In this study, we present a novel low-frequency electrical stimulation paradigm for mesial temporal lobe epilepsy (MTLE). The paradigm utilizes the hippocampal commissure as a unique stimulation target to simultaneously influence large portions of the bilateral hippocampal network. When applied to an acute rat model of MTLE, animals that received stimulation exhibited an 88% reduction in the signal power of the bilateral epileptiform activity relative to the control group. In addition, the stimulation entrained the hippocampal network's spontaneous epileptiform activity and disrupted its bilateral synchrony.
Automating the detection of epileptic seizures could reduce the significant human resources necessary for the care of patients suffering from intractable epilepsy and offer improved solutions for closed-loop therapeutic devices such as implantable electrical stimulation systems. While numerous detection algorithms have been published, an effective detector in the clinical setting remains elusive. There are significant challenges facing seizure detection algorithms. The epilepsy EEG morphology can vary widely among the patient population. EEG recordings from the same patient can change over time. EEG recordings can be contaminated with artifacts that often resemble epileptic seizure activity. In order for an epileptic seizure detector to be successful, it must be able to adapt to these different challenges. In this study, a novel detector is proposed based on a support vector machine assembly classifier (SVMA). The SVMA consists of a group of SVMs each trained with a different set of weights between the seizure and non-seizure data and the user can selectively control the output of the SVMA classifier. The algorithm can improve the detection performance compared to traditional methods by providing an effective tuning strategy for specific patients. The proposed algorithm also demonstrates a clear advantage over threshold tuning. When compared with the detection performances reported by other studies using the publicly available epilepsy dataset hosted by the University of BONN, the proposed SVMA detector achieved the best total accuracy of 98.72%. These results demonstrate the efficacy of the proposed SVMA detector and its potential in the clinical setting.
The ability to extract physiological source signals to control various prosthetics offer tremendous therapeutic potential to improve the quality of life for patients suffering from motor disabilities. Regardless of the modality, recordings of physiological source signals are contaminated with noise and interference along with crosstalk between the sources. These impediments render the task of isolating potential physiological source signals for control difficult. In this paper, a novel Bayesian Source Filter for signal Extraction (BSFE) algorithm for extracting physiological source signals for control is presented. The BSFE algorithm is based on the source localization method Champagne and constructs spatial filters using Bayesian methods that simultaneously maximize the signal to noise ratio of the recovered source signal of interest while minimizing crosstalk interference between sources. When evaluated over peripheral nerve recordings obtained in-vivo, the algorithm achieved the highest signal to noise interference ratio (>7.00±3.45dB) amongst the group of methodologies compared with average correlation between the extracted source signal and the original source signal > 0.93. The results support the efficacy of the BSFE algorithm for extracting source signals from the peripheral nerve or as a pre-filtering stage for BCI methods.
In vivo studies of epileptiform discharges in the hippocampi of rodents have shown that bilateral seizure activity can sometimes be synchronized with very small delays (< 2 ms). This observed small time delay of epileptiform activity between the left and right CA3 regions is unexpected given the physiological propagation time across the hemispheres (> 6 ms). The goal of this study is to determine the mechanisms of this tight synchronization with in-vitro electrophysiology techniques and computer simulations. The hypothesis of a common source was first eliminated by using an in-vitro preparation containing both hippocampi with a functional ventral hippocampal commissure (VHC) and no other tissue. Next, the hypothesis that a noisy baseline could mask the underlying synchronous activity between the two hemispheres was ruled out by low noise in-vivo recordings and computer simulation of the noisy environment. Then we built a novel bilateral CA3 model to test the hypothesis that the phenomenon of very small left-to-right propagation delay of seizure activity is a product of epileptic cell network dynamics. We found that the commissural tract connectivity could decrease the delay between seizure events recorded from two sides while the activity propagated longitudinally along the CA3 layer thereby yielding delays much smaller than the propagation time between the two sides. The modeling results indicate that both recurrent and feedforward inhibition were required for shortening the bilateral propagation delay and depended critically on the length of the commissural fiber tract as well as the number of cells involved in seizure generation. These combined modeling/experimental studies indicate that it is possible to explain near perfect synchronization between the two hemispheres by taking into account the structure of the hippocampal network.
Extracting physiological signals to control external devices such as prosthetics is a field of research that offers great hope for patients suffering from disabilities. In this paper, a novel source signal extraction algorithm, based on the source localization method Champagne, is presented. The algorithm constructs spatial filters that not only maximizes the signal to noise ratio (SNR > 13 dB) of the source activities but also minimizes the cross-talk interference between the sources 10log((P(source of interest)/P(interference sources)) > 14 dB.
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