Seizure Prediction Research Articles

Supervised and Unsupervised Deep Learning Approaches for EEG Seizure Prediction.

Epilepsy affects more than 50 million people worldwide, making it one of the world's most prevalent neurological diseases. The main symptom of epilepsy is seizures, which occur abruptly and can cause serious injury or death. The ability to predict the occurrence of an epileptic seizure could alleviate many risks and stresses people with epilepsy face. We formulate the problem of detecting preictal (or pre-seizure) with reference to normal EEG as a precursor to incoming seizure. To this end, we developed several supervised deep learning approaches model to identify preictal EEG from normal EEG. We further develop novel unsupervised deep learning approaches to train the models on only normal EEG, and detecting pre-seizure EEG as an anomalous event. These deep learning models were trained and evaluated on two large EEG seizure datasets in a person-specific manner. We found that both supervised and unsupervised approaches are feasible; however, their performance varies depending on the patient, approach and architecture. This new line of research has the potential to develop therapeutic interventions and save human lives.

Domain adaptation for EEG-based, cross-subject epileptic seizure prediction.

The ability to predict the occurrence of an epileptic seizure is a safeguard against patient injury and health complications. However, a major challenge in seizure prediction arises from the significant variability observed in patient data. Common patient-specific approaches, which apply to each patient independently, often perform poorly for other patients due to the data variability. The aim of this study is to propose deep learning models which can handle this variability and generalize across various patients. This study addresses this challenge by introducing a novel cross-subject and multi-subject prediction models. Multiple-subject modeling broadens the scope of patient-specific modeling to account for the data from a dedicated ensemble of patients, thereby providing some useful, though relatively modest, level of generalization. The basic neural network architecture of this model is then adapted to cross-subject prediction, thereby providing a broader, more realistic, context of application. For accrued performance, and generalization ability, cross-subject modeling is enhanced by domain adaptation. Experimental evaluation using the publicly available CHB-MIT and SIENA data datasets shows that our multiple-subject model achieved better performance compared to existing works. However, the cross-subject faces challenges when applied to different patients. Finally, through investigating three domain adaptation methods, the model accuracy has been notably improved by 10.30% and 7.4% for the CHB-MIT and SIENA datasets, respectively.

Distilling Invariant Representations With Domain Adversarial Learning for Cross-Subject Children Seizure Prediction

Seizure prediction based on electroencephalogram (EEG) has great potential to improve patients’ life quality. Due to the high heterogeneity in distributions of EEG signals among different patients, conventional studies usually show poor generalization ability when transferring the model to new patients, which also leads to difficulties in clinical applications. To alleviate the challenging issue concerning cross-subject domain shift, we propose a transformer-based domain adversarial model. Our model first adopts a pre-trained general neural network to extract common features from the EEG signals of available patients. Then, we design a distiller module and a domain discriminator module to perform domain adaptation training based on a small amount of labeled data from the new-coming patient. During adaptation process, conditional domain adversarial training with the addition of label information is employed to remove patient-related information from the extracted features to learn a common seizure feature space among different patients. Our proposed seizure prediction method is evaluated on the CHB-MIT EEG database. The proposed model achieves a sensitivity of 79.5%, a false alarm rate (FPR) of 0.258/h, and an AUC of 0.814. Experimental results demonstrate that the proposed method can effectively reduce inter-patient domain disparity compared to state-of-the-art methods.

Abstract TMP62: The Delayed Seizure CVT Score Predicts Delayed Seizure After Cerebral Venous Thrombosis - A Secondary Analysis of ACTION-CVT

