基于二阶C0复杂度的癫痫发作预测

癫痫发作的预测是近年来在临床医学和神经系统科学研究领域中备受关注的问题。如果癫痫发作能够被可靠地预测,则可以提前采取有效的临床预防措施,从而能较大程度地改善癫痫患者的生活质量。文章提出了一种基于二阶C0复杂度的预测算法用于预测癫痫发作。该算法通过分析癫痫患者颅内脑电信号的二阶C0复杂度,利用发作前期复杂度曲线的变化特征预测癫痫发作。作者运用该算法对21组癫痫病人87次发作的临床颅内脑电数据和4组大鼠4次发作的颅内脑电数据进行分析计算,预测准确率分别为94.3%和100%。实验结果表明该算法可以有效地预测癫痫发作,具有潜在的重要临床应用价值。     

基于脑电信号的癫痫发作预测特征及识别

解码癫痫发作前脑电信号的神经元集群异常痫样放电活动,对癫痫发作进行有效预测并实施病前干预,可显著减少疾病病损,是癫痫防治的研究热点之一。基于脑电信号的癫痫发作预测研究关键在于发作间期和前期的异常状态识别,研究上述两状态间的神经动力学特征差异对明确癫痫发病机制、选取高分辨特征,进而有效识别该渐进性疾病所处的发作阶段具有重要价值。目前,研究者已对当前主流特征提取及模式识别方法进行了充分的调研梳理,但忽视了神经动态特征变化对于癫痫发作预测的重要意义。基于此,本文归纳总结了5类典型的发作预测特征分析方法及其优缺点,重点剖析了发作间期至前期神经生理特征的动态变化及其动力学特性,类比分析了当前该领域主流的机器学习和深度学习特征识别方法,以期为进一步建立精准、高效的癫痫发作预测技术提供新思路。     

Patient-Specific Epileptic Seizure Prediction in Long-Term Scalp EEG Signal Using Multivariate Statistical Process Control

An accurate epileptic seizure prediction algorithm can alleviate the problem and reduce risks in the life of a patient suffering from epilepsy. The main motive of this work is to propose a model which can predict seizures well in advance of its occurrence. Multivariate statistical process control (MSPC) has been used for seizure predictions in long-term scalp EEG signal. It has been observed that excessive neuronal activity in the preictal period of seizure changes the electrical characteristic from chaotic to rhythmic behavior. These changes have been utilized for prediction. Eight temporal based features are used for predicting the seizures by using multivariate statistical process control, which is widely known as an anomaly monitoring method. 90 seizures from the CHB-MIT EEG data of ten patients are analyzed.ResultThe results of the proposed method demonstrated that 80 seizures out of 90 in preictal period were correctly predicted prior to the seizure onset, thereby giving a sensitivity of 88.89%. The false positive rate is observed to 0.39 per hour.ConclusionThis study proposed a temporal based patient-specific epileptic seizure prediction method using MSPC in long-term scalp EEG signals. It also provides the possibility of realizing an EEG-based epileptic seizure prediction system which requires less computational power.SignificanceThe proposed method does not require preictal data for modeling. The extracted features are computationally easy. The tested result shows good accuracy on the CHB-MIT data base.     

小波能量评价EEG的不同成分对癫痫发作预报的价值

癫痫是一种严重危害人类健康的常见疾病,对癫痫发作进行预报具有重要的重要意义。通过对3例部分性继发全身性发作的癫痫病人在发作最长约30min的8导EEG进行小波分解,将EEG中的棘波、尖波成分与慢波成分分别突出到不同的尺度上,并计算相应尺度上这些成分的能量,考察这些不同成分在发作前的变化趋势。发现在发作前的若干分钟,8导EEG的慢波能量都有显著增大,而与棘波／尖波有关的快波能量基本上没有什么变化趋势,说明EEG慢波成分的增大对部分性继发全身性发作的预报具有重要价值,EEG的“慢波过大”可能是癫痫从发作间状态转变为发作的重要因素。     

