Heart rate variability (CHRIS baseline)

md_x0_0113

Indicators of heart rate variability (HRV) derived from the 20-minute resting ECG performed during the visit at the study center using commercial HRV analysis software

No sub-modules.
total length of the ECG

Total length of the ECG recording, expressed in seconds

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effective length

Effective length of the ECG which is used for HRV analysis, expressed in seconds

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effective length (%)

Effective data length, expressed as percentage of the total length

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segment start

Start point of the data segment which is used for HRV analysis, expressed in seconds

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segment end

End point of the data segment which is used for HRV analysis, expressed in seconds

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length RR series

Length of the RR series

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t_RR start

Time point of the first beat in the RR series, expressed in seconds

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t_RR end

Time point of the last beat in the RR series, expressed in seconds

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total number of beats

Total number of beats

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number of corrected beats

Number of corrected beats

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number of corrected beats (%)

Number of corrected beats, expressed as percentage of the total number of beats

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number of noise segments

Number of noise segments

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noise segments

List of all detected noise segments, expressed as tuple (start point of the noisy region, end point of the noisy region), expressed in seconds

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PNS index

PNS index

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PNS param: nMeanRR

First parameter used for PNS index calculation: MeanRR normalized by scaling with the standard deviations of normal population

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PNS param: nRMSSD

Second parameter used for PNS index calculation: RMSSD normalized by scaling with the standard deviations of normal population

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PNS param: nSD1

Third parameter used for PNS index calculation: SD1 normalized by scaling with the standard deviations of normal population

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SNS index

SNS index

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SNS param: nMeanHR

First parameter used for SNS index calculation: MeanHR normalized by scaling with the standard deviations of normal population

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SNS param: nSI

Second parameter used for SNS index calculation: SI normalized by scaling with the standard deviations of normal population

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SNS param: nSD2

Third parameter used for SNS index calculation: SD2 normalized by scaling with the standard deviations of normal population

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mean RR

Mean value of RR intervals, expressed in milliseconds

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SDNN

SDNN: Standard deviation of RR intervals, expressed in milliseconds

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RMSSD

RMSSD: Square root of the mean squared differences between successive RR intervals, expressed in milliseconds

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NN50

NN50: Number of successive RR interval pairs that differ more than 50 ms

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pNN50

pNN50: NN50 divided by the total number of RR intervals

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SDANN

SDANN: Standard deviation of the averages of RR intervals in 5-min segments, expressed in milliseconds

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SDNNI

SDNNI: Mean of the standard deviations of RR intervals in 5-min segments, expressed in milliseconds

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mean HR

Mean heart rate, expressed in beats per minute

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std HR

Standard deviation of heart rate

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min HR

Minimum HR computed using N=5 beat moving average

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max HR

Maximum HR computed using N=5 beat moving average

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SI (Stress Index)

SI: Baevsky stress index, computed according to the formula given in Baevsky 2009, is a geometric measure of HRV reflecting cardiovascular system stress

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HRV triangular index

HRV triangular index: integral of the histogram (i.e. total number of RR intervals) divided by the height of the histogram computed with a bin width of 1/128 seconds

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TINN

TINN: baseline width of the RR histogram evaluated through triangular interpolation

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AC

AC: Acceleration Capacity captures the shortening of RR interval within 2-4 successive beats. It is computed as a four point difference from the acceleration PRSA signal (phase-rectified signal averaging), expressed in milliseconds

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DC

DC: Deceleration Capacity is a measure of cardiac parasympathetic modulation as it captures the lengthening of RR interval within 2-4 successive beats. It is computed as a four point difference from the deceleration PRSA signal (phase-rectified signal averaging), expressed in milliseconds

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ACmod

ACmod: modified Acceleration Capacity, computed according to the formula given in Nasario-Junior et al. 2014, expressed in milliseconds

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DCmod

DCmod: modified Deceleration Capacity, computed according to the formula given in Nasario-Junior et al. 2014, expressed in milliseconds

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VLF peak (Welch)

Very Low Frequency (VLF) band peak frequency, with PSD estimated using Fast Fourier transformation (FFT) based Welch's periodogram method, expressed in Hz

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LF peak (Welch)

Low Frequency (LF) band peak frequency, with PSD estimated using Fast Fourier transformation (FFT) based Welch's periodogram method, expressed in Hz

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HF peak (Welch)

High Frequency (HF) band peak frequency, with PSD estimated using Fast Fourier transformation (FFT) based Welch's periodogram method, expressed in Hz

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VLF power (Welch)

Absolute power of VLF band, with PSD estimated using Fast Fourier transformation (FFT) based Welch's periodogram method, expressed in ms^2

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LF power (Welch)

Absolute power of LF band, with PSD estimated using Fast Fourier transformation (FFT) based Welch's periodogram method, expressed in ms^2

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HF power (Welch)

Absolute power of HF band, with PSD estimated using Fast Fourier transformation (FFT) based Welch's periodogram method, expressed in ms^2

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total power (Welch)

Absolute power of the total spectrum, with PSD estimated using Fast Fourier transformation (FFT) based Welch's periodogram method, expressed in ms^2

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logVLF power (Welch)

Natural logarithm transformed values of VLF band, with PSD estimated using Fast Fourier transformation (FFT) based Welch's periodogram method

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logLF power (Welch)

Natural logarithm transformed values of LF band, with PSD estimated using Fast Fourier transformation (FFT) based Welch's periodogram method

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logHF power (Welch)

Natural logarithm transformed values of HF band, with PSD estimated using Fast Fourier transformation (FFT) based Welch's periodogram method

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logtot power (Welch)

Natural logarithm transformed values of total spectrum power, with PSD estimated using Fast Fourier transformation (FFT) based Welch's periodogram method

