Preictal Stage Research Articles

Nonlinear analysis and recognition of epileptic EEG signals in different stages.

It is a hot problem in epilepsy research to detect and predict seizures by EEG signals. Clinically, it is generally observed that there are only sudden abnormal signals during the ictal stage, but there is no significant difference in the EEG signal between the interictal and preictal stages. To solve the problem that preictal signals are difficult to recognize clinically, and then effectively improve the recognition efficiency of epileptic seizures, so, in this paper, some nonlinear methods are comprehensively used to extract the hidden information in the EEG signals in different stages, namely, phase space reconstruction (PSR), Poincaré section (PS), synchroextracting transform (SET), and machine learning for EEG signal analysis. First, PSR based on C-C method is used, and the results show that there are different diffuse attractor trajectories of the signals in different stages. Second, the confidence ellipse (CE) is constructed by using the scatter diagram of the corresponding trajectory on PS, and the aspect ratio and area of the ellipse are calculated. The results show that there is an interesting transitional phenomenon in preictal stage. To recognize ictal and preictal signals, time-frequency (TF) spectrums, which are processed by SET, are fed into the convolutional neural network (CNN) classifier. The accuracy of recognizing ictal and preictal signals reaches 99.7% and 93.7%, respectively. To summarize, our results based on nonlinear method provide new research ideas for seizure detection and prediction.NEW & NOTEWORTHY Our results based on nonlinear method have better practical significance and clinical application value and improved the prediction efficiency of epileptic EEG signals effectively. This work provides direct insight into the application of these biomarkers for seizure detection and prediction.

Seizure stage detection of epileptic seizure using convolutional neural networks

According to the World Health Organization (WHO), seventy million individuals worldwide suffer from epilepsy, a neurological disorder. While electroencephalography (EEG) is crucial for diagnosing epilepsy and monitoring the brain activity of epilepsy patients, it requires a specialist to examine all EEG recordings to find epileptic behavior. This procedure needs an experienced doctor, and a precise epilepsy diagnosis is crucial for appropriate treatment. To identify epileptic seizures, this study employed a convolutional neural network (CNN) based on raw scalp EEG signals to discriminate between preictal, ictal, postictal, and interictal segments. The possibility of these characteristics is explored by examining how well time-domain signals work in the detection of epileptic signals using intracranial Freiburg Hospital (FH), scalp Children's Hospital Boston-Massachusetts Institute of Technology (CHB-MIT) databases, and Temple University Hospital (TUH) EEG. To test the viability of this approach, two types of experiments were carried out. Firstly, binary class classification (preictal, ictal, postictal each versus interictal) and four-class classification (interictal versus preictal versus ictal versus postictal). The average accuracy for stage detection using CHB-MIT database was 84.4%, while the Freiburg database's time-domain signals had an accuracy of 79.7% and the highest accuracy of 94.02% for classification in the TUH EEG database when comparing interictal stage to preictal stage.

Automatic classification of seizure and seizure-free EEG signals based on phase space reconstruction features.

Epilepsy is a type of brain disorder triggered by an abrupt electrical imbalance of neuronal networks. An electroencephalogram (EEG) is a diagnostic tool to capture the underlying brain mechanisms and detect seizure onset in epileptic patients. To detect seizures, neurologists need to manually monitor EEG recordings for long periods, which is challenging and susceptible to errors depending on expertise and experience. Therefore, automatic identification of seizure and seizure-free EEG signals becomes essential. This study introduces a method based on the features extracted from the phase space reconstruction for classifying seizure and seizure-free EEG signals. The computed features are derived from the elliptical area and interquartile range of the Euclidean distance by varying percentage values of data points ranging from 50 to 100%. We consider two public datasets and evaluate these features in each EEG epoch that includes the healthy, interictal, preictal, and ictal stages of epileptic subjects, utilizing the K-nearest neighbor classifier for classification. Results show that the features have higher values during the seizure than the seizure-free EEG signals and healthy subjects. Furthermore, the proposed features can effectively discriminate seizure EEG signals from the seizure-free and normal subjects with 100% accuracy, sensitivity, and specificity in both datasets. Likewise, the classification between the preictal stage and seizure EEG signals attains 98% accuracy. Overall, the reconstructed phase space features significantly enhance the accuracy of detecting epileptic EEG signals compared with existing methods. This advancement holds great potential in assisting neurologists in swiftly and accurately diagnosing epileptic seizures from EEG signals.

