BCG signal analysis based on improved VMD algorithm

Abstract

To achieve accurate ballistocardiogram (BCG) conversion to the electrocardiogram (ECG) and complete quantitative alignment for wearable portable flexible monitoring. Based on the use of fiber optic sensing, an algorithm for heart rate signal analysis with optimized variational modal decomposition (OVMD) is proposed. Fiber Bragg Grating Sensors (FBGs) embedded in nylon bandages can be made into a flexible smart sensing fabric for monitoring BCG signals. And the parameters of the variational modal decomposition (VMD) are optimized by the mean correlation coefficient of adjacent components and the negative entropy of the envelope spectrum, which are used to demodulate and analyze the FBGs echo signal. At the same time, the demodulated BCG signals are compared with ECG signals for feature point analysis. The feature point mapping relationship between BCG and ECG is tested and decoded for three different postures: standing, sitting, and lying. The experimental results showed that there is a strong correlation and high agreement between the heart rate variability (HRV) parameters of the BCG signal and the HRV parameters of the ECG signal. The heart rate interval (RR-JJ) correlation is 0.994 and the consistency limit is 10 ms. In addition, the remaining feature points also showed good consistency, which verified the feasibility of the algorithm to improve the accuracy of BCG heart rate signal solving.