Identification Of Seizures Research Articles

Identification and referral of patients with refractory epilepsy from the primary to the tertiary care interface in New South Wales, Australia

ObjectivesThis mixed-method feasibility study conducted in New South Wales (NSW), Australia, aimed to explore clinical practices around the identification of patients with refractory epilepsy and referral from primary care to Tertiary Epilepsy Centers. The perceptions of general practitioners, neurologists, and adults living with refractory epilepsy were considered. MethodsFifty-two data collection events were achieved through 22 semi-structured interviews with six neurologists and 12 adults who currently have, or have had refractory epilepsy, and four family members, 10 clinical observations of patient consultations and 20 surveys with general practitioners. A thematic analysis was conducted on the qualitative data alongside assessment of observational fieldnotes and survey data. FindingsTwo main themes emerged: 1) Patient healthcare pathways and care experiences highlighted the complex and deeply contextualized experiences of both patients and healthcare professionals, from first identification of people's seizures, in primary and community care settings, to referral to Tertiary Epilepsy Centers, shedding light on a fragmented, nonstandardized referral process, influenced by both individual and shared-care practices. 2) Factors impacting referrals and patient pathways indicated that onward referral to a Tertiary Epilepsy Center is affected by the knowledge, or the lack thereof, of healthcare professionals regarding treatment options. Barriers include limited person-centered care, shared decision-making, and refractory epilepsy education for healthcare professionals, which can delay patients' disease identification and can hinder speedy referral pathways and processes, in Australia for up to 17 years. In addition, person-centered communication around care pathways is affected by relationships between clinicians, patients, and family members. ConclusionThis study has identified a noticeable lack of standardized care across epilepsy-related healthcare sectors, which recognizes a need for developing and implementing clearer epilepsy-related guidelines and Continuing Professional Development in the primary and community care settings. This, however, requires greater collaboration and commitment in the primary, community, and tertiary care sectors to address the ongoing misconceptions around professional roles and responsibilities to optimize shared-care practices. Ultimately, prioritizing person-centered care on both patients' and professionals' agendas, in order to improve satisfaction with care experiences of people living with complex epilepsy.

Deep Convolutional Neural Networks for the Accurate Identification of High-Amplitude Stereotypic Epileptiform Seizures in the Post-Hypoxic-Ischemic EEG of Preterm Fetal Sheep.

Neonatal seizures after birth may contribute to brain injury after an hypoxic-ischemic (HI) event, impaired brain development and a later life risk for epilepsy. Despite neural immaturity, seizures can also occur in preterm infants. However, surprisingly little is known about their evolution after an HI insult or patterns of expression. An improved understanding of preterm seizures will help facilitate diagnosis and prognosis and the implementation of treatments. This requires improved detection of seizures, including electrographic seizures. We have established a stable preterm fetal sheep model of HI that results in different types of post-HI seizures. These including the expression of epileptiform transients during the latent phase (0-6 h) of cerebral energy recovery, and bursts of high amplitude stereotypic evolving seizures (HAS) during the secondary phase of cerebral energy failure (∼6-72 h). We have previously developed successful automated machine-learning strategies for accurate identification and quantification of the evolving micro-scale EEG patterns (e.g. gamma spikes and sharp waves), during the latent phase. The current paper introduces, for the first time, a real-time approach that employs a 15-layer deep convolutional neural network (CNN) classifier, directly fed with the raw EEG time-series, to identify HAS in the 1024Hz and 256Hz down-sampled data in our preterm fetuses post-HI. The classifier was trained and tested using EEG segments during ∼6 to 48 hours post-HI recordings. The classifier accurately identified HAS with 98.52% accuracy in the 1024Hz and 97.78% in the 256Hz data. Clinical relevance-Results highlight the promising ability of the proposed CNN classifier for accurate identification of HI related seizures in the neonatal preterm brain, if further applied to the current 256Hz clinical recordings, in real-world.

Expanding Access to Continuous EEG Monitoring in Neonatal Intensive Care Units.

