Epileptic Seizures in Neonates Treated with Hypothermia for Hypoxo-Ischemic Encephalopathy in Brazzaville, Congo: Types and Evolution

Background: Moderate to severe hypoxic-ischemic encephalopathy (HIE) in neonates is often treated with hypothermia. However, some neonates may experience epileptic seizures during therapeutic hypothermia (TH). Data on the electrophysiologic and evolutionary aspects of these seizures are scarce in African countries. Objectives: To determine the types of epileptic seizures caused by HIE in neonates in Brazzaville;to describe the evolution of background EEG activities during TH and rewarming;to report the evolution of epileptic seizures. Methods: This was a cross-sectional, descriptive study conducted from January 2020 to July 2022. It took place in Brazzaville in the Neonatology Department of the Blanche Gomez Mother and Child Hospital. It focused on term neonates suffering from moderate or severe HIE. They were treated with hypothermia combined with phenobarbital for 72 hours. Results: Among 36 neonates meeting inclusion criteria, there were 18 boys and 18 girls. Thirty-one (86.1%) neonates had grade 2 and 5 (13.9%) grade 3 HIE. In our neonates, HIE had induced isolated electrographic seizures (n = 11;30.6%), electroclinical seizures (n = 25;69.4%), and 6 types of background EEG activity. During TH and rewarming, there were 52.8% of patients with improved background EEG activity, 41.7% of patients with unchanged background EEG activity, and 5.5% of patients with worsened background EEG activity. At the end of rewarming, only 9 (25%) patients still had seizures. Conclusion: Isolated electrographic and electroclinical seizures are the only pathological entities found in our studied population. In neonates with moderate HIE, the applied therapeutic strategy positively influences the evolution of both seizures and background EEG activity. On the other hand, in neonates with severe HIE, the same therapeutic strategy is ineffective. .

Classification of low-density EEG for epileptic seizures by energy and fractal features based on EMD

We are here to present a new method for the classification of epileptic seizures from electroencephalogram(EEG) signals.It consists of applying empirical mode decomposition(EMD) to extract the most relevant intrinsic mode functions(IMFs) and subsequent computation of the Teager and instantaneous energy,Higuchi and Petrosian fractal dimension,and detrended fluctuation analysis(DFA) for each IMF.We validated the method using a public dataset of 24 subjects with EEG signals from 22 channels and showed that it is possible to classify the epileptic seizures,even with segments of six seconds and a smaller number of channels(e.g.,an accuracy of0.93 using five channels).We were able to create a general machine-learning-based model to detect epileptic seizures of new subjects using epileptic-seizure data from various subjects,after reducing the number of instances,based on the k-means algorithm.

Differential diagnosis between epileptic seizures and psychogenic nonepileptic seizures based on semiology

Psychogenic nonepileptic seizures present as paroxysmal symptoms and signs mimicking epileptic seizures.The gold standard test is the synchronous recording by video,electrocardiogram and electroencephalogram.However,video electroencephalogram is not available at many centers and not entirely independent of semiology.Recent studies have focused on semiological characteristics distinguishing these two circumstances.Clinical signs and symptoms provide important clues when making differential diagnosis.The purpose of this review is to help physicians differentiating psychogenic nonepileptic seizures better from epileptic seizures based on semiology,and improve care for those patients.

Evolutionary Algorithsm with Machine Learning Based Epileptic Seizure Detection Model

Machine learning (ML) becomes a familiar topic among decisionmakers in several domains, particularly healthcare. Effective design of MLmodels assists to detect and classify the occurrence of diseases using healthcaredata. Besides, the parameter tuning of the ML models is also essentialto accomplish effective classification results. This article develops a novelred colobuses monkey optimization with kernel extreme learning machine(RCMO-KELM) technique for epileptic seizure detection and classification.The proposed RCMO-KELM technique initially extracts the chaotic, time,and frequency domain features in the actual EEG signals. In addition, the minmax normalization approach is employed for the pre-processing of the EEGsignals. Moreover, KELM model is used for the detection and classificationof epileptic seizures utilizing EEG signal. Furthermore, the RCMO techniquewas utilized for the optimal parameter tuning of the KELM technique insuch a way that the overall detection outcomes can be considerably enhanced.The experimental result analysis of the RCMO-KELM technique has beenexamined using benchmark dataset and the results are inspected under severalaspects. The comparative result analysis reported the better outcomes of theRCMO-KELM technique over the recent approaches with the accuy of 0.956.

