Accurate identification of seizure activity, both clinical and subclinical, has important implications in the management of epilepsy. Accurate recognition of seizure activity is essential for diagnostic, management and forecasting purposes, but patient-reported seizures have been shown to be unreliable. Earlier work has revealed accurate capture of electrographic seizures and forecasting is possible with an implantable intracranial device, but less invasive electroencephalography (EEG) recording systems would be optimal. Here, we present preliminary results of seizure detection and forecasting with a minimally invasive sub-scalp device that continuously records EEG. Five participants with refractory epilepsy who experience at least two clinically identifiable seizures monthly have been implanted with sub-scalp devices (Minder®), providing two channels of data from both hemispheres of the brain. Data is continuously captured via a behind-the-ear system, which also powers the device, and transferred wirelessly to a mobile phone, from where it is accessible remotely via cloud storage. EEG recordings from the sub-scalp device were compared to data recorded from a conventional system during a 1-week ambulatory video-EEG monitoring session. Suspect epileptiform activity (EA) was detected using machine learning algorithms and reviewed by trained neurophysiologists. Seizure forecasting was demonstrated retrospectively by utilizing cycles in EA and previous seizure times. The procedures and devices were well-tolerated and no significant complications have been reported. Seizures were accurately identified on the sub-scalp system, as visually confirmed by periods of concurrent conventional scalp EEG recordings. The data acquired also allowed seizure forecasting to be successfully undertaken. The area under the receiver operating characteristic curve (AUC score) achieved (0.88), which is comparable to the best score in recent, state-of-the-art forecasting work using intracranial EEG.
A closed-loop system for the automated detection and control of epileptic seizures was created and tested in three Genetic Absence Epilepsy Rats from Strasbourg (GAERS) rats. In this preliminary study, a set of four EEG features were used to detect seizures and three different electrical stimulation strategies (standard (130 Hz), very high (500 Hz) and ultra high (1000 Hz)) were delivered to terminate seizures. Seizure durations were significantly shorter with all three stimulation strategies when compared to non-stimulated (control) seizures. We used mean seizure duration of epileptiform discharges persisting beyond the end of electrical stimulation as a measure of stimulus efficacy. When compared to the duration of seizures stimulated in the standard approach (7.0 s ± 10.1), both very high and ultra high frequency stimulation strategies were more effective at shortening seizure durations (1.3 ± 2.2 s and 3.5 ± 6.4 s respectively). Further studies are warranted to further understand the mechanisms by which this therapeutic effect may be conveyed, and which of the novel aspects of the very high and ultra high frequency stimulation strategies may have contributed to the improvement in seizure abatement performance when compared to standard electrical stimulation approaches.
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