ecgdatakit 0.0.7

Multi-format ECG file parsing and signal processing library

ECGDataKit

A Python library for parsing, processing, and visualizing multi-format ECG files.

Developed at UMMISCO / IRD by Ahmad Fall.

ecgdatakit.ummisco.fr — Full documentation, API reference, and getting started guide.

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Features

Parsing — 12 ECG formats, one unified data model

Format	File Types	Detection
HL7 aECG	.xml	<AnnotatedECG in header
Philips Sierra XML	.xml	<restingecgdata in header
ISHNE Holter	.ecg, .hol	ISHNE1.0 or ANN 1.0 magic bytes
Mortara EL250	.xml	<ECG + <CHANNEL in header
EDF/EDF+	.edf	"0 " at offset 0
SCP-ECG	.scp	Valid Section 0 pointer table at offset 6
GE MUSE XML	.xml	<RestingECG> in header
DICOM Waveform	.dcm	DICM at offset 128
WFDB (PhysioNet)	.hea + .dat	.hea extension + valid header
MFER	.mwf, .mfer	Valid MFER tag + BER length
Mindray BeneHeart R12	.xml	<BeneHeartR12> or <MindrayECG>
GE MAC 2000	.xml	<MAC2000> or <GE_MAC>

Signal Processing

Category	Capabilities
Filtering	Butterworth (lowpass, highpass, bandpass, notch), baseline removal, diagnostic & monitoring presets
Peak Detection	Pan-Tompkins, Shannon energy
Heart Rate	Average HR, RR intervals, instantaneous beat-by-beat HR
HRV Analysis	Time-domain (SDNN, RMSSD, pNN50), frequency-domain (VLF/LF/HF), Poincaré (SD1/SD2)
Spectral	FFT, Welch PSD, beat segmentation, ensemble averaging
Quality	Signal quality index (SQI), SNR estimation
Leads	Derive III, aVR/aVL/aVF, full 12-lead assembly
Cleaning	Built-in, BioSPPy, NeuroKit2, combined, DeepFADE neural-net denoising

Visualization

Type	Plots
ECG Waveforms	Single lead, multi-lead, standard 12-lead grid with paper background
Annotations	R-peak markers, RR intervals, heart rate overlay
Beat Analysis	Segmented beats, ensemble-averaged beat with SD shading
Spectral	Power spectrum (PSD/FFT), spectrogram
HRV	Tachogram, Poincaré plot, frequency bands, metrics dashboard
Reports	Signal quality per lead, full ECG report with patient info
Interactive	All plots available as interactive Plotly versions (zoom, pan, hover)

Installation

# Core (parsing only)
pip install ecgdatakit

# With signal processing
pip install "ecgdatakit[processing]"

# With static plots (matplotlib)
pip install "ecgdatakit[plotting]"

# With interactive plots (plotly)
pip install "ecgdatakit[plotting-interactive]"

# With ECG cleaning backends
pip install "ecgdatakit[cleaning]"

# With DeepFADE neural-net denoising (requires torch)
pip install "ecgdatakit[denoising]"

# Everything (except torch — install separately if needed)
pip install "ecgdatakit[all]"

Optional extras for specific formats:

pip install "ecgdatakit[holter]"   # ISHNE Holter CRC validation
pip install "ecgdatakit[dicom]"    # DICOM waveform support

Quick Start

Parse an ECG file

from ecgdatakit import FileParser

record = FileParser().parse("path/to/ecg_file.xml")

print(record.source_format)            # "sierra_xml"
print(record.patient.first_name)       # "John"
print(record.patient.age)              # 55
print(record.recording.sample_rate)    # 500
print(record.measurements.heart_rate)  # 75
print(record.device.manufacturer)      # "Philips"
print(record.signal.data_encoding)     # "base64"
print(len(record.leads))               # 12

json_str = record.to_json()

Process signals

from ecgdatakit.processing import (
    diagnostic_filter, detect_r_peaks, heart_rate,
    rr_intervals, time_domain, signal_quality_index, clean_ecg,
)

lead = record.leads[1]

filtered = diagnostic_filter(lead)

peaks = detect_r_peaks(filtered)
peaks_se = detect_r_peaks(filtered, method="shannon_energy")

hr = heart_rate(filtered, peaks)
rr = rr_intervals(filtered, peaks)

hrv = time_domain(rr)
print(hrv["sdnn"], hrv["rmssd"], hrv["pnn50"])

sqi = signal_quality_index(lead)

cleaned = clean_ecg(lead)
cleaned = clean_ecg(lead, method="neurokit2")
cleaned = clean_ecg(lead, method="deepfade")

Visualize

from ecgdatakit.plotting import (
    plot_lead, plot_12lead, plot_peaks, plot_hrv_summary,
    iplot_lead, iplot_12lead,
)

# Static plots auto-display by default
plot_12lead(record)
plot_peaks(filtered, peaks)
plot_hrv_summary(rr)

# To get the figure without displaying (e.g. for saving):
fig = plot_12lead(record, show=False)
fig.savefig("ecg_12lead.png", dpi=150)

# Use sample indices instead of time on the x-axis:
plot_lead(filtered, x_axis="samples")

# Interactive plots (plotly) — opens in browser
iplot_lead(filtered, peaks).show()
iplot_12lead(record).show()

Batch processing

from pathlib import Path
from ecgdatakit import parse_batch

files = list(Path("ecg_data/").glob("*.xml"))
for record in parse_batch(files, max_workers=4):
    print(record.patient.patient_id, record.measurements.heart_rate)

Data Model

All parsers produce the same ECGRecord:

ECGRecord
  patient: PatientInfo        # ID, name, birth date, sex, age, weight, height, medications
  recording: RecordingInfo    # date, duration, sample rate, ADC gain, technician, physician
  device: DeviceInfo          # manufacturer, model, name, serial number, software version
  filters: FilterSettings     # highpass, lowpass, notch frequencies
  signal: SignalCharacteristics  # bits/sample, encoding, compression, channel counts
  leads: list[Lead]           # label, samples (float64 array), sample rate, units
  interpretation: Interpretation  # statements, severity, source, interpreter
  measurements: GlobalMeasurements  # HR, PR, QRS, QT, QTc, axes, RR interval
  median_beats: list[Lead]    # median/template beats if available
  annotations: dict[str, str] # additional key-value annotations
  source_format: str          # parser identifier
  raw_metadata: dict          # original format-specific metadata

Exceptions

All exceptions inherit from ECGDataKitError:

Exception	When raised
UnsupportedFormatError	File format not recognized
CorruptedFileError	File is truncated or structurally invalid
MissingElementError	Required element or field is missing
ChecksumError	Checksum validation failed

Testing

pip install -e ".[all,dev,holter,dicom]"
pytest tests/ -v

Author

Ahmad Fall — UMMISCO / IRD

License

Apache 2.0 — see LICENSE for details.