Electrical brain stimulation and continuous behavioral state tracking in ambulatory humans

Mivalt F, Kremen V, Sladky V, Balzekas I, Nejedly P, Gregg N, Lundstrom BN, Lepkova K, Pridalova T, Brinkmann BH, Jurak P, Van Gompel JJ, Miller K, Denison TJ, St Louis EK, Worrell GA.

J Neural Eng. 2022 Jan 17. doi:10.1088/1741-2552/ac4bfd. Epub ahead of print. PMID: 35038687.


25 Jan 2022

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Objective: Electrical deep brain stimulation (DBS) is an established treatment for patients with drug-resistant epilepsy. Sleep disorders are common in people with epilepsy, and DBS may actually further disturb normal sleep patterns and sleep quality. Novel devices capable of DBS and continuous intracranial EEG (iEEG) telemetry enable detailed assessments of therapy efficacy and tracking sleep related 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).

Approach: The iEEG recorded from HPC is used to classify sleep during concurent DBS targeting ANT. Simultaneous polysomnography and HPC sensing were used to train, validate and test an automated classifier for a range of ANT DBS frequencies: no stimulation, 2 Hz, 7 Hz, and high frequency (>100 Hz).

Main results: 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 DBS frequencies with an average F1-score 0.894, and provided viable classification into awake and major sleep categories, rapid eye movement (REM) and non-REM. 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.

Significance: The ability to continuously track behavioral state and fully characterize sleep should prove useful for optimizing DBS for epilepsy and associated sleep, cognitive and mood comorbidities.

Keywords: Ambulatory intracranial EEG; Automated Sleep Scoring; Deep brain stimulation; Electrical Brain Stimulation; Epilepsy; Implantable Devices.

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