Methods in heart rate variability analysis: which tachogram should we choose?

Janssen, Marc; Swenne, Cees A.; Bie, J. de; Rompelman, O.; Bemmel, Jan H. van

doi:10.1016/0169-2607(93)90061-o

“…Next, it was subjected to power spectral analysis using the Sparse Direct Fourier Transform algorithm (Rompelman, Snijders, & van Spronsen, 1982) that was directly applied on the arrival times of the R-waves in the ECG without interpolation of the non-equidistant time series. As absolute power spectral density estimates of HRV are not independent of the mean heart period and the metric of the heart beat time series (i.e., heart period series, instantaneous heart rate series, or R-wave events) (Janssen, Swenne, de Bie, Rompelman, & van Bemmel, 1993;van Dellen, Aasman, Mulder, & Mulder, 1985), spectral estimates were normalized by expressing them as squared coefficients of variation (the squared modulation index). It has been theoretically and empirically demonstrated that normalized spectral estimates of HRV are unrelated to mean heart period and utilized metric (de Boer, Karemaker, & Strackee, 1984;Janssen et al, 1993;van Dellen et al, 1985).…”

Section: Apparatus and Physiological Recordingsmentioning

confidence: 99%

Facial EMG and heart rate responses to emotion‐inducing film clips in boys with disruptive behavior disorders

Wied

¹

,

Boxtel

²

,

Posthumus

³

et al. 2009

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We examined aspects of emotional empathy across different physiological response systems in boys with disruptive behavior disorders (DBD) and normal controls. Heart rate (HR) and electromyographic (EMG) reactivity in zygomaticus major and corrugator supercilii muscles were monitored during sadness-, anger-, or happiness-inducing film clips. Relative to controls, DBD boys showed significantly less HR reduction during sadness, and a smaller increase in corrugator EMG activity both during sadness and anger. No significant group differences emerged in HR and zygomaticus EMG responsivity during happiness. We also examined cardiac activity at rest and found higher resting HR and lower respiratory sinus arrhythmia in DBD boys compared to controls. Findings give evidence for a selective impairment in empathy with sadness and anger (not happiness) among DBD boys who exhibit relatively high levels of anxiety and poor emotional control.

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“…They concluded that analysis directly based on R-R intervals could give biased results with respect to the underlying autonomic activity. Janssen et al [14] compared interbeat interval series, spectrum of counts and instantaneous HR series and concluded that spectra were incomparable without normalization of the tachogram with respect to HR. Castiglioni [16] has reported extensively on the differences between HR and HP and emphasized that the link between HR and HP is non-linear, and that this nonlinearity causes discrepancy between HR and HP variabilities.…”

Section: Introductionmentioning

confidence: 98%

“…Several recent reports discuss the problems of using HP versus HR in various time and frequency domain analyses [13][14][15][16][17][18][19]. Niklasson et al [13] have shown considerable differences between measurements based on beat-by-beat HR and measurements based on HP.…”

Section: Introductionmentioning

confidence: 99%

Fractal dimension and approximate entropy of heart period and heart rate: awake versus sleep differences and methodological issues

Yeragani¹,

SOBOLEWSKI²,

JAMPALA³

et al. 1998

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1. Investigations that assess cardiac autonomic function include non-linear techniques such as fractal dimension and approximate entropy in addition to the common time and frequency domain measures of both heart period and heart rate. This article evaluates the differences in using heart rate versus heart period to estimate fractal dimensions and approximate entropies of these time series.2. Twenty-four-hour ECG was recorded in 23 normal subjects using Holter records. Time series of heart rate and heart period were analysed using fractal dimensions, approximate entropies and spectral analysis for the quantification of absolute and relative heart period variability in bands of ultra low (<0.0033 Hz), very low (0. 0033-0.04 Hz), low (0.04-0.15 Hz) and high (0.15-0.5 Hz) frequency.3. Linear detrending of the time series did not significantly change the fractal dimension or approximate entropy values. We found significant differences in the analyses using heart rate versus heart period between waking up and sleep conditions for fractal dimensions, approximate entropies and absolute spectral powers, especially for the power in the band of 0.0033-0.5 Hz. Log transformation of the data revealed identical fractal dimension values for both heart rate and heart period. Mean heart period correlated significantly better with fractal dimensions and approximate entropies of heart period than did corresponding heart rate measures.4. Studies using heart period measures should take the effect of mean heart period into account even for the analyses of fractal dimension and approximate entropy. As the sleep-awake differences in fractal dimensions and approximate entropies are different between heart rate and heart period, the results should be interpreted accordingly.

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“…Niklasson et al [13] have shown considerable differences between measurements based on beat-by-beat HR and measurements based on HP. Janssen et al [14] compared interbeat interval series, spectrum of counts and instantaneous HR series and concluded that spectra were incomparable without normalization of the tachogram with respect to HR. Janssen et al [14] compared interbeat interval series, spectrum of counts and instantaneous HR series and concluded that spectra were incomparable without normalization of the tachogram with respect to HR.…”

Section: Introductionmentioning

confidence: 99%

Fractal dimension and approximate entropy of heart period and heart rate: awake versus sleep differences and methodological issues

Yeragani

¹

,

Sobolewski

²

,

Jampala

³

et al. 1998

View full text Add to dashboard Cite

1. Investigations that assess cardiac autonomic function include non-linear techniques such as fractal dimension and approximate entropy in addition to the common time and frequency domain measures of both heart period and heart rate. This article evaluates the differences in using heart rate versus heart period to estimate fractal dimensions and approximate entropies of these time series.2. Twenty-four-hour ECG was recorded in 23 normal subjects using Holter records. Time series of heart rate and heart period were analysed using fractal dimensions, approximate entropies and spectral analysis for the quantification of absolute and relative heart period variability in bands of ultra low (<0.0033 Hz), very low (0. 0033-0.04 Hz), low (0.04-0.15 Hz) and high (0.15-0.5 Hz) frequency.3. Linear detrending of the time series did not significantly change the fractal dimension or approximate entropy values. We found significant differences in the analyses using heart rate versus heart period between waking up and sleep conditions for fractal dimensions, approximate entropies and absolute spectral powers, especially for the power in the band of 0.0033-0.5 Hz. Log transformation of the data revealed identical fractal dimension values for both heart rate and heart period. Mean heart period correlated significantly better with fractal dimensions and approximate entropies of heart period than did corresponding heart rate measures.4. Studies using heart period measures should take the effect of mean heart period into account even for the analyses of fractal dimension and approximate entropy. As the sleep-awake differences in fractal dimensions and approximate entropies are different between heart rate and heart period, the results should be interpreted accordingly.

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Methods in heart rate variability analysis: which tachogram should we choose?

Cited by 21 publications

References 22 publications

Facial EMG and heart rate responses to emotion‐inducing film clips in boys with disruptive behavior disorders

Facial EMG and heart rate responses to emotion‐inducing film clips in boys with disruptive behavior disorders

Fractal dimension and approximate entropy of heart period and heart rate: awake versus sleep differences and methodological issues

Fractal dimension and approximate entropy of heart period and heart rate: awake versus sleep differences and methodological issues

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