An end-to-end seizure prediction approach using long short-term memory network.

There are increasing epilepsy patients suffering from the pain of seizure onsets, and effective prediction of seizures could improve their quality of life. To obtain high sensitivity for epileptic seizure prediction, current studies generally need complex feature extraction operations, which heavily depends on the artificial experience (or domain knowledge) and is highly subjective. To address these issues, in this paper we propose an end-to-end epileptic seizure prediction approach based on the long short-term memory network (LSTM). In the new method, only the gamma band of raw electroencephalography (EEG) signals is extracted as network input directly for seizure prediction, thus avoiding subjective and expensive feature design process. Despite its simplicity, the proposed method achieves the mean sensitivity of 91.76% and false prediction rate (FPR) of 0.29/h on Children's Hospital Boston-MIT (CHB-MIT) scalp EEG Database, respectively, when identifying the preictal stage from the EEG signals. Furthermore, different from traditional methods that only consider the classification of preictal and interictal EEG, we introduce the postictal stage as an extra class in the proposed method. As a result, the performance of seizure prediction is further improved, obtaining a higher sensitivity of 92.17% and a low FPR of 0.27/h. The mean warning time is 44.46 min, which suggests that sufficient time is reserved for patients to take intervention measures by this prediction method.

An Epileptic Seizure Prediction Method Based on CBAM-3D CNN-LSTM Model.

Epilepsy as a common disease of the nervous system, with high incidence, sudden and recurrent characteristics. Therefore, timely prediction of seizures and intervention treatment can significantly reduce the accidental injury of patients and protect the life and health of patients. Epilepsy seizures is the result of temporal and spatial evolution, Existing deep learning methods often ignore its spatial features, in order to make better use of the temporal and spatial characteristics of epileptic EEG signals. We propose a CBAM-3D CNN-LSTM model to predict epilepsy seizures. First, we apply short-time Fourier transform(STFT) to preprocess EEG signals. Secondly, the 3D CNN model was used to extract the features of preictal stage and interictal stage from the preprocessed signals. Thirdly, Bi-LSTM is connected to 3D CNN for classification. Finally CBAM is introduced into the model. Different attention is given to the data channel and space to extract key information, so that the model can accurately extract interictal and pre-ictal features. Our proposed approach achieved an accuracy of 97.95%, a sensitivity of 98.40%, and a false alarm rate of 0.017 h-1 on 11 patients from the public CHB-MIT scalp EEG dataset. Clinical and Translational Impact Statement-Timely prediction of epileptic seizures and intervention treatment can significantly reduce the accidental injury of patients and protect the life and health of patients.

Dynamic analysis of epileptic causal brain networks based on directional transfer function

Epilepsy is a neurological disease with disordered brain network connectivity. It is important to analyze the brain network mechanism of epileptic seizure from the perspective of directed functional connectivity. In this paper, causal brain networks were constructed for different sub-bands of epileptic electroencephalogram (EEG) signals in interictal, preictal and ictal phases by directional transfer function method, and the information transmission pathway and dynamic change process of brain network under different conditions were analyzed. Finally, the dynamic changes of characteristic attributes of brain networks with different rhythms were analyzed. The results show that the topology of brain network changes from stochastic network to rule network during the three stage and the node connections of the whole brain network show a trend of gradual decline. The number of pathway connections between internal nodes of frontal, temporal and occipital regions increase. There are a lot of hub nodes with information outflow in the lesion region. The global efficiency in ictal stage of α, β and γ waves are significantly higher than in the interictal and the preictal stage. The clustering coefficients in preictal stage are higher than in the ictal stage and the clustering coefficients in ictal stage are higher than in the interictal stage. The clustering coefficients of frontal, temporal and parietal lobes are significantly increased. The results of this study indicate that the topological structure and characteristic properties of epileptic causal brain network can reflect the dynamic process of epileptic seizures. In the future, this study has important research value in the localization of epileptic focus and prediction of epileptic seizure.

