scholarly journals P.129 Bi-insular Responsive Neurostimulation Artifact on Scalp Electroencephalogram

2021 ◽  
Vol 48 (s3) ◽  
pp. S56-S56
Author(s):  
EM Paredes-Aragón ◽  
M Chávez-Castillo ◽  
GL Barkley ◽  
JG Burneo ◽  
A Suller-Martí
Keyword(s):  
Scientific Literature ◽  
Seizure Activity ◽  
Epileptiform Activity ◽  
Scalp Eeg ◽  
Proven Efficacy ◽  
Eeg Recordings ◽  
Scalp Electroencephalogram ◽  
Intracranial Electroencephalography

Background: Background: Responsive Neurostimulation (RNS) has proven efficacy in treating medically resistant epilepsy as an intracranial system detecting, recording and treating seizures automatically. No information exists pertaining to artifact characteristics of RNS findings in scalp EEG. Methods: A 30 year-old female was diagnosed using intracranial electroencephalography(iEEG), with bi-insular epilepsy, of unknown cause. She presented large number of focal unaware non-motor seizures and seizures with progression to bilateral tonic-clonic. She was implanted with bi-insular RNS. Results: During scalp EEG recordings, a prominent artifact was seen corresponding to an automatized discharge suspectedly evoked by the RNS trying to minimize the frequent epileptiform activity in her case. Figure 1 and 2 depict these findings. Conclusions: Artifact seen by the RNS in scalp EEG has not been previously described in scientific literature. These findings must be identified to better characterize the role of the RNS in EEG and treatment of seizure activity visible on scalp recordings.

scholarly journals Canonical Decomposition of Ictal Scalp EEG and Accurate Source Localisation: Principles and Simulation Study

2007 ◽  
Vol 2007 ◽  
pp. 1-10 ◽  
Author(s):  
Maarten De Vos ◽  
Lieven De Lathauwer ◽  
Bart Vanrumste ◽  
Sabine Van Huffel ◽  
W. Van Paesschen
Keyword(s):  
Simulation Study ◽  
Seizure Activity ◽  
Canonical Decomposition ◽  
Scalp Eeg ◽  
Activity Frequency ◽  
Ictal Eeg ◽  
Single Dipole ◽  
Eeg Recordings ◽  
Frequency Changes ◽  
Dipole Localisation

Long-term electroencephalographic (EEG) recordings are important in the presurgical evaluation of refractory partial epilepsy for the delineation of the ictal onset zones. In this paper, we introduce a new concept for an automatic, fast, and objective localisation of the ictal onset zone in ictal EEG recordings. Canonical decomposition of ictal EEG decomposes the EEG in atoms. One or more atoms are related to the seizure activity. A single dipole was then fitted to model the potential distribution of each epileptic atom. In this study, we performed a simulation study in order to estimate the dipole localisation error. Ictal dipole localisation was very accurate, even at low signal-to-noise ratios, was not affected by seizure activity frequency or frequency changes, and was minimally affected by the waveform and depth of the ictal onset zone location. Ictal dipole localisation error using 21 electrodes was around 10.0 mm and improved more than tenfold in the range of 0.5–1.0 mm using 148 channels. In conclusion, our simulation study of canonical decomposition of ictal scalp EEG allowed a robust and accurate localisation of the ictal onset zone.

scholarly journals Seizure Forecasting Using a Novel Sub-Scalp Ultra-Long Term EEG Monitoring System

2021 ◽  
Vol 12 ◽  
Author(s):  
Rachel E. Stirling ◽  
Matias I. Maturana ◽  
Philippa J. Karoly ◽  
Ewan S. Nurse ◽  
Kate McCutcheon ◽  
...  
Keyword(s):  
Seizure Activity ◽  
Characteristic Curve ◽  
Epileptiform Activity ◽  
Machine Learning Algorithms ◽  
Eeg Monitoring ◽  
Accurate Identification ◽  
Patient Reported ◽  
Diagnostic Management ◽  
Eeg Recordings ◽  
Brain Data

