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+# Annotation (or segmentation) of the electrocardiogram (ECG) with a long short-term memory neural network. 
+Here, I experimented with annotating peaks of the ECG using a recurrent neural network in tensorflow's Keras.
+In the beginning I struggled a bit to get the input/output right, which had to do with the way I tried to format ECG-peaks (as a sparse vector containing peaks (1) vs no peaks (0)). Aproaching it as a semantic segmentation problem (e.g. Seq2Seq) solved it for me. 
+<br>It seems to work well on the QT database of physionet, but there are some cases that it has never seen where it fails; I haven't played with augmenting the ecgs yet.
+ 
+[5oct 2019]
+Since posting this 3 years ago, I noticed several publications using the (exact) same principle:
+- Matlab: https://www.mathworks.com/help/signal/examples/waveform-segmentation-using-deep-learning.html
+- IEEE: https://ieeexplore.ieee.org/document/8333406
+
+
+## Model
+
+```
+model = Sequential()
+model.add(Dense(32,W_regularizer=regularizers.l2(l=0.01), input_shape=(seqlength, features)))
+model.add(Bidirectional(LSTM(32, return_sequences=True)))#, input_shape=(seqlength, features)) ) ### bidirectional ---><---
+model.add(Dropout(0.2))
+model.add(BatchNormalization())
+model.add(Dense(64, activation='relu',W_regularizer=regularizers.l2(l=0.01)))
+model.add(Dropout(0.2))
+model.add(BatchNormalization())
+model.add(Dense(dimout, activation='softmax'))
+adam = optimizers.adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.0)
+model.compile(loss='categorical_crossentropy', optimizer=adam, metrics=['accuracy']) 
+```
+
+## Getting Started
+- Download ECG data using something like <code>wget -r -l1 --no-parent https://physionet.org/physiobank/database/qtdb/</code>
+- Run, with as the first argument the directory where the ECG data is stored; or set <code>qtdbpath</code>.
+
+## Output
+A 2 lead ECG, the colors indicate P-Pend(yellow),Pend-Q(green), Q-R(red),R-S(purple),S-Tend(brown),Tend-P(pink). Training took about an hour on 6 cores. 
+![example output](https://github.com/niekverw/Deep-Learning-Based-ECG-Annotator/blob/master/images/example.png?raw=true)
+- Colors at the top show true annotations
+- Colors at the bottom show predicted annotations
+
+## Dependencies
+I haven't got a list of all dependencies; but use this:  
+- wfdb 1.3.4 ( not the newest >2.0, thanks BrettMontague); pip install wfdb==1.3.4