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Processing pipeline for ECG records and quantification of basic and advanced electrocardiographic markers, natively compatible with the PTB and PTBXL databases.

Project description

ECGquant

A robust Python processing pipeline for Electrocardiogram (ECG) records, specifically tailored to handle and analyse data from the PTB and PTB-XL databases.

Overview

ECGquant automates the extraction, processing, and visualization of electrocardiographic features. Built on top of standard scientific libraries, it provides a reliable and clean interface for clinical data analysis, precise wave delineation, and biomarker quantification.

Features

  • Extensively validated
  • Disease-agnostic
  • Compatible with Physionet databases: native support for loading and parsing PTB and PTB-XL database records via wfdb.
  • Signal processing: advanced noise filtering and baseline wander removal utilising scipy and numpy.
  • Wave delineation: accurate detection and localization of P, Q, R, S, and T wave peaks, onsets, and offsets.
  • Clinical markers: automated identification of critical cardiac markers, including the J-point and the ST segment (isoelectric line).
  • Advanced pattern recognition: fQRS detection, tombstone patterns, biphasic T waves, T wave inversion...
  • Data management: export, manipulate, and analyse structured patient datasets seamlessly with pandas.
  • Visualisation: built-in plotting tools via matplotlib to inspect clean signals and verify extracted fiducial points.¨

Currently, the library provides lead-derived markers. Future versions will incorporate new complex clinically-relevant ECG markers comprehending information from multimple leads.

Installation

You can install the package directly from PyPI:

pip install ECGquant

ECGquant.py provides a working pipeline for processing ECG data from PTB or PTB-XL databases. Make sure you update the path of the database downloaded and decompressed in your computer.

Requirements

The library requires Python >= 3.10 and depends on the following core packages:

  • numpy
  • scipy
  • pandas
  • matplotlib
  • wfdb

License

This project is licensed under the MIT License. See the LICENSE file for details.

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