Accurate identification of seizure activity, both clinical and subclinical, has important implications in the management of epilepsy. Accurate recognition of seizure activity is essential for diagnostic, management and forecasting purposes, but patient-reported seizures have been shown to be unreliable. Earlier work has revealed accurate capture of electrographic seizures and forecasting is possible with an implantable intracranial device, but less invasive electroencephalography (EEG) recording systems would be optimal. Here, we present preliminary results of seizure detection and forecasting with a minimally invasive sub-scalp device that continuously records EEG. Five participants with refractory epilepsy who experience at least two clinically identifiable seizures monthly have been implanted with sub-scalp devices (Minder®), providing two channels of data from both hemispheres of the brain. Data is continuously captured via a behind-the-ear system, which also powers the device, and transferred wirelessly to a mobile phone, from where it is accessible remotely via cloud storage. EEG recordings from the sub-scalp device were compared to data recorded from a conventional system during a 1-week ambulatory video-EEG monitoring session. Suspect epileptiform activity (EA) was detected using machine learning algorithms and reviewed by trained neurophysiologists. Seizure forecasting was demonstrated retrospectively by utilizing cycles in EA and previous seizure times. The procedures and devices were well-tolerated and no significant complications have been reported. Seizures were accurately identified on the sub-scalp system, as visually confirmed by periods of concurrent conventional scalp EEG recordings. The data acquired also allowed seizure forecasting to be successfully undertaken. The area under the receiver operating characteristic curve (AUC score) achieved (0.88), which is comparable to the best score in recent, state-of-the-art forecasting work using intracranial EEG.

scholarly journals Features importance in seizure classification using scalp EEG reduced to single timeseries

2021 ◽  
Author(s):  
Sebastien Naze ◽  
Jianbin Tang ◽  
James Kozloski ◽  
Stefan Harrer
Keyword(s):  
Support Vector ◽  
Seizure Type ◽  
Low Frequencies ◽  
Scalp Eeg ◽  
Eeg Spectrum ◽  
Wearable Systems ◽  
Vector Machines ◽  
Eeg Recordings ◽  
Single Trace ◽  
Scalp Electroencephalogram

Seizure detection and seizure-type classification are best performed using intra-cranial or full-scalp electroencephalogram (EEG). In embedded wearable systems however, recordings from only a few electrodes are available, reducing the spatial resolution of the signals to a handful of timeseries at most. Taking this constraint into account, we tested the performance of multiple classifiers using a subset of the EEG recordings by selecting a single trace from the montage or performing a dimensionality reduction over each hemispherical space. Our results support that Random Forest (RF) classifiers lead most efficient and stable classification performances over Support Vector Machines (SVM). Interestingly, tracking the feature importances using permutation tests reveals that classical EEG spectrum power bands display different rankings across the classifiers: low frequencies (delta, theta) are most important for SVMs while higher frequencies (alpha, gamma) are more relevant for RF and Decision Trees. We reach up to 94.3% +/- 5.3% accuracy in classifying absence from tonic-clonic seizures using state-of-art sampling methods for unbalanced datasets and leave-patients-out 3-fold cross-validation policy.

scholarly journals Monitoring the Burden of Seizures and Highly Epileptiform Patterns in Critical Care with a Novel Machine Learning Method

2020 ◽  
Author(s):  
Baharan Kamousi ◽  
Suganya Karunakaran ◽  
Kapil Gururangan ◽  
Matthew Markert ◽  
Barbara Decker ◽  
...  
Keyword(s):  
Negative Predictive Value ◽  
Sensitivity And Specificity ◽  
Predictive Value ◽  
Seizure Activity ◽  
Epileptiform Activity ◽  
High Sensitivity ◽  
Machine Learning Method ◽  
High Negative Predictive Value ◽  
Majority Consensus ◽  
Eeg Recordings

