case showed improvement after a week and was recovered after one month.

Keywords: calvarial hyperostosis, persian mastiff, frontal bone, seizure

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36 Depressive Symptoms in Children with Epilepsy Attending a Tertiary Care Hospital in Oman

Authors: Hamood Al Kiyumi, Salim Al Huseini, Khalid Al Risi, Hassan Mirza, Amira Al Hosni, Sanjay Jaju, Asaad Al Habsi

Abstract:

Objectives: The aim of this study was to assess the proportion of depressive symptoms along with demographic data in children diagnosed with epilepsy in a tertiary care institution in Oman. Methods: This cross-sectional study was conducted between June 2016 and August 2018. We have included 75 children with age group from five to 12 years old, attending epilepsy clinic at Sultan Qaboos University Hospital who were diagnosed with epilepsy and already on treatment. Patients were excluded if they have mental retardation. Validated Depression Scale for Children (CES-DC) questionnaire was utilized to assess the level of depressive symptoms among children. In addition, we have looked at associated factors including seizure status in the last three months, compliance with antiepileptic medications, type of epilepsy, and number of antiepileptic medications. Results: In this study, we found that depressive symptoms were present in 39 (52%) of patients. We also found that 96% of the patients were compliant to medications. In addition, seizure was present in the last three months in 48% of the sample studies. There was no statistically significant association between any of the studied variables and depression. Conclusions: Although depression is highly prevalent in children with epilepsy, this study did not find any significant association between the CES-DC scores and the studied factors.

Keywords: depression, children, epilepsy, Oman

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35 Lennox-gastaut Syndrome Associated with Dysgenesis of Corpus Callosum

Authors: A. Bruce Janati, Muhammad Umair Khan, Naif Alghassab, Ibrahim Alzeir, Assem Mahmoud, M. Sammour

Abstract:

Rationale: Lennox-Gastaut syndrome(LGS) is an electro-clinical syndrome composed of the triad of mental retardation, multiple seizure types, and the characteristic generalized slow spike-wave complexes in the EEG. In this article, we report on two patients with LGS whose brain MRI showed dysgenesis of corpus callosum(CC). We review the literature and stress the role of CC in the genesis of secondary bilateral synchrony(SBS). Method: This was a clinical study conducted at King Khalid Hospital. Results: The EEG was consistent with LGS in patient 1 and unilateral slow spike-wave complexes in patient 2. The MRI showed hypoplasia of the splenium of CC in patient 1, and global hypoplasia of CC combined with Joubert syndrome in patient 2. Conclusion: Based on the data, we proffer the following hypotheses: 1-Hypoplasia of CC interferes with functional integrity of this structure. 2-The genu of CC plays a pivotal role in the genesis of secondary bilateral synchrony. 3-Electrodecremental seizures in LGS emanate from pacemakers generated in the brain stem, in particular the mesencephalon projecting abnormal signals to the cortex via thalamic nuclei. 4-Unilateral slow spike-wave complexes in the context of mental retardation and multiple seizure types may represent a variant of LGS, justifying neuroimaging studies.

Keywords: EEG, Lennox-Gastaut syndrome, corpus callosum , MRI

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34 Factors Contributing to Adverse Maternal and Fetal Outcome in Patients with Eclampsia

Authors: T. Pradhan, P. Rijal, M. C. Regmi

Abstract:

Background: Eclampsia is a multisystem disorder that involves vital organs and failure of these may lead to deterioration of maternal condition and hypoxia and acidosis of fetus resulting in high maternal and perinatal mortality and morbidity. Thus, evaluation of the contributing factors for this condition and its complications leading to maternal deaths should be the priority. Formulating the plan and protocol to decrease these losses should be our goal. Aims and Objectives: To evaluate the risk factors associated with adverse maternal and fetal outcome in patients with eclampsia and to correlate the risk factors associated with maternal and fetal morbidity and mortality. Methods: All patients with eclampsia admitted in Department of Obstetrics and Gynecology, B. P. Koirala Institute of Health Sciences were enrolled after informed consent from February 2013 to February 2014. Questions as per per-forma were asked to patients, and attendants like Antenatal clinic visits, parity, number of episodes of seizures, duration from onset of seizure to magnesium sulfate and the patients were followed as per the hospital protocol, the mode of delivery, outcome of baby, post partum maternal condition like maternal Intensive Care Unit admission, neurological impairment and mortality were noted before discharge. Statistical analysis was done using Statistical Package for the Social Sciences (SPSS 11). Mean and percentage were calculated for demographic variables. Pearson’s correlation test and chi-square test were applied to find the relation between the risk factors and the outcomes. P value less than 0.05 was considered significant. Results: There were 10,000 antenatal deliveries during the study period. Fifty-two patients with eclampsia were admitted. All of the patients were unbooked for our institute. Thirty-nine patients were antepartum eclampsia. Thirty-one patients required mechanical ventilator support. Twenty-four patients were delivered by emergency c-section and 21 babies were Low Birth Weight and there were 9 stillbirths. There was one maternal mortality and 45 patients were discharged with improvement but 3 patients had neurological impairment. Mortality was significantly related with number of seizure episodes and time interval between seizure onset and administration of magnesium sulphate. Conclusion: Early detection and management of hypertensive complicating pregnancy during antenatal clinic check up. Early hospitalization and management with magnesium sulphate for eclampsia can help to minimize the maternal and fetal adverse outcomes.

