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.

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 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 Breakthrough spikes in rapid eye movement sleep from the epilepsy monitoring unit are associated with peak seizure frequency

SLEEP ◽  
2019 ◽  
Vol 43 (5) ◽  
Author(s):  
Marna B McKenzie ◽  
Michelle-Lee Jones ◽  
Aoife O’Carroll ◽  
Demitre Serletis ◽  
Leigh Anne Shafer ◽  
...  
Keyword(s):  
Eye Movement ◽  
Seizure Frequency ◽  
Epileptiform Activity ◽  
Rapid Eye Movement ◽  
Rapid Eye Movement Sleep ◽  
Epileptiform Discharges ◽  
Interictal Epileptiform Discharges ◽  
Patient Reported ◽  
Eeg Recordings ◽  
Epilepsy Monitoring

Abstract Study Objectives Rapid eye movement sleep (REM) usually suppresses interictal epileptiform discharges (IED) and seizures. However, breakthrough IEDs in REM sometimes continue. We aimed to determine if the amount of IED and seizures in REM, or REM duration, is associated with clinical trajectories. Methods Continuous electroencephalogram (EEG) recordings from the epilepsy monitoring unit (EMU) were clipped to at least 3 h of concatenated salient findings per day including all identified REM. Concatenated EEG files were analyzed for nightly REM duration and the “REM spike burden” (RSB), defined as the proportion of REM occupied by IED or seizures. Patient charts were reviewed for clinical data, including patient-reported peak seizure frequency. Logistic and linear regressions were performed, as appropriate, to explore associations between two explanatory measures (duration of REM and RSB) and six indicators of seizure activity (clinical trajectory outcomes). Results The median duration of REM sleep was 43.3 (IQR 20.9–73.2) min per patient per night. 59/63 (93.7%) patients achieved REM during EMU admission. 39/59 (66.1%) patients had breakthrough IEDs or seizures in REM with the median RSB at 0.7% (IQR 0%–8.4%). Every 1% increase in RSB was associated with 1.69 (95% CI = 0.47–2.92) more seizures per month during the peak seizure period of one’s epilepsy (p = 0.007). Conclusions Increased epileptiform activity during REM is associated with increased peak seizure frequency, suggesting an overall poorer epilepsy trajectory. Our findings suggest that RSB in the EMU is a useful biomarker to help guide about what to expect over the course of one’s epilepsy.

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.

scholarly journals INNV-38. RATE OF EEG IN GLIOMA PATIENTS: SHOULD THIS BE PART OF STANDARD OF CARE FOR NEW BRAIN TUMOR PATIENTS?

2019 ◽  
Vol 21 (Supplement_6) ◽  
pp. vi137-vi138
Author(s):  
Wendy Sherman
Keyword(s):  
Seizure Activity ◽  
Clinical Course ◽  
Epileptiform Activity ◽  
Standard Of Care ◽  
Eeg Monitoring ◽  
Current Recommendation ◽  
Tumor Patients ◽  
Electrographic Seizure ◽  
Clinical Seizure ◽  
Current Guidelines

Abstract Current guidelines recommend antiepileptic drug (AED) therapy for glioma patients only in patients who have experienced a seizure. There is no current recommendation in regards to performing EEG monitoring in glioma patients. Thus, typically only clinical seizures have prompted AED therapy in glioma patients. Our purpose was to investigate the rate of EEG monitoring in glioma patients entered into our glioma registry, along with seizure and AED rates. Using our glioma registry to date, of 167 glioma patients, 119 patients had EEGs performed during their clinical course. Of those 119 patients, 104 glioma patients had either clinical or electrographic seizure activity and all 104 were on AEDs. This observation indicates that baseline eeg monitoring might be considered in all newly diagnosed glioma patients to reduce seizure incidence, potentially, starting AED prophylaxis if epileptiform activity is seen on EEG prior to clinical seizure activity to potentially reduce morbidity and healthcare costs related to clinical seizure activity. This warrants further prospective study.

