0554373 - ÚPT 2023 RIV GB eng J - Článek v odborném periodiku
Mívalt, F. - Křemen, V. - Sladký, V. - Balzekas, I. - Nejedlý, Petr - Gregg, N. M. - Lundstrom, B. N. - Lepková, K. - Přidalová, T. - Brinkmann, B. H. - Jurák, Pavel - Van Gompel, J. J. - Miller, K. - Denison, T. - St Louis, E. K. - Worrell, G. A.
Electrical brain stimulation and continuous behavioral state tracking in ambulatory humans.
Journal of Neural Engineering. Roč. 19, č. 1 (2022), č. článku 016019. ISSN 1741-2560. E-ISSN 1741-2552
Institucionální podpora: RVO:68081731
Klíčová slova: electrical brain stimulation * deep brain stimulation * implantable devices * automated sleep scoring * ambulatory intracranial EEG * epilepsy
Obor OECD: Medical engineering
Impakt faktor: 4, rok: 2022
Způsob publikování: Open access
https://iopscience.iop.org/article/10.1088/1741-2552/ac4bfd

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.
Trvalý link: https://hdl.handle.net/11104/0333660