A peptide blocking the ADORA1-neurabin interaction is anticonvulsant and inhibits epilepsy in an Alzheimer's model

Epileptic seizures are common sequelae of stroke, acute brain injury, and chronic neurodegenerative diseases, including Alzheimer's disease (AD), and cannot be effectively controlled in approximately 40% of patients, necessitating the development of novel therapeutic agents. Activation of the A1 receptor (A1R) by endogenous adenosine is an intrinsic mechanism to self-terminate seizures and protect neurons from excitotoxicity. However, targeting A1R for neurological disorders has been hindered by side effects associated with its broad expression outside the nervous system. Here we aim to target the neural-specific A1R/neurabin/regulator of G protein signaling 4 (A1R/neurabin/RGS4) complex that dictates A1R signaling strength and response outcome in the brain. We developed a peptide that blocks the A1R-neurabin interaction to enhance A1R activity. Intracerebroventricular or i.n. administration of this peptide shows marked protection against kainate-induced seizures and neuronal death. Furthermore, in an AD mouse model with spontaneous seizures, nasal delivery of this blocking peptide reduces epileptic spike frequency. Significantly, the anticonvulsant and neuroprotective effects of this peptide are achieved through enhanced A1R function in response to endogenous adenosine in the brain, thus, avoiding side effects associated with A1R activation in peripheral tissues and organs. Our study informs potentially new anti-seizure therapy applicable to epilepsy and other neurological illness with comorbid seizures.

Automatic epileptic seizure detection via Stein kernel-based sparse representation

Epileptic seizure detection is of great significance in the diagnosis of epilepsy and relieving the heavy workload of visual inspection of electroencephalogram (EEG) recordings. This paper presents a novel method for seizure detection using the Stein kernel-based sparse representation (SR) for EEG recordings. Different from the traditional SR scheme that works with vector data in Euclidean space, the Stein kernel-based SR framework is constructed for seizure detection in the space of the symmetric positive definite (SPD) matrices, which form a Riemannian manifold. Due to the non-Euclidean geometry of the Riemannian manifold, the Stein kernel on the manifold permits the embedding of the manifold in a high-dimensional reproducing kernel Hilbert space (RKHS) to perform SR. In the Stein kernel-based SR framework, EEG samples are described by SPD matrices in the form of covariance descriptors (CovDs). Then, a test EEG sample is sparsely represented on the training set, and the test sample is classified as a member of the class, which leads to the minimum reconstructed residual. Finally, by using three widely used EEG datasets to evaluate the detection performance of the proposed method, the experimental results demonstrate that it achieves good classification accuracy on each dataset. Furthermore, the fast computational speed of the Stein kernel-based SR also meets the basic requirements for real-time seizure detection.

EEG Synchronization Analysis for Seizure Prediction: A Study on Data of Noninvasive Recordings

Objective: Epilepsy is a neurological disorder arising from anomalies of the electrical activity in the brain, affecting ~65 million individuals worldwide. Prediction methods, typically based on machine learning methods, require a large amount of data for training, in order to correctly classify seizures with small false alarm rates. Methods: In this work, we present a new database containing EEG scalp signals of 14 epileptic patients acquired at the Unit of Neurology and Neurophysiology of the University of Siena, Italy. Furthermore, a patient-specific seizure prediction method, based on the detection of synchronization patterns in the EEG, is proposed and tested on the data of the database. The use of noninvasive EEG data aims to explore the possibility of developing a noninvasive monitoring/control device for the prediction of seizures. The prediction method employs synchronization measures computed over all channel pairs and a computationally inexpensive threshold-based classification approach. Results and conclusions: The experimental analysis, performed by inspection and by the proposed threshold-based classifier on all the patients of the database, shows that the features extracted by the synchronization measures are able to detect preictal and ictal states and allow the prediction of the seizures few minutes before the seizure onsets.