Comparison of PROMIS Anxiety and Depression, PHQ-8, and GAD-7 to screen for anxiety and depression among patients presenting for spine surgery

2019 ◽  
Vol 30 (4) ◽  
pp. 524-531
Author(s):  
Taylor E. Purvis ◽  
Brian J. Neuman ◽  
Lee H. Riley ◽  
Richard L. Skolasky
Keyword(s):  
Major Depression ◽  
Spine Surgery ◽  
Characteristic Curve ◽  
Depression Scale ◽  
Data Driven ◽  
Anxiety And Depression ◽  
Measurement Information ◽  
Generalized Anxiety ◽  
Cut Points ◽  
Patient Reported

OBJECTIVEIn this paper, the authors demonstrate to spine surgeons the prevalence and severity of anxiety and depression among patients presenting for surgery and explore the relationships between different legacy and Patient-Reported Outcomes Measurement Information System (PROMIS) screening measures.METHODSA total of 512 adult spine surgery patients at a single institution completed the 7-item Generalized Anxiety Disorder questionnaire (GAD-7), 8-item Patient Health Questionnaire (PHQ-8) depression scale, and PROMIS Anxiety and Depression computer-adaptive tests (CATs) preoperatively. Correlation coefficients were calculated between PROMIS scores and GAD-7 and PHQ-8 scores. Published reference tables were used to determine the presence of anxiety or depression using GAD-7 and PHQ-8. Sensitivity and specificity of published guidance on the PROMIS Anxiety and Depression CATs were compared. Guidance from 3 sources was compared: published GAD-7 and PHQ-8 crosswalk tables, American Psychiatric Association scales, and expert clinical consensus. Receiver operator characteristic curves were used to determine data-driven cut-points for PROMIS Anxiety and Depression. Significance was accepted as p < 0.05.RESULTSIn 512 spine surgery patients, anxiety and depression were prevalent preoperatively (5% with any anxiety, 24% with generalized anxiety screen-positive; and 54% with any depression, 24% with probable major depression). Correlations were moderately strong between PROMIS Anxiety and GAD-7 scores (r = 0.72; p < 0.001) and between PROMIS Depression and PHQ-8 scores (r = 0.74; p < 0.001). The observed correlation of the PROMIS Depression score was greater with the PHQ-8 cognitive/affective score (r = 0.766) than with the somatic score (r = 0.601) (p < 0.001). PROMIS Anxiety and Depression CATs were able to detect the presence of generalized anxiety screen-positive (sensitivity, 86.0%; specificity, 81.6%) and of probable major depression (sensitivity, 82.3%; specificity, 81.4%). Receiver operating characteristic curve analysis demonstrated data-driven cut-points for these groups.CONCLUSIONSPROMIS Anxiety and Depression CATs are reliable tools for identifying generalized anxiety screen-positive spine surgery patients and those with probable major depression.

Identify Compounds' Target Against Alzheimer's Disease Based on In-Silico Approach

2019 ◽  
Vol 16 (3) ◽  
pp. 193-208 ◽  
Author(s):  
Yan Hu ◽  
Guangya Zhou ◽  
Chi Zhang ◽  
Mengying Zhang ◽  
Qin Chen ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Small Molecule ◽  
Characteristic Curve ◽  
Machine Learning Algorithms ◽  
Learner Model ◽  
Mao B ◽  
Number Of Patients ◽  
Sar Model ◽  
Set Up

Background: Alzheimer's disease swept every corner of the globe and the number of patients worldwide has been rising. At present, there are as many as 30 million people with Alzheimer's disease in the world, and it is expected to exceed 80 million people by 2050. Consequently, the study of Alzheimer’s drugs has become one of the most popular medical topics. Methods: In this study, in order to build a predicting model for Alzheimer’s drugs and targets, the attribute discriminators CfsSubsetEval, ConsistencySubsetEval and FilteredSubsetEval are combined with search methods such as BestFirst, GeneticSearch and Greedystepwise to filter the molecular descriptors. Then the machine learning algorithms such as BayesNet, SVM, KNN and C4.5 are used to construct the 2D-Structure Activity Relationship(2D-SAR) model. Its modeling results are utilized for Receiver Operating Characteristic curve(ROC) analysis. Results: The prediction rates of correctness using Randomforest for AChE, BChE, MAO-B, BACE1, Tau protein and Non-inhibitor are 77.0%, 79.1%, 100.0%, 94.2%, 93.2% and 94.9%, respectively, which are overwhelming as compared to those of BayesNet, BP, SVM, KNN, AdaBoost and C4.5. Conclusion: In this paper, we conclude that Random Forest is the best learner model for the prediction of Alzheimer’s drugs and targets. Besides, we set up an online server to predict whether a small molecule is the inhibitor of Alzheimer's target at http://47.106.158.30:8080/AD/. Furthermore, it can distinguish the target protein of a small molecule.