Application of a dissimilarity index of EEG and its sub-bands on prediction of induced epileptic seizures from rat's EEG signals

ObjectiveEpileptic seizures are defined as manifest of excessive and hyper-synchronous activity of neurons in the cerebral cortex that cause frequent malfunction of the human central nervous system. Therefore, finding precursors and predictors of epileptic seizure is of utmost clinical relevance to reduce the epileptic seizure induced nervous system malfunction consequences. Researchers for this purpose may even guide us to a deep understanding of the seizure generating mechanisms. The goal of this paper is to predict epileptic seizures in epileptic rats.MethodsSeizures were induced in rats using pentylenetetrazole (PTZ) model. EEG signals in interictal, preictal, ictal and postictal periods were then recorded and analyzed to predict epileptic seizures. Epileptic seizures were predicted by calculating an index in consecutive windows of EEG signal and comparing the index with a threshold. In this work, a newly proposed dissimilarity index called Bhattacharyya Based Dissimilarity Index (BBDI), dynamical similarity index and fuzzy similarity index were investigated.ResultsBBDI, dynamical similarity index and fuzzy similarity index were examined on case and control groups and compared to each other. The results show that BBDI outperforms dynamical and fuzzy similarity indices. In order to improve the results, EEG sub-bands were also analyzed. The best result achieved when the proposed dissimilarity index was applied on Delta sub-band that predicts epileptic seizures in all rats with a mean of 299.5 s.ConclusionThe dissimilarity of neural network activity between reference window and present window of EEG signal has a significant increase prior to an epileptic seizure and the proposed dissimilarity index (BBDI) can reveal this variation to predict epileptic seizures. In addition, analyzing EEG sub-bands results in more accurate information about constituent neuronal activities underlying the EEG since certain changes in EEG signal may be amplified when each sub-band is analyzed separately.SignificanceThis paper presents application of a dissimilarity index (BBDI) on EEG signals and its sub-bands to predict PTZ-induced epileptic seizures in rats. Based on the results of this work, BBDI will predict epileptic seizures more accurately and more reliably compared to current indices that increases epileptic patient comfort and improves patient outcomes.     

The influence of potassium concentration on epileptic seizures in a coupled neuronal model in the hippocampus

Experiments on hippocampal slices have recorded that a novel pattern of epileptic seizures with alternating excitatory and inhibitory activities in the CA1 region can be induced by an elevated potassium ion (K⁺) concentration in the extracellular space between neurons and astrocytes (ECS-NA). To explore the intrinsic effects of the factors (such as glial K⁺ uptake, Na⁺–K⁺-ATPase, the K⁺ concentration of the bath solution, and K⁺ lateral diffusion) influencing K⁺ concentration in the ECS-NA on the epileptic seizures recorded in previous experiments, we present a coupled model composed of excitatory and inhibitory neurons and glia in the CA1 region. Bifurcation diagrams showing the glial K⁺ uptake strength with either the Na⁺–K⁺-ATPase pump strength or the bath solution K⁺ concentration are obtained for neural epileptic seizures. The K⁺ lateral diffusion leads to epileptic seizure in neurons only when the synaptic conductance values of the excitatory and inhibitory neurons are within an appropriate range. Finally, we propose an energy factor to measure the metabolic demand during neuron firing, and the results show that different energy demands for the normal discharges and the pathological epileptic seizures of the coupled neurons.     

癫痫先兆探测治疗仪的设计

本文介绍一种能自动探测和识别癫痫先兆时的信号,并在病发早期自动发出音频电脉冲刺激患者,减弱癫痫病灶细胞活性的新型治疗仪的设计思路。     

The control of seizure-like activity in the rat hippocampal slice

The sudden and transient hypersynchrony of neuronal firing that characterizes epileptic seizures can be considered as the transitory stabilization of metastable states present within the dynamical repertoire of a neuronal network. Using an in vitro model of recurrent spontaneous seizures in the rat horizontal hippocampal slice preparation, we present an approach to characterize the dynamics of the transition to seizure, and to use this information to control the activity and avoid the occurrence of seizure-like events. The transition from the interictal activity (between seizures) to the seizure-like event is aborted by brief (20-50 s) low-frequency (0.5 Hz) periodic forcing perturbations, applied via an extracellular stimulating electrode to the mossy fibers, the axons of the dentate neurons that synapse onto the CA3 pyramidal cells. This perturbation results in the stabilization of an interictal-like low-frequency firing pattern in the hippocampal slice. The results derived from this work shed light on the dynamics of the transition to seizure and will further the development of algorithms that can be used in automated devices to stop seizure occurrence.     

