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

Abstract

Introduction: It remains controversial whether the periodic discharges (PDs) pattern is an ictal or interictal phenomenon. The aims of the study are to apply time-frequency and power spectrum analysis to study the PDs pattern and prediction of seizures.Methods: We retrospectively searched continuous electroencephalography (cEEG) recordings to identify patients exhibiting the PDs pattern. Artifact-free cEEG segments demonstrating the PDs pattern with stable baselines were chosen for time-frequency and power spectrum analysis.Results: In total, 72 patients (1.3%) exhibited the PDs pattern, with a mean age 36.0 ± 20.7 years (range, 8–76 years). The median spectral power of PDs with a length of 60 s was 70.94 μV2 and that of PDs with a length of 20 s was 195.80 μV2. During follow-up, patients with spectral power of PDs of length 60 and 20 s lower than 28.65 and 36.09 μV2, respectively, exhibited no seizure. For predicting seizures, when the spectral power for PDs of 60 and 20 s equaled to 17.26 and 21.40 μV2, respectively, the diagnostic sensitivity was 100% and specificity was 86%. The locations of maximal spectral power of PDs, crude seizure onset zone (SOZ) judged from scalp EEG, and the most prominent regions of hyper- or hypo-metabolism on FDG-PET were congruent.Conclusions: Spectral power might be a candidate seizure marker of the PDs pattern. High spectral power predicted a high risk of seizures, and low spectral power was associated with a low risk of seizures.

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

“…This effect tends to be higher in those animals previously treated with magnet, reaching significant values at theta band. The reduction we observed in these bands agrees with other authors 38 that found that any increase is associated to a greater number of seizures and the other way around. Our study showed that the theta band was the most affected frequency, and it fits well with proposals from other authors, namely, that the theta band is the preferred frequency for investigating the brain-network dysfunction 26 , and that this frequency band is a good epileptic marker 39 .…”

“…(iii) Power spectral analysis, a measure related with the severity of the epilepsy 38 shows a decrease in all bands studied (delta, theta, alfa, and beta) after applying the diazepam. This effect tends to be higher in those animals previously treated with magnet, reaching significant values at theta band.…”

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

“…PSD analysis of EEG is a widely used method for quantifying power in a specific frequency band reflecting the process of regional brain activities 27 , 28 , which has been used for evaluating dynamic changes in neural activity during the initiation and propagation of seizures 29 and for exploring markers to predict the risk of seizures 30 . According to previous literature 29 , 30 , increased PSD can represent increases in neural activities, which may be a predisposition to seizure.…”

EEG Oscillatory Power and Complexity for Epileptic Seizure Detection

“…Investigations of EEG patterns of seizure have shown that, in general, seizure events have been observed within a wide range of EEG bandwidths. Likewise, their amplitude and frequency in the delta (1-4 Hz), theta (4-8 Hz), alpha (8)(9)(10)(11)(12)(13)(14), beta (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), and gamma bands are significantly different than normal [17,18]. In line with this observation, several studies have used EEG spectral power density (PSD) for seizure detection [3,7,8,19].…”

“…In a recent study, a comparison of power spectrum analysis and time-frequency analysis was performed. The results revealed that power spectrum features are likely to be seizure markers, and there was a significant difference between the distribution of the power spectrum in seizure segments versus segments in which there were no seizures [23]. Time and frequency domain signal complexity measures have also been used to describe EEG data and serve for the classification of normal and seizure EEG data patterns [2,5,7,[11][12][13][17][18][19]24].…”