Variational Mode Extraction: A New Efficient Method to Derive Respiratory Signals from ECG.

Abstract

ECG-derived respiratory (EDR) signal is an effective and inexpensive method to monitor the respiration. Previous studies have shown that the empirical mode decomposition (EMD) techniques can satisfactorily extract the EDR signal, however, their performances are degraded at the presence of noise. On the other hand, variational mode decomposition (VMD) performs good robustness against noise. In applications such as EDR extraction, where a specific mode is in interest, VMD imposes unnecessary computational cost. In this paper, we consider the extraction of EDR as a problem of obtaining a specific mode of a signal and suggest a new method named as variational mode extraction (VME). The method is established on the similar basis as VMD, with a new criterion: The residual signal after extracting the specific mode should have no or less energy at the center frequency of the mode. In this regard, VME is capable of solving the EDR problem by considering the EDR signal as a mode with approximate center frequency of zero. For verification, the respiratory rate signal is detected from EDR signal extracted by VME and compared it with those obtained by VMD, EMD-based methods, and bandpass filtering. The results confirm that the new method can extract the EDR signal with a better accuracy, while performing much lower computational cost and higher convergence rate.