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Noise Detection and Classification in Chagasic ECG Signals Based on One-Dimensional Convolutional Neural Networks
Resource type
Authors/contributors
- Caldas, Weslley Lioba (Author)
- do Vale Madeiro, João Paulo (Author)
- Pedrosa, Roberto Coury (Author)
- Gomes, João Paulo Pordeus (Author)
- Du, Wencai (Author)
- Marques, João Alexandre Lobo (Author)
- Lee, Roger (Editor)
Title
Noise Detection and Classification in Chagasic ECG Signals Based on One-Dimensional Convolutional Neural Networks
Abstract
Continuous cardiac monitoring has been increasingly adopted to prevent heart diseases, especially the case of Chagas disease, a chronic condition that can degrade the heart condition, leading to sudden cardiac death. Unfortunately, a common challenge for these systems is the low-quality and high level of noise in ECG signal collection. Also, generic techniques to assess the ECG quality can discard useful information in these so-called chagasic ECG signals. To mitigate this issue, this work proposes a 1D CNN network to assess the quality of the ECG signal for chagasic patients and compare it to the state of art techniques. Segments of 10 s were extracted from 200 1-lead ECG Holter signals. Different feature extractions were considered such as morphological fiducial points, interval duration, and statistical features, aiming to classify 400 segments into four signal quality types: Acceptable ECG, Non-ECG, Wandering Baseline (WB), and AC Interference (ACI) segments. The proposed CNN architecture achieves a $$0.90 \pm 0.02$$accuracy in the multi-classification experiment and also $$0.94 \pm 0.01$$when considering only acceptable ECG against the other three classes. Also, we presented a complementary experiment showing that, after removing noisy segments, we improved morphological recognition (based on QRS wave) by 33% of the entire ECG data. The proposed noise detector may be applied as a useful tool for pre-processing chagasic ECG signals.
Book Title
Computer and Information Science
Series
Studies in Computational Intelligence
Place
Cham
Publisher
Springer International Publishing
Date
2023
Pages
117-129
Language
en
ISBN
978-3-031-12127-2
Accessed
8/1/23, 12:50 PM
Library Catalog
Springer Link
Extra
Citation
Caldas, W. L., do Vale Madeiro, J. P., Pedrosa, R. C., Gomes, J. P. P., Du, W., & Marques, J. A. L. (2023). Noise Detection and Classification in Chagasic ECG Signals Based on One-Dimensional Convolutional Neural Networks. In R. Lee (Ed.), Computer and Information Science (pp. 117–129). Springer International Publishing. https://doi.org/10.1007/978-3-031-12127-2_8
Academic Units
United Nations SDGs
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