Amplitude Suppression and Chaos Control in Epileptic EEG Signals

In this paper we have proposed a novel amplitude suppression algorithm for EEG signals collected during epileptic seizure. Then we have proposed a measure of chaoticity for a chaotic signal, which is somewhat similar to measuring sensitive dependence on initial conditions by measuring Lyapunov exponent in a chaotic dynamical system. We have shown that with respect to this measure the amplitude suppression algorithm reduces chaoticity in a chaotic signal (EEG signal is chaotic). We have compared our measure with the estimated largest Lyapunov exponent measure by the largelyap function, which is similar to Wolf's algorithm. They fit closely for all but one of the cases. How the algorithm can help to improve patient specific dosage titration during vagus nerve stimulation therapy has been outlined

Low-Power Implantable Device for Onset Detection and Subsequent Treatment of Epileptic Seizures: A Review

Over the past few years, there has been growing interest in neuro-responsive intracerebral local treatments of seizures, such as focal drug delivery, focal cooling, or electrical stimulation. This mode of treatment requires an effective intracerebral electroencephalographic acquisition system, seizure detector, brain stimulator, and wireless system that consume ultra-low power. This review focuses on alternative brain stimulation treatments for medically intractable epilepsy patients. We mainly discuss clinical studies of long-term responsive stimulation and suggest safer optimized therapeutic options for epilepsy. Finally, we conclude our study with the proposed low-power, implantable fully integrated device that automatically detects low-voltage fast activity ictal onsets and triggers focal treatment to disrupt seizure progression. The detection performance was verified using intracerebral electroencephalographic recordings from two patients with epilepsy. Further experimental validation of this prototype is underway

Simulation of Abnormal/Normal Brain States Using the KIV Model

Recent studies have focused on the phenomena of abnormal electrical brain activity which may transition into a debilitating seizure state through the entrainment of large populations of neurons.Starting from the initial epileptogenisis of a small population of abnormally firing neurons, to the mobilization of mesoscopic neuron populations behaving in a synchronous manner, a model has been formulated that captures the initial epileptogenisis to the semi-periodic entrainment of distant neuron populations.The normal non-linear dynamic signal captured through EEG, moves into a semi-periodic state, which can be quantified as the seizure state.Capturing the asynchronous/synchronous behavior of the normal/pathological brain state will be discussed.This model will also demonstrate how electrical stimulation applied to the limbic system restores the seizure state of the brain back to its original normal condition.Human brain states are modeled using a biologically inspired neural network, the KIV model.The KIV model exhibits the noisy, chaotic attributes found in the limbic system of brains of higher forms of organisms, and in its normal basal state, represents the homogeneous activity of millions of neuron activations.The KIV can exhibit the ’unbalanced state’ of neural activity, whereas when a small cluster of abnormal firing neurons starts to exhibit periodic neural firings that eventually entrain all the neurons within the limbic system, the network has moved into the ‘seizure’ state.These attributes have been found in human EEG recordings and have been duplicated in this model of the brain.The discussion in this dissertation covers the attributes found in human EEG data and models these attributes.Additionally, this model proposes a methodology to restore the modeled ‘seizure’ state, and by doing so, proposes a manner for external electrical titration to restore the abnormal seizure state back to a normal chaotic EEG signal state.Quantification measurements of normal, abnormal, and restoration to normal brain states will be exhibited using the following approaches:Analysis of human EEG dataQuantification measurements of brain states.Development of models of the different brain states, i.e. fit parameters of the model on individual personal data/history.Implementation of quantitative measurements on “restored” simulated seizure state