Application of a Computer Vision Tool for Automated Glottic Tracking to Vocal Fold Paralysis Patients

2021 ◽  
pp. 019459982198960
Author(s):  
Tiffany V. Wang ◽  
Nat Adamian ◽  
Phillip C. Song ◽  
Ramon A. Franco ◽  
Molly N. Huston ◽  
...  
Keyword(s):  
Vocal Fold ◽  
Assessment Tool ◽  
Medical Center ◽  
Characteristic Curve ◽  
Academic Medical Center ◽  
Vocal Fold Paralysis ◽  
Control Group ◽  
Large Patient ◽  
Patient Reported ◽  
Before And After

Objectives (1) Demonstrate true vocal fold (TVF) tracking software (AGATI [Automated Glottic Action Tracking by artificial Intelligence]) as a quantitative assessment of unilateral vocal fold paralysis (UVFP) in a large patient cohort. (2) Correlate patient-reported metrics with AGATI measurements of TVF anterior glottic angles, before and after procedural intervention. Study Design Retrospective cohort study. Setting Academic medical center. Methods AGATI was used to analyze videolaryngoscopy from healthy adults (n = 72) and patients with UVFP (n = 70). Minimum, 3rd percentile, 97th percentile, and maximum anterior glottic angles (AGAs) were computed for each patient. In patients with UVFP, patient-reported outcomes (Voice Handicap Index 10, Dyspnea Index, and Eating Assessment Tool 10) were assessed, before and after procedural intervention (injection or medialization laryngoplasty). A receiver operating characteristic curve for the logistic fit of paralysis vs control group was used to determine AGA cutoff values for defining UVFP. Results Mean (SD) 3rd percentile AGA (in degrees) was 2.67 (3.21) in control and 5.64 (5.42) in patients with UVFP ( P < .001); mean (SD) 97th percentile AGA was 57.08 (11.14) in control and 42.59 (12.37) in patients with UVFP ( P < .001). For patients with UVFP who underwent procedural intervention, the mean 97th percentile AGA decreased by 5 degrees from pre- to postprocedure ( P = .026). The difference between the 97th and 3rd percentile AGA predicted UVFP with 77% sensitivity and 92% specificity ( P < .0001). There was no correlation between AGA measurements and patient-reported outcome scores. Conclusions AGATI demonstrated a difference in AGA measurements between paralysis and control patients. AGATI can predict UVFP with 77% sensitivity and 92% specificity.

scholarly journals Depth Electrode Guided Anterior Insulectomy: 2-Dimensional Operative Video

2021 ◽  
Author(s):  
Mauricio Mandel ◽  
Layton Lamsam ◽  
Pue Farooque ◽  
Dennis Spencer ◽  
Eyiyemisi Damisah
Keyword(s):  
Right Hemisphere ◽  
Preoperative Evaluation ◽  
Epileptiform Activity ◽  
Neurological Deficits ◽  
Depth Electrodes ◽  
Depth Electrode ◽  
Patient Reported ◽  
Frontal Operculum ◽  
History Of ◽  
Electroencephalogram Eeg

Abstract The insula is well established as an epileptogenic area.1 Insular epilepsy surgery demands precise anatomic knowledge2-4 and tailored removal of the epileptic zone with careful neuromonitoring.5 We present an operative video illustrating an intracranial electroencephalogram (EEG) depth electrode guided anterior insulectomy.  We report a 17-yr-old right-handed woman with a 4-yr history of medically refractory epilepsy. The patient reported daily nocturnal ictal vocalization preceded by an indescribable feeling. Preoperative evaluation was suggestive of a right frontal-temporal onset, but the noninvasive results were discordant. She underwent a combined intracranial EEG study with a frontal-parietal grid, with strips and depth electrodes covering the entire right hemisphere. Epileptiform activity was observed in contact 6 of the anterior insula electrode. The patient consented to the procedure and to the publication of her images.  A right anterior insulectomy was performed. First, a portion of the frontal operculum was resected and neuronavigation was used for the initial insula localization. However, due to unreliable neuronavigation (ie, brain shift), the medial and anterior borders of the insular resection were guided by the depth electrode reference. The patient was discharged 3 d after surgery with no neurological deficits and remains seizure free.  We demonstrate that depth electrode guided insular surgery is a safe and precise technique, leading to an optimal outcome.

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