Missing-event gap filling¶
What it does¶
This method reconstructs plausible event timestamps inside abnormally long intervals before interval-based HRV or pulse-rate variability analysis.
When to use it¶
Use it after false-positive detections have been removed and when missed beats or pulses have merged several physiological intervals into one long observed interval.
Canonical ID: hrv.fillgaps
Inputs¶
| Name | Meaning | Type | Unit | Requirements |
|---|---|---|---|---|
tk |
Strictly increasing event occurrence times after false-positive removal. | real vector | s | minimum length: 1; no NaN; finite |
Parameters¶
| Name | Meaning | Type | Unit | Default | Requirements |
|---|---|---|---|---|---|
gap_detection_factor |
Multiplier that marks an observed interval as locally long. | real scalar | 1 | 1.5 |
greater than: 0 |
correction_upper_factor |
Upper acceptance bound for reconstructed intervals. | real scalar | 1 | 1.15 |
greater than: 0 |
correction_lower_factor |
Lower acceptance bound used while choosing insertion counts. | real scalar | 1 | 0.75 |
greater than: 0 |
minimum_interval |
Smallest physiologically accepted reconstructed interval. | real scalar | s | 0.5 |
minimum: 0 |
max_gap_duration |
Longest gap duration that the method will attempt to reconstruct. | real scalar | s | 10 |
greater than: 0 |
Outputs¶
| Name | Meaning | Type | Unit |
|---|---|---|---|
tn |
Event occurrence times including accepted reconstructed events. | real vector | s |
dtn |
Intervals between output events, with unresolved spans kept explicit. | real vector | s |
How it works¶
Locally long intervals are detected against a median-based adaptive baseline. The method tries increasing insertion counts and uses shape-preserving interpolation from surrounding valid intervals. Reconstructions must remain inside local acceptance bounds and preserve the duration between observed events.
Interpretation and limitations¶
Inserted timestamps are deterministic estimates, not observed events. Abrupt rhythm changes, insufficient context, or long missing spans may remain unresolved; those spans stay explicit in the interval output.
References¶
- Diego Cajal et al. (2022). Effects of Missing Data on Heart Rate Variability Metrics. Sensors. doi:10.3390/s22155774