API for a high precision ECG Database with annotated R peaks (GUDB)
Project description
This is an API which provides transparent online accesses 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
Module
The module is called ecg_gudb_database:
from ecg_gudb_database import GUDb
The constructor loads the ECG data of one subject and one 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’.
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_II ecg_class.einthoven_III
Chest strap:
ecg_class.cs_V2_V1
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:
Project details
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