Search results for: seizure onset

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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Authors: Marwa Qaraqe, Muhammad Ismail, Erchin Serpedin

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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

Procedia PDF Downloads 477

Authors: Mostafa Mohammadpour, Christoph Kapeller, Christy Li, Josef Scharinger, Christoph Guger

Abstract:

Interictal spikes provide valuable information on electrocorticography (ECoG), which aids in surgical planning for patients who suffer from refractory epilepsy. However, the shape and temporal dynamics of these spikes remain unclear. The purpose of this work was to analyze the shape of interictal spikes and measure their distance to the seizure onset zone (SOZ) to use in epilepsy surgery. Thirteen patients' data from the iEEG portal were retrospectively studied. For analysis, half an hour of ECoG data was used from each patient, with the data being truncated before the onset of a seizure. Spikes were first detected and grouped in a sequence, then clustered into interictal epileptiform discharges (IEDs) and non-IED groups using two-step clustering. The distance of the spikes from IED and non-IED groups to SOZ was quantified and compared using the Wilcoxon rank-sum test. Spikes in the IED group tended to be in SOZ or close to it, while spikes in the non-IED group were in distance of SOZ or non-SOZ area. At the group level, the distribution for sharp wave, positive baseline shift, slow wave, and slow wave to sharp wave ratio was significantly different for IED and non-IED groups. The distance of the IED cluster was 10.00mm and significantly closer to the SOZ than the 17.65mm for non-IEDs. These findings provide insights into the shape and spatiotemporal dynamics of spikes that could influence the network mechanisms underlying refractory epilepsy.

Keywords: spike propagation, spike pattern, clustering, SOZ

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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

Procedia PDF Downloads 171

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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Authors: Natalia Espinosa, Arthur Amorim, Rudolf Huebner

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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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Authors: Noha Seddik, Sherine Youssef, Mohamed Kholeif

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

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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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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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Authors: Maurice Ntahobari, Levin Kuhlmann, Mario Boley, Zhinoos Razavi Hesabi

Abstract:

For machine learning based epileptic seizure prediction, it is important for the model to be implemented in small implantable or wearable devices that can be used to monitor epilepsy patients; however, current state-of-the-art methods are complex and computationally intensive. We use Shapley Additive Explanation (SHAP) to find relevant intracranial electroencephalogram (iEEG) features and improve the computational efficiency of a state-of-the-art seizure prediction method based on the extra trees classifier while maintaining prediction performance. Results for a small contest dataset and a much larger dataset with continuous recordings of up to 3 years per patient from 15 patients yield better than chance prediction performance (p < 0.004). Moreover, while the performance of the SHAP-based model is comparable to that of the benchmark, the overall training and prediction time of the model has been reduced by a factor of 1.83. It can also be noted that the feature called zero crossing value is the best EEG feature for seizure prediction. These results suggest state-of-the-art seizure prediction performance can be achieved using efficient methods based on optimal feature selection.

Keywords: machine learning, seizure prediction, extra tree classifier, SHAP, epilepsy

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Authors: Mona Hejazi, Ali Motie Nasrabadi

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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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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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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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Authors: Paolo Castaldo, Bruno Palazzo, Laura Lodato

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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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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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Authors: Karan Sharma, Ajay Kumar

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

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

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

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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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Authors: Seolah Lee, Yoohyk Jang, Soyoung Lee, Kon Chu, Sang Kun Lee

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Objective: The International League Against Epilepsy (ILAE) currently suggests a topiramate serum level range of 5-20 mg/L. However, numerous institutions have observed substantial drug response at lower levels. This study aims to investigate the correlation between topiramate serum levels, drug responsiveness, and adverse events to establish a more accurate and tailored therapeutic range. Methods: We retrospectively analyzed topiramate serum samples collected between January 2017 and January 2022 at Seoul National University Hospital. Clinical data, including serum levels, antiseizure regimens, seizure frequency, and adverse events, were collected. Patient responses were categorized as "insufficient" (reduction in seizure frequency <50%) or "sufficient" (reduction ≥ 50%). Within the "sufficient" group, further subdivisions included seizure-free and tolerable seizure subgroups. A population pharmacokinetic model estimated serum levels from spot measurements. ROC curve analysis determined the optimal serum level cut-off. Results: A total of 389 epilepsy patients, with 555 samples, were reviewed, having a mean dose of 178.4±117.9 mg/day and a serum level of 3.9±2.8 mg/L. Out of the samples, only 5.6% (n=31) exhibited insufficient response, with a mean serum level of 3.6±2.5 mg/L. In contrast, 94.4% (n=524) of samples demonstrated sufficient response, with a mean serum level of 4.0±2.8 mg/L. This difference was not statistically significant (p = 0.45). Among the 78 reported adverse events, logistic regression analysis identified a significant association between ataxia and serum concentration (p = 0.04), with an optimal cut-off value of 6.5 mg/L. In the subgroup of patients receiving monotherapy, those in the tolerable seizure group exhibited a significantly higher serum level compared to the seizure-free group (4.8±2.0 mg/L vs 3.4±2.3 mg/L, p < 0.01). Notably, patients in the tolerable seizure group displayed a higher likelihood of progressing into drug-resistant epilepsy during follow-up visits compared to the seizure-free group. Significance: This study proposed an optimal therapeutic concentration for topiramate based on the patient's responsiveness to the drug and the incidence of adverse effects. We employed a population pharmacokinetic model and analyzed topiramate serum levels to recommend a serum level below 6.5 mg/L to mitigate the risk of ataxia-related side effects. Our findings also indicated that topiramate dose elevation is unnecessary for suboptimal responders, as the drug's effectiveness plateaus at minimal doses.

