Metadata-Version: 2.4
Name: ECGquant
Version: 0.1.1
Summary: Processing pipeline for ECG records and quantification of basic and advanced electrocardiographic markers, natively compatible with the PTB and PTBXL databases.
Author-email: Hector Martinez-Navarro <hector.martinez-navarro@uv.es>, Ignacio Garcia-Fernandez <ignacio.garcia@uv.es>
Requires-Python: >=3.10
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy
Requires-Dist: scipy
Requires-Dist: pandas
Requires-Dist: matplotlib
Requires-Dist: wfdb
Dynamic: license-file

# 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.	
