Location Of Spikes Research Articles

Semantic Segmentation of Solar Radio Spikes at Low Frequencies

Solar radio spikes are short lived, narrow bandwidth features in low frequency solar radio observations. The timing of their occurrence and the number of spikes in a given observation is often unpredictable. The high temporal and frequency of resolution of modern radio telescopes such as NenuFAR mean that manually identifying radio spikes is an arduous task. Machine learning approaches to data exploration in solar radio data is on the rise. Here we describe a convolutional neural network to identify the per pixel location of radio spikes as well as determine some simple characteristics of duration, spectral width and drift rate. The model, which we call SpikeNet, was trained using an Nvidia Tesla T4 16GB GPU with ~100000 sample spikes in a total time of 2.2 hours. The segmentation performs well with an intersection over union in the test set of ~0.85. The root mean squared error for predicted spike properties is of the order of 23%. Applying the algorithm to unlabelled data successfully generates segmentation masks although the accuracy of the predicted properties is less reliable, particularly when more than one spike is present in the same 64 X 64 pixel time-frequency range. We have successfully demonstrated that our convolutional neural network can locate and characterise solar radio spikes in a number of seconds compared to the weeks it would take for manual identification.

Correlation between scalp high-frequency oscillations and prognosis in patients with benign epilepsy of childhood with centrotemporal spikes.

The study aimed to explore whether high-frequency oscillations (HFOs) can predict seizure risk and atypical manifestations of benign epilepsy of childhood with centrotemporal spikes (BECTS). We recruited 60 patients and divided them into three groups: (1)seizure-free BECTS, (2) active typical BECTS, and (3) active atypical forms of BECTS. Electroencephalogram was used to record the number, location, average amplitude, and duration of spikes, and spike ripples were analyzed using time-frequency technology. Multivariable logistic regression analysis was used to investigate independent predictive factors for prognosis. The number of sleep spike ripples, rather than spikes, was an independent risk factor for the active period of the disease (odds ratio [OR] = 4.714, p = 0.003) and atypical forms of BECTS (OR = 1.455, p = 0.049); the optimal thresholds for the spike ripple rate were >0 (area under the curve [AUC] = 0.885, sensitivity = 96.15%, specificity = 73.33%) and >0.6/min (AUC = 0.936, sensitivity = 84.21%, specificity = 96.15%), respectively. Furthermore, in typical BECTS, the spike ripple rate showed significant negative correlations with time since the last seizure (ρ = -0.409, p = 0.009) and age (ρ = -0.379, p = 0.016), while the spike rate did not. Spike ripple was a marker for distinguishing typical and atypical forms of BECTS and reflected the risk of seizure recurrence better than the spike alone. The present findings might assist clinicians in BECTS treatment.

Hybrid Machine Learning Scheme for Classification of BECTS and TLE Patients Using EEG Brain Signals

Approximately 50 million people have epilepsy worldwide. Prognosis may vary among patients depending on their seizure semiology, age of onset, seizure onset location, and features of electroencephalogram (EEG). Several researchers have focused on EEG patterns and demonstrated that EEG patterns of individuals with epilepsy can be used to predict prognosis and treatment responses. However, accurate EEG analysis requires an experienced epileptologist with several years of training, who are often unavailable in small or medium sized hospitals. In this paper, a novel machine learning (ML) model that accurately distinguishes Benign Epilepsy with Centrotemporal Spikes (BECTS) from Temporal Lobe Epilepsy (TLE) is proposed. BECTS and TLE show different seizure types and age of onset, but differential diagnosis can be challenging due to the similar location and patterns of the EEG spikes. The proposed hybrid machine learning (HML) model processes the diagnosis in the order of (1) creating feature matrices using statistical indexes after signal decomposition, (2) processing feature selection using Support Vector Machine (SVM) technology, and (3) classifying the results through ensemble learning based on decision trees. Simulation was performed using real patient data of 112 BECTS and 112 TLE EEG signals, where training was performed using 80% of the data and 20% of the data was used in the performance analysis comparison with the actual labeled data based on the diagnosis of medical doctors. The performance of the hybrid classification model is compared with other representative ML algorithms, which include logistic regression, KNN, SVM, and ensemble learning based decision tree. The model proposed in this paper shows an accuracy performance exceeding 99%, which is higher than the performance obtainable from the other ML classification models. The purpose of this study is to introduce a novel EEG diagnostic system that shows maximum efficiency to support clinical real-time diagnosis that can accurately distinguish epilepsy types. Future research will focus on expanding this ML model to categorize other types of epilepsies beyond BECTS and TLE and implement the HML diagnostic blockchain database into the hospital system.

