README.md

metadata

license: cc-by-nc-4.0
task_categories:
  - tabular-classification
  - tabular-regression
  - time-series-forecasting
language:
  - en
tags:
  - tsfile
  - timeseries
  - synthetic
  - healthcare
  - icu
  - vital-signs
  - continuous-monitoring
  - mimic-iv
  - eicu-crd
  - time-series
  - apache-ii
  - sofa
  - news2
  - qsofa
  - charlson
  - alarm-fatigue
  - joint-commission
  - iec-60601
  - ventilation
  - vasopressor
  - sepsis
  - early-warning-score
  - deterioration-prediction
  - signal-quality
  - monitoring-devices
  - philips-intellivue
  - ge-carescape
  - masimo
  - nihon-kohden
  - format:tsfile
pretty_name: >-
  HLT-009 Synthetic Continuous Vital Sign Monitoring Dataset (Sample Preview)
  (TsFile)
size_categories:
  - 10K<n<100K
configs:
  - config_name: default
    data_files:
      - split: alarm_events
        path: alarm_events.tsfile
      - split: episode_summary
        path: episode_summary.tsfile
      - split: interventions
        path: interventions.tsfile
      - split: vitals_timeseries
        path: vitals_timeseries.tsfile

HLT-009 Synthetic Continuous Vital Sign Monitoring Dataset (Sample Preview) (TsFile)

Apache TsFile version of xpertsystems/hlt009-sample.

Overview

A fully synthetic ICU continuous vital sign monitoring dataset combining 12-stream time-series vitals (HR/SpO2/RR/NBP/IBP/Temp/EtCO2/CVP/CO), alarm event logs with true/false labels, intervention logs (medication boluses, ventilator adjustments, code events), and 53-column episode-level summary data — calibrated to MIMIC-IV / eICU-CRD benchmarks with APACHE-II, SOFA, NEWS2, qSOFA, and CCI severity scoring.

• Tables: 4 TsFile table(s), 26,888 rows in total.
  • alarm_events: 166 rows, 2022-03-19 15:20:00 → 2023-09-19 22:15:00 (UTC)
  • episode_summary: 25 rows, 1970-01-01 00:00:00 → 1970-01-01 00:00:00.024000 (UTC)
  • interventions: 20 rows, 1970-01-01 00:00:00 → 2023-09-19 14:23:03.165000 (UTC)
  • vitals_timeseries: 26,677 rows, 2022-03-15 00:00:00 → 2023-12-07 06:30:00 (UTC)

Schema (TsFile structure)

alarm_events.tsfile

• Time (INT64, milliseconds) — the source alarm_onset_ts column.
• TAG: none — the table is stored as a single device.
• FIELD (14): alarm_id (string), episode_id (string), alarm_type (string), alarm_priority (string), alarm_duration_sec (int64), true_alarm_flag (int64), false_alarm_subtype (string), response_time_min (double), intervention_triggered (int64), override_flag (int64), limit_at_alarm_low (double), limit_at_alarm_high (double), alarm_cascade_id (int64), shift (string)

episode_summary.tsfile

• Time (INT64, milliseconds) — synthetic row index over the stable source order; this table has no clock column.
• TAG: none — the table is stored as a single device.
• FIELD (54): episode_id (string), monitoring_setting (string), icu_unit_type (string), admit_dt (string), discharge_dt (string), episode_duration_days (double), bed_id (string), age (int64), sex (string), apache2_score (int64), sofa_score (int64), sofa_at_discharge (int64), primary_dx_group (string), cci_score (int64), ventilation_status (int64), vasopressor_flag (int64), rrt_flag (int64), has_arterial_line (int64), has_central_line (int64), has_pa_catheter (int64), trajectory (string), monitor_manufacturer (string), rpm_device_type (double), lead_configuration (string), device_uptime_pct (double), connectivity_drops (int64), true_alarm_rate (double), total_alarms (int64), alarms_per_patient_day (double), actionable_alarm_rate (double), alarm_override_rate (double), median_response_time_min (double), alarm_limit_modification_count (int64), alarm_cascade_count (int64), fatigue_index_score (double), news2_max (double), news2_mean (double), qsofa_max (int64), deterioration_6h_label (int64), in_hospital_mortality (int64), readmission_30d (int64), rapid_response_event (int64), sqi_hr_bpm (double), sqi_spo2_pct (double), sqi_rr_bpm (double), sqi_nbp_sys_mmhg (double), sqi_nbp_dia_mmhg (double), sqi_nbp_map_mmhg (double), sqi_ibp_sys_mmhg (double), sqi_ibp_dia_mmhg (double), sqi_temp_c (double), sqi_etco2_mmhg (double), sqi_cvp_mmhg (double), sqi_cardiac_output_lpm (double)

interventions.tsfile

• Time (INT64, milliseconds) — the source intervention_ts column.
• TAG: none — the table is stored as a single device.
• FIELD (10): intervention_id (string), episode_id (string), intervention_type (string), alarm_trigger_id (string), latency_min (double), drug_class (string), dose_delta (double), vent_param_changed (string), rosc_flag (double), rapid_response_team_id (double)

vitals_timeseries.tsfile

• Time (INT64, milliseconds) — the source timestamp column.
• TAG: none — the table is stored as a single device.
• FIELD (18): episode_id (string), hr_bpm (double), spo2_pct (double), rr_bpm (double), nbp_sys_mmhg (double), nbp_dia_mmhg (double), nbp_map_mmhg (double), ibp_sys_mmhg (double), ibp_dia_mmhg (double), temp_c (double), etco2_mmhg (double), cvp_mmhg (double), cardiac_output_lpm (double), artifact_flag (int64), news2_score (double), qsofa_score (double), news2_roll_max_4h (double), news2_rate_of_rise (double)

Conversion notes

• One TsFile per source CSV (4 tables). Every table is stored as a single device, so all source columns (episode/acility ids, alarm ids, device vendors) are FIELDs.
• Time = the source timestamp column parsed as INT64 milliseconds: timestamp (5-minute vitals grid), alarm_onset_ts, intervention_ts. episode_summary is an episode-level table with no per-row clock, so its Time is a synthetic row index (0..N-1).
• No column is dropped.

Usage

Install the Apache TsFile Python SDK (pip install tsfile) and read a converted file:

from pathlib import Path
from tsfile import TsFileReader

path = Path("alarm_events.tsfile")
with TsFileReader(str(path)) as reader:
    schemas = reader.get_all_table_schemas()
    print("tables:", list(schemas))
    table_name = next(iter(schemas))
    table = schemas[table_name]
    columns = [column.get_column_name() for column in table.get_columns()]
    print("columns:", columns)
    field_names = [
        column.get_column_name()
        for column in table.get_columns()
        if column.get_column_name() not in {"Time", "time"}
    ]
    if field_names:
        with reader.query_table(table_name, field_names[:3], batch_size=1024) as result:
            batch = result.read_arrow_batch()
            if batch is not None:
                print(batch.to_pandas().head())

Source & license

• Original dataset: xpertsystems/hlt009-sample
• Source card title / publisher: HLT-009 Synthetic Continuous Vital Sign Monitoring Dataset (Sample Preview)
• License: cc-by-nc-4.0 (as declared by the original dataset).