Neonatal seizures are common and difficult to identify clinically because the majority are subclinical and correct identification of electroclinical seizures based on semiology is unreliable. Therefore, continuous EEG monitoring (CEEG) is critical for seizure identification in neonates and is recommended as the gold standard method in American Clinical Neurophysiology Society guidelines. Despite these recommendations, barriers to implementing widespread CEEG exist. To expand access to CEEG for at-risk neonates, a framework for providing remote CEEG was established at two network hospital neonatal intensive care units. Utilization and clinical impact were tracked as a quality improvement study. In a 27-month period from June 2017 through September 2019, 76 neonates underwent CEEG between the two network neonatal intensive care units. Electrographic seizures occurred in about one quarter of records (18/76; 24%), though their incidence varied by CEEG indication. Care notes indicated that CEEG impacted clinical care in three quarters of cases (57/76; 75%). Continuous EEG impacted decisions to treat with anti-seizure medications in approximately one half of patients (impact: 28/57 [49%]; no impact 29/57 [51%]), and CEEG impacted prognostic discussions in approximately two thirds of patients (impact: 39/57 [68%]; no impact 18/57 [32%]). Establishment of a remote CEEG program for neonates is feasible, effective at identifying seizures, and improves the quality of care provided to neonates hospitalized at these network hospitals.

Implementation and Early Evaluation of a Quantitative Electroencephalography Program for Seizure Detection in the PICU.

To describe implementation and early evaluation of using quantitative electroencephalography for electrographic seizure detection by PICU clinician staff. Prospective observational study of electrographic seizure detection by PICU clinicians in patients monitored with quantitative electroencephalography. Quantitative electroencephalography program implementation included a continuous education and training package. Continuous quantitative electroencephalography monitoring consisted of two-channel amplitude-integrated electroencephalography, color density spectral array, and raw-electroencephalography. PICU. Children less than 18 years old admitted to the PICU during the 14-month study period and deemed at risk of electrographic seizure. None. Real time electrographic seizure detection by a PICU team was analyzed for diagnostic accuracy and promptness, against electrographic seizure identification by a trained neurophysiologist, retrospectively reading the same quantitative electroencephalography and blinded to patient details. One-hundred one of 1,510 consecutive admissions (6.7%) during the study period underwent quantitative electroencephalography monitoring. Status epilepticus (35%) and suspected hypoxic-ischemic injury (32%) were the most common indications for quantitative electroencephalography. Electrographic seizure was diagnosed by the neurophysiologist in 12% (n = 12) of the cohort. PICU clinicians correctly diagnosed all 12 patients (100% sensitivity and negative predictive value). An additional eleven patients had a false-positive diagnosis of electrographic seizure (false-positive rate = 52% [31-73%]) leading to a specificity of 88% (79-94%). Median time to detect seizures was 25 minutes (5-218 min). Delayed recognition of electrographic seizure (> 1 hr from onset) occurred in five patients (5/12, 42%). Early evaluation of quantitative electroencephalography program to detect electrographic seizure by PICU clinicians suggested good sensitivity for electrographic seizure detection. However, the high false-positive rate is a challenge. Ongoing work is needed to reduce the false positive diagnoses and avoid electrographic seizure detection delays. A comprehensive training program and regular refresher updates for clinical staff are key components of the program.

Management of Multi Organ Dysfunction in Neonatal Encephalopathy.

Neonatal Encephalopathy (NE) describes neonates with disturbed neurological function in the first post-natal days of life. NE is an overall term that does not specify the etiology of the encephalopathy although it often involves hypoxia-ischaemia. In NE, although neurological dysfunction is part of the injury and is most predictive of long-term outcome, these infants may also have multiorgan injury and compromise, which further contribute to neurological impairment and long-term morbidities. Therapeutic hypothermia (TH) is the standard of care for moderate to severe NE. Infants with NE may have co-existing immune, respiratory, endocrine, renal, hepatic, and cardiac dysfunction that require individualized management and can be impacted by TH. Non-neurological organ dysfunction not only has a negative effect on long term outcome but may also influence the efficacy of treatments in the acute phase. Post resuscitative care involves stabilization and decisions regarding TH and management of multi-organ dysfunction. This management includes detailed neurological assessment, cardio-respiratory stabilization, glycaemic and fluid control, sepsis evaluation and antibiotics, seizure identification, and monitoring and responding to biochemical and coagulation derangements. The emergence of new biomarkers of specific organ injury may have predictive value and improve the definition of organ injury and prognosis. Further evidence-based research is needed to optimize management of NE, prevent further organ dysfunction and reduce neurodevelopmental impairment.

Seizure Detection Algorithms in Critically Ill Children: A Comparative Evaluation.

