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+TensorFlow implementation of ecg classification. 
+
+# Prepare data
+To prepare the dataset *create_traindataset_mitdb.py* extract the beats from all patients, compute the RR interval information and set their corresponding label from the annotation files.
+
+# Models
+
+## DNN classifier
+In *dnn_mitdb.py* a DNN default classifier from tensorflow is used
+
+```python
+    mitdb_classifier = tf.contrib.learn.DNNClassifier(feature_columns=feature_columns,
+    hidden_units=[10, 20, 10],
+    n_classes=5)
+```
+
+## My own model classifier
+Due to the imbalanced data (common in that problem) between N class and anomalies class (SVEB, VEB, F). In *my_dnn_mitdb.py* a classifier that adjust the weight for loss computation during training step is defined. 
+
+```python
+def my_model_fn(features, targets, mode, params):
+    ...
+    loss = tf.losses.softmax_cross_entropy(targets_onehot, output_layer, weights=weights_tf)
+    ...
+
+my_nn = tf.contrib.learn.Estimator(model_fn=my_model_fn, params=model_params)
+```
+
+# Requirements
+
+[Installation guide](installation_guide.md)
+
+Tensorflow
+
+python-matplotlib
+
+pywavelets