https://github.com/niekverw/Deep-Learning-Based-ECG-Annotator
Annotation of ECG signals using deep learning, tensorflow’ Keras
https://github.com/niekverw/Deep-Learning-Based-ECG-Annotator
annotations deep-learning ecg ecg-data electrocardiogram example keras-tutorials lstm peak peaks qrs-detection segmentation tensorflow time-series tutorial wfdb
Last synced: 11 months ago
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Annotation of ECG signals using deep learning, tensorflow’ Keras
- Host: GitHub
- URL: https://github.com/niekverw/Deep-Learning-Based-ECG-Annotator
- Owner: niekverw
- Created: 2017-12-29T18:32:53.000Z (over 8 years ago)
- Default Branch: master
- Last Pushed: 2020-01-09T15:21:16.000Z (over 6 years ago)
- Last Synced: 2024-11-27T03:35:00.227Z (over 1 year ago)
- Topics: annotations, deep-learning, ecg, ecg-data, electrocardiogram, example, keras-tutorials, lstm, peak, peaks, qrs-detection, segmentation, tensorflow, time-series, tutorial, wfdb
- Language: Python
- Homepage:
- Size: 162 KB
- Stars: 146
- Watchers: 8
- Forks: 48
- Open Issues: 4
-
Metadata Files:
- Readme: README.md
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README
# 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.
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 wget -r -l1 --no-parent https://physionet.org/physiobank/database/qtdb/
- Run, with as the first argument the directory where the ECG data is stored; or set qtdbpath.
## 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.
- 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