Introduction: Seizures are a common initial manifestation of cerebral venous thrombosis (CVT). We sought to establish predictive factors for delayed seizures to stratify risk and guide clinical management. Methods: We analyzed data from the Direct Oral Anticoagulants Versus Warfarin in the Treatment of Cerebral Venous Thrombosis (ACTION-CVT) study of consecutive patients with CVT from 2015 - 2020. The primary outcome was delayed seizure(s), occurring after 7 days from CVT diagnosis. Patients with outcome data for late seizures and follow-up of at least 7 days from time of diagnosis were included. Univariate analysis was performed using Chi-square, rank-sum, and t-tests. Variables with P-value < 0.2 were included in the backward stepwise regression model with 0.2 as cutoff, based on which we constructed the Delayed Seizure CVT (Delayed Seizures after CVT) score. Results: We identified 599 patients who met inclusion criteria: mean age 44.9(± 16.6) years, 62.9% female. Delayed seizures occurred in 37 (6.1%) patients. Risk factors included intracranial hemorrhage (OR 2.0 95% CI 0.7-5.74 p=0.196), alcohol abuse (OR 3.88 95% CI 1.45-10.38 p=0.007), focal neurologic deficit (OR 2.05 95% CI 0.95-4.44 p=0.067), seizure on presentation (OR 2.88 95% CI 1.32-6.29 p=0.008), hormonal birth control (OR 2.38 95% CI 1.06-5.37 p=0.0036), endovascular therapy (OR 2.86 95% CI 1.16-7.07 p=0.023) and decompressive hemicraniectomy (OR 3.357 95% CI 1.02-10.99 p=0.046), respectively. Based on coefficients of these variables, a 7-item score was created with acceptable performance for seizure prediction (AUC 0.796, 95% CI 0.72 - 0.86) (Table 1). Conclusions: Delayed seizure CVT score is a clinical tool to identify risk of delayed seizure in CVT. Further research is needed to validate these findings in an external cohort.

Heart Rate Variability as a Tool for Seizure Prediction: A Scoping Review

The most critical burden for People with Epilepsy (PwE) is represented by seizures, the unpredictability of which severely impacts quality of life. The design of real-time warning systems that can detect or even predict ictal events would enhance seizure management, leading to high benefits for PwE and their caregivers. In the past, various research works highlighted that seizure onset is anticipated by significant changes in autonomic cardiac control, which can be assessed through heart rate variability (HRV). This manuscript conducted a scoping review of the literature analyzing HRV-based methods for detecting or predicting ictal events. An initial search on the PubMed database returned 402 papers, 72 of which met the inclusion criteria and were included in the review. These results suggest that seizure detection is more accurate in neonatal and pediatric patients due to more significant autonomic modifications during the ictal transitions. In addition, conventional metrics are often incapable of capturing cardiac autonomic variations and should be replaced with more advanced methodologies, considering non-linear HRV features and machine learning tools for processing them. Finally, studies investigating wearable systems for heart monitoring denoted how HRV constitutes an efficient biomarker for seizure detection in patients presenting significant alterations in autonomic cardiac control during ictal events.

Smart Patch for Non-Invasive Blood Pressure Monitoring in Epileptic Seizure Patients via the Sole

Epileptic seizures can cause sudden blood pressure changes, requiring continuous monitoring. However, traditional blood pressure monitoring methods are often invasive and uncomfortable for the patient. In addition, it is difficult to measure blood pressure during seizures. This research aims to design a non-invasive, comfortable device to monitor blood pressure during epileptic seizures continuously. Photoplethysmography (PPG) signals from the sole of the patient's foot were used to extract blood pressure data. A smart patch was designed to be worn comfortably on foot for continuous monitoring during seizures. The results show that the average systolic and diastolic blood pressure errors were 2.838 and 4.494 mmHg during epileptic seizures, respectively. These blood pressure changes could be related to the onset of seizures, suggesting that the device and methodology could be combined with other measures to analyze and predict seizure activity. This research offers a non-invasive and comfortable solution for continuous blood pressure monitoring during seizures, which may affect seizure prediction and management.

Smart Societal Optimization-based Deep Learning Convolutional Neural Network Model for Epileptic Seizure Prediction

ABSTRACT Epilepsy is a long-term neurological condition that disrupts brain function in people of all ages, epilepsy is a condition that is analysed through the brain signals via electroencephalogram (EEG) signal. To analyse epilepsy using spatial and temporal data, various machine-learning-based techniques are used. However, most of the techniques suffer from inaccuracy issues in dealing with the dynamic and raw EEG signal. In this research, an intelligent societal optimisation-driven classifier is introduced based on convolutional neural networks (CNN) for epileptic seizure prediction using EEG signals. To boost predictive accuracy, we extract frequency band features from the EEG signal utilising wavelet decomposition. The frequency band features form the feature vector, is provided smart societal optimisation- CNN such that the prediction performance is enhanced through the optimal tuning of the CNN with the smart societal optimisation. Smart societal optimisation is proposed by integrating the behaviour of the Lobos wolf and the Moggie. The smart societal optimisation-based CNN attains 87.673% accuracy, 84.949% sensitivity91.274%specificity for the K-Fold-10 for CHB-MIT scalp EEG database.