Epileptic Signal Classification With Deep EEG Features by Stacked CNNs

The scalp electroencephalogram (EEG)-based epileptic seizure/nonseizure detection has been comprehensively studied, and fruitful achievements have been reported in the past. Yet, few investigations have been paid to the preictal stage detection, which is practically more crucial to epileptics in taking precautions before seizure onset. In this article, a novel epileptic preictal state classification and seizure detection algorithm based on deep features learned by stacked convolutional neural networks (SCNNs) is developed. The mean amplitude of sub-band spectrum map (MAS) obtained from the average sub-band spectra of multichannel EEGs is adopted for representation. The probability feature vectors by stacked convolutional neural networks (CNNs) are extracted in the softmax layer of CNNs, where an adaptive and discriminative feature weighting fusion (AWF) is developed for performance enhancement. Following the deep extraction layer, the effective kernel extreme learning machine (KELM) is adopted for feature learning and epileptic classification. Experiments on the benchmark CHB-MIT database and a real recorded epileptic database are conducted for performance demonstration. Comparisons to many state-of-the-art epileptic classification methods are provided to show the superiority of the proposed SCNN+AWF algorithm.

An Evaluation of the Channel Effect on Detecting the Preictal Stage in Patients With Epilepsy.

Decreasing the processor load to an acceptable level challenges researchers as an important threshold in the study of real-time detection and the prediction of epileptic seizures. The main methods in overcoming this problem are feature selection, dimension reduction, and electrode selection. This study is an evaluation of the performances of EEG signals, obtained from different channels in the detection processes of epileptic stages, in epileptic individuals. In particular, it aimed to analyze the separation levels of preictal periods from other periods and to evaluate the effects of the electrode selection on seizure prediction studies. The EEG signals belong to 14 epileptic patients. A feature set was formed for each patient using 20 features widely used in epilepsy studies. The number of features was decreased to 8 using principal component analysis. The reduced feature set was divided into testing and training data, using the cross-validation method. The testing data were classified with linear discriminant analysis and the results of the classification were evaluated individually for each patient and channel. Variability of up to 29.48 % was observed in the average of classification accuracy due to the selection of channels.

A lightweight solution to epileptic seizure prediction based on EEG synchronization measurement

It is critical to determine whether the brain state of an epilepsy patient is indicative of a possible seizure onset; thus, appropriate therapy or alarm may be delivered in time. Successful seizure prediction relies on the capability of accurately separating the preictal stage from the interictal stage of ictal electroencephalography (EEG). With the booming of brain e-health technologies, there exists a pressing need for an approach that provides accurate seizure prediction while operating efficiently on edge computing platforms with very limited computing resources in Internet of Things environments. This study proposes a lightweight solution to this problem based on synchronization measurement of multivariate EEG captured from multiple brain regions consisting of two phases, i.e., synchronization measurement and classification. For phase one, Pearson correlation coefficient is calculated to obtain the correlation matrices. For phase two, the correlation matrices are classified to distinguish the preictal states from the interictal ones with a simple CNN model, and seizure onset can then be predicted. Experiments have been performed to evaluate the performance of the lightweight solution on the CHB-MIT scalp EEG dataset. The experimental results indicate that: (1) the solution outperforms most of the state-of-the-art counterparts with a high accuracy of seizure prediction ( $$89.98\%$$ for 15 mins alarm in advance) for all subjects, and (2) the solution incurs a very low computational overhead and holds potentials in brain e-health applications.

Aberrant Connectivity During Pilocarpine-Induced Status Epilepticus.

