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  1. Article ; Online: Targeted continuous EEG monitoring in critically ill patients: The long and the short of a scalability problem.

    Haider, Hiba A / Perucca, Piero

    Epilepsia open

    2023  Volume 8, Issue 3, Page(s) 721–723

    MeSH term(s) Humans ; Critical Illness ; Monitoring, Physiologic ; Seizures ; Electroencephalography
    Language English
    Publishing date 2023-06-27
    Publishing country United States
    Document type Journal Article ; Comment
    ISSN 2470-9239
    ISSN (online) 2470-9239
    DOI 10.1002/epi4.12777
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  2. Article ; Online: Depth versus surface: A critical review of subdural and depth electrodes in intracranial electroencephalographic studies.

    Wu, Shasha / Issa, Naoum P / Rose, Sandra L / Haider, Hiba A / Nordli, Douglas R / Towle, Vernon L / Warnke, Peter C / Tao, James X

    Epilepsia

    2024  

    Abstract: Intracranial electroencephalographic (IEEG) recording, using subdural electrodes (SDEs) and stereoelectroencephalography (SEEG), plays a pivotal role in localizing the epileptogenic zone (EZ). SDEs, employed for superficial cortical seizure foci ... ...

    Abstract Intracranial electroencephalographic (IEEG) recording, using subdural electrodes (SDEs) and stereoelectroencephalography (SEEG), plays a pivotal role in localizing the epileptogenic zone (EZ). SDEs, employed for superficial cortical seizure foci localization, provide information on two-dimensional seizure onset and propagation. In contrast, SEEG, with its three-dimensional sampling, allows exploration of deep brain structures, sulcal folds, and bihemispheric networks. SEEG offers the advantages of fewer complications, better tolerability, and coverage of sulci. Although both modalities allow electrical stimulation, SDE mapping can tessellate cortical gyri, providing the opportunity for a tailored resection. With SEEG, both superficial gyri and deep sulci can be stimulated, and there is a lower risk of afterdischarges and stimulation-induced seizures. Most systematic reviews and meta-analyses have addressed the comparative effectiveness of SDEs and SEEG in localizing the EZ and achieving seizure freedom, although discrepancies persist in the literature. The combination of SDEs and SEEG could potentially overcome the limitations inherent to each technique individually, better delineating seizure foci. This review describes the strengths and limitations of SDE and SEEG recordings, highlighting their unique indications in seizure localization, as evidenced by recent publications. Addressing controversies in the perceived usefulness of the two techniques offers insights that can aid in selecting the most suitable IEEG in clinical practice.
    Language English
    Publishing date 2024-05-09
    Publishing country United States
    Document type Journal Article ; Review
    ZDB-ID 216382-2
    ISSN 1528-1167 ; 0013-9580
    ISSN (online) 1528-1167
    ISSN 0013-9580
    DOI 10.1111/epi.18002
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    Zs.A 517: Show issues Location:
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  3. Article ; Online: Putative roles for homeostatic plasticity in epileptogenesis.

    Issa, Naoum P / Nunn, Katherine C / Wu, Shasha / Haider, Hiba A / Tao, James X

    Epilepsia

    2023  Volume 64, Issue 3, Page(s) 539–552

    Abstract: Homeostatic plasticity allows neural circuits to maintain an average activity level while preserving the ability to learn new associations and efficiently transmit information. This dynamic process usually protects the brain from excessive activity, like ...

    Abstract Homeostatic plasticity allows neural circuits to maintain an average activity level while preserving the ability to learn new associations and efficiently transmit information. This dynamic process usually protects the brain from excessive activity, like seizures. However, in certain contexts, homeostatic plasticity might produce seizures, either in response to an acute provocation or more chronically as a driver of epileptogenesis. Here, we review three seizure conditions in which homeostatic plasticity likely plays an important role: acute drug withdrawal seizures, posttraumatic or disconnection epilepsy, and cyclic seizures. Identifying the homeostatic mechanisms active at different stages of development and in different circuits could allow better targeting of therapies, including determining when neuromodulation might be most effective, proposing ways to prevent epileptogenesis, and determining how to disrupt the cycle of recurring seizure clusters.
    MeSH term(s) Humans ; Epilepsy ; Seizures ; Brain ; Homeostasis/physiology ; Neuronal Plasticity
    Language English
    Publishing date 2023-01-18
    Publishing country United States
    Document type Journal Article ; Review ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
    ZDB-ID 216382-2
    ISSN 1528-1167 ; 0013-9580
    ISSN (online) 1528-1167
    ISSN 0013-9580
    DOI 10.1111/epi.17500
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  4. Article ; Online: Association of Epileptiform Abnormality on Electroencephalography with Development of Epilepsy After Acute Brain Injury.

