Time-domain HRV metrics¶
What it does¶
This method computes standard time-domain variability metrics from cleaned beat-to-beat or pulse-to-pulse intervals.
When to use it¶
Use it after event detection and interval cleaning. The input may contain NaN markers for intervals that should be omitted, but valid intervals must be positive and expressed in seconds.
Canonical ID: hrv.tdmetrics
Inputs¶
| Name | Meaning | Type | Unit | Requirements |
|---|---|---|---|---|
dtk |
Clean beat-to-beat or pulse-to-pulse intervals. | real vector | s | greater than: 0; NaN allowed; finite |
Outputs¶
| Name | Meaning | Type | Unit |
|---|---|---|---|
mhr |
Mean heart or pulse rate computed from valid intervals. | real scalar | beats/min |
sdnn |
Sample standard deviation of valid intervals. | real scalar | ms |
sdsd |
Sample standard deviation of successive valid interval differences. | real scalar | ms |
rmssd |
Root mean square of successive valid interval differences. | real scalar | ms |
pnn50 |
Percentage of successive valid interval differences greater than 50 ms. | real scalar | % |
How it works¶
Mean rate and SDNN use all valid intervals. SDSD, RMSSD, and pNN50 use successive differences only when both adjacent intervals are valid. The sample standard-deviation convention is used where applicable.
Interpretation and limitations¶
Results depend strongly on recording duration, preprocessing, missing data, activity, posture, and physiological context. Metrics from different protocols should not be compared without accounting for those factors.
References¶
- Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology (1996). Heart rate variability: standards of measurement, physiological interpretation and clinical use. European Heart Journal.
- Diego Cajal et al. (2022). Effects of Missing Data on Heart Rate Variability Metrics. Sensors. doi:10.3390/s22155774