Video-EEG

Role of specific interictal and ictal EEG onset patterns

The objective of this review is to present the role of specific interictal and ictal EEG onset patterns during scalp video-EEG monitoring. Specific non-epileptiform abnormalities include temporal intermittent rhythmic delta activity (TIRDA) and temporal intermittent rhythmic theta activity (TIRTA) indicating a temporal lobe seizure onset, while interictal rhythmical midline theta activity occurs more frequently in frontal epilepsy. Specific interictal epileptiform abnormalities comprise Type 1 spikes pointing towards a mesial and Type 2spikes indicating a lateral temporal irritative zone. Unilateral temporal interictal epileptiform discharges (IEDs) are predictive for a good surgical seizure outcome in temporal lobe epilepsy. Small sharp spikes (SSS) named Benign Epileptiform Transients of Sleep (BETS) in the past represent scalp EEG markers of hippocampal epileptic activity. While the localizing value of IEDs in extratemporal epilepsies is often limited, a consistently localized spike focus predicts a good surgical seizure outcome in non-lesional extratemporal patients. A specific ictal EEG pattern for mesial temporal lobe epilepsy consists of a 5-9 Hz rhythmic temporal activity which also predicts a good surgical outcome. In extratemporal epilepsies, ictal scalp EEG frequently is non-localized. Concerning the correspondence of ictal scalp-EEG and intracranial EEG (iEEG) patterns there is no simple one-to-one relationship. Scalp-EEG and iEEG patterns correspond closer to each other when there is no delay between clinical and scalp-EEG onset. Paroxysmal fast activity on scalp-EEG matches with low-voltage fast activity on iEEG. Repetitive epileptiform discharges on scalp EEG indicate an underlying focal cortical dysplasia.

The ENCEVIS algorithm in the EMU and the factors affecting its performance: Our experience

The study's purpose was to assess the seizure detection performance of ENCEVIS 1.7, identify factors that may influence algorithm performance, and explore its potential for implementation and application in long-term video EEG monitoring units. The study included video-EEG recordings containing at least one epileptic seizure. Forty-three recordings, encompassing 112 seizures, were included in the analysis. True positive, false negative, and false positive seizure detections were defined. Factors that may influence algorithm performance were studied. ENCEVIS demonstrated an overall sensitivity of 71.2%, significantly higher (75.1%) in focal compared to generalized seizures (62%). Ictal patterns rhythmicity (rhythmic 59.4 %, arrhythmic 41.7 %), seizure duration (<10 sec 6.3 %, >60 sec. 63.9 % (p < 0.05)) and patient age (<18 years 39.5 %, >18 years 58.1 % (P < 0.05)) influenced ENCEVIS sensitivity. The coexistence of extracerebral signal changes did not influence sensitivity. ENCEVIS with 79.1% accuracy annotates at least one seizure in those recordings containing epileptic seizures. ENCEVIS seizure detection performance was reasonable for generalized/focal to bilateral tonic-clonic seizures and seizures with temporal lobe onset. Rhythmic ictal patterns, longer seizure duration, and adult age positively influenced algorithm performance. ENCEVIS can be a valuable tool for identifying recordings containing seizures and can potentially reduce the workload of neurophysiologists.

Machine Learning Characterization of Ictal and Interictal States in EEG Aimed at Automated Seizure Detection

Background: The development of automated seizure detection methods using EEG signals could be of great importance for the diagnosis and the monitoring of patients with epilepsy. These methods are often patient-specific and require high accuracy in detecting seizures but also very low false-positive rates. The aim of this study is to evaluate the performance of a seizure detection method using EEG signals by investigating its performance in correctly identifying seizures and in minimizing false alarms and to determine if it is generalizable to different patients. Methods: We tested the method on about two hours of preictal/ictal and about ten hours of interictal EEG recordings of one patient from the Freiburg Seizure Prediction EEG database using machine learning techniques for data mining. Then, we tested the obtained model on six other patients of the same database. Results: The method achieved very high performance in detecting seizures (close to 100% of correctly classified positive elements) with a very low false-positive rate when tested on one patient. Furthermore, the model portability or transfer analysis revealed that the method achieved good performance in one out of six patients from the same dataset. Conclusions: This result suggests a strategy to discover clusters of similar patients, for which it would be possible to train a general-purpose model for seizure detection.