    Chen, Denise F / Kumari, Polly / Haider, Hiba A / Ruiz, Andres Rodriguez / Lega, Julia / Dhakar, Monica B

    Neurocritical care

    2021  Volume 35, Issue 2, Page(s) 428–433

    Abstract: Background/objectives: Epileptiform abnormalities (EA) on continuous electroencephalography (cEEG) are associated with increased risk of acute seizures; however, data on their association with development of long-term epilepsy are limited. We aimed to ... ...

    Abstract Background/objectives: Epileptiform abnormalities (EA) on continuous electroencephalography (cEEG) are associated with increased risk of acute seizures; however, data on their association with development of long-term epilepsy are limited. We aimed to investigate the association of EA in patients with acute brain injury (ABI): ischemic or hemorrhagic stroke, traumatic brain injury, encephalitis, or posterior reversible encephalopathy syndrome, and subsequent development of epilepsy.
    Methods: This was a retrospective, single-center study of patients with ABI who had at least 6 hours of cEEG during the index admission between 1/1/2017 and 12/31/2018 and at least 12 months of follow-up. We compared patients with EAs; defined as lateralized periodic discharges (LPDs), lateralized rhythmic delta activity (LRDA), generalized periodic discharges (GPDs), and sporadic interictal epileptiform discharges (sIEDs) to patients without EAs on cEEG. The primary outcome was the new development of epilepsy, defined as the occurrence of spontaneous clinical seizures following hospital discharge. Secondary outcomes included time to development of epilepsy and use of anti-seizure medications (ASMs) at the time of last follow-up visit.
    Results: One hundred and one patients with ABI met study inclusion criteria. Thirty-one patients (30.7%) had EAs on cEEG. The median (IQR) time to cEEG was 2 (1-5) days. During a median (IQR) follow-up period of 19.1 (16.2-24.3) months, 25.7% of patients developed epilepsy; the percentage of patients who developed epilepsy was higher in those with EAs compared to those without EAs (41.9% vs. 18.6%, p = 0.025). Patients with EAs were more likely to be continued on ASMs during follow-up compared to patients without EAs (67.7% vs. 38.6%, p = 0.009). Using multivariable Cox regression analysis, after adjusting for age, mental status, electrographic seizures on cEEG, sex, ABI etiology, and ASM treatment on discharge, patients with EAs had a significantly increased risk of developing epilepsy compared to patients without EA (hazard ratio 3.39; 95% CI 1.39-8.26; p = 0.007).
    Conclusions: EAs on cEEG in patients with ABI are associated with a greater than three-fold increased risk of new-onset epilepsy. cEEG findings in ABI may therefore be a useful risk stratification tool for assessing long-term risk of seizures and serve as a biomarker for new-onset epilepsy.
    MeSH term(s) Brain Injuries ; Electroencephalography ; Epilepsy/drug therapy ; Epilepsy/epidemiology ; Epilepsy/etiology ; Humans ; Posterior Leukoencephalopathy Syndrome ; Retrospective Studies
    Language English
    Publishing date 2021-01-20
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2381896-7
    ISSN 1556-0961 ; 1541-6933
    ISSN (online) 1556-0961
    ISSN 1541-6933
    DOI 10.1007/s12028-020-01182-0
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  5. Article ; Online: Thirty-day readmission after status epilepticus in the United States: Insights from the nationwide readmission database.

    Dhakar, Monica B / Thurman, David J / Haider, Hiba A / Rodriguez, Andres R / Jette, Nathalie / Faught, Edward

    Epilepsy research

    2020  Volume 165, Page(s) 106346

    Abstract: Objective: To determine the incidence, causes, predictors, and costs of 30-day readmissions in patients admitted with status epilepticus (SE) from a large representative United States (US) population.: Methods: Adults (age ≥18 years) hospitalized ... ...

