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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
+
+![](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)
+