Keywords: topiramate, therapeutic range, low dos, antiseizure effect

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Authors: Narupon Kunbootsri, J. Kraipoj, K. Phandech, P. Sirasaporn

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Physiotherapy is one of the treatments for frozen shoulder but there was no data about the treatment of physiotherapy. Moreover, it is question about onset of symptom before physiotherapy program and obtaining physical modalities and delayed start physiotherapy program lead to delayed improvement. Thus the aim of this study was to investigate physiotherapy program for frozen shoulder on pain score, onset of symptom and obtaining physical modalities. A retrospective study design was conducted. 182 medical records of patients with frozen shoulder were reviewed. These frozen shoulders were treated at physiotherapy unit, department of Rehabilitation last 3 years (January, 2014- December, 2016). The data consist of onset of symptom, pain score and obtaining physical modalities were recorded. There was a statistically significant improve in pain score, pretreatment score mean 7.24±1.52 and the last follow up pain score mean 3.88± 1.0 [mean difference 3.18 with 95%CI were [2.45- 3.92]. In addition, the onset of symptoms was 145 days before obtaining physiotherapy program. The physical modalities used frequently were hot pack 14.8% and ultrasound diathermy 13.7%. In conclusion, the retrospective study show physiotherapy program including, hot pack and ultrasound diathermy seem to be useful for frozen shoulder in term of pain score. But onset of symptom is too long to start physiotherapy programs.

Keywords: frozen shoulder, physiotherapy, pain score, onset of symptom, physical modality

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Authors: Moses Balina, Archbald Bahizi

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Introduction: Early onset neonatal sepsis is a systemic infection in a newborn baby during the first week after birth and contributes to 50% of neonatal deaths each year. Risk factors for early onset neonatal sepsis, which can be maternal, health care provider, or health care facility associated, can be prevented with access to quality antenatal care. Objective: The objective of the study was to assess early onset neonatal sepsis and antenatal factors associated with Fort Portal Regional Referral Hospital. Methodology: A cross sectional study design was used. The study involved 60 respondents who were mothers of breastfeeding neonates being treated for early onset neonatal sepsis at Fort Portal Regional Referral Hospital neonatal intensive care unit. Simple random sampling was used to select study participants. Data were collected using questionnaires, entered in Stata 16, and analysed using logistic regression. Results: The prevalence of early onset neonatal sepsis at Fort Portal Regional Referral Hospital was 25%. Multivariate analysis revealed that institutional factors were the only antenatal factors found to be significantly associated with early onset neonatal sepsis at Fort Portal Regional Referral Hospital (p < 0.01). Bivariate analysis revealed that attending antenatal care at a health centre III or IV instead of a hospital (p = 0.011) and attending antenatal care in health care facilities with no laboratory investigations (p = 0.048) were risk factors for early onset neonatal sepsis in the newborn at Fort Portal Regional Referral Hospital. Conclusion: Antenatal factors were associated with early onset neonatal sepsis, and health care facility factors like lower level health centre and unavailability of quality laboratory investigations to pregnant women contributed to early onset neonatal sepsis in the newborn. Mentorships, equipping/stocking laboratories, and improving staffing levels were necessary to reduce early onset neonatal sepsis.

