Automatic Seizure Detection System Research Articles

Phase spectrogram of EEG from S-transform Enhances epileptic seizure detection

Automatic epilepsy seizure detection has high clinical value since it can alleviate the burden of manual monitoring. Nevertheless, it remains a technically challenging task to achieve a reliable system. In this study, we investigated the significance of the phase information in EEG signals in seizure detection using machine learning. We used the Stockwell transform (S-transform) to extract both phase and power spectra of the EEG signal in epilepsy patients. A dual-stream convolution neural network (CNN) model was adopted as the classifier, which takes both spectra as inputs. We demonstrated that the phase input allows the CNN model to capture the heightened phase synchronization among EEG channels in seizure and add network attention to both the low- and high-frequency features of the inputs in the CHB-MIT and Bonn databases. We improved the detection AUC-ROC by 6.68% on the CHB-MIT database when adding phase inputs to the power inputs. By incorporating a channel fusion post-processing to the outputs of this CNN model, it achieves a sensitivity and specificity of 79.59% and 92.23%, respectively, surpassing some of the state-of-the-art methods. Our results show that the phase inputs are useful features in seizure detection. This discovery has significant implications for improving the effectiveness of automatic seizure detection systems.

High-precision stereotactic irradiation for focal drug-resistant epilepsy versus standard treatment: a randomized waitlist-controlled trial (the PRECISION trial)

IntroductionThe standard treatment for patients with focal drug-resistant epilepsy (DRE) who are not eligible for open brain surgery is the continuation of anti-seizure medication (ASM) and neuromodulation. This treatment does not cure epilepsy but only decreases severity. The PRECISION trial offers a non-invasive, possibly curative intervention for these patients, which consist of a single stereotactic radiotherapy (SRT) treatment. Previous studies have shown promising results of SRT in this patient population. Nevertheless, this intervention is not yet available and reimbursed in the Netherlands. We hypothesize that: SRT is a superior treatment option compared to palliative standard of care, for patients with focal DRE, not eligible for open surgery, resulting in a higher reduction of seizure frequency (with 50% of the patients reaching a 75% seizure frequency reduction at 2 years follow-up).MethodsIn this waitlist-controlled phase 3 clinical trial, participants are randomly assigned in a 1:1 ratio to either receive SRT as the intervention, while the standard treatments consist of ASM continuation and neuromodulation. After 2-year follow-up, patients randomized for the standard treatment (waitlist-control group) are offered SRT. Patients aged ≥ 18 years with focal DRE and a pretreatment defined epileptogenic zone (EZ) not eligible for open surgery will be included. The intervention is a LINAC-based single fraction (24 Gy) SRT treatment. The target volume is defined as the epileptogenic zone (EZ) on all (non) invasive examinations. The seizure frequency will be monitored on a daily basis using an electronic diary and an automatic seizure detection system during the night. Potential side effects are evaluated using advanced MRI, cognitive evaluation, Common Toxicity Criteria, and patient-reported outcome questionnaires. In addition, the cost-effectiveness of the SRT treatment will be evaluated.DiscussionThis is the first randomized trial comparing SRT with standard of care in patients with DRE, non-eligible for open surgery. The primary objective is to determine whether SRT significantly reduces the seizure frequency 2 years after treatment. The results of this trial can influence the current clinical practice and medical cost reimbursement in the Netherlands for patients with focal DRE who are not eligible for open surgery, providing a non-invasive curative treatment option.Trial registrationClinicaltrials.gov Identifier: NCT05182437. Registered on September 27, 2021.

Pediatric and Adolescent Seizure Detection: A Machine Learning Approach Exploring the Influence of Age and Sex in Electroencephalogram Analysis