To evaluate the performance of commercially available seizure detection algorithms in critically ill children. Diagnostic accuracy comparison between commercially available seizure detection algorithms referenced to electroencephalography experts using quantitative electroencephalography trends. Multispecialty quaternary children's hospital in Canada. Critically ill children undergoing electroencephalography monitoring. Continuous raw electroencephalography recordings (n = 19) were analyzed by a neurophysiologist to identify seizures. Those recordings were then converted to quantitative electroencephalography displays (amplitude-integrated electroencephalography and color density spectral array) and evaluated by six independent electroencephalography experts to determine the sensitivity and specificity of the amplitude-integrated electroencephalography and color density spectral array displays for seizure identification in comparison to expert interpretation of raw electroencephalography data. Those evaluations were then compared with four commercial seizure detection algorithms: ICTA-S (Stellate Harmonie Version 7; Natus Medical, San Carlos, CA), NB (Stellate Harmonie Version 7; Natus Medical), Persyst 11 (Persyst Development, Prescott, AZ), and Persyst 13 (Persyst Development) to determine sensitivity and specificity in comparison to amplitude-integrated electroencephalography and color density spectral array. Of the 379 seizures identified on raw electroencephalography, ICTA-S detected 36.9%, NB detected 92.3%, Persyst 11 detected 75.9%, and Persyst 13 detected 74.4%, whereas electroencephalography experts identified 76.5% of seizures using color density spectral array and 73.7% using amplitude-integrated electroencephalography. Daily false-positive rates averaged across all recordings were 4.7 with ICTA-S, 126.3 with NB, 5.1 with Persyst 11, 15.5 with Persyst 13, 1.7 with color density spectral array, and 1.5 with amplitude-integrated electroencephalography. Both Persyst 11 and Persyst 13 had sensitivity comparable to that of electroencephalography experts using amplitude-integrated electroencephalography and color density spectral array. Although Persyst 13 displayed the highest sensitivity for seizure count and seizure burden detected, Persyst 11 exhibited the best trade-off between sensitivity and false-positive rate among all seizure detection algorithms. Some commercially available seizure detection algorithms demonstrate performance for seizure detection that is comparable to that of electroencephalography experts using quantitative electroencephalography displays. These algorithms may have utility as early warning systems that prompt review of quantitative electroencephalography or raw electroencephalography tracings, potentially leading to more timely seizure identification in critically ill patients.

Atmospheric Solids Analysis Probe Coupled to a Portable Mass Spectrometer for Rapid Identification of Bulk Drug Seizures.

The emergence of ambient ionization techniques and their combination with smaller, cheaper mass spectrometers is beginning to make real the possibility of mass spectrometry measurements being made routinely outside of traditional laboratory settings. Here, we describe the development of an atmospheric solids analysis probe (ASAP) source for a commercially available miniaturized, single-quadrupole mass spectrometer and subsequent modification of the instrument to allow it to run as a deployable system; we further go on to describe the application of this instrument to the identification of the contents of drug seizures. For the drug seizure analysis, a small quantity of the material (powder, tablet, resin, etc.) was dissolved in ethanol and shaken to extract the analytes, the resulting solutions were then sampled by dipping a sealed glass capillary into the solution prior to analysis by ASAP-MS. Identification of the contents of the seizures was carried out using a NIST searching approach utilizing a bespoke spectral library containing 46 compounds representative of those most commonly encountered in UK forensic laboratories. In order to increase confidence in identification the library sample and subsequent analyses were carried out using a four-channel acquisition method; each channel in this method used a different cone voltage (15, 30, 50, and 70 V) inducing differing levels of in-source fragmentation in each channel; the match score across each channel was then used for identification. Using this developed method, a set of 50 real-life drug samples was analyzed with each of these being identified correctly using the library searching method.

