CN102631198A - Dynamic spectrum data processing method based on difference value extraction - Google Patents
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Abstract
The invention discloses a dynamic spectrum data processing method based on difference value extraction and relates to the technical field of spectrum analysis. The dynamic spectrum data processing method comprises the steps of: synchronously acquiring photoelectric volume pulse waves under N wavelengths of a full-wave band of a part to be detected; setting a range of the number of interval points; extracting difference value dynamic spectrum groups corresponding to all numerical values SA within the range of the number of the interval points by adopting a difference value extraction method; and comparing the difference value dynamic spectrum groups corresponding to all numerical values SA and selecting a group with lowest dispersion degree, which serves as the final dynamic spectrum result after being subjected to superposition averaging. The dynamic spectrum data processing method can obtain a large amount of difference value dynamic spectra by virtue of difference value operation, fully utilizes experimental data, enhances computational efficiency, lowers experimental complexity, eliminates the dynamic spectrum with a gross error by virtue of the average effect of the difference value dynamic spectra during the elimination process of the gross error, greatly increases the signal to noise ratio of the dynamic spectra and improves the precision of detection on non-invasive blood constituents by the dynamic spectra.
Description
Technical Field
The invention relates to the technical field of spectral analysis, in particular to a dynamic spectral data processing method based on difference extraction, which can improve the accuracy and efficiency of dynamic spectral analysis.
Background
Among the many non-invasive optical methods for detecting blood components, transmission spectroscopy has a significant advantage over other spectroscopic methods, in which dynamic spectroscopy theoretically eliminates the interference of the optical background such as skin, fat, etc. with the spectrum of arterial blood in the pulsating part of the measurement. The basic principle of the dynamic spectrum method is that visible and near-infrared wave bands are adopted to irradiate fingers to obtain photoplethysmography containing blood component information under each wavelength, and the dynamic spectrum can be formed by extracting the peak value of the photoplethysmography after logarithm is taken under each wavelength. Because the amount of light absorbed by the pulsating arterial blood is very weak compared with the tissue background, and due to the influences of factors such as spectral overlap, abnormal waveform interference, limited sampling rate of a data acquisition system and the like, how to more fully utilize the acquired photoplethysmography pulse wave data of each wavelength is more important, and a high-quality dynamic spectrum is more effectively obtained at a high speed.
In order to more simply and effectively obtain the difference of absorbance corresponding to the same blood volume change, the peak-to-peak value of the photoplethysmographic pulse wave (the difference between the maximum value and the minimum value in a single photoplethysmographic pulse wave period) is extracted to correspond to the maximum change amount of the pulsatile arterial blood, and then a dynamic spectrum is formed. The existing dynamic spectrum extraction methods mainly comprise a frequency domain extraction method (the invention patent 'method for non-invasively measuring blood spectrum and components' publication number: CN101507607, published date: 2009, 8 and 19 days) and a time domain single-beat extraction method (the invention patent 'dynamic spectrum data processing method based on single edge extraction' publication number: CN101912256A, published date: 2010, 12 and 15 days), wherein the two methods are used for extracting the peak value of the photoplethysmogram to form the dynamic spectrum.
The following disadvantages and drawbacks are found in both of the above two methods by analysis:
1. the frequency domain extraction method is an indirect extraction method which is provided for solving the problems that the peak value of the logarithmic photoplethysmography is relatively difficult to extract in the time domain and has large error, although all data of the photoplethysmography under each wavelength are processed, only the maximum harmonic component information is utilized, operation redundancy is caused, operation efficiency is reduced, the influence of factors such as abnormal waveform and baseline drift existing in a time domain signal is difficult to suppress in the operation process, and effective real-time evaluation on data quality cannot be carried out in the operation process;
2. the time domain single-beat extraction method preliminarily solves the difficulty of dynamic spectrum time domain extraction, realizes the direct extraction of logarithmic pulse peak values, can well inhibit the influence of abnormal waveforms in photoplethysmography on dynamic spectrum precision, and improves the data processing speed to some extent.