Implantable Micro-Device for Epilepsy Seizure Detection and Subsequent Treatment

RÉSUMÉ L’émergence des micro-dispositifs implantables est une voie prometteuse pour le traitement de troubles neurologiques. Ces systèmes biomédicaux ont été exploités comme traitements non-conventionnels sur des patients chez qui les remèdes habituels sont inefficaces. Les récents progrès qui ont été faits sur les interfaces neuronales directes ont permis aux chercheurs d’analyser l’activité EEG intracérébrale (icEEG) en temps réel pour des fins de traitements. Cette thèse présente un dispositif implantable à base de microsystèmes pouvant capter efficacement des signaux neuronaux, détecter des crises d’épilepsie et y apporter un traitement afin de l’arrêter. Les contributions principales présentées ici ont été rapportées dans cinq articles scientifiques, publiés ou acceptés pour publication dans les revues IEEE, et plusieurs autres tels que «Low Power Electronics» et «Emerging Technologies in Computing». Le microsystème proposé inclus un circuit intégré (CI) à faible consommation énergétique permettant la détection de crises d’épilepsie en temps réel. Cet CI comporte une pré-amplification initiale et un détecteur de crises d’épilepsie. Le pré-amplificateur est constitué d’une nouvelle topologie de stabilisateur d’hacheur réduisant le bruit et la puissance dissipée. Les CI fabriqués ont été testés sur des enregistrements d’icEEG provenant de sept patients épileptiques réfractaires au traitement antiépileptique. Le délai moyen de la détection d’une crise est de 13,5 secondes, soit avant le début des manifestations cliniques évidentes. La consommation totale d’énergie mesurée de cette puce est de 51 μW. Un neurostimulateur à boucle fermée (NSBF), quant à lui, détecte automatiquement les crises en se basant sur les signaux icEEG captés par des électrodes intracrâniennes et permet une rétroaction par une stimulation électrique au même endroit afin d’interrompre ces crises. La puce de détection de crises et le stimulateur électrique à base sur FPGA ont été assemblés à des électrodes afin de compléter la prothèse proposée. Ce NSBF a été validé en utilisant des enregistrements d’icEEG de dix patients souffrant d’épilepsie réfractaire. Les résultats révèlent une performance excellente pour la détection précoce de crises et pour l’auto-déclenchement subséquent d’une stimulation électrique. La consommation énergétique totale du NSBF est de 16 mW. Une autre alternative à la stimulation électrique est l’injection locale de médicaments, un traitement prometteur de l’épilepsie. Un système local de livraison de médicament basé sur un nouveau détecteur asynchrone des crises est présenté.----------ABSTRACT Emerging implantable microdevices hold great promise for the treatment of patients with neurological conditions. These biomedical systems have been exploited as unconventional treatment for the conventionally untreatable patients. Recent progress in brain-machine-interface activities has led the researchers to analyze the intracerebral EEG (icEEG) recording in real-time and deliver subsequent treatments. We present in this thesis a long-term safe and reliable low-power microsystem-based implantable device to perform efficient neural signal recording, seizure detection and subsequent treatment for epilepsy. The main contributions presented in this thesis are reported in five journal manuscripts, published or accepted for publication in IEEE Journals, and many others such as Low Power Electronics, and Emerging Technologies in Computing. The proposed microsystem includes a low-power integrated circuit (IC) intended for real-time epileptic seizure detection. This IC integrates a front-end preamplifier and epileptic seizure detector. The preamplifier is based on a new chopper stabilizer topology that reduces noise and power dissipation. The fabricated IC was tested using icEEG recordings from seven patients with drug-resistant epilepsy. The average seizure detection delay was 13.5 sec, well before the onset of clinical manifestations. The measured total power consumption of this chip is 51 µW. A closed-loop neurostimulator (CLNS) is next introduced, which is dedicated to automatically detect seizure based on icEEG recordings from intracranial electrode contacts and provide an electrical stimulation feedback to the same contacts in order to disrupt these seizures. The seizure detector chip and a dedicated FPGA-based electrical stimulator were assembled together with common recording electrodes to complete the proposed prosthesis. This CLNS was validated offline using recording from ten patients with refractory epilepsy, and showed excellent performance for early detection of seizures and subsequent self-triggering electrical stimulation. Total power consumption of the CLNS is 16 mW. Alternatively, focal drug injection is the promising treatment for epilepsy. A responsive focal drug delivery system based on a new asynchronous seizure detector is also presented. The later system with data-dependent computation reduces up to 49% power consumption compared to the previous synchronous neurostimulator