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Electrical brain stimulation and continuous behavioral state tracking in ambulatory humans.
Mivalt, Filip; Kremen, Vaclav; Sladky, Vladimir; Balzekas, Irena; Nejedly, Petr; Gregg, Nicholas M; Lundstrom, Brian Nils; Lepkova, Kamila; Pridalova, Tereza; Brinkmann, Benjamin H; Jurak, Pavel; Van Gompel, Jamie J; Miller, Kai; Denison, Timothy; St Louis, Erik K; Worrell, Gregory A.
Afiliação
  • Mivalt F; Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, United States of America.
  • Kremen V; Department of Biomedical Engineering, Faculty of Electrical Engineering and Communication, Brno University of Technology, Brno, Czech Republic.
  • Sladky V; Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, United States of America.
  • Balzekas I; Czech Institute of Informatics, Robotics, and Cybernetics, Czech Technical University in Prague, Prague, Czech Republic.
  • Nejedly P; Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, United States of America.
  • Gregg NM; Faculty of Biomedical Engineering, Czech Technical University in Prague, Kladno, Czech Republic.
  • Lundstrom BN; International Clinical Research Center, St. Anne's University Hospital, Brno, Czech Republic.
  • Lepkova K; Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, United States of America.
  • Pridalova T; Mayo Clinic School of Medicine and the Mayo Clinic Medical Scientist Training Program, Rochester, MN, United States of America.
  • Brinkmann BH; Biomedical Engineering and Physiology Graduate Program, Mayo Clinic Graduate School of Biomedical Sciences, Rochester, MN, United States of America.
  • Jurak P; Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, United States of America.
  • Van Gompel JJ; The Czech Academy of Sciences, Institute of Scientific Instruments, Brno, Czech Republic.
  • Miller K; Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, United States of America.
  • Denison T; Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, United States of America.
  • St Louis EK; Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, United States of America.
  • Worrell GA; Faculty of Biomedical Engineering, Czech Technical University in Prague, Kladno, Czech Republic.
J Neural Eng ; 19(1)2022 02 08.
Article em En | MEDLINE | ID: mdl-35038687
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.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Transtornos do Sono-Vigília / Núcleos Anteriores do Tálamo / Estimulação Encefálica Profunda Tipo de estudo: Diagnostic_studies / Observational_studies / Risk_factors_studies Limite: Humans Idioma: En Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Transtornos do Sono-Vigília / Núcleos Anteriores do Tálamo / Estimulação Encefálica Profunda Tipo de estudo: Diagnostic_studies / Observational_studies / Risk_factors_studies Limite: Humans Idioma: En Ano de publicação: 2022 Tipo de documento: Article