Literature DB >> 27184021

Interaction with slow waves during sleep improves discrimination of physiologic and pathologic high-frequency oscillations (80-500 Hz).

Nicolás von Ellenrieder1,2, Birgit Frauscher1, François Dubeau1, Jean Gotman1.   

Abstract

OBJECTIVE: To characterize the interaction between physiologic and pathologic high-frequency oscillations (HFOs) and slow waves during sleep, and to evaluate the practical significance of these interactions by automatically classifying channels as recording from normal or epileptic brain regions.
METHODS: We automatically detected HFOs in intracerebral electroencephalography (EEG) recordings of 45 patients. We characterized the interaction between the HFOs and the amplitude and phase of automatically detected slow waves during sleep. We computed features associated with HFOs, and compared classic features such as rate, amplitude, duration, and frequency to novel features related to the interaction between HFOs and slow waves. To quantify the practical significance of the difference in these features we classified the channels as recording from normal/epileptic regions using logistic regression. We assessed the results in different brain regions to study differences in the HFO characteristics at the lobar level.
RESULTS: We found a clear difference in the coupling between the phase of slow waves during sleep and the occurrence of HFOs. In channels recording physiologic activity, the HFOs tend to occur after the peak of the deactivated state of the slow wave, and in channels with epileptic activity, the HFOs occur more often before this peak. This holds for HFOs in the ripple (80-250 Hz) and fast ripple (250-500 Hz) bands, and different regions of the brain. When using this interaction to automatically classify channels as recording from normal/epileptic brain regions, the performance is better than when using other HFO characteristics. We confirmed differences in the HFO characteristics in mesiotemporal structures and in the occipital lobe. SIGNIFICANCE: We found the association between slow waves and HFOs to be different in normal and epileptic brain regions, emphasizing their different origin. This is also of practical significance, since it improves the separation between channels recording from normal and epileptic brain regions. Wiley Periodicals, Inc.
© 2016 International League Against Epilepsy.

Entities:  

Keywords:  Automatic detection; Classification; Depth EEG; HFO; NREM sleep

Mesh:

Year:  2016        PMID: 27184021     DOI: 10.1111/epi.13380

Source DB:  PubMed          Journal:  Epilepsia        ISSN: 0013-9580            Impact factor:   5.864


  29 in total

Review 1.  Localizing epileptogenic regions using high-frequency oscillations and machine learning.

Authors:  Shennan A Weiss; Zachary Waldman; Federico Raimondo; Diego Slezak; Mustafa Donmez; Gregory Worrell; Anatol Bragin; Jerome Engel; Richard Staba; Michael Sperling
Journal:  Biomark Med       Date:  2019-05-02       Impact factor: 2.851

Review 2.  High-frequency oscillations: The state of clinical research.

Authors:  Birgit Frauscher; Fabrice Bartolomei; Katsuhiro Kobayashi; Jan Cimbalnik; Maryse A van 't Klooster; Stefan Rampp; Hiroshi Otsubo; Yvonne Höller; Joyce Y Wu; Eishi Asano; Jerome Engel; Philippe Kahane; Julia Jacobs; Jean Gotman
Journal:  Epilepsia       Date:  2017-06-30       Impact factor: 5.864

3.  Spike-related haemodynamic responses overlap with high frequency oscillations in patients with focal epilepsy.

Authors:  Karina A González Otárula; Hui Ming Khoo; Nicolás von Ellenrieder; Jeffery A Hall; François Dubeau; Jean Gotman
Journal:  Brain       Date:  2018-03-01       Impact factor: 13.501

4.  Bimodal coupling of ripples and slower oscillations during sleep in patients with focal epilepsy.

Authors:  Inkyung Song; Iren Orosz; Inna Chervoneva; Zachary J Waldman; Itzhak Fried; Chengyuan Wu; Ashwini Sharan; Noriko Salamon; Richard Gorniak; Sandra Dewar; Anatol Bragin; Jerome Engel; Michael R Sperling; Richard Staba; Shennan A Weiss
Journal:  Epilepsia       Date:  2017-09-26       Impact factor: 5.864

5.  A semi-automated method for rapid detection of ripple events on interictal voltage discharges in the scalp electroencephalogram.

Authors:  Catherine J Chu; Arthur Chan; Dan Song; Kevin J Staley; Steven M Stufflebeam; Mark A Kramer
Journal:  J Neurosci Methods       Date:  2016-12-14       Impact factor: 2.390

6.  Progress and Remaining Challenges in the Application of High Frequency Oscillations as Biomarkers of Epileptic Brain.

Authors:  Fatemeh Khadjevand; Jan Cimbalnik; Gregory A Worrell
Journal:  Curr Opin Biomed Eng       Date:  2017-09-22

7.  Scalp recorded spike ripples predict seizure risk in childhood epilepsy better than spikes.

Authors:  Mark A Kramer; Lauren M Ostrowski; Daniel Y Song; Emily L Thorn; Sally M Stoyell; McKenna Parnes; Dhinakaran Chinappen; Grace Xiao; Uri T Eden; Kevin J Staley; Steven M Stufflebeam; Catherine J Chu
Journal:  Brain       Date:  2019-05-01       Impact factor: 13.501

8.  Stereotyped high-frequency oscillations discriminate seizure onset zones and critical functional cortex in focal epilepsy.

Authors:  Su Liu; Candan Gurses; Zhiyi Sha; Michael M Quach; Altay Sencer; Nerses Bebek; Daniel J Curry; Sujit Prabhu; Sudhakar Tummala; Thomas R Henry; Nuri F Ince
Journal:  Brain       Date:  2018-03-01       Impact factor: 13.501

9.  Phase-amplitude coupling between interictal high-frequency activity and slow waves in epilepsy surgery.

Authors:  Hirotaka Motoi; Makoto Miyakoshi; Taylor J Abel; Jeong-Won Jeong; Yasuo Nakai; Ayaka Sugiura; Aimee F Luat; Rajkumar Agarwal; Sandeep Sood; Eishi Asano
Journal:  Epilepsia       Date:  2018-08-26       Impact factor: 5.864

10.  Phase-amplitude coupling and epileptogenesis in an animal model of mesial temporal lobe epilepsy.

Authors:  Soheila Samiee; Maxime Lévesque; Massimo Avoli; Sylvain Baillet
Journal:  Neurobiol Dis       Date:  2018-02-24       Impact factor: 5.996

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