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

Abstract

Early implantable epilepsy therapy devices provided open-loop electrical stimulation without brain sensing, computing, or an interface for synchronized behavioral inputs from patients. Recent epilepsy stimulation devices provide brain sensing but have not yet developed analytics for accurately tracking and quantifying behavior 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. Automated analysis of continuous streaming electrophysiology is synchronized with patient reports using a hand-held device and integrated with distributed cloud computing resources for quantifying seizures, interictal epileptiform spikes, and patient symptoms during therapeutic electrical brain stimulation. The classification algorithms for interictal epileptiform spikes and seizures were developed and parameterized using long-term ambulatory data from 9 humans and 8 canines with epilepsy, and then implemented prospectively in out-of-sample testing in 2 pet canines and 4 humans with drug resistant epilepsy living in their natural environments. Accurate seizure diaries are needed as the primary clinical outcome measure of epilepsy therapy and to guide brain stimulation optimization. The brain co-processor system described here enables tracking interictal epileptiform spikes, seizures, and correlation with patient behavioral reports. In the future correlation of spikes and seizures with behavior will allow more detailed investigation of the clinical impact of spikes and seizures on patients.

Integrated human-machine interface for closed-loop stimulation using implanted and wearable devices

“…Patients annotate seizure diaries and medication entries and perform mood, memory, and other surveys and tasks. All the data are streamed into the cloud environment and analyzed by automated algorithms [2] and then by manual review of the clinical team. The system battery performance requires charging tablet, phone, and watch at least once per day.…”

“…This task of synchronization is important to many research applications [10], [11]–[14]. Here we developed a system of commercial electronics (Software tools) that enables us to use it with the implantable RC+S™ system [1], [2], [15]. Fig.…”

“…In recent papers [1], [2], we have described a distributed brain co-processor system coupled with an implantable neurostimulator and sensing device (INSS) that enables long-term wireless streaming of continuous intracranial EEG. The system is linked to the cloud environment that utilizes of high-cost computational algorithms to extract clinically useful information from the collected data.…”

“…The system is linked to the cloud environment that utilizes of high-cost computational algorithms to extract clinically useful information from the collected data. Automated tools for biomarker tracking include interictal epileptiform spike (IES) rates, accurate seizure reports [2], and classification of behavioral states. [3], [4]…”

“…The system is linked to the cloud environment that utilizes of highcost computational algorithms to extract clinically useful information from the collected data. Automated tools for biomarker tracking include interictal epileptiform spike (IES) rates, accurate seizure reports [2], and classification of behavioral states. [3], [4] A particular challenge with the system is its limitation of data collection using the MS Windows running tablet computer (Epilepsy Patient Assist Device -EPAD) and the clock synchronization between the INSS and EPAD.…”