Predicting Epileptic Seizures in Advance

Epilepsy is the second most common neurological disorder, affecting 0.6–0.8% of the world''s population. In this neurological disorder, abnormal activity of the brain causes seizures, the nature of which tend to be sudden. Antiepileptic Drugs (AEDs) are used as long-term therapeutic solutions that control the condition. Of those treated with AEDs, 35% become resistant to medication. The unpredictable nature of seizures poses risks for the individual with epilepsy. It is clearly desirable to find more effective ways of preventing seizures for such patients. The automatic detection of oncoming seizures, before their actual onset, can facilitate timely intervention and hence minimize these risks. In addition, advance prediction of seizures can enrich our understanding of the epileptic brain. In this study, drawing on the body of work behind automatic seizure detection and prediction from digitised Invasive Electroencephalography (EEG) data, a prediction algorithm, ASPPR (Advance Seizure Prediction via Pre-ictal Relabeling), is described. ASPPR facilitates the learning of predictive models targeted at recognizing patterns in EEG activity that are in a specific time window in advance of a seizure. It then exploits advanced machine learning coupled with the design and selection of appropriate features from EEG signals. Results, from evaluating ASPPR independently on 21 different patients, suggest that seizures for many patients can be predicted up to 20 minutes in advance of their onset. Compared to benchmark performance represented by a mean S1-Score (harmonic mean of Sensitivity and Specificity) of 90.6% for predicting seizure onset between 0 and 5 minutes in advance, ASPPR achieves mean S1-Scores of: 96.30% for prediction between 1 and 6 minutes in advance, 96.13% for prediction between 8 and 13 minutes in advance, 94.5% for prediction between 14 and 19 minutes in advance, and 94.2% for prediction between 20 and 25 minutes in advance.     

Analysis of inherited epilepsy using single locus mutations in mice.

The neurological expression of mutations at defined gene loci in isogenic mice provides a singular opportunity to investigate the developmental pathophysiology of inherited central nervous system (CNS) diseases. Analysis of the single locus mutants that are currently available shows that CNS diseases that include spontaneous seizures as symptoms can be inherited as simple recessive traits. Mutant gene dose is highly correlated with the spontaneous occurrence of seizures. Single gene defects at one of multiple chromosomal loci may give rise to similar epileptic patterns. One mutation, tottering (tg, chromosome 8, recessive) produces in young mice a focal motor seizure pattern with a somatotopic progression, and behavioral absence seizures accompanied by abnormal bursts of bilaterally synchronous, spike-wave discharges in the electrocorticogram. Spontaneous electrographic and clinical seizures of this general pattern bear close resemblance to common forms of human epilepsy. Defined alterations in restricted neuronal pathways of the mouse brain produced by single locus mutations can be used to infer general principles of inherited epileptogenesis, and may provide specific biological test systems for the development of more selective chemical antagonists of seizure activity.     

The Search for New Screening Models of Pharmacoresistant Epilepsy: Is Induction of Acute Seizures in Epileptic Rodents a Suitable Approach?