Keywords: antenatal factors, early onset neonatal sepsis, neonates 0-7 days, fort portal regional referral hospital

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Authors: Liudmila Garzanova, Lidia Ananyeva, Olga Koneva, Olga Ovsyannikova, Oxana Desinova, Mayya Starovoytova, Rushana Shayahmetova, Anna Khelkovskaya-Sergeeva

Abstract:

Objectives. The age of the disease onset could have an impact on the effect of therapy in systemic sclerosis(SSc). Late-age onset in SSc could have a more severe course of the disease and worse clinical effects on therapy. The aim of our study was to evaluate changes in skin fibrosis on rituximab(RTX) therapy in patients with SSc and different ages of the disease onset. Methods. 151 patients with SSc were included in this study. Patients were divided into groups depending on the age of the disease onset: group 1 - younger than 30 years (40 patients(26%), group 2 - 31-59 years (90 patients(60%) and group 3 – more than 60 years (21 patients(14%). The mean follow-up period was 13±2.3month. The mean age was 48±13years, female-83% of patients, and the diffuse cutaneous subset of the disease had 52% of patients. The mean disease duration was 6.4±5years. The cumulative mean dose of RTX was 1.5±0.6grams. Patients received RTX as a therapy for interstitial lung disease. All patients received prednisone at a dose of 11.6±4.8mg/day, immunosuppressants received 48% of them. The results at baseline and at the end of the follow-up are presented in the form of mean values. Results. There was a significant decrease of modified Rodnan skin score(mRss) in all groups: in group 1 - from 10.2±8 to 7.7±6.5(p=0.01); in group 2 - from 9±7.2 to 6.2±4.7(p=0.0001); in group 3 - from 20.5±14.1 to 10.8±9.4(p=0.001). There was a significant decrease of the activity index (EScSG-AI): in group 1 from 2.5±1.8 to 1.3±1.1; in group 2 – from 3.2±1.6 to 1.5±1.2; in group 3 – from 4.2±2.1 to 1.3±1. Conclusion. There was a significant improvement in skin fibrosis in a year after initiation of RTX therapy regardless of the age of the disease onset. The improvement was more pronounced in the group with late-age onset of the disease, but these data require further investigations.

Keywords: skin fibrosis, systemic sclerosis, rituximab, disease onset

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Authors: Ramzi Rihane, Yassine Benayed

Abstract:

Epilepsy is a chronic neurological disorder characterized by recurrent, unprovoked seizures resulting from abnormal electrical activity in the brain. Timely and accurate detection of these seizures is essential for improving patient care. In this study, we leverage the UK Bonn University open-source EEG dataset and employ advanced deep-learning techniques to automate the detection of epileptic seizures. By extracting key features from both time and frequency domains, as well as Spectrogram features, we enhance the performance of various deep learning models. Our investigation includes architectures such as Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), 1D Convolutional Neural Networks (1D-CNN), and hybrid CNN-LSTM and CNN-BiLSTM models. The models achieved impressive accuracies: LSTM (98.52%), Bi-LSTM (98.61%), CNN-LSTM (98.91%), CNN-BiLSTM (98.83%), and CNN (98.73%). Additionally, we utilized a data augmentation technique called SMOTE, which yielded the following results: CNN (97.36%), LSTM (97.01%), Bi-LSTM (97.23%), CNN-LSTM (97.45%), and CNN-BiLSTM (97.34%). These findings demonstrate the effectiveness of deep learning in capturing complex patterns in EEG signals, providing a reliable and scalable solution for real-time seizure detection in clinical environments.

Keywords: electroencephalogram, epileptic seizure, deep learning, LSTM, CNN, BI-LSTM, seizure detection

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Authors: Mohamed A. Hammad, Dzul Azri Mohamed Noor, Syed Azhar Syed Sulaiman, Majed Ahmed Al-Mansoub, Muhammad Qamar

Abstract:

There is a progressive increase in the prevalence of early onset Type 2 diabetes mellitus. Early detection of Type 2 diabetes enhances the length and/or quality of life which might result from a reduction in the severity, frequency or prevent or delay of its long-term complications. The study aims to determine the onset age for the first diagnosis of Type 2 diabetes mellitus. A retrospective study conducted in the endocrine clinic at Hospital Pulau Pinang in Penang, Malaysia, January- December 2016. Records of 519 patients with Type 2 diabetes mellitus were screened to collect demographic data and determine the age of first-time diabetes mellitus diagnosis. Patients classified according to the age of diagnosis, gender, and ethnicity. The study included 519 patients with age (55.6±13.7) years, female 265 (51.1%) and male 254 (48.9%). The ethnicity distribution was Malay 191 (36.8%), Chinese 189 (36.4%) and Indian 139 (26.8%). The age of Type 2 diabetes diagnosis was (42±14.8) years. The female onset of diabetes mellitus was at age (41.5±13.7) years, while male (42.6±13.7) years. Distribution of diabetic onset by ethnicity was Malay at age (40.7±13.7) years, Chinese (43.2±13.7) years and Indian (42.3±13.7) years. Diabetic onset was classified by age as follow; ≤20 years’ cohort was 33 (6.4%) cases. Group >20- ≤40 years was 190 (36.6%) patients, and category >40- ≤60 years was 270 (52%) subjects. On the other hand, the group >60 years was 22 (4.2%) patients. The range of diagnosis was between 10 and 73 years old. Conclusion: Malay and female have an earlier onset of diabetes than Indian, Chinese and male. More than half of the patients had diabetes between 40 and 60 years old. Diabetes mellitus is becoming more common in younger age <40 years. The age at diagnosis of Type 2 diabetes mellitus has decreased with time.

