Metadata-Version: 1.1
Name: ecg_gudb_database
Version: 1.0.1
Summary: API for a high precision ECG Database with annotated R peaks (GUDB)
Home-page: https://github.com/berndporr/ECG-GUDB
Author: Bernd Porr
Author-email: bernd.porr@glasgow.ac.uk
License: GPL 3.0
Description: ==================================================================================================
        High precision ECG Database with annotated R peaks, recorded and filmed under realistic conditions
        ==================================================================================================
        
        This is an API which provides transparent online access to the ECG GUDB
        http://researchdata.gla.ac.uk/716/ without the need of downloading it.
        
        It contains ECGs from 25 subjects. Each subject was recorded performing 5 different tasks for two minutes:
           * sitting
           * a maths test on a tablet
           * walking on a treadmill
           * running on a treadmill
           * using a hand bike
        
        The following channels were recorded with two Attys (https://www.attys.tech/) running synchronously:
           * Einthoven II and III with standard cables and the amplifier worn around the waist
           * Exercise cheststrap ECG which resembles approximtely V2-V1 with the ECG amplifier directly mounted on the strap
           * Acceleration in X/Y/Z whith the sensor mounted directly on the chest strap
           
        The cheststrap ECG allowed R peak detection even while jogging at a
        very high precision (+/- one sample). The sampling rate was 250Hz at a
        resolution of 24 bits. The database contains the unfiltered,
        DC-coupled signals as originally recorded. In order to be able to link
        the ECG artefacts to the behaviour of the subject all but one subject
        gave permission to be filmed and the videos are also part of the
        database.
        
        
        
        Installation
        ============
        
        Simply install via pip or pip3::
        
           pip install ecg_gudb_database
           pip3 install ecg_gudb_database
        
        
           
        Usage
        =====
        
        Check out `usage_example.py` on github which plots the ECG and the heartrate of one subject.
        
        
        Module
        ------
        
        The module is called `ecg_gudb_database`::
        
            from ecg_gudb_database import GUDb
        
        
        The constructor loads the ECG data of one subject/experiment from github::
        
            ecg_class = GUDb(subject_number, experiment)
        
        where `subject_number` is from 0..24 and `experiment` is 'sitting', 'maths', 'walking', 'hand_bike' or 'jogging'.
        The array `ecg_class.experiments` is an array of all experiments so that one can loop through the different experiments.
        
        Retrieve the ECG data
        ---------------------
        
        The data is available as numpy arrays. The sampling rate is 250Hz for all experiments (`ecg_class.fs`).
        we have recorded Einthoven and from a chest strap.
        
        Einthoven::
        
            ecg_class.einthoven_I, ecg_class.einthoven_I_filt
            ecg_class.einthoven_II, ecg_class.einthoven_II_filt
            ecg_class.einthoven_III, ecg_class.einthoven_III_filt
        
        
        Chest strap::
        
            ecg_class.cs_V2_V1, ecg_class.cs_V2_V1_filt
        
        where the filtered versions have 50Hz mains and DC removed.
        
        
        R peak annotations
        ------------------
        
        The two boolean variables `ecg_class.anno_cs_exists` and `ecg_class.anno_cables_exists`
        tell the user if annotations exist. If yes they can be obtained::
        
        
            if ecg_class.anno_cs_exists:
                chest_strap_anno = ecg_class.anno_cs
            else:
                print('No chest strap annotations')
            if ecg_class.anno_cables_exists:
                cables_anno = ecg_class.anno_cables
            else:
                print("No cables annotations")
        
        
        
        Videos
        ======
        
        Where the participant has consented, there is a video for each of the tasks.
        The video and ECG data have been synchronised so they start and end at the same time. The videos can be requested here:
        
        http://researchdata.gla.ac.uk/716/
        
Platform: UNKNOWN
Classifier: Intended Audience :: Developers
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
