Keras implementations of deep learning architectures used in our publication on heart rate estimation from BCG data.
The models are implemented with tf.keras
of Tensorflow 1.13, but should work
with most versions of Tensorflow.
You can install all required packages (except Tensorflow) with the provided requirements file:
git clone https://github.com/SamProell/bcg-hr-dl.git
cd bcg-hr-dl
pip install -r requirements.txt
The example jupyter notebooks highlight how to obtain and train the models.
In essence, you can import any network from the models subfolder and use
create
to get a compiled Keras model:
# from models import stacked_cnn_rnn_improved as architecture
from models import baseline_fcn as architecture
patchsize, n_channels = 400, 1
model = architecture.create(input_shape=(patchsize, n_channels), enlarge=1)
# with x_data and y_data in the correct shape:
model.fit(x_data, y_data, batch_size=32)