A Review of Decomposition Methods for ECG-Derived Respiratory Signal Extraction: Principles, Performance, and Applications

Electrocardiogram (ECG)-Derived respiratory methods have become a non-invasive technique for monitoring and assessing respiratory functions. The purpose of this study was to evaluate the efficacy of various ECG-derived respiratory (EDR) methods based on signal decomposition techniques, including empirical mode decomposition (EMD), variational mode decomposition (VMD), empirical wavelet transforms (EWT), and kernel principal component analysis (KPCA). The contributions obtained Were able to provide information about respiratory function non-invasively. This research compares several approaches’ concepts, benefits, limitations, and comparative performance using the correlation and coherence coefficients as evaluation metrics. With correlation coefficients of 0.91 and 0.94, respectively, and coherence coefficients of 0.95 and 0.97, EWT and KPCA perform the best in accuracy and robustness. The paper indicates that EWT is the most effective and efficient decomposition approach for extracting respiratory signals from ECG data, as it can adapt to the dynamically varying features of ECG signals, decrease noise, and provide smooth and accurate EDR signals. The article suggests that EDR techniques based on decomposition techniques can provide a non-invasive and continuous monitoring strategy for respiratory parameters. Nonetheless, they encounter obstacles and constraints that must be addressed to improve their clinical usefulness.