The use of ECG in cardiovascular health monitoring is well established. The signal is collected using specialised equipment, capturing the electrical discharge properties of the human heart. This produces a well-structured signal trace, which can be characterised through its peaks and troughs. The signal can then be used by clinicians to diagnose cardiac disorders. However, as with any measuring equipment, the ECG output signal can experience deterioration resulting from noise. This can happen due to environmental interference, human issues or measuring equipment failure, necessitating the development of various denoising strategies to reduce, or remove, the noise. In this paper, we study typically occurring types of noise and implement popular strategies used to rectify them. We also show, that the given strategy's denoising potential is directly related to R-wave detection, and provide best strategies to apply when faced with specific noise type.
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| http://dx.doi.org/10.1109/EMBC46164.2021.9629495 | DOI Listing |
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Article Synopsis
- The study addresses issues in fault diagnosis related to Fast Ensemble Empirical Mode Decomposition (FEEMD), like mode mixing and selection bias, and proposes a new method combining Fast Complementary Ensemble Empirical Mode Decomposition (FCEEMD) with Time-shift Multiscale Fuzzy Dispersion Entropy (TSMFDE) and adaptive Optuna-CatBoost to improve diagnostic accuracy.
- By introducing paired white noise in FCEEMD, the method effectively reduces mode aliasing while retaining critical fault-related Intrinsic Mode Function components for better signal reconstruction.
- The overall accuracy of fault diagnosis using this method is remarkably high, reaching 99.76% and 99.33%, demonstrating its potential
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