Status epilepticus (SE) is a common, life-threatening neurological disorder that may lead to permanent brain damage. In rodent models, SE is an acute phase of seizures that could be reproduced by injecting with pilocarpine and then induce chronic temporal lobe epilepsy (TLE) seizures. However, how SE disrupts brain activity, especially communications among brain regions, is still unclear. In this study, we aimed to identify the characteristic abnormalities of network connections among the frontal cortex, hippocampus and thalamus during the SE episodes in a pilocarpine model with functional and effective connectivity measurements. We showed that the coherence connectivity among these regions increased significantly during the SE episodes in almost all frequency bands (except the alpha band) and that the frequency band with enhanced connections was specific to different stages of SE episodes. Moreover, with the effective analysis, we revealed a closed neural circuit of bidirectional effective interactions between the frontal regions and the hippocampus and thalamus in both ictal and post-ictal stages, implying aberrant enhancement of communication across these brain regions during the SE episodes. Furthermore, an effective connection from the hippocampus to the thalamus was detected in the delta band during the pre-ictal stage, which shifted in an inverse direction during the ictal stage in the theta band and in the theta, alpha, beta and low-gamma bands during the post-ictal stage. This specificity of the effective connection between the hippocampus and thalamus illustrated that the hippocampal structure is critical for the initiation of SE discharges, while the thalamus is important for the propagation of SE discharges. Overall, our results demonstrated enhanced interaction among the frontal cortex, hippocampus and thalamus during the SE episodes and suggested the modes of information flow across these structures for the initiation and propagation of SE discharges. These findings may reveal an underlying mechanism of aberrant network communication during pilocarpine-induced SE discharges and deepen our knowledge of TLE seizures.

Epileptic Seizures: Glia-Neuron Interactions For Better or For Worse.

Investigations of the mechanisms generating epileptic seizures have primarily focused on neurons. However, more systemic research of brain circuits has highlighted an important role of non-neuronal cells such as glia in the genesis and spreading of generalized seizures in the brain.

Hypothalamic regulation of headache and migraine.

The clinical picture, but also neuroimaging findings, suggested the brainstem and midbrain structures as possible driving or generating structures in migraine. This has been intensely discussed in the last decades and the advent of modern imaging studies refined the involvement of rostral parts of the pons in acute migraine attacks, but more importantly suggested a predominant role of the hypothalamus and alterations in hypothalamic functional connectivity shortly before the beginning of migraine headaches. This was shown in the NO-triggered and also in the preictal stage of native human migraine attacks. Another headache type that is clinically even more suggestive of hypothalamic involvement is cluster headache, and indeed a structure in close proximity to the hypothalamus has been identified to play a crucial role in attack generation. It is very likely that spontaneous oscillations of complex networks involving the hypothalamus, brainstem, and dopaminergic networks lead to changes in susceptibility thresholds that ultimately start but also terminate headache attacks. We will review clinical and neuroscience evidence that puts the hypothalamus in the center of scientific attention when attack generation is discussed.

Optimization of pathology diagnosis by applying chaos theory and fractal analysis to EEG-signal processing

By 2018, there are more than 70 million people suffering from various forms of one of the most common neurological diseases – epilepsy. In fact, epipelsy is a central nervous system (neurological) disorder, manifesting itself in anomalous brain activity. Nowadays, most of the patients do not have the ability to foresee the onset of an attack in advance, which is due to the complex symptoms that are difficult to predict. One of the best way to analyze such kind of behaviour is electroencephalography (EEG). This research paper considers the problem of EEG epileptic seizures prediction from the point of the theory of nonlinear dynamical systems. According to the study, signals of cerebral cortex neural networks corresponds to multifractal nature which gives an opportunity to analyze changes between states in phase space during the abnormal electrical activity. Therefore, monitoring the preictal stage and early indication of an attack may help patients to avoid problems related to sudden seizures. This research will provide valuable information regarding the mechanism of epilepsy onset and introduce a prediction model using machine learning algorithm based on fractal analysis of RQA.

Predictive analytics & modeling for modern health care system for cerebral palsy patients