The additional diagnostic benefits of performing both video-polysomnography and prolonged video-EEG-monitoring: when and why

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Video-polysomnography (VPSG) and prolonged video-EEG-monitoring (pVEEG-M) are indicated for different suspected diagnoses.

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Here, we report on 109 patients who underwent both, VPSG and pVEEG-M, within a 10 year period.

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Both examinations were performed in case of comorbidities, to achieve a final diagnosis or to refute differential diagnoses.

Objective

Video-polysomnography (VPSG) and prolonged video-EEG-monitoring (pVEEG-M) are neurophysiological investigation modalities. Depending on indication either is performed, but occasionally patients undergo both (during the same or separate stays). We sought to assess the reasons and potential benefits of dual diagnostic assessments with both modalities.

Methods

A retrospective chart-review was performed to identify patients who underwent both VPSG and pVEEG-M during the 10 year period between 2007 and 2017. One-hundred-nine patients were identified who had undergone both studies. Patients were grouped according to indication and outcome.

Results

One-hundred-nine patients had both, a VPSG and pVEEG-M, in 62 (56.9%) the studies were performed because of separate diagnoses independent from each other. In 47 patients (43.1%) investigation with both modalities was needed to clarify the suspected diagnosis or to refute differential diagnoses. Out of these 47, 11 (10.1% of the whole group) arrived a new final diagnosis whereas in 36 (33%) the primary diagnosis was corroborated with the second modality.

Conclusions

In the majority of cases VPSG plus pVEEG-M were indicated to diagnose or monitor different comorbid diseases (e.g. sleep-related breathing disorder and epilepsy). In the other cases, performing both modalities was useful to achieve a higher diagnostic accuracy or to refute differential diagnoses.

Significance

VPSG and pVEEG-M are neurophysiological investigations which complement each other, especially in case of two different comorbid diseases in a single patient, to rule out differential diagnosis or when a higher diagnostic certainty is seeked.

Factors Associated With Prolonged Length of Stay in Patients Hospitalized With Generalized Convulsive Status Epilepticus in the United States

Objective

Generalized convulsive status epilepticus (GCSE) is a severe complication of epilepsy, which typically requires extended hospitalization, resulting in substantial resource utilization, hospital expenditures, and patient costs. In this nationwide analysis, we examined hospital length of stay (LOS) patterns for GCSE, and the factors that influence prolonged LOS.

Methods

We extracted data for adult patients (age 18 years and above) with a primary discharge diagnosis of GCSE from the National Inpatient Sample (NIS) from 2006-2014, the largest all-payer inpatient care database in the United States. We computed LOS (≤1, 2-6, and ≥7 days), overall, and across pre-specified patient-related, hospital-related, and healthcare system-related variables available in the NIS. We identified factors independently associated with prolonged hospitalization (2 or more days), using a multivariable logistic regression model.

Results

Of 57,832 discharged with a primary diagnosis of GCSE, 6,133 (10.7%) had a LOS ≤1 day, 27,327 (7.3%) stayed for 2-6 days, and 24,372 (42.1%) stayed for ≥7 days. After adjusting for confounders, patients who were older, female, Black, and Hispanic, who underwent continuous EEG video monitoring, were Medicare beneficiaries, had medical comorbidities, or were admitted to large/urban hospitals, were all significantly more likely to have prolonged LOS.

Conclusion

Over 40% of patients hospitalized for GCSE in the United States spend at least a week in the hospital. Efforts to shorten hospitalization for GCSE may need to primarily focus on patient groups with select sociodemographic and clinical characteristics.

Activation patterns of interictal epileptiform discharges in relation to sleep and seizures: An artificial intelligence driven data analysis

OBJECTIVE

To quantify effects of sleep and seizures on the rate of interictal epileptiform discharges (IED) and to classify patients with epilepsy based on IED activation patterns.

METHODS

We analyzed long-term EEGs from 76 patients with at least one recorded epileptic seizure during monitoring. IEDs were detected with an AI-based algorithm and validated by visual inspection. We then used unsupervised clustering to characterize patient sub-cohorts with similar IED activation patterns regarding circadian rhythms, deep sleep activation, and seizure occurrence.

