ekgtools 1.0.1

ECG XML parsing, fast loading, and plotting utilities for research pipelines

ekgtools

Utilities for working with multi-format ECG waveforms in research and production settings.

Features

• Unified parser interface for Philips iECG, GE MUSE, ELI, WFDB, and MIMIC records.
• Centralised configuration with per-call overrides for filter settings and signal scaling.
• ECGDataset that reads from local storage or S3/MinIO buckets with transparent caching.
• Plotting helpers for rapid visual inspection of 12-lead rhythms.

Installation

pip install ekgtools

Python 3.10+ is required. Install the appropriate PyTorch/TorchVision wheels for your platform separately if needed.

Quick Start

Parse an ECG file

from ekgtools.parser import ECGParser

parser = ECGParser(
    path="/data/ecg/philips_001.xml",
    config_overrides={
        "bandpass_lower": 1.0,
        "bandpass_higher": 40.0,
        "median_filter_size": 3,
    },
)

ecg = parser.float_array        # (12, n_samples)
metadata = parser.text_data     # demographics & machine measurements

Local datasets for PyTorch

import pandas as pd
from torch.utils.data import DataLoader

from ekgtools.dataset import ECGDataset

df = pd.read_csv("/data/labels.csv")

dataset = ECGDataset(
    directory="/data/ecg",
    df=df,
    filename_column="filename",
    label_column="label",
    specify_leads=["I", "II", "V1", "V2", "V3", "V4", "V5", "V6"],
    fast=True,
)

loader = DataLoader(dataset, batch_size=32, shuffle=True, num_workers=4)

signals, labels, complete = next(iter(loader))

Stream from S3 with caching

from ekgtools.dataset import ECGDataset
from ekgtools.s3 import S3Setup

s3 = S3Setup(
    bucket="ecg-data",
    prefix="records",
    endpoint_url="https://minio.example.com",
    access_key_id="ACCESS",
    secret_key="SECRET",
    region_name="us-east-1",
    use_ssl=True,
)

dataset = ECGDataset(
    directory="public/12lead",   # prefix under the bucket
    df=df,
    filename_column="filename",
    label_column="label",
    storage="s3",
    s3_setup=s3,
    cache_dir="~/.cache/ekgtools",
)

Plotting helpers

import numpy as np
from ekgtools.plot import plot

signals = np.random.randn(12, 5000)
fig = plot(signals, sample_rate=500)
fig.savefig("preview.png", dpi=200)

Configuration

Default values live in ekgtools/config.py and can be overridden per parser invocation via config_overrides. To reuse overrides across multiple parsers:

from ekgtools.config import resolve_config

custom_config = resolve_config(overrides={"bandpass_lower": 0.67})
parser = ECGParser(path, config=custom_config)

Development

pip install -e .[dev]
pytest

License

MIT