Automatic Identification of Epileptic Seizures From EEG Signals Using Sparse Representation-Based Classification

Identifying seizure activities in non-stationary electroencephalography (EEG) is a challenging task, since it is time-consuming, burdensome, and dependent on expensive human resources and subject to error and bias. A computerized seizure identification scheme can eradicate the above problems, assist clinicians and benefit epilepsy research. So far, several attempts were made to develop automatic systems to help neurophysiologists accurately identify epileptic seizures. In this research, a fully automated system is presented to automatically detect the various states of the epileptic seizure. The proposed method is based on sparse representation-based classification (SRC) theory and the proposed dictionary learning using electroencephalogram (EEG) signals. Furthermore, the proposed method does not require additional preprocessing and extraction of features which is common in the existing methods. The proposed method reached the sensitivity, specificity and accuracy of 100% in 8 out of 9 scenarios. It is also robust to the measurement noise of level as much as 0 dB. Compared to state-of-the-art algorithms and other common methods, the proposed method outperformed them in terms of sensitivity, specificity and accuracy. Moreover, it includes the most comprehensive scenarios for epileptic seizure detection, including different combinations of 2 to 5 class scenarios. The proposed automatic identification of epileptic seizures method can reduce the burden on medical professionals in analyzing large data through visual inspection as well as in deprived societies suffering from a shortage of functional magnetic resonance imaging (fMRI) equipment and specialized physician.

Epilepsy seizure detection using complete ensemble empirical mode decomposition with adaptive noise

Background: Epileptic seizure detection is traditionally performed by visual observation of Electroencephalogram (EEG) signals. Owing to its onerous and time-consuming nature, seizure detection based on visual inspection hinders epilepsy diagnosis, monitoring, and large-scale data analysis in epilepsy research. So, there is a dire need of an automatic seizure detection scheme.Method: An automated scheme for epileptic seizure identification is developed in this study. Here we utilize a signal processing technique, namely-complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) for epileptic seizure identification. First, we decompose segments of EEG signals into intrinsic mode functions by CEEMDAN. The mode functions are then modeled by normal inverse Gaussian (NIG) pdf parameters. In this work, NIG modeling is employed in conjunction with CEEMDAN for epileptic seizure detection for the first time. The efficacy of the NIG parameters in the CEEMDAN domain is demonstrated by intuitive, graphical, and statistical analyses. Adaptive Boosting, an eminent ensemble learning based classification model, is implemented to perform classification.Results: Experimental outcomes suggest that the algorithmic performance of the proposed scheme is promising in all the cases of clinical significance. Comparative evaluation of algorithmic performance with the state-of-the-art schemes manifest that the seizure detection scheme proposed herein outperforms competing algorithms in terms of accuracy, sensitivity, specificity, and Cohen’s Kappa coefficient.Conclusions: Upon its implementation in clinical practice, the proposed seizure detection scheme will eliminate the onus of medical professionals and expedite epilepsy research and diagnosis.

DNA-based species identification of shark finning seizures in Southwest Atlantic: implications for wildlife trade surveillance and law enforcement

Sharks developed life history traits that make them susceptible to overfishing. This is, in turn, a risk for extinction, and several species are affected. The high price of shark fins in the international trade has triggered the widespread capture of sharks at unsustainable levels, prompting illegal and unethical practices, such as finning. To address these concerns, the present study aimed to identify species composition using molecular techniques based on DNA barcoding and DNA polymorphism on samples taken from illegal shark fin seizures conducted by the Federal Environmental Agency of Brazil. A species-specific DNA-based identification from three finning seizures in Brazil found at least 20 species from 747 shark fins, some of which were identified as endangered and protected under Brazilian legislation, while others were representative of restricted catches, according to Appendix II of CITES. In the seizure from Belem, 338 fins were identified as belonging to at least 19 different species, while in the seizure from Natal 211, fins belonging to at least 8 different species were identified. Furthermore, 198 fins from Cananeia were identified through PCR-Multiplex as belonging to Isurus oxyrinchus. These results raise concerns about the environmental and socioeconomic effects of finning on developing countries. Furthermore, this study represents the first finning evaluation from Brazil in the Southwest Atlantic, highlighting the importance of developing policies aimed toward restricting and regulating the shark trade and detecting IUU fisheries and illegal trade of endangered species, mainly in developing countries, where fisheries management, surveillance, and species-specific fisheries catch data are often sporatic.

MSBE Analysis with Frequency Spectra for Automated Identification of Epileptic Seizure

Objective-This study introduces a reliable automated seizure detection technique based on MSBE (Multi scale bubble entropy) and frequency spectral analysis. Method- This paper aims to develop a novel seizure detection technique that incorporates AM FM model for decomposition of EEG into different sub bands. In our approach, integrated feature set is constructed using multi scale bubble entropy analysis at each sub band and frequency spectral analysis at each electrode. Result-In this paper, an application of bubble entropy with different frequency parameter such as PPF and PSD is provided in order to access its stable and outstanding performance on epileptic seizer detection. The experimental results show that classification accuracy is improved with this algorithm. These finding suggest that extracted features can be used for treatment of epilepsy. Significance- This method provides greater stability and discriminative power, so this technique could be used to detect wider range of seizures.

