tensorflow

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

    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.

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

Tensorflow

python-matplotlib

pywavelets