Background: Epilepsy, a prevalent neurological disorder characterized by recurrent seizures affecting an estimated 70 million people worldwide, poses a significant diagnostic challenge. EEG serves as an important tool in identifying these seizures, but the manual examination of EEGs by experts is time-consuming. To expedite this process, automated seizure detection methods have emerged as powerful aids for expert EEG analysis. It is worth noting that while such methods are well-established for adult EEGs, they have been underdeveloped for pediatric and adolescent EEGs. This study sought to address this gap by devising an automatic seizure detection system tailored for pediatric and adolescent EEG data. Methods: Leveraging publicly available datasets, the TUH pediatric and adolescent EEG and CHB-MIT EEG datasets, the machine learning-based models were constructed. The TUH pediatric and adolescent EEG dataset was divided into training (n = 118), validation (n = 19), and testing (n = 37) subsets, with special attention to ensure a clear demarcation between the individuals in the training and test sets to preserve the test set’s independence. The CHB-MIT EEG dataset was used as an external test set. Age and sex were incorporated as features in the models to investigate their potential influence on seizure detection. Results: By leveraging 20 features extracted from both time and frequency domains, along with age as an additional feature, the method achieved an accuracy of 98.95% on the TUH test set and 64.82% on the CHB-MIT external test set. Our investigation revealed that age is a crucial factor for accurate seizure detection in pediatric and adolescent EEGs. Conclusion: The outcomes of this study hold substantial promise in supporting researchers and clinicians engaged in the automated analysis of seizures in pediatric and adolescent EEGs.

Vagus Nerve Stimulation Therapy for Drug-Resistant Epilepsy in Children—A Literature Review

Vagus nerve stimulation (VNS) is a palliative treatment for drug-resistant epilepsy (DRE) that has been in use for over two decades. VNS suppresses epileptic seizures, prevents emotional disorders, and improves cognitive function and sleep quality, a parallel effect associated with the control of epileptic seizures. The seizure suppression rate with VNS increases monthly to annually, and the incidence of side effects reduces over time. This method is effective in treating DRE in children as well as adults, such as epilepsy associated with tuberous sclerosis, Dravet syndrome, and Lennox–Gastaut syndrome. In children, it has been reported that seizures decreased by >70% approximately 8 years after initiating VNS, and the 50% responder rate was reported to be approximately 70%. VNS regulates stimulation and has multiple useful systems, including self-seizure suppression using magnets, additional stimulation using an automatic seizure detection system, different stimulation settings for day and night, and an automatic stimulation adjustment system that reduces hospital visits. VNS suppresses seizures and has beneficial behavioral effects in children with DRE. This review describes the VNS system, the mechanism of the therapeutic effect, the specific stimulation adjustment method, antiepileptic effects, and other clinical effects in patients with childhood DRE.

Automatic epileptic seizure detection based on EEG using a moth-flame optimization of one-dimensional convolutional neural networks.

Frequent epileptic seizures can cause irreversible damage to the brains of patients. A potential therapeutic approach is to detect epileptic seizures early and provide artificial intervention to the patient. Currently, extracting electroencephalogram (EEG) features to detect epileptic seizures often requires tedious methods or the repeated adjustment of neural network hyperparameters, which can be time- consuming and demanding for researchers. This study proposes an automatic detection model for an EEG based on moth-flame optimization (MFO) optimized one-dimensional convolutional neural networks (1D-CNN). First, according to the characteristics and need for early epileptic seizure detection, a data augmentation method for dividing an EEG into small samples is proposed. Second, the hyperparameters are tuned based on MFO and trained for an EEG. Finally, the softmax classifier is used to output EEG classification from a small-sample and single channel. The proposed model is evaluated with the Bonn EEG dataset, which verifies the feasibility of EEG classification problems that involve up to five classes, including healthy, preictal, and ictal EEG from various brain regions and individuals. Compared with existing advanced optimization algorithms, such as particle swarm optimization, genetic algorithm, and grey wolf optimizer, the superiority of the proposed model is further verified. The proposed model can be implemented into an automatic epileptic seizure detection system to detect seizures in clinical applications.

Seizure detection using dynamic memristor-based reservoir computing and leaky integrate-and-fire neuron for post-processing

Epilepsy is a prevalent neurological disorder, rendering the development of automated seizure detection systems imperative. While complex machine learning models are powerful, their training and hardware deployment remain challenging. The reservoir computing system offers a low-cost solution in terms of both hardware requirements and training. In this paper, we introduce a compact reservoir computing system for seizure detection, based on the α-In2Se3 dynamic memristors. Leaky integrate-and-fire neurons are used for post-processing the output of the system, and experimental results indicate their effectiveness in suppressing erroneous outputs, where both accuracy and specificity are enhanced by over 2.5%. The optimized compact reservoir system achieves 96.40% accuracy, 86.34% sensitivity, and 96.56% specificity in seizure detection tasks. This work demonstrates the feasibility of using reservoir computing for seizure detection and shows its potential for future application in extreme edge devices.

