Publication detail

Automated Sleep Classification and Brain Stimulation with Implantable Devices

MÍVALT, F. SLADKÝ, V. NEJEDLÝ, P. BALZEKAS, I. BRINKMANN, B. DENISON, T. WORRELL, G. KŘEMEN, V.

Original Title

Automated Sleep Classification and Brain Stimulation with Implantable Devices

Type

presentation, poster

Language

English

Original Abstract

Rationale: Electrical brain stimulation (EBS) is an established therapy for drug-resistant epilepsy. Novel implantable neural sense and stimulation devices (INSS) enabling continuous intracranial electroencephalographic (iEEG) streaming provide new opportunities for objective outcome evaluation with seizure diaries and sleep scoring. However, new challenges arise when collecting iEEG data with concurrent EBS. EBS-induced stimulation artifacts and brain electrophysiology changes are factors affecting the performance of automated classification algorithms. We investigated the feasibility of utilizing iEEG data to build an automated behavioral state classifier under different EBS paradigms. Methods: Four human subjects underwent chronic ambulatory monitoring using the Medtronic investigational Summit RC+STM INSS device with electrodes implanted in bilateral hippocampus (HC) & anterior nucleus of the thalamus (ANT). Standard sleep clinical polysomnography (PSG) and continuous iEEG data streaming from the INSS were recorded simultaneously over three nights in the epilepsy monitoring unit. Different EBS parameters (2, 7 and 145 Hz) were trialed in 5–15-minute-long epochs with wash-out no-stim periods. PSG data were scored according to gold standard sleep categories using AASM2012 rules. An automated classification algorithm was designed and trained using only no-stim iEEG data and tested under the 2, 7, and 145 Hz EBS setups. Results: An automated behavioral sleep state iEEG classifier (wake, rapid eye movement (REM and non-REM) had the overall average F1-score was 0.889 across all modes of stimulation. The models were deployed on long-term data (over 30 months of continuous iEEG in total) to create a sleep/wake profile for all patients. Conclusions: The trained sleep classification models enable the assessment of behavioral states under different low (2&7 Hz) and high ( >100Hz) frequency EBS for four human subjects implanted with Medtronic investigational Summit RC+STM INSS device for epilepsy treatment. The study shows that the behavioral state classification models can be trained using no-stim data, resulting in high classification rates in EBS.

Keywords

epilepsy; deep brain stimulation; sleep classification; implantable stimulators

Authors

MÍVALT, F.; SLADKÝ, V.; NEJEDLÝ, P.; BALZEKAS, I.; BRINKMANN, B.; DENISON, T.; WORRELL, G.; KŘEMEN, V.

Released

6. 12. 2021

Publisher

American Epilepsy Society

Location

Chicago

URL

https://cms.aesnet.org/abstractslisting/automated-sleep-classification-and-brain-stimulation-with-implantable-devices

BibTex

@misc{BUT177615,
  author="Filip {Mívalt} and Vladimír {Sladký} and Petr {Nejedlý} and Irena {Balzekas} and Benjamin H. {Brinkmann} and Timothy {Denison} and Gregory {Worrell} and Václav {Křemen}",
  title="Automated Sleep Classification and Brain Stimulation with Implantable Devices",
  year="2021",
  publisher="American Epilepsy Society",
  address="Chicago",
  url="https://cms.aesnet.org/abstractslisting/automated-sleep-classification-and-brain-stimulation-with-implantable-devices",
  note="presentation, poster"
}