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Over the last two decades, the evidence has been growing that in addition to epileptic spikes high frequency oscillations (HFOs) are important biomarkers of epileptogenic tissue. New methods of artificial intelligence such as deep learning neural networks can provide additional tools for automated analysis of EEG. Here we present a Long Short-Term Memory neural network for detection of spikes, ripples and ripples-on-spikes (RonS). We used intracranial EEG (iEEG) from two independent datasets. First dataset (7 patients) was used for network training and testing. The second dataset (5 patients) was used for cross-institutional validation. 1000 events of each class (spike, RonS, ripple and baseline) were selected from the candidates initially found using a novel threshold method. Network training was performed using random selections of 50-500 events (per class) from all patients from the 1 st dataset. This 'global' network was then tested on other events for each patient from both datasets. The network was able to detect events with a good generalisability namely, with total accuracy and specificity for each class exceeding 90% in all cases, and sensitivity less than 86% in only two cases (82.5% for spikes in one patient and 81.9% for ripples in another patient). The deep learning networks can significantly accelerate the analysis of iEEG data and increase their diagnostic value which may improve surgical outcome in patients with localization-related intractable epilepsy.

As epilepsy remains a refractory condition in about 30% of patients with complex partial seizures, electrical stimulation of the brain has recently shown potential for additive seizure control therapy. Previously, we applied noninvasive transcranial focal stimulation via novel tripolar concentric ring electrodes (TCREs) on the scalp of rats after inducing seizures with pentylenetetrazole (PTZ). We developed a close-loop system to detect seizures and automatically trigger the stimulation and evaluated its effect on the electrographic activity recorded by TCREs in rats. In our previous work the detectors of seizure onset were based on seizure-induced changes in signal power in the frequency range up to 100 Hz, while in this preliminary study we assess the feasibility of recording high frequency oscillations (HFOs) in the range up to 300 Hz noninvasively with scalp TCREs during PTZ-induced seizures. Grand average power spectral density estimate and generalized likelihood ratio tests were used to compare power of electrographic activity at different stages of seizure development in a group of rats (n= 8). The results suggest that TCREs have the ability to record HFOs from the scalp as well as that scalp-recorded HFOs can potentially be used as features for seizure onset detection.

Epilepsy is a neurological disorder that affects approximately one percent of the world population. Anti-epileptic drugs are ineffective in 25~30% of cases. Electrical stimulation to control seizures may be an additive therapy. We applied noninvasive transcutaneous focal electrical stimulation (TFES) via concentric ring electrodes on the scalp of rats after inducing seizures with pentylenetetrazole. We found a significant increase in synchrony within the beta-gamma bands during seizures and that TFES significantly reduced the synchrony of the beta-gamma activity and increased synchrony in the delta band.

Epilepsy affects approximately one percent of the world population. Antiepileptic drugs are ineffective in approximately 30% of patients and have side effects. We are developing a noninvasive, or minimally invasive, transcranial focal electrical stimulation (TFS) system through our novel concentric ring electrodes to control seizures. Here we report on the development of a seizure detecting algorithm to be used for automatic application of TFS. A cumulative sum (CUSUM) algorithm was evaluated that detected the electrographic seizure activity in all experiments well in advance of the behavioral seizure activity.

Even with the latest advancements in antiepileptic drugs (AEDs) there are still many persons whose seizures are not controlled. There are also side effects reported associated with the AEDs. Electrical stimulation of the brain has shown promise toward controlling seizures. However, most brain stimulation techniques involve invasive procedures to implant electrodes and electronic stimulators. There are no conclusive descriptions of where to place the implanted electrodes to control seizures. Noninvasive electrical stimulation does not require the risks of implantation, and the electrodes can be moved easily as needed to determine where they may be the most effective in reducing seizure activity. Herein we review the progress of our group in the development of noninvasive electrical stimulation via concentric ring electrodes to control seizures in rats induced by penicillin G, pilocarpine, and pentylenetetrazole (PTZ).

Spectral signs of genetically conditioned convulsibility of rats of the Krushinskiĭ-Molodkina (KM) strain were determined when studying the background electrical brain activity. These signs were: an increase of the delta-activity power in motor and visual cortical zones; considerable increase of the hippocampal potential power; weakening of the caudate nucleus potential power. Spectra of biopotentials being approximated by theoretical spectral function, model parameters simulating physiological characteristics of neural networks were estimated (an inverse problem of modelling). Spectral signs of convulsibility were shown to reflect changes in properties of the neural network. On the basis of calculations it is supposed that genetically determined convulsibility of KM rats results from weakening of inhibitory functions of the caudate nucleus as well as from increased excitability (reactivity) of hippocampal and neocortical neurons.

Rats of the Krushinskiĭ-Molodkina (KM) strain genetically predisposed to audiogenic seizures were repeatedly injected camphor for 4-5 months in gradually increasing doses (beginning from those below the seizure threshold up to those above it). As a result of "adaptation" to the convulsant action rats endured without seizures camphor doses 1.5-3 times higher than seizure threshold ones. Parallel with this "adaptation" the motor cortex of KM rats accumulated properties peculiar to the epileptic focus: spontaneous epileptiform spikes were recorded there after the camphor application. Estimation of the neural network parameters from spectra of the motor cortex biopotentials using the network model has shown a decrease of a parameter reflecting efficacy of the recurrent inhibition mechanisms. It is supposed that development of compensatory processes making rats able to avoid generalized camphor seizures are related to an increase of inhibitory functions of the caudate nucleus and weakening of the hippocampal excitability.

The resting EEGs of several brain structures (motor and visual cortex, caudate nucleus and intralaminar thalamic nuclei) were submitted to spectral and coherence computer analyses in two rat strains. Genetically predisposed to convulsive state KM rats were shown to differ from nonpredisposed Wistar rats in EEG spectral properties. KM rats EEG pattern was characterized by increase of low frequencies (1-2 Hz) power and decrease of faster activity (5-12 Hz) power in cortical spectrograms as well as by decrease of caudate nucleus EEG absolute power. The coherence value between cortical or subcortical structures at below 4 Hz was intensified in KM rats. Reinforcement of cortical auto-oscillating properties manifested by ECoG synchronization in cortical-thalamic resonance interaction as well as weakening of striatal inhibitory system may constitute neurophysiological mechanisms of enhanced convulsive readiness. The probable role of mediator imbalance in these mechanisms is discussed.

Desynchronous (low voltage fast activity), synchronous (high voltage slow waves) as well as convulsive brain activities were stimulated by a computer model of neuronal population. Network excitatory and inhibitory elements possessed fundamental dynamic properties of real neurones. Being independent both of the excitability of elements and of external influence efficacy, synchronous (desynchronous) network activity resulted from the increase (decrease) of the average power of "neuronal" interconnections which imitated mutual and recurrent excitation and inhibition. The inhibition efficacy being reduced as compared with excitation, synchronization of elements became intensified. As a consequence, the rhythmic activity amplitude increased and the appearance of self-sustained oscillations simulating convulsive activity was facilitated. The probable mechanism of EEG activation by virtue of the reduction of mutual and recurrent excitation and inhibition efficacy as well as the significance of inhibitory mechanism deficiency for epileptogenesis are discussed.