Deep learning Scheme for Acquisition Errors Elimination in ECG Data by the Empirical Mode Values of Independent Components Associated Decomposition

DOI

10.2991/978-94-6239-674-6_28How to use a DOI?

Keywords

ECG; EEG; ICA; Artificial Intelligence and Empirical Mode of Decomposition

Abstract

A proposed methods seeks to improve the processing of Electrocardiogram signals with the intent of reducing the impacts of power-line interference and magnetic-field defects that commonly result in uncertainties of medical diagnosis. The initial step of our proposed approach involves decomposition of Electrocardiogram signals through Empirical Mode Decomposition to identify the resolution of the signals into intrinsic mode functions to identify the underlying patterns. The subsequent step involves application of Independent Component Analysis (ICA) through Independent Component Generation step thru the intent of identifying the independent components of the signal to facilitate easier interpretation of the signals to distinguish signal components from noise. After rigorous noise elimination, our proposed approach applies the inverse of ICA and Empirical Mode Decomposition to generate an improved version of the signals with increased realism due to the elimination of noise that may still exist.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Amit Kumar Pandey
AU  - Chhote Lal Prasad Gupta
PY  - 2026
DA  - 2026/05/28
TI  - Deep learning Scheme for Acquisition Errors Elimination in ECG Data by the Empirical Mode Values of Independent Components Associated Decomposition
BT  - Proceedings of the International Conference on Sustainable Computing and Artificial Intelligence (ICSCAI 2025)
PB  - Atlantis Press
SP  - 325
EP  - 333
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6239-674-6_28
DO  - 10.2991/978-94-6239-674-6_28
ID  - Pandey2026
ER  -