Automatic detection of non-convulsive seizures: A reduced complexity approach

Nonconvulsive status epilepticus is a condition where the patient is exposed to abnormally prolonged epileptic seizures without evident physical symptoms. Since thesecontinuous seizures may cause permanent brain damage, it constitutes a medical emergency. This paper proposes a method to detect nonconvulsive seizures for a further nonconvulsive status epilepticus diagnosis. To differentiate between the normal and seizure electroencephalogram (EEG), a K-Nearest Neighbor, a Radial Basis Support Vector Machine, and a Linear Discriminant Analysis classifier are used. The classifier features are obtained from the Canonical Polyadic Decomposition (CPD) and Block Term Decomposition of the EEG data represented as third order tensor. To expand the EEG into a tensor, Wavelet or Hilbert-Huang transform are used. The algorithm is tested on a scalp EEG database of 139 seizures of different duration. The experimental results suggest that a Hilbert-Huang tensor representation and the CPD analysis provide the most suitable framework for nonconvulsive seizure detection. The Radial Basis Support Vector Machine classifier shows the best performance with sensitivity, specificity, and accuracy values over 98%. A rough comparison with other methods proposed in the literature shows the superior performance of the proposed method for nonconvulsive epileptic seizure detection.

Non-convulsive seizures (NCS) are common among critically ill children with acute encephalopathy. Continuous electroencephalogram (CEEG) monitoring is an indispensable tool to detect NCS, which is essential to guiding management and assessing prognosis. Risk factors for NCS are highest in pediatric intensive care unit (PICU) patients with altered mental status (AMS) and a recently witnessed clinical seizure, acute changes on neuroimaging, and/or interictal abnormalities on CEEG. Screening for at least 24 hours in at risk pediatric populations is ideal, but around half of NCS may be detected within the first hour. Rapid treatment of prolonged seizures or status epilepticus is critical, as higher seizure burdens have been associated with poorer outcomes in critically ill children. This review integrates current information on critically ill children with AMS and the use of CEEGs, risk factors for NCS, duration of CEEG monitoring, and how the detection of NCS impacts management and outcomes.

Continuous electroencephalography in a mixed non-neurological intensive care population, an observational study

Electroencephalography in stroke Care: From acute monitoring to Post-Stroke epilepsy.

American Journal of HematologyVolume 70, Issue 3 p. 266-267 Letters and CorrespondenceFree Access Autosomal non-dominant hereditary spherocytosis: Does it occur in India? Akhilesh S. Karan, Akhilesh S. Karan Department of Haematology, All-India Institute of Medical Sciences, New Delhi, IndiaSearch for more papers by this authorRenu Saxena, Renu Saxena Department of Haematology, All-India Institute of Medical Sciences, New Delhi, IndiaSearch for more papers by this authorV.P. Choudhry, V.P. Choudhry Department of Haematology, All-India Institute of Medical Sciences, New Delhi, IndiaSearch for more papers by this author Akhilesh S. Karan, Akhilesh S. Karan Department of Haematology, All-India Institute of Medical Sciences, New Delhi, IndiaSearch for more papers by this authorRenu Saxena, Renu Saxena Department of Haematology, All-India Institute of Medical Sciences, New Delhi, IndiaSearch for more papers by this authorV.P. Choudhry, V.P. Choudhry Department of Haematology, All-India Institute of Medical Sciences, New Delhi, IndiaSearch for more papers by this author First published: 27 June 2002 https://doi.org/10.1002/ajh.10122Citations: 2AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Citing Literature Volume70, Issue3July 2002Pages 266-267 ReferencesRelatedInformation

Automatic epileptic seizure detection technologies for clinical diagnosis mainly rely on electroencephalogram (EEG) recordings, which are immensely useful tools for epileptic location and identification. Currently, traditional seizure detection methods based only on single-view features have great limitations for the typical dynamic and nonlinear EEG signals. An objective of this paper is to investigate the effect of multiview feature selection and multilevel spectral analysis methods on the identification of the EEG signals for seizure detection. Here, multiview features are extracted from time domain, frequency domain, and information theory to collect adequate information of EEG signals. And a feature selection algorithm based on particle swarm optimization (PSO) is proposed for automatic seizure detection. Moreover, due to the different frequency components of the EEG signals, they are divided into four kinds of brain waves for multilevel spectral analysis. The effect of these four rhythm waves on seizure detection is compared. Three well-known classifiers are employed to classify EEG signals concerning seizure or nonseizure events. The result shows that the average accuracy, specificity, and sensitivity of classification with the CHB-MIT database are 98.14%, 98.64%, and 96.79%, respectively. The application of the PSO-based feature selection method for automatic seizure detection improves accuracy by 5.99% with the SVM classifier. Compared with the state-of-the-art methods, the proposed method has superior competence with high performance for automatic seizure detection. It is further shown that the feature selection method is an indispensable step in seizure detection. With PSO-based feature selection and multilevel spectral analysis, the θ wave in the frequency range of 4-7 Hz shows better performance in the identification of EEG signals and is more suitable for the proposed method. The PSO-based feature selection algorithm for automatic seizure detection can be a useful assistant tool for clinical diagnosis.