Having your cake and eating it: Faster responses with reduced muscular activation while learning a temporal interval

We examined how motor responses to a stimulus evolve as individuals learn to predict when a stimulus will appear, by comparing responses to a regular versus irregular stimulus train. The study was conducted with two groups of adults - one responded to the regular appearance of a visual stimulus every 3 s (R group) and the second responded to the irregular presentation of the same stimulus (IR group) at intervals varying between 2 and 4 s. Participants responded to the appearance of the stimulus by bending over to press a button that was slightly out of reach. This whole body reach requires muscular activation at the ankles. Over the course of 50 consecutive responses, the response times in the R group were found to decrease more than those for participants in the IR group. The electromyographs (EMGs) of two ankle antagonist muscles, the anterior tibialis and soleus were also modified as participants progressively learnt the temporal regularity of a sequence. Tibialis onset times for the R group were found to decrease faster. A less predictable observation was the faster reduction in post stimulus activation of the tibialis muscle for the R group. Soleus muscle deactivation is an indicator of movement preparation. EMG integrals for this muscle a little before stimulus onset showed a trend for greater decrease in the R group. In summary, our study shows that temporal expectations over repeated stimulus presentation permit the dynamic optimization of motor activity with progressively faster response times, muscle activation onset times and lower muscle activation amplitudes.

Using Dictionary Pair Learning for Seizure Detection

Automatic seizure detection is extremely important in the monitoring and diagnosis of epilepsy. The paper presents a novel method based on dictionary pair learning (DPL) for seizure detection in the long-term intracranial electroencephalogram (EEG) recordings. First, for the EEG data, wavelet filtering and differential filtering are applied, and the kernel function is performed to make the signal linearly separable. In DPL, the synthesis dictionary and analysis dictionary are learned jointly from original training samples with alternating minimization method, and sparse coefficients are obtained by using of linear projection instead of costly [Formula: see text]-norm or [Formula: see text]-norm optimization. At last, the reconstructed residuals associated with seizure and nonseizure sub-dictionary pairs are calculated as the decision values, and the postprocessing is performed for improving the recognition rate and reducing the false detection rate of the system. A total of 530[Formula: see text]h from 20 patients with 81 seizures were used to evaluate the system. Our proposed method has achieved an average segment-based sensitivity of 93.39%, specificity of 98.51%, and event-based sensitivity of 96.36% with false detection rate of 0.236/h.

A Patient-Specific Approach for Short-Term Epileptic Seizures Prediction Through the Analysis of EEG Synchronization

Epilepsy is a neurological disorder arising from anomalies of the electrical activity in the brain, affecting about 65 millions individuals worldwide.

OBJECTIVE

This paper proposes a patient-specific approach for short-term prediction (i.e., within few minutes) of epileptic seizures.

METHODS

We use noninvasive EEG data, since the aim is exploring the possibility of developing a noninvasive monitoring/control device for the prediction of seizures. Our approach is based on finding synchronization patterns in the EEG that allow to distinguish in real time preictal from interictal states. In practice, we develop easily computable functions over a graph model to capture the variations in the synchronization, and employ a classifier for identifying the preictal state.

RESULTS

We compare two state-of-the-art classification algorithms and a simple and computationally inexpensive threshold-based classifier developed ad hoc. Results on publicly available scalp EEG database and on scalp data of the patients of the Unit of Neurology and Neurophysiology at University of Siena show that this simple and computationally viable processing is able to highlight the changes in synchronization when a seizure is approaching.

CONCLUSION AND SIGNIFICANCE

The proposed approach, characterized by low computational requirements and by the use of noninvasive techniques, is a step toward the development of portable and wearable devices for real-life use.

The earth mover's distance and Bayesian linear discriminant analysis for epileptic seizure detection in scalp EEG

Since epileptic seizure is unpredictable and paroxysmal, an automatic system for seizure detecting could be of great significance and assistance to patients and medical staff. In this paper, a novel method is proposed for multichannel patient-specific seizure detection applying the earth mover's distance (EMD) in scalp EEG. Firstly, the wavelet decomposition is executed to the original EEGs with five scales, the scale 3, 4 and 5 are selected and transformed into histograms and afterwards the distances between histograms in pairs are computed applying the earth mover's distance as effective features. Then, the EMD features are sent to the classifier based on the Bayesian linear discriminant analysis (BLDA) for classification, and an efficient postprocessing procedure is applied to improve the detection system precision, finally. To evaluate the performance of the proposed method, the CHB-MIT scalp EEG database with 958 h EEG recordings from 23 epileptic patients is used and a relatively satisfactory detection rate is achieved with the average sensitivity of 95.65% and false detection rate of 0.68/h. The good performance of this algorithm indicates the potential application for seizure monitoring in clinical practice.

