Human pulse wave signal noise suppression method

Abstract

The invention discloses a human pulse wave signal noise suppression method. In order to clearly display a waveform, firstly, acquired original pulse wave signal amplitude is normalized. Secondly, in order to decrease the influence of edge effects, two ends of a signal are periodically extended. Finally, double median filters are designed and used for human pulse wave signal noise suppression, a first median filter is used for eliminating high-frequency noise in the pulse wave signal, a second median filter is used for estimating low-frequency noise in the signal, and the estimated low-frequency noise is removed from the signal after high-frequency noise suppression to acquire a denoised signal. The high-frequency noise, baseline drift and part of motion noise in the human pulse wave signalcan be inhibited, increase of an adjunct circuit or use of a complex denoising algorithm can be avoided, and the method is a simple real-time noise suppression method which can be applied to a microprocessor.

Description

technical field [0001] The invention relates to a real-time pulse wave signal noise suppression method based on double median filtering, which is especially suitable for a portable pulse oximeter. Background technique [0002] Human pulse wave signal detection has been widely used in the evaluation of cardiovascular system, respiratory system and blood circulation system. The human pulse wave signal is weak and will inevitably be interfered by human movement and heavy breathing, which will affect the quality of signal acquisition and the accuracy of later calculation of clinical physiological parameters. [0003] At present, the noise suppression algorithms at home and abroad mainly include EMD denoising method, adaptive filter denoising method, wavelet multi-resolution analysis denoising method, SVD denoising method, ICA denoising method, high-order statistical denoising method and step-by-step denoising method. Periodic Fourier series analysis, etc. In addition, wavelet d...

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Problems solved by technology

[0004] However, the market share of traditional pulse oximeters based on ordinary microprocessors is relatively high. Considering factors such as cost, volume, real-time and implementation methods, the above methods may need to add additional hardware circuits to meet the requirements of motion interference signals. Acquisition, or need to use a high-end microcontroller to implement complex noise reduction algorithms, or need to implement pulse wave signal data transmission and use the host computer to run complex algorithms, not suitable for the use of portable pulse oximeters

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