CN112274159B - Compression domain electrocardiosignal quality assessment method based on improved band-pass filter - Google Patents
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Abstract
The invention discloses a compression domain electrocardiosignal quality assessment method based on an improved band-pass filter, which is used for selecting a high-quality electrocardiosignal segment after compression sampling, distinguishing an interfered inferior signal from a high-quality signal, and skipping a complex signal reconstruction process. The method comprises the steps of performing compressed sampling on an original electrocardiosignal at a sensing sampling node to obtain a compressed sampling signal, and then respectively performing filtering processing on the compressed sampling signal by using six compression domain band-pass filters to obtain equivalent forms of wavelet coefficients on six frequency bands of the original electrocardiosignal in a compression domain, and obtaining similar energy and similar wavelet entropy of each layer. And finally, the similar energy of each frequency band and similar wavelet entropy are taken as evaluation indexes together, and a support vector machine (Support Vector Machine, SVM) is used for judging whether the compressed sampling signal is acceptable or unacceptable, so that the compressed sampling signal segment with higher signal quality is judged in real time, and a judgment basis is provided for the subsequent direct processing of the compressed acquisition electrocardiographic data.
Description
Technical Field
The invention relates to a compressed electrocardiosignal quality evaluation method, in particular to a compressed domain electrocardiosignal quality evaluation method based on an improved band-pass filter.
Background
The world health organization issues report on the status of non-infectious diseases worldwide (2014) indicating that the incidence of cardiovascular disease is the leading cause of death in non-infectious diseases. In 2012 only, cardiovascular diseases cause 1750 thousands of deaths worldwide, 46% of the deaths of non-infectious diseases are shown according to statistics of cardiovascular disease centers in China, in recent years, the number of patients with cardiovascular diseases in China is increased rapidly, meanwhile, the implantation amount of cardiac pacemakers in China is continuously increased, the annual growth rate is stabilized to be more than 10%, and the cost for treating the cardiovascular diseases correspondingly presents a year-by-year rising situation. Cardiovascular diseases have become an important killer for threatening the life and health of people in China. At present, research results in medical clinics indicate that cardiovascular disease is a disease that can be controlled and prevented. Therefore, the method has very important significance and research value for preventing and diagnosing the cardiovascular diseases.
Arrhythmia is one of the most common cardiovascular diseases and will cause heart beat disorders and pump dysfunction, even sudden cardiac arrest or sudden cardiac death. And an Electrocardiographic (ECG) signal is a main basis for clinical diagnosis of arrhythmia, and is also an important physiological signal collected by a heart health monitoring system. Currently, wearable health monitoring systems (Wearable Health Monitoring System, WHMS) are widely used for ECG acquisition and processing. Due to the limited battery capacity of WHMS, communication constraints play a critical role in low power consumption sensing systems with limited resources. The compressed sensing (Compressed Sensing, CS) is adopted at the sensing sampling node, so that data compression can be completed at the same time of data sampling, thereby greatly reducing data transmission quantity and system energy consumption, effectively solving the problem, but facing the new problems that whether the compressed acquisition electrocardiosignals are interfered by noise and are not easy to identify, and the signals must be reconstructed through a complex reconstruction process before arrhythmia identification diagnosis is carried out.
Therefore, there is a need for a method that can evaluate the signal quality of compressed sensing electrocardiographic signals (compressed sensing electrocardiosignal, CSECG), evaluate the signal quality before performing arrhythmia beat identification diagnosis on the CSECG, and make a choice as to whether the signal can be used as a diagnostic basis based on the signal quality.
Disclosure of Invention
In order to solve the technical problems, the invention provides a method for evaluating the quality of electrocardiosignals in a compression domain. When the method is used for quality evaluation of the compressed electrocardiosignals, an evaluation result can be obtained rapidly, the accuracy is high, and a basis can be provided for noise removal of later signals or selection of acquired data.
