Distributed brain co-processor for tracking electrophysiology and behavior during electrical brain stimulation

Sladky,; Nejedlý, Petr; Mívalt, Filip; Brinkmann, Benjamin H.; I, Kim; Louis, Erik K. St.; Nm, Gregg; Lundstrom, Brian Nils; Cm, Crowe; Tp, Attia; Crepeau, Daniel; Balzekas, Irena; Маркс,; Lp, Wheeler; Cimbálník, Jan; Cook, Mark; Janča, Radek; Bk, Sturges; Leyde, Kent; Miller, Kevin J; Jj, Van Gompel; Denison, Timothy; Ga, Worrell; Kremen, Václav

doi:10.1101/2021.03.08.434476

Cited by 12 publications

(22 citation statements)

References 86 publications

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“…To investigate comorbid psychiatric disorders that are very common in drug-resistant epilepsy our group used the investigational Medtronic Summit RC+S TM combined with the EPAD system to underline connections between epileptiform activity, mood, and therapeutic deep brain stimulation. Finally, in our group recent work we described the first application of a distributed brain co-processor, made possible by the EPAD system, providing an intuitive, bi-directional interface between device and patient, and implement it with human and canine epilepsy patients in their natural environments ( 30 ). Different algorithms, including automated behavioral state (wake and sleep) and electrophysiologic biomarker (interictal epileptiform spikes and seizures), were first developed and parameterized using long-term retrospective data from 10 humans and 11 canines with epilepsy and then implemented prospectively in implanted co-processors for two pet canines and one human with drug-resistant epilepsy as they live naturally in society.…”

Section: Discussionmentioning

confidence: 99%

Epilepsy Personal Assistant Device—A Mobile Platform for Brain State, Dense Behavioral and Physiology Tracking and Controlling Adaptive Stimulation

et al. 2021

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Epilepsy is one of the most common neurological disorders, and it affects almost 1% of the population worldwide. Many people living with epilepsy continue to have seizures despite anti-epileptic medication therapy, surgical treatments, and neuromodulation therapy. The unpredictability of seizures is one of the most disabling aspects of epilepsy. Furthermore, epilepsy is associated with sleep, cognitive, and psychiatric comorbidities, which significantly impact the quality of life. Seizure predictions could potentially be used to adjust neuromodulation therapy to prevent the onset of a seizure and empower patients to avoid sensitive activities during high-risk periods. Long-term objective data is needed to provide a clearer view of brain electrical activity and an objective measure of the efficacy of therapeutic measures for optimal epilepsy care. While neuromodulation devices offer the potential for acquiring long-term data, available devices provide very little information regarding brain activity and therapy effectiveness. Also, seizure diaries kept by patients or caregivers are subjective and have been shown to be unreliable, in particular for patients with memory-impairing seizures. This paper describes the design, architecture, and development of the Mayo Epilepsy Personal Assistant Device (EPAD). The EPAD has bi-directional connectivity to the implanted investigational Medtronic Summit RC+STM device to implement intracranial EEG and physiological monitoring, processing, and control of the overall system and wearable devices streaming physiological time-series signals. In order to mitigate risk and comply with regulatory requirements, we developed a Quality Management System (QMS) to define the development process of the EPAD system, including Risk Analysis, Verification, Validation, and protocol mitigations. Extensive verification and validation testing were performed on thirteen canines and benchtop systems. The system is now under a first-in-human trial as part of the US FDA Investigational Device Exemption given in 2018 to study modulated responsive and predictive stimulation using the Mayo EPAD system and investigational Medtronic Summit RC+STM in ten patients with non-resectable dominant or bilateral mesial temporal lobe epilepsy. The EPAD system coupled with an implanted device capable of EEG telemetry represents a next-generation solution to optimizing neuromodulation therapy.

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Section: Discussionmentioning

confidence: 99%

Epilepsy Personal Assistant Device—A Mobile Platform for Brain State, Dense Behavioral and Physiology Tracking and Controlling Adaptive Stimulation

et al. 2021

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“…Regarding integrated systems, clinically available infrastructures to support sensing and closed-loop responsive stimulation devices lack the computing power and data storage capacity needed to track the electrophysiologic, therapeutic, and behavioral features associated with seizures. The Mayo Epilepsy Personal Assistant Device (EPAD) is a neurological disease management system that integrates the implanted device, intracranial EEG telemetry, electrical stimulation, behavioral state classifiers, remote parameter control, hand-held computational device, and cloud environment (Kremen et al, 2018 ; Sladky et al, 2021 ). Our group has developed custom software that runs on the patients’ tablet computer and enables continuous streaming of LFP data to a physician cloud; allowing remote adjustment of stimulation parameters and customized algorithms (Kremen et al, 2018 ).…”

Section: Clinical Technologies For Ambulatory Invasive Electrophysiologymentioning

confidence: 99%

Invasive Electrophysiology for Circuit Discovery and Study of Comorbid Psychiatric Disorders in Patients With Epilepsy: Challenges, Opportunities, and Novel Technologies

Balzekas

Sladky

Nejedlý

et al. 2021

Front. Hum. Neurosci.