RESULTS

Five sub-cohorts with similar IED activation patterns were found: "Sporadic" (14%, n = 10) without or few IEDs, "Continuous" (32%, n = 23) with weak circadian/deep sleep or seizure modulation, "Nighttime & seizure activation" (23%, n = 17) with high IED rates during normal sleep times and after seizures but without deep sleep modulation, "Deep sleep" (19%, n = 14) with strong IED modulation during deep sleep, and "Seizure deactivation" (12%, n = 9) with deactivation of IEDs after seizures. Patients showing "Deep sleep" IED pattern were diagnosed with temporal lobe epilepsy in 86%, while 80% of the "Sporadic" cluster were extratemporal.

CONCLUSIONS

Patients with epilepsy can be characterized by using temporal relationships between rates of IEDs, circadian rhythms, deep sleep and seizures.

SIGNIFICANCE

This work presents the first approach to data-driven classification of epilepsy patients based on their fully validated temporal pattern of IEDs.

Systematic analysis and comparison of commercial seizure-detection software

OBJECTIVE

To determine if three different commercially available seizure-detection software packages (Besa 2.0, Encevis 1.7, and Persyst 13) accurately detect seizures with high sensitivity, high specificity, and short detection delay in epilepsy patients undergoing long-term video-electroencephalography (EEG) monitoring (VEM).

METHODS

Comparison of sensitivity (detection rate), specificity (false alarm rate), and detection delay of three commercially available seizure-detection software packages in 81 randomly selected patients with epilepsy undergoing long-term VEM.

RESULTS

Detection rates on a per-patient basis were not significantly different between Besa (mean 67.6%, range 0-100%), Encevis (77.8%, 0-100%) and Persyst (81%, 0-100%; P = .059). False alarm rate (per hour) was significantly different between Besa (mean 0.7/h, range 0.01-6.2/h), Encevis (0.2/h, 0.01-0.5/h), and Persyst (0.9/h, 0.04-6.5/h; P < .001). Detection delay was significantly different between Besa (mean 30 s, range 0-431 s), Encevis (25 s, 2-163 s), and Persyst (20 s, 0-167 s; P = .007). Kappa statistics showed moderate to substantial agreement between the reference standard and each seizure-detection software (Besa: 0.47, 95% confidence interval [CI] 0.36-0.59; Encevis: 0.59, 95% CI 0.47-0.7; Persyst: 0.63, 95% CI 0.51-0.74).

SIGNIFICANCE

Three commercially available seizure-detection software packages showed similar, reasonable sensitivities on the same data set, but differed in false alarm rates and detection delay. Persyst 13 showed the highest detection rate and false alarm rate with the shortest detection delay, whereas Encevis 1.7 had a slightly lower sensitivity, the lowest false alarm rate, and longer detection delay.

Sudden cardiac arrest in epilepsy patients undergoing continuous video electroencephalogram monitoring: The national inpatient sample

OBJECTIVE

To examine the relationship between epilepsy and sudden cardiac arrest (SCA) and identify clinical and healthcare system related predictors of SCA in patients with a discharge diagnosis of epilepsy undergoing continuous video EEG (cVEEG) monitoring.

METHODS

The national inpatient sample was used as data source to identify adults (18 years and older) with a primary discharge diagnosis of epilepsy who were at some point during their hospitalization on cVEEG monitoring. We applied a logistic regression model to identify independent patient-related and hospital/healthcare system-related factors associated with SCA.

RESULTS

A total of weighted 10,059 (0.71 %) patients with epilepsy on cVEEG had a secondary discharge diagnosis of SCA. The main independent factors associated with SCA were the presence of any of the following secondary discharge diagnoses: paroxysmal arrhythmia (OR: 2.29, 95 %CI: 1.96-2.66), myocardial infarction (OR: 3.78, 95 %CI: 2.83-5.05), congestive heart failure (OR: 2.27, 95 %CI: 1.93-2.62), and anoxic brain injury (OR: 57.6, 95 %CI: 50.83-67.27). There was no association between refractory epilepsy and SCA (OR: 0.99, 95 %CI: 0.51-1.93).

CONCLUSION

SCA is a rare event occurring in < 1% of patients with epilepsy undergoing cVEEG monitoring in the United States. Key independent contributors to occurrence of SCA are presence of select cardiovascular conditions and anoxic brain injury.