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ECG Signal Classification with Binarized Convolutional Neural Network source code

This code is the source code of our paper. Please refer to the paper for the specific parameters.

Dataset

The dataset we used for model verification is from the PhysioNet/CinC arrhythmia detection challenge 2017, which contains 8,528 single-lead ECG recordings, each of which is derived from participants with length ranging from 9 seconds to 61 seconds. The data were sampled at 300 Hz (the shortest data has 2,714 data points and the longest data has 18,286).

Please using read_data.py to create the tfrecoed format data.

Train

You can edit the file myscript.sh to adjust the hyper parameters of the training, and run entry.py to start running the program.

Test

Set the is_test to True in myscrips.sh to test the model.

Dependent Library

python==2.7 tensorflow==1.5.0 tensorlayer==1.8.3 CUDA==9.1 cudnn==7.3.1

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Contributors

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