Abstract Introduction Current electroencephalography (EEG) practice relies on interpretation by expert neurologists, which introduces diagnostic and therapeutic delays that can impact patients’ clinical outcomes. As EEG practice expands, these experts are becoming increasingly limited resources. A highly sensitive and specific automated seizure detection system would streamline practice and expedite appropriate management for patients with possible nonconvulsive seizures. We aimed to test the performance of a recently FDA-cleared machine learning method (Claritγ, Ceribell Inc.) that measures the burden of seizure activity in real time and generates bedside alerts for possible status epilepticus (SE). Methods We retrospectively identified adult patients (n = 353) who underwent evaluation of possible seizures with Rapid Response EEG system (Rapid-EEG, Ceribell Inc.). Automated detection of seizure activity and seizure burden throughout a recording (calculated as the percentage of ten-second epochs with seizure activity in any 5-min EEG segment) was performed with Claritγ, and various thresholds of seizure burden were tested (≥ 10% indicating ≥ 30 s of seizure activity in the last 5 min, ≥ 50% indicating ≥ 2.5 min of seizure activity, and ≥ 90% indicating ≥ 4.5 min of seizure activity and triggering a SE alert). The sensitivity and specificity of Claritγ’s real-time seizure burden measurements and SE alerts were compared to the majority consensus of at least two expert neurologists. Results Majority consensus of neurologists labeled the 353 EEGs as normal or slow activity (n = 249), highly epileptiform patterns (HEP, n = 87), or seizures [n = 17, nine longer than 5 min (e.g., SE), and eight shorter than 5 min]. The algorithm generated a SE alert (≥ 90% seizure burden) with 100% sensitivity and 93% specificity. The sensitivity and specificity of various thresholds for seizure burden during EEG recordings for detecting patients with seizures were 100% and 82% for ≥ 50% seizure burden and 88% and 60% for ≥ 10% seizure burden. Of the 179 EEG recordings in which the algorithm detected no seizures, seizures were identified by the expert reviewers in only two cases, indicating a negative predictive value of 99%. Discussion Claritγ detected SE events with high sensitivity and specificity, and it demonstrated a high negative predictive value for distinguishing nonepileptiform activity from seizure and highly epileptiform activity. Conclusions Ruling out seizures accurately in a large proportion of cases can help prevent unnecessary or aggressive over-treatment in critical care settings, where empiric treatment with antiseizure medications is currently prevalent. Claritγ’s high sensitivity for SE and high negative predictive value for cases without epileptiform activity make it a useful tool for triaging treatment and the need for urgent neurological consultation.

Fentanyl-induced electrocorticographic seizures in patients with complex partial epilepsy

1992 ◽  
Vol 77 (2) ◽  
pp. 201-208 ◽  
Author(s):  
René Tempelhoff ◽  
Paul A. Modica ◽  
Kerry L. Bernardo ◽  
Isaac Edwards
Keyword(s):  
Temporal Lobe ◽  
Seizure Activity ◽  
Epileptiform Activity ◽  
Temporal Lobectomy ◽  
Electrode Arrays ◽  
Complex Partial Seizures ◽  
Content Type ◽  
Temporal Lobe Seizures ◽  
Eeg Recordings ◽  
The Right

✓ Although electrical seizure activity in response to opioids such as fentanyl has been well described in animals, scalp electroencephalographic (EEG) recordings have failed to demonstrate epileptiform activity following narcotic administration in humans. The purpose of this study was to determine whether fentanyl is capable of evoking electrical seizure activity in patients with complex partial (temporal lobe) seizures. Nine patients were studied in whom recording electrode arrays had been placed in the bitemporal epidural space several days earlier to determine which temporal lobe gave rise to their seizures. The symptomatic temporal lobe was localized by correlating clinical and electrical seizure activity obtained during continuous simultaneous videotape and epidural EEG monitoring. In each patient, clinical seizures and electrical seizure activity were consistently demonstrated to arise unilaterally from one temporal lobe (four on the right, five on the left). During fentanyl induction of anesthesia in preparation for secondary craniotomy for anterior temporal lobectomy, eight of the nine patients exhibited electrical seizure activity at fentanyl doses ranging from 17.7 to 35.71 µg · kg−1 (mean 25.75 µg · kg−1). More importantly, four of these eight seizures occurred initially in the “healthy” temporal lobe contralateral to the surgically resected lobe from which the clinical seizures had been shown to arise. These findings indicate that, in patients with complex partial seizures, moderate doses of fentanyl can evoke electrical seizure activity. The results of this study could have important implications for neurosurgical centers where electrocorticography is used during surgery for the purpose of determining the extent of the resection.

