| --- |
| 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} |
| } |
| ``` |
|
|
