阅读说明：本技术 一种基于增强的高斯冗余字典脉搏信号去噪方法 (Enhanced Gaussian redundant dictionary pulse signal denoising method ) 是由 罗堪 刘肖 李建兴 邹复民 马莹 陈炜 黄炳法 于 2019-09-20 设计创作，主要内容包括：本发明能够有效的抑制脉搏信号的高频噪声、低频噪声、基线漂移、工频干扰和肌电干扰等噪声,有效提高了信噪比。本发明去噪后的信号没有多余震荡,极大地提高了运算效率,节约了大量资源,可以用于低功耗的前端脉搏信号采集,为后续脉搏波特征提取及分类进一步工作提供了很好的效果。本发明不止可以用于去除脉搏信号的噪声,也可以用于去除心电信号等其他信号的噪声。(The invention can effectively inhibit the high-frequency noise, the low-frequency noise, the baseline drift, the power frequency interference, the myoelectric interference and other noises of the pulse signal, and effectively improves the signal-to-noise ratio. The denoised signal has no redundant oscillation, thereby greatly improving the operation efficiency, saving a large amount of resources, being used for collecting the front-end pulse signal with low power consumption and providing good effect for further work of subsequent pulse wave feature extraction and classification. The invention can be used for removing noise of pulse signals and other signals such as electrocardiosignals.)

1. A pulse signal denoising method based on an enhanced Gaussian redundant dictionary is characterized by comprising the following steps: which comprises the following steps:

step 1, pre-generating an enhanced Gaussian redundant dictionary, wherein the mathematical model of the enhanced Gaussian redundant dictionary is as follows:

ψ＝[D₁ D₂ D₃…D_u B₁ B₂ B₃…B_q E P₅₀] (1)

Wherein u NxN-dimensional Gaussian dictionaries D_uCorresponding to noiseless pulse signal, q Gauss dictionaries B with dimension of NxN_qCorresponding to low and high frequency noise, 1 NxN dimensional identity matrix E corresponding to random noise, and 1 NxN dimensional sine matrix P with frequency of 50Hz₅₀Interference corresponding to power frequency noise of 50 Hz;

step 2, acquiring a pulse signal containing noise, and calculating and acquiring a sparse vector of the pulse signal corresponding to the enhanced Gaussian redundant dictionary;

Wherein the content of the first and second substances,A sparse vector of a gaussian dictionary is represented,representing sparse vectors, θ, detecting low and high frequency noise_Erepresenting sparse vectors, θ, detecting random noise_P50representing a sparse vector for detecting power frequency noise;

step 3, constructing a noise suppression index template vector l, and obtaining the noise suppression index template vector l by using the transpose of the splicing combination of p vectors with dimension of Nx 1 and element value of 1 and k vectors with dimension of Nx 1 and element value of 0, wherein the expression is as follows:

l＝[1_N×1,1_N×1,…,1_N×1,0_N×1,0_N×1,…,0_N×1]^T (7)

and 4, denoising the noise-containing pulse signals based on the enhanced Gaussian redundant dictionary, which comprises the following specific steps

Step 4-1, linearly representing the pulse signal x based on the enhanced Gaussian redundant dictionary psi as:

x＝ψθ (9)

wherein, theta is a sparse vector of the pulse signal x corresponding to psi;

And 4-2, multiplying the sparse vector theta by the vector point of the noise suppression index template to obtain the noise-component-suppressing pulse signal sparse vector estimation theta'.