Robust detection of neural spikes using sparse coding based features.

The detection of neural spikes plays an important role in studying and processing extracellular recording signals, which promises to be able to extract the necessary spike data for all subsequent analyses. The existing algorithms for spike detection have achieved great progress but there still remains much room for improvement in terms of the robustness to noise and the flexibility in the spike shape. To address this issue, this paper presents a novel method for spike detection based on the theory of sparse representation. By analyzing the characteristics of extracellular neural recordings, a targetdriven sparse representation framework is firstly constructed, with which the neural spike signals can be effectively separated from background noise. In addition, considering the fact that the spikes emitted by different neurons have different shapes, we then learn a universal dictionary to give a sparse representation of various spike signals. Finally, the information (location and number) of spikes in the recorded signal are achieved by comprehensively analyzing the sparse features. Experimental results demonstrate that the proposed method outperforms the existing methods in the spike detection problem.

EEG as a Predictor of Poorly- Controlled Childhood Epilepsy

Introduction:The aim of the present study was to determine electroencephalographic factors associated with poorly controlled epilepsy.
 Materials and Methods: This retrospective study was performed from January 2007 to December 2008 at Paediatric Neurology outpatient department in Bangabandhu Sheikh Mujib Medical University, Dhaka, among the children with epilepsy of 7 months to 15 years age who had history of at least 6 months treatment with rational antiepileptic drugs daily with adequate compliance. There were two groups of patients; group 1, consisted of 50 poorly controlled epilepsy patients and group 2, comprised 50 well-controlled epilepsy patients. We retrospectively reviewed EEGs and medical records from these children. Features of initial EEGs findings were compared between the two groups.
 Results: Significant electroencephalographic predictors of poorly controlled epilepsy were: abnormal initial EEG (p=0.025), EEG background abnormality (p<0.001), frequent sharp wave/spike (p<0.001) and hypsarrhythmia (p=0.046). No significant difference was noticed between the two groups in respect to the location of spikes/ sharp waves. With multiple logistic regression, independent predictors of poor seizure control were EEG background abnormality and frequent sharp wave/spike.
 Conclusion: The study showed several Electroencehalographic factors that can be identified early in the course of childhood epilepsy which can predict development of poor seizure control. Knowledge of these factors will help us to discriminate our patients and pay more attention to those at risk of developing poorly controlled epilepsy.
 Medicine Today 2020 Vol.32(1): 15-20

Spatial distribution of interictal spikes fluctuates over time and localizes seizure onset.

The location of interictal spikes is used to aid surgical planning in patients with medically refractory epilepsy; however, their spatial and temporal dynamics are poorly understood. In this study, we analysed the spatial distribution of interictal spikes over time in 20 adult and paediatric patients (12 females, mean age = 34.5 years, range = 5-58) who underwent intracranial EEG evaluation for epilepsy surgery. Interictal spikes were detected in the 24 h surrounding each seizure and spikes were clustered based on spatial location. The temporal dynamics of spike spatial distribution were calculated for each patient and the effects of sleep and seizures on these dynamics were evaluated. Finally, spike location was assessed in relation to seizure onset location. We found that spike spatial distribution fluctuated significantly over time in 14/20 patients (with a significant aggregate effect across patients, Fisher's method: P < 0.001). A median of 12 sequential hours were required to capture 80% of the variability in spike spatial distribution. Sleep and postictal state affected the spike spatial distribution in 8/20 and 4/20 patients, respectively, with a significant aggregate effect (Fisher's method: P < 0.001 for each). There was no evidence of pre-ictal change in the spike spatial distribution for any patient or in aggregate (Fisher's method: P = 0.99). The electrode with the highest spike frequency and the electrode with the largest area of downstream spike propagation both localized the seizure onset zone better than predicted by chance (Wilcoxon signed-rank test: P = 0.005 and P = 0.002, respectively). In conclusion, spikes localize seizure onset. However, temporal fluctuations in spike spatial distribution, particularly in relation to sleep and post-ictal state, can confound localization. An adequate duration of intracranial recording-ideally at least 12 sequential hours-capturing both sleep and wakefulness should be obtained to sufficiently sample the interictal network.

