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项目作者： CVxTz

项目描述 ：
CNN for heartbeat classification
高级语言： Python
项目地址: git://github.com/CVxTz/ECG_Heartbeat_Classification.git
创建时间： 2018-07-13T18:30:07Z
项目社区：https://github.com/CVxTz/ECG_Heartbeat_Classification
开源协议：MIT License
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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


Figure 1 :
https://en.wikipedia.org/wiki/Electrocardiography

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.

Dataset

The original datasets used are the MIT-BIH Arrhythmia
Dataset and The PTB
Diagnostic ECG Database
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 :


Figure 2 : Example of preprocessed sample from the MIT-BIH dataset

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 :

Figure 3 : Keras model

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