The invention aims at realizing the following technical scheme:
a compressed domain electrocardiosignal quality evaluation method based on an improved band-pass filter comprises the following steps:
step 1, classifying the quality evaluation result of the electrocardiosignal into two types of acceptable and unacceptable;
step 2, according to the preset interception length, intercepting electrocardiograph data x (N) from the obtained electrocardiograph signals, and marking the signals in categories, wherein the categories are marked as acceptable and unacceptable;
step 3, performing compressed sampling on the intercepted electrocardiograph data x (N) to obtain a compressed sampling signal y (M);
step 4, constructing a correction compression domain band-pass filter based on Discrete Wavelet Transform (DWT), and carrying out frequency band decomposition on a compressed sampling signal y (M) to obtain equivalent forms of wavelet coefficients on a plurality of frequency bands of electrocardiograph data x (N) in a compression domain;
step 5, based on the step 4, similar frequency band energy and similar wavelet entropy under each sub-band of the compressed sampling signal y (M) are calculated, and are used as characteristic data of the signal, and a learning and classifying system is built based on a support vector machine (Support Vector Machine, SVM) method;
step 6, intercepting a preset length of a new electrocardiosignal signal, performing step 3 and step 4 to obtain new electrocardiosignal characteristic data, and classifying based on the trained SVM classification system in step 5;
and 7, taking the classification result of the SVM classification system as an evaluation result of the signal quality.
The invention has the following advantages:
1. the invention can directly acquire the energy information of the CSECG signal, evaluate the signal quality by taking the energy information as the compressed signal characteristic data, provide a theoretical basis for evaluating the signal quality directly in a compressed domain, and avoid complex signal reconstruction process.
2. According to the invention, the sparse binary random matrix is used as a measurement matrix to perform compressive sampling on an original signal, so that on one hand, the computational complexity can be reduced when the compressive sampling is completed, and the computational power consumption is further reduced by properly reducing the number of 1 element; on the other hand, the matrix is composed of only two elements of 0 and 1, the number of the 1 elements is very small, and 0 and 1 just respectively represent the switching states of the two circuits, so that the hardware implementation is facilitated.
3. The invention aims at a wearable health monitoring system adopting compressed sensing at a sampling node, and can realize sampling with the sampling frequency far lower than Nyquist (Nyquist), thereby greatly reducing the data acquisition amount and the data amount required to be transmitted, further reducing the energy consumption of the system, further realizing better matching between algorithm performance and hardware equipment and improving the cost performance.
4. The method provided by the invention has higher accuracy, can effectively evaluate the quality of the multi-lead CSECG signal, and has the advantages of simple method, low cost and the like.
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FIG. 1 is a flow chart of a method for compressed domain electrocardiosignal quality assessment based on an improved bandpass filter;
FIG. 2 is a schematic diagram of a compressed domain electrocardiosignal quality assessment method based on an improved bandpass filter;
fig. 3 is a diagram showing the original signal compared with the electrocardiosignal after compressed sampling.
Detailed Description
The technical scheme of the invention is further explained below with reference to the accompanying drawings. However, the present invention is not limited thereto, and all modifications and equivalents made to the technical scheme of the present invention without departing from the spirit and scope of the technical scheme of the present invention shall be covered in the protection scope of the present invention.
The invention provides a compressed domain electrocardiosignal quality evaluation method based on an improved band-pass filter, wherein a flow chart is shown in fig. 1, a scheme schematic diagram is shown in fig. 2, and the specific implementation steps are as follows:
and step 1, classifying the quality evaluation result of the electrocardiosignal into two acceptable classes and two unacceptable classes, namely, only two grades of evaluation results of the signal quality.
Step 2, according to the preset interception length, intercepting electrocardiograph data x (N) from the obtained electrocardiograph signals, and marking the signals in categories, wherein the categories are marked as acceptable and unacceptable;
specifically, in order to reduce the data calculation amount and ensure the accuracy of the evaluation result, the specific interception length is the length of the electrocardiographic data acquired when the electrocardiographic data sampling frequency is fixed and the sampling point number is 2048.
