Publication detail

Electrical brain stimulation and continuous behavioral state tracking in ambulatory humans

MÍVALT, F. KŘEMEN, V. SLADKÝ, V. BALZEKAS, I. NEJEDLÝ, P. GREGG, N. LUNDSTROM, B. LEPKOVÁ, K. PŘIDALOVÁ, T. BRINKMANN, B. JURÁK, P. VAN GOMPEL, J. MILLER, K. DENISON, T. ST LOUIS, E. WORRELL, G.

Original Title

Electrical brain stimulation and continuous behavioral state tracking in ambulatory humans

Type

journal article in Web of Science

Language

English

Original Abstract

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 implantable devices capable of DBS and streaming of continuous intracranial electroencephalography (iEEG) signals enable detailed assessments of therapy efficacy and tracking of 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 concurrent DBS targeting ANT. Simultaneous polysomnography (PSG) and sensing from HPC 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 iEEG 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 PSG 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

electrical brain stimulation; deep brain stimulation; implantable devices; automated sleep scoring; ambulatory intracranial EEG; epilepsy

Authors

MÍVALT, F.; KŘEMEN, V.; SLADKÝ, V.; BALZEKAS, I.; NEJEDLÝ, P.; GREGG, N.; LUNDSTROM, B.; LEPKOVÁ, K.; PŘIDALOVÁ, T.; BRINKMANN, B.; JURÁK, P.; VAN GOMPEL, J.; MILLER, K.; DENISON, T.; ST LOUIS, E.; WORRELL, G.

Released

8. 2. 2022

Publisher

IOP Publishing Ltd

Location

BRISTOL

ISBN

1741-2552

Periodical

Journal of Neural Engineering

Year of study

19

Number

1

State

United Kingdom of Great Britain and Northern Ireland

Pages from

1

Pages to

13

Pages count

13

URL

https://iopscience.iop.org/article/10.1088/1741-2552/ac4bfd

BibTex

@article{BUT176644,
  author="Filip {Mívalt} and Václav {Křemen} and Vladimír {Sladký} and Irena {Balzekas} and Petr {Nejedlý} and Nicholas M. {Gregg} and Brian {Lundstrom} and Kamila {Lepková} and Tereza {Přidalová} and Benjamin H. {Brinkmann} and Pavel {Jurák} and Jamie J. {Van Gompel} and Kai J. {Miller} and Timothy {Denison} and Erik {St Louis} and Gregory {Worrell}",
  title="Electrical brain stimulation and continuous behavioral state tracking in ambulatory humans",
  journal="Journal of Neural Engineering",
  year="2022",
  volume="19",
  number="1",
  pages="1--13",
  doi="10.1088/1741-2552/ac4bfd",
  issn="1741-2552",
  url="https://iopscience.iop.org/article/10.1088/1741-2552/ac4bfd"
}