θ′＝l·θ (10)

Step 4-3, reconstructing the signal by using matrix multiplication to obtain a clean pulse signal x' as follows:

x′＝ψθ′ (11)。

2. The enhanced gaussian redundancy dictionary-based pulse signal denoising method according to claim 1, wherein: u NxN-dimensional Gaussian dictionaries D in step 1_uelement d in (1)_d(i, j) is:

Wherein i, j is belonged to [1, N ∈]Is the row and column in which the elements of the matrix are located, a_das a morphological parameter, corresponds to D_uA total of u;

a_dparameter generated D_uSatisfy the minimum sparse vector theta_crepresents any section of noiseless pulse signal cPG;

Wherein cPPPG is clean pulse signal and theta_cIs a sparse vector obtained by carrying out sparse decomposition on the clean pulse signal, and theta_cSatisfying that its 1 norm has a minimum.

3. The enhanced Gaussian redundancy dictionary-based pulse signal denoising method according to claim 2, wherein: determination of morphological parameters a in step 1_dThe specific method comprises the following steps: randomly selecting several segments of clean pulse signals, a_dTaking value within a certain search range to generate D_udetermining the minimum number of a when the formula (3) is satisfied by means of grid search_dThe combination is the optimal D_uAnd generating parameters.

4. the enhanced gaussian redundancy dictionary-based pulse signal denoising method according to claim 1, wherein: q N × N dimensional Gaussian dictionaries B in step 1_qElement d in (1)_b(i, j) is:

Wherein i, j is belonged to [1, N ∈]is the row and column in which the elements of the matrix are located, a_dAs a noise morphological parameter, corresponds to B_qQ in total;

a_dB of parameter generation_qsatisfy the requirement of the sparse vector theta capable of being minimized_nTo represent any piece of low and high frequency noise nS;

where nS is low and high frequency noise, θ_nIs a sparse vector obtained by sparse decomposition of noise, and theta_nSatisfying that its 1 norm has a minimum.

5. The enhanced gaussian redundancy dictionary-based pulse signal denoising method according to claim 1, wherein: in step 1, 1N × N identity matrix E is a square matrix with a main diagonal element of 1 and other position elements of 0.

6. the enhanced gaussian redundancy dictionary-based pulse signal denoising method according to claim 1, wherein: 1 NXN sine matrix P in step 1₅₀Element p of (1)₅₀the expression of (i, j) is:

p₅₀(i,j)＝sin(2*π*50*(i-1-j+1/fs)) (6)

Where i, j ∈ [1, N ] is the row and column in which the element in the matrix is located.

7. the enhanced gaussian redundancy dictionary-based pulse signal denoising method according to claim 1, wherein: and (2) carrying out sparse decomposition on the noise-containing pulse signal by adopting a basis tracking algorithm, a greedy algorithm, an IRLS algorithm or an SL0 algorithm to obtain a sparse vector.

Technical Field

the invention relates to the technical field of signal noise processing, in particular to a pulse signal denoising method based on an enhanced Gaussian redundancy dictionary.

Background

the pulse condition signal of human body has important clinical diagnosis value in Chinese traditional medicine (traditional Chinese medicine) and modern medicine (western medicine). In order to objectively and quantitatively reveal the mechanism of pulse condition, since the 20 th century 80 years, many researchers have devoted themselves to the detection and analysis of human pulse signals, however, the human pulse signals are weak signals with low signal-to-noise ratio, the pulse condition signals of normal people are in the frequency range of 0-20 Hz, and about 99% of energy is distributed between 0-10 Hz, and the following noises often exist in the collected signals due to the influence of instruments, human bodies and the like during detection and collection: baseline drift, human respiration and other low-frequency interference, and the frequency is less than 1 Hz; myoelectric interference, which is caused by limb trembling and muscle tension, and has a large frequency range; power frequency interference and high frequency noise. Therefore, denoising the pulse signal is an important task.

The existing pulse signal denoising method has the following schemes: (1) the threshold denoising method compresses the transformation coefficient of a signal according to a certain preset threshold, and then reconstructs the signal by using the compressed coefficient so as to achieve the purpose of eliminating noise. Although the signal-to-noise ratio of the signal is improved, when the signal has abrupt discontinuity points or the signal-to-noise ratio is low, the threshold value method denoising can generate a pseudo Gibbs phenomenon, namely, the signal near the discontinuity points jumps up and down on the original signal level.

