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.

Bioelectronics Neurophysiology and Engineering Laboratory Department of Neurology Mayo Clinic Rochester MN United States of America

Biomedical Engineering and Physiology Graduate Program Mayo Clinic Graduate School of Biomedical Sciences Rochester MN United States of America

Center for Sleep Medicine Departments of Neurology and Medicine Divisions of Sleep Neurology and Pulmonary and Critical Care Medicine Mayo Clinic Rochester MN United States of America

Czech Institute of Informatics Robotics and Cybernetics Czech Technical University Prague Prague Czech Republic

Department of Biomedical Engineering Faculty of Electrical Engineering and Communication Brno University of Technology Brno Czech Republic

Department of Engineering Science Oxford University Oxford United Kingdom

Department of Neurosurgery Mayo Clinic Rochester MN United States of America

Department of Physiology and Biomedical Engineering Mayo Clinic Rochester MN United States of America

Faculty of Biomedical Engineering Czech Technical University Prague Kladno Czech Republic

International Clinical Research Center St Anne's University Hospital Brno Czech Republic

Mayo Clinic School of Medicine and the Mayo Clinic Medical Scientist Training Program Rochester MN United States of America

The Czech Academy of Sciences Institute of Scientific Instruments Brno Czech Republic

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Case report: Bridging limbic network epilepsy with psychiatric, memory, and sleep comorbidities: case illustrations of reversible psychosis symptoms during continuous, high-frequency ANT-DBS

Acute to long-term characteristics of impedance recordings during neurostimulation in humans

Method for cycle detection in sparse, irregularly sampled, long-term neuro-behavioral timeseries: Basis pursuit denoising with polynomial detrending of long-term, inter-ictal epileptiform activity

Acute to long-term characteristics of impedance recordings during neurostimulation in humans

Impedance Rhythms in Human Limbic System

Automated sleep classification with chronic neural implants in freely behaving canines

Utilization of temporal autoencoder for semi-supervised intracranial EEG clustering and classification

Distributed brain co-processor for tracking spikes, seizures and behaviour during electrical brain stimulation

Embedding digital chronotherapy into bioelectronic medicines