EEG epileptic seizure detection and classification based on dual-tree complex wavelet transform and machine learning algorithms

The visual analysis of common neurological disorders such as epileptic seizures in electroencephalography(EEG) is an oversensitive operation and prone to errors,which has motivated the researchers to develop effective automated seizure detection methods.This paper proposes a robust automatic seizure detection method that can establish a veritable diagnosis of these diseases.The proposed method consists of three steps:(i) remove artifact from EEG data using Savitzky-Golay filter and multi-scale principal component analysis(MSPCA),(ii) extract features from EEG signals using signal decomposition representations based on empirical mode decomposition(EMD),discrete wavelet transform(DWT),and dual-tree complex wavelet transform(DTCWT) allowing to overcome the non-linearity and non-stationary of EEG signals,and(iii) allocate the feature vector to the relevant class(i.e.,seizure class "ictal" or free seizure class "interictal") using machine learning techniques such as support vector machine(SVM),k-nearest neighbor(k-NN),and linear discriminant analysis(LDA).The experimental results were based on two EEG datasets generated from the CHB-MIT database with and without overlapping process.The results obtained have shown the effectiveness of the proposed method that allows achieving a higher classification accuracy rate up to 100% and also outperforms similar state-of-the-art methods.

Low-frequency repetitive transcranial magnetic simulation prevents chronic epileptic seizure

Although low-frequency repetitive transcranial magnetic simulation can potentially treat epilepsy, its underlying mechanism remains unclear. This study investigated the influence of low-frequency re-petitive transcranial magnetic simulation on changes in several nonlinear dynamic electroenceph-alographic parameters in rats with chronic epilepsy and explored the mechanism underlying repeti-tive transcranial magnetic simulation-induced antiepileptic effects. An epilepsy model was estab-lished using lithium-pilocarpine intraperitoneal injection into adult Sprague-Dawley rats, which were then treated with repetitive transcranial magnetic simulation for 7 consecutive days. Nonlinear elec-electroencephalographic parameters were obtained from the rats at 7, 14, and 28 days post-stimulation. Results showed significantly lower mean correlation-dimension and Kolmogo-rov-entropy values for stimulated rats than for non-stimulated rats. At 28 days, the complexity and point-wise correlation dimensional values were lower in stimulated rats. Low-frequency repetitive transcranial magnetic simulation has suppressive effects on electrical activity in epileptic rats, thus explaining its effectiveness in treating epilepsy.

Improving control effects of absence seizures using single-pulse alternately resetting stimulation (SARS) of corticothalamic circuit

Presently,we develop a simplified corticothalamic(SCT)model and propose a single-pulse alternately resetting stimulation(SARS)with sequentially applying anodic(A,“+”)or cathodic(C,“−”)phase pulses to the thalamic reticular(RE)nuclei,thalamus-cortex(TC)relay nuclei,and cortical excitatory(EX)neurons,respectively.Abatement effects of ACC-SARS of RE,TC,and EX for the 2 Hz-4 Hz spike and wave discharges(SWD)of absence seizures are then concerned.The m∶n on-off ACC-SARS protocol is shown to effectively reduce the SWD with the least current consumption.In particular,when its frequency is out of the 2 Hz-4 Hz SWD dominant rhythm,the desired seizure abatements can be obtained,which can be further improved by our proposed directional steering(DS)stimulation.The dynamical explanations for the SARS induced seizure abatements are lastly given by calculating the averaged mean firing rate(AMFR)of neurons and triggering averaged mean firing rates(TAMFRs)of 2 Hz-4 Hz SWD.

Epileptic seizure detection using EEG signals and extreme gradient boosting

The problem of automated seizure detection is treated using clinical electroencephalograms(EEG) and machine learning algorithms on the Temple University Hospital EEG Seizure Corpus(TUSZ).Performances on this complex data set are still not encountering expectations.The purpose of this work is to determine to what extent the use of larger amount of data can help to improve the performances.Two methods are explored:a standard partitioning on a recent and larger version of the TUSZ,and a leave-one-out approach used to increase the amount of data for the training set.XGBoost,a fast implementation of the gradient boosting classifier,is the ideal algorithm for these tasks.The performances obtained are in the range of what is reported until now in the literature with deep learning models.We give interpretation to our results by identifying the most relevant features and analyzing performances by seizure types.We show that generalized seizures tend to be far better predicted than focal ones.We also notice that some EEG channels and features are more important than others to distinguish seizure from background.