Epilepsy, a prevalent neurological disease characterized by spontaneous recurrent seizures (SRS), is often refractory to treatment with anti-seizure drugs (ASDs), so that more effective ASDs are urgently needed. For this purpose, it would be important to develop, validate, and implement new animal models of pharmacoresistant epilepsy into drug discovery. Several chronic animal models with difficult-to-treat SRS do exist; however, most of these models are not suited for drug screening, because drug testing on SRS necessitates laborious video-EEG seizure monitoring. More recently, it was proposed that, instead of monitoring SRS, chemical or electrical induction of acute seizures in epileptic rodents may be used as a surrogate for testing the efficacy of novel ASDs against refractory SRS. Indeed, several ASDs were shown to lose their efficacy on acute seizures, when such seizures were induced by pentylenetetrazole (PTZ) in epileptic rather than nonepileptic rats, whereas this was not observed when using the maximal electroshock seizure test. Subsequent studies confirmed the loss of anti-seizure efficacy of valproate against PTZ-induced seizures in epileptic mice, but several other ASDs were more potent against PTZ in epileptic than nonepileptic mice. This was also observed when using the 6-Hz model of partial seizures in epileptic mice, in which the potency of levetiracetam, in particular, was markedly increased compared to nonepileptic animals. Overall, these observations suggest that performing acute seizure tests in epileptic rodents provides valuable information on the pharmacological profile of ASDs, in particular those with mechanisms inherent to disease-induced brain alterations. However, it appears that further work is needed to define optimal approaches for acute seizure induction and generation of epileptic/drug refractory animals that would permit reliable screening of new ASDs with improved potential to provide seizure control in patients with pharmacoresistant epilepsy.     

Models of epileptic seizures in immature rats

Models of basic types of epileptic seizures are elaborated not only in adult but also in immature rodents. It is important because at least half of human epilepsies starts during infancy and childhood. This paper presents a review of chemically and electrically induced models of generalized convulsive and nonconvulsive (absence) seizures as well as models of partial simple (neocortical) and complex (limbic) seizures in immature rats. These models can also serve as a tool for study the development of central nervous system and motor abilities because the level of maturation is reflected in seizure semiology. Age-dependent models of epileptic seizures (absences and flexion seizures) are discussed. Models of seizures in immature animals should be used for testing of potential antiepileptic drugs.     

Role of oxidative stress in epileptic seizures

Oxidative stress resulting from excessive free-radical release is likely implicated in the initiation and progression of epilepsy. Therefore, antioxidant therapies aimed at reducing oxidative stress have received considerable attention in epilepsy treatment. However, much evidence suggests that oxidative stress does not always have the same pattern in all seizures models. Thus, this review provides an overview aimed at achieving a better understanding of this issue. We summarize work regarding seizure models (i.e., genetic rat models, kainic acid, pilocarpine, pentylenetetrazol, and trimethyltin), oxidative stress as an etiologic factor in epileptic seizures (i.e., impairment of antioxidant systems, mitochondrial dysfunction, involvement of redox-active metals, arachidonic acid pathway activation, and aging), and antioxidant strategies for seizure treatment. Combined, this review highlights pharmacological mechanisms associated with oxidative stress in epileptic seizures and the potential for neuroprotection in epilepsy that targets oxidative stress and is supported by effective antioxidant treatment.     

Machine Learning Approach for the Outcome Prediction of Temporal Lobe Epilepsy Surgery

Epilepsy surgery is effective in reducing both the number and frequency of seizures, particularly in temporal lobe epilepsy (TLE). Nevertheless, a significant proportion of these patients continue suffering seizures after surgery. Here we used a machine learning approach to predict the outcome of epilepsy surgery based on supervised classification data mining taking into account not only the common clinical variables, but also pathological and neuropsychological evaluations. We have generated models capable of predicting whether a patient with TLE secondary to hippocampal sclerosis will fully recover from epilepsy or not. The machine learning analysis revealed that outcome could be predicted with an estimated accuracy of almost 90% using some clinical and neuropsychological features. Importantly, not all the features were needed to perform the prediction; some of them proved to be irrelevant to the prognosis. Personality style was found to be one of the key features to predict the outcome. Although we examined relatively few cases, findings were verified across all data, showing that the machine learning approach described in the present study may be a powerful method. Since neuropsychological assessment of epileptic patients is a standard protocol in the pre-surgical evaluation, we propose to include these specific psychological tests and machine learning tools to improve the selection of candidates for epilepsy surgery.     