The time-shifting elements and high between individual changeability make early forecast of a seizure express a difficult errand. Numerous examinations have demonstrated that EEG signals do have profitable data that if effectively broke down, could help in the expectation of seizures in epileptic patients before their event. A few numerical changes have been broke down for its connection with seizure beginning forecast and a progression of analyses were done to ensure their qualities. New calculations are exhibited to help elucidate, screen, and cross-approve the order of EEG signs to foresee the ictal (for example seizure) states, explicitly the preictal, interictal, and postictal states in the mind. These new strategies show promising outcomes in distinguishing the nearness of a preictal stage before the ictal state. Artificial Intelligence and Machine Learning are playing major role in Diagnosis and Predicting the diseases, EEG signal were recorded with 16 channel system from brain scalp and stored in system. From recorded database seizure levels were analyzed from each channel by articrafting with ICA and PCA algorithms and processed through Band pass filter to identify the Delta, Theta, Alpha and Beta levels. After collecting the brain signals from each channel they were bided with FFT to get the data in time domain series. A machine learning model is developed using python programming by sampling on obtained EEG data, sampled data is trained on to the system. Machine Learning algorithms were applied to the data to accuracy and predict the accuracy on developed model neural network is applied to identify the True-False values. The analyzed data is integrated with IOT framework to Diagnosis the Cerebral Palsy patient remotely. Remote monitoring system for the persons with Intellectual disabilities and update the health status to care takers time to time while they are at work. It’s also reduces the patient work load by checking his details staying at home rather going to a hospital. If system identifies any changes in patient heart rate, brain signals, body temperature, the system alerts the doctor and respective relatives about patient’s status over IOT and also stores the patient details in the cloud.

Epileptic Signal Classification with Deep Transfer Learning Feature on Mean Amplitude Spectrum.

Epilepsy, as a sudden and life-threatening nervous system disease, seriously affected around 6% population in the world. Epileptic classification has attracted wide attention in the past and a number of methods have been developed. But currently studies are mainly on three epileptic states classification (preictal, ictal, interictal) or seizure/non-seizure detection. Among them, the one hour before seizure onset was generally considered as preictal, where the division is actually not fine enough for some practical applications. In this paper, the epileptic signal classification with a more granular time-scale of the preictal stage is studied and a novel deep Electroencephalogram (EEG) feature extraction with the convolutional neural network (CNN) based transfer learning is developed. The subband mean amplitude spectrum map (MAS) of multichannel EEGs is computed for signal representation and three popular deep CNNs are exploited for feature transfer learning, respectively. Experiments on the benchmark CHI-MIT epilepsy EEG database show that the proposed algorithm achieves a highest overall accuracy of 92.77% when the one hour preictal stage is divided into small segments with a fine resolution of 20-minutes scale.

Automatic seizure detection using three-dimensional CNN based on multi-channel EEG

BackgroundAutomated seizure detection from clinical EEG data can reduce the diagnosis time and facilitate targeting treatment for epileptic patients. However, current detection approaches mainly rely on limited features manually designed by domain experts, which are inflexible for the detection of a variety of patterns in a large amount of patients’ EEG data. Moreover, conventional machine learning algorithms for seizure detection cannot accommodate multi-channel Electroencephalogram (EEG) data effectively, which contains both temporal and spatial information. Recently, deep learning technology has been widely applied to perform image processing tasks, which could learns useful features from data and process multi-channel data automatically. To provide an effective system for automatic seizure detection, we proposed a new three-dimensional (3D) convolutional neural network (CNN) structure, whose inputs are multi-channel EEG signals.MethodsEEG data of 13 patients were collected from one center hospital, which has already been inspected by experts. To represent EEG data in CNN, firstly time series of each channel of EEG data was converted into the two-dimensional image. Then all channel images were combined into 3D images according to the mutual correlation intensity between different electrodes. Finally, a CNN was constructed using 3D kernels to predict different stages of EEG data, including inter-ictal, pre-ictal, and ictal stages. The system performance was evaluated and compared with the traditional feature-based classifier and the two-dimensional (2D) deep learning method.ResultsIt demonstrated that multi-channel EEG data could provide more information for increasing the specificity and sensitivity in cpmparison result between the single and multi-channel. And the 3D CNN based on multi-channel outperformed the 2D CNN and traditional signal processing methods with an accuracy of more than 90%, an sensitivity of 88.90% and an specificity of 93.78%.ConclusionsThis is the first effort to apply 3D CNN in detecting seizures from EEG. It provides a new way of learning patterns simultaneously from multi-channel EEG signals, and demonstrates that deep neural networks in combination with 3D kernels can establish an effective system for seizure detection.

