systole.hrv.time_domain#
- systole.hrv.time_domain(rr: Union[List, ndarray], input_type: str = 'rr_ms') DataFrame[source]#
Extract all time domain parameters from R-R intervals.
- 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
Time domain summary statistics. * ‘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 (%).
See also
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:func:pandas.pivot_table function: >>> pd.pivot_table(stats, values=’Values’, columns=’Metric’)
All time-domain results have been tested against Kubios HVR 2.2 (<https://www.kubios.com>).