Step 3, performing 2 times of compression sampling on the intercepted electrocardiograph data x (N) to obtain a compression sampling signal y (M);
in the present embodiment, a sparse binary random matrix is used as the measurement matrix Φ for the compressed sampling of the electrocardiographic data x (N). A comparison of the original signal (using Nyquist sampling) with the compressed electrocardiographic signal (using compressed sensing sampling) is shown in fig. 3.
Step 4, under the condition of Discrete Wavelet Transform (DWT), carrying out 5-layer DWT decomposition on a compressed sampling signal y (M) by adopting db6 wavelet through a correction compression domain band-pass filter, decomposing 6 compression domain sub-bands, and obtaining the equivalent form of wavelet coefficients on six frequency bands of electrocardiograph data x (N) in the compression domain, wherein the specific construction steps of each frequency band correction compression domain band-pass filter are as follows:
referring to the DWT decomposition of ECG under the conventional domain, it is assumed that one nxn square matrix D can represent the L-layer DWT of ECG, namely:
wherein: d in the frame n (1 is not less than N is not more than L+1) represents a solving matrix corresponding to the wavelet coefficient of the nth layer, x is a vector form of intercepting electrocardio data x (N), is an N-dimensional column vector, and z represents the wavelet coefficient of the signal x after L-layer decomposition. The number of wavelet coefficients of each scale of DWT decomposition is a multiple relation of 2, the number of the wavelet coefficients is equal to the number of rows of a solving matrix, so that the number of rows of the wavelet coefficient solving matrix is different, but the number of columns is kept uniform, all the solving matrices are sequentially arranged according to rows and are equal to a matrix D, and the solving mode is as follows:
wherein C is N/2 Representing a downsampling matrix of dimension N/2 xn,and->Respectively representing the high-pass filtering and the low-pass filtering processes. Sequentially maintaining one or several continuous layers of wavelet coefficient solving matrix, and setting zero at other positions to form an N×N band-pass filter matrix B i I represents the number of band decompositions. According to noise interference and the frequency distribution condition of the ECG, the invention carries out DWT on the electrocardiosignal and divides the electrocardiosignal into 6 frequency bands, and the wavelet coefficient of each frequency band is expressed as follows by matrix multiplication:
f i ＝B i x；
wherein: f (f) i Wavelet coefficient of ith frequency band after DWT decomposition for uncompressed electrocardiograph data x (N), B i Representing an ith bandpass filter matrix;
on the basis of L+1 wavelet coefficients and band-pass filters after L-layer DWT decomposition of electrocardiograph data x (N) under the non-compressed domain, the compressed sampling signal y (M) is assumed to be subjected to compressed domain band-pass filtering, so that the band-pass filtering effect under each frequency band is equivalent to direct compressed sampling of the result of conventional band-pass filtering, and matrix and vector multiplication are applied to represent the result, and the equation is expressed as follows:
wherein phi is an MxN matrix, which is a measurement matrix of electrocardiographic data x (N) in the conventional domain, f i Wavelet coefficients of an ith frequency band after DWT decomposition for uncompressed electrocardiographic data x (N),is a matrix of MxM, representing the ith modified compressed domain bandpass filter, y is the vector form of the compressed sampled signal y (M), y ε R M×1 Is an M-dimensional column vector;
from the equation y=Φx and f i ＝B i x (x is the vector form of intercepting the electrocardiographic data x (N), is an N-dimensional column vector, B i Representing the ith band pass filter matrix) can be derived as follows;
further simplifying the correction compression-domain band-pass filter that can derive each bandThe expression of (2) is as follows:
in the method, in the process of the invention,represents the generalized inverse of Φ.
