Motion state heart rate monitoring method based on photoplethysmography and spectrum analysis

A photoplethysmography and heart rate monitoring technology, which is applied in the measurement of pulse rate/heart rate, etc., can solve the problems of high computational complexity, difficulty in heart rate measurement, and inaccurate results, and achieves high accuracy of calculation results and low complexity of design methods. , the effect of good application value

Active Publication Date: 2015-12-02
南京盟联信息科技股份有限公司
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Problems solved by technology

[0002] Traditional heart rate monitoring methods based on photoplethysmography signals mostly rely on the processing of the monitored signals in a static state, but in the state of exercise, the existence of motion noise makes it difficult to measure heart rate
The traditional methods of heart rate monitoring based on photoplethysmography signals in the state of exercise are independent component analysis, adaptive denoising, sparse spectral analysis, spectral analysis based on Fourier transform, spectral subtraction, etc., and most of these methods are used to characterize motion. Interfering accelerometers measure signal data, and have high computational complexity, and the results obtained are not very accurate

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  • Motion state heart rate monitoring method based on photoplethysmography and spectrum analysis
  • Motion state heart rate monitoring method based on photoplethysmography and spectrum analysis

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Embodiment Construction

[0023] Refer to attached Figure 1~2 A heart rate monitoring method in an exercise state based on photoplethysmography and spectrum analysis, comprising the steps of:

[0024] 1) Use a zero-phase filter to perform baseline filtering on the source signal collected from the hardware circuit to remove the baseline drift caused by breathing noise in the source signal;

[0025] The signal obtained at this time photoplethysmographic signal and noise signal The interfering signal of; Formula (1) represents the composition of the signal that obtains, and wherein t represents time;

[0026] (1)

[0027] The baseline filtering refers to smoothing the signal using the mean filtering method, and then subtracting the smoothed signal from the source signal to remove the baseline drift interference caused by the breathing signal, so as to facilitate better signal analysis.

[0028] Equations (2) and (3) describe the mean filter method, where, represents the cut-off coefficient, ...

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Abstract

The invention relates to motion state heart rate monitoring, in particular to a motion state heart rate monitoring method based on photoplethysmography and a spectrum analysis. The method comprises the following steps: performing zero-phase base line filtering on a source signal; partitioning a processed signal by a sliding window method; obtaining a frequency spectrum by a short-time Fourier transformation method; finding out a spectrum peak representing heart rate data by a spectrum peak tracing method to obtain the heart rate data; processing the heart rate data by a calculatedly moving mean filter to obtain a final heart rate result. The motion state heart rate monitoring method is simple in design and efficient; by monitoring and the analysis of the method, a user can acquire the heart rate data under a motion state. The motion state heart rate monitoring method can be applied to wearable equipment so as to realize healthy monitoring of the heart rate and has higher application value.

Description

technical field [0001] The invention relates to heart rate monitoring in exercise state, in particular to a heart rate monitoring method in exercise state based on photoplethysmography and spectrum analysis. Background technique [0002] Traditional heart rate monitoring methods based on photoplethysmography signals mostly rely on the processing of the monitored signals in the static state, but in the state of exercise, the existence of motion noise makes the measurement of heart rate more difficult. The traditional methods of heart rate monitoring based on photoplethysmography signals in the state of exercise are independent component analysis, adaptive denoising, sparse spectral analysis, spectral analysis based on Fourier transform, spectral subtraction, etc. Most of these methods are used to characterize motion Interfering accelerometers measure signal data and have high computational complexity, and the results obtained are not very accurate. Contents of the invention...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): A61B5/024
Inventor 汪丰赵大地潘日劲
Owner 南京盟联信息科技股份有限公司
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