Epilepsy Identification Based on VMD, RELIEFF Algorithm and Machine Learning Classification Techniques

Epilepsy identification is done by visual observation of electroencephalography (EEG) signals, which is more sensitive to bias and time consuming. In most of the previous research of epileptic seizure detection suffers from unsuitability and low power for processing large datasets. To eliminate aforementioned problems a computerized detection method is required to aid medical professionals. In this paper, a new technique is proposed to identify the epilepsy based on VMD, RELIEFF algorithm and machine learning approach. To investigate the proposed method performance a public EEG dataset is adopted from university hospital bonn, Germany. The technique starts with the VMD, which is used to extract the features from each EEG signal. And then RELIEFF algorithm is adopted to identify the best features. Finally to categorize the normal and epilepsy EEG signals a machine learning classification (ANN, KNN, and SVM) approach is used. The results demonstrate that the adopted method (VMD+RELIEFF+SVM) can achieve a better accuracy, shows that a commanding method to identification and classification of epileptic seizures.

Applications of advanced signal processing and machine learning in the neonatal hypoxic-ischemic electroencephalogram.

Perinatal hypoxic-ischemic-encephalopathy significantly contributes to neonatal death and life-long disability such as cerebral palsy. Advances in signal processing and machine learning have provided the research community with an opportunity to develop automated real-time identification techniques to detect the signs of hypoxic-ischemic-encephalopathy in larger electroencephalography/amplitude-integrated electroencephalography data sets more easily. This review details the recent achievements, performed by a number of prominent research groups across the world, in the automatic identification and classification of hypoxic-ischemic epileptiform neonatal seizures using advanced signal processing and machine learning techniques. This review also addresses the clinical challenges that current automated techniques face in order to be fully utilized by clinicians, and highlights the importance of upgrading the current clinical bedside sampling frequencies to higher sampling rates in order to provide better hypoxic-ischemic biomarker detection frameworks. Additionally, the article highlights that current clinical automated epileptiform detection strategies for human neonates have been only concerned with seizure detection after the therapeutic latent phase of injury. Whereas recent animal studies have demonstrated that the latent phase of opportunity is critically important for early diagnosis of hypoxic-ischemic-encephalopathy electroencephalography biomarkers and although difficult, detection strategies could utilize biomarkers in the latent phase to also predict the onset of future seizures.

Electroencephalographic seizures in critically ill children: Management and adverse events.

Guidelines recommend that encephalopathic critically ill children undergo continuous electroencephalographic (CEEG) monitoring for electrographic seizure (ES) identification and management. However, limited data exist on antiseizure medication (ASM) safety for ES treatment in critically ill children. We performed a single-center prospective observational study of encephalopathic critically ill children undergoing CEEG. Clinical and EEG features and ASM utilization patterns were evaluated. We determined the incidence, types, and risk factors for adverse events associated with ASM administration. A total of 472 consecutive critically ill children undergoing CEEG were enrolled. ES occurred in 131 children (28%). Clinicians administered ASM to 108 children with ES (82%). ES terminated after the initial ASM in 38% of patients who received one ASM, after the second ASM in 35% of patients who received two ASMs, after the third ASM in 50% of patients who received three ASMs, and after the fourth ASM in 53% of patients who received four ASMs. Thirty patients (28%) received anesthetic infusions for ES management. Adverse events occurred in 18 patients (17%). Adverse effects were expected and resolved in all patients, and they were generally serious (in 15 patients) and definitely related (in 12 patients). Adverse events were rare in patients with acute symptomatic seizures requiring only one to two ASMs for treatment, but were more common in children with epilepsy, ictal-interictal continuum EEG patterns, or patients requiring more extensive ASM management. ES ceased after one ASM in only 38% of critically ill children but ceased after two ASMs in 73% of critically ill children. Thus, ES management was often accomplished with readily available medications, but optimization of multistep ES management strategies might be beneficial. Adverse events were rare and manageable in children with acute symptomatic seizures requiring only one to two ASMs for treatment. Future studies are needed to determine whether management of acute symptomatic ES improves neurobehavioral outcomes.

