An efficient method for extracting respiratory activity from single-lead-ECG based on variational mode decomposition

Recording and monitoring of respiratory signal has a great importance in medical fields. Old methods for recording this signal are mostly expensive, affected from the environmental conditions and troublesome for the patient. Consequently, using indirect methods like ECG-derived respiratory signal (EDR) is an appropriate solution for reducing above problems. In this regard, multi resolution decomposition methods such as empirical mode decomposition (EMD) methods were proposed to solve the problem, however they could not get satisfactory results if the noise were present in the ECG signal. We previously proposed that the variational mode decomposition (VMD) method could be used as a precise and robust method to extract EDR, however the high computational burden of VMD was a problem. In this paper, we propose a new method based on VMD with a lowered computational complexity and a better precision in EDR detection. several tests on artificial and real ECG data confirm the good performance of the new method.