EEG-based epileptic seizure detection using binary dragonfly algorithm and deep neural network

Electroencephalogram (EEG) is one of the most common methods used for seizure detection as it records the electrical activity of the brain. Symmetry and asymmetry of EEG signals can be used as indicators of epileptic seizures. Normally, EEG signals are symmetrical in nature, with similar patterns on both sides of the brain. However, during a seizure, there may be a sudden increase in the electrical activity in one hemisphere of the brain, causing asymmetry in the EEG signal. In patients with epilepsy, interictal EEG may show asymmetric spikes or sharp waves, indicating the presence of epileptic activity. Therefore, the detection of symmetry/asymmetry in EEG signals can be used as a useful tool in the diagnosis and management of epilepsy. However, it should be noted that EEG findings should always be interpreted in conjunction with the patient's clinical history and other diagnostic tests. In this paper, we propose an EEG-based improved automatic seizure detection system using a Deep neural network (DNN) and Binary dragonfly algorithm (BDFA). The DNN model learns the characteristics of the EEG signals through nine different statistical and Hjorth parameters extracted from various levels of decomposed signals obtained by using the Stationary Wavelet Transform. Next, the extracted features were reduced using the BDFA which helps to train DNN faster and improve its performance. The results show that the extracted features help to differentiate the normal, interictal, and ictal signals effectively with 100% accuracy, sensitivity, specificity, and F1 score with a 13% selected feature subset when compared to the existing approaches.

Automated diagnosis of epileptic seizures using EEG image representations and deep learning

BackgroundThe identification of seizure and its complex waveforms in electroencephalography (EEG) through manual examination is time consuming, tedious, and susceptible to human mistakes. These issues have prompted the design of an automated seizure detection system that can assist the neurophysiologists by providing a fast and accurate analysis. MethodsExisting automated seizure detection systems are either machine learning based or deep learning based. Machine learning based algorithms employ handcrafted features with sophisticated feature selection approaches. As a result of which their performance varies with the choice of the feature extraction and selection techniques employed. On the other hand, deep learning-based methods automatically deduce the best subset of features required for the categorization task but they are computationally expensive and lacks generalization on clinical EEG datasets. To address the above stated limitations and motivated by the advantage of continuous wavelet transform's (CWT) in elucidating the non-stationary nature of the EEG signals in a better way, we propose an approach based on EEG image representations (constructed via applying WT at different scale and time intervals) and transfer learning for seizure detection. Firstly, the pre-trained model is fine-tuned on the EEG image representations and thereafter features are extracted from the trained model by performing activations on different layers of the network. Subsequently, the features are passed through a Support Vector Machine (SVM) for categorization using a 10-fold data partitioning scheme. Results and comparison with existing methodsThe proposed mechanism results in a ceiling level of classification performance (accuracy=99.50/98.67, sensitivity=100/100 & specificity=99/96) for both the standard and the clinical dataset that are better than the existing state-of-the art works. ConclusionThe rapid advancement in the field of deep learning has created a paradigm shift in automated diagnosis of epilepsy. The proposed tool has effectually marked the relevant EEG segments for the clinician to review thereby reducing the time burden in scanning the long duration EEG records.

Evaluation of Automated Seizure Detection Measurements in Cardiac Arrest Patients Undergoing Ceribell Rapid-EEG (S30.009)