EEG epilepsy seizure prediction: the post-processing stage as a chronology

Almost one-third of epileptic patients fail to achieve seizure control through anti-epileptic drug administration. In the scarcity of completely controlling a patient’s epilepsy, seizure prediction plays a significant role in clinical management and providing new therapeutic options such as warning or intervention devices. Seizure prediction algorithms aim to identify the preictal period that Electroencephalogram (EEG) signals can capture. However, this period is associated with substantial heterogeneity, varying among patients or even between seizures from the same patient. The present work proposes a patient-specific seizure prediction algorithm using post-processing techniques to explore the existence of a set of chronological events of brain activity that precedes epileptic seizures. The study was conducted with 37 patients with Temporal Lobe Epilepsy (TLE) from the EPILEPSIAE database. The designed methodology combines univariate linear features with a classifier based on Support Vector Machines (SVM) and two post-processing techniques to handle pre-seizure temporality in an easily explainable way, employing knowledge from network theory. In the Chronological Firing Power approach, we considered the preictal as a sequence of three brain activity events separated in time. In the Cumulative Firing Power approach, we assumed the preictal period as a sequence of three overlapping events. These methodologies were compared with a control approach based on the typical machine learning pipeline. We considered a Seizure Prediction horizon (SPH) of 5 mins and analyzed several values for the Seizure Occurrence Period (SOP) duration, between 10 and 55 mins. Our results showed that the Cumulative Firing Power approach may improve the seizure prediction performance. This new strategy performed above chance for 62% of patients, whereas the control approach only validated 49% of its models.

RETRACTED ARTICLE: Designing a cognitive smart healthcare framework for seizure prediction using multimodal convolutional neural network

RETRACTED ARTICLE: Designing a cognitive smart healthcare framework for seizure prediction using multimodal convolutional neural network

B2-ViT Net: Broad Vision Transformer Network With Broad Attention for Seizure Prediction.

Seizure prediction are necessary for epileptic patients. The global spatial interactions among channels, and long-range temporal dependencies play a crucial role in seizure onset prediction. In addition, it is necessary to search for seizure prediction features in a vast space to learn new generalized feature representations. Many previous deep learning algorithms have achieved some results in automatic seizure prediction. However, most of them do not consider global spatial interactions among channels and long-range temporal dependencies together, and only learn the feature representation in the deep space. To tackle these issues, in this study, an novel bi-level programming seizure prediction model, B2-ViT Net, is proposed for learning the new generalized spatio-temporal long-range correlation features, which can characterize the global interactions among channels in spatial, and long-range dependencies in temporal required for seizure prediction. In addition, the proposed model can comprehensively learn generalized seizure prediction features in a vast space due to its strong deep and broad feature search capabilities. Sufficient experiments are conducted on two public datasets, CHB-MIT and Kaggle datasets. Compared with other existing methods, our proposed model has shown promising results in automatic seizure prediction tasks, and provides a certain degree of interpretability.

Combination of Channel Reordering Strategy and Dual CNN-LSTM for Epileptic Seizure Prediction Using Three iEEG Datasets.

Intracranial electroencephalogram (iEEG) signals are generally recorded using multiple channels, and channel selection is therefore a significant means in studying iEEG-based seizure prediction. For n channels, 2n-1 channel cases can be generated for selection. However, by this means, an increase in n can cause an exponential increase in computational consumption, which may result in a failure of channel selection when n is too large. Hence, it is necessary to explore reasonable channel selection strategies under the premise of controlling computational consumption and ensuring high classification accuracy. Given this, we propose a novel method of channel reordering strategy combined with dual CNN-LSTM for effectively predicting seizures. First, for each patient with n channels, interictal and preictal iEEG samples from each single channel are input into the CNN-LSTM model for classification. Then, the F1-score of each single channel is calculated, and the channels are reordered in descending order according to the size of F1-scores (channel reordering strategy). Next, iEEG signals with an increasing number of channels are successively fed into the CNN-LSTM model for classification again. Finally, according to the classification results from n channel cases, the channel case with the highest classification rate is selected. Our method is evaluated on the three iEEG datasets: the Freiburg, the SWEC-ETHZ and the American Epilepsy Society Seizure Prediction Challenge (AES-SPC). At the event-based level, the sensitivities of 100%, 100% and 90.5%, and the false prediction rates (FPRs) of 0.10/h, 0/h and 0.47/h, are achieved for the three datasets, respectively. Moreover, compared to an unspecific random predictor, our method also shows a better performance for all patients and dogs from the three datasets. At the segment-based level, the sensitivities-specificities-accuracies-AUCs of 88.1%-94.0%-93.5%-0.9101, 99.1%-99.7%-99.6%-0.9935, and 69.2%-79.9%-78.2%-0.7373, are attained for the three datasets, respectively. Our method can effectively predict seizures and address the challenge of an excessive number of channels during channel selection.