Change of centrotemporal spikes from onset to remission in self-limited epilepsy with centrotemporal spikes (SLECTS)

ObjectiveTo reveal the changes of centrotemporal spikes that occur during the disease course of self-limited epilepsy with centrotemporal spikes (SLECTS). MethodWe retrospectively reviewed the serial EEGs of 63 patients with SLECTS from initial diagnosis to remission. There were 32 patients who did not undergo treatment and 31 patients who underwent treatment with oxcarbazepine (OXC). The change of occurrence or abundance, voltage, and location of centrotemporal spikes of serial EEGs were analyzed and compared between the two groups. Clinical seizure evidenced and reported was counted. The time gap between seizure remission and EEG remission was measured in the two groups. ResultChanges of occurrence or abundance of the centrotemporal spikes were either abrupt (sudden disappearance of the frequent spikes on following EEG) or gradual (decline in number over 2 or more serial EEGs). Pattern of spike disappearance was not significantly different between the medication naïve group and OXC treated group. The spike voltage or the location of centrotemporal spikes did not change during the disease course in most cases. Delay between seizure remission and EEG normalization was 3.34 ± 1.75 (mean ± standard deviation, range: 0.77–7.97) years in untreated patients and 3.03 ± 1.41 (0.95–6.61) years in OXC-treated group. ConclusionPattern of spike disappearance in SLECTS was either abrupt or gradual. Treatment with OXC had no effect in the disappearance pattern. Precise data regarding the pattern of disappearance and delay between seizure remission and EEG normalization can help to understand the evolution of spike in SLECTS and to predict the timing of normalization of EEG after seizure remission.

Benign Rolandic epilepsy: widespread increases in connectivity in a focal epilepsy syndrome.

Benign Rolandic epilepsy (benign epilepsy with centrotemporal spikes; recently renamed self-limited epilepsy with centrotemporal spikes) is associated with widespread deficits in cognition and behavior, suggesting abnormalities in networks that extend beyond the centrotemporal region. To assess functional connectivity in children with benign Rolandic epilepsy, we assessed EEG spectral power and coherence during awake and sleep records in 27 children with centrotemporal spikes. Coherence represents the consistency of the phase difference between two EEG signals when compared over time and serves as a measure of synchronization between two EEG signals based mainly on phase consistency. Epochs of EEG with and without centrotemporal spikes were compared during both waking and sleep. During the spike epochs, there was an increase in spectral power at all frequencies, although statistical significance was seen primarily in the delta, theta and alpha bandwidths. This increase in absolute power was seen at all electrode sites and was similar in left and right-sided electrodes. During centrotemporal spikes, there were significant changes in coherence compared to the EEG segments without spikes. In the theta, alpha and beta bandwidths, there were significant increases in coherence. The increases in coherences were widespread and bilateral, and involved electrode pairs outside the central and temporal regions. To determine if there was a relationship between location of the spikes and coherence values, right-sided, left-sided and bilateral centrotemporal spikes were compared. There was no relationship between location of the centrotemporal spikes and power or coherence values. These findings indicate that benign Rolandic epilepsy results in generalized changes in spectral power and connectivity and raises the suggestion that from a functional standpoint, benign Rolandic epilepsy resembles a generalized rather than focal seizure disorder.

Evaluation of a dual signal subspace projection algorithm in magnetoencephalographic recordings from patients with intractable epilepsy and vagus nerve stimulators

