Description

This work was presented at the Physionet Challenge 2017 presented at the Computing in Cardiology conference 2017. The Challenge consisted of classifying short single-lead ECG segments with 10-60 seconds duration into one of the following classes:

Class Description
N normal sinus rhythm
A atrial fibrillation (AF)
O other cardiac rhythms
~ noise segment

Two methodologies are proposed and described in distict forlder within this repo:

  • Classic feature-based MATLAB approach (featurebased-approach folder)
  • Deep Convolutional Network Approach in Python (deeplearn-approach folder)

The complete code is available on my Github .

When using this code, please cite our paper :

Andreotti, F., Carr, O., Pimentel, M.A.F., Mahdi, A., & De Vos, M. (2017). Comparing Feature Based Classifiers and Convolutional Neural Networks to Detect Arrhythmia from Short Segments of ECG. In Computing in Cardiology. Rennes (France).