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Ictal quantitative surface electromyography correlates with postictal EEG suppression.

ABSTRACT: OBJECTIVE:To test the hypothesis that neurophysiologic biomarkers of muscle activation during convulsive seizures reveal seizure severity and to determine whether automatically computed surface EMG parameters during seizures can predict postictal generalized EEG suppression (PGES), indicating increased risk for sudden unexpected death in epilepsy. Wearable EMG devices have been clinically validated for automated detection of generalized tonic-clonic seizures. Our goal was to use quantitative EMG measurements for seizure characterization and risk assessment. METHODS:Quantitative parameters were computed from surface EMGs recorded during convulsive seizures from deltoid and brachial biceps muscles in patients admitted to long-term video-EEG monitoring. Parameters evaluated were the durations of the seizure phases (tonic, clonic), durations of the clonic bursts and silent periods, and the dynamics of their evolution (slope). We compared them with the duration of the PGES. RESULTS:We found significant correlations between quantitative surface EMG parameters and the duration of PGES (p < 0.001). Stepwise multiple regression analysis identified as independent predictors in deltoid muscle the duration of the clonic phase and in biceps muscle the duration of the tonic-clonic phases, the average silent period, and the slopes of the silent period and clonic bursts. The surface EMG-based algorithm identified seizures at increased risk (PGES ≥20 seconds) with an accuracy of 85%. CONCLUSIONS:Ictal quantitative surface EMG parameters correlate with PGES and may identify seizures at high risk. CLASSIFICATION OF EVIDENCE:This study provides Class II evidence that during convulsive seizures, surface EMG parameters are associated with prolonged postictal generalized EEG suppression.

SUBMITTER: Arbune AA

PROVIDER: S-EPMC7455333 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:It is not fully understood how seizures terminate and why some seizures are followed by a period of complete brain activity suppression, postictal generalized EEG suppression. This is clinically relevant as there is a potential association between postictal generalized EEG suppression, cardiorespiratory arrest and sudden death following a seizure. We combined human encephalographic seizure data with data of a computational model of seizures to elucidate the neuronal network dynamics underlying seizure termination and the postictal generalized EEG suppression state. A multi-unit computational neural mass model of epileptic seizure termination and postictal recovery was developed. The model provided three predictions that were validated in EEG recordings of 48 convulsive seizures from 48 subjects with refractory focal epilepsy (20 females, age range 15-61 years). The duration of ictal and postictal generalized EEG suppression periods in human EEG followed a gamma probability distribution indicative of a deterministic process (shape parameter 2.6 and 1.5, respectively) as predicted by the model. In the model and in humans, the time between two clonic bursts increased exponentially from the start of the clonic phase of the seizure. The terminal interclonic interval, calculated using the projected terminal value of the log-linear fit of the clonic frequency decrease was correlated with the presence and duration of postictal suppression. The projected terminal interclonic interval explained 41% of the variation in postictal generalized EEG suppression duration (P < 0.02). Conversely, postictal generalized EEG suppression duration explained 34% of the variation in the last interclonic interval duration. Our findings suggest that postictal generalized EEG suppression is a separate brain state and that seizure termination is a plastic and autonomous process, reflected in increased duration of interclonic intervals that determine the duration of postictal generalized EEG suppression.

Project description:Postictal generalized EEG suppression is the state of suppression of electrical activity at the end of a seizure. Prolongation of this state has been associated with increased risk of sudden unexpected death in epilepsy, making characterization of underlying electrical rhythmic activity during postictal suppression an important step in improving epilepsy treatment. Phase-amplitude coupling in EEG reflects cognitive coding within brain networks and some of those codes highlight epileptic activity; therefore, we hypothesized that there are distinct phase-amplitude coupling features in the postictal suppression state that can provide an improved estimate of this state in the context of patient risk for sudden unexpected death in epilepsy. We used both intracranial and scalp EEG data from eleven patients (six male, five female; age range 21-41 years) containing 25 seizures, to identify frequency dynamics, both in the ictal and postictal EEG suppression states. Cross-frequency coupling analysis identified that during seizures there was a gradual decrease of phase frequency in the coupling between delta (0.5-4 Hz) and gamma (30+ Hz), which was followed by an increased coupling between the phase of 0.5-1.5 Hz signal and amplitude of 30-50 Hz signal in the postictal state as compared to the pre-seizure baseline. This marker was consistent across patients. Then, using these postictal-specific features, an unsupervised state classifier-a hidden Markov model-was able to reliably classify four distinct states of seizure episodes, including a postictal suppression state. Furthermore, a connectome analysis of the postictal suppression states showed increased information flow within the network during postictal suppression states as compared to the pre-seizure baseline, suggesting enhanced network communication. When the same tools were applied to the EEG of an epilepsy patient who died unexpectedly, ictal coupling dynamics disappeared and postictal phase-amplitude coupling remained constant throughout. Overall, our findings suggest that there are active postictal networks, as defined through coupling dynamics that can be used to objectively classify the postictal suppression state; furthermore, in a case study of sudden unexpected death in epilepsy, the network does not show ictal-like phase-amplitude coupling features despite the presence of convulsive seizures, and instead demonstrates activity similar to postictal. The postictal suppression state is a period of elevated network activity as compared to the baseline activity which can provide key insights into the epileptic pathology.

Project description:ObjectivesIn refractory status epilepticus (RSE), the optimal degree of suppression (EEG burst suppression or merely suppressing seizures) remains unknown. Many centers lacking continuous EEG must default to serial intermittent recordings where uncertainty from lack of data may prompt more aggressive suppression. In this study, we sought to determine whether the quantitative burst suppression ratio (QBSR) from serial intermittent EEG recording is associated with RSE patient outcome.MethodsWe screened the EEG database to identify non-anoxic adult RSE patients for EEG and chart review. QBSR was calculated per 10-second EEG epoch as the percentage of time during which EEG amplitude was <3 µV. Patients who survived 1-3 months after discharge from ICU and hospital comprised the favorable group. Further to initial unadjusted univariate analysis of all pooled QBSR, we conducted multivariate analyses to account for individual patient confounders ("per-capita analysis"), uneven number of EEG recordings ("per-session analysis"), and uneven number of epochs ("per-epoch analysis"). We analyzed gender, anesthetic number, and adjusted status epilepticus severity score (aSTESS) as confounders.ResultsIn 135,765 QBSR values over 160 EEG recordings (median 2.17 h every ≥24 h) from 17 patients on Propofol, Midazolam, and/or Ketamine, QBSR was deeper in the favorable group (p < 0.001) on initial unadjusted analysis. However, on adjusted multivariate analysis, there was consistently no association between QBSR and outcome. Higher aSTESS consistently associated with unfavorable outcome on per-capita (p = 0.033), per-session (p = 0.048) and per-epoch (p < 0.001) analyses. Greater maximal number of non-barbiturate anesthetic associated with favorable outcome on per-epoch analysis (p < 0.001).ConclusionsThere was no association between depth of EEG suppression using non-barbiturate anesthetic and RSE patient outcome based on QBSR from serial intermittent EEG. A per-epoch association between non-barbiturate anesthetic and favorable outcome suggests an effect from non-suppressive time-varying EEG content.SignificanceTargeting and following deeper burst suppression through non-barbiturate anesthetics on serial intermittent EEG monitoring of RSE is of limited utility.

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Ictal quantitative surface electromyography correlates with postictal EEG suppression.

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