    Abstract Objective: To determine the incidence, causes, predictors, and costs of 30-day readmissions in patients admitted with status epilepticus (SE) from a large representative United States (US) population.
    Methods: Adults (age ≥18 years) hospitalized with a primary diagnosis of SE (International Classification of Diseases-Ninth Revision-CM codes 345.2 or 345.3) between January 2013 and September 2015 were identified using the Nationwide Readmissions Database. A multivariable logistic regression model was used to identify predictors of 30-day readmissions.
    Results: Of 42,232 patients with index SE, 6372 (15.0%) were readmitted within 30 days. In the multivariable analysis, intracranial hemorrhage (odds ratio, 1.56; 95% confidence interval, 1.12-2.18), psychosis (1.26 95%, 1.05-1.50), diabetes mellitus (1.12, 95%, 1.00-1.25), chronic kidney disease (1.50, 95%, 1.31-1.72), chronic liver disease (1.51; 95%, 1.24-1.84), >3 Elixhauser comorbidities (1.18; 95%, 1.06-1.31), length of stay >4 days during index hospitalization (1.41; 95%, 1.28-1.56) and discharge to skilled nursing facility (SNF) (1.14; 95%, 1.01-1.28) were independent predictors of 30-day readmission. The most common reason for readmission was seizures (45.1%). Median length of stay and costs of readmission were 4 days (interquartile range [IQR], 2-7 days) and $7882 (IQR, $4649-$15,012), respectively.
    Conclusion: Thirty-day readmissions after SE occurs in 15% of patients, the majority of which were due to seizures. Readmitted patients are more likely to have multiple comorbidities, a longer length of stay, and discharge to SNF. Awareness of these predictors can help identify and target high-risk patients for interventions to reduce readmissions and costs.
    MeSH term(s) Adult ; Aged ; Female ; Humans ; Length of Stay/economics ; Male ; Middle Aged ; Patient Discharge/economics ; Patient Readmission/economics ; Postoperative Complications/economics ; Postoperative Complications/epidemiology ; Risk Factors ; Time Factors
    Language English
    Publishing date 2020-05-18
    Publishing country Netherlands
    Document type Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
    ZDB-ID 632939-1
    ISSN 1872-6844 ; 0920-1211
    ISSN (online) 1872-6844
    ISSN 0920-1211
    DOI 10.1016/j.eplepsyres.2020.106346
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    Zs.A 2193: Show issues Location:
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  6. Article ; Online: Epileptiform Abnormalities in Acute Ischemic Stroke: Impact on Clinical Management and Outcomes.

    Dhakar, Monica B / Sheikh, Zubeda / Kumari, Polly / Lawson, Eric C / Jeanneret, Valerie / Desai, Dhaval / Rodriguez Ruiz, Andres / Haider, Hiba A

    Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society

    2020  Volume 39, Issue 6, Page(s) 446–452

    Abstract: Purpose: Studies examining seizures (Szs) and epileptiform abnormalities (EAs) using continuous EEG in acute ischemic stroke (AIS) are limited. Therefore, we aimed to describe the prevalence of Sz and EA in AIS, its impact on anti-Sz drug management, ... ...

    Abstract Purpose: Studies examining seizures (Szs) and epileptiform abnormalities (EAs) using continuous EEG in acute ischemic stroke (AIS) are limited. Therefore, we aimed to describe the prevalence of Sz and EA in AIS, its impact on anti-Sz drug management, and association with discharge outcomes.
    Methods: The study included 132 patients with AIS who underwent continuous EEG monitoring >6 hours. Continuous EEG was reviewed for background, Sz and EA (lateralized periodic discharges [LPD], generalized periodic discharges, lateralized rhythmic delta activity, and sporadic epileptiform discharges). Relevant clinical, demographic, and imaging factors were abstracted to identify risk factors for Sz and EA. Outcomes included all-cause mortality, functional outcome at discharge (good outcome as modified Rankin scale of 0-2 and poor outcome as modified Rankin scale of 3-6) and changes to anti-Sz drugs (escalation or de-escalation).
    Results: The frequency of Sz was 7.6%, and EA was 37.9%. Patients with Sz or EA were more likely to have cortical involvement (84.6% vs. 67.5% P = 0.028). Among the EAs, the presence of LPD was associated with an increased risk of Sz (25.9% in LPD vs. 2.9% without LPD, P = 0.001). Overall, 21.2% patients had anti-Sz drug changes because of continuous EEG findings, 16.7% escalation and 4.5% de-escalation. The presence of EA or Sz was not associated with in-hospital mortality or discharge functional outcomes.
    Conclusions: Despite the high incidence of EA, the rate of Sz in AIS is relatively lower and is associated with the presence of LPDs. These continuous EEG findings resulted in anti-Sz drug changes in one-fifth of the cohort. Epileptiform abnormality and Sz did not affect mortality or discharge functional outcomes.
    MeSH term(s) Electroencephalography/methods ; Humans ; Ischemic Stroke ; Monitoring, Physiologic ; Retrospective Studies ; Risk Factors ; Seizures
    Language English
    Publishing date 2020-12-08
    Publishing country United States
    Document type Journal Article
    ZDB-ID 605640-4
    ISSN 1537-1603 ; 0736-0258
    ISSN (online) 1537-1603
    ISSN 0736-0258
    DOI 10.1097/WNP.0000000000000801
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    Zs.A 1921: Show issues Location:
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  7. Article ; Online: Electroencephalography in epilepsy: look for what could be beyond the visual inspection.