Seizure Detection and Network Dynamics of Generalized Convulsive Seizures: Towards Rational Designing of Closed-Loop Neuromodulation

Objective

Studies have demonstrated the utility of closed-loop neuromodulation in treating focal onset seizures. There is an utmost need of neurostimulation therapy for generalized tonic-clonic seizures. The study goals are to map the thalamocortical network dynamics during the generalized convulsive seizures and identify targets for reliable seizure detection.

Methods

Local field potentials were recorded from bilateral cortex, hippocampi, and centromedian thalami in Sprague-Dawley rats. Pentylenetetrazol was used to induce multiple convulsive seizures. The performances of two automated seizure detection methods (line length and P-operators) as a function of different cortical and subcortical structures were estimated. Multiple linear correlations-Granger's Causality was used to determine the effective connectivity.

Results

Of the 29 generalized tonic-clonic seizures analyzed, line length detected 100% of seizures in all the channels while the P-operator detected only 35% of seizures. The detection latencies were shortest in the thalamus in comparison to the cortex. There was a decrease in amplitude correlation within the thalamocortical network during the seizure, and flow of information was decreased from thalamus to hippocampal-parietal nodes.

Significance

The preclinical study confirms thalamus as a superior target for automated detection of generalized seizures and modulation of synchrony to increase coupling may be a strategy to abate seizures.

Epileptic seizure onset detection based on EEG and ECG data fusion

This paper presents a novel method for seizure onset detection using fused information extracted from multichannel electroencephalogram (EEG) and single-channel electrocardiogram (ECG). In existing seizure detectors, the analysis of the nonlinear and nonstationary ECG signal is limited to the time-domain or frequency-domain. In this work, heart rate variability (HRV) extracted from ECG is analyzed using a Matching-Pursuit (MP) and Wigner-Ville Distribution (WVD) algorithm in order to effectively extract meaningful HRV features representative of seizure and nonseizure states. The EEG analysis relies on a common spatial pattern (CSP) based feature enhancement stage that enables better discrimination between seizure and nonseizure features. The EEG-based detector uses logical operators to pool SVM seizure onset detections made independently across different EEG spectral bands. Two fusion systems are adopted. In the first system, EEG-based and ECG-based decisions are directly fused to obtain a final decision. The second fusion system adopts an override option that allows for the EEG-based decision to override the fusion-based decision in the event that the detector observes a string of EEG-based seizure decisions. The proposed detectors exhibit an improved performance, with respect to sensitivity and detection latency, compared with the state-of-the-art detectors. Experimental results demonstrate that the second detector achieves a sensitivity of 100%, detection latency of 2.6s, and a specificity of 99.91% for the MAJ fusion case.

Epileptic Seizure Detection with Log-Euclidean Gaussian Kernel-Based Sparse Representation

Epileptic seizure detection plays an important role in the diagnosis of epilepsy and reducing the massive workload of reviewing electroencephalography (EEG) recordings. In this work, a novel algorithm is developed to detect seizures employing log-Euclidean Gaussian kernel-based sparse representation (SR) in long-term EEG recordings. Unlike the traditional SR for vector data in Euclidean space, the log-Euclidean Gaussian kernel-based SR framework is proposed for seizure detection in the space of the symmetric positive definite (SPD) matrices, which form a Riemannian manifold. Since the Riemannian manifold is nonlinear, the log-Euclidean Gaussian kernel function is applied to embed it into a reproducing kernel Hilbert space (RKHS) for performing SR. The EEG signals of all channels are divided into epochs and the SPD matrices representing EEG epochs are generated by covariance descriptors. Then, the testing samples are sparsely coded over the dictionary composed by training samples utilizing log-Euclidean Gaussian kernel-based SR. The classification of testing samples is achieved by computing the minimal reconstructed residuals. The proposed method is evaluated on the Freiburg EEG dataset of 21 patients and shows its notable performance on both epoch-based and event-based assessments. Moreover, this method handles multiple channels of EEG recordings synchronously which is more speedy and efficient than traditional seizure detection methods.

