Add zenodo link to readme

README.md

@@ -44,7 +44,8 @@ The code training and evaluation is implemented in Python, contains
 
 The model can be trained using the script `train.py`. Alternatively, 
 pre-trained weighs trained on the code dataset for the model described in the paper 
-is available in
+is available in [doi.org/10.5281/zenodo.4892365](https://doi.org/10.5281/zenodo.4892365)
+in the following dropbox mirror
 [here](https://www.dropbox.com/s/thvqwaryeo8uemo/model.zip?dl=0).
 Using the command line, the weights can be downloaded using:
 ```
@@ -53,7 +54,6 @@ unzip model.zip
 ```
 The model is described in the module ``
 - input: shape = (N, 12, 4096). The input tensor should contain the 4096 points of the ECG tracings sampled at 400Hz (i.e., a signal of approximately 10 seconds). Both in the training and in the test set, when the signal was not long enough, we filled the signal with zeros, so 4096 points were attained. The last dimension of the tensor contains points of the 12 different leads. The leads are ordered in the following order: {DI, DII, DIII, AVR, AVL, AVF, V1, V2, V3, V4, V5, V6}. All signal are represented as 32 bits floating point numbers at the scale 1e-4V: so if the signal is in V it should be multiplied by 1000 before feeding it to the neural network model.
-
 - output: shape = (N, 1). With the entry being the predicted age from the ECG.
 
 ## Requirements