Automated unsupervised behavioral state classification using intracranial electrophysiology. (21st January 2019)

• Record Type: Journal Article
• Authors: Kremen, Vaclav; Brinkmann, Benjamin H; Van Gompel, Jamie J; Stead, Matt; St Louis, Erik K; Worrell, Gregory A
• Is Part Of: Journal of neural engineering. Volume 16:Number 2(2019:Apr.)
• Publication Date: 2019-01-21
• Subjects: intracranial EEG -- behavioral states -- sleep staging -- machine learning -- classification -- epilepsy -- deep brain stimulation; Neurosciences -- Periodicals; Biomedical engineering -- Periodicals; 612.8
• DOI: 10.1088/1741-2552/aae5ab
• Languages: English
• ISSNs: 1741-2560

Abstract

Abstract: Objective . Automated behavioral state classification in intracranial EEG (iEEG) recordings may be beneficial for iEEG interpretation and quantifying sleep patterns to enable behavioral state dependent neuromodulation therapy in next generation implantable brain stimulation devices. Here, we introduce a fully automated unsupervised framework to differentiate between awake (AW), sleep (N2), and slow wave sleep (N3) using intracranial EEG (iEEG) only and validated with expert scored polysomnography. Approach . Data from eight patients undergoing evaluation for epilepsy surgery (age, three female) with intracranial depth electrodes for iEEG monitoring were included. Spectral power features (0.1–235 Hz) spanning several frequency bands from a single electrode were used to classify behavioral states of patients into AW, N2, and N3. Main results . Overall, classification accuracy of 94%, with 94% sensitivity and 93% specificity across eight subjects using multiple spectral power features from a single electrode was achieved. Classification performance of N3 sleep was significantly better (95%, sensitivity 95%, specificity 93%) than that of the N2 sleep phase (87%, sensitivity 78%, specificity 96%). Significance . Automated, unsupervised, and robust classification of behavioral states based on iEEG data is possible, and it is feasible to incorporate these algorithms into future implantable devices with limited computational power, memory, and number of electrodes for brain monitoring and stimulation.