    Mesraoua, Boulenouar / Deleu, Dirk / Al Hail, Hassan / Melikyan, Gayane / Boon, Paul / Haider, Hiba A / Asadi-Pooya, Ali A

    Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology

    2019  Volume 40, Issue 11, Page(s) 2287–2291

    Abstract: Since its starting point in 1929, human scalp electroencephalography (EEG) has been routinely interpreted by visual inspection of waveforms using the assumption that the activity at a given electrode is a representation of the activity of the cerebral ... ...

    Abstract Since its starting point in 1929, human scalp electroencephalography (EEG) has been routinely interpreted by visual inspection of waveforms using the assumption that the activity at a given electrode is a representation of the activity of the cerebral cortex under it, but such a method has some limitations. In this review, we will discuss three advanced methods to obtain valuable information from scalp EEG in epilepsy using innovative technologies. Authors who had previous publications in the field provided a narrative review. Spike voltage topography of interictal spikes is a potential way to improve non-invasive EEG localization in focal epilepsies. Electrical source imaging is also a complementary technique in localization of the epileptogenic zone in patients who are candidates for epilepsy surgery. Quantitative EEG simplifies the large amount of information in continuous EEG by providing a static graphical display. Scalp electroencephalography has the potential to offer more spatial and temporal information than the traditional way of visual inspection alone in patients with epilepsy. Fortunately, with the help of modern digital EEG equipment and computer-assisted analysis, this information is more accessible.
    MeSH term(s) Electroencephalography/methods ; Electroencephalography/trends ; Epilepsy/diagnosis ; Humans
    Language English
    Publishing date 2019-07-27
    Publishing country Italy
    Document type Journal Article ; Review
    ZDB-ID 2016546-8
    ISSN 1590-3478 ; 1590-1874
    ISSN (online) 1590-3478
    ISSN 1590-1874
    DOI 10.1007/s10072-019-04026-8
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    Zs.A 1877: Show issues Location:
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  8. Article ; Online: A standardized nomenclature for spectrogram EEG patterns: Inter-rater agreement and correspondence with common intensive care unit EEG patterns.

    Zafar, Sahar F / Amorim, Edilberto / Williamsom, Craig A / Jing, Jin / Gilmore, Emily J / Haider, Hiba A / Swisher, Christa / Struck, Aaron / Rosenthal, Eric S / Ng, Marcus / Schmitt, Sarah / Lee, Jong W / Brandon Westover, M

    Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology

    2020  Volume 131, Issue 9, Page(s) 2298–2306

    Abstract: Objective: To determine the inter-rater agreement (IRA) of a standardized nomenclature for EEG spectrogram patterns, and to estimate the probability distribution of ictal-interictal continuum (IIC) patterns vs. other EEG patterns within each category in ...

