Effect of vagus nerve stimulation against generalized seizure and status epilepticus recurrence

ObjectiveVagus nerve stimulation (VNS) is a palliative surgery for drug-resistant epilepsy. The two objectives of this study were to (1) determine the seizure type most responsive to VNS and (2) investigate the preventive effect on status epilepticus (SE) recurrence.MethodsWe retrospectively reviewed 136 patients with drug-resistant epilepsy who underwent VNS implantation. We examined seizure outcomes at 6, 12, and 24 months following implantation of VNS as well as at the last visit to the Juntendo Epilepsy Center. Univariate analysis and multivariate logistic regression models were used to estimate the prognostic factors.Results125 patients were followed up for at least 1 year after VNS implantation. The percentage of patients with at least a 50% reduction in seizure frequency compared with prior to VNS implantation increased over time at 6, 12, and 24 months after VNS implantation: 28, 41, and 52%, respectively. Regarding overall seizure outcomes, 70 (56%) patients responded to VNS. Of the 40 patients with a history of SE prior to VNS implantation, 27 (67%) showed no recurrence of SE. The duration of epilepsy, history of SE prior to VNS implantation and seizure type were correlated with seizure outcomes after VNS implantation in univariate analysis (p = 0.05, p < 0.01, and p = 0.03, respectively). In multivariate logistic regression analysis, generalized seizure was associated with VNS response [odds ratio (OR): 4.18, 95% CI: 1.13–15.5, p = 0.03]. A history of SE prior to VNS implantation was associated with VNS non-responders [(OR): 0.221, 95% CI: 0.097–0.503, p < 0.01]. The duration of epilepsy, focal to bilateral tonic–clonic seizure and epileptic spasms were not significantly associated with VNS responders (p = 0.07, p = 0.71, and p = 0.11, respectively).ConclusionFollowing 125 patients with drug-resistant epilepsy for an average of 69 months, 56% showed at least 50% reduction in seizure frequency after VNS implantation. This study suggests that generalized seizure is the most responsive to VNS, and that VNS may reduce the risk of recurrence of SE. VNS was shown to be effective against generalized seizure and also may potentially influence the risk of further events of SE, two marker of disease treatment that can lead to improved quality of life

Seizure onset zone classification based on imbalanced iEEG with data augmentation.

Objective. Identifying the seizure onset zone (SOZ) in patients with focal epilepsy is the critical information required for surgery. However, collecting this information is challenging, time-consuming, and subjective. Some machine learning methods reduce the workload of clinical experts in intracranial electroencephalogram (iEEG) visual diagnosis but face significant challenges because interictal iEEG clinical data often suffer from a significant class imbalance. We aim to generate synthetic data for the minority class.Approach. To make the clinically imbalanced data suitable for machine learning, we introduce an EEG augmentation method (EEGAug). The EEGAug method randomly selects several samples from the minority class and transforms them into the frequency domain. Then, different frequency bands from different samples are used to compose new data. Finally, a synthetic sample is generated after converting the new data back to the time domain.Main results. The imbalanced clinical iEEG data can be balanced and applied to machine learning models using the method. A one-dimensional convolutional neural network model is used to classify the SOZ and non-SOZ data. We compare the EEGAug method with other data augmentation methods and another method of class-balanced focal loss function, which is also used for solving the data imbalance problem by adjusting the weights between the minority and majority classes. The results show that the EEGAug method performs best in most data.Significance. Data imbalance is a widespread clinical problem. The EEGAug method can flexibly generate synthetic data for the minority class, yielding synthetic and raw data with a high distribution similarity. By using the EEGAug method, clinical data can be used in machine learning models

Statistical Features in High-Frequency Bands of Interictal iEEG Work Efficiently in Identifying the Seizure Onset Zone in Patients with Focal Epilepsy

The design of a computer-aided system for identifying the seizure onset zone (SOZ) from interictal and ictal electroencephalograms (EEGs) is desired by epileptologists. This study aims to introduce the statistical features of high-frequency components (HFCs) in interictal intracranial electroencephalograms (iEEGs) to identify the possible seizure onset zone (SOZ) channels. It is known that the activity of HFCs in interictal iEEGs, including ripple and fast ripple bands, is associated with epileptic seizures. This paper proposes to decompose multi-channel interictal iEEG signals into a number of subbands. For every 20 s segment, twelve features are computed from each subband. A mutual information (MI)-based method with grid search was applied to select the most prominent bands and features. A gradient-boosting decision tree-based algorithm called LightGBM was used to score each segment of the channels and these were averaged together to achieve a final score for each channel. The possible SOZ channels were localized based on the higher value channels. The experimental results with eleven epilepsy patients were tested to observe the efficiency of the proposed design compared to the state-of-the-art methods

Classification of the Epileptic Seizure Onset Zone Based on Partial Annotation.

Epilepsy is a chronic disorder caused by excessive electrical discharges. Currently, clinical experts identify the seizure onset zone (SOZ) channel through visual judgment based on long-time intracranial electroencephalogram (iEEG), which is a very time-consuming, difficult and experience-based task. Therefore, there is a need for high-accuracy diagnostic aids to reduce the workload of clinical experts. In this article, we propose a method in which, the iEEG is split into the 20-s segment and for each patient, we ask clinical experts to label a part of the data, which is used to train a model and classify the remaining iEEG data. In recent years, machine learning methods have been successfully applied to solve some medical problems. Filtering, entropy and short-time Fourier transform (STFT) are used for extracting features. We compare them to wavelet transform (WT), empirical mode decomposition (EMD) and other traditional methods with the aim of obtaining the best possible discriminating features. Finally, we look for their medical interpretation, which is important for clinical experts. We achieve high-performance results for SOZ and non-SOZ data classification by using the labeled iEEG data and support vector machine (SVM), fully connected neural network (FCNN) and convolutional neural network (CNN) as classification models. In addition, we introduce the positive unlabeled (PU) learning to further reduce the workload of clinical experts. By using PU learning, we can learn a binary classifier with a small amount of labeled data and a large amount of unlabeled data. This can greatly reduce the amount and difficulty of annotation work by clinical experts. All together, we show that using 105 minutes of labeled data we achieve a classification result of 91.46% on average for multiple patients

Classification of the Epileptic Seizure Onset Zone Based on Partial Annotation.

