A New Complementary Empirical Ensemble Mode Decomposition Method for Respiration Extraction

Respiration monitoring is essential for diagnosing and managing a variety of diseases. It is a non-invasive, convenient and effective method to derive breathing from ECG signals. This paper proposes a new complementary ensemble empirical mode decomposition (NCEEMD) method for respiration extraction. By additional ensemble empirical mode decomposition (EEMD) of the auxiliary white gaussian noise, the noise residue of the corresponding respiratory band after the EEMD decomposition of original ECG signal is subtracted. The new IMF was selected for correlation analysis with the measured respiratory signal, and the optimal amplitude noise coefficient was determined adaptively by the principle of maximum correlation increment. Then IMF in the respiratory band is selected to reconstruct the respiratory signal which is ECG-derived respiration (EDR). A comparative experiment of respiration extraction was conducted using the data of the MIT-BIH Polysomnographic database. The experimental results show that compared with the complementary ensemble empirical mode decomposition (CEEMD) method, the proposed EDR extraction method reduces the average MSE by 3.95%, RMSE by 2.74%, and MAE by 2.52% and the physical significance of the IMF component is more explicit. This method has good accuracy, robustness and adaptability, and provides a new solution idea for the extraction of respiratory signals.

@article{Wan2024,
  title     = {A New Complementary Empirical Ensemble Mode Decomposition Method for Respiration Extraction},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {6},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Xiangkui Wan and Wenxin Gong and Yunfan Chen and Yang Liu},
  doi       = {10.14569/IJACSA.2024.01506120},
  url       = {https://doi.org/10.14569/IJACSA.2024.01506120}
}