Synaptic Vesicle Protein2A Decreases in Amygdaloid-kindling Pharmcoresistant Epileptic Rats

Synaptic vesicle protein 2A（SV2A） involvement has been reported in the animal models of epilepsy and in human intractable epilepsy. The difference between pharmacosensitive epilepsy and pharmacoresistant epilepsy remains poorly understood. The present study aimed to observe the hippocampus SV2 A protein expression in amygdale-kindling pharmacoresistant epileptic rats. The pharmacosensitive epileptic rats served as control. Amygdaloid-kindling model of epilepsy was established in 100 healthy adult male Sprague-Dawley rats. The kindled rat model of epilepsy was used to select pharmacoresistance by testing their seizure response to phenytoin and phenobarbital. The selected pharmacoresistant rats were assigned to a pharmacoresistant epileptic group（PRE group）. Another 12 pharmacosensitive epileptic rats（PSE group） served as control. Immunohistochemistry,real-time PCR and Western blotting were used to determine SV2 A expression in the hippocampus tissue samples from both the PRE and the PSE rats. Immunohistochemistry staining showed that SV2 A was mainly accumulated in the cytoplasm of the neurons,as well as along their dendrites throughout all subfields of the hippocampus. Immunoreactive staining level of SV2A-positive cells was 0.483±0.304 in the PRE group and 0.866±0.090 in the PSE group（P〈0.05）. Real-time PCR analysis demonstrated that 2-ΔΔCt value of SV2 A m RNA was 0.30±0.43 in the PRE group and 0.76±0.18 in the PSE group（P〈0.05）. Western blotting analysis obtained the similar findings（0.27±0.21 versus 1.12±0.21,P〈0.05）. PRE rats displayed a significant decrease of SV2 A in the brain. SV2 A may be associated with the pathogenesis of intractable epilepsy of the amygdaloid-kindling rats.

Editorial commentary on special issue of Advances in EEG Signal Processing and Machine Learning for Epileptic Seizure Detection and Prediction

This special issue of The Journal of Biomedical Research features novel studies on epileptic seizure detection and prediction based on advanced EEG signal processing and machine learning algorithms.The articles selected present important findings including new experimental results and theoretical studies.

Fuzzy-Based Automatic Epileptic Seizure Detection Framework

Detection of epileptic seizures on the basis of Electroencephalogram(EEG)recordings is a challenging task due to the complex,non-stationary and non-linear nature of these biomedical signals.In the existing literature,a number of automatic epileptic seizure detection methods have been proposed that extract useful features from EEG segments and classify them using machine learning algorithms.Some characterizing features of epileptic and non-epileptic EEG signals overlap;therefore,it requires that analysis of signals must be performed from diverse perspectives.Few studies analyzed these signals in diverse domains to identify distinguishing characteristics of epileptic EEG signals.To pose the challenge mentioned above,in this paper,a fuzzy-based epileptic seizure detection model is proposed that incorporates a novel feature extraction and selection method along with fuzzy classifiers.The proposed work extracts pattern features along with time-domain,frequencydomain,and non-linear analysis of signals.It applies a feature selection strategy on extracted features to get more discriminating features that build fuzzy machine learning classifiers for the detection of epileptic seizures.The empirical evaluation of the proposed model was conducted on the benchmark Bonn EEG dataset.It shows significant accuracy of 98%to 100%for normal vs.ictal classification cases while for three class classification of normal vs.inter-ictal vs.ictal accuracy reaches to above 97.5%.The obtained results for ten classification cases(including normal,seizure or ictal,and seizure-free or inter-ictal classes)prove the superior performance of proposed work as compared to other state-of-the-art counterparts.

Aortic dissection with epileptic seizure: A case report

BACKGROUND Aortic dissection(AoD)is a life-threatening disease.Its diversified clinical manifestations,especially the atypical ones,make it difficult to diagnose.The epileptic seizure is a neurological problem caused by various kinds of diseases,but AoD with epileptic seizure as the first symptom is rare.CASE SUMMARY A 53-year-old male patient suffered from loss of consciousness for 1 h and tonicclonic convulsion for 2 min.The patient performed persistent hypomania and chest discomfort for 30 min after admission.He had a history of hypertension without regular antihypertensive drugs,and the results of his bilateral blood pressure varied greatly.Then the electroencephalogram showed the existence of epileptic waves.The thoracic aorta computed tomography angiography showed the appearance of AoD,and it originated at the lower part of the ascending aorta.Finally,the diagnosis was AoD(DeBakey,type I),acute aortic syndrome,hypertension(Grade 3),and secondary epileptic seizure.He was given symptomatic treatment to relieve symptoms and prevent complications.Thereafter,the medical therapy was effective but he refused our surgical advice.CONCLUSION The AoD symptoms are varied.When diagnosing the epileptic seizure etiologically,AoD is important to consider by clinical and imaging examinations.