(2) A wavelet transformation denoising method. The pulse signal and noise have different properties under the wavelet transform, and the purpose of separating the signal from the noise is achieved by processing through a wavelet decomposition coefficient. But this method is computationally complex. (3) Digital filtering (FIR, IIR) denoising method. The coefficient of the digital filter is obtained through design, and the noise removal is realized by utilizing multiplication, time delay and accumulation calculation. But this method cannot deal with noise that has spectrally aliased parts with the signal.

Disclosure of Invention

The invention aims to provide a pulse signal denoising method based on an enhanced Gaussian redundant dictionary.

The technical scheme adopted by the invention is as follows:

a pulse signal denoising method based on an enhanced Gaussian redundant dictionary comprises the following steps:

Step 1, pre-generating an enhanced Gaussian redundant dictionary, wherein the mathematical model of the enhanced Gaussian redundant dictionary is as follows:

ψ＝[D₁ D₂ D₃…D_u B₁ B₂ B₃…B_q E P₅₀] (1)

wherein u NxN-dimensional Gaussian dictionaries D_ucorresponding to noiseless pulse signal, q Gauss dictionaries B with dimension of NxN_qCorresponding to low and high frequency noise, 1 NxN dimensional identity matrix E corresponding to random noise, and 1 NxN dimensional sine matrix P with frequency of 50Hz₅₀Interference corresponding to power frequency noise of 50 Hz;

Step 2, acquiring a pulse signal containing noise, and calculating and acquiring a sparse vector of the pulse signal corresponding to the enhanced Gaussian redundant dictionary;

Wherein the content of the first and second substances,A sparse vector of a gaussian dictionary is represented,Representing sparse vectors, θ, detecting low and high frequency noise_ERepresents a sparse vector of detected random noise,Representing a sparse vector for detecting power frequency noise;

Step 3, constructing a noise suppression index template vector l, and obtaining the noise suppression index template vector l by using the transpose of the splicing combination of p vectors with dimension of Nx 1 and element value of 1 and k vectors with dimension of Nx 1 and element value of 0, wherein the expression is as follows:

l＝[1_N×1,1_N×1,…,1_N×1,0_N×1,0_N×1,…,0_N×1]^T (7)

Wherein, the result of multiplying 0 element by any number is 0, and the result of multiplying 1 element by any number is the multiplicand itself;

And 4, denoising the noise-containing pulse signals based on the enhanced Gaussian redundant dictionary, which comprises the following specific steps

Step 4-1, linearly representing the pulse signal x based on the enhanced Gaussian redundant dictionary psi as:

x＝ψθ (9)

wherein, theta is a sparse vector of the pulse signal x corresponding to psi;

And 4-2, multiplying the sparse vector theta by the vector point of the noise suppression index template to obtain the noise-component-suppressing pulse signal sparse vector estimation theta'.

θ′＝l·θ (10)

Step 4-3, reconstructing the signal by using matrix multiplication to obtain a clean pulse signal x' as follows:

x′＝ψθ′ (11)

Further, u N × N dimensional Gaussian dictionaries D_uElement d in (1)_d(i, j) is:

Wherein i, j is belonged to [1, N ∈]is the row and column in which the elements of the matrix are located, a_das a morphological parameter, corresponds to D_uA total of u;

a_dparameter generated D_uSatisfy the minimum sparse vector theta_cRepresents any section of noiseless pulse signal cPG;

Wherein cPPPG is clean pulse signal and theta_cIs a sparse vector obtained by carrying out sparse decomposition on the clean pulse signal, and theta_csatisfying that its 1 norm has a minimum.

Further, a morphological parameter a is determined_dThe specific method comprises the following steps: randomly selecting several segments of clean pulse signals, a_dTaking value within a certain search range to generate D_udetermining the minimum number of a when the formula (3) is satisfied by means of grid search_dThe combination is the optimal D_uand generating parameters.