Role of Potassium and Calcium in the Generation of Cellular Bursts in the Dentate Gyrus

1997 ◽  
Vol 77 (5) ◽  
pp. 2293-2299 ◽  
Author(s):  
Enhui Pan ◽  
Janet L. Stringer
Keyword(s):  
Dentate Gyrus ◽  
Granule Cells ◽  
Seizure Activity ◽  
Epileptiform Activity ◽  
Hippocampal Slices ◽  
Extracellular Potassium ◽  
Sprague Dawley

Pan, Enhui and Janet L. Stringer. Role of potassium and calcium in the generation of cellular bursts in the dentate gyrus. J. Neurophysiol. 77: 2293–2299, 1997. Epileptiform activity, which appears to be endogenous, has been recorded in the granule cells of the dentate gyrus before the onset of synchronized seizure activity and has been termed cellular bursts. It has been postulated that an increase in input to the dentate gyrus causes a local increase in extracellular K+ concentration ([K+]o) and a decrease in [Ca2+]o that results in this cellular bursting. The first test of this hypothesis is to determine whether the cellular bursts appear in ionic conditions that occur in vivo before the onset of synchronized epileptic activity. This hypothesis was tested in vitro by varying the ionic concentrations in the perfusing solution and recording changes in the granule cells of the dentate gyrus. Intra- and extracellular recordings were made in the dentate gyri of hippocampal slices prepared from anesthetized adult Sprague-Dawley rats. Increasing the extracellular potassium or decreasing the extracellular calcium of the perfusing solution caused three forms of spontaneous activity to appear: depolarizing potentials, action potentials, and cellular bursts. Increasing potassium or decreasing calcium also caused the granule cells to depolarize and reduced their input resistance. No synchronized extracellular field activity was detected. Simultaneously increasing potassium and decreasing calcium caused cellular bursts to appear at concentrations recorded in vivo before the onset of synchronized reverberatory seizure activity.

scholarly journals Review: Cav2.3 R-type Voltage-Gated Ca2+ Channels - Functional Implications in Convulsive and Non-convulsive Seizure Activity

2016 ◽  
Vol 10 (1) ◽  
pp. 99-126 ◽  
Author(s):  
Carola Wormuth ◽  
Andreas Lundt ◽  
Christina Henseler ◽  
Ralf Müller ◽  
Karl Broich ◽  
...  
Keyword(s):  
Seizure Activity ◽  
Epileptiform Activity ◽  
Juvenile Myoclonic Epilepsy ◽  
Absence Epilepsy ◽  
Ca1 Neurons ◽  
Voltage Gated ◽  
Hippocampal Seizures ◽  
Cellular Ca ◽  
Ca2 Channels

Background: Researchers have gained substantial insight into mechanisms of synaptic transmission, hyperexcitability, excitotoxicity and neurodegeneration within the last decades. Voltage-gated Ca2+ channels are of central relevance in these processes. In particular, they are key elements in the etiopathogenesis of numerous seizure types and epilepsies. Earlier studies predominantly targeted on Cav2.1 P/Q-type and Cav3.2 T-type Ca2+ channels relevant for absence epileptogenesis. Recent findings bring other channels entities more into focus such as the Cav2.3 R-type Ca2+ channel which exhibits an intriguing role in ictogenesis and seizure propagation. Cav2.3 R-type voltage gated Ca2+ channels (VGCC) emerged to be important factors in the pathogenesis of absence epilepsy, human juvenile myoclonic epilepsy (JME), and cellular epileptiform activity, e.g. in CA1 neurons. They also serve as potential target for various antiepileptic drugs, such as lamotrigine and topiramate. Objective: This review provides a summary of structure, function and pharmacology of VGCCs and their fundamental role in cellular Ca2+ homeostasis. We elaborate the unique modulatory properties of Cav2.3 R-type Ca2+ channels and point to recent findings in the proictogenic and proneuroapoptotic role of Cav2.3 R-type VGCCs in generalized convulsive tonic–clonic and complex-partial hippocampal seizures and its role in non-convulsive absence like seizure activity. Conclusion: Development of novel Cav2.3 specific modulators can be effective in the pharmacological treatment of epilepsies and other neurological disorders.

scholarly journals Theta bursts precede, and spindles follow, cortical and thalamic downstates in human NREM sleep

2018 ◽  
Author(s):  
Chris Gonzalez ◽  
Rachel Mak-McCully ◽  
Burke Rosen ◽  
Sydney S. Cash ◽  
Patrick Chauvel ◽  
...  
Keyword(s):  
Brain Activity ◽  
Intractable Epilepsy ◽  
Nrem Sleep ◽  
Human Cortex ◽  
Scalp Eeg ◽  
Temporal Coupling ◽  
Healthy Cohort ◽  
Eeg Recordings ◽  
Generative Mechanisms

