Datasets:

solsticestudioai
/

bio-pulse-pack

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 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	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 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

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:

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 — ~25K records, one subject, 72-hour delivery.

Full pack + enterprise scope:

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 — available via Datarade / Monda.

Citation

@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}
}