systole.hrv.all_domain#

systole.hrv.all_domain(rr: List | ndarray, input_type: str = 'rr_ms') DataFrame[source]#

Extract all the HRV indices implemented for the time domain, frequency domain and linear domain.

Parameters:
rr

R-R interval time-series, peaks or peaks index vectors. The default expected vector is R-R intervals in milliseconds. Other data format can be provided by specifying the “input_type” (can be “rr_s”, “peaks” or “peaks_idx”).

input_type

The type of input provided. Can be “peaks”, “peaks_idx”, “rr_ms” or “rr_s”. Defaults to “rr_ms”.

Returns:
stats

Summary of the HRV indices extracted. * ‘MeanRR’ : Mean of R-R intervals (ms). * ‘MeanBPM’ : Mean of beats per minutes (bpm). * ‘MedianRR’ : Median of R-R intervals’ (ms). * ‘MedianBPM’ : Median of beats per minutes (bpm). * ‘MinRR’ : Minimum R-R intervals (ms). * ‘MinBPM’ : Minimum beats per minutes (bpm). * ‘MaxRR’ : Maximum R-R intervals (ms). * ‘MaxBPM’ : Maximum beats per minutes (bpm). * ‘SDNN’ : Standard deviation of RR intervals (ms). * ‘SDSD’ : Standard deviation of the Successive difference (ms). * ‘RMSSD’ : Root Mean Square of the Successive Differences (ms). * ‘nn50’ : number of successive differences larger than 50ms (count). * ‘pnn50’ : Proportion of successive difference larger than 50ms (%). * ‘vlf_peak’ : Very low frequency peak (HZ). * ‘vlf_power’ : Very low frequency power (ms²). * ‘lf_peak’ : Low frquency peak (Hz). * ‘lf_power’ : Low frequency power (ms²). * ‘hf_peak’ : High frequency peak (Hz). * ‘hf_power’ : High frequency power (ms²). * ‘vlf_power_per’ : Very low frequency power (%). * ‘lf_power_per’ : Low frequency power (%). * ‘hf_power_per’ : High frequency power (%). * ‘lf_power_nu’ : Low frequency power (normalized units). * ‘hf_power_nu’ : High frequency power (normalized units). * ‘total_power’ : Total frequency power (ms²). * ‘lf_hf_ratio’ : Low / high frequency ratio (normalized units). * ‘SD1’ : SD1, the standard deviation of the poincare plot orthogonal to the identity line (ms). * ‘SD2’ : SD2, the standard deviation of the poincare plot along the identity line (ms). * ‘recurrence_rate’ : The recurrence rate in the recurrence plot (%). * ‘l_max’ : The maximun diagonal length in the recurrence plot (beats). * ‘l_mean’ : The mean diagonal length in the recurrence plot (beats). * ‘determinism_rate’ : The determinism rate in the recurrence plot (%). * ‘shannon_entropy’ : The Shannon entropy.

Notes

The dataframe containing the summary statistics is returned in the long format to facilitate the creation of group summary data frame that can easily be transferred to other plotting or statistics library. You can easily convert it into a wide format for a subject-level inline report using the py:pandas.pivot_table() function: >>> pd.pivot_table(stats, values=’Values’, columns=’Metric’)