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VLF power (%) (Welch)

Relative power of VLF band, with PSD estimated using Fast Fourier transformation (FFT) based Welch's periodogram method, expressed in ms^2

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LF power (%) (Welch)

Relative power of LF band, with PSD estimated using Fast Fourier transformation (FFT) based Welch's periodogram method, expressed in ms^2

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HF power (%) (Welch)

Relative power of HF band, with PSD estimated using Fast Fourier transformation (FFT) based Welch's periodogram method, expressed in ms^2

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LF power (n.u.) (Welch)

Power of LF band in normalised units, LF [n.u.] = LF [ms2] / (total power [ms2] - VLF [ms2]) x 100%, with PSD estimated using Fast Fourier transformation (FFT) based Welch's periodogram method, expressed in n.u

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HF power (n.u.) (Welch)

Power of HF band in normalised units, HF [n.u.] = LF [ms2] / (total power [ms2] - VLF [ms2]) x 100%, with PSD estimated using Fast Fourier transformation (FFT) based Welch's periodogram method, expressed in n.u.

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LF-HF power (Welch)

Ratio between LF and HF band powers, with PSD estimated using Fast Fourier transformation (FFT) based Welch's periodogram method

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VLF peak (AR)

Very Low Frequency (VLF) band peak frequency, with PSD estimated using parametric autoregressive (AR) modeling method, expressed in Hz

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LF peak (AR)

Low Frequency (LF) band peak frequency, with PSD estimated using parametric autoregressive (AR) modeling method, expressed in Hz

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HF peak (AR)

High Frequency (HF) band peak frequency, with PSD estimated using parametric autoregressive (AR) modeling method, expressed in Hz

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VLF power (AR)

Absolute power of VLF band, with PSD estimated using parametric autoregressive (AR) modeling method, expressed in ms^2

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LF power (AR)

Absolute power of LF band, with PSD estimated using parametric autoregressive (AR) modeling method, expressed in ms^2

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HF power (AR)

Absolute power of HF band, with PSD estimated using parametric autoregressive (AR) modeling method, expressed in ms^2

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total power (AR)

Absolute power of the total spectrum, with PSD estimated using parametric autoregressive (AR) modeling method, expressed in ms^2

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logVLF power (AR)

Natural logarithm transformed values of VLF band, with PSD estimated using parametric autoregressive (AR) modeling method

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logLF power (AR)

Natural logarithm transformed values of LF band, with PSD estimated using parametric autoregressive (AR) modeling method

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logHF power (AR)

Natural logarithm transformed values of HF band, with PSD estimated using parametric autoregressive (AR) modeling method

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logtot power (AR)

Natural logarithm transformed values of total spectrum power, with PSD estimated using parametric autoregressive (AR) modeling method

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VLF power (%) (AR)

Relative power of VLF band, with PSD estimated using parametric autoregressive (AR) modeling method, expressed in ms^2

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LF power (%) (AR)

Relative power of LF band, with PSD estimated using parametric autoregressive (AR) modeling method, expressed in ms^2

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HF power (%) (AR)

Relative power of HF band, with PSD estimated using parametric autoregressive (AR) modeling method, expressed in ms^2

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LF power (n.u.) (AR)

Power of LF band in normalised units, LF [n.u.] = LF [ms2] / (total power [ms2] - VLF [ms2]) x 100%, with PSD estimated using parametric autoregressive (AR) modeling method, expressed in n.u

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HF power (n.u.) (AR)

Power of HF band in normalised units, HF [n.u.] = LF [ms2] / (total power [ms2] - VLF [ms2]) x 100%, with PSD estimated using parametric autoregressive (AR) modeling method, expressed in n.u.

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LF/HF power (AR)

Ratio between LF and HF band powers, with PSD estimated using parametric autoregressive (AR) modeling method

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EDR (ECG Derived Respiration)

EDR (ECG Derived Respiration), expressed in Hz

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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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Measurements related to Detrended Fluctuation Analysis (DFA) in nonlinear domain Heart Rate Variability analysis

2 variables.
Metadata related to the ECG recording used for Heart Rate Variability analysis

5 variables.
Measurements related to Entropy calculation in nonlinear domain Heart Rate Variability analysis

2 variables.
Measurements related to non parametric methods in frequency domain Heart Rate Variability analysis

17 variables.
Measurements related to parametric methods in frequency domain Heart Rate Variability analysis

17 variables.
Measurements related to frequency domain analysis of Heart Rate Variability

35 variables.
Measurements related to geometrical methods in time domain Heart Rate Variability analysis

3 variables.
Measurements related to Multi Scale Entropy (MSE) in nonlinear domain Heart Rate Variability analysis

20 variables.
Metadata in Heart Rate Variability analysis

13 variables.
Noise annotations along the ECG reconding period used for Heart Rate Variability analysis

2 variables.
Measurements related to nonlinear domain analysis of Heart Rate Variability

35 variables.
Measurements related to PNS index in Heart Rate Variability analysis

4 variables.
Measurements related to Phase Rectified Signal Averaging (PRSA) in time domain Heart Rate Variability analysis

4 variables.
Measurements related to Poincare plot in nonlinear domain Heart Rate Variability analysis

3 variables.
Measurements related to Recurrence Plot Analysis (RPA) in nonlinear domain Heart Rate Variability analysis

6 variables.
Metadata related to the RR series used for Heart Rate Variability analysis

6 variables.
Measurements related to SNS index in Heart Rate Variability analysis

4 variables.
Measurements related to statistical methods in time domain Heart Rate Variability analysis

11 variables.
Summary measurements in Heart Rate Variability analysis, related to PNS and SNS indexes

8 variables.
Measurements related to time domain analysis of Heart Rate Variability

18 variables.