A case study on Discrete Wavelet Transform based Hurst exponent for epilepsy detection

Epileptic seizures are manifestations of epilepsy. Careful analysis of EEG records can provide valuable insight and improved understanding of the mechanism causing epileptic disorders. The detection of epileptic form discharges in EEG is an important component in the diagnosis of epilepsy. As EEG signals are non-stationary, the conventional frequency and time domain analysis does not provide better accuracy. So, in this work an attempt has been made to provide an overview of the determination of epilepsy by implementation of Hurst exponent (HE)-based discrete wavelet transform techniques for feature extraction from EEG data sets obtained during ictal and pre ictal stages of affected person and finally classifying EEG signals using SVM and KNN Classifiers. The The highest accuracy of 99% is obtained using SVM.

Of generators, networks and migraine attacks.

One of the most discussed topics in migraine pathophysiology is where migraine attacks originate. Although recent evidence suggests central attack generating loci, there is an ongoing debate about the involved centres of the brain and brainstem. Recent neuroimaging studies focussing on the preictal stage of migraine attacks suggest a predominant role of the hypothalamus and its functional connectivity shortly before the beginning of migraine headaches. In interictal migraineurs, changes in resting state functional connectivity of the dorsal pons and the hypothalamus have been found. Based on the clinical presentation of the premonitory phase of migraine, the hypothalamus and changes within the dopaminergic system have been discussed as likely candidates for attack generation. Neuroimaging studies however suggested the dorsal pons as attack generator. Taking into account the recent findings of hypothalamic involvement and changing connectivity in the preictal stage, the available evidence suggests that the idea of a single migraine generator within the human brain is probably too simplistic. More likely, spontaneous oscillations of complex networks lead to activity changes in certain subcortical and brainstem areas. This in turn might constitute functional changes of descending pain-modulating pathways, and thus the generation of migraine pain.

Understanding migraine as a cycling brain syndrome: reviewing the evidence from functional imaging.

Due to the clinical picture and also based on early imaging data (Weiller et al. Nat Med 1:658-660, 1995), the brainstem and midbrain structures have been intensely discussed as possible driving or generating structures in migraine. The fact that the brainstem activation persisted after treatment makes it unlikely that this activation was only due to increased activity of the endogenous anti-nociceptive system. It was consequently (and somewhat simplifying) coined the "migraine generator". Since then several studies have focussed on this region when investigating episodic, but also chronic migraine. Denuelle et al. were the first to not only demonstrate significant activations in the midbrain and pons but also in the hypothalamus, which, just like the brainstem activation in the first study, persisted after headache relief with sumatriptan. Expanding these studies into f-MRI studies, refined the involvement of rostral parts of the pons in acute migraine attacks. However, they also focused on the preictal stage of NO-triggered and native human migraine attacks and suggested a predominant role of the hypothalamus shortly before the beginning of migraine headaches as well as alterations in hypothalamic functional connectivity. Additionally, changes in resting-state functional connectivity of the dorsal pons and the hypothalamus in interictal migraineurs has recently been found. The pathophysiology and genesis of migraine attacks is probably not just the result of one single "brainstem generator". Spontaneous oscillations of complex networks involving the hypothalamus, brainstem, and dopaminergic networks lead to changes in activity in certain subcortical and brainstem areas, thus changing susceptibility thresholds and not only starting but also terminating headache attacks.

EEG-Based Prediction of Epileptic Seizures Using Phase Synchronization Elicited from Noise-Assisted Multivariate Empirical Mode Decomposition

In this study, we examined the phase locking value (PLV) for seizure prediction, particularly, in the gamma frequency band. We prepared simulation data and 65 clinical cases of seizure. In addition, various filtering algorithms including bandpass filtering, empirical mode decomposition, multivariate empirical mode decomposition and noise-assisted multivariate empirical mode decomposition (NA-MEMD) were used to decompose spectral components from the data. Moreover, in the case of clinical data, the PLVs were used to classify between interictal and preictal stages using a support vector machine. The highest PLV was achieved with NA-MEMD with 0-dB white noise algorithm (0.9988), which exhibited statistically significant differences compared to other filtering algorithms. Moreover, the classification rate was the highest for the NA-MEMD with 0-dB algorithm (83.17%). In terms of frequency components, examining the gamma band resulted in the highest classification rates for all algorithms, compared to other frequency bands such as theta, alpha, and beta bands. We found that PLVs calculated with the NA-MEMD algorithm could be used as a potential biological marker for seizure prediction. Moreover, the gamma frequency band was useful for discriminating between interictal and preictal stages.