Further, q number of N × N dimensional Gaussian dictionaries B_qelement d in (1)_b(i, j) the expression is:

Wherein i, j is belonged to [1, N ∈]Is the row and column in which the elements of the matrix are located, a_das a noise morphological parameter, corresponds to B_qq in total;

a_dB of parameter generation_qSatisfy the requirement of the sparse vector theta capable of being minimized_nTo represent any piece of low and high frequency noise nS;

Where nS is low and high frequency noise, θ_nIs a sparse vector obtained by sparse decomposition of noise, and theta_nsatisfies that its 1 norm has a minimum; b is_qNoise shape parameter a of_bDetermined by experimental methods.

further, the 1N × N identity matrix E is a square matrix having a main diagonal element of 1 and other position elements of 0.

Further, 1N sine matrix P₅₀Element p of (1)₅₀The expression of (i, j) is:

p₅₀(i,j)＝sin(2*π*50*(i-1-j+1/fs)) (6)

Where i, j ∈ [1, N ] is the row and column in which the element in the matrix is located.

Further, in step 2, a Basis Pursuit (BP) algorithm, a greedy algorithm, an IRLS (Iterative Re-weighted Least Squares) algorithm or SL0 is adopted to perform sparse decomposition on the noise-containing pulse signal to obtain a sparse vector.

By adopting the technical scheme, the constructed enhanced Gaussian redundant dictionary is utilized, then the sparse vector capable of suppressing the noise is obtained through the index template vector for suppressing the noise, and finally the clean pulse signal is reconstructed.

The invention can effectively inhibit the high-frequency noise, the low-frequency noise, the baseline drift, the power frequency interference, the myoelectric interference and other noises of the pulse signal, and effectively improves the signal-to-noise ratio. The denoised signal has no redundant oscillation, thereby greatly improving the operation efficiency, saving a large amount of resources, being used for collecting the front-end pulse signal with low power consumption and providing good effect for further work of subsequent pulse wave feature extraction and classification. The invention can be used for removing noise of pulse signals and other signals such as electrocardiosignals.

drawings

The invention is described in further detail below with reference to the accompanying drawings and the detailed description;

FIG. 1 is a schematic flow chart of a method for denoising a pulse signal based on an enhanced Gaussian redundancy dictionary according to the present invention.

Detailed Description

As shown in fig. 1, the invention discloses an enhanced gaussian redundancy dictionary-based pulse signal denoising method, which comprises the following steps:

step 1, pre-generating an enhanced Gaussian redundant dictionary, wherein the mathematical model of the enhanced Gaussian redundant dictionary is as follows:

ψ＝[D₁ D₂ D₃…D_u B₁ B₂ B₃…B_q E P₅₀] (1)

Wherein u NxN-dimensional Gaussian dictionaries D_uCorresponding to a noise-free pulseSignal, q number of N × N dimensional Gaussian dictionaries B_qCorresponding to low and high frequency noise, 1 NxN dimensional identity matrix E corresponding to random noise, and 1 NxN dimensional sine matrix P with frequency of 50Hz₅₀interference corresponding to power frequency noise of 50 Hz;

further, u N × N dimensional Gaussian dictionaries D_uElement d in (1)_d(i, j) is:

Wherein i, j is belonged to [1, N ∈]Is the row and column in which the elements of the matrix are located, a_dAs a morphological parameter, corresponds to D_uA total of u;

a_dParameter generated D_usatisfy the minimum sparse vector theta_crepresents any section of noiseless pulse signal cPG;

Wherein cPPPG is clean pulse signal and theta_cIs a sparse vector obtained by carrying out sparse decomposition on the clean pulse signal, and theta_csatisfying that its 1 norm has a minimum.

