Requirement : Keras, tensorflow, numpy

An ECG is a 1D signal that is the result of recording the electrical activity of the heart using an electrode. It is one of the tool that cardiologists use to diagnose heart anomalies and diseases.

In this blog post we are going to use an annotated dataset of heartbeats already preprocessed by the authors of this paper to see if we can train a model to detect abnormal heartbeats.

Similar to [1] I use a neural network based on 1D convolutions but without the residual blocks :

Figure 3 : Keras model

Code :

MIT-BIH Arrhythmia dataset :

• Accuracy : 98.5
• F1 score : 91.5

The PTB Diagnostic ECG Database

• Accuracy : 98.3
• F1 score : 98.8

From Scratch :

• Accuracy : 98.3
• F1 score :** 98.8**

Freezing the Convolution Layer and Training the Fully connected ones :

• Accuracy : 95.6
• F1 score : 96.9

Training all layers :

• Accuracy : 99.2
• F1 score : 99.4

We can see the freezing the first layers does not work very well. But if we initialize the weights with those learned on MIT-BIH and train all layers we are able to improve the performance compared to training from scratch.