Magnetoencephalography (MEG) data is subject to many sources of environmental noise, and interference rejection is a necessary step in the processing of MEG data. Large amplitude interference caused by sources near the brain have been common in clinical settings and are difficult to reject. Artifact from vagal nerve stimulators (VNS) is a prototypical example. In this study, we describe a novel MEG interference rejection algorithm called dual signal subspace projection (DSSP), and evaluate its performance in clinical MEG data from people with epilepsy and implanted VNS. The performance of DSSP was evaluated in a retrospective cohort study of patients with epilepsy and VNS who had MEG scans for source localization of interictal epileptiform discharges. DSSP was applied to the MEG data and compared with benchmark for performance. We evaluated the clinical impact of interference rejection based on human expert detection and estimation of the location and time-course of interictal spikes, using an empirical Bayesian source reconstruction algorithm (Champagne). Clinical recordings, after DSSP processing, became more readable and a greater number of interictal epileptic spikes could be clearly identified. Source localization results of interictal spikes also significantly improved from those achieved before DSSP processing, including meaningful estimates of activity time courses. Therefore, DSSP is a valuable novel interference rejection algorithm that can be successfully deployed for the removal of strong artifacts and interferences in MEG.

High-frequency oscillations detected in ECoG recordings correlate with cavernous malformation and seizure-free outcome in a child with focal epilepsy: A case report.

SummaryEpilepsy associated with cavernous malformation (CM) often requires surgical resection of seizure focus to achieve seizure‐free outcome. High‐frequency oscillations (HFOs) in intracranial electroencephalogram (EEG) are reported as potential biomarkers of epileptogenic regions, but to our knowledge there are no data on the existence of HFOs in CM‐caused epilepsy. Here we report our experience of the identification of the seizure focus in a 3‐year‐old pediatric patient with intractable epilepsy associated with CM. The electrocorticographic recordings were obtained from a 64‐contact grid over 2 days in the epilepsy monitoring unit (EMU). The spatial distribution of HFOs and epileptic spikes were estimated from recording segments right after the electrode placement, during sleep and awake states separately. The HFO distribution showed consistency with the perilesional region; the location of spikes varied over days and did not correlate with the lesion. The HFO spatial distribution was more compact in sleep state and pinpointed the contacts sitting on the CM border. Following the resection of the CM and the hemosiderin ring, the patient became seizure‐free. This is the first report describing HFOs in a pediatric patient with intractable epilepsy associated with CM and shows their potential in identifying the seizure focus.

Surgery for “Long-term epilepsy associated tumors (LEATs)”: Seizure outcome and its predictors

Objectives“Long-term epilepsy associated tumors (LEATs)” by definition are tumors primarily causing drug-resistant seizures for two years or more. They include low-grade glial and glioneuronal tumors with normal life expectancy. We studied a large cohort of patients with LEATs who underwent surgery through our epilepsy program. Patients & methodsFrom 1998–2011, 105 patients with LEATs underwent surgery in our center. We utilized their data archived in a prospective registry to evaluate their electro-clinical-imaging characteristics affecting the long-term seizure outcome. ResultsOf 105 patients (age 3–50 years), mean age at surgery was 20 years and mean pre-surgical duration of epilepsy was 10.9 years. 66 (62.8%) had secondary generalized seizures. 82 had temporal tumors, 23 had extra temporal (13 frontal, 3 parietal, 2 occipital and 5 multilobar lesions) and four had associated hippocampal sclerosis. The interictal discharges and ictal onset were concordant to the lesion in 82 (78%) and 98 (93%) patients respectively. Lesionectomy and/or adjoining corticectomy or temporal lobectomy was done. Ganglioglioma was the most dominant pathological substrate in 61 (58%). During a mean follow-up of 7.5 years (range 3–16 years), 78/105 (74.2%) were seizure-free and 45 (57.4%) were totally off drugs. Secondary generalized seizures (p-0.02), temporal location of tumor (p-0.008) and spikes in third month post-operative EEG (p-0.03) caused unfavorable seizure outcome. A pre-surgical duration of epilepsy of more than 6.6 years caused less than optimal surgical outcome ConclusionsEarly surgery should be considered a priority in LEATs. Presence of secondary generalized seizures is the single most important predictor of a poor seizure outcome.

Natural Hybridization within theCarex flavaComplex (Cyperaceae) in Poland: Morphometric Studies

Natural populations of hybrids and populations of parental species of the Carex flava aggregate, growing in seven localities in Poland, were studied. Statistical methods were used to analyse 30 morphological characters measured from 399 dried specimens. The results allowed us to recognize the following hybrids : C. × alsatica [C. flava × C. demissa], C. × ruedtii [C. flava × C. lepidocarpa], C. × schatzii [C. lepidocarpa × C. viridula] and C. demissa × C. viridula. We found that (i) the utricle and beak lengths in the hybrid are usually similar to those of the parents that have longer utricles and beaks, and that (ii) introgression typically occurs towards the parental taxon dominant in an area and that with an earlier flowering period. The morphological characters most useful in distinguishing between hybrids and their parental species in the C. flava aggregate are the size of the utricle and the beak as well as characters transgressive in the hybrids, associated with inflorescence length, location of female spikes, and length of male utricles and their peduncles.

