Longitudinal wearable HRV
Turn a long ECG, Holter, or wearable recording into a time-series of HRV instead of a single number — slide overlapping windows across the recording, track how autonomic state evolves, and compare temporal variability across groups.
Research question
How does HRV change over the course of a long recording, and does its level or its temporal variability differ across subjects or groups?
Who this is for
- Researchers working with 24-hour Holter, overnight, or multi-hour wearable recordings
- Digital health teams building longitudinal autonomic dashboards
- Studies where when HRV changes matters as much as its average
Data requirements
| Data | Required | Purpose |
|---|---|---|
| ECG waveform or RR series | Yes | Beat detection and per-window HRV |
| Long recording (minutes to hours) | Recommended | Run-level HRV hides within-recording change |
Workflow
Upload → Preprocess (ECG) → Windowed HRV → Trend chart → Windowed cohort comparison (optional) → Interpret
Step 1 — Preprocess
Upload an ECG dataset and run Preprocess ECG (or use an RR CSV directly).
Step 2 — Windowed HRV
Open the Windowed HRV panel and click Compute windows. The pipeline slides overlapping windows (default 5-minute window, 1-minute step) and computes per-window metrics:
| Output | Description |
|---|---|
| Per-window HRV | Mean HR, SDNN, RMSSD, pNN50, Poincaré SD1/SD2 |
| Quality / missingness | Beats per window, coverage, valid-window flag |
| Recording summary | Median, IQR, and across-window CV per metric, plus a first-half vs second-half trend |
| Feature matrix | A columns × windows matrix for export and cohort modeling |
Step 3 — Inspect the trend
Use the longitudinal chart to see how RMSSD, SDNN, mean HR, or pNN50 move across the recording.
Step 4 — Windowed cohort comparison (optional)
In the cohort workspace, set Feature source → Windowed features to compare groups on per-subject medians and temporal variability (CV) — variability that run-level HRV can't express.
Step 5 — Interpret (optional)
Run interpretation; windowed metrics and trends are cited so the narrative can describe longitudinal change (e.g. a decline in RMSSD over the night).
Expected outputs
- Per-window HRV feature matrix with quality/missingness
- Recording-level median/IQR/CV and trend per metric
- Longitudinal trend chart
- Group comparison on windowed features (when run)
Example insight
RMSSD declines steadily across the overnight recording (second-half median 18% below the first half), and the treatment group shows higher across-window variability than controls.