Nonconvulsive seizures (NCS) and nonconvulsive status epilepticus (NCSE) are frequently observed in human patients. Diagnosis of NCS and NCSE only can be achieved by the use of electroencephalography (EEG). Electroencephalographic monitoring is rare in veterinary medicine and consequently there is limited data on frequency of NCS and NCSE. Determine the prevalence of NCS and NCSE in dogs and cats with a history of cluster seizures. Twenty-six dogs and 12 cats. Retrospective study. Medical records of dogs and cats with cluster seizures were reviewed. Electroencephalography was performed in order to identify electrographic seizure activity after the apparent cessation of convulsive seizure activity. Nonconvulsive seizures were detected in 9 dogs and 2 cats out of the 38 patients (29%). Nonconvulsive status epilepticus was detected in 4 dogs and 2 cats (16%). Five patients had both NCS and NCSE. A decreased level of consciousness was evident in 6/11 patients with NCS, 3/6 also had NCSE. Mortality rate for patients with NCS (73%) and NCSE (67%) was much higher than that for patients with no seizure activity on EEG (27%). Prevalence of NCS and NCSE is high in dogs and cats with a history of cluster seizures. Nonconvulsive seizures and NCSE are difficult to detect clinically and are associated with higher in hospital mortality rates. Results indicate that prompt EEG monitoring should be performed in dogs and cats with cluster seizures.

Epilepsy is one of the most common neurological disorders. Epileptic seizures are the manifestation of abnormal hypersynchronous discharges of cortical neurons that impair brain function. Most of the people affected can be treated successfully with drug therapy or neurosurgical procedures. But there is still a large group of epilepsy patients that continues to have frequent seizures. For these patients automated detection of epileptic seizures can be of great clinical importance. Seizure detection can influence daily care or can be used to evaluate treatment effect. Furthermore automated detection can be used to trigger an alarm system during seizures that might be harmful to the patient. This thesis focusses on accelerometry (ACM) based seizure detection. A detailed overview is provided, on the perspectives for long-term epilepsy monitoring and automated seizure detection. The value of accelerometry for seizure detection is shown by means of a clinical evaluation and the first steps are made towards automatic detection of epileptic seizures based on ACM. With accelerometers movements are recorded. A large group of epileptic seizures manifest in specific movement patterns, so called motor seizures. Chapter 2 of this thesis presents an overview of the published literature on available methods for epileptic seizure detection in a long-term monitoring context. Based on this overview recommendations are formulated that should be used in seizure detection research and development. It is shown that for seizure detection in home environments, other sensor modalities besides EEG become more important. The use of alternative sensor modalities (such as ACM) is relatively new and so is the algorithm development for seizure detection based on these measures. It was also found that for both the adaptation of existing techniques and the development of new algorithms, clinical information should be taken more into account. The value of ACM for seizure detection is shown by means of a clinical evaluation in chapter 3. Here 3-D ACM- and EEG/video-recordings of 18 patients with severe epilepsy are visually analyzed. A striking outcome presented in this chapter is the large number of visually detected seizures versus the number of seizures that was expected on forehand and the number of seizures that was observed by the nurses. These results underscore the need for an automatic seizure detection device even more, since in the current situation many seizures are missed and therefore it is possible that patients do not get the right (medical) treatment. It was also observed that 95% of the ACM-patterns during motor seizures are sequences of three elementary patterns: myoclonic, tonic and clonic patterns. These characteristic patterns are a starting point for the development of methods for automated seizure detection based on ACM. It was decided to use a modular approach for the detection methodology and develop algorithms separately for motor activity in general, myoclonic seizures and tonic seizures. Furthermore, clinical information is incorporated in the detection methodology. Therefore in this thesis features were used that are either based on the shape of the patterns of interest as described in clinical practice (chapter 4 and 7), or the features were based on a physiological model with parameters that are related to seizure duration and intensity (chapter 5 and 6). In chapter 4 an algorithm is developed to distinguish periods with and without movement from ACM-data. Hence, when there is no movement there is no motor seizure. The amount of data that needs further analysis for seizure detection is thus reduced. From 15 ACM-signals (measured on five positions on the body), two features are computed, the variance and the jerk. In the resulting 2-D feature space a linear threshold function is used for classification. For training and testing the algorithm ACM data along with video data are used from nocturnal recordings in mentally retarded patients with severe epilepsy. Using this algorithm the amount of data that needs further analysis is reduced considerably. The results also indicate that the algorithm is robust for fluctuations across patients and thus there is no need for training the algorithm for each new patient. For the remaining data it needs to be established whether the detected movement is seizure related or not. To this purpose a model is developed for the accelerometer pattern measured on the arm during a myoclonic seizure (chapter 5). The model consists of a mechanical and an electrophysiological part. This model is used as a matched wavelet filter to detect myoclonic seizures. In chapter 6 the model based wavelet is compared to three other time frequency measures: the short time Fourier transform, the Wigner distribution and the continuous wavelet transform using a Daubechies wavelet. All four time-frequency methods are evaluated in a linear classification setup. Data from mentally retarded patients with severe epilepsy are used for training and evaluation. The results show that both wavelets are useful for detection of myoclonic seizures. On top of that, our model based wavelet has the advantage that it consists of parameters that are related to seizure duration and intensity that are physiological meaningful. Besides myoclonic seizures, the model is also useful for the detection of clonic seizures; physiologically these are repetitive myoclonic seizures. Finally for the detection of tonic seizures, in chapter 7 a set of features is studied that incorporate the mean characteristics of ACM-patterns associated with tonic seizures. Linear discriminant analysis is used for classification in the multi-dimensional feature space. For training and testing the algorithm, again data are used from recordings in mentally retarded patients with severe epilepsy. The results show that our approach is useful for the automated detection of tonic seizures based on 3-D ACM and that it is a promising contribution in a complete multi-sensor seizure detection setup.