Interpretable Multi-Branch Architecture for Spatiotemporal Neural Networks and Its Application in Seizure Prediction.

Currently, spatiotemporal convolutional neural networks (CNNs) for electroencephalogram (EEG) signals have emerged as promising tools for seizure prediction (SP), which explore the spatiotemporal biomarkers in an epileptic brain. Generally, these CNNs capture spatiotemporal features at single spectral resolution. However, epileptiform EEG signals contain irregular neural oscillations of different frequencies in different brain regions. Therefore, it may be underperforming and uninterpretable for the CNNs without capturing complex spectral properties sufficiently. This study proposed a novel interpretable multi-branch architecture for spatiotemporal CNNs, namely MultiSincNet. On the one hand, the MultiSincNet could directly show the frequency boundaries using the interpretable sinc-convolution layers. On the other hand, it could extract and integrate multiple spatiotemporal features across varying spectral resolutions using parallel branches. Moreover, we also constructed a post-hoc explanation technique for multi-branch CNNs, using the first-order Taylor expansion and chain rule based on the multivariate composite function, which demonstrates the crucial spatiotemporal features learned by the proposed multi-branch spatiotemporal CNN. When combined with the optimal MultiSincNet, ShallowConvNet, DeepConvNet, and EEGWaveNet had significantly improved the subject-specific performance on most metrics. Specifically, the optimal MultiSincNet significantly increased the average accuracy, sensitivity, specificity, binary F1-score, weighted F1-score, and AUC of EEGWaveNet by about 7%, 8%, 7%, 8%, 7%, and 7%, respectively. Besides, the visualization results showed that the optimal model mainly extracts the spectral energy difference from the high gamma band focalized to specific spatial areas as the dominant spatiotemporal EEG feature.

Deep Multiview Module Adaption Transfer Network for Subject-Specific EEG Recognition.

Transfer learning is one of the popular methods to solve the problem of insufficient data in subject-specific electroencephalogram (EEG) recognition tasks. However, most existing approaches ignore the difference between subjects and transfer the same feature representations from source domain to different target domains, resulting in poor transfer performance. To address this issue, we propose a novel subject-specific EEG recognition method named deep multiview module adaption transfer (DMV-MAT) network. First, we design a universal deep multiview (DMV) network to generate different types of discriminative features from multiple perspectives, which improves the generalization performance by extensive feature sets. Second, module adaption transfer (MAT) is designed to evaluate each module by the feature distributions of source and target samples, which can generate an optimal weight sharing strategy for each target subject and promote the model to learn domain-invariant and domain-specific features simultaneously. We conduct extensive experiments in two EEG recognition tasks, i.e., motor imagery (MI) and seizure prediction, on four datasets. Experimental results demonstrate that the proposed method achieves promising performance compared with the state-of-the-art methods, indicating a feasible solution for subject-specific EEG recognition tasks. Implementation codes are available at https://github.com/YangLibuaa/DMV-MAT.

Seizure prediction in stroke survivors who experienced an infection at skilled nursing facilities-a machine learning approach.

Infections and seizures are some of the most common complications in stroke survivors. Infections are the most common risk factor for seizures and stroke survivors that experience an infection are at greater risk of experiencing seizures. A predictive model to determine which stroke survivors are at the greatest risk for a seizure after an infection can be used to help providers focus on prevention of seizures in higher risk residents that experience an infection. A predictive model was generated from a retrospective study of the Long-Term Care Minimum Data Set (MDS) 3.0 (2014-2018, n = 262,301). Techniques included three data balancing methods (SMOTE for up sampling, ENN for down sampling, and SMOTEENN for up and down sampling) and three feature selection methods (LASSO, Recursive Feature Elimination, and Principal Component Analysis). One balancing and one feature selection technique was applied, and the resulting dataset was then trained on four machine learning models (Logistic Regression, Random Forest, XGBoost, and Neural Network). Model performance was evaluated with AUC and accuracy, and interpretation used SHapley Additive exPlanations. Using data balancing methods improved the prediction performances of the machine learning models, but feature selection did not remove any features and did not affect performance. With all models having a high accuracy (76.5%-99.9%), interpretation on all four models yielded the most holistic view. SHAP values indicated that therapy (speech, physical, occupational, and respiratory), independence (activities of daily living for walking, mobility, eating, dressing, and toilet use), and mood (severity score, anti-anxiety medications, antidepressants, and antipsychotics) features contributed the most. Meaning, stroke survivors who received fewer therapy hours, were less independent, had a worse overall mood were at a greater risk of having a seizure after an infection. The development of a tool to predict seizure following an infection in stroke survivors can be interpreted by providers to guide treatment and prevent complications long term. This promotes individualized treatment plans that can increase the quality of resident care.