<h3>Objective:</h3> To retrospectively evaluate the performance of the Clarity™ automated seizure detection system (Ceribell, Inc) for the detection of seizures and status epilepticus in cardiac arrest patients. <h3>Background:</h3> In cardiac arrest patients who remain comatose after return of spontaneous circulation, seizures and EEG abnormalities are common. Thus, guidelines recommend urgent EEG initiation for evaluation of seizures in this population. Point-of-care EEG systems, such as Ceribell™ Rapid-EEG, allow for prompt initiation of EEG monitoring, albeit through a limited-channel montage. Rapid-EEG incorporates an automated seizure detection software (Clarity™) to measure seizure burden in real time and alert clinicians when a high seizure burden, consistent with possible status epilepticus, is identified. External validation of Clarity is still needed. <h3>Design/Methods:</h3> This study was a retrospective review of Ceribell Rapid-EEG recordings from all the patients who were admitted to the medical intensive care unit at Kent Hospital (Warwick, RI, USA) between 6/1/2021 and 3/18/2022 for management after cardiac arrest and who underwent Rapid-EEG monitoring as part of their routine clinical care (n=21). Board-certified epileptologists identified events that met criteria for seizures or status epilepticus, as per the 2021 American Clinical Neurophysiology Society’s Standardized Critical Care EEG Terminology, and evaluated any seizure burden detections generated by Clarity. <h3>Results:</h3> In this study, 4 of 21 cardiac arrest patients (19.0%) who underwent Rapid-EEG monitoring had multiple electrographic seizures, and 2 of those patients (9.5%) had electrographic status epilepticus within the first 24 hours of the study. None of these ictal abnormalities were detected by the Clarity seizure detection system. Clarity showed 0% seizure burden during all these events. <h3>Conclusions:</h3> The presence of frequent seizures and/or status epilepticus may go undetected by currently available automated seizure detection systems. Timely and careful review of all raw Rapid-EEG recordings by a qualified human EEG reader is necessary to guide clinical care. <b>Disclosure:</b> Dr. Villamar has a non-compensated relationship as a Member of Editorial Board with Neurology: Clinical Practice that is relevant to AAN interests or activities. Dr. Villamar has a non-compensated relationship as a Member of Editorial Board with The Neurohospitalist that is relevant to AAN interests or activities. The institution of Dr. Ayub has received research support from Brown Physicians Incorporated. Dr. Koenig has received personal compensation in the range of $5,000-$9,999 for serving on a Speakers Bureau for Fuji SonoSite. Dr. Koenig has received personal compensation in the range of $5,000-$9,999 for serving on a Speakers Bureau for Medscape.

Barnes–Hut approximation based accelerating t-SNE for seizure detection

Background:Automatic detection of epileptic seizures is critical in the paradigm of epilepsy diagnosis and in relieving the cumbersome visual inspection of electroencephalogram (EEG) recordings. A speedy algorithm could help in more reliable monitoring and detection of seizures. Methods and materials:In this study, we aim to provide an EEG-based seizure detection system with computational efficiency and improved performance. In the proposed work, many features including temporal, spectral, and non-linear features from each intrinsic mode function (IMF) of empirical mode decomposition (EMD) have been used. Barnes–Hut approximation-based t-stochastic neighborhood embedding (bh t-SNE) was explored for the first time to observe the reduction in computational time (CT) period in the automatic seizure detection system. Three classes of widely-used EEG Bonn datasets were used to assess the performance of the proposed method. Results:The proposed Barnes–Hut-based accelerating t-SNE along with SVM and KNN reduced more than half of the classification time with the same accuracy. The classifier takes 2.147±0.1 s for SVM and 1.216±0.1 s for KNN without the proposed t-SNE and 1.31±0.1 s for SVM and 0.736±0.1 s for KNN (at the trade-off parameter θ=0.5) with the proposed Barnes–hut based t-SNE (bh t-SNE) at an accuracy of 100%. Conclusions: The findings of the experimental work indicate that the proposed method is effective in reducing the computational time while maintaining the required efficacy. As a result, the inclusion of these algorithms in hardware might prove to be effective in assisting neurologists in detecting seizures.

An effectual IOT coupled EEG analysing model for continuous patient monitoring

A persistent neurological condition known as epilepsy is characterized by aberrant brain electrical activity. Epilepsy is a disorder characterized by sporadic symptoms and aberrant electrical brain activity that is spasmodic. Such aberrations are supported by non-stationary, non-linear, and multidimensional clinical data. Such data processing and analysis present a variety of computing research difficulties. One of the most often used signals obtained from the human brain in both science and therapeutic settings is the electroencephalogram (EEG). It is an effective resource for offering insightful knowledge of the mechanics of the brain. EEG signal analyses that are precise and thorough are crucial in the diagnosis of brain diseases like epilepsy. The EEG signal is well-liked in the field of biomedical study because of its notable temporal solution, non-invasive recording setup, and convenience with minimal costs. Currently, skilled neurologists make a diagnosis by visually examining EEG information to determine the most likely course of action. It takes time and is error-prone to visually inspect high-dimensional and non-stationary EEG recordings. An automated seizure detector is a crucial tool for better understanding how seizures are generated as well as a relief for medical experts who become exhausted when viewing a continuous, massive, long-term recording. As it records from at least 16 channels, the multichannel automatic seizure detection system is large. It is difficult to use such a method on distantly located subjects. The suggested system gathers data from IoT devices, and patient history-related electronic clinical data that are saved in the cloud were subjected to predictive analytics. The Bi-LSTM-based intelligent healthcare system for tracking and precisely forecasting brain risk. The trade-off between the current system and the current methods is anticipated in the results section.