Angelman syndrome in three adult patients with atypical presentation and severe neurological complications

Angelman syndrome (AS) is a distinct neurogenetic disorder and the phenotype is well known in childhood and adolescence. However, with advancing age the clinical and behavioral phenotype changes. In adulthood, the phenotype can be rather aspecific. We report on AS in 3 severely to profoundly mentally retarded patients, who developed severe neurologic complications of severe tremor, spasticity and coordination problems, resulting into severe loss of function. They presented atypical craniofacial features, short stature, epileptic seizures, microcephaly, brachytelephalangy and absent speech. Two patients presented at an older age a change in day-night rhythm. Based on this experience, we conclude that all severely to profoundly mentally retarded patients with atypical phenotype, spasticity, absent speech, epileptic seizures and changed day-night rhythm are candidates for further cytogenetic and molecular investigation for AS. Clinical photographs of the patient at a younger age can be helpful. The presence of the typical EEG pattern with frontal triphasic delta waves may direct to the diagnosis of AS.     

Complicated relationship between autism with regression and epilepsy

We retrospectively evaluated a set of 205 children with autism and compared it to the partial sub-set of 71 (34.6%) children with a history of regression. From 71 children with regression, signs of epileptic processes were present in 43 (60.6%), 28 (65.12%) suffered clinical epileptic seizures, and 15 (34.9%) just had an epileptiform abnormality on the EEG. In our analysis, autistic regression is substantially more associated with epileptic process symptoms than in children with autism and no history of regression. More than 90% of children with a history of regression also show IQ < 70 and reduced functionality. Functionality and IQ further worsens with the occurrence of epileptic seizures (98% of children with regression and epilepsy have IQ < 70). We proved that low IQ and reduced functionality significantly correlate rather with epileptic seizures than just sub-clinical epileptiform abnormality on EEG. Clinical epileptic seizures associated with regression significantly influence the age of regression and its clinical type. The age of regression is higher compared to children with regression without epileptic seizures (in median: 35 months of age in patients with seizures while only 24 months in other patients). Patients with seizures revealed regression after 24th months of age in 68% of cases, while patients without seizures only in 27%. However, coincidence with epilepsy also increased the occurrence of regression before the 18th month of age (23% of patients), while only 4% of patients without epilepsy revealed regression before the 18th month. Epileptic seizures are significantly associated especially with behaviour regression rather than speech regression or regression in both behaviour and speech. Also epileptic seizures diagnosed before correct diagnosis of autism were significantly associated with delayed regression (both behavioural and speech regression).     

Classification of epileptic seizures using wavelet packet log energy and norm entropies with recurrent Elman neural network classifier

Electroencephalogram shortly termed as EEG is considered as the fundamental segment for the assessment of the neural activities in the brain. In cognitive neuroscience domain, EEG-based assessment method is found to be superior due to its non-invasive ability to detect deep brain structure while exhibiting superior spatial resolutions. Especially for studying the neurodynamic behavior of epileptic seizures, EEG recordings reflect the neuronal activity of the brain and thus provide required clinical diagnostic information for the neurologist. This specific proposed study makes use of wavelet packet based log and norm entropies with a recurrent Elman neural network (REN) for the automated detection of epileptic seizures. Three conditions, normal, pre-ictal and epileptic EEG recordings were considered for the proposed study. An adaptive Weiner filter was initially applied to remove the power line noise of 50 Hz from raw EEG recordings. Raw EEGs were segmented into 1 s patterns to ensure stationarity of the signal. Then wavelet packet using Haar wavelet with a five level decomposition was introduced and two entropies, log and norm were estimated and were applied to REN classifier to perform binary classification. The non-linear Wilcoxon statistical test was applied to observe the variation in the features under these conditions. The effect of log energy entropy (without wavelets) was also studied. It was found from the simulation results that the wavelet packet log entropy with REN classifier yielded a classification accuracy of 99.70 % for normal-pre-ictal, 99.70 % for normal-epileptic and 99.85 % for pre-ictal-epileptic.     