Here, on the basis of analyzing DWT decomposition subbands in the conventional domain, it is proposed to modify the conventional band-pass filter to obtain compressed domain subbands similar to the conventional subbands. Since each layer of wavelet coefficient corresponds to one frequency band, the modified compressed domain band-pass filterObtaining the equivalent form of wavelet coefficients on six frequency bands of the electrocardio data x (N) in a compression domain, namely similar wavelet coefficients +.>Representing the output of the filter after the i-th layer subspace correction, i=1, 2, …,6, with the equation +.>
Step 5, similar wavelet coefficient under the compressed domain calculated based on step 4Calculating similar frequency band energy and similar wavelet entropy under each sub-band of the compressed sampling signal y (M), taking the similar frequency band energy and similar wavelet entropy as characteristic data of the signal, and training an SVM classification system by combining the class marks of the signal in the step 2, wherein the specific steps of extracting the characteristic data are as follows:
feature 1: calculation of similar band energies for compressed domains under each sub-band
According to the Johnson-Lindenstrauss lemma, the inner product of the vector remains unchanged under random conditions, and the constrained equidistant nature of compressed sensing constrains the energy conservation nature of compressed sampling. Performing a similar band energy calculation of the compressed domain according to the following formula;
wherein E is i Similar band energies of the domain are compressed for the i-layer subspace signal,representing the output of the filter after the i-th layer subspace correction,>
feature 2: computation of similar wavelet entropy of compressed domain under each sub-band
The specific calculation process of the similar wavelet entropy of each sub-band is as follows:
first calculate the total energy E of the signal band t ；
Next, the specific gravity P of the energy of each frequency band to the total energy is obtained i ；
P i ＝E i /E t ；
Similar wavelet entropy S of compressed domain under each sub-band i Represented as;
S i ＝-P i log P i ；
where the subscript i is 1,2, …, n, n is the number of subbands.
And 6, intercepting a preset length of a new electrocardiosignal signal, performing step 3 and step 4 to obtain new electrocardiosignal characteristic data, and classifying based on the trained SVM classification system in step 5.
And 7, taking the classification result of the SVM classification system as an evaluation result of the signal quality.
The foregoing description of the preferred embodiments of the present invention has been presented only in terms of those specific and detailed descriptions, and is not, therefore, to be construed as limiting the scope of the invention. It should be noted that modifications, improvements and substitutions can be made by those skilled in the art without departing from the spirit of the invention, which are all within the scope of the invention. Accordingly, the scope of protection of the present invention is to be determined by the appended claims.
Claims (3)
1. The compressed domain electrocardiosignal quality evaluation method based on the improved band-pass filter is characterized by comprising the following steps of:
step 1, classifying the quality evaluation result of the electrocardiosignal into two types of acceptable and unacceptable;
step 2, according to the preset interception length, intercepting electrocardiograph data x (N) from the obtained electrocardiograph signals, and marking the signals in categories, wherein the categories are marked as acceptable and unacceptable;
step 3, performing compressed sampling on the intercepted electrocardiograph data x (N) to obtain a compressed sampling signal y (M);
step 4, constructing a correction compression domain band-pass filter based on Discrete Wavelet Transform (DWT), and carrying out band decomposition on a compression sampling signal y (M) to obtain equivalent forms of wavelet coefficients on a plurality of frequency bands of electrocardiograph data x (N) in a compression domain;
step 5, based on the step 4, similar frequency band energy and similar wavelet entropy under each sub-band of the compressed sampling signal y (M) are calculated, and are used as characteristic data of the signal, and a learning and classifying system is built based on an SVM method;
step 6, intercepting a preset length of a new electrocardiosignal signal, performing step 3 and step 4 to obtain new electrocardiosignal characteristic data, and classifying based on the trained SVM classification system in step 5;
step 7, taking a classification result of the SVM classification system as an evaluation result of signal quality;
the specific construction steps of the correction compression domain band-pass filter in the step 4 are as follows:
referring to the DWT decomposition of ECG under the conventional domain, it is assumed that one nxn square matrix D can represent the L-layer DWT of ECG, namely:
wherein: d in the frame n (1 is more than or equal to N is more than or equal to L+1) and represents a solving matrix corresponding to an nth layer wavelet coefficient, x is a vector form of intercepting electrocardio data x (N), is an N-dimensional column vector, and z represents the wavelet coefficient of the signal x after L layer decomposition; the number of wavelet coefficients of each scale of DWT decomposition is a multiple relation of 2, the number of the wavelet coefficients is equal to the number of rows of a solving matrix, so that the number of rows of the wavelet coefficient solving matrix is different, but the number of columns is kept uniform, all the solving matrices are sequentially arranged according to rows and are equal to a matrix D, and the solving mode is as follows:
wherein C is N/2 Representing a downsampling matrix of dimension N/2 xn,and->Respectively representing a high-pass filtering process and a low-pass filtering process; sequentially combining one layer orSuccessive layers of wavelet coefficients are solved and maintained in matrix, and zero is set at other positions to form an N x N band-pass filter matrix B i I represents the number of frequency band decomposition; according to noise interference and the frequency distribution condition of the ECG, carrying out DWT on the electrocardiosignal, dividing the electrocardiosignal into 6 frequency bands, and expressing wavelet coefficients of each frequency band as follows by matrix multiplication:
f i ＝B i x；
wherein: f (f) i Wavelet coefficient of ith frequency band after DWT decomposition for uncompressed electrocardiograph data x (N), B i Representing an ith bandpass filter matrix;
on the basis of L+1 wavelet coefficients and band-pass filters after L-layer DWT decomposition of electrocardiograph data x (N) under the non-compressed domain, the compressed sampling signal y (M) is assumed to be subjected to compressed domain band-pass filtering, so that the band-pass filtering effect under each frequency band is equivalent to direct compressed sampling of the result of conventional band-pass filtering, and matrix and vector multiplication are applied to represent the result, and the equation is expressed as follows:
where Φ is an MxN matrix, which is a conventional domain signal measurement matrix, f i For wavelet coefficients of the i-th layer subspace after DWT decomposition,is a matrix of MxM, representing the ith modified compressed domain bandpass filter, y is the vector form of the compressed sampled signal y (M), y ε R M×1 Is an M-dimensional column vector;
from the equation y=Φx and f i ＝B i x, x is the vector form of intercepting the electrocardiographic data x (N), is an N-dimensional column vector, B i Representing the ith band-pass filter matrix, the following equation can be derived;
further simplification can lead to a modified compressed domain bandpass filterIs an expression of (2);
in the method, in the process of the invention,a generalized inverse matrix representing Φ;
the specific steps of the calculation in the step 5 are as follows:
feature 1: calculation of similar band energies for compressed domains under each sub-band
According to the Johnson-Lindenstrauss theorem, the inner product of the vector is kept unchanged under random conditions, and the compressive sensing constraint equidistant property constrains the compressive sampling energy conservation property, and the similar band energy of the compressive domain is calculated according to the following formula;
wherein E is i Similar band energies of the domain are compressed for the i-layer subspace signal,representing the output of the filter after the i-th layer subspace correction,>
feature 2: computation of similar wavelet entropy of compressed domain under each sub-band
The calculation process of the similar wavelet entropy is as follows;
first calculate the total energy E of the signal band t ；
Next, the specific gravity P of the energy of each frequency band to the total energy is obtained i ；
P i ＝E i /E t ；
Similar wavelet entropy S of compressed domain under each sub-band i Represented as;
S i ＝-P i log P i ；
where the subscript i is 1,2, …, n, n is the number of subbands.
2. The improved band-pass filter-based compressed domain electrocardiosignal quality assessment method of claim 1 wherein the interception length is unified to be the length of electrocardiosignals acquired when the sampling frequency of the electrocardiosignals is fixed and the number of sampling points is 2048.
3. The improved band-pass filter-based compressed domain electrocardiograph signal quality assessment method according to claim 1, wherein in the step 3, a sparse binary random matrix is adopted as a measurement matrix to perform compressed sampling on the truncated electrocardiograph data x (N).
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