Poincare SD1

Poincare SD1: in Poincare plot, SD1 represents the standard deviation perpendicular to the line-of-identity

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Poincare SD2

Poincare SD2: in Poincare plot, SD2 represents the standard deviation along the line-of-identity

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Poincare SD2/SD1

Poincare SD2/SD1: ratio between SD2 and SD1

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EnoughData (flag for nonlinear analysis)

EnoughData: boolean variable to flag whether the nonlinear analysis was carried out or not

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Approximate Entropy

Approximate Entropy: measures the complexity or irregularity of the signal. Large values of ApEn indicate high irregularity and smaller values of ApEn more regular signal

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Sample Entropy

Sample Entropy: similar to ApEn, but it presents some differences in the calculation

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Correlation Dimension D2

Correlation Dimension D2: it is a method for measuring the complexity or strangeness of the RR series. This index is expected to give information on the minimum number of dynamic variables needed to model the underlying system

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DFA alpha1

DFA alpha1: Detrended Fluctuation Analysis measures the correlation within the signal. Slope alpha1 characterizes short-term fluctuations or the RR series

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DFA alpha2

DFA alpha2: Detrended Fluctuation Analysis measures the correlation within the signal. Slope alpha2 characterizes long-term fluctuations or the RR series

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RPA recurrence rate

RPA recurrence rate: Recurrence Plot Analysis is another approach for analyzing the complexity of the RR series, which consists in creating a symmetrical matrix of zeros and ones. The recurrence rate (REC) is defined as the ratio of ones and zeros in this RP matrix

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RPA determinism

RPA determinism: Recurrence Plot Analysis is another approach for analyzing the complexity of the RR series, which consists in creating a symmetrical matrix of zeros and ones. The determinism (DET) is defined as the percentage of recurrence points which form diagonal lines

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RPA divergence

RPA divergence: Recurrence Plot Analysis is another approach for analyzing the complexity of the RR series, which consists in creating a symmetrical matrix of zeros and ones. The divergence (DIV) is defined as the inverse of Lmax

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RPA Lmax

RPA Lmax: Recurrence Plot Analysis is another approach for analyzing the complexity of the RR series, which consists in creating a symmetrical matrix of zeros and ones. Lmax is maximum line length of the diagonal lines

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RPA Lmean

RPA Lmean: Recurrence Plot Analysis is another approach for analyzing the complexity of the RR series, which consists in creating a symmetrical matrix of zeros and ones. Lmean is the average diagonal line length

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RPA Shannon Entropy

RPA Shannon Entropy: it is the entropy of the probability distribution of the diagonal line lengths

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MSE (t=1)

MSE (t=1): Multi Scale Entropy, extension of SampEn to multiple time scales, here calculated for time scale factor equal to 1

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MSE (t=2)

MSE (t=2): Multi Scale Entropy, extension of SampEn to multiple time scales, here calculated for time scale factor equal to 2

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MSE (t=3)

MSE (t=3): Multi Scale Entropy, extension of SampEn to multiple time scales, here calculated for time scale factor equal to 3

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MSE (t=4)

MSE (t=4): Multi Scale Entropy, extension of SampEn to multiple time scales, here calculated for time scale factor equal to 4

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MSE (t=5)

MSE (t=5): Multi Scale Entropy, extension of SampEn to multiple time scales, here calculated for time scale factor equal to 5

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MSE (t=6)

MSE (t=6): Multi Scale Entropy, extension of SampEn to multiple time scales, here calculated for time scale factor equal to 6

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MSE (t=7)

MSE (t=7): Multi Scale Entropy, extension of SampEn to multiple time scales, here calculated for time scale factor equal to 7

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MSE (t=8)

MSE (t=8): Multi Scale Entropy, extension of SampEn to multiple time scales, here calculated for time scale factor equal to 8

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MSE (t=9)

MSE (t=9): Multi Scale Entropy, extension of SampEn to multiple time scales, here calculated for time scale factor equal to 9

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MSE (t=10)

MSE (t=10): Multi Scale Entropy, extension of SampEn to multiple time scales, here calculated for time scale factor equal to 10

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MSE (t=11)

MSE (t=11): Multi Scale Entropy, extension of SampEn to multiple time scales, here calculated for time scale factor equal to 11

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MSE (t=12)

MSE (t=12): Multi Scale Entropy, extension of SampEn to multiple time scales, here calculated for time scale factor equal to 12

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MSE (t=13)

MSE (t=13): Multi Scale Entropy, extension of SampEn to multiple time scales, here calculated for time scale factor equal to 13

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MSE (t=14)

MSE (t=14): Multi Scale Entropy, extension of SampEn to multiple time scales, here calculated for time scale factor equal to 14

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MSE (t=15)

MSE (t=15): Multi Scale Entropy, extension of SampEn to multiple time scales, here calculated for time scale factor equal to 15

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MSE (t=16)

MSE (t=16): Multi Scale Entropy, extension of SampEn to multiple time scales, here calculated for time scale factor equal to 16

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MSE (t=17)

MSE (t=17): Multi Scale Entropy, extension of SampEn to multiple time scales, here calculated for time scale factor equal to 17

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MSE (t=18)

MSE (t=18): Multi Scale Entropy, extension of SampEn to multiple time scales, here calculated for time scale factor equal to 18

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MSE (t=19)

MSE (t=19): Multi Scale Entropy, extension of SampEn to multiple time scales, here calculated for time scale factor equal to 19

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MSE (t=20)

MSE (t=20): Multi Scale Entropy, extension of SampEn to multiple time scales, here calculated for time scale factor equal to 20

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