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Intracranial electroencephalographic (iEEG) recordings from patients with epilepsy provide distinct opportunities and novel data for the study of co-occurring psychiatric disorders. Comorbid psychiatric disorders are very common in drug-resistant epilepsy and their added complexity warrants careful consideration. In this review, we first discuss psychiatric comorbidities and symptoms in patients with epilepsy. We describe how epilepsy can potentially impact patient presentation and how these factors can be addressed in the experimental designs of studies focused on the electrophysiologic correlates of mood. Second, we review emerging technologies to integrate long-term iEEG recording with dense behavioral tracking in naturalistic environments. Third, we explore questions on how best to address the intersection between epilepsy and psychiatric comorbidities. Advances in ambulatory iEEG and long-term behavioral monitoring technologies will be instrumental in studying the intersection of seizures, epilepsy, psychiatric comorbidities, and their underlying circuitry.

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“…The study is registered at https://clinicaltrials.gov/ct2/show/NCT03946618. The patients provided written consent in accordance with the IRB and FDA requirements [37]. Four subjects with drug resistant medial temporal lobe epilepsy (mTLE) (H1-4) were consented and implanted with the investigational Medtronic Summit RC+S TM device [29,37,38], with four 4-contact leads, targetting the hippocampus (HPC) and anterior nucleus of the thalamus (ANT) bilaterally.…”

Section: Experimental Protocolmentioning

confidence: 99%

“…Intracranial EEG data were continuously collected using wireless streaming to a tablet computer as previously described by Kremen and Sladky et al [37][38][39] with 250 Hz or 500 Hz sampling frequency. Data acquired at 500 Hz was down sampled to 250 Hz.…”

Section: Intracranial Eeg Data Acquisitionmentioning

confidence: 99%

“…The expert gold standard sleep scoring from polysomnography is used to create labeled data to develop the automated sleep classifier [37]. C) Direct comparison of the expert sleep scores that are used to evaluate performance of the iEEG based classifier.…”

Section: Block-diagram Of the Process For Training Validation And Testing Of An Automated Sleep Classifier Using Simultaneous Polysomnogrmentioning

confidence: 99%

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Electrical Brain Stimulation and Continuous Behavioral State Tracking in Ambulatory Humans

Mívalt

Kremen

Sladky

et al. 2021

Preprint

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Electrical brain stimulation (EBS) is an established treatment for patients with drug-resistant epilepsy. Sleep disorders are common in people with epilepsy and EBS therapies may actually further disturb normal sleep patterns and sleep quality. Novel devices capable of EBS and continuous intracranial EEG (iEEG) telemetry enable detailed assessments of therapy efficacy and tracking of sleep and comorbidities. Here, we investigate the feasibility of automated sleep classification using continuous iEEG data recorded from Papez's circuit in four patients with drug resistant mesial temporal lobe epilepsy using an investigational implantable sensing and stimulation device with electrodes implanted in bilateral hippocampus (HPC) and anterior nucleus of thalamus (ANT). The iEEG recorded from HPC are used to classify sleep during concurent ANT stimulation. Simultaneous polysomnography and HPC sensing was were used to train, validate and test an automated classifier for a range of ANT EBS frequencies (2 Hz, 7Hz, 100Hz, and 145 Hz). We show that it is possible to build a patient specific automated sleep staging classifier using power in band features extracted from one HPC sensing channel. The patient specific classifiers performed well under all thalamic EBS frequencies with an average F1-score 0.894, and provided viable classification into major sleep categories (Awake, NREM, REM). Within this project, we retrospectively analyzed classification performance with gold-standard polysomnography annotations, and then prospectively deployed the classifier on chronic continuous iEEG data spanning multiple months to characterize sleep patterns in ambulatory patients living in their home environment. The ability to continuously track behavioral state and fully characterize sleep should prove useful for optimizing EBS for epilepsy and associated sleep, cognitive and mood comorbidities.

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Distributed brain co-processor for tracking electrophysiology and behavior during electrical brain stimulation

Cited by 12 publications

References 86 publications

Epilepsy Personal Assistant Device—A Mobile Platform for Brain State, Dense Behavioral and Physiology Tracking and Controlling Adaptive Stimulation

Epilepsy Personal Assistant Device—A Mobile Platform for Brain State, Dense Behavioral and Physiology Tracking and Controlling Adaptive Stimulation

Invasive Electrophysiology for Circuit Discovery and Study of Comorbid Psychiatric Disorders in Patients With Epilepsy: Challenges, Opportunities, and Novel Technologies

Electrical Brain Stimulation and Continuous Behavioral State Tracking in Ambulatory Humans

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