    Abstract Objective: To determine the inter-rater agreement (IRA) of a standardized nomenclature for EEG spectrogram patterns, and to estimate the probability distribution of ictal-interictal continuum (IIC) patterns vs. other EEG patterns within each category in this nomenclature.
    Methods: We defined seven spectrogram categories: "Solid Flames", "Irregular Flames", "Broadband-monotonous", "Narrowband-monotonous", "Stripes", "Low power", and "Artifact". Ten electroencephalographers scored 115 spectrograms and the corresponding raw EEG samples. Gwet's agreement coefficient was used to calculate IRA.
    Results: Solid Flames represented seizures or IIC patterns 69.4% of the time. Irregular Flames represented seizures or IIC patterns 38.7% of the time. Broadband-monotonous primarily corresponded with seizures or IIC (54.3%) and Narrowband-monotonous with focal or generalized slowing (43.8%). Stripes were associated with burst-suppression (37.2%) and generalized suppression (34.4%). Low Power category was associated with generalized suppression (94%). There was "near perfect" agreement for Solid Flames (κ = 94.36), Low power (κ = 92.61), and Artifact (κ = 93.72). There was "substantial agreement" for all other categories (κ = 74.65-79.49).
    Conclusions: This EEG spectrogram nomenclature has high IRA among electroencephalographers.
    Significance: The nomenclature can be a useful tool for EEG screening. Future studies are needed to determine if using this nomenclature shortens time to IIC identification, and how best to use it in practice to reduce time to intervention.
    MeSH term(s) Brain/physiopathology ; Electroencephalography ; Humans ; Intensive Care Units ; Reference Standards ; Seizures/diagnosis ; Seizures/physiopathology ; Terminology as Topic
    Language English
    Publishing date 2020-06-24
    Publishing country Netherlands
    Document type Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
    ZDB-ID 1463630-x
    ISSN 1872-8952 ; 0921-884X ; 1388-2457
    ISSN (online) 1872-8952
    ISSN 0921-884X ; 1388-2457
    DOI 10.1016/j.clinph.2020.05.032
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  9. Article ; Online: Validation of the 2HELPS2B Seizure Risk Score in Acute Brain Injury Patients.

    Moffet, Eric W / Subramaniam, Thanujaa / Hirsch, Lawrence J / Gilmore, Emily J / Lee, Jong Woo / Rodriguez-Ruiz, Andres A / Haider, Hiba A / Dhakar, Monica B / Jadeja, Neville / Osman, Gamaledin / Gaspard, Nicolas / Struck, Aaron F

    Neurocritical care

    2020  Volume 33, Issue 3, Page(s) 701–707

    Abstract: Background and objective: Seizures are common after traumatic brain injury (TBI), aneurysmal subarachnoid hemorrhage (aSAH), subdural hematoma (SDH), and non-traumatic intraparenchymal hemorrhage (IPH)-collectively defined herein as acute brain injury ( ... ...

    Abstract Background and objective: Seizures are common after traumatic brain injury (TBI), aneurysmal subarachnoid hemorrhage (aSAH), subdural hematoma (SDH), and non-traumatic intraparenchymal hemorrhage (IPH)-collectively defined herein as acute brain injury (ABI). Most seizures in ABI are subclinical, meaning that they are only detectable with EEG. A method is required to identify patients at greatest risk of seizures and thereby in need of prolonged continuous EEG monitoring. 2HELPS2B is a simple point system developed to address this need. 2HELPS2B estimates seizure risk for hospitalized patients using five EEG findings and one clinical finding (pre-EEG seizure). The initial 2HELPS2B study did not specifically assess the ABI subpopulation. In this study, we aim to validate the 2HELPS2B score in ABI and determine its relative predictive accuracy compared to a broader set of clinical and electrographic factors.
    Methods: We queried the Critical Care EEG Monitoring Research Consortium database for ABI patients age ≥ 18 with > 6 h of continuous EEG monitoring; data were collected between February 2013 and November 2018. The primary outcome was electrographic seizure. Clinical factors considered were age, coma, encephalopathy, ABI subtype, and acute suspected or confirmed pre-EEG clinical seizure. Electrographic factors included 18 EEG findings. Predictive accuracy was assessed using a machine-learning paradigm with area under the receiver operator characteristic (ROC) curve as the primary outcome metric. Three models (clinical factors alone, EEG factors alone, EEG and clinical factors combined) were generated using elastic-net logistic regression. Models were compared to each other and to the 2HELPS2B model. All models were evaluated by calculating the area under the curve (AUC) of a ROC analysis and then compared using permutation testing of AUC with bootstrapping to generate confidence intervals.
    Results: A total of 1528 ABI patients were included. Total seizure incidence was 13.9%. Seizure incidence among ABI subtype varied: IPH 17.2%, SDH 19.1%, aSAH 7.6%, TBI 9.2%. Age ≥ 65 (p = 0.015) and pre-cEEG acute clinical seizure (p < 0.001) positively affected seizure incidence. Clinical factors AUC = 0.65 [95% CI 0.60-0.71], EEG factors AUC = 0.82 [95% CI 0.77-0.87], and EEG and clinical factors combined AUC = 0.84 [95% CI 0.80-0.88]. 2HELPS2B AUC = 0.81 [95% CI 0.76-0.85]. The 2HELPS2B AUC did not differ from EEG factors (p = 0.51), or EEG and clinical factors combined (p = 0.23), but was superior to clinical factors alone (p < 0.001).
    Conclusions: Accurate seizure risk forecasting in ABI requires the assessment of EEG markers of pathologic electro-cerebral activity (e.g., sporadic epileptiform discharges and lateralized periodic discharges). The 2HELPS2B score is a reliable and simple method to quantify these EEG findings and their associated risk of seizure.
    MeSH term(s) Brain Injuries/complications ; Brain Injuries/diagnosis ; Electroencephalography ; Humans ; Monitoring, Physiologic ; Risk Factors ; Seizures/diagnosis ; Seizures/etiology
    Language English
    Publishing date 2020-02-26
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2381896-7
    ISSN 1556-0961 ; 1541-6933
    ISSN (online) 1556-0961
    ISSN 1541-6933
    DOI 10.1007/s12028-020-00939-x
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  10. Article ; Online: Comparison of machine learning models for seizure prediction in hospitalized patients.

