vitabel

vitabel is an open-source Python framework for post-hoc loading, visualizing, aligning, and annotating high-resolution physiological time series.

It is designed for retrospective critical care and perioperative research, where recordings are often heterogeneous, noisy, and distributed across multiple devices and file formats. vitabel helps turn these data into curated, analysis-ready datasets by combining interactive visualization, manual annotation, timeline alignment, and reusable processing workflows in a single Jupyter-based environment.

The framework provides sensible defaults for common use cases while remaining flexible and extensible for project-specific analysis and annotation pipelines. Data can be added, for example, via Vitals.add_defibrillator_recording or Vitals.add_vital_db_recording; multiple defibrillator formats and VitalDB-based workflows are supported.

A typical use of this package reads as follows:

from vitabel import Vitals, Label

# create case and load data
case = Vitals()
case.add_defibrillator_recording("path/to/ZOLL_data_file.json")

# use in-built methods for processing available data, compute etco2
# and predict circulatory state
case.compute_etco2_and_ventilations()
case.predict_circulation()

# create a new label for ROSC events
ROSC_label = Label('ROSC', plotstyle={'marker': '$\u2665$', 'color': 'red', 'ms': 10, 'linestyle': ''})
case.add_global_label(ROSC_label)

# display an interactive plot that allows annotations and further data adjustments
case.plot_interactive(
    channels=[['cpr_acceleration'], ['capnography'], ['ecg_pads'], []],
    labels = [['ROSC'], ['etco2_from_capnography', 'ROSC'], ['ROSC'], ['ROSC', 'rosc_probability']],
    channel_overviews=[['cpr_acceleration']],
    time_unit='s',
    subplots_kwargs={'figsize': (22, 9)}
)

• Quickstart
  • Use case: German Resuscitation Registry
• Examples
  • Fundamentals: Channels, labels, and collections
    • 1. Imports
    • 2. Create channels
    • 3. Create labels
    • 4. Inspect the collection
    • 5. Plot channels with attached labels and automatic overviews
    • 6. Plot with an explicit subplot layout
    • Summary
  • Use Case 1: Defibrillator data from out-of-hospital cardiac arrest case
    • 1. Loading Data
    • 2. Processing Data
    • 3. Adding new Labels
    • 4. Plotting Data interactively
    • 5. Storing Data
  • Use Case 2: Data from multiple sources in an animal experiment
    • 1. Loading Data
    • 2. Processing Data
    • 3. Plotting Data interactively
    • 4. Storing Data
  • Use Case 3: User interface to research intra-anaesthesisa hypotension
    • 1. Loading Data
    • 2. Processing Data
    • 3. Plotting and Labeling Data Interactively
    • 4. Building a Custom User Interface
• Development
  • Changelog
• Bibliography
• API Reference
  • vitabel
    • Submodules