AF Classification from a Short Single Lead ECG Recording: The PhysioNet/Computing in Cardiology Challenge 2017

health life sciences medicine

Description

The 2017 PhysioNet/CinC Challenge aims to encourage the development of algorithms to classify, from a single short ECG lead recording (between 30 s and 60 s in length), whether the recording shows normal sinus rhythm, atrial fibrillation (AF), an alternative rhythm, or is too noisy to be classified.

Update Frequency

Not updated

License

Open Data Commons Attribution License v1.0

Documentation

https://doi.org/10.13026/d3hm-sf11

Managed By

PhysioNet

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Contact

https://physionet.org/about/#contact_us

How to Cite

AF Classification from a Short Single Lead ECG Recording: The PhysioNet/Computing in Cardiology Challenge 2017 was accessed on DATE from https://registry.opendata.aws/challenge-2017.

Resources on AWS

  • Description
    https://doi.org/10.13026/d3hm-sf11
    Resource type
    S3 Bucket
    Amazon Resource Name (ARN)
    arn:aws:s3:::physionet-open/challenge-2017/
    AWS Region
    us-east-1
    AWS CLI Access (No AWS account required)
    aws s3 ls --no-sign-request s3://physionet-open/challenge-2017/

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