Real-time seizure onset detection has been an active area of research in the study of epilepsy. Electroencephalography (EEG) measurements are widely used in seizure detection due to their high temporal resolution, cost-effective, and noninvasive nature. Various approaches based on machine learning are used for epileptic seizure detection, but these approaches do not explicitly reveal the underlying dynamics, require larger datasets for training, and are computationally demanding. Although high-frequency oscillations (HFOs) are the new biomarkers of epilepsy, they cannot be used with the existing data acquisition systems as they require high sampling rates and high cutoff frequency of the used filters. In this article, we present a novel approach for real-time seizure detection using high spatial frequencies. Since eigenvalues of the graph Laplacian represent spatial frequencies, we conjecture that higher eigenvalues and eigenvectors will contain the detailed information of seizure and non-seizure brain states. Hence, we have formed sub-band characteristic response vector (sub-band CRV) using weighted sum of eigenvectors corresponding to higher spatial frequencies and analyzed it over time. We have used a publicly available dataset to demonstrate the efficacy of the proposed approach. We observed that the proposed approach performs satisfactorily well in real-time automated seizure detection without requiring any kind of prior training. Moreover, our approach is not only accurate in seizure detection but also is independent of sampling rates, hence can be implemented easily in clinical realm for developing an automated seizure detection tool with the existing data acquisition systems operating at low sampling rates.

ObjectiveIn recent years, with the development of machine learning and deep learning technologies, an increasing number of research works have begun using these technologies for automatic seizure detection in EEG signals. However, existing automatic seizure detection algorithms primarily focus on the features of individual EEG channels and pay less attention to the inter-channel relationships. This results in insufficient extraction of spatiotemporal information from multi-channel EEG data, affecting the final seizure detection performance.MethodsTherefore, this paper proposes an automatic seizure detection method based on the combination of Graph Attention Networks (GAT) and Transformer networks. Specifically, GAT is used as the front end for extracting spatial features, fully leveraging the topological structure of different EEG channels. Meanwhile, the Transformer network is used as the back end to explore temporal relationships and make final decisions based on the states before and after the current moment.ResultsExperiments were conducted on the CHB-MIT and TUH datasets with ten-fold cross-validation. The final seizure detection accuracies on the two datasets were 98.62 and 98.12%, respectively, with the model’s performance surpassing or being comparable to current state-of-the-art models.ConclusionThe proposed hybrid algorithm combines the advantages of two deep learning models, fully exploring the spatiotemporal correlations between EEG channels. Experiments on public datasets demonstrate the effectiveness of this method, significantly advancing the development of automatic seizure detection.

To examine the relation between the quality of physicians and migration among alumni of All India Institute of Medical Sciences (AIIMS), New Delhi, India over the period 1989-2000. In a retrospective cohort study, data on graduates of AIIMS were collected from entrance exam qualifier lists, the AIIMS alumni directory, convocation records, the American Medical Association and informal alumni networks. The data were analysed by use of 2x2 contingency tables and logistic regression models. Nearly 54% of AIIMS graduates during 1989-2000 now reside outside India. Students admitted under the general category are twice as likely to reside abroad (95% confidence interval: 1.53-2.99) as students admitted under the affirmative-action category. Recipients of multiple academic awards were 35% more likely to emigrate than non-recipients of awards (95% confidence interval: 1.04-1.76). Multivariate analyses do not change these basic conclusions. Graduates from higher quality institutions account for a disproportionately large share of emigrating physicians. Even within high-end institutions, such as AIIMS, better physicians are more likely to emigrate. Interventions should focus on the highly trained individuals in the top institutions that contribute disproportionately to the loss of human resources for health. Our findings suggest that affirmative-action programmes may have an unintended benefit in that they may help retain a subset of such personnel.