Keywords: eclampsia, maternal mortality, perinatal mortality, risk factors

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33 Anti-Phospholipid Antibody Syndrome Presenting with Seizure, Stroke and Atrial Mass: A Case Report

Authors: Rajish Shil, Amal Alduhoori, Vipin Thomachan, Jamal Teir, Radhakrishnan Renganathan

Abstract:

Background: Antiphospholipid antibody syndrome (APS) has a broad spectrum of thrombotic and non-thrombotic clinical manifestations. We present a case of APS presenting with seizure, stroke, and atrial mass. Case Description: A 38-year-old male presented with headache of 10 days duration and tonic-clonic seizure. The neurological examination was normal. Magnetic resonance imaging of brain showed small acute right cerebellar infarct. Magnetic resonance angiography of brain and neck showed a focal narrowing in the origin of the internal carotid artery bilaterally. Electroencephalogram was normal. He was started on aspirin, atorvastatin, and carbamazepine. Transthoracic and trans-esophageal echocardiography showed a pedunculated and lobular atrial mass, measuring 1 X 1.5 cm, which was freely mobile across mitral valve opening across the left ventricular inflow. Autoimmune screening showed positive Antiphospholipid antibodies in high titer (Cardiolipin IgG > 120 units/ml, B2 glycoprotein IgG 90 units/mL). Anti-nuclear antibody was negative. Erythrocyte sedimentation rate and C-reactive protein levels were normal. Platelet count was low (111 x 109/L). The patient underwent successful surgical removal of the mass, which looked like a thrombotic clot, and Histopathological analysis confirmed it as a fibrinous clot, with no evidence of tumor cells. The patient was started on full anticoagulation treatment and was followed up regularly in the clinic, where our patient did not have any further complications from the disease. Discussion: Our patient was diagnosed to have APS based on the features of high positive anticardiolipin antibody IgG and B2 glycoprotein IgG levels, Stroke, thrombocytopenia, and abnormal echo findings. Thrombotic vegetation can mimic an atrial myxoma on echo. Conclusion: APS can present with neurological and cardiac manifestations, and therefore a high index of suspicion is necessary for a diagnosis of the disease as it can affect both short and long term treatment plans and prognosis. Therefore, in patients presenting with neurological symptoms like seizures, weakness and radiological diagnosis of stroke in a young patient, where atrial masses could be thought to be the cause of stroke, they should be screened for any concomitant findings of thrombocytopenia and/or activated partial thromboplastin time prolongation, which should raise the suspicion of vasculitis, specifically APS to be the primary cause of the clinical presentation.

Keywords: antiphospholipid syndrome, seizures, atrial mass, stroke

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32 Effects of Bilateral Electroconvulsive Therapy on Autobiographical Memories in Asian Patients

Authors: Lai Gwen Chan, Yining Ong, Audrey Yoke Poh Wong

Abstract:

Background. The efficacy of electroconvulsive therapy (ECT) as a form of treatment to a range of mental disorders is well-established. However, ECT is often associated with either temporary or persistent cognitive side-effects, resulting in the failure of wider prescription. Of which, retrograde amnesia is the most commonly reported cognitive side-effect. Most studies found a recalling deficit in autobiographical memories to be short-term, although a few have reported more persistent amnesic effects. Little is known about ECT-related amnesic effects in Asian population. Hence, this study aims to resolve conflicting findings, as well as to better elucidate the effects of ECT on cognitive functioning in a local sample. Method: 12 patients underwent bilateral ECT under the care of Psychological Medicine Department, Tan Tock Seng Hospital, Singapore. Participants’ cognition and level of functioning were assessed at four time-points: before ECT, between the third and fourth induced seizure, at the end of the whole course of ECT, and two months after the index course of ECT. Results: It was found that Global Assessment of Functioning scores increased significantly at the completion of ECT. Case-by-case analyses also revealed an overall improvement in Personal Semantic and Autobiographical memory two months after the index course of ECT. A transient dip in both personal semantic and autobiographical memory scores was observed in one participant between the third and fourth induced seizure, but subsequently resolved and showed better performance than at baseline. Conclusions: The findings of this study suggest that ECT is an effective form of treatment to alleviate the severity of symptoms of the diagnosis. ECT does not affect attention, language, executive functioning, personal semantic and autobiographical memory adversely. The findings suggest that Asian patients may respond to bilateral ECT differently from Western samples.