Disclosure of Invention
In order to solve the defects that the operation efficiency is low, abnormal waveform influence cannot be effectively evaluated and overcome in operation and the like in the conventional dynamic spectrum frequency domain extraction method, and the problems that pulse wave positioning is difficult and operation is complex and the like in a time domain single-beat extraction method, the invention provides a dynamic spectrum data processing method based on difference value extraction, which comprises the following steps of:
a method of dynamic spectral data processing based on difference extraction, the method comprising the steps of:
(1) synchronously collecting the photoplethysmography of the part to be measured under the full-wave-band N wavelengths, and setting an interval point number range S;
(2) extracting a difference dynamic spectrum group corresponding to each numerical value SA in the interval point number range S by adopting a difference extraction method;
(3) and comparing the difference dynamic spectrum groups corresponding to the numerical values SA, and selecting one group with the minimum dispersion degree to perform superposition averaging to obtain a final dynamic spectrum result.
The step (2) of extracting the difference dynamic spectrum group corresponding to each numerical value in the interval point number range S by using a difference extraction method specifically comprises the following steps:
1) taking logarithm of the full-waveband photoelectric volume pulse waves to obtain full-waveband logarithmic pulse waves, selecting any numerical value SA in the interval point number range S, calculating the absolute value of the difference value of two sampling points which are separated by the numerical value SA according to time sequence to obtain a full-waveband difference value sequence, wherein the length of the difference value sequence under each wavelength is M-SA, and M is the number of the sampling points;
2) carrying out superposition averaging on the difference values at the same position in the full-waveband difference value sequence to obtain an average difference value sequence;
3) for all difference values D in the average difference value sequenceiCalculating the average difference
According to the average difference value
Setting a difference threshold range, screening the differences in the average difference sequence through the difference threshold range, and acquiring L screened differences, wherein the value of L is less than or equal to that of M-SA (1, 2, 3.), and the value of M-SA;
4) according to the positions of the L screened difference values, extracting the difference values at the same position in the full-waveband difference value sequence according to the wavelength sequence to form L initial difference value dynamic spectrums;
5) normalizing the L initial difference dynamic spectrums to obtain a normalized difference dynamic spectrum XjWherein j is 1, 2, 3, …, L;
6) dynamic spectrum X of the normalized differencejCarrying out superposition averaging to obtain a dynamic spectrum template of difference values
7) Dynamic spectrum X for describing various normalized difference values by using Euclidean distancejAnd the difference value dynamic spectrum template
The degree of similarity of (c);
8) judging the dynamic spectrum X of each normalized difference value according to the 3 sigma criterion and the similarity degreejWhether a gross error exists or not, if so, rejecting the corresponding normalized difference dynamic spectrum Xj(ii) a If the difference value does not exist, the screening is finished, and finally a difference value dynamic spectrum group corresponding to the numerical value SA is obtained;
9) and circularly executing the steps 2) -8), and sequentially extracting the difference dynamic spectrum groups corresponding to other numerical values in the interval point number range.
Normalizing the L initial difference dynamic spectrums to obtain normalized difference dynamic lightSpectrum XjThe method specifically comprises the following steps:
carrying out superposition averaging on the L initial difference dynamic spectrums to obtain an average optical path length difference dynamic spectrum;
dividing the spectrum value of each wavelength in the initial difference dynamic spectrum by the spectrum value corresponding to the average optical path length difference dynamic spectrum to obtain a group of proportionality coefficients KλWherein λ 1, 2, 3, N;
for all proportionality coefficients KλCarrying out superposition averaging to obtain an average optical path normalization coefficient
Multiplying the spectrum value of each wavelength in the initial difference dynamic spectrum by
Obtaining the normalized difference dynamic spectrum Xj。
The minimum degree of dispersion in step (3), i.e., the value of the standard deviation σ, is minimum.
The dynamic spectrum data processing method based on difference value extraction has the beneficial effects that:
compared with the existing frequency domain extraction method and time domain difference extraction method, the method provided by the invention can obtain a large amount of difference dynamic spectra through difference operation, thereby realizing more sufficient utilization of experimental data, improving the calculation efficiency and reducing the complexity of the test; in the processing process, the average effect of the difference sequence template is firstly utilized to realize effective elimination of the difference dynamic spectrum with lower signal-to-noise ratio and singularity, and then the average effect of the difference dynamic spectrum is utilized to eliminate the dynamic spectrum containing the gross error in the gross error elimination process, so that the signal-to-noise ratio of the dynamic spectrum is greatly improved, and the precision of the dynamic spectrum noninvasive blood component detection is improved.
