Physiological signal separation and extraction system and method based on optical fiber sensing

Abstract

The invention discloses a physiological signal separation and extraction system based on optical fiber sensing. A physiological signal preprocessing module of the physiological signal separation and extraction system is used for sequentially removing direct current components and artifacts from original physiological signals; a respiratory signal separation module is used for obtaining respiratory signals in the physiological signals; a ballistocardiogram model building module is used for building a ballistocardiogram model with noise aliasing; a heartbeat signal extraction module is used for obtaining a cardiac impulse diagram signal. According to the system and the method, effective physiological signals can be separated from aliasing signals with a low signal-to-noise ratio, a heartbeat denoising extraction model is constructed in a matrix iterative decomposition mode, and the signals are reconstructed according to the model, so that the operation complexity is greatly reduced, effective components of the signals are ensured, and thus the demodulation difficulty of the non-contact sensing signal and the system design cost are reduced.

Description

technical field [0001] The invention relates to the technical field of monitoring human vital signs, in particular to a system and method for separating and extracting physiological signals based on optical fiber sensing. Background technique [0002] With the development of social economy and technology and the improvement of people's requirements for quality of life, people's demand for testing their own health is increasing. Respiratory rate and heart rate are the most basic vital signs of the human body. Morbid signs of the human body are often reflected in abnormal respiration rate and heart rate. Therefore, daily real-time monitoring of respiration rate and heart rate is of great significance for human health assessment and disease prevention . Long-term breathing rate and heartbeat monitoring can enable the health status of the monitored person to be monitored and mastered in real time. However, most of the traditional monitoring systems use contact devices, which ha...

AI Technical Summary

Problems solved by technology

The sensing system based on the principle of optical fiber sensing has the characteristics of high sensitivity, good comfort, and anti-electromagnetic interference. The frequency of the signal and the heart rate signal is low, and there is a certain crossover in the frequency. It is difficult to completely separate the respiratory signal and the heart rate signal by using the traditional filtering method; in reference [1], wavelet decomposition is used to denoise the ECG signal. The signal-to-noise ratio of the BCG physiological signal aimed at in the present invention is low, and the respiratory frequency and the heartbeat frequency are relatively close, so it is necessary to decompose multiple layers to improve the resolution of signal extraction. In order to meet the signal extraction requirements, the algorithm based on wavelet decomposition has an extremely Large, requires a high-performance processor, so it will increase the design cost and hardware design difficulty

References [2-3] use the empirical mode decomposition method EMD to separate signals, but for signals with low signal-to-noise ratio, EMD cannot adaptively process different noises. For aliased signals with low signal-to-noise ratio, the signal The separation effect is not ideal; the multimodal decomposition (VMD) algorithm is proposed in reference [4], and the signal is denoised and extracted by this method. When the signal-to-noise ratio is high, the signal extraction effect is better, but for the signal-noise When the ratio is low, the extracted signal will be seriously distorted, resulting in the loss of a lot of useful information of the signal

Images