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Nonlinear filters and deep learning for removing chest compression artefacts from electrocardiogram measurements during cardiac arrest

Verboom, S.D. (2020) Nonlinear filters and deep learning for removing chest compression artefacts from electrocardiogram measurements during cardiac arrest.

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Abstract:The key treatment actions of cardiac arrest include high-quality chest compression and quick defibrillation. Quick defibrillation is an effective treatment for so-called shockable underlying rhythms, which can be distinguished from non-shockable rhythms by analysis of the electrocardiogram (ECG). Unfortunately, chest compressions create artifacts in the ECG which impedes the analysis of the ECG. Interruptions of chest compressions are therefore necessary, however, pauses of the chest compressions decrease the chances of survival. The importance of continuing chest compressions on one side and ECG analysis on the other side poses a contradiction in the treatment of cardiac arrest. A solution to this contradiction would be to create a method that would allow interpretation of the ECG even during ongoing chest compressions. Many methods have been proposed to remove chest compression artifacts from ECGs during resuscitation. Most existing filters are post-processing techniques based on the Fourier transform. However, the frequency spectra of chest compression artifacts and cardiac rhythms during cardiac arrest overlap considerably, leading to a problem in filtering in the frequency spectrum. A shift from Fourier based filters to filters based on a nonlinear transform could improve chest compression artifact reduction. In this thesis, we generalize existing filter methods based on linear transforms to nonlinear alternatives such as nonlinear spectral analysis and deep learning-based methods. We proposed a deep learning-based filter from the field of audio processing. This neural network embeds a recurrent separation network that separates the chest compression artifact from the ECG in an autoencoder. The resulting filter learns both the transform (autoencoder) and a transfer function (recurrent network) and can operate nearly in real-time. This introduced deep learning filter significantly improved the signal to noise ratio of artificially mixed ECG signals more compared to an existing linear Fourier based filter. Furthermore, the amplitude spectral area (AMSA) can be calculated after filtering artificially mixed ECG signals with only a small error after filtering. The association between pre-shock AMSA and shock success remained present for filtered ECG but with a different cut-off value. It remains unclear what the added value is of the deep learning filter on measured corrupted ECG signals. To conclude, we have introduced a nonlinear deep learning method for removing chest compression artifacts from ECG measurements during cardiac arrest. This method has shown to improve the signal quality of artificially mixed ECG and can be further improved to be generalized to measured corrupted ECG.
Item Type:Essay (Master)
Clients:
Radboudumc, Nijmegen, The Netherlands
Faculty:TNW: Science and Technology
Subject:31 mathematics, 44 medicine
Programme:Technical Medicine MSc (60033)
Link to this item:http://purl.utwente.nl/essays/84685
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