README.md

---
license: cc-by-4.0
task_categories:
  - tabular-classification
  - time-series-forecasting
language:
  - en
tags:
  - synthetic
  - healthcare
  - digital-health
  - wearables
  - remote-patient-monitoring
  - precision-medicine
  - physiological-telemetry
  - early-warning-detection
  - clinical-decision-support
  - time-series
  - hipaa-safe
pretty_name: Bio-Pulse Physiological Telemetry Pack
size_categories:
  - 10K<n<100K
configs:
  - config_name: default
    data_files:
      - split: train
        path: bio_pulse_sample.parquet
---

# Bio-Pulse Physiological Telemetry Pack (Sample)

**A synthetic wearable-sensor physiological telemetry dataset for early-warning detection, digital-health model training, and remote patient monitoring research.** Each row is a complete physiological event sequence — from baseline vitals through a progressing clinical scenario (drug interaction, septic cascade, nocturnal hypoglycemia) — with phenotype context, causal-chain labels, and anomaly-signature detection metadata.

Built by [SolsticeAI](https://www.solsticestudio.ai/datasets) as a free sample of a larger commercial pack. 100% synthetic. No real patient, wearable, or clinical data — fully HIPAA-safe: no PHI, no identifiers, no re-identification risk.

## What is included

| File | Rows | Format | Purpose |
|---|---:|---|---|
| `bio_pulse_sample.parquet` | 10,000 | Parquet | Columnar, typed, best for analytics |
| `bio_pulse_sample.jsonl` | 10,000 | JSON Lines | Streaming / LLM training friendly |

**This sample:** 10,000 physiological event sequences, balanced across 4 criticality tiers and 3 diagnostic classes.  
**Criticality classes:** `low`, `medium`, `high`, `life_threatening` (~2,500 each)  
**Diagnostic classes:** `Drug_Interaction_Toxicity`, `Septic_Shock_Cascade`, `Nocturnal_Hypoglycemia` (~3,300 each)  
**Patient phenotypes:** `pediatric_asthmatic`, `active_longevity`, `high_stress_corporate`, `sedentary_diabetic`, `geriatric_frailty`  
**Sensor assets covered:** Continuous Glucose Monitor, Smart Watch v3, O2 Ring, Neural Patch v2

## Record structure

Each record is one physiological event sequence with 7 top-level fields:

| Field | Type | Contents |
|---|---|---|
| `schema_version` | string | Pack schema version (`1.0.0-bio-pulse-sample`) |
| `event` | struct | `id`, `trace_id`, `timestamp`, `criticality`, `outcome`, `confidence` |
| `patient_context` | struct | `phenotype`, `age_group`, `existing_conditions[]` |
| `medical_logic` | struct | `diagnostic_class`, `causal_chain[]`, `prediction_window_minutes`, `tracked_biomarkers[]` |
| `vitals_telemetry` | list<struct> | Ordered sensor readings: `timestamp`, `sensor_id`, `sensor_asset`, `event_name`, `vitals` (hr, glucose, spo2, temp_c, cortisol_ugdl, systolic_bp, qt_interval_ms), `patient_id` |
| `detection_logic` | struct | `signature`, `anomaly_score`, `baseline_deviation` |
| `simulation` | struct | `synthetic`, `model`, `fidelity`, `target_diagnoses[]` |

See [SCHEMA.md](./SCHEMA.md) for the full nested field breakdown.

## Why this dataset is useful

Most public physiological datasets are either highly-scoped single-signal recordings (MIMIC ICU, PhysioNet challenges) or flat aggregated reports. This pack is shaped around what digital-health and wearable-AI teams actually need to train predictive monitoring models:

- Multi-signal sequences rather than single-vital recordings
- Balanced criticality tiers across low → life-threatening
- Causal-chain labels that connect trigger events to outcomes (not just end-state labels)
- Phenotype-aware variability so models don't overfit one population
- Explicit prediction-window fields for early-warning benchmarks
- Cross-sensor coverage (CGM, smart watch, pulse ox, wearable patch)
- Synthetic-by-design — safe to share across vendors, regulators, and research teams

## Typical use cases

- Early-warning detection model training
- Wearable-device AI and firmware validation
- Remote patient monitoring (RPM) pipelines
- Clinical decision support prototypes
- Digital-health analytics and dashboards
- Phenotype-aware risk stratification
- LLM fine-tuning on physiological narratives + reasoning
- Benchmarking anomaly scores and false-alarm reduction

## Quick start

```python
import pandas as pd
import pyarrow.parquet as pq

df = pq.read_table("bio_pulse_sample.parquet").to_pandas()

# Criticality distribution (balanced)
print(df["event"].apply(lambda e: e["criticality"]).value_counts())

# Average anomaly score by diagnostic class
df["dx"] = df["medical_logic"].apply(lambda m: m["diagnostic_class"])
df["anomaly"] = df["detection_logic"].apply(lambda d: d["anomaly_score"])
print(df.groupby("dx")["anomaly"].mean().round(3))

# Pull the QT-interval trajectory for one episode
row = df.iloc[0]
traj = [(v["timestamp"], v["vitals"]["qt_interval_ms"]) for v in row["vitals_telemetry"]]
print(traj)
```

Streaming form:

```python
import json

with open("bio_pulse_sample.jsonl") as f:
    for line in f:
        episode = json.loads(line)
        # one physiological sequence per line
```

## Responsible use

This dataset is intended for **digital-health research and model development**. It contains synthesized physiological sequences and clinical-scenario labels — it does **not** contain real patient records, PHI, wearable-device telemetry, or identifiable data of any kind. It is HIPAA-safe by construction (no covered data is present). Models trained on this data must be independently validated against real clinical data before any deployment in patient care.

## License

Released under **CC BY 4.0**. Use freely for research, prototyping, education, and commercial development with attribution.

## Get the full pack

This Hugging Face repo is a **10K-episode sample**. The production pack scales to 1M+ episodes, wider phenotype and age coverage, additional diagnostic classes (atrial fibrillation, asthma exacerbation, hypertensive crisis, post-op recovery, stroke precursors), richer biomarker panels, device-specific signal-fidelity variants, parquet + JSONL + FHIR-aligned formats, and buyer-specific configurations.

**Self-serve (Stripe checkout):**
- [**Sample Scale tier — $5,000**](https://buy.stripe.com/7sY5kD2j85QTfSb5lfeEo03) — ~25K records, one subject, 72-hour delivery.

**Full pack + enterprise scope:**
- [www.solsticestudio.ai/datasets](https://www.solsticestudio.ai/datasets) — per-SKU pricing across Starter / Professional / Enterprise tiers, plus commercial licensing, custom generation, and buyer-specific variants.

**Procurement catalog:**
- [SolsticeAI Data Storefront](https://solsticeai.mydatastorefront.com) — available via Datarade / Monda.

## Citation

```bibtex
@dataset{solstice_bio_pulse_pack_2026,
  title        = {Bio-Pulse Physiological Telemetry Pack (Sample)},
  author       = {SolsticeAI},
  year         = {2026},
  publisher    = {Hugging Face},
  url          = {https://huggingface.co/datasets/solsticestudioai/bio-pulse-pack}
}
```