Next-generation neuromodulator for epilepsy prevention

Closed-loop neurostimulation systems have emerged as a prominent method for treating seizures. However, most of the proposed solutions do not consider the need for fast (real-time) seizure detection or their energy overheads, resulting in systems not suitable for wearable or implantable applications. This thesis describes the design and implementation of a novel closed-loop system that is capable of real-time seizure detection and suppression, while requiring minimal power and energy consumption. The proposed system utilizes a complex Morlet wavelet in combination with a thresholding mechanism to detect the presence of ictal-activity in ECoG signals. We evaluate our system in terms of detection performance (sensitivity, specificity and delay) considering various filter parameters, such as the filter order and various (static) detection thresholds. Additionally, we consider the system’s suitability for implantable applications by evaluating its computational overheads (execution time, energy consumption) when executed on the SiMS low-power processor. We show that decreasing the filter order results in less accurate detection (sensitivity, specificity), a faster detection (delay), and less overheads. In addition, we show that we may further improve the detection accuracy and delay with minimal overheads by considering an input-dependent (adaptive) threshold mechanism. Furthermore, we show that we can effectively trade-off detection accuracy and energy consumption: For example, shrinking filter order by 70% results in a decrease in detection accuracy of only 1%, while allowing us to obtain an improvement in delay by 190 ms (from 710 ms to 520 ms) and in energy consumption by 70% (from 5.04?J to 1.51?J). Compared to related work, we show that we can detect seizures significantly faster (492 ms, compared to 970 ms) with the same sensitivity (94%) and at a minimal decrease in specificity of 4.6% (93.60% compared to 98.2%). A prototype implementation of the closed-loop system has successfully been applied in in-vivo experiments, demonstrating its potential for epilepsy treatment.Computer EngineeringBioelectronicsElectrical Engineering, Mathematics and Computer Scienc

Balancing accuracy, delay and battery autonomy for pervasive seizure detection

A promising alternative for treating absence seizures has emerged through closed-loop neurostimulation, which utilizes a wearable or implantable device to detect and subsequently suppress epileptic seizures. Such devices should detect seizures fast and with high accuracy, while respecting the strict energy budget on which they operate. Previous work has overlooked one or more of these requirements, resulting in solutions which are not suitable for continuous closed-loop stimulation. In this paper, we perform an in-depth design space exploration of a novel seizure-detection algorithm, which uses a complex Morlet wavelet filter and a static thresholding mechanism to detect absence seizures. We consider both the accuracy and speed of our detection algorithm, as well as various trade-offs with device autonomy when executed on a low-power processor. For example, we demonstrate that a minimal decrease in average detection rate of only 1.83% (from 92.72% to 90.89%) allows for a substantial increase in device autonomy (of 3.7x) while also facilitating faster detection (from 710 ms to 540 ms)

An implementation of a wavelet-based seizure detection filter suitable for realtime closed-loop epileptic seizure suppression

This paper presents the design and implementation of a real-time epilepsy detection filter that is suitable for closed-loop seizure suppression. The design aims to minimize the detection delay, while a reasonable average detection rate is obtained. The filter is based on a complex Morlet wavelet and uses an adaptive thresholding strategy for the seizure discrimination. This relatively simple configuration allows the algorithm to run on a cheap and readily available microprocessor prototyping platform. The performance of the filter is verified using both in vivo real-time measurements as well as simulations over a pre-recorded EEG dataset (29.75 hours with 1914 seizures). An average detection delay of 492 ms is achieved with a sensitivity of 96.03% and a specificity of 93.60%

Cerebellar output controls generalized spike-and-wave discharge occurrence

ObjectiveDisrupting thalamocortical activity patterns has proven to be a promising approach to stop generalized spike-and-wave discharges (GSWDs) characteristic of absence seizures. Here, we investigated to what extent modulation of neuronal firing in cerebellar nuclei (CN), which are anatomically in an advantageous position to disrupt cortical oscillations through their innervation of a wide variety of thalamic nuclei, is effective in controlling absence seizures. MethodsTwo unrelated mouse models of generalized absence seizures were used: the natural mutant tottering, which is characterized by a missense mutation in Cacna1a, and inbred C3H/HeOuJ. While simultaneously recording single CN neuron activity and electrocorticogram in awake animals, we investigated to what extent pharmacologically increased or decreased CN neuron activity could modulate GSWD occurrence as well as short-lasting, on-demand CN stimulation could disrupt epileptic seizures. ResultsWe found that a subset of CN neurons show phase-locked oscillatory firing during GSWDs and that manipulating this activity modulates GSWD occurrence. Inhibiting CN neuron action potential firing by local application of the -aminobutyric acid type A (GABA-A) agonist muscimol increased GSWD occurrence up to 37-fold, whereas increasing the frequency and regularity of CN neuron firing with the use of GABA-A antagonist gabazine decimated its occurrence. A single short-lasting (30-300 milliseconds) optogenetic stimulation of CN neuron activity abruptly stopped GSWDs, even when applied unilaterally. Using a closed-loop system, GSWDs were detected and stopped within 500 milliseconds. InterpretationCN neurons are potent modulators of pathological oscillations in thalamocortical network activity during absence seizures, and their potential therapeutic benefit for controlling other types of generalized epilepsies should be evaluated. Ann Neurol 2015;77:1027-104