    Struck, Aaron F / Rodriguez-Ruiz, Andres A / Osman, Gamaledin / Gilmore, Emily J / Haider, Hiba A / Dhakar, Monica B / Schrettner, Matthew / Lee, Jong W / Gaspard, Nicolas / Hirsch, Lawrence J / Westover, M Brandon

    Annals of clinical and translational neurology

    2019  Volume 6, Issue 7, Page(s) 1239–1247

    Abstract: Objective: To compare machine learning methods for predicting inpatient seizures risk and determine the feasibility of 1-h screening EEG to identify low-risk patients (<5% seizures risk in 48 h).: Methods: The Critical Care EEG Monitoring Research ... ...

    Abstract Objective: To compare machine learning methods for predicting inpatient seizures risk and determine the feasibility of 1-h screening EEG to identify low-risk patients (<5% seizures risk in 48 h).
    Methods: The Critical Care EEG Monitoring Research Consortium (CCEMRC) multicenter database contains 7716 continuous EEGs (cEEG). Neural networks (NN), elastic net logistic regression (EN), and sparse linear integer model (RiskSLIM) were trained to predict seizures. RiskSLIM was used previously to generate 2HELPS2B model of seizure predictions. Data were divided into training (60% for model fitting) and evaluation (40% for model evaluation) cohorts. Performance was measured using area under the receiver operating curve (AUC), mean risk calibration (CAL), and negative predictive value (NPV). A secondary analysis was performed using Monte Carlo simulation (MCS) to normalize all EEG recordings to 48 h and use only the first hour of EEG as a "screening EEG" to generate predictions.
    Results: RiskSLIM recreated the 2HELPS2B model. All models had comparable AUC: evaluation cohort (NN: 0.85, EN: 0.84, 2HELPS2B: 0.83) and MCS (NN: 0.82, EN; 0.82, 2HELPS2B: 0.81) and NPV (absence of seizures in the group that the models predicted to be low risk): evaluation cohort (NN: 97%, EN: 97%, 2HELPS2B: 97%) and MCS (NN: 97%, EN: 99%, 2HELPS2B: 97%). 2HELPS2B model was able to identify the largest proportion of low-risk patients.
    Interpretation: For seizure risk stratification of hospitalized patients, the RiskSLIM generated 2HELPS2B model compares favorably to the complex NN and EN generated models. 2HELPS2B is able to accurately and quickly identify low-risk patients with only a 1-h screening EEG.
    MeSH term(s) Aged ; Aged, 80 and over ; Cohort Studies ; Critical Care ; Electroencephalography ; Female ; Humans ; Machine Learning ; Male ; Monitoring, Physiologic ; Neural Networks, Computer ; Seizures/diagnosis ; Young Adult
    Language English
    Publishing date 2019-06-27
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2740696-9
    ISSN 2328-9503 ; 2328-9503
    ISSN (online) 2328-9503
    ISSN 2328-9503
    DOI 10.1002/acn3.50817
    Database MEDical Literature Analysis and Retrieval System OnLINE

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