Parametric Description of EEG Profiles for Assessment of Sleep Architecture in Disorders of Consciousness

We propose a fully parametric approach to the assessment of sleep architecture, based upon the classical electroencephalographic criteria, applicable also to the recordings of patients with disorders of consciousness (DOC). Sleep spindles and slow waves are automatically detected from the matching pursuit decomposition of overnight EEG recordings. Their evolution can be presented in the form of EEG profiles, yielding a continuous description of sleep architecture, compatible with the classical criteria used in sleep staging. We propose assessment of these EEG profiles by five parameters, which can be combined by a linear classifier, assessing the quality of sleep architecture. Proposed methodology is evaluated on 59 overnight EEG recordings from 19 patients from a hospital for children with severe brain damage, in relation to their behavioral diagnosis according to the Coma Recovery Scale-Revised. Presented results indicate robustness of the proposed approach, which may serve as a valuable aid in diagnosis of DOC patients. Complete software environment for computing and presentation of EEG profiles is freely available from http://svarog.pl .

[1]  D. Percival,et al.  Physiological time series: distinguishing fractal noises from motions , 2000, Pflügers Archiv.

[2]  E. Parati,et al.  Significance of multiple neurophysiological measures in patients with chronic disorders of consciousness , 2015, Clinical Neurophysiology.

[3]  G. Tononi,et al.  Electrophysiological correlates of behavioural changes in vigilance in vegetative state and minimally conscious state. , 2011, Brain : a journal of neurology.

[4]  E. Molteni,et al.  Sleep/Wake Modulation of Polysomnographic Patterns has Prognostic Value in Pediatric Unresponsive Wakefulness Syndrome. , 2016, Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine.

[5]  Steven Laureys,et al.  Electroencephalographic profiles for differentiation of disorders of consciousness , 2013, Biomedical engineering online.

[6]  Monika Schönauer,et al.  Night sleep in patients with vegetative state , 2017, Journal of sleep research.

[7]  T Sarraf,et al.  Long-term outcomes of chronic minimally conscious and vegetative states , 2010, Neurology.

[8]  Hugo Vereecke,et al.  Spectral Entropy as an Electroencephalographic Measure of Anesthetic Drug Effect: A Comparison with Bispectral Index and Processed Midlatency Auditory Evoked Response , 2004, Anesthesiology.

[9]  P Achermann,et al.  Power and coherence of sleep spindle frequency activity following hemispheric stroke. , 2002, Brain : a journal of neurology.

[10]  Steven Laureys,et al.  Automated EEG entropy measurements in coma, vegetative state/unresponsive wakefulness syndrome and minimally conscious state. , 2011, Functional neurology.

[11]  Steven Laureys,et al.  Sleep in disorders of consciousness. , 2010, Sleep medicine reviews.

[12]  G. Gigli,et al.  Polysomnographic Sleep Patterns in Children and Adolescents in Unresponsive Wakefulness Syndrome , 2015, The Journal of head trauma rehabilitation.

[13]  Steven Laureys,et al.  EEG ultradian rhythmicity differences in disorders of consciousness during wakefulness , 2016, Journal of Neurology.

[14]  Christian O'Reilly,et al.  Spindles in Svarog: framework and software for parametrization of EEG transients , 2015, Front. Hum. Neurosci..

[15]  Davide Tonon,et al.  The importance of polysomnography in the evaluation of prolonged disorders of consciousness: sleep recordings more adequately correlate than stimulus-related evoked potentials with patients' clinical status. , 2014, Sleep medicine.

[16]  R. Huber,et al.  High-density electroencephalographic recordings during sleep in children with disorders of consciousness , 2016, NeuroImage: Clinical.

[17]  J. Giacino,et al.  The minimally conscious state: Definition and diagnostic criteria , 2002, Neurology.

[18]  Steven Laureys,et al.  Sleep in the unresponsive wakefulness syndrome and minimally conscious state. , 2013, Journal of neurotrauma.

[19]  F. Nobili,et al.  The prognostic value of sleep patterns in disorders of consciousness in the sub-acute phase , 2016, Clinical Neurophysiology.

[20]  Rafał Kuś,et al.  Multivariate matching pursuit in optimal Gabor dictionaries: theory and software with interface for EEG/MEG via Svarog , 2013, BioMedical Engineering OnLine.

[21]  Steven Laureys,et al.  From unresponsive wakefulness to minimally conscious PLUS and functional locked-in syndromes: recent advances in our understanding of disorders of consciousness , 2011, Journal of Neurology.

[22]  Piotr J. Durka,et al.  High resolution parametric description of slow wave sleep , 2005, Journal of Neuroscience Methods.

[23]  Piotr J. Durka,et al.  On the Robust Parametric Detection of EEG Artifacts in Polysomnographic Recordings , 2009, Neuroinformatics.

[24]  G. Tononi,et al.  Sleep and the Price of Plasticity: From Synaptic and Cellular Homeostasis to Memory Consolidation and Integration , 2014, Neuron.

[25]  J. Giacino,et al.  The JFK Coma Recovery Scale-Revised: measurement characteristics and diagnostic utility. , 2004, Archives of physical medicine and rehabilitation.

[26]  C. Peng,et al.  Mosaic organization of DNA nucleotides. , 1994, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[27]  M. Boly,et al.  Diagnostic accuracy of the vegetative and minimally conscious state: Clinical consensus versus standardized neurobehavioral assessment , 2009, BMC neurology.

[28]  N. Graham,et al.  Areas beneath the relative operating characteristics (ROC) and relative operating levels (ROL) curves: Statistical significance and interpretation , 2002 .

[29]  A. Luxen,et al.  Automated Analysis of Background EEG and Reactivity During Therapeutic Hypothermia in Comatose Patients After Cardiac Arrest , 2014, Clinical EEG and neuroscience.

[30]  Piotr J. Durka,et al.  Fully Parametric Sleep Staging Compatible with the Classical Criteria , 2009, Neuroinformatics.

[31]  Steven Laureys,et al.  Sleep in patients with disorders of consciousness characterized by means of machine learning , 2018, PloS one.

[32]  R. Huber,et al.  High-Density Electroencephalographic Recordings During Sleep in Children and Adolescents With Acquired Brain Injury , 2017, Neurorehabilitation and Neural Repair.