An Improved Sparse Representation over Learned Dictionary Method for Seizure Detection

Automatic seizure detection has played an important role in the monitoring, diagnosis and treatment of epilepsy. In this paper, a patient specific method is proposed for seizure detection in the long-term intracranial electroencephalogram (EEG) recordings. This seizure detection method is based on sparse representation with online dictionary learning and elastic net constraint. The online learned dictionary could sparsely represent the testing samples more accurately, and the elastic net constraint which combines the 11-norm and 12-norm not only makes the coefficients sparse but also avoids over-fitting problem. First, the EEG signals are preprocessed using wavelet filtering and differential filtering, and the kernel function is applied to make the samples closer to linearly separable. Then the dictionaries of seizure and nonseizure are respectively learned from original ictal and interictal training samples with online dictionary optimization algorithm to compose the training dictionary. After that, the test samples are sparsely coded over the learned dictionary and the residuals associated with ictal and interictal sub-dictionary are calculated, respectively. Eventually, the test samples are classified as two distinct categories, seizure or nonseizure, by comparing the reconstructed residuals. The average segment-based sensitivity of 95.45%, specificity of 99.08%, and event-based sensitivity of 94.44% with false detection rate of 0.23/h and average latency of -5.14 s have been achieved with our proposed method.

Seizure detection approach using S-transform and singular value decomposition

Automatic seizure detection plays a significant role in the diagnosis of epilepsy. This paper presents a novel method based on S-transform and singular value decomposition (SVD) for seizure detection. Primarily, S-transform is performed on EEG signals, and the obtained time-frequency matrix is divided into submatrices. Then, the singular values of each submatrix are extracted using singular value decomposition (SVD). Effective features are constructed by adding the largest singular values in the same frequency band together and fed into Bayesian linear discriminant analysis (BLDA) classifier for decision. Finally, postprocessing is applied to obtain higher sensitivity and lower false detection rate. A total of 183.07 hours of intracranial EEG recordings containing 82 seizure events from 20 patients were used to evaluate the system. The proposed method had a sensitivity of 96.40% and a specificity of 99.01%, with a false detection rate of 0.16/h.

Band-sensitive seizure onset detection via CSP-enhanced EEG features

This paper presents two novel epileptic seizure onset detectors. The detectors rely on a common spatial pattern (CSP)-based feature enhancement stage that increases the variance between seizure and nonseizure scalp electroencephalography (EEG). The proposed feature enhancement stage enables better discrimination between seizure and nonseizure features. The first detector adopts a conventional classification stage using a support vector machine (SVM) that feeds the energy features extracted from different subbands to an SVM for seizure onset detection. The second detector uses logical operators to pool SVM seizure onset detections made independently across different EEG spectral bands. The proposed detectors exhibit an improved performance, with respect to sensitivity and detection latency, compared with the state-of-the-art detectors. Experimental results have demonstrated that the first detector achieves a sensitivity of 95.2%, detection latency of 6.43s, and false alarm rate of 0.59perhour. The second detector achieves a sensitivity of 100%, detection latency of 7.28s, and false alarm rate of 1.2per hour for the MAJORITY fusion method.

