Search
Abstract
OBJECTIVE: Automated behavioral state classification can benefit next generation implantable epilepsy devices. In this study we explored the feasibility of automated awake (AW) and slow wave sleep (SWS) classification using wide bandwidth intracranial EEG (iEEG) in patients undergoing evaluation for epilepsy surgery. APPROACH: Data from seven patients (age [Formula: see text], 4 women) who underwent intracranial depth electrode implantation for iEEG monitoring were included. Spectral power features (0.1-600 Hz) spanning several frequency bands from a single electrode were used to train and test a support vector machine classifier. MAIN RESULTS: Classification accuracy of 97.8 ± 0.3% (normal tissue) and 89.4 ± 0.8% (epileptic tissue) across seven subjects using multiple spectral power features from a single electrode was achieved. Spectral power features from electrodes placed in normal temporal neocortex were found to be more useful (accuracy 90.8 ± 0.8%) for sleep-wake state classification than electrodes located in normal hippocampus (87.1 ± 1.6%). Spectral power in high frequency band features (Ripple (80-250 Hz), Fast Ripple (250-600 Hz)) showed comparable performance for AW and SWS classification as the best performing Berger bands (Alpha, Beta, low Gamma) with accuracy ⩾90% using a single electrode contact and single spectral feature. SIGNIFICANCE: Automated classification of wake and SWS should prove useful for future implantable epilepsy devices with limited computational power, memory, and number of electrodes. Applications include quantifying patient sleep patterns and behavioral state dependent detection, prediction, and electrical stimulation therapies.
Citations
-
2 9
CrossRef
-
0
Web of Science
-
2 6
Scopus
Authors (10)
-
Vaclav Kremen
- Mayo Clinic Department of Neurology
-
Juliano J. Duque
- Mayo Clinic Department of Neurology
-
Benjamin Brinkmann
- Mayo Clinic Department of Neurology
-
Brent M. Berry
- Mayo Clinic Department of Neurology
-
-
Fatemeh Khadjevand
- Mayo Clinic Department of Neurology
-
Jamie Van Gompel
- Mayo Clinic Department of Neurology
-
Matt Stead
- Mayo Clinic Department of Neurology
-
Erik K. ST.Louis
- Mayo Clinic Department of Neurology
-
Gregory A. Worrell
- Mayo Clinic Department of Neurology
Cite as
Full text
full text is not available in portal
Keywords
Details
- Category:
- Articles
- Type:
- artykuł w czasopiśmie wyróżnionym w JCR
- Published in:
-
Journal of Neural Engineering
no. 14,
edition 2,
pages 1 - 9,
ISSN: 1741-2560 - Language:
- English
- Publication year:
- 2017
- Bibliographic description:
- Kremen V., Duque J. J., Brinkmann B., Berry B. M., Kucewicz M. T., Khadjevand F., Van Gompel J., Stead M., St.louis E. K., Worrell G. A.: Behavioral state classification in epileptic brain using intracranial electrophysiology// Journal of Neural Engineering. -Vol. 14, iss. 2 (2017), s.1-9
- DOI:
- Digital Object Identifier (open in new tab) 10.1088/1741-2552/aa5688
- Verified by:
- Gdańsk University of Technology
seen 83 times
Recommended for you
Unsupervised machine-learning classification of electrophysiologically active electrodes during human cognitive task performance
- K. Saboo,
- Y. Varatharajah,
- B. M. Berry
- + 10 authors
High frequency oscillations are associated with cognitive processing in human recognition memory
- M. T. Kucewicz,
- J. Cymbalnik,
- J. Matsumoto
- + 8 authors
Dissecting gamma frequency activities during human memory processing
- M. T. Kucewicz,
- B. M. Berry,
- V. Kremen
- + 13 authors