Quantifying chirp in sleep spindles

Sleep spindles are considered as a marker of integrity for thalamo-cortical circuits. Recently, attention has been given to internal frequency variation in sleep spindles. In this study, a procedure based on matching pursuit with a Gabor-chirplet dictionary was applied in order to measure chirp rate in atoms representing sleep spindles, also categorized into negative, positive or zero chirp types. The sample comprised 707 EEG segments containing visual sleep spindles, labeled TP, obtained from nine healthy male volunteers (aged 20-34, average 24.6 y). Control datasets were 333 non-REM (NREM) sleep background segments and 287 REM sleep intervals, each with 16s duration. Analyses were carried out on the C3-A2 EEG channel. In TP and NREM groups, the proportion of non-null chirp types was non-random and total chirp distribution was asymmetrical towards negative values, in contrast to REM. Median negative chirp rate in the TP and NREM groups was significantly lower than in REM (-0.4 Hz/s vs -0.3 Hz/s, P < 0.05). Negative chirp atoms outnumbered positives by 50% in TP, while in NREM and REM, they were, respectively, only 22% and 12% more prevalent. TP negative chirp atoms were significantly higher in amplitude compared to positive or zero types. Considering individual subjects, 88.9% had a TP negative/positive chirp ratio above 1 (mean ± sd=1.64 ± 0.65). We propose there is increasing evidence, corroborated by the present study, favoring systematic measurement of sleep spindle chirp rate or internal frequency variation. Preferential occurrence of negatively chirping spindles is consistent with the hypothesis of electrophysiological modulation of neocortical memory consolidation.

[1]  H. Schulz,et al.  Topographical analysis of sleep spindle activity. , 1992, Neuropsychobiology.

[2]  K. Blinowska,et al.  High resolution study of sleep spindles , 1999, Clinical Neurophysiology.

[3]  A. Chesson,et al.  The American Academy of Sleep Medicine (AASM) Manual for the Scoring of Sleep and Associated Events: Rules, Terminology and Technical Specifications , 2007 .

[4]  P. Anderer,et al.  Topographic distribution of sleep spindles in young healthy subjects , 1997, Journal of sleep research.

[5]  M. Lehtokangas,et al.  Optimization of sigma amplitude threshold in sleep spindle detection , 2000, Journal of sleep research.

[6]  M. Steriade Brain Electrical Activity and Sensory Processing during Waking and Sleep States , 2005 .

[7]  János Körmendi,et al.  The individual adjustment method of sleep spindle analysis: Methodological improvements and roots in the fingerprint paradigm , 2009, Journal of Neuroscience Methods.

[8]  F. H. Lopes da Silva,et al.  Computer-assisted EEG diagnosis: pattern recognition and brain mapping , 1998 .

[9]  George Adelman,et al.  Encyclopedia of neuroscience , 2004 .

[10]  G Buzsáki,et al.  Memory consolidation during sleep: a neurophysiological perspective. , 1998, Journal of sleep research.

[11]  Terrence J. Sejnowski,et al.  Sleep and Sleep States: Thalamic Regulation , 2009 .

[12]  J Hasan,et al.  Quantitative topographic electroencephalographic mapping during drowsiness and sleep onset. , 1995, Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society.

[13]  Piotr J. Durka,et al.  Stochastic time-frequency dictionaries for matching pursuit , 2001, IEEE Trans. Signal Process..

[14]  M. Kryger,et al.  Principles and Practice of Sleep Medicine , 1989 .

[15]  T. Sejnowski,et al.  A model of spindle rhythmicity in the isolated thalamic reticular nucleus. , 1994, Journal of neurophysiology.

[16]  S. Himanen,et al.  Diffuse sleep spindles show similar frequency in central and frontopolar positions , 2008, Journal of Neuroscience Methods.

[17]  M. Zervakis,et al.  Time–frequency analysis methods to quantify the time-varying microstructure of sleep EEG spindles: Possibility for dementia biomarkers? , 2009, Journal of Neuroscience Methods.

[18]  Stéphane Mallat,et al.  Matching pursuits with time-frequency dictionaries , 1993, IEEE Trans. Signal Process..

[19]  Laura B Ray,et al.  Validating an automated sleep spindle detection algorithm using an individualized approach , 2010, Journal of sleep research.