Automatic seizure detection using Stockwell transform and boosting algorithm for long-term EEG

Automatic detection of seizures has vital significance for epileptic diagnosis and can efficiently reduce the workload of the medical staff. In this study, a novel seizure detection method based on Stockwell transform is proposed for intracranial long-term EEG data. The Stockwell transform is employed to obtain the time-frequency representation of the EEG signals, and then the power spectral density is calculated in the time-frequency plane to characterize the behavior of EEG recordings. After that, a classifier based on gradient boosting algorithm is used to make the classification. Finally, the postprocessing is utilized on the outputs of the classifier to obtain more stable and accurate detection results, which includes Kalman filter, threshold judgment, and collar technique. The performance of this method is assessed on the publicly available EEG database which contains approximately 533h of intracranial EEG recordings. The experimental results indicate that the proposed method can achieve a satisfactory sensitivity of 94.26%, a specificity of 96.34%, as well as a very short delay time of 0.56s.

Kernel Collaborative Representation-Based Automatic Seizure Detection in Intracranial EEG

Automatic seizure detection is of great significance in the monitoring and diagnosis of epilepsy. In this study, a novel method is proposed for automatic seizure detection in intracranial electroencephalogram (iEEG) recordings based on kernel collaborative representation (KCR). Firstly, the EEG recordings are divided into 4s epochs, and then wavelet decomposition with five scales is performed. After that, detail signals at scales 3, 4 and 5 are selected to be sparsely coded over the training sets using KCR. In KCR, l2-minimization replaces l1-minimization and the sparse coefficients are computed with regularized least square (RLS), and a kernel function is utilized to improve the separability between seizure and nonseizure signals. The reconstructed residuals of each EEG epoch associated with seizure and nonseizure training samples are compared and EEG epochs are categorized as the class that minimizes the reconstructed residual. At last, a multi-decision rule is applied to obtain the final detection decision. In total, 595 h of iEEG recordings from 21 patients with 87 seizures are employed to evaluate the system. The average sensitivity of 94.41%, specificity of 96.97%, and false detection rate of 0.26/h are achieved. The seizure detection system based on KCR yields both a high sensitivity and a low false detection rate for long-term EEG.

Robust deep network with maximum correntropy criterion for seizure detection

Effective seizure detection from long-term EEG is highly important for seizure diagnosis. Existing methods usually design the feature and classifier individually, while little work has been done for the simultaneous optimization of the two parts. This work proposes a deep network to jointly learn a feature and a classifier so that they could help each other to make the whole system optimal. To deal with the challenge of the impulsive noises and outliers caused by EMG artifacts in EEG signals, we formulate a robust stacked autoencoder (R-SAE) as a part of the network to learn an effective feature. In R-SAE, the maximum correntropy criterion (MCC) is proposed to reduce the effect of noise/outliers. Unlike the mean square error (MSE), the output of the new kernel MCC increases more slowly than that of MSE when the input goes away from the center. Thus, the effect of those noises/outliers positioned far away from the center can be suppressed. The proposed method is evaluated on six patients of 33.6 hours of scalp EEG data. Our method achieves a sensitivity of 100% and a specificity of 99%, which is promising for clinical applications.

Epileptic EEG classification based on kernel sparse representation

The automatic identification of epileptic EEG signals is significant in both relieving heavy workload of visual inspection of EEG recordings and treatment of epilepsy. This paper presents a novel method based on the theory of sparse representation to identify epileptic EEGs. At first, the raw EEG epochs are preprocessed via Gaussian low pass filtering and differential operation. Then, in the scheme of sparse representation based classification (SRC), a test EEG sample is sparsely represented on the training set by solving l1-minimization problem, and the represented residuals associated with ictal and interictal training samples are computed. The test EEG sample is categorized as the class that yields the minimum represented residual. So unlike the conventional EEG classification methods, the choice and calculation of EEG features are avoided in the proposed framework. Moreover, the kernel trick is employed to generate a kernel version of the SRC method for improving the separability between ictal and interictal classes. The satisfactory recognition accuracy of 98.63% for ictal and interictal EEG classification and for ictal and normal EEG classification has been achieved by the kernel SRC. In addition, the fast speed makes the kernel SRC suit for the real-time seizure monitoring application in the near future.