Deep Multi-View Feature Learning for EEG-Based Epileptic Seizure Detection.

Epilepsy is a neurological illness caused by abnormal discharge of brain neurons, where epileptic seizure can lead to life-threatening emergencies. By analyzing the encephalogram (EEG) signals of patients with epilepsy, their conditions can be monitored and seizure can be detected and intervened in time. As the identification of effective features in EEG signals is important for accurate seizure detection, this paper proposes a multi-view deep feature extraction method in attempt to achieve this goal. The method first uses fast Fourier transform (FFT) and wavelet packet decomposition (WPD) to construct the initial multi-view features. Convolutional neural network (CNN) is then used to automatically learn deep features from the initial multi-view features, which reduces the dimensionality and obtain the features with better seizure identification ability. Furthermore, the multi-view Takagi-Sugeno-Kang fuzzy system (MV-TSK-FS), an interpretable rule-based classifier, is used to construct a classification model with strong generalizability based on the deep multi-view features obtained. Experimental studies show that the classification accuracy of the proposed multi-view deep feature extraction method is at least 1% higher than that of common feature extraction methods such as principal component analysis (PCA), FFT and WPD. The classification accuracy is also at least 4% higher than the average accuracy achieved with single-view deep features.

Acute Symptomatic Seizures in Neonates

Seizures are an important sign of neurologic dysfunction in neonates, and they most often represent acute brain injury such as hypoxic-ischemic encephalopathy, stroke, or intracranial hemorrhage (acute symptomatic seizures). Clinical identification of seizures is not reliable since seizures in neonates often do not have an apparent clinical correlate; therefore, electroencephalography should be used to accurately diagnose and manage neonatal seizures. Seizures are refractory to initial loading doses of standard medications in >50% of cases. Since seizures are commonly associated with adverse acute and long-term outcomes, and the seizures themselves may result in additional brain injury, it is important to quickly recognize, diagnose, and treat seizures in neonates. Local practice pathways may optimize efficiency in assessment and treatment for affected newborns. Herein, we review the etiology, methods of diagnosis, treatment, and current knowledge gaps for neonatal seizures.

Identification of epileptic seizures in EEG signals using time-scale decomposition (ITD), discrete wavelet transform (DWT), phase space reconstruction (PSR) and neural networks

Traditionally, detection of epileptic seizures based on the visual inspection of neurologists is tedious, laborious and subjective. To overcome such disadvantages, numerous seizure detection techniques involving signal processing and machine learning tools have been developed. However, there still remain the problems of automatic detection with high efficiency and accuracy in distinguishing normal, interictal and ictal electroencephalogram (EEG) signals. In this study we propose a novel method for automatic identification of epileptic seizures in singe-channel EEG signals based upon time-scale decomposition (ITD), discrete wavelet transform (DWT), phase space reconstruction (PSR) and neural networks. First, EEG signals are decomposed into a series of proper rotation components (PRCs) and a baseline signal by using the ITD method. The first two PRCs of the EEG signals are extracted, which contain most of the EEG signals’ energy and are considered to be the predominant PRCs. Second, four levels DWT is employed to decompose the predominant PRCs into different frequency bands, in which third-order Daubechies (db3) wavelet function is selected for analysis. Third, phase space of the PRCs is reconstructed based on db3, in which the properties associated with the nonlinear EEG system dynamics are preserved. Three-dimensional (3D) PSR together with Euclidean distance (ED) has been utilized to derive features, which demonstrate significant difference in EEG system dynamics between normal, interictal and ictal EEG signals. Fourth, neural networks are then used to model, identify and classify EEG system dynamics between normal (healthy), interictal and ictal EEG signals. Finally, experiments are carried out on the University of Bonn’s widely used and publicly available epilepsy dataset to assess the effectiveness of the proposed method. By using the 10-fold cross-validation style, the achieved average classification accuracy for eleven cases is reported to be 98.15%. Compared with many state-of-the-art methods, the results demonstrate superior performance and the proposed method can serve as a potential candidate for the automatic detection of seizure EEG signals in the clinical application.