[20]  Günther J. L. Gerhardt,et al.  Characteristics of human EEG sleep spindles assessed by Gabor transform , 2003 .

[21]  S. Mallat A wavelet tour of signal processing , 1998 .

[22]  Willy Wong,et al.  Approximating the Time-Frequency Representation of Biosignals with Chirplets , 2010, EURASIP J. Adv. Signal Process..

[23]  M. Walker,et al.  Daytime Naps, Motor Memory Consolidation and Regionally Specific Sleep Spindles , 2007, PloS one.

[24]  P. Achermann,et al.  Spindle frequency activity in the sleep EEG: individual differences and topographic distribution. , 1997, Electroencephalography and clinical neurophysiology.

[25]  C. Rosenberg,et al.  Electroencephalography: Basic Principles, Clinical Applications, and Related Fields, 3rd Ed. , 1994 .

[26]  Qinye Yin,et al.  A fast refinement for adaptive Gaussian chirplet decomposition , 2002, IEEE Trans. Signal Process..

[27]  A. Rechtschaffen,et al.  A manual of standardized terminology, technique and scoring system for sleep stages of human subjects , 1968 .

[28]  Simon Haykin,et al.  The chirplet transform: physical considerations , 1995, IEEE Trans. Signal Process..

[29]  Rémi Gribonval,et al.  Fast matching pursuit with a multiscale dictionary of Gaussian chirps , 2001, IEEE Trans. Signal Process..

[30]  Alexander E. Hramov,et al.  Sleep spindles and spike–wave discharges in EEG: Their generic features, similarities and distinctions disclosed with Fourier transform and continuous wavelet analysis , 2009, Journal of Neuroscience Methods.

[31]  Nima Dehghani,et al.  Topographical frequency dynamics within EEG and MEG sleep spindles , 2011, Clinical Neurophysiology.

[32]  G Klösch,et al.  Low-resolution brain electromagnetic tomography revealed simultaneously active frontal and parietal sleep spindle sources in the human cortex , 2001, Neuroscience.

[33]  Piotr J. Durka,et al.  Matching pursuit parametrization of sleep spindles , 1996, Proceedings of 18th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[34]  Douglas L. Jones,et al.  Shear madness: new orthonormal bases and frames using chirp functions , 1993, IEEE Trans. Signal Process..

[35]  M. Ferrara,et al.  Sleep spindles: an overview. , 2003, Sleep medicine reviews.

[36]  J. Born,et al.  Sleep to Remember , 2006, The Neuroscientist : a review journal bringing neurobiology, neurology and psychiatry.

[37]  Manuel Schabus,et al.  Hemodynamic cerebral correlates of sleep spindles during human non-rapid eye movement sleep , 2007, Proceedings of the National Academy of Sciences.

[38]  D. McCormick,et al.  Cellular mechanisms of a synchronized oscillation in the thalamus. , 1993, Science.

[39]  Fabrice Wendling,et al.  Computational modeling of high-frequency oscillations at the onset of neocortical partial seizures: From ‘altered structure’ to ‘dysfunction’ , 2010, NeuroImage.

[40]  F. L. D. Silva,et al.  EEG analysis: Theory and Practice , 1998 .

[41]  Günther J. L. Gerhardt,et al.  Benchmarking matching pursuit to find sleep spindles , 2006, Journal of Neuroscience Methods.

[42]  M. Steriade Corticothalamic resonance, states of vigilance and mentation , 2000, Neuroscience.

[43]  Antti Saastamoinen,et al.  Development and comparison of four sleep spindle detection methods , 2007, Artif. Intell. Medicine.

[44]  K. Blinowska,et al.  Adaptive time–frequency parametrization in pharmaco EEG , 2002, Journal of Neuroscience Methods.

[45]  D. Colella,et al.  Brain chirps: spectrographic signatures of epileptic seizures , 2000, Clinical Neurophysiology.

[46]  W. Wong,et al.  Time-frequency analysis of visual evoked potentials by means of matching pursuit with chirplet atoms , 2004, The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[47]  Ernst Fernando Lopes Da Silva Niedermeyer,et al.  Electroencephalography, basic principles, clinical applications, and related fields , 1982 .