Further, a morphological parameter a is determined_dThe specific method comprises the following steps: randomly selecting several segments of clean pulse signals, a_dTaking value within a certain search range to generate D_uDetermining the minimum number of a when the formula (3) is satisfied by means of grid search_dThe combination is the optimal D_uand generating parameters.

Further, q number of N × N dimensional Gaussian dictionaries B_qelement d in (1)_b(i, j) the expression is:

Wherein i, j is belonged to [1, N ∈]is the row and column in which the elements of the matrix are located, a_dAs a noise morphological parameter, corresponds to B_qQ in total;

a_db of parameter generation_qSatisfy the requirement of the sparse vector theta capable of being minimized_nTo represent any piece of low and high frequency noise nS;

Where nS is low and high frequency noise, θ_nIs a sparse vector obtained by sparse decomposition of noise, and theta_nSatisfies that its 1 norm has a minimum; b is_qNoise shape parameter a of_bdetermined by experimental methods.

Further, the 1N × N identity matrix E is a square matrix having a main diagonal element of 1 and other position elements of 0.

Further, 1N sine matrix P₅₀Element p of (1)₅₀The expression of (i, j) is:

p₅₀(i,j)＝sin(2*π*50*(i-1-j+1/fs)) (6)

Where i, j ∈ [1, N ] is the row and column in which the element in the matrix is located.

Step 2, acquiring a pulse signal containing noise, and calculating and acquiring a sparse vector of the pulse signal corresponding to the enhanced Gaussian redundant dictionary;

wherein the content of the first and second substances,A sparse vector of a gaussian dictionary is represented,Representing sparse vectors, θ, detecting low and high frequency noise_ERepresents a sparse vector of detected random noise,Representing a sparse vector for detecting power frequency noise;

further, in step 2, a Basis Pursuit (BP) algorithm, a greedy algorithm, an IRLS (Iterative Re-weighted Least Squares) algorithm or SL0 is adopted to perform sparse decomposition on the noise-containing pulse signal to obtain a sparse vector.

Step 3, constructing a noise suppression index template vector l, and obtaining the noise suppression index template vector l by using the transpose of the splicing combination of p vectors with dimension of Nx 1 and element value of 1 and k vectors with dimension of Nx 1 and element value of 0, wherein the expression is as follows:

l＝[1_N×1,1_N×1,…,1_N×1,0_N×1,0_N×1,…,0_N×1]^T (7)

Wherein, the result of multiplying 0 element by any number is 0, and the result of multiplying 1 element by any number is the multiplicand itself;

And 4, denoising the noise-containing pulse signals based on the enhanced Gaussian redundant dictionary, which comprises the following specific steps

Step 4-1, linearly representing the pulse signal x based on the enhanced Gaussian redundant dictionary psi as:

x＝ψθ (9)

wherein, theta is a sparse vector of the pulse signal x corresponding to psi;

and 4-2, multiplying the sparse vector theta by the vector point of the noise suppression index template to obtain the noise-component-suppressing pulse signal sparse vector estimation theta'.

θ′＝l·θ (10)

step 4-3, reconstructing the signal by using matrix multiplication to obtain a clean pulse signal x' as follows:

x′＝ψθ′ (11)

By adopting the technical scheme, the constructed enhanced Gaussian redundant dictionary is utilized, then the sparse vector capable of suppressing the noise is obtained through the index template vector for suppressing the noise, and finally the clean pulse signal is reconstructed.

the invention can effectively inhibit the high-frequency noise, the low-frequency noise, the baseline drift, the power frequency interference, the myoelectric interference and other noises of the pulse signal, and effectively improves the signal-to-noise ratio. The denoised signal has no redundant oscillation, thereby greatly improving the operation efficiency, saving a large amount of resources, being used for collecting the front-end pulse signal with low power consumption and providing good effect for further work of subsequent pulse wave feature extraction and classification. The invention can be used for removing noise of pulse signals and other signals such as electrocardiosignals.