Automated ECG Quality Assessment Using Deep Learning
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2023-06-11
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en
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Abstract
Cardiovascular diseases are becoming more prevalent with the increase in the global
population. Due to this, there is a greater need for diagnostic techniques like ECGs.
ECG recordings need to be checked for quality to ensure that only non-noisy recordings
are used for analysis. As this is a time-consuming process, automated ECG
quality assessment techniques have been developed. Previous research on automated
ECG quality assessment techniques used unbalanced datasets or noise datasets
to create balanced datasets to train their models. Our model was trained on balanced
datasets generated without artificially added noise to generate extra noisy recordings.
The proposed CNN-LSTM model has an accuracy of 97.27% on the test set and
an accuracy of 98.04% on the external test set. The model was trained using real
noise sources which likely results in better real-life performance and usability of the
models compared to models trained and tested on simulated data. The proposed
CNN-LSTM was incorporated into a toolbox for easy usage. The layout of the toolbox
was changed to be more user-friendly. Furthermore, the toolbox is fullscreen
and is equipped with faster datastructures.
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Faculteit der Sociale Wetenschappen