Keywords: age of onset, diabetes diagnosis, diabetes mellitus, Malaysia, outpatients, type 2 diabetes, retrospective study

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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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Authors: Narupon Kunbootsri, P. Sirasaporn

Abstract:

Frozen shoulder is a common problem present by pain and limit range of motion. The prevalence of frozen shoulder showed 18-31% of population. The effect of frozen shoulder lead to limit activities daily living life, high medical care cost and so on. Physiotherapy is one of the treatments for frozen shoulder but there was no data about the treatment of physiotherapy. Moreover, it is question about onset of symptom relate to physiotherapy program and obtaining physical modalities and delayed start physiotherapy program lead to delayed improvement. Thus the aim of this study was to investigate physiotherapy program for frozen shoulder relate to onset of symptom, range of motion and obtaining physical modalities. A retrospective study design was conducted. 182 medical records of patients with frozen shoulder were reviewed. These frozen shoulders were treated at physiotherapy unit, department of Rehabilitation last 3 years (January, 2014- December, 2016). The data consist of onset of symptom, range of motion and obtaining physical modalities were recorded. There was a statistically significant increase in shoulder flexion [mean difference 38.88 with 95%CI were [16.00-61.77], shoulder abduction [mean difference 48.47 with 95%CI were 16.07-90.59], shoulder internal rotation [mean difference 22.36 with 95%CI were 2.81-37.18] and shoulder external rotation [mean difference 32.12 with 95%CI were [(-2.47)-(46.91)]. In addition, the onset of symptom was 76.42±46.90 days. And the physical modalities used frequently were hot pack 14.8% and ultrasound diathermy 13.7%. In conclusion, the physiotherapy program including, hot pack and ultrasound diathermy seem to be useful for frozen shoulder. But onset of symptom is too long to start physiotherapy programs.

Keywords: frozen shoulder, range of motions, onset of symptom, physiotherapy, physical modality

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Authors: Bliss Singhal

Abstract:

Epilepsy is a prevalent neurological disorder affecting approximately 50 million individuals worldwide and 1.2 million Americans. There exist millions of pediatric patients with intractable epilepsy, a condition in which seizures fail to come under control. The occurrence of seizures can result in physical injury, disorientation, unconsciousness, and additional symptoms that could impede children's ability to participate in everyday tasks. Predicting seizures can help parents and healthcare providers take precautions, prevent risky situations, and mentally prepare children to minimize anxiety and nervousness associated with the uncertainty of a seizure. This research proposes a comprehensive framework to predict seizures in pediatric patients by evaluating machine learning algorithms on unimodal neuroimaging data consisting of electroencephalogram signals. The bandpass filtering and independent component analysis proved to be effective in reducing the noise and artifacts from the dataset. Various machine learning algorithms’ performance is evaluated on important metrics such as accuracy, precision, specificity, sensitivity, F1 score and MCC. The results show that the deep learning algorithms are more successful in predicting seizures than logistic Regression, and k nearest neighbors. The recurrent neural network (RNN) gave the highest precision and F1 Score, long short-term memory (LSTM) outperformed RNN in accuracy and convolutional neural network (CNN) resulted in the highest Specificity. This research has significant implications for healthcare providers in proactively managing seizure occurrence in pediatric patients, potentially transforming clinical practices, and improving pediatric care.

Keywords: intractable epilepsy, seizure, deep learning, prediction, electroencephalogram channels

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Authors: Bhaveek Maini, Sanjay Dhanka, Surita Maini

Abstract:

Electroencephalography (EEG) is used to classify several epileptic seizures worldwide. It is a very crucial task for the neurologist to identify the epileptic seizure with manual EEG analysis, as it takes lots of effort and time. Human error is always at high risk in EEG, as acquiring signals needs manual intervention. Disease diagnosis using machine learning (ML) has continuously been explored since its inception. Moreover, where a large number of datasets have to be analyzed, ML is acting as a boon for doctors. In this research paper, authors proposed two different ML models, i.e., logistic regression (LR) and Naïve Bayes (NB), to predict epileptic seizures based on general parameters. These two techniques are applied to the epileptic seizures recognition dataset, available on the UCI ML repository. The algorithms are implemented on an 80:20 train test ratio (80% for training and 20% for testing), and the performance of the model was validated by 10-fold cross-validation. The proposed study has claimed accuracy of 81.87% and 95.49% for LR and NB, respectively.

Keywords: epileptic seizure recognition, logistic regression, Naïve Bayes, machine learning

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