Regulating absence seizures by tri-phase delay stimulation applied to globus pallidus internal

In this paper,a reduced globus pallidus internal(GPI)-corticothalamic(GCT)model is developed,and a tri-phase delay stimulation(TPDS)with sequentially applying three pulses on the GPI representing the inputs from the striatal D_(1)neurons,subthalamic nucleus(STN),and globus pallidus external(GPE),respectively,is proposed.The GPI is evidenced to control absence seizures characterized by 2 Hz–4 Hz spike and wave discharge(SWD).Hence,based on the basal ganglia-thalamocortical(BGCT)model,we firstly explore the triple effects of D_(1)-GPI,GPE-GPI,and STN-GPI pathways on seizure patterns.Then,using the GCT model,we apply the TPDS on the GPI to potentially investigate the alternative and improved approach if these pathways to the GPI are blocked.The results show that the striatum D_(1),GPE,and STN can indeed jointly and significantly affect seizure patterns.In particular,the TPDS can effectively reproduce the seizure pattern if the D_(1)-GPI,GPE-GPI,and STN-GPI pathways are cut off.In addition,the seizure abatement can be obtained by well tuning the TPDS stimulation parameters.This implies that the TPDS can play the surrogate role similar to the modulation of basal ganglia,which hopefully can be helpful for the development of the brain-computer interface in the clinical application of epilepsy.

Unilateral Femoral Neck Fracture after Epileptic Seizure in Young Patient: A Case Report

Femoral neck fracture occurring after an epileptic seizure is a rare and under-diagnosed injury. The majority of the reported cases in literature are old patients with osteoporosis. Younger patients present several risk factors of osteopenia and the treatment remains controversial. We present an outcome of a 23 years old patient with unilateral femoral neck fracture occurring during an epileptic seizure and we discuss the associated multiple risk factors of osteopenia and osteonecrosis of the hip. The patient was brought to the emergency department of Teaching Hospital of Kamenge (CHUK) complaining of pain in his left hip that had been progressing for one month after an epileptic seizure. There is a history of HIV infection since birth and epileptic seizures with ongoing treatments for both diseases. Despite the high risk of avascular necrosis, the treatment choice has been influenced by the patient’s age and a conservative surgery by internal fixation with Dynamic Hip Screw has been made. Unfortunately, this treatment early resulted in osteonecrosis of the hip since HIV infection itself and the highly active anti-retroviral therapy increase its risk.

Complexity analysis and dynamic characteristics of EEG using MODWT based entropies for identification of seizure onset

In this paper,complexity analysis and dynamic characteristics of electroencephalogram(EEG) signal based on maximal overlap discrete wavelet transform(MODWT) has been exploited for the identification of seizure onset.Since wavelet-based studies were well suited for classification of normal and epileptic seizure EEG,we have applied MODWT which is an improved version of discrete wavelet transform(DWT).The selection of optimal wavelet sub-band and features plays a crucial role to understand the brain dynamics in epileptic patients.Therefore,we have investigated MODWT using four different wavelets,namely Haar,Coif4,Dmey,and Sym4 sub-bands until seven levels.Further,we have explored the potentials of six entropies,namely sigmoid,Shannon,wavelet,Renyi,Tsallis,and Steins unbiased risk estimator(SURE) entropies in each sub-band.The sigmoid entropy extracted from Haar wavelet in sub-band D4 showed the highest accuracy of 98.44% using support vector machine classifier for the EEG collected from Ramaiah Medical College and Hospitals(RMCH).Further,the highest accuracy of 100% and 94.51% was achieved for the University of Bonn(UBonn) and CHB-MIT databases respectively.The findings of the study showed that Haar and Dmey wavelets were found to be computationally economical and expensive respectively.Besides,in terms of dynamic characteristics,MODWT results revealed that the highest energy present in sub-bands D2,D3,and D4 and entropies in those respective sub-bands outperformed other entropies in terms of classification results for RMCH database.Similarly,using all the entropies,sub-bands D5 and D6 outperformed other sub-bands for UBonn and CHB-MIT databases respectively.In conclusion,the comparison results of MODWT outperformed DWT.