Seizure detection algorithm based on improved functional brain network structure feature extraction

Epilepsy is one of the most common neurological disorders. Accurate detection of epileptic seizures is essential for treatment. A seizure detection method with the structure of functional brain network, time, and frequency multi-domain features is presented in this paper. Pearson correlation coefficient (PCC) and mutual information (MI) can characterize the correlation between channels of EEG, and be used to construct PCC and MI combined functional brain networks (PMNet). Then the complex network theory is used to calculate the structure features of PMNet to explore whether it reflects seizure or not. After extraction of the multi-domain features, principal component analysis (PCA) is used to eliminate irrelevant feature information before feeding the features into the support vector machine (SVM) classification method. The method is tested on two open datasets (CHB-MIT and SIENA) and verified on the West syndrome (WS) dataset from the Children’s Hospital, Zhejiang University School of Medicine. The results demonstrate that the method based on the structure of functional brain networks and other multi-domain EEG features is competitive with other comparison methods. Furthermore, base on the method with brain network features, this paper has developed a simple automatic seizure detection system in hospital applications, which can reduce the workload of EEG technicians in reading EEG and marking seizures.

Deep-learning-based seizure detection and prediction from electroencephalography signals.

Electroencephalography (EEG) is among the main tools used for analyzing and diagnosing epilepsy. The manual analysis of EEG must be conducted by highly trained clinicians or neuro-physiologists; a process that is considered to have a comparatively low inter-rater agreement. Furthermore, the new data interpretation consumes an excessive amount of time and resources. Hence, an automatic seizure detection and prediction system can improve the quality of patient care in terms of shortening the diagnosis period, reducing manual errors, and automatically detecting debilitating events. Moreover, for patient treatment, it is important to alert the patients of epilepsy seizures prior to seizure occurrence. Various distinguished studies presented good solutions for two-class seizure detection problems with binary classification scenarios. To deal with these challenges, this paper puts forward effective approaches for EEG signal classification for normal, pre-ictal, and ictal activities. Three models are presented for the classification task. Two of them are patient-specific, while the third one is patient non-specific, which makes it better for the general classification tasks. The two-class classification is implemented between normal and pre-ictal activities for seizure prediction and between normal and ictal activities for seizure detection. A more generalized three-class classification framework is considered to identify all EEG signal activities. The first model depends on a Convolutional Neural Network (CNN) with residual blocks. It contains thirteen layers with four residual learning blocks. It works on spectrograms of EEG signal segments. The second model depends on a CNN with three layers. It also works on spectrograms. On the other hand, the third model depends on Phase Space Reconstruction (PSR) to eliminate the limitations of the spectrograms used in the first models. A five-layer CNN is used with this strategy. The advantage of the PSR is the direct projection from the time domain, which keeps the main trend of different signal activities. The third model deals with all signal activities, and it was tested for all patients of the CHB-MIT dataset. It has a superior performance compared to the first models and the state-of-the-art models.

Automatic seizure detection and seizure pattern morphology

ObjectiveWe studied the influence of seizure pattern morphology on detection rate and detection delay of an automatic seizure detection system. We correlated seizure pattern morphology with seizure onset zone and assessed the influence of seizure onset zone on the performance of the seizure detection system. MethodsWe analyzed 10.000 hours of EEG in 129 patients, 193 seizures in 67 patients were included in the final analysis. Seizure pattern morphologies were classified as rhythmic activity (alpha, theta and delta), paroxysmal fast activity, suppression of activity, repetitive epileptiform and arrhythmic activity. The seizure detection system EpiScan was compared with visual analysis. ResultsDetection rates were significantly higher for rhythmic and repetitive epileptiform activities than for paroxysmal fast activity. Seizure patterns significantly correlated with seizure onset zone. Detection rate was significantly higher in temporal lobe (TL) seizures than in frontal lobe (FL) seizures. Detection delay tended to be shorter in seizures with rhythmic alpha or theta activity. TL seizures were significantly more often detected within 10 seconds than FL seizures. ConclusionsSeizure morphology is critical for optimization of automatic seizure detection algorithms. SignificanceThis study is unique in exploring the influence of seizure pattern morphology on automatic seizure detection and can help future research on seizure detection in epilepsy.