Nonconvulsive seizures (NCS) and nonconvulsive status epilepticus (NCSE) are electrographic seizures (ESz) that are not associated with overt clinical seizure activity. NCS are distinct ESz, whereas NCSE has ongoing, continuous electrographic seizure activity. Both are common in critically ill patients admitted to hospital intensive care units (ICUs), and studies have shown that about 20% of ICU patients undergoing continuous electroencephalography (cEEG) monitoring will have NCS/NCSE. Although the treatment for convulsive SE is well established, there is no clear consensus for the treatment of NCS/NCSE. Antiepileptic drugs (AEDs), such as phenytoin (PHT) and fosphenytoin (fPHT), used in convulsive SE are also used to treat NCS/NCSE despite lack of data for their appropriateness for these conditions. Recent studies have shown that very aggressive treatment of NCSss/NCSE can lead to worse outcomes because the AEDs used can have significant adverse effects. Recently, several intravenous (IV) AEDs have become available for substitution therapy when their oral use is not possible. There are retrospective case reports and case series that suggest that these AEDs may be beneficial for treatment of NCS/NCSE. The Treatment of Recurrent Electrographic Nonconvulsive Seizures (TRENdS) Study will compare the efficacy and tolerability of fPHT and lacosamide in patients having NCS as noted by cEEG monitoring. The study is currently open to recruitment and has 13 sites in the United States. A total of 200 subjects will be randomized, 100 to each treatment arm.

Non-immobilizers put to the test: F6 and the GABAA receptor

Epilepsy is a chronic neurological disorder that affects more than 50 million people world wide, characterized by recurrent seizures (World Health Organization [WHO], 2006). An epileptic seizure is a transient occurrence of signs and/or symptoms due to abnormal excessive or synchronous neuronal activity in the brain (Fisher et al., 2005 & Berg et al., 2010). This electrical hyperactivity can have its source in different parts of the brain and produces physical symptoms such as short periods of inattention and loose of memory, a sensory hallucination, or a whole-body convulsion. The frequency of these events can vary from one in a year to several in a day. The majority of the patients suffer from unpredictable, persistent and frequent seizures which limit the independence of an individual, increase the risk of serious injury and mobility, and result in both social isolation and economic hardship (Friedman & Gilliam, 2010). In addition, the patients with epilepsy have an increased mortality risk of approximately 2 to 3 times that of the general population (Ficker, 2000). The first line of treatment for epilepsy is with multiple anti-epileptic drugs and it is effective in about 70% of the cases. From the 30% remaining affected individuals only less than 10% could benefit from surgical therapy leaving a 20% of the total of people with epilepsy who will continue suffering sudden, incontrollable seizures and for whom other forms of treatment are being investigated (Theodore & Ficker, 2004; WHO, 2006). For any of the reasons exposed before the seizure detection is an important component in the diagnosis of epilepsy and for the seizures control. In the clinical practice this detection basically involves visual scanning of Electroencephalogram (EEG) long recordings by the physicians in order to detect and classify the seizure activity present in the EEG signal. Usually these are multichannel records of 24 to 72 hours length which implies a very time consuming task and it is also kwon that the conclusions are very subjective so disagreement between physicians are not rare. The seek here is to detect automatically in long term EEG records those segments of the signal that present epileptic seizures for the shake of reducing the high amount of information to be analyzed by the neurologists. Thus them could focus their attention in these part of the information so a more precisely and quick diagnosis can be made. Seizure detection is also a useful tool for treatments such us timely drug delivery, electrical stimulation and seizure alert systems. Automated seizure detection, quantification and recognition have been of interest of the biomedical community researchers since the 1970s. In some initial works a number of

Epileptic seizure detection using EEGs is a heavy workload of traditional visual inspection for diagnosing epilepsy. Therefore, more and more research on automatic seizure detection have been developed in recent years. The appropriate feature extraction method and efficient classifier are recognized to be crucial in the successful realization. In this paper, we first create a novel feature extraction method based on fuzzy conditional Renyi entropy (FCRE), which focuses on characterizing the physiological complexity of the underlying signal transduction processes. Then we propose an automatic seizure detection method FCRE-ELM, which integrates the fuzzy conditional Renyi entropy FCRE and extreme learning machine (ELM) for differentiating seizure EEGs from non-seizure EEGs or normal EEGs. The experimental results on the open EEG database have shown that the proposed method FCRE-ELM does a good job in epileptic seizure detection while preserving the efficiency and simplicity.