Recurrence quantification analysis across sleep stages

Rolink, J Jérôme; Kutz, Martin; Fonseca, Pedro; Long, Xi; Misgeld, Berno J.E.; Leonhardt, Steffen

doi:10.1016/j.bspc.2015.04.006

Cited by 18 publications

(10 citation statements)

References 40 publications

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“…It determines the number of rhythmic patterns and its duration in a time series signal [80]. The nonlinearity of a signal is analyzed by calculating different features, such as determinism (det), laminarity (lam), recurrence rate (rr), recurrence time (t1, t2), entropy (ent), longest vertical line (vmax), largest diagonal line (lmax), mean diagonal length (meanlen) and trapping time (Tt) [81] listed in table 3. The recurrence plot is a 2D plot of dotted arrays that helps in the detection of masked periodic nature and drift of a random signal in the time domain.…”

Section: Analysis Of Sleep Eeg Signals Using Nonlinear Dynamics Mementioning

confidence: 99%

Nonlinear Dynamics Measures for Automated EEG-Based Sleep Stage Detection

et al. 2015

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Background: The brain's continuous neural activity during sleep can be monitored by electroencephalogram (EEG) signals. The EEG wave pattern and frequency vary during five stages of sleep. These subtle variations in sleep EEG signals cannot be easily detected through visual inspection. Summary: A range of time, frequency, time-frequency and nonlinear analysis methods can be applied to understand the complex physiological signals and their chaotic behavior. This paper presents a comprehensive comparative review and analysis of 29 nonlinear dynamics measures for EEG-based sleep stage detection. Key Messages: The characteristic ranges of these features are reported for the five different sleep stages. All nonlinear measures produce clinically significant results, that is, they can discriminate the individual sleep stages. Feature ranking based on the statistical F-value, however, shows that the third order cumulant of higher order spectra yields the most discriminative result. The distinct value ranges for each sleep stage and the discriminative power of the features can be used for sleep disorder diagnosis, treatment monitoring, and drug efficacy assessment.

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Section: Analysis Of Sleep Eeg Signals Using Nonlinear Dynamics Mementioning

confidence: 99%

Nonlinear Dynamics Measures for Automated EEG-Based Sleep Stage Detection

et al. 2015

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“…Both axes of the RP are time axes. The dots or pixels located at (i, j) and (j, i) on the RP are black if the distance between points x i and x j in the phase space fall inside a ball or threshold corridor of radius ε, the threshold distance [21,22]. In this case, the black points refer to recurring states also termed ε-recurrent states since they occur in an ε-neighbourhood.…”

Section: Recurrence Plotsmentioning

confidence: 99%

On Modeling Affect in Audio with Non-Linear Symbolic Dynamics

Mouawad¹,

Dubnov²

2017

Adv. sci. technol. eng. syst. j.

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“…Diagonal lines indicate deterministic behavior, regular patterns of parallel lines reveal a periodicity. Isolated points (islands) are typical for chaotic systems, while a rectangular patch results from laminar states [21].…”

Section: Signal Post-processingmentioning

confidence: 99%

“…The first quantitative parameter used in this work is the determinism in percentage (%DET), as it seemed efficient in the detection of system transitions [3,18,19,21]. %DET is a simple quotient of the number of RP points that lie on lines of the assumed minimal length (parallel to the main diagonal) to the total number of black RP points.…”

Section: Signal Post-processingmentioning

confidence: 99%