Generalized multiscale Lempel–Ziv complexity of cyclic alternating pattern during sleep
- 112 Downloads
Increasing evidences show that multiscale complexity measure is an intuitive and effective measure in quantifying various physical and physiological states. In this study, we demonstrate that the classical algorithm of multiscale Lempel–Ziv complexity (multiscale LZC or MLZ) has a critical limitation in neglecting rapid rhythms in complex systems. To this end, simulations added with different levels of white noise are designed to examine whether or not MLZ calculation neglects the effects of high-frequency noise. In addition, an algorithm by obtaining coarse-grained multiscale LZC, so-called generalized multiscale LZC (gMLZ), is proposed to yield a spectrum of complexity. A series of simulated non-stationary signals are generated for comparing the performances between MLZ and gMLZ. Besides, cyclic alternating pattern (CAP), characterized by the excessive synchronization of neuronal activity, has been associated with its power and physiological states. To understand how the synchronization of neuronal activities in different phase-A subtypes in exerting an influence over its power and complexity, we analyze the gMLZ of the real CAP database and compare it to its power spectra as well as modified multiscale entropy (MMSE), which is one of the most well-known multiscale complexity-based measures. The novel algorithm reveals that the evaluated complexities in different phase-A subtypes are inversely related to both the power and excessive synchronization in different timescales in general. The impact of frequencies, sleep stages and pathophysiological conditions on these two complexity measures is also examined. The discerning abilities of different phase-A subtypes using coarse-grained complexity measures (gMLZ and MMSE) are more consistent than power across different time scales. Our approach makes up a deficiency in handling with high-frequency oscillations and enables us to examine complexities of nonlinear systems in a wide-range of timescales.
KeywordsLempel–Ziv complexity Multiscale complexity Cyclic alternating pattern Sleep
This research is sponsored by the China Postdoctoral Science Foundation (Grant 043206005).
Compliance with ethical standard
Conflict of interest
The authors declare that they have no conflict of interest.
- 12.Richman, J.S., Moorman, J.R.: Physiological time-series analysis using approximate entropy and sample entropy. Am. J. Psychol. 278, H2039–H2049 (2000)Google Scholar
- 19.Dauwels, J., Srinivasan, K., Ramasubba Reddy, M., Musha, T., Vialatte, F.B., Latchoumane, C., Jeong, J., Cichocki, A.: Slowing and loss of complexity in Alzheimer’s EEG: two sides of the same coin? Int. J. Alzheimers Dis. 2011, 539621 (2011)Google Scholar
- 21.Niedermeyer, E.: The normal EEG in the waking adult.Electroencephalography: Basic Principles, Clinical Applications, and Related field. Williams & Wilkins, Baltimore (1999)Google Scholar
- 26.Hu, J., Gao, J., Wang, X.: Multifractal analysis of sunspot time series: the effects of the 11-year cycle and Fourier truncation. J. Stat. Mech. C Theory Exp. 2, P02066 (2009)Google Scholar
- 27.Bell, I.R., Howerter, A., Jackson, N., Aickin, M., Bootzin, R.R., Brooks, A.J.: Nonlinear dynamical systems effects of homeopathic remedies on multiscale entropy and correlation dimension of slow wave sleep EEG in young adults with histories of coffee-induced insomnia. Homeopathy 101, 182–192 (2012)CrossRefGoogle Scholar
- 33.Terzano, M.G., Parrino, L., Sherieri, A., Chervin, R., Chokroverty, S., Guilleminault, C., Hirshkowitz, M., Mahowald, M., Moldofsky, H., Rosa, A., Thomas, R., Walters, A.: Atlas, rules, and recording techniques for the scoring of cyclic alternating pattern (CAP) in human sleep. Sleep Med. 2, 537–553 (2001)CrossRefGoogle Scholar
- 34.Lundberg, N.: Continuous recording and control of ventricular fluid pressure in neurosurgical practice. Acta. Psychiatr. Scand. 36, 1–193 (1960)Google Scholar
- 37.Goldberger, A.L., Amaral, L.A., Glass, L., Hausdorff, J.M., Ivanov, P.C., Mark, R.G., Mietus, J.E., Moody, G.B., Peng, C.K., Stanley, H.E.: PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation 101, e215–e220 (2000)CrossRefGoogle Scholar
- 38.Rechtscahffen, A.: A manual of standardized terminology, techniques and scoring system for sleep stages of human subjects. U.S. National Institutes of Health 204 (1968)Google Scholar
- 44.Terzano, M.G., Parrino, L., Boselli, M., Smerieri, A., Spaggiari, M.C.: CAP components and EEG synchronization in the first 3 sleep cycles. Clin. Neurophysiol. 111, 283–290 (2000)Google Scholar
- 45.Ferrillo, F., Gabarra, M., Nobili, L., Parrino, L., Schiavi, G., Stubinski, B., Terzano, M.G.: Comparison between visual scoring of cyclic alternating pattern (CAP) and computerized assessment of slow EEG oscillations in the transition from light to deep non-REM sleep. J. Clin. Neurophysiol. 14, 210–216 (1997)CrossRefGoogle Scholar