Automatic seizure detection using diffusion distance and BLDA in intracranial EEG

Approximately 1% of the world's population suffers from epilepsy. An automatic seizure detection system is of great significance in the monitoring and diagnosis of epilepsy. In this paper, a novel method is proposed for automatic seizure detection in intracranial EEG recordings. The EEG recordings are divided into 4-s epochs, and then wavelet decomposition with five scales is performed to the EEG epochs. Detail signals at scales 3, 4, and 5 are selected to form a signal distribution. The diffusion distances are extracted as features, and Bayesian linear discriminant analysis (BLDA) is used as the classifier. A total of 193.75h of intracranial EEG recordings from 21 patients having 87 seizures are employed to evaluate the system, and the average sensitivity of 94.99%, specificity of 98.74%, and false-detection rate of 0.24/h are achieved. The seizure detection system based on diffusion distance yields a high sensitivity as well as a low false-detection rate for long-term EEG recordings.

A boosted cascade for efficient epileptic seizure detection

Seizure detection from electroencephalogram (EEG) plays an important role for epilepsy therapy. Due to the diversity of seizure EEG patterns between different individuals, multiple features are necessary for high accuracy since a single feature could hardly encode all types of epileptiform discharges. However, a large feature set inevitably causes the increase of the computational cost. This paper proposes a boosted cascade chain to obtain both high detection performance and high computational efficiency. Sixteen features that are widely used in seizure detection are implemented. Considering the sequential characteristics of EEG signals, the features are extracted on each 1-second segment and its former three segments. Thus, a total of 64 features are used to construct a feature pool. Based on the feature pool, Real AdaBoost is used to select a group of effective features, on which weak classifiers are learned to assemble a strong classifier. The strong classifier is transformed to a cascade classifier by reordering the weak classifiers and learning a threshold for each weak classifier. The cascade classifier still has the similar classification strength to the original strong classifier. More importantly, it is able to reject easy non-seizure samples by the first a few weak classifiers in the cascade, thus high computational efficiency can be obtained. To evaluate our method, 90.6-hour EEG signals from four patients are tested. The experimental results show that our method can achieve an average accuracy of 95.31% and an average detection rate of 91.29% with the false positive rate of 4.68%. On average, only about 4 features are used. Compared with support vector machine (SVM), our method is much more efficient with the similar detection performance.

Epileptic seizure detection with linear and nonlinear features

Automatic seizure detection is significant in both diagnosis of epilepsy and relieving the heavy workload of inspecting prolonged EEG. This paper presents a new seizure detection method for multi-channel long-term EEG. The fractal intercept derived from fractal geometry is extracted as a novel nonlinear feature of EEG signals, and the relative fluctuation index is calculated as a linear feature. The feature vector, consisting of the two EEG descriptors, is fed into a single-layer neural network for classification. Extreme learning machine (ELM) algorithm is adopted to train the neural network. Finally, post-processing including smoothing, channel fusion, and collar technique is employed to obtain more accurate and stable results. Both the segment-based and event-based assessments are used for the performance evaluation of this method on the 21-patient Freiburg dataset. The segment-based sensitivity of 91.72% and specificity of 94.89% were achieved. For the event-based assessment, this method yielded a sensitivity of 93.85% with a false detection rate of 0.35/h.

Morphology-based automatic seizure detector for intracerebral EEG recordings

In this paper, a new seizure detection system aimed at assisting in a rapid review of prolonged intracerebral EEG recordings is described. It is based on quantifying the sharpness of the waveform, one of the most important electrographic EEG features utilized by experts for an accurate and reliable identification of a seizure. The waveform morphology is characterized by a measure of sharpness as defined by the slope of the half-waves. A train of abnormally sharp waves resulting from subsequent filtering are used to identify seizures. The method was optimized using 145 h of single-channel depth EEG from seven patients, and tested on another 158 h of single-channel depth EEG from another seven patients. Additionally, 725 h of depth EEG from 21 patients was utilized to assess the system performance in a multichannel configuration. Single-channel test data resulted in a sensitivity of 87% and a specificity of 71%. The multichannel test data reported a sensitivity of 81% and a specificity of 58.9%. The new system detected a wide range of seizure patterns that included rhythmic and nonrhythmic seizures of varying length, including those missed by the experts. We also compare the proposed system with a popular commercial system.