Phase-amplitude coupling between infraslow and high-frequency activities well discriminates between the preictal and interictal states

Infraslow activity (ISA) and high-frequency activity (HFA) are key biomarkers for studying epileptic seizures. We aimed to elucidate the relationship between ISA and HFA around seizure onset. We enrolled seven patients with drug-resistant focal epilepsy who underwent intracranial electrode placement. We comparatively analyzed the ISA, HFA, and ISA-HFA phase-amplitude coupling (PAC) in the seizure onset zone (SOZ) or non-SOZ (nSOZ) in the interictal, preictal, and ictal states. We recorded 15 seizures. HFA and ISA were larger in the ictal states than in the interictal or preictal state. During seizures, the HFA and ISA of the SOZ were larger and occurred earlier than those of nSOZ. In the preictal state, the ISA-HFA PAC of the SOZ was larger than that of the interictal state, and it began increasing at approximately 87 s before the seizure onset. The receiver-operating characteristic curve revealed that the ISA-HFA PAC of the SOZ showed the highest discrimination performance in the preictal and interictal states, with an area under the curve of 0.926. This study demonstrated the novel insight that ISA-HFA PAC increases before the onset of seizures. Our findings indicate that ISA-HFA PAC could be a useful biomarker for discriminating between the preictal and interictal states.

Predication and Analysis of Epileptic Seizure Neurological Disorder using Intracranial Electroencephalography (iEEG)

Epileptic seizure is one of the neurological brain disorder approximately 50 million of world’s population is affected. Diagnosis of seizure is done using medical test Electroencephalography. Electroencephalography is a technique to record brain signal by placing electrodes on scalp. Electroencephalography suffers from disadvantage such as low spatial resolution and presence of artifact. Intracranial Electroencephalography is used to record brain electrical activity by mounting strip, grid and depth electrodes on surface of brain by surgery. Online standard Intracranial Electroencephalography data is analyzed by our system for predication and analysis of Epileptic seizure. The pre-processing of Intracranial Electroencephalography signal is done and is further analyzed in wavelet domain by implementation of Daubechies Discrete Wavelet Transform. Features were extracted to classify as preictal and ictal state. Analysis of preictal state was carried out for predication of seizure. Intracranial Electroencephalography signals provide better result and accuracy in seizure detection and predication. Earlier warning can also be issued to control seizure with anti- epileptic drugs

Viability of Preictal High-Frequency Oscillation Rates as a Biomarker for Seizure Prediction

Motivation: There is an ongoing search for definitive and reliable biomarkers to forecast or predict imminent seizure onset, but to date most research has been limited to EEG with sampling rates <1,000 Hz. High-frequency oscillations (HFOs) have gained acceptance as an indicator of epileptic tissue, but few have investigated the temporal properties of HFOs or their potential role as a predictor in seizure prediction. Here we evaluate time-varying trends in preictal HFO rates as a potential biomarker of seizure prediction.Methods: HFOs were identified for all interictal and preictal periods with a validated automated detector in 27 patients who underwent intracranial EEG monitoring. We used LASSO logistic regression with several features of the HFO rate to distinguish preictal from interictal periods in each individual. We then tested these models with held-out data and evaluated their performance with the area-under-the-curve (AUC) of their receiver-operating curve (ROC). Finally, we assessed the significance of these results using non-parametric statistical tests.Results: There was variability in the ability of HFOs to discern preictal from interictal states across our cohort. We identified a subset of 10 patients in whom the presence of the preictal state could be successfully predicted better than chance. For some of these individuals, average AUC in the held-out data reached higher than 0.80, which suggests that HFO rates can significantly differentiate preictal and interictal periods for certain patients.Significance: These findings show that temporal trends in HFO rate can predict the preictal state better than random chance in some individuals. Such promising results indicate that future prediction efforts would benefit from the inclusion of high-frequency information in their predictive models and technological architecture.

Preictal and Interictal Recognition for Epileptic Seizure Prediction Using Pre-trained 2D-CNN Models

Epilepsy is a neurological disease affecting almost 1% of world population. Predicting a possible seizure will make a significant contribution to improving the quality of life of patients suffering from this disease. One of the most important steps in seizure prediction studies is the preictal activity recognition stage. In many previous studies, the preictal state was determined to end at the onset of the seizure, which makes it difficult for the physician to intervene in the patient in a possible seizure. In the proposed method, unlike previous studies, the preictal state was determined as the 30-minute interval ending 30 minutes before the onset of an epileptic seizure. The method consisted of three stages; (I) preictal and interictal activities were divided into five-second segments, (ii) the separated signals were converted into spectrograms, and (iii) the spectrogram images were classified using three different pre-trained CNN models (VGG19, ResNet, DenseNet) and the results were compared among these models. Classification was performed separately using the predetermined four EEG channels for 20 cases in the CHB-MIT dataset. The best classification accuracy value in preictal/interictal discrimination (91.05%) was obtained on channel 8 (P3-O1). An important contribution of this study was that the proposed approach provided important information about the preictal and interictal discrimination of the section 30 minutes before the onset of seizures. In addition, by examining the four channels separately, channel-based information on preictal/interictal discrimination was also obtained. Based on these results, we consider that the proposed method will bring a different perspective to seizure prediction studies.

Glia-neuron interactions underlie state transitions to generalized seizures

SUMMARYBrain activity and connectivity alter drastically during epileptic seizures. Throughout this transition, brain networks shift from a balanced resting state to a hyperactive and hypersynchronous state, spreading across the brain. It is, however, less clear which mechanisms underlie these state transitions. By studying neuronal and glial activity across the zebrafish brain, we observed striking differences between these networks. During the preictal period, neurons displayed a small increase in synchronous activity only locally, while the entire glial network was highly active and strongly synchronized across large distances. We observed that the transition from a preictal state to a generalized seizure leads to an abrupt increase in neuronal activity and connectivity, which is accompanied by a strong functional coupling between glial and neuronal networks. Optogenetic activation of glia induced strong and transient burst of neuronal activity, emphasizing a potential role for glia-neuron connections in the generation of epileptic seizures.