Towards Real-Time ECG Signal Denoising using Sparse and Shallow Running Denoising Autoencoder
Embargo bis
auf unbestimmte Zeit
Autor:in
Gutachter:in
Schanze, Thomas
Fiebich, Martin
Datum
2025-03
Metadata
Zur Langanzeige
Dissertation oder Habilitation

Zusammenfassung
Background: Electrocardiogram (ECG) is a widely used diagnostic tool in medical
practice, but noise often compromises the quality of the recorded ECG signals, especially
during prolonged monitoring like Ambulatory Cardiac Monitoring (ACM). Denoising Au-
toencoders (DAE) have shown promise in biomedical signal denoising due to their ability
to learn complex features. However, the current state-of-the-art DAE models need long
non-aligned input segment with multiple hidden layers to achieve acceptable denoising,
leading to complex models. This complexity poses challenges for model’s interpretability
and for practical implementation in real-time scenarios, particularly on portable devices
like ACM.
Methods: Various DAE models utilizing either dense or convolutional layers have been
examined with respect to different input lengths, including QRS-aligned, non-aligned, and
overlapping ECG segments. In this work, a novel Sparse Running Denoising Autoencoder
(SRunDAE) model is proposed to denoise relatively short ECG segments without the need
for R-peak detection algorithms for segment alignment. The proposed RunDAE model
employs a sliding-window processing approach, which takes into account the correlation
between consecutive, overlapping ECG segments. This work investigates the effects of two
weight regularization techniques, L1- and L2-norm, with the aim of enhancing denoising
performance, achieving more interpretable, meaningful, and sparse representations, and
simplifying the architecture of the current RunDAE model.
Results and Discussions: The results indicate that the running DAE models outper-
form the traditional DAE models in denoising ECG signals. Moreover, introducing spar-
sity to RunDAE model help to improve the generalization of the model in removing un-
seen noise/artifacts and retaining ECG morphology, and the interpretability of the model
by learning meaningful features. The simplicity and the efficiency of SRunDAE model
facilitate the implementation of reliable real-time ECG denoising on limited-resources
mircrocontroller, like Arduino.
Conclusions: The concept of running DAEs has shown a significant advancement com-
pared to the traditional DAEs. By imposing sparsity, the SRunDAE model has delivered
remarkable denoising and offers practical benefits such as improved generalization, inter-
pretability, simplicity, and suitability for real-time implementation. Besides, the sparse
DAEs yield weights that effectively acting as basis functions suitable for sparse coding.
Schlagworte
Denoising autoencoder
Running denoising autoencoder
Non-aligned ECG segments
QRS-aligned ECG segments
Sliding-window ECG segments
Running denoising autoencoder
Non-aligned ECG segments
QRS-aligned ECG segments
Sliding-window ECG segments
Umfang
XIX, 104 S.
Beziehung zu anderer Publikation
Link zur Veröffentlichung
Sammlungen