Distributed brain co-processor for tracking spikes, seizures and behaviour during electrical brain stimulation.

Early implantable epilepsy therapy devices provided open-loop electrical stimulation without brain sensing, computing, or an interface for synchronized behavioural inputs from patients. Recent epilepsy stimulation devices provide brain sensing but have not yet developed analytics for accurately tracking and quantifying behaviour and seizures. Here we describe a distributed brain co-processor providing an intuitive bi-directional interface between patient, implanted neural stimulation and sensing device, and local and distributed computing resources.

Thalamic deep brain stimulation modulates cycles of seizure risk in epilepsy.

Chronic brain recordings suggest that seizure risk is not uniform, but rather varies systematically relative to daily (circadian) and multiday (multidien) cycles. Here, one human and seven dogs with naturally occurring epilepsy had continuous intracranial EEG (median 298 days) using novel implantable sensing and stimulation devices. Two pet dogs and the human subject received concurrent thalamic deep brain stimulation (DBS) over multiple months.

Spatiotemporal Patterns of High-Frequency Activity (80-170 Hz) in Long-Term Intracranial EEG.

Objective: To determine the utility of high-frequency activity (HFA) and epileptiform spikes as biomarkers for epilepsy, we examined the variability in their rates and locations using long-term ambulatory intracranial EEG (iEEG) recordings.

Methods: This study used continuous iEEG recordings obtained over an average of 1.4 years from 15 patients with drug-resistant focal epilepsy.

Neural State Monitoring in the Treatment of Epilepsy: Seizure Prediction-Conceptualization to First-In-Man Study.

This research study is part of a therapy development effort in which a novel approach was taken to develop an implantable electroencephalographic (EEG) based brain monitoring and seizure prediction system. Previous attempts to predict seizures by other groups had not been demonstrated to be statistically more successful than chance. The primary clinical findings from this group were published in a clinical paper; however much of the fundamental technology, including the strategy and techniques behind the development of the seizure advisory system have not been published.

Adding wisdom to 'smart' bioelectronic systems: a design framework for physiologic control including practical examples.

This perspective provides an overview of how risk can be effectively considered in physiological control loops that strive for semi-to-fully automated operation. The perspective first introduces the motivation, user needs and framework for the design of a physiological closed-loop controller. Then, we discuss specific risk areas and use examples from historical medical devices to illustrate the key concepts.

The circadian profile of epilepsy improves seizure forecasting.

It is now established that epilepsy is characterized by periodic dynamics that increase seizure likelihood at certain times of day, and which are highly patient-specific. However, these dynamics are not typically incorporated into seizure prediction algorithms due to the difficulty of estimating patient-specific rhythms from relatively short-term or unreliable data sources. This work outlines a novel framework to develop and assess seizure forecasts, and demonstrates that the predictive power of forecasting models is improved by circadian information.

Intracranial EEG fluctuates over months after implanting electrodes in human brain.

Objective: Implanting subdural and penetrating electrodes in the brain causes acute trauma and inflammation that affect intracranial electroencephalographic (iEEG) recordings. This behavior and its potential impact on clinical decision-making and algorithms for implanted devices have not been assessed in detail. In this study we aim to characterize the temporal and spatial variability of continuous, prolonged human iEEG recordings.

Crowdsourcing seizure detection: algorithm development and validation on human implanted device recordings.

There exist significant clinical and basic research needs for accurate, automated seizure detection algorithms. These algorithms have translational potential in responsive neurostimulation devices and in automatic parsing of continuous intracranial electroencephalography data. An important barrier to developing accurate, validated algorithms for seizure detection is limited access to high-quality, expertly annotated seizure data from prolonged recordings.

Interictal spikes and epileptic seizures: their relationship and underlying rhythmicity.

We report on a quantitative analysis of electrocorticography data from a study that acquired continuous ambulatory recordings in humans over extended periods of time. The objectives were to examine patterns of seizures and spontaneous interictal spikes, their relationship to each other, and the nature of periodic variation. The recorded data were originally acquired for the purpose of seizure prediction, and were subsequently analysed in further detail.

Human focal seizures are characterized by populations of fixed duration and interval.

Objective: We report on a quantitative analysis of data from a study that acquired continuous long-term ambulatory human electroencephalography (EEG) data over extended periods. The objectives were to examine the seizure duration and interseizure interval (ISI), their relationship to each other, and the effect of these features on the clinical manifestation of events.

Methods: Chronic ambulatory intracranial EEG data acquired for the purpose of seizure prediction were analyzed and annotated.

The dynamics of the epileptic brain reveal long-memory processes.

The pattern of epileptic seizures is often considered unpredictable and the interval between events without correlation. A number of studies have examined the possibility that seizure activity respects a power-law relationship, both in terms of event magnitude and inter-event intervals. Such relationships are found in a variety of natural and man-made systems, such as earthquakes or Internet traffic, and describe the relationship between the magnitude of an event and the number of events.

Forecasting seizures in dogs with naturally occurring epilepsy.

Seizure forecasting has the potential to create new therapeutic strategies for epilepsy, such as providing patient warnings and delivering preemptive therapy. Progress on seizure forecasting, however, has been hindered by lack of sufficient data to rigorously evaluate the hypothesis that seizures are preceded by physiological changes, and are not simply random events. We investigated seizure forecasting in three dogs with naturally occurring focal epilepsy implanted with a device recording continuous intracranial EEG (iEEG).

Feasibility study of a caregiver seizure alert system in canine epilepsy.

A device capable of detecting seizures and alerting caregivers would be a major advance for epilepsy management, and could be used to guide early intervention and prevent seizure-related injuries. The objective of this work was to evaluate a seizure advisory system (SAS) that alerts caregivers of seizures in canines with naturally occurring epilepsy. Four dogs with epilepsy were implanted with a SAS that wirelessly transmits continuous intracranial EEG (iEEG) to an external device embedded with a seizure detection algorithm and the capability to alert caregivers.

Prediction of seizure likelihood with a long-term, implanted seizure advisory system in patients with drug-resistant epilepsy: a first-in-man study.

Background: Seizure prediction would be clinically useful in patients with epilepsy and could improve safety, increase independence, and allow acute treatment. We did a multicentre clinical feasibility study to assess the safety and efficacy of a long-term implanted seizure advisory system designed to predict seizure likelihood and quantify seizures in adults with drug-resistant focal seizures.

Methods: We enrolled patients at three centres in Melbourne, Australia, between March 24, 2010, and June 21, 2011.

A novel implanted device to wirelessly record and analyze continuous intracranial canine EEG.

We present results from continuous intracranial electroencephalographic (iEEG) monitoring in 6 dogs with naturally occurring epilepsy, a disorder similar to the human condition in its clinical presentation, epidemiology, electrophysiology and response to therapy. Recordings were obtained using a novel implantable device wirelessly linked to an external, portable real-time processing unit. We demonstrate previously uncharacterized intracranial seizure onset patterns in these animals that are strikingly similar in appearance to human partial onset epilepsy.