# ECG_Heartbeat_Classification

Description of the approach : https://blog.goodaudience.com/heartbeat-classification-detecting-abnormal-heartbeats-and-heart-diseases-from-ecgs-913449c2665

Requirement : Keras, tensorflow, numpy 

# Heartbeat Classification : Detecting abnormal heartbeats and heart diseases from

ECGs

![](https://cdn-images-1.medium.com/max/1600/1*QLO4ZfqK44tmBzpsOx2VjQ.jpeg)

<span class="figcaption_hack">Figure 1 :

[https://en.wikipedia.org/wiki/Electrocardiography](https://en.wikipedia.org/wiki/Electrocardiography)</span>

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](https://www.kaggle.com/shayanfazeli/heartbeat) of heartbeats already

preprocessed by the authors of [this paper](https://arxiv.org/abs/1805.00794) to

see if we can train a model to detect abnormal heartbeats.

### Dataset

The original datasets used are [the MIT-BIH Arrhythmia

Dataset](https://www.physionet.org/physiobank/database/mitdb/) and [The PTB

Diagnostic ECG Database](https://www.physionet.org/physiobank/database/ptbdb/)

that were preprocessed by [1] based on the methodology described in III.A of the

paper in order to end up with samples of a single heartbeat each and normalized

amplitudes as :

![](https://cdn-images-1.medium.com/max/1600/1*1iDuoH9i1LR-BDwuuid3PQ.png)

<span class="figcaption_hack">Figure 2 : Example of preprocessed sample from the MIT-BIH dataset</span>

MIT-BIH Arrhythmia dataset :

* Number of Categories: 5

* Number of Samples: 109446

* Sampling Frequency: 125Hz

* Data Source: Physionet’s MIT-BIH Arrhythmia Dataset

* Classes: [’N’: 0, ‘S’: 1, ‘V’: 2, ‘F’: 3, ‘Q’: 4]

The PTB Diagnostic ECG Database

* Number of Samples: 14552

* Number of Categories: 2 ( Normal vs Abnomal)

* Sampling Frequency: 125Hz

* Data Source: Physionet’s PTB Diagnostic Database

The published preprocessed version of the MIT-BIH dataset does not fit the

description that authors provided of it in their paper as the former is heavily

unbalanced while the latter is not. This made it so my results are not directly

comparable to theirs. I sent the authors an email to have the same split as them

and I’ll update my results if I get a reply. A similar issue exists for the PTB

dataset.

### Model

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

residual blocks :

<span class="figcaption_hack">Figure 3 : Keras model</span>

Code :

### Results

MIT-BIH Arrhythmia dataset :

* Accuracy : **98.5**

* F1 score : **91.5**

The PTB Diagnostic ECG Database

* Accuracy : **98.3**

* F1 score : **98.8**

### Transferring representations

Since the PTB dataset is much smaller than the MIT-BIH dataset we can try and

see if the representations learned from MIT-BIH dataset can generalize and be

useful to the PTB dataset and improve the performance.

This can be done by loading the weights learned in MIT-BIH as initial point of

training the PTB model.

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.

Code to reproduce the results is available at :

[https://github.com/CVxTz/ECG_Heartbeat_Classification](https://github.com/CVxTz/ECG_Heartbeat_Classification)