A Compact Graph Convolutional Network With Adaptive Functional Connectivity for Seizure Prediction.

Seizure prediction using EEG has significant implications for the daily monitoring and treatment of epilepsy patients. However, the task is challenging due to the underlying spatiotemporal correlations and patient heterogeneity. Traditional methods often use large-scale models with independent components to capture the spatial and temporal features of EEG separately or explore shared patterns among patients with the help of pre-defined functional connectivity. In this paper, we propose a compact model, called the graph convolutional network based on adaptive functional connectivity (AFC-GCN), for seizure prediction. The model can adaptively infer evolution of functional connectivity in epilepsy patients during seizures through data-driven methods and synchronously analyze spatiotemporal response of functional connectivity in multiple topologies. On CHB-MIT datasets, the experimental results demonstrate that AFC-GCN achieves accurate and robust performance with low complexity. (AUC: 0.9820, accuracy: 0.9815, sensitivity: 0.9802, FPR: 0.0172). The proposed method has the potential to predict seizure during daily monitoring.

New Approaches to Epileptic Seizure Prediction Based on EEG Signals Using Hybrid CNNs

New Approaches to Epileptic Seizure Prediction Based on EEG Signals Using Hybrid CNNs

The present and future of seizure detection, prediction, and forecasting with machine learning, including the future impact on clinical trials.

Seizures have a profound impact on quality of life and mortality, in part because they can be challenging both to detect and forecast. Seizure detection relies upon accurately differentiating transient neurological symptoms caused by abnormal epileptiform activity from similar symptoms with different causes. Seizure forecasting aims to identify when a person has a high or low likelihood of seizure, which is related to seizure prediction. Machine learning and artificial intelligence are data-driven techniques integrated with neurodiagnostic monitoring technologies that attempt to accomplish both of those tasks. In this narrative review, we describe both the existing software and hardware approaches for seizure detection and forecasting, as well as the concepts for how to evaluate the performance of new technologies for future application in clinical practice. These technologies include long-term monitoring both with and without electroencephalography (EEG) that report very high sensitivity as well as reduced false positive detections. In addition, we describe the implications of seizure detection and forecasting upon the evaluation of novel treatments for seizures within clinical trials. Based on these existing data, long-term seizure detection and forecasting with machine learning and artificial intelligence could fundamentally change the clinical care of people with seizures, but there are multiple validation steps necessary to rigorously demonstrate their benefits and costs, relative to the current standard.

Cyber Security Based Application-Specific Integrated Circuit for Epileptic Seizure Prediction Using Convolutional Neural Network

In the event of an epileptic attack, the Field-Programmable Gate Array (FPGA)-accelerated Convolutional Neural Network (CNN) model is paired with Electroencephalogram (EEG) acquisition equipment to produce a reliable production system that can be used in clinical medical diagnosis. Additionally, this study includes cybersecurity to protect both the epileptic patient’s data and the prediction system. Epilepsy is a frequent neurological disorder that manifests as recurrent seizures, a sign that indicates rapid intervention is necessary to minimize adverse events and improve patient health. The study provides a new real-time design for predicting epileptic seizures based on the Application-Specific Integrated Circuit (ASIC)-based Very Large-Scale Integration (VLSI) architecture. As a first step, EEG data from epilepsy patients were captured and pre-processed. Afterwards, faults and artefacts in the data were removed. Additionally, data was divided into short-time windows and then classified as either ictal, pre-seizure, or interictal. The CNN model was adapted for EEG signal analysis and then trained with categorized data. This technique is more effective and efficient for predicting epileptic seizures accurately, which is advantageous for patient monitoring and treatment. Additionally, cybersecurity measures were implemented to secure patient data and the prediction system.

Semi-Supervised Seizure Prediction Based on Deep Pairwise Representation Alignment of Epileptic EEG Signals

Semi-Supervised Seizure Prediction Based on Deep Pairwise Representation Alignment of Epileptic EEG Signals

Multi-Feature Fusion Based Convolutional Neural Networks for EEG Epileptic Seizure Prediction in Consumer Internet of Things

Multi-Feature Fusion Based Convolutional Neural Networks for EEG Epileptic Seizure Prediction in Consumer Internet of Things