Epilepsy is a chronic disorder caused by excessive electrical discharges. Currently, clinical experts identify the seizure onset zone (SOZ) channel through visual judgment based on long-time intracranial electroencephalogram (iEEG), which is a very time-consuming, difficult and experience-based task. Therefore, there is a need for high-accuracy diagnostic aids to reduce the workload of clinical experts. In this article, we propose a method in which, the iEEG is split into the 20-s segment and for each patient, we ask clinical experts to label a part of the data, which is used to train a model and classify the remaining iEEG data. In recent years, machine learning methods have been successfully applied to solve some medical problems. Filtering, entropy and short-time Fourier transform (STFT) are used for extracting features. We compare them to wavelet transform (WT), empirical mode decomposition (EMD) and other traditional methods with the aim of obtaining the best possible discriminating features. Finally, we look for their medical interpretation, which is important for clinical experts. We achieve high-performance results for SOZ and non-SOZ data classification by using the labeled iEEG data and support vector machine (SVM), fully connected neural network (FCNN) and convolutional neural network (CNN) as classification models. In addition, we introduce the positive unlabeled (PU) learning to further reduce the workload of clinical experts. By using PU learning, we can learn a binary classifier with a small amount of labeled data and a large amount of unlabeled data. This can greatly reduce the amount and difficulty of annotation work by clinical experts. All together, we show that using 105 minutes of labeled data we achieve a classification result of 91.46% on average for multiple patients

Leucine-rich α2-glycoprotein is a novel biomarker of neurodegenerative disease in human cerebrospinal fluid and causes neurodegeneration in mouse cerebral cortex.

Leucine-rich α2-glycoprotein (LRG) is a protein induced by inflammation. It contains a leucine-rich repeat (LRR) structure and easily binds with other molecules. However, the function of LRG in the brain during aging and neurodegenerative diseases has not been investigated. Here, we measured human LRG (hLRG) concentration in the cerebrospinal fluid (CSF) and observed hLRG expression in post-mortem human cerebral cortex. We then generated transgenic (Tg) mice that over-expressed mouse LRG (mLRG) in the brain to examine the effects of mLRG accumulation. Finally, we examined protein-protein interactions using a protein microarray method to screen proteins with a high affinity for hLRG. The CSF concentration of hLRG increases with age and is significantly higher in patients with Parkinson's disease with dementia (PDD) and progressive supranuclear palsy (PSP) than in healthy elderly people, idiopathic normal pressure hydrocephalus (iNPH) patients, and individuals with Alzheimer's disease (AD). Tg mice exhibited neuronal degeneration and neuronal decline. Accumulation of LRG in the brains of PDD and PSP patients is not a primary etiological factor, but it is thought to be one of the causes of neurodegeneration. It is anticipated that hLRG CSF levels will be a useful biomarker for the early diagnosis of PDD and PSP

Analysis of Epileptic Discharges from Implanted Subdural Electrodes in Patients with Sturge-Weber Syndrome

<div><p>Objective</p><p>Almost two-thirds of patients with Sturge-Weber syndrome (SWS) have epilepsy, and half of them require surgery for it. However, it is well known that scalp electroencephalography (EEG) does not demonstrate unequivocal epileptic discharges in patients with SWS. Therefore, we analyzed interictal and ictal discharges from intracranial subdural EEG recordings in patients treated surgically for SWS to elucidate epileptogenicity in this disorder.</p><p>Methods</p><p>Five intractable epileptic patients with SWS who were implanted with subdural electrodes for presurgical evaluation were enrolled in this study. We examined the following seizure parameters: seizure onset zone (SOZ), propagation speed of seizure discharges, and seizure duration by visual inspection. Additionally, power spectrogram analysis on some frequency bands at SOZ was performed from 60 s before the visually detected seizure onset using the EEG Complex Demodulation Method (CDM).</p><p>Results</p><p>We obtained 21 seizures from five patients for evaluation, and all seizures initiated from the cortex under the leptomeningeal angioma. Most of the patients presented with motionless staring and respiratory distress as seizure symptoms. The average seizure propagation speed and duration were 3.1 ± 3.6 cm/min and 19.4 ± 33.6 min, respectively. Significant power spectrogram changes at the SOZ were detected at 10–30 Hz from 15 s before seizure onset, and at 30–80 Hz from 5 s before seizure onset.</p><p>Significance</p><p>In patients with SWS, seizures initiate from the cortex under the leptomeningeal angioma, and seizure propagation is slow and persists for a longer period. CDM indicated beta to low gamma-ranged seizure discharges starting from shortly before the visually detected seizure onset. Our ECoG findings indicate that ischemia is a principal mechanism underlying ictogenesis and epileptogenesis in SWS.</p></div

CDM band analysis associated with seizure onset on ECoG.

<p>Values for all x-axes are given in seconds. Values for all y-axes are given in Hz. The upper row showed ECoG at SOZ and non-SOZ electrodes, with the ECoG shown 20 s before and after seizure onset by visual inspection. The lower rows showed the CDM band graph between 0 and 100 Hz. Oscillations between 10 and 30 Hz were detected at SOZ from 15 s before seizure onset. Oscillations higher than 30 Hz were detected at SOZ from 5 s before seizure onset.</p