A Two-Dimensionally Coincident Second Difference Cosmic Ray Spike Removal Method for the Fully Automated Processing of Raman Spectra

Charge-coupled device detectors are vulnerable to cosmic rays that can contaminate Raman spectra with positive going spikes. Because spikes can adversely affect spectral processing and data analyses, they must be removed. Although both hardware-based and software-based spike removal methods exist, they typically require parameter and threshold specification dependent on well-considered user input. Here, we present a fully automated spike removal algorithm that proceeds without requiring user input. It is minimally dependent on sample attributes, and those that are required (e.g., standard deviation of spectral noise) can be determined with other fully automated procedures. At the core of the method is the identification and location of spikes with coincident second derivatives along both the spectral and spatiotemporal dimensions of two-dimensional datasets. The method can be applied to spectra that are relatively inhomogeneous because it provides fairly effective and selective targeting of spikes resulting in minimal distortion of spectra. Relatively effective spike removal obtained with full automation could provide substantial benefits to users where large numbers of spectra must be processed.

Can EEG characteristics predict development of epilepsy in autistic children?

BackgroundThe high occurrence of epilepsy in children with autism spectrum disorders (ASD) is a clear indication that ASD has a neurobiological basis. The current understanding of the association between epilepsy and ASD is still limited, but from a clinical point of view, this association should not be overlooked. AimsWe investigated the electroencephalogram (EEG) paroxysmal abnormality in children with ASD and the incidence of later development of epilepsy. MethodsParticipants were recruited from University of Yamanashi hospital and 5 satellite hospitals between April 1, 2001 and March 31, 2005. EEG recordings and clinical evaluations were performed every 6 months for at least 6 years, focusing on paroxysmal abnormality. We scored the occurrence and the location of spikes and evaluated the relation with later development of epilepsy. ResultsThe prospective study included 21 patients with ASD (12 males and 9 females) between the ages of 3 and 6 years. EEG paroxysmal abnormalities were present in 11/21 patients (52.4%). In addition, six of 21 patients (28.6%) had epilepsy at some point in their lives. The presence of frontal paroxysms was significantly associated with later development of epilepsy compared with centrotemporal paroxysmus (p < 0.003). The type of seizure diagnosed was mainly partial; in particular, partial with secondary generalization in 4/6 (66.7%). ConclusionThe presence of frontal paroxysms may indicate a higher risk of epilepsy in ASD.

Regional Differences in the Sensitivity of MEG for Interictal Spikes in Epilepsy

MEG interictal spikes as recorded in epilepsy patients are a reflection of intracranial interictal activity. This study investigates the relationship between the estimated sources of MEG spikes and the location, distribution and size of interictal spikes in the invasive ECoG of a group of 38 epilepsy patients that are monitored for pre-surgical evaluation. An amplitude/surface area measure is defined to quantify and rank ECoG spikes. It is found that all MEG spikes are associated with an ECoG spike that is among the three highest ranked in a patient. Among the different brain regions considered, the fronto-orbital, inter-hemispheric, tempero-lateral and central regions stand out. In an accompanying simulation study it is shown that for hypothesized extended sources of larger sizes, as suggested by the data, source location, orientation and curvature can partly explain the observed sensitivity of MEG for interictal spikes.

The sweet spot of a biological hammer: the centre of percussion of glyptodont (Mammalia: Xenarthra) tail clubs.

The importance of the centre of percussion (CP) of some hand-held sporting equipment (such as tennis rackets and baseball bats) for athletic performance is well known. In order to avoid injuries it is important that powerful blows are located close to the CP. Several species of glyptodont (giant armoured mammals) had tail clubs that can be modelled as rigid beams (like baseball bats) and it is generally assumed that these were useful for agonistic behaviour. However, the variation in tail club morphology among known genera suggests that a biomechanical and functional analysis of these structures could be useful. Here, we outline a novel method to determine the CP of the glyptodont tail clubs. We find that the largest species had the CP very close to the possible location of horny spikes. This is consistent with the inference that they were adapted to delivering powerful blows at that point. Our new analysis reinforces the case for agonistic use of tail clubs in several glyptodont species.