Adaptive prediction of epileptic seizures from intracranial recordings

Previous work has demonstrated that some dynamic properties of intracranial EEG signals are indicative of epileptic seizures and hence could be used for prediction in order to realize counter measures. However, most previous studies only investigated predictability via offline analysis of EEG signals as compared to actually predicting seizures in a setting applicable to implantable devices. Here we address this problem, which calls for simple and fast online methods, and based on previous offline analyses we hypothesize that prediction can be further improved when using multiple features to detect the preictal patterns. We propose a simple adaptive online method (an evolving neuro-fuzzy model) to adaptively learn such combined features. The classifier starts out with a simple structure and patient-independent parameters and then grows into a personal seizure predictor as recursive methods tune the model structure and parameters. We apply the adaptive classifier to a publicly available database of intracranial recordings from 21 patients and demonstrate that seizure prediction is improved with our online method as compared to offline non-adaptive techniques. We show that our method is robust with respect to those few model parameters, which are not adapted. Moreover, as we report the performance on data from a publicly available seizure database, our results can serve as a yardstick for future method developments.     

Absence-like and tonic seizures in aspartoacylase/attractin double-mutant mice.

The Spontaneously Epileptic Rat (SER), a double-mutant for tremor and zitter mutations, shows spontaneous occurrences of absence-like and tonic seizures. Several lines of evidence suggest that the combined effect of Aspa and Atrn mutations is the most likely cause of the epileptic phenotype of the SER. To address this issue, we produced a new double-mutant mouse line carrying both homozygous Aspa-knockout and Atrn(mg-3J) mutant alleles. The Aspa/Atrn double-mutant mice exhibited absence-like and tonic seizures that were characterized by the appearance of 5-7 Hz spike-wave-like complexes and low voltage fast waves on EEGs. These results demonstrate directly that the simultaneous loss of the Aspa and Atrn gene functions causes epileptic seizures in the mouse and suggest that both Aspa and Atrn deficiencies might be responsible for epileptic seizures in the SER.     

Correlation between brain glycogen and convulsive state in mice submitted to methionine sulfoximine

It is now well established that in epileptic patients, hypometabolic foci appear during interictal periods. The meaning and the mechanism of such an hypometabolism are as yet unclear. The aim of the present investigation was to look for a putative relationship between glucose metabolism in the brain and the genesis of seizures in mice using administration of the convulsant, methionine sulfoximine. Besides its epileptic action, methionine sulfoximine is a powerful glycogenic agent. We analyzed the epileptogenic and glycogenic effects of methionine sulfoximine in two inbred mouse strains with different susceptibility towards the convulsant. CBA/J mice displayed high response to methionine sulfoximine. The tonic convulsions appeared 5-6 h after MSO administration, without brain glycogen content variations during the preconvulsive period. These mice died of status epilepticus during the first seizure(s). Conversely, C57BL/6J mice displayed low response to MSO. The tonic and clonic seizures appeared 8 to 14 h after MSO administration with only 2% mortality. The seizures were preceded by an increase in brain glycogen content during the preconvulsive period. Moreover, during seizures, C57BL/6J mice were able to mobilize this accumulated brain glycogen, that returned to high value after seizures. The epileptic and glycogenic responses of the parental strains were also observed in mice of the F2 generation. The F2 mice that convulsed early (16%) did not utilize their small increase in brain glycogen content, and resembled CBA/J mice; while the F2 mice that seized tardily (24%) increased their brain glycogen content before convulsion, utilized it during convulsions, and resembled C57BL/6J mice. Sixty percent of the F2 mice presented an intermediate pattern in epileptogenic responses to the convulsant. These data suggest a possible genetic link between the two MSO effects, epileptiform seizures and increase in brain glycogen content. The increase in brain glycogen content and the capability of its mobilization during seizures could delay the seizure's onset and could be considered a "resistance factor" against the seizures.