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FIG. 1 is a flow chart of a dynamic spectrum data processing method based on difference extraction according to the present invention;
fig. 2 is a flowchart of extracting a difference dynamic spectrum group corresponding to each value in the interval point number range S according to the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, embodiments of the present invention will be described in detail with reference to the accompanying drawings.
In order to solve the problems that the operation efficiency is low in the current dynamic spectrum frequency domain extraction method, abnormal waveform influence cannot be effectively evaluated and overcome in the operation, the pulse wave positioning is difficult and the operation is complex in the time domain single-beat extraction method, and the like, the embodiment of the invention provides a dynamic spectrum data processing method based on difference value extraction, which is described in detail in the following description with reference to fig. 1 and fig. 2:
101: synchronously collecting the photoplethysmography of the part to be measured under the full-wave-band N wavelengths, and setting an interval point number range S;
the number of sampling points of each photoplethysmogram is M, and the parts to be detected can be fingers, earlobes and the like, and the embodiment of the invention is not limited in the concrete implementation;
the interval point number range S is set according to the sampling rate and the precision of the photoelectric volume pulse wave data acquisition device and in combination with the characteristics of human pulse waves, the photoelectric pulse wave data acquisition device adopts a device which is universal in the prior art and only can realize synchronous acquisition, and the embodiment of the invention is not limited in the concrete implementation;
102: extracting a difference dynamic spectrum group corresponding to each numerical value SA in the interval point number range S by adopting a difference extraction method;
wherein, the steps specifically include steps 1021-:
1021: taking logarithm of the full-waveband photoelectric volume pulse waves to obtain full-waveband logarithm pulse waves, selecting any numerical value SA in an interval point number range S, calculating absolute values of difference values of two sampling points which are separated by the SA according to time sequence to obtain a full-waveband difference value sequence;
wherein the steps are as follows: and carrying out logarithmic transformation on the collected photoplethysmography at all wavelengths according to the corrected Lambert-beer law to obtain full-waveband logarithmic sphygmia, wherein the length of the difference sequence at each wavelength is M-SA.
Wherein, the value SA can be selected from any value in the interval point number range S, such as: the interval point number range S is 1 to 5, and the value SA may be 1, 2, 3, 4, or 5, which is not limited in this embodiment of the present invention in specific implementation.
1022: the difference values of the same position in the full-waveband difference value sequence are subjected to superposition averaging to obtain an average difference value sequence;
because the photoplethysmographic pulses under each wavelength are synchronously acquired at the same position, the photoplethysmographic pulses have strict consistency in time and have similarity in graphs. The full-waveband difference sequence obtained through logarithm and difference operation also has time consistency and graph consistency, so that the difference at the same position in the difference sequence of each wavelength can be superposed and averaged to obtain an average difference sequence.
1023: for all difference values D in the average difference value sequencei(i ═ 1, 2, 3.., M-SA) average difference
According to the average difference
Setting a difference threshold range, and screening the differences in the average difference sequence through the difference threshold range to obtainTaking L screened difference values;
wherein the steps are as follows: in the difference calculation process, the difference is too small or the difference caused by abnormal waveforms is abnormal, which seriously affects the signal-to-noise ratio of the difference dynamic spectrum and needs to be eliminated. In the process, all the difference values D in the average difference value sequence are comparedi(i ═ 1, 2, 3.., M-SA) average difference
According to the average difference
And setting a difference threshold range, and screening to obtain L differences within the difference range, wherein the value of L is less than or equal to M-SA. Wherein, the range of the difference threshold value in the embodiment of the invention is selected as
In a specific implementation, other ranges may be set, and the embodiment of the present invention is not limited thereto.
1024: according to the positions of the L screened difference values, extracting the difference values at the same position in the full-waveband difference value sequence according to the wavelength sequence to form L initial difference value dynamic spectrums;
according to the dynamic spectrum theory, the difference values at the same position in the full-waveband difference value sequence can form a difference value dynamic spectrum; the average difference sequence is an ideal sequence of all wavelength difference sequences of the full-waveband, and the selection of the difference in the average difference sequence is essentially the optimization of the difference at the same position in the full-waveband difference sequence, namely the selection of the dynamic spectrum of the difference; and respectively acquiring corresponding L initial difference dynamic spectrums according to the positions of the L differences obtained by screening.
