Releases¶

All published releases of ECGDataKit, fetched from GitHub Releases at build time.

v1.0.2¶

Released 2026-07-06 · View on GitHub

Full Changelog: https://github.com/UMMISCO/ECGDataKit/compare/v1.0.0…v1.0.2

v1.0.0 - Stable Release¶

Released 2026-03-19 · View on GitHub

New features¶

Per-lead normalization — normalize_minmax, normalize_zscore, and normalize_amplitude now accept a list[Lead] and return a list[Lead], normalizing each lead independently. Pass record.leads directly instead of looping manually
Resampling and normalization (min-max, z-score, amplitude) now support batch processing across all leads (independently) of an ECG record

Full Changelog: https://github.com/UMMISCO/ECGDataKit/compare/v0.0.9…v1.0.0

v0.0.9 - ADC resolution pipeline¶

Released 2026-03-07 · View on GitHub

Breaking changes¶

sample_rate renamed to sampling_rate in Lead and SignalCharacteristics
Lead.units semantics changed — now empty ("") when is_raw=True (raw ADC counts); set to the physical unit (voltages) only after to_physical() or when data is already in physical units
Lead.is_raw is now auto-detected — parsers no longer hardcode is_raw=True; instead is_raw = not (resolution == 1.0 and offset == 0.0)

New features¶

Automatic ADC-to-physical scaling — FileParser.parse(auto_scale=True) (default) converts raw ADC samples to mV via to_physical() + convert_units("mV"). Disable with auto_scale=False
Lead.to_physical() — converts raw ADC samples using physical = samples × resolution + offset
Lead.convert_units(target) — converts between voltage units (uV, mV, V)
ECGRecord.to_physical() / ECGRecord.convert_units(target) — batch conversion for all leads and median beats
FileParser.supported_formats() — returns format metadata for all 12 parsers
ECGRecord.__repr__() and ECGRecord.plot() — YAML-style console display and quick plotting
Multi-lead numpy array support — plot_leads, plot_12lead, iplot_leads, iplot_12lead accept raw numpy arrays with fs= parameter
LeadsLike type alias — for multi-lead inputs (list of Lead, ECGRecord, 2D array, list of arrays)

New Lead fields¶

resolution_unit — unit of the resolution scale factor (e.g. "uV", "mV"); what samples will be in after to_physical()
adc_resolution — original ADC resolution as stored in the source file (e.g. 153.0 for 153 nV/count in ISHNE)
adc_resolution_unit — unit of adc_resolution as defined by the format (e.g. "nV" for ISHNE and SCP-ECG)

Improvements¶

Simplified multi-lead plots: full signal by default with configurable rows/cols grid layout
plot_12lead and iplot_12lead assign standard 12-lead names to unnamed inputs
Static plots no longer force matplotlib Agg backend

Full Changelog: https://github.com/UMMISCO/ECGDataKit/commits/v0.0.9

v0.0.8 - Visualization¶

Released 2026-03-03 · View on GitHub

Visualization¶

Multi-lead plotting functions (plot_leads, plot_12lead, plot_quality, iplot_leads, iplot_12lead) now accept raw numpy arrays directly with fs= parameter — pass a 2D array (n_leads × n_samples) or a list of 1D arrays alongside the sample rate
New LeadsLike type alias for multi-lead inputs: list[Lead] | ECGRecord | NDArray | list[NDArray]
Static plots no longer force the Agg backend — plots display inline in Jupyter and GUI environments by default

Full Changelog: https://github.com/UMMISCO/ECGDataKit/commits/v0.0.8

v0.0.7 - Signal Characteristics, Flexible Inputs & Sphinx Docs¶

Released 2026-03-02 · View on GitHub

What’s New¶

Signal Characteristics Model¶

New SignalCharacteristics dataclass on every ECGRecord — captures technical signal encoding metadata directly from each format:

bits_per_sample, signal_offset, signal_signed
data_encoding (e.g. base64_int16le, format_212, int16)
compression (e.g. none, huffman)
number_channels_allocated / number_channels_valid
electrode_placement, acsetting, and signal processing flags

All 12 parsers now populate record.signal from format-specific fields.

Expanded Metadata Extraction¶

Parsers now extract significantly more metadata from each format:

Technician — Sierra XML, GE MUSE, DICOM, GE MAC 2000
Referring physician — Sierra XML, EDF, GE MUSE, GE MAC 2000
Patient weight — SCP-ECG (Tag 12), BeneHeart R12, GE MAC 2000
Patient height — BeneHeart R12, GE MAC 2000
Patient race — HL7 aECG, Mortara EL250, GE MAC 2000
RR interval & QRS count — Mortara EL250, BeneHeart R12, GE MAC 2000
Device serial number — HL7 aECG, GE MAC 2000
Notch filter active flag — now properly set across all parsers

Flexible Inputs — Numpy Arrays Everywhere¶

All processing and plotting functions now accept raw numpy arrays directly alongside Lead objects. Pass the sample rate via fs=:

import numpy as np
from ecgdatakit.processing import diagnostic_filter, detect_r_peaks
from ecgdatakit.plotting import plot_lead

signal = np.random.randn(5000)
filtered = diagnostic_filter(signal, fs=500)
peaks = detect_r_peaks(filtered)
plot_lead(filtered, peaks=peaks)

Plotting Improvements¶

Auto-display: static plots now call plt.show() by default — pass show=False to get the figure for saving
Sample index axis: plot_lead(lead, x_axis=”samples”) for sample indices instead of time
Cleaner layout: reduced margins, integer time ticks on x-axis

Documentation Migration¶

Migrated from Hugo to Sphinx with the Furo theme
Full autodoc API reference generated from source docstrings
Hosted at https://ecgdatakit.ummisco.fr

Bug Fixes

BeneHeart R12: fixed assignment to non-existent RecordingInfo.device field
SCP-ECG: now reads patient weight from Tag 12 (was silently skipped)
MFER: notch filter now correctly extracted from filter tag when 12+ bytes present

Full Changelog: https://github.com/UMMISCO/ECGDataKit/commits/v0.0.0.7