Artificial Intelligence in Healthcare:Review and Prediction Case Studies

Artificial intelligence(AI)has been developing rapidly in recent years in terms of software algorithms,hardware implementation,and applications in a vast number of areas.In this review,we summarize the latest developments of applications of AI in biomedicine,including disease diagnostics,living assistance,biomedical information processing,and biomedical research.The aim of this review is to keep track of new scientific accomplishments,to understand the availability of technologies,to appreciate the tremendous potential of AI in biomedicine,and to provide researchers in related fields with inspiration.It can be asserted that,just like AI itself,the application of AI in biomedicine is still in its early stage.New progress and breakthroughs will continue to push the frontier and widen the scope of AI application,and fast developments are envisioned in the near future.Two case studies are provided to illustrate the prediction of epileptic seizure occurrences and the filling of a dysfunctional urinary bladder.

Anticonvulsant effects of medicinal plants with emphasis on mechanisms of action

Epilepsy is a disorder in brain in which clusters of nerve cells, or neurons, occasionally signal abnormally and cause strange emotions, sensations, and behavior, or sometimes muscle spasms, convulsions, and loss of consciousness. Neurotransmitters in central nervous system greatly affect and play a very important part in neuronal excitability.Traditional treatments are still a component of health care system in many communities despite the fact that well-established alternatives are available. In this review article, we addressed epilepsy and its treatments with emphasis on medical plants and introduction of antiepileptic plants and their action mechanisms. Relevant articles published since 2010 were retrieved using the search terms including epileptic seizure, anticonvulsant, medicinal plants, and oxidative stress. Most plants/herbal preparations that are ethnomedically used to treat epilepsy or those which have been tested for anticonvulsant activity were reported. Overall, the results of the published articles show that the symptoms of epilepsy seizure can be inhibited or treated by active ingredients derived from medicinal plants.

Acknowledgments

Epilepsy is the most common neurological disorder of the brain that affects people worldwide at any age from newborn to adult. It is characterized by recurrent seizures, which are brief episodes of signs or symptoms due to abnormal excessive or synchronous neuronal activity in the brain. The electroencephalogram, or EEG, is a physiological method to measure and record the electrical

High-frequency and brief-pulse stimulation pulses terminate cortical electrical stimulation-induced afterdischarges

Brief-pulse stimulation at 50 Hz has been shown to terminate afterdischarges observed in epilepsy patients. However, the optimal pulse stimulation parameters for terminating cortical electrical stimulation-induced afterdischarges remain unclear. In the present study, we examined the effects of different brief-pulse stimulation frequencies（5, 50 and 100 Hz） on cortical electrical stimulation-induced afterdischarges in 10 patients with refractory epilepsy. Results demonstrated that brief-pulse stimulation could terminate cortical electrical stimulation-induced afterdischarges in refractory epilepsy patients. In conclusion,（1） a brief-pulse stimulation was more effective when the afterdischarge did not extend to the surrounding brain area.（2） A higher brief-pulse stimulation frequency（especially 100 Hz） was more likely to terminate an afterdischarge.（3） A low current intensity of brief-pulse stimulation was more likely to terminate an afterdischarge.

Anticonvulsant Effect of Xingnaojing Injection on Acute Seizure Models in Mice

Background:Epilepsy is characterized by acute recurrent seizures.The control of seizures is largely hampered by the tolerance to current anti-seizure drugs.Complementary anti-convulsant pharmacotherapies are urgently needed.Objective:Here,we aimed to investigate the anti-convulsant effects of Xingnaojing Injection(XNJ)which is an approved Traditional Chinese Medicine injection on different acute seizure models in mice.Methods:The effects of XNJ were tested on the maximal electroshock(MES),pentylenetetrazol(PTZ)and kainic acid(KA)acute seizure models.Also,whether XNJ can directly inhibit hippocampal neuronal firings were exam-ined by in vitro electrophysiology.Results:XNJ could shorten the durations of generalized tonic-clonic seizures in the MES model.It also significantly prolonged the latencies to generalized myo-clonic seizures in the PTZ model.In the KA model,XNJ showed various efficacies including inhibiting the seizure stages,prolonging the latency to the occurrence of the first seizures or generalized seizures,shortening the seizure durations,decreasing the numbers of generalized seizures.In vitro electrophysiological recordings further verified XNJ directly inhibited both the spontaneous and evoked action potentials of hippocampal pyramidal neurons,but did not influence the excitatory or inhibitory synaptic transmissions.Conclusion:These findings proposed XNJ as an alternative anti-convulsant pharmacotherapy for controlling acute epileptic seizures.