Abstract:Since their discovery, slow oscillations have been observed to group spindles during non-REM sleep. Previous studies assert that the slow oscillation downstate (DS) is preceded by slow spindles (10-12Hz), and followed by fast spindles (12-16Hz). Here, using both direct transcortical recordings in patients with intractable epilepsy (n=10, 8 female), as well as scalp EEG recordings from a healthy cohort (n=3, 1 female), we find in multiple cortical areas that both slow and fast spindles follow the DS. Although discrete oscillations do precede DSs, they are theta bursts (TB) centered at 5-8Hz. TBs were more pronounced for DSs in NREM stage N2 compared with N3. TB with similar properties occur in the thalamus, but unlike spindles they have no clear temporal relationship with cortical TB. These differences in corticothalamic dynamics, as well as differences between spindles and theta in coupling high frequency content, are consistent with NREM theta having separate generative mechanisms from spindles. The final inhibitory cycle of the TB coincides with the DS peak, suggesting that in N2, TB may help trigger the DS. Since the transition to N1 is marked by the appearance of theta, and the transition to N2 by the appearance of DS and thus spindles, a role of TB in triggering DS could help explain the sequence of electrophysiological events characterizing sleep. Finally, the coordinated appearance of spindles and DSs are implicated in memory consolidation processes, and the current findings redefine their temporal coupling with theta during NREM sleep.Significance StatementSleep is characterized by large slow waves which modulate brain activity. Prominent among these are ‘downstates,’ periods of a few tenths of a second when most cells stop firing, and ‘spindles,’ oscillations at about twelve times a second lasting for about a second. In this study, we provide the first detailed description of another kind of sleep wave: ‘theta bursts,’ a brief oscillation at about six cycles per second. We show, recording during natural sleep directly from the human cortex and thalamus, as well as on the human scalp, that theta bursts precede, and spindles follow downstates. Theta bursts may help trigger downstates in some circumstances, and organize cortical and thalamic activity so that memories can be consolidated during sleep.

scholarly journals Seizure Forecasting Using a Novel Sub-Scalp Ultra-Long Term EEG Monitoring System

2021 ◽  
Author(s):  
Rachel E Stirling ◽  
Philippa J Karoly ◽  
Matias Maturana ◽  
Ewan S Nurse ◽  
Kate McCutcheon ◽  
...  
Keyword(s):  
Seizure Activity ◽  
Characteristic Curve ◽  
Epileptiform Activity ◽  
Machine Learning Algorithms ◽  
Eeg Monitoring ◽  
Accurate Identification ◽  
Patient Reported ◽  
Diagnostic Management ◽  
Eeg Recordings ◽  
Brain Data

Accurate identification of seizure activity, both clinical and subclinical, has important implications in the management of epilepsy. Accurate recognition of seizure activity is essential for diagnostic, management and forecasting purposes, but patient-reported seizures have been shown to be unreliable. Earlier work has revealed accurate capture of electrographic seizures and forecasting is possible with an implantable intracranial device, but less invasive electroencephalography (EEG) recording systems would be optimal. Here, we present preliminary results of seizure detection and forecasting with a minimally invasive sub-scalp device that continuously records EEG. Five participants with refractory epilepsy who experience at least two clinically identifiable seizures monthly have been implanted with sub-scalp devices (Minder TM), providing two channels of data from both hemispheres of the brain. Data is continuously captured via a behind-the-ear system, which also powers the device, and transferred wirelessly to a mobile phone, from where it is accessible remotely via cloud storage. EEG recordings from the sub-scalp device were compared to data recorded from a conventional system during a 1-week ambulatory video-EEG monitoring session. Suspect epileptiform activity (EA) was detected using machine learning algorithms and reviewed by trained neurophysiologists. Seizure forecasting was demonstrated retrospectively by utilising cycles in EA and previous seizure times. The procedures and devices were well tolerated and no significant complications have been reported. Seizures were accurately identified on the sub-scalp system, as confirmed by periods of concurrent conventional scalp EEG recordings. The data acquired also allowed seizure forecasting to be successfully undertaken. The area under the receiver operating characteristic curve (AUC score) achieved (0.88) is comparable to the best score in recent, state-of-the-art forecasting work using intracranial EEG.

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