1025: normalizing the L initial difference dynamic spectrums to obtain a normalized difference dynamic spectrum Xj(j＝1，2，3，…，L)；
Since the optical path length difference exists between the initial difference dynamic spectra, the initial difference dynamic spectra need to be normalized. Because the difference dynamic spectrums at different moments have similarity but have difference in optical path length, the L initial difference dynamic spectrums are subjected to superposition averaging to obtain an average optical path length difference dynamic spectrum.
Because the average optical path length difference dynamic spectrum has a high signal-to-noise ratio, the average optical path length difference dynamic spectrum is taken as a standard to normalize each initial difference dynamic spectrum, and finally each difference dynamic spectrum and the average optical path length difference dynamic spectrum have the same optical path length.
Taking a certain difference dynamic spectrum as an example, the normalization comprises the following specific steps: dividing the spectrum value of each wavelength in the initial difference dynamic spectrum by the corresponding spectrum value of the average optical path length difference dynamic spectrum to obtain a group of proportionality coefficients Kλ(λ ═ 1, 2, 3.., N); for all proportionality coefficients KλCarrying out superposition averaging to obtain an average optical path normalization coefficient
Multiplying the spectrum value of each wavelength in the initial difference dynamic spectrum byObtaining normalized difference dynamic spectrum Xj。
1026: dynamic spectrum X of normalized differencejCarrying out superposition averaging to obtain a dynamic spectrum template of difference values
1027: dynamic spectrum X for describing various normalized difference values by using Euclidean distancejAnd difference value dynamic spectrum template
The degree of similarity of (c);
wherein the steps are as follows: according to the definition of Euclidean distance, each normalized difference value dynamic spectrum XjAnd difference value dynamic spectrum template
Is a distance of
To be provided with
To describe the similarity of the two,
the smaller the number, the higher the similarity between the two.
Wherein,
Xj，λ，
are each Xj，
A spectral value corresponding to a wavelength λ, λ 1, 2, 3.
1028: judging each normalized difference dynamic spectrum X according to the 3 sigma criterion and the similarity degreejWhether a gross error exists or not, if so, rejecting the corresponding normalized difference dynamic spectrum Xj(ii) a If the difference value does not exist, the screening is finished, and a difference value dynamic spectrum group corresponding to the numerical value SA is finally obtained;
during the measurement process, due to the existence of external noise or interference such as baseline drift, the factors can generate gross errors, thereby affecting the precision of the dynamic spectrum. Therefore, the normalized difference dynamic spectrum with gross errors needs to be removed to improve the signal-to-noise ratio of the dynamic spectrum.
Wherein, thick error is rejected the step and is specifically: calculating each normalized difference dynamic spectrum XjAnd difference dynamic lightSpectrum template
Mean euclidean distance betweenResidual vjStandard deviation σ; if the residual error of a normalized difference dynamic spectrum is greater than 3 sigma, i.e. | vjIf the absolute value is greater than 3 sigma, the normalized difference dynamic spectrum is considered to contain a gross error and is removed, otherwise, the normalized difference dynamic spectrum is retained; after finishing a round of gross errors under the dynamic spectrum template for all the normalized difference dynamic spectrums, re-executing the step 1026 to the step 1028 for the rest of the normalized difference dynamic spectrums after screening, and re-obtaining the difference dynamic spectrum template to remove the dynamic spectrum containing the gross errors; the number L of the normalized difference value spectrums after each round of screening is correspondingly reduced until all the normalized difference value dynamic spectrums containing large errors are eliminated; finally, a difference dynamic spectrum group corresponding to the numerical value SA is obtained.
1029: and (5) circularly executing steps 1022-1028, and sequentially extracting the difference dynamic spectrum groups corresponding to other numerical values in the interval point number range S.
103: and comparing the difference dynamic spectrum groups corresponding to the numerical values, and selecting one group with the minimum dispersion degree to perform superposition averaging to obtain a final dynamic spectrum result.
The minimum degree of dispersion is the minimum value of σ.
The 3 sigma decision criterion applied in the method of the embodiment of the present invention is a well-known technique in a data processing method, and is well known to the engineers skilled in the art.