Early ontogenesis of the sea poacher Brachyopsis segaliensis (Osteichthyes: Agonidae) from peter the Great Bay, Sea of Japan

The description of developing larvae and fry of Brachyopsis segaliensis is given for the first time. B. segaliensis larvae and fry pass through a planktonic stage. In the course of growth and development, they undergo the following morphological changes: the height of the head and the body decreases, the eyes become shifted to the upper head profile, the bases of the pectoral fins become shifted to the lower region of the body; the location of spikes and pigmentation change.

Gradient magnetic-field topography for dynamic changes of epileptic discharges

We developed gradient magnetic-field topography (GMFT) for magnetoencephalography (MEG). We plotted the Euclidean norms of gradient magnetic fields occurring at the centers of 102 sensors onto 49-point grids and projected these norms onto the MRI brain surface of a 12-year-old boy who presented with neocortical epilepsy secondary to a left temporal tumor. The peak gradient magnetic field located posterior to the tumor and correlated to MEG dipoles. The gradient magnetic field propagated to the temporo-parietal region and corresponded with spike locations on electrocorticography. GMFT revealed the location and distribution of spikes while avoiding the inverse problem.

Data Assimilation as Synchronization of Truth and Model: Experiments with the Three-Variable Lorenz System*

Abstract The potential use of chaos synchronization techniques in data assimilation for numerical weather prediction models is explored by coupling a Lorenz three-variable system that represents “truth” to another that represents “the model.” By adding realistic “noise” to observations of the master system, an optimal value of the coupling strength was clearly identifiable. Coupling only the y variable yielded the best results for a wide range of higher coupling strengths. Coupling along dynamically chosen directions identified by either singular or bred vectors could improve upon simpler chaos synchronization schemes. Generalized synchronization (with the parameter r of the slave system different from that of the master) could be easily achieved, as indicated by the synchronization of two identical slave systems coupled to the same master, but the slaves only provided partial information about regime changes in the master. A comparison with a standard data assimilation technique, three-dimensional variational analysis (3DVAR), demonstrated that this scheme is slightly more effective in producing an accurate analysis than the simpler synchronization scheme. Higher growth rates of bred vectors from both the master and the slave anticipated the location and size of error spikes in both 3DVAR and synchronization. With less frequent observations, synchronization using time-interpolated observational increments was competitive with 3DVAR. Adaptive synchronization, with a coupling parameter proportional to the bred vector growth rate, was successful in reducing episodes of large error growth. These results suggest that a hybrid chaos synchronization–data assimilation approach may provide an avenue to improve and extend the period for accurate weather prediction.

Automated localization of magnetoencephalographic interictal spikes by adaptive spatial filtering

Objective Automated adaptive spatial filtering techniques can be applied to magnetoencephalographic (MEG) data collected from people with epilepsy. Source waveforms estimated by these methods have higher signal-to-noise ratio (SNR) than spontaneous MEG data, allowing identification and location of interictal spikes. The software tool SAM( g 2) provides an adaptive spatial filtering algorithm for MEG data that yields source images of excess kurtosis and provides source time-courses in voxels exhibiting high excess kurtosis. The sensitivity and specificity of SAM( g 2) in epilepsy is unknown. Methods Interictal MEG data from 36 patients with intractable epilepsy were analyzed using SAM( g 2), and results compared with equivalent current dipole (ECD) fit procedures. Results When SNR of interictal spikes was high (compared to background) with a clear single focus, in most cases there was good agreement between ECD and SAM( g 2). With multiple foci, there was typically overlap but imperfect concordance between results of ECD and SAM( g 2). Conclusions SAM( g 2) may in some cases be equivalent to manual ECD fit for localizing interictal spikes with single locus and good SNR. Further studies are required to validate SAM( g 2) with multiple foci or poor SNR. Significance In some cases, SAM( g 2) might eventually assist or replace manual ECD analysis of MEG data.