CMDA

CMDA is a Python package specifically tailored for time series data research. It streamlines tasks related to temporal data, encompassing importing, preprocessing, and feature extraction for comprehensive analysis.

This tool has been developed by Joint Research Center for Computational Biomedicine (JRC-COMBINE), a research institute of RWTH Aachen University.

The key features are:

Data Import: Read high resolution signals from local files, server or Physionet Database.
Data Pre-processing: Outlier removal, noise filtering and missing value imputation.
Feature Extraction: Extract features from the imported signals using built-in features or user-defined features.
Feature Engineering Pipeline: Build an automated pipeline for importing data, pre-processing and feature extraction.

Installation

Requirements

Python (>=3.8)
NumPy (>=1.13.3)
Pandas (>=1.2.2)
Scipy (>=0.19.1)
WFDB (>= 4.0.0)
pathos (>= 0.2.9)
tqdm
PyWavelets

User Installation

with pip

CMDA can be installed with pip:

pip install git+https://github.com/JRC-COMBINE/cmda

This will automatically install compatible versions of all dependencies.

with git

CMDA can be directly used from GitHub by cloning the repository into a subfolder of your project root.

git clone https://github.com/JRC-COMBINE/cmda

When cloning from git, all required dependencies must be installed as follows:

pip install -r cmda/requirements.txt