Metadata-Version: 2.1
Name: cardiopy
Version: 0.0.4
Summary: Analysis package for single-lead clinical EKG data
Home-page: https://github.com/CardioPy/CardioPy
Author: Jackie Gottshall
Author-email: jackie.gottshall@gmail.com
License: UNKNOWN
Description: # Cardiopy
        
        A flexibile package for R-peak detection and heart rate variability analysis of single-lead EKG data. <br>
        
        ## How to use Cardiopy
        Cardiopy can be used in two ways:<br>
           1. __As a preprocessing module for the import and cleaning of clinical EKG data in conjuction
        		with HRV analyses by standard software packages.__ For this use, run through feature sets 1 and 2 (listed below). The exported '*_nn.txt*' file is compatible with all major HRV software packages <br>
           2. __As a stand-alone HRV analysis toolkit.__ For this use, continue through the workflow from feature set 1 through 4 (listed below). To ensure analytic reproducibilty, we highly recommend exporting cleaned nn detections at feature set 2.
        
        ## Features
        __1. Data preprocessing and cleaning__<br>
           * Load single-lead EKG data<br>
           * Detect R-peaks with flexible thresholding parameters for adjustment to noisy data and varying amplitudes<br>
        		- Option to filter especially noisy data prior to peak detection<br>
           * Built-in detection visualization methods<br>
           * Simple artifact removal methods for manual inspection of detected peaks<br>
          
        __2. Export methods for cleaned peak detections__<br>
           * Compatible with commonly used software such as Kubios HRV and Artiifact<br>
           
        __3. HRV analysis methods__<br>
           * Standard time-domain statistics<br>
           * Standard frequency domain statistics<br>
        		- Option for Multitaper or Welch power spectral estimates<br>
            
        __4. HRV statistics export__<br>
           * Single-file report exports in json format<br>
           * Multi-file exports into .csv spreadsheets for group statistics<br>
        
        ## Installation
        Use the package manager [pip](https://pip.pypa.io/en/stable/) to install CardioPy.
        
        ```bash
        pip install cardiopy
        ```
        
        ## Usage
        Best when run with jupyter notebook. For detailed instructions download the [example jupyter notebook file](https://github.com/CardioPy/CardioPy/blob/master/example_run/CardioPy_Example_2020.ipynb) and [de-identified data segment](https://github.com/CardioPy/CardioPy/blob/master/example_run/HCXXX_2001-01-01_awake_cycle1_epoch1_222000.csv) from [github](https://github.com/CardioPy/CardioPy/blob/master/example_run) <br>
        	*For optimal performance, close figure interactions ('off' button on the top right corner) when finished with each window.*
        
        ### Parameter Optimization & Cleaning Tips
        * Remove false interbeat intervals LAST, after all cleaning (addition/removal of peaks) has been done.
        * To maintain integrity of the artifact logs:
        	- Only remove incorrectly added peaks with EKG.undo_add_peak NOT with EKG.rm_peak.
        	- Only re-add incorrectly removed peaks with EKG.undo_rm_peak NOT with EKG.add_peak.
        
        * If R peaks are not very pronounced, try: 
        	1. reducing the moving window size
        	2. reducing the upshift percentage
        	3. both<br>
                <img src="https://github.com/CardioPy/CardioPy/blob/master/example_run/advice_images/EKG_paramshift_new-edited.png">
        
        ## Contributing
        Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.
        
        ## License
        BSD 3-Clause
        
        ## Roadmap
        The authors plan for future versions of CardioPy to include:
        * Support for additional commonly used data formats
        * Automatic parameter detection<br> 
                - *This would include upshift, moving window and smoothing window suggestions for optimal peak detection*
        * A graphical user interface
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: BSD License
Classifier: Operating System :: OS Independent
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Medical Science Apps.
Requires-Python: >=3.6
Description-Content-Type: text/markdown