Epileptic seizure identification using entropy of FBSE based EEG rhythms

This paper has proposed a new method for classification of epileptic seizures based on weighted multiscale Renyi permutation entropy (WMRPE) and rhythms obtained with Fourier–Bessel series expansion (FBSE) of electroencephalogram (EEG) signals. In this method, each EEG signal is considered for the FBSE to determine the coefficients then the selected order values of these coefficients are used to obtain the rhythms (δ, θ, α, β, and γ). The feature used in this work is WMRPE which has been extracted from rhythms of EEG signal. These computed feature values are then used in distinct classifiers such as random forest (RF), least squares support vector machine (LS-SVM), and regression for classification of epileptic seizure EEG signals. The classification is validated using 10-fold cross-validation technique in order to verify the robustness of the proposed work and the optimization of classification performance has been implemented with feature ranking methods. The proposed method is also tested with additive white Gaussian noise (AWGN) at different signal to noise ratio (SNR) levels. In this present work, the proposed method provides better classification accuracy results for seven different classification problems in which six are two-class classification problems and the remaining one is three-class classification problem. In two-class classification problems, the EEG signals of epileptic patients at the time of seizure interval has been classified with six distinct classes of EEG signals i.e., healthy subjects with eyes open condition, healthy subjects with eyes closed condition, epileptic patients at the time of seizure-free interval captured from hippocampal formation zone, epileptic patients at the time of seizure-free interval captured from epileptogenic zone, epileptic patients at the time of seizure-free interval, and subjects at the time of non-seizure (healthy and seizure-free) interval. In three-class classification problem, the EEG signals corresponding to epileptic patients during seizure interval, healthy subjects with eyes open condition, and epileptic patients at the time of seizure-free interval captured from hippocampal formation zone have been classified with the help of proposed method. The obtained results show that the WMRPE with FBSE based rhythms provides better classification accuracies in comparison to the existing rhythms based method and other existing methods. The proposed method is also perform well in noisy environment at different SNR levels.

Hyperdimensional Computing With Local Binary Patterns: One-Shot Learning of Seizure Onset and Identification of Ictogenic Brain Regions Using Short-Time iEEG Recordings.

We develop a fast learning algorithm combining symbolic dynamics and brain-inspired hyperdimensional computing for both seizure onset detection and identification of ictogenic (seizure generating) brain regions from intracranial electroencephalography (iEEG). Our algorithm first transforms iEEG time series from each electrode into symbolic local binary pattern codes, from which a holographic distributed representation of the brain state of interest is constructed across all the electrodes and over time in a hyperdimensional space. The representation is used to quickly learn from few seizures, detect their onset, and identify the spatial brain regions that generated them. We assess our algorithm on our dataset that contains 99 short-time iEEG recordings from 16 drug-resistant epilepsy patients being implanted with 36-100 electrodes. For the majority of the patients (ten out of 16), our algorithm quickly learns from one or two seizures and perfectly (100%) generalizes on novel seizures using k-fold cross-validation. For the remaining six patients, the algorithm requires three to six seizures for learning. Our algorithm surpasses the state-of-the-art including deep learning algorithms by achieving higher specificity (94.84% versus 94.77%) and macroaveraging accuracy (95.42% versus 94.96%), and 74× lower memory footprint, but slightly higher average latency in detection (15.9s versus 14.7s). Moreover, the algorithm can reliably identify (with a p-value ) the relevant electrodes covering an ictogenic brain region at two levels of granularity: cerebral hemispheres and lobes. Our algorithm provides: 1) a unified method for both learning and classification tasks with end-to-end binary operations; 2) one-shot learning from seizure examples; 3) linear computational scalability for increasing number of electrodes; and 4) generation of transparent codes that enables post-translational support for clinical decision making. Our source code and anonymized iEEG dataset are freely available at http://ieeg-swez.ethz.ch.

A review on the pattern detection methods for epilepsy seizure detection from EEG signals.

Over several years, research had been conducted for the detection of epileptic seizures to support an automatic diagnosis system to comfort the clinicians' encumbrance. In this regard, a number of research papers have been published for the identification of epileptic seizures. A thorough review of all these papers is required. So, an attempt has been made to review on the pattern detection methods for epilepsy seizure detection from EEG signals. More than 150 research papers have been discussed to determine the techniques for detecting epileptic seizures. Further, the literature review confirms that the pattern recognition techniques required to detect epileptic seizures varies across the electroencephalogram (EEG) datasets of different conditions. This is mostly owing to the fact that EEG detected under different conditions have different characteristics. This consecutively necessitates the identification of the pattern recognition technique to efficiently differentiate EEG epileptic data from the EEG data of various conditions.