Keywords: electroconvulsive therapy (ECT), autobiographical memory, cognitive impairment, psychiatric disorder

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31 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: classification, seizure, KNN, SVM, LDA, ANN, epilepsy

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30 Statistical Wavelet Features, PCA, and SVM-Based Approach for EEG Signals Classification

Authors: R. K. Chaurasiya, N. D. Londhe, S. Ghosh

Abstract:

The study of the electrical signals produced by neural activities of human brain is called Electroencephalography. In this paper, we propose an automatic and efficient EEG signal classification approach. The proposed approach is used to classify the EEG signal into two classes: epileptic seizure or not. In the proposed approach, we start with extracting the features by applying Discrete Wavelet Transform (DWT) in order to decompose the EEG signals into sub-bands. These features, extracted from details and approximation coefficients of DWT sub-bands, are used as input to Principal Component Analysis (PCA). The classification is based on reducing the feature dimension using PCA and deriving the support-vectors using Support Vector Machine (SVM). The experimental are performed on real and standard dataset. A very high level of classification accuracy is obtained in the result of classification.

Keywords: discrete wavelet transform, electroencephalogram, pattern recognition, principal component analysis, support vector machine

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29 Acute Severe Hyponatremia in Patient with Psychogenic Polydipsia, Learning Disability and Epilepsy

Authors: Anisa Suraya Ab Razak, Izza Hayat

Abstract:

Introduction: The diagnosis and management of severe hyponatremia in neuropsychiatric patients present a significant challenge to physicians. Several factors contribute, including diagnostic shadowing and attributing abnormal behavior to intellectual disability or psychiatric conditions. Hyponatraemia is the commonest electrolyte abnormality in the inpatient population, ranging from mild/asymptomatic, moderate to severe levels with life-threatening symptoms such as seizures, coma and death. There are several documented fatal case reports in the literature of severe hyponatremia secondary to psychogenic polydipsia, often diagnosed only in autopsy. This paper presents a case study of acute severe hyponatremia in a neuropsychiatric patient with early diagnosis and admission to intensive care. Case study: A 21-year old Caucasian male with known epilepsy and learning disability was admitted from residential living with generalized tonic-clonic self-terminating seizures after refusing medications for several weeks. Evidence of superficial head injury was detected on physical examination. His laboratory data demonstrated mild hyponatremia (125 mmol/L). Computed tomography imaging of his brain demonstrated no acute bleed or space-occupying lesion. He exhibited abnormal behavior - restlessness, drinking water from bathroom taps, inability to engage, paranoia, and hypersexuality. No collateral history was available to establish his baseline behavior. He was loaded with intravenous sodium valproate and leveritircaetam. Three hours later, he developed vomiting and a generalized tonic-clonic seizure lasting forty seconds. He remained drowsy for several hours and regained minimal recovery of consciousness. A repeat set of blood tests demonstrated profound hyponatremia (117 mmol/L). Outcomes: He was referred to intensive care for peripheral intravenous infusion of 2.7% sodium chloride solution with two-hourly laboratory monitoring of sodium concentration. Laboratory monitoring identified dangerously rapid correction of serum sodium concentration, and hypertonic saline was switched to a 5% dextrose solution to reduce the risk of acute large-volume fluid shifts from the cerebral intracellular compartment to the extracellular compartment. He underwent urethral catheterization and produced 8 liters of urine over 24 hours. Serum sodium concentration remained stable after 24 hours of correction fluids. His GCS recovered to baseline after 48 hours with improvement in behavior -he engaged with healthcare professionals, understood the importance of taking medications, admitted to illicit drug use and drinking massive amounts of water. He was transferred from high-dependency care to ward level and was initiated on multiple trials of anti-epileptics before achieving seizure-free days two weeks after resolution of acute hyponatremia. Conclusion: Psychogenic polydipsia is often found in young patients with intellectual disability or psychiatric disorders. Patients drink large volumes of water daily ranging from ten to forty liters, resulting in acute severe hyponatremia with mortality rates as high as 20%. Poor outcomes are due to challenges faced by physicians in making an early diagnosis and treating acute hyponatremia safely. A low index of suspicion of water intoxication is required in this population, including patients with known epilepsy. Monitoring urine output proved to be clinically effective in aiding diagnosis. Early referral and admission to intensive care should be considered for safe correction of sodium concentration while minimizing risk of fatal complications e.g. central pontine myelinolysis.

Keywords: epilepsy, psychogenic polydipsia, seizure, severe hyponatremia

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28 Cost Implications of Natural Resources Conflicts on Livelihoods of Farmers and Pastoralists in the North East Arid Zone of Nigeria

Authors: Ibrahim Ahmed Jajere

Abstract:

Resource use conflicts capable of undermining of pastoralists’ livelihoods are on the increase in the North East Arid Zone of Nigeria. In order to expose socio - economic effects of conflicts and benefits of peace, this study assessed cost implications of farmers/pastoralists conflicts over natural resources. Interviews were conducted with 94 farmers, 90 agro-pastoralists and 91 pastoralists’ household heads. The farmers and agro-pastoralists were systematically sampled while pastoralists were located using snowballing. Both farmers and pastoralists suffered losses in the form of injuries to, and even death of household members, and loss of shelter. Farmers sustained losses of facilities and farm produce while pastoralists suffered loss and seizure of livestock, arrest of household members and forced migrations. The material losses in monetary terms amounted to 14,242,200.00 nairas for farmers, a figure higher than the 10,915,500.00 nairas incurred by pastoralists.

Keywords: cost, conflicts, farmers, pastoralists

Procedia PDF Downloads 149