Neuromorphic Architecture Accelerated Automated Seizure Detection in Multi-Channel Scalp EEG.

Epileptic focal seizures can be localized in the brain using tracer injections during or immediately after the incidence of a seizure. A real-time automated seizure detection system with minimal latency can help time the injection properly to find the seizure origin accurately. Reliable real-time seizure detection systems have not been clinically reported yet. We developed an anomaly detection-based automated seizure detection system, using scalp-electroencephalogram (EEG) data, which can be trained using a few seizure sessions, and implemented it on commercially available hardware with parallel, neuromorphic architecture—the NeuroStack. We extracted nonlinear, statistical, and discrete wavelet decomposition features, and we developed a graphical user interface and traditional feature selection methods to select the most discriminative features. We investigated Reduced Coulomb Energy (RCE) networks and K-Nearest Neighbors (k-NN) for its several advantages, such as fast learning no local minima problem. We obtained a maximum sensitivity of and a specificity of with 5 s epoch duration. The system’s latency was 12 s, which is within most seizure event windows, which last for an average duration of 60 s. Our results showed that the CD feature consumes large computation resources and excluding it can reduce the latency to 3.6 s but at the cost of lower performance 80% sensitivity and 97% specificity. We demonstrated that the proposed methodology achieves a high specificity and an acceptable sensitivity within a short delay. Our results indicated also that individual-based RCE are superior to population-based RCE. The proposed RCE networks has been compared to SVM and ANN as a baseline for comparison as they are the most common machine learning seizure detection methods. SVM and ANN-based systems were trained on the same data as RCE and K-NN with features optimized specifically for them. RCE nets are superior to SVM and ANN. The proposed model also achieves comparable performance to the state-of-the-art deep learning techniques while not requiring a sizeable database, which is often expensive to build. These numbers indicate that the system is viable as a trigger mechanism for tracer injection.

Automatic Epileptic Seizure Detection Using Graph-Regularized Non-Negative Matrix Factorization and Kernel-Based Robust Probabilistic Collaborative Representation.

Automatic seizure detection system can serve as a meaningful clinical tool for the treatment and analysis of epilepsy using electroencephalogram (EEG) and has obtained rapid development. An automatic detection of epileptic seizure method based on kernel-based robust probabilistic collaborative representation (ProCRC) combined with graph-regularized non-negative matrix factorization (GNMF) is proposed in this work. The raw EEG signals are pre-processed through the wavelet transform to obtain time-frequency distribution of EEG signals as preliminary feature information and GNMF is further employed for dimension reduction, retaining and enhancing the productive feature information of EEG signals. Then, the test sample is represented using robust ProCRC that can decide whether the testing sample belongs to each class (seizure or non-seizure) by jointly maximizing the likelihood. In addition, the kernel trick is applied to improve the separability of non-linear high dimensional EEG signals in robust ProCRC. Finally, post-processing techniques are introduced to generate more accurate and reliable results. The average epoch-based sensitivity of 96.48%, event-based sensitivity of 93.65% and specificity of 98.55% are acquired in this method, which is evaluated on the public Freiburg EEG database.

An automated approach for electroencephalography-based seizure detection using machine learning algorithms

Epilepsy is a chronic neurological disease that causes recurrent life-threatening seizures due to irregular brain activity. The purpose of seizure detection algorithms is to detect seizures from electroencephalography (EEG) recordings accurately. The main goal of our work is to facilitate an automated seizure detection system using machine learning algorithms. We proposed a model that evaluates eight machine learning algorithms on the Bonn University dataset. Two classifiers, Random Forest and Gaussian Naive Bayes, achieve the highest accuracy of 100% with 100% sensitivity, 100% specificity, 0.01 FPR, and 0.99 AUC with feature extraction. These two algorithms also work better without using feature extraction. This performance is superior to existing seizure detection approaches and comparable to deep learning approaches. Our work comprehensively compares the traditional machine learning algorithms and reinforces the effectiveness of feature extraction. Our work contributes to aiding neurologists in making faster and more precise decisions for epilepsy treatment.

