Can Spectral Power Be Used as a Candidate Seizure Marker of the Periodic Discharges Pattern?

Inherent seizure susceptibility in patients with antihistamine-induced acute symptomatic seizure: a resting-state EEG analysis

We compared neural activities and network properties between the antihistamine-induced seizures (AIS) and seizure-free groups, with the hypothesis that patients with AIS might have inherently increased neural activities and network properties that are easily synchronized. Resting-state electroencephalography (EEG) data were collected from 27 AIS patients and 30 healthy adults who had never had a seizure. Power spectral density analysis was used to compare neural activities in each localized region. Functional connectivity (FC) was measured using coherence, and graph theoretical analyses were performed to compare network properties between the groups. Machine learning algorithms were applied using measurements found to be different between the groups in the EEG analyses as input features. Compared with the seizure-free group, the AIS group showed a higher spectral power in the entire regions of the delta, theta, and beta bands, as well as in the frontal areas of the alpha band. The AIS group had a higher overall FC strength, as well as a shorter characteristic path length in the theta band and higher global efficiency, local efficiency, and clustering coefficient in the beta band than the seizure-free group. The Support Vector Machine, k-Nearest Neighbor, and Random Forest models distinguished the AIS group from the seizure-free group with a high accuracy of more than 99%. The AIS group had seizure susceptibility considering both regional neural activities and functional network properties. Our findings provide insights into the underlying pathophysiological mechanisms of AIS and may be useful for the differential diagnosis of new-onset seizures in the clinical setting.

Synergistic effects of applying static magnetic fields and diazepam to improve EEG abnormalities in the pilocarpine epilepsy rat model

The lithium-pilocarpine rat model is a well-known model of temporal epilepsy. Recently we found that transcranial static magnetic stimulation (tSMS) delay and reduce the signs of EEG in this model. We aim to test the effect of combining the therapeutic action of tSMS and diazepam, a drug used to treat status epilepticus. We induce epilepsy in 12 Sprague-Dawley rats. Animals were classified as "magnet" when a magnetic neodymium cylinder was placed over the skull or "control" when a stainless-steel replica was used. Diazepam was injected 60-min after the second doses of pilocarpine injection. We found a reduction in the number of spikes/minute for magnet condition compared with sham condition, reaching significance at 60 min after diazepam injection. The Root-Mean-Square shown a significant reduction in magnet animals compared with those receiving diazepam (Tukey's-test 30 and 60 min after diazepam injection, p < 0.01; 40 and 50 min after diazepam injection, p < 0.05). Furthermore, the power spectrum analysis shown a reduction in delta, theta, alpha and beta bands, on the diazepam + magnet animals compared to the diazepam + sham group. Analysis of high-frequency oscillations revealed an increased in the ripples due to pilocarpine being reduced by diazepam. Our results demonstrate that application of tSMS previously to diazepam potentiates the effect of the drug by reducing the electroencephalographic pattern associated with epileptiform discharges. We suggest a new synergistic cooperation between pharmacology and neuromodulation as a future treatment for epilepsy.

Power spectrum analysis and outcomes of non-convulsive status epilepticus: a single-center study

OBJECTIVES

Diagnosis of non-convulsive status epilepticus (NCSE) is challenging and outcomes during follow-up are not clear. This study aimed to conduct power spectrum analysis in NCSE and measure outcomes of patients.

METHODS

We searched continuous EEG monitoring (cEEG) recordings to identify patients of NCSE. An artifact-free cEEG epoch of continuous 60 s was chosen for spectral power analysis. We also collected electronic medical records of the patients for extracting clinical information. Patients recruited were followed up at least every half a year.

RESULTS

There were 48 patients with 64 independent NCSE episodes during different course of disease recruited in the study, with a mean age of 40.3 ± 19.1 years (range, 12-72 years), including 24 males (50%) and 24 females (50%). When the spectral power of 60 s equaled to 11.30 μV² for predicting impairment of consciousness, (sensitivity, specificity) = (0.979, 0.625). When the spectral power of 60 s equaled to 52.70 μV² for predicting myoclonic jerks, (sensitivity, specificity) = (0.783, 0.756). There were 27 patients (56.3%) followed up with a duration over 12 months. Nineteen patients (70.4%) continued to have seizures. Eleven (40.7%) resisted to at least two kinds of appropriate anti-seizure medication at maximum tolerated levels. Five patients with prolonged NCSE suffered from loss of brain parenchymal volume on follow-up MRI scans.

CONCLUSION

Spectral power analysis can be used to detect mental status and limb jerks. Early diagnosis and treatment of NCSE are important, which can influence outcomes of the patients during follow-up.

Brain network analysis of interictal epileptiform discharges from ECoG to identify epileptogenic zone in pediatric patients with epilepsy and focal cortical dysplasia type II: A retrospective study

Objective

For patients with drug-resistant focal epilepsy, intracranial monitoring remains the gold standard for surgical intervention. Focal cortical dysplasia (FCD) is the most common cause of pharmacoresistant focal epilepsy in pediatric patients who usually develop seizures in early childhood. Timely removal of the epileptogenic zone (EZ) is necessary to achieve lasting seizure freedom and favorable developmental and cognitive outcomes to improve the quality of life. We applied brain network analysis to investigate potential biomarkers for the diagnosis of EZ that will aid in the resection for pediatric focal epilepsy patients with FCD type II.

Methods

Ten pediatric patients with focal epilepsy diagnosed as FCD type II and that had a follow-up after resection surgery (Engel class I [n = 9] and Engel class II [n = 1]) were retrospectively included. Time-frequency analysis of phase transfer entropy, graph theory analysis, and power spectrum compensation were combined to calculate brain network parameters based on interictal epileptiform discharges from ECoG.

Results

Clustering coefficient, local efficiency, node out-degree, and node out-strength with higher values are the most reliable biomarkers for the delineation of EZ, and the differences between EZ and margin zone (MZ), and EZ and normal zone (NZ) were significant (p < 0.05; Mann-Whitney U-test, two-tailed). In particular, the difference between MZ and NZ was significant for patients with frontal FCD (MZ > NZ; p < 0.05) but was not significant for patients with extra-frontal FCD.

Conclusions

Brain network analysis, based on the combination of time-frequency analysis of phase transfer entropy, graph theory analysis, and power spectrum compensation, can aid in the diagnosis of EZ for pediatric focal epilepsy patients with FCD type II.

EEG Oscillatory Power and Complexity for Epileptic Seizure Detection

Monitoring patients at risk of epileptic seizure is critical for optimal treatment and ensuing the reduction of seizure risk and complications. In general, seizure detection is done manually in hospitals and involves time-consuming visual inspection and interpretation by experts of electroencephalography (EEG) recordings. The purpose of this study is to investigate the pertinence of band-limited spectral power and signal complexity in order to discriminate between seizure and seizure-free EEG brain activity. The signal complexity and spectral power are evaluated in five frequency intervals, namely, the delta, theta, alpha, beta, and gamma bands, to be used as EEG signal feature representation. Classification of seizure and seizure-free data was performed by prevalent potent classifiers. Substantial comparative performance evaluation experiments were performed on a large EEG data record of 341 patients in the Temple University Hospital EEG seizure database. Based on statistically validated criteria, results show the efficiency of band-limited spectral power and signal complexity when using random forest and gradient-boosting decision tree classifiers (95% of the area under the curve (AUC) and 91% for both F-measure and accuracy). These results support the use of these automatic classification schemes to assist the practicing neurologist interpret EEG records more accurately and without tedious visual inspection.