Welcome
The Cardiology area of Gradient Biotech is a web workspace for multimodal cardiovascular and physiological signal research. Upload ECG, RR intervals, blood pressure, PPG, and respiration recordings; run validated pipelines; link data to subjects; compare cohorts; and generate interpretations grounded in computed metrics.
What you can do today
| Capability | Status |
|---|---|
| ECG preprocessing + HRV | Full workflow: upload → preprocess → metrics → waveform explorer |
| RR-only HRV | CSV upload → compute HRV directly |
| PPG + PRV | Preprocess PPG → pulse rate variability |
| Blood pressure | Variability, dipping classification |
| Baroreflex, RSA, respiration | Cross-modal coupling pipelines |
| Cohort comparison | Group stats, tests, outliers, batch HRV |
| AI interpretation | Descriptive and mechanistic modes with metric citations |
| Risk stratification (research) | Signal-derived cohort risk scores |
How the product is organized
Every analysis lives inside a study. From the cardiology dashboard you create or open a study, then work through four top-level tabs:
- Data — upload/manage datasets (Datasets sub-tab) and check cohort-wide completeness (Readiness sub-tab)
- Analyze — Signal Quality (per-dataset SQI + bulk preprocessing), Signal Explorer (waveform viewer, run pipelines, annotations, run history for one dataset), Bulk Pipelines (run HRV/BP/PRV/arrhythmia across every eligible dataset at once), and Cohort comparison (group stats, batch HRV, risk scores)
- Interpret — generate a study-level AI interpretation synthesizing every completed analysis run into a grounded summary
- History — every pipeline run for the study, with status and delete
Pipeline jobs run asynchronously. Track progress via the Jobs panel in the navbar, or the inline progress bar shown under the button that started the job.
Who this is for
- Cardiovascular researchers running HRV, telemetry, or wearable analysis without maintaining local MATLAB/Python toolchains
- Hospital research programs processing Holter or telemetry cohorts with reproducible runs
- Digital health teams ingesting RR or PPG exports at scale
- Translational teams attaching outcomes metadata and comparing treatment groups
What this is not
- Not a regulated clinical decision support device
- Not a passive ECG viewer — waveforms support QC and context for cohort intelligence
- Not automatic diagnosis — arrhythmia outputs are burden metrics for research
Next steps
- Use cases — scenario guides for Holter HRV, wearable cohorts, multimodal autonomic profiling, and cohort endpoints
- Quick start — run your first ECG analysis in minutes
- Key concepts — studies, datasets, runs, and subjects
- Study workflow — how the main sections fit together