Peri‐ictal and non‐seizure EEG event detection using generated metadata

AbstractLack of open access, seizure specific database has hindered the development of Automated Seizure Detection System (ASDS) along with state‐of‐the‐art feature selection and classification methods. Available databases contain noise, artefacts, different length &amp; time EEG segments, associated comorbidities, clinical settings, and epilepsy/seizure types etc. Pre‐processing of such continuous EEG segments requires significant amount of time and may feed redundant information (with reference to a seizure event) to the classification model leading to inaccurate, real‐time seizure event detection systems. Hence, this paper proposes a metadata generation of large EEG databases (here, CHB‐MIT EEG scalp database v.1.0.0) for ASDS. We elucidate the need to generate seizure sensitive data through peri‐ictal and non‐seizure EEG segments. This paper performs multi‐variate analysis and two class (non‐seizure and seizure event) classification between these fixed length and time EEG segments from support vector machine (SVM) and k‐NN classifier. We thoroughly analysed the variation and dependence of different kernels, cost function, gamma, and degree for SVM based pipeline. The proposed pipeline has been compared with state‐of‐the‐art pipelines and has achieved good classification score. Such methods will help in development of generalised approach toward handling large EEG databases and machine learning applications for seizure detection and prediction.

Automatic detection of epileptiform potentials and seizures in the EEG

Automatic computer-based algorithms for the detection of epileptiform potentials and seizure patterns on EEG facilitate a time-saving, objective method of quantitative EEG interpretation which is available 7/24. For the automatic detection of interictal epileptiform potentials sensitivities range from 65 to 99% with false positive detections of 0,09 to 13,4 per minute. Recent studies documented equal or even better performance of automatic spike detection programs compared with experienced human EEG readers. The seizure detection problem-one of the major problems in clinical epileptology-consists of the fact that the majority of focal onset seizures with impaired awareness and of seizures arising out of sleep occur unnoticed by patients and their caregivers. Automatic seizure detection systems could facilitate objective seizure documentation and thus help to solve the seizure detection problem. Furthermore, seizure detection systems may help to prevent seizure-related injuries and sudden unexpected death in epilepsy (SUDEP), and could be an integral part of novel, seizure-triggered on-demand therapies in epilepsy. During long-term video-EEG monitoring seizure detection systems could improve patient safety, provide a time-saving objective and reproducible analysis of seizure patterns and facilitate automatic computer-based patient testing during seizures. Sensitivities of seizure detection systems range from 75 to 90% with extratemporal seizures being more difficult to detect than temporal seizures. The false positive alarm rate ranges from 0,1 to 5 per 24 hours. Finally, machine learning algorithms, especially deep learning approaches, open a new highly promising era in automatic spike and seizure detection.

A channel independent generalized seizure detection method for pediatric epileptic seizures

Background and objectiveEpilepsy the disorder of the central nervous system has its worldwide presence in roughly 50 million people as estimated by the World Health Organization. Electroencephalogram (EEG) is one of the most common and non-invasive ways of analyzing and studying the subtle changes in neuronal activity of the brain during an epileptic seizure attack. These changes can be analyzed for developing an automated system that would assert the chances of an impending seizure. As changeable nature of seizure affects the patients from having a normal life, hence progress in developing new methods will improve the quality of life and also provide assistance in the medical sector. Objective of the proposed method is to avoid EEG channel selection and use all input EEG channel features to design a generalized epileptic seizure detection framework. MethodIn this work, a long short-term memory network has been proposed that is not complex and has the capability of effectively detecting epileptic seizures from both non-invasive and invasive electroencephalogram recordings. The proposed framework is simple and effective and designed in such capacity that raw electroencephalogram signals can be used to detect seizures. Also, a generalized approach has been followed that is channel independent such that EEG signals belonging to any hemisphere of the brain can be detected effectively by the proposed architecture. ResultsThe automated seizure detection system achieved high seizure detection sensitivity of 99.9%, and a low false-positive rate of 0.003 per hour for the Children's Hospital Boston-Massachusetts Institute of Technology dataset. While for the Sleep-Wake-Epilepsy-Center of the University Department of Neurology at the Inselspital Bern dataset, the sensitivity is 99.4% and false-positive rate of 0.006 per hour. Convergence analysis of the proposed model provides a significant amount of reliability and correctness in the efficient detection of epileptic seizures. ConclusionAssessment of the proposed framework on non-invasive as well as invasive EEG signals showed that the framework worked well for different type of EEG recordings as different metrics gave satisfactory results. As the framework is simple and did not require any additional parameter optimization techniques, it reduced the processing overheads without affecting the accuracy. Hence, it can be used as an efficient method for monitoring epileptic seizures.