In summary, the embodiments of the present invention provide a dynamic spectrum data processing method based on difference extraction, and compared with the existing frequency domain extraction method and time domain difference extraction method, the method can obtain a large number of difference dynamic spectra through difference calculation, thereby achieving more sufficient utilization of experimental data, improving calculation efficiency, and reducing complexity of tests; in the processing process, the average effect of the difference sequence template is firstly utilized to realize effective elimination of the difference dynamic spectrum with lower signal-to-noise ratio and singularity, and then the average effect of the difference dynamic spectrum is utilized to eliminate the dynamic spectrum containing the gross error in the gross error elimination process, so that the signal-to-noise ratio of the dynamic spectrum is greatly improved, and the precision of the dynamic spectrum noninvasive blood component detection is improved.
Those skilled in the art will appreciate that the drawings are only schematic illustrations of preferred embodiments, and the above-described embodiments of the present invention are merely provided for description and do not represent the merits of the embodiments.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (4)
1. A dynamic spectral data processing method based on difference extraction is characterized by comprising the following steps:
(1) synchronously collecting the photoplethysmography of the part to be measured under the full-wave-band N wavelengths, and setting an interval point number range S;
(2) extracting a difference dynamic spectrum group corresponding to each numerical value SA in the interval point number range S by adopting a difference extraction method;
(3) and comparing the difference dynamic spectrum groups corresponding to the numerical values SA, and selecting one group with the minimum dispersion degree to perform superposition averaging to obtain a final dynamic spectrum result.
2. The method as claimed in claim 1, wherein the step (2) of extracting the difference dynamic spectrum group corresponding to each value in the interval point number range S by using the difference extraction method specifically includes:
1) taking logarithm of the full-waveband photoelectric volume pulse waves to obtain full-waveband logarithmic pulse waves, selecting any numerical value SA in the interval point number range S, calculating the absolute value of the difference value of two sampling points which are separated by the numerical value SA according to time sequence to obtain a full-waveband difference value sequence, wherein the length of the difference value sequence under each wavelength is M-SA, and M is the number of the sampling points;
2) carrying out superposition averaging on the difference values at the same position in the full-waveband difference value sequence to obtain an average difference value sequence;
3) for all difference values D in the average difference value sequenceiCalculating the average difference
According to the average difference valueSetting a difference threshold range, screening the differences in the average difference sequence through the difference threshold range, and acquiring L screened differences, wherein the value of L is less than or equal to that of M-SA (1, 2, 3.), and the value of M-SA;
4) according to the positions of the L screened difference values, extracting the difference values at the same position in the full-waveband difference value sequence according to the wavelength sequence to form L initial difference value dynamic spectrums;
5) normalizing the L initial difference dynamic spectrums to obtain a normalized difference dynamic spectrum XjWherein j is 1, 2, 3, …, L;
6) dynamic spectrum X of the normalized differencejCarrying out superposition averaging to obtain a dynamic spectrum template of difference values
7) Dynamic spectrum X for describing various normalized difference values by using Euclidean distancejAnd the difference value dynamic spectrum template
The degree of similarity of (c);
8) judging the dynamic spectrum X of each normalized difference value according to the 3 sigma criterion and the similarity degreejWhether a gross error exists or not, if so, rejecting the corresponding normalized difference dynamic spectrum Xj(ii) a If the difference value does not exist, the screening is finished, and finally a difference value dynamic spectrum group corresponding to the numerical value SA is obtained;
9) and circularly executing the steps 2) -8), and sequentially extracting the difference dynamic spectrum groups corresponding to other numerical values in the interval point number range.
3. The difference extraction-based dynamic spectrum data processing method as claimed in claim 2, wherein the L initial difference dynamic spectra are normalized to obtain a normalized difference dynamic spectrum XjThe method specifically comprises the following steps:
carrying out superposition averaging on the L initial difference dynamic spectrums to obtain an average optical path length difference dynamic spectrum;
dividing the spectrum value of each wavelength in the initial difference dynamic spectrum by the spectrum value corresponding to the average optical path length difference dynamic spectrum to obtain a group of proportionality coefficients KλWherein λ 1, 2, 3, N;
for all proportionality coefficients KλCarrying out superposition averaging to obtain an average optical path normalization coefficient
Multiplying the spectrum value of each wavelength in the initial difference dynamic spectrum by
Obtaining the normalized difference dynamic spectrumXj。
4. The method according to claim 1, wherein the minimum degree of dispersion in step (3) is the minimum value of standard deviation σ.
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