A patient-specific seizure prediction algorithm is proposed using a classifier to differentiate pre-ictal from inter-ictal EEG signals. The spectral power of EEG processed in four different fashions is used as features: raw, time-differential, space-differential, and time/space-differential EEG. The features are classified using cost-sensitive support vector machines by the double cross-validation methodology. The proposed algorithm has been applied to EEG recordings of 18 patients in the Freiburg EEG database, totaling 80 seizures and 437 h long inter-ictal recordings. Classification with the feature obtained from time/space-differential ECoG demonstrates the performance of 86.25% sensitivity and 0.1281 false positives per hour in out-of-sample testing.
Additional treatment options for temporal lobe epilepsy are needed, and potential interventions targeting the cerebellum are of interest. Previous animal work has shown strong inhibition of hippocampal seizures through on-demand optogenetic manipulation of the cerebellum. However, decades of work examining electrical stimulation – a more immediately translatable approach – targeting the cerebellum has produced very mixed results. We were therefore interested in exploring the impact that stimulation parameters may have on seizure outcomes. Using a mouse model of temporal lobe epilepsy, we conducted on-demand electrical stimulation of the cerebellar cortex, and varied stimulation charge, frequency, and pulse width, resulting in over a thousand different potential combinations of settings. To explore this parameter space in an efficient, data-driven, manner, we utilized Bayesian optimization with Gaussian process regression, implemented in Matlab with an Expected Improvement Plus acquisition function. We examined three different fitting conditions and two different electrode orientations. Following the optimization process, we conducted additional on-demand experiments to test the effectiveness of selected settings. Regardless of experimental setup, we found that Bayesian optimization allowed identification of effective intervention settings. Additionally, generally similar optimal settings were identified across animals, suggesting that personalized optimization may not always be necessary. While optimal settings were effective, stimulation with settings predicted from the Gaussian process regression to be ineffective failed to provide seizure control. Taken together, our results provide a blueprint for exploration of a large parameter space for seizure control, and illustrate that robust inhibition of seizures can be achieved with electrical stimulation of the cerebellum, but only if the correct stimulation parameters are used.
Objective: The objective of this study was to investigate the effects of micromagnetic stimuli strength and frequency from the Magnetic Pen (MagPen) on the rat right sciatic nerve. The nerve s response would be measured by recording muscle activity and movement of the right hind limb. Approach: The MagPen was custom-built such that it can be held over the sciatic nerve in a stable manner. Rat leg muscle twitches were captured on video and movements were extracted using image processing algorithms. EMG recordings were also used to measure muscle activity. Main results: The MagPen prototype when driven by alternating current, generates time-varying magnetic field which as per Faradays Law of Electromagnetic Induction, induces an electric field for neuromodulation. The orientation dependent spatial contour maps for the induced electric field from the MagPen prototype has been numerically simulated. Furthermore, in this in vivo work on μMS, a dose-response relationship has been reported by experimentally studying how the varying amplitude (Range: 25 mVp-p through 6 Vp-p) and frequency (Range: 100 Hz through 5 kHz) of the MagPen stimuli alters the hind limb movement. The primary highlight of this dose-response relationship is that at a higher frequency of the μMS stimuli, significantly smaller amplitudes can trigger hind limb muscle twitch. This frequency-dependent activation can be justified following directly from the Faradays Law as the magnitude of the induced electric field is directly proportional to frequency. Significance: This work reports that μMS can successfully activate the sciatic nerve in a dose-dependent manner. The MagPen probe, unlike electrodes, does not have a direct electrochemical interface with tissues rendering it much safer than an electrode. Magnetic fields create more precise activation than electrodes because they induce smaller volumes of activation. Finally, unique features of μMS such as orientation dependence, directionality and spatial selectivity have been demonstrated.
Epilepsy is a neurological disorder affecting approximately 70 million people worldwide. It is characterized by seizures that are complex aberrant dynamical events typically treated with drugs and surgery. Unfortunately, not all patients become seizure-free, and there is an opportunity for novel approaches to treat epilepsy using a network view of the brain. The traditional seizure focus theory presumed that seizures originated within a discrete cortical area with subsequent recruitment of adjacent cortices with seizure progression. However, a more recent view challenges this concept, suggesting that epilepsy is a network disease, and both focal and generalized seizures arise from aberrant activity in a distributed network. Changes in the anatomical configuration or widespread neural activities spanning lobes and hemispheres could make the brain more susceptible to seizures. In this perspective paper, we summarize the current state of knowledge, address several important challenges that could further improve our understanding of the human brain in epilepsy, and invite novel studies addressing these challenges.
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