CN102631198A - Dynamic spectrum data processing method based on difference value extraction - Google Patents
Dynamic spectrum data processing method based on difference value extraction Download PDFInfo
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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 present invention relates to field of spectral analysis technology, particularly a kind of method for processing dynamic spectral data based on the difference extraction that can improve dynamic spectrum analysis precision and efficient.
Background technology
In numerous noinvasive blood constituent optical detecting methods; Transmission spectrum method is compared other spectral measurement methods and is had obvious superiority, and wherein the dynamic light spectrometry can be eliminated optics backgrounds such as skin, fat in theory to measuring the spectrographic interference of arterial blood of pulsation part.The ultimate principle of dynamic light spectrometry is the photoelectricity volume pulsation wave that adopts the rayed finger of visible and near infrared band and then obtain containing under each wavelength blood constituent information, can form dynamic spectrum through the peak-to-peak value that extracts the photoelectricity volume pulsation wave after taking the logarithm under each wavelength.Because pulsation arterial blood extinction amount is faint a lot of compared to background tissue; The influence of factors such as spectra overlapping, unusual waveforms disturb in addition, the data acquisition system sampling rate is limited; How more to make full use of each the wavelength light Power Capacity pulse wave data that collects, obtaining high-quality dynamic spectrum effectively just seems particularly important more at a high speed.
For the more simple and effective difference of obtaining the corresponding absorbance of same blood volume-variation; Usually adopt the peak-to-peak value that extracts the photoelectricity volume pulsation wave (difference in the single photoelectricity volume pulse wave cycle between maximum and the minima) to come corresponding pulsation arterial blood maximum variable quantity, and then the composition dynamic spectrum.Existing dynamic spectrum method for distilling mainly contains frequency domain extraction method (patent of invention " method of noninvasive measurement of blood spectra and composition " publication number: CN101507607; Open day: on August 19th, 2009) with single extraction method (patent of invention " a kind of " publication number: the CN101912256A that claps of time domain based on the method for processing dynamic spectral data of list along extraction method; Open day: on December 15th, 2010), the two all is to form dynamic spectrum through the peak-to-peak value that extracts the photoelectricity volume pulsation wave.
Through being analyzed, above-mentioned two kinds of methods find that the two all exists following deficiency and defective:
1, the frequency domain extraction method utilizes Fourier transform method that the photoelectricity volume pulsation wave after the taking the logarithm under each wavelength is carried out the conversion of time domain to frequency domain; The humorous wave amplitude that extracts amplitude maximum in the frequency domain substitutes the peak-to-peak value of logarithm photoelectricity volume pulsation wave; This method is to extract logarithm photoelectricity volume pulsation wave peak-to-peak value difficulty and the bigger problem of error and the indirect extracting mode that proposes relatively in order to solve time domain; Although the total data to photoelectricity volume pulsation wave under each wavelength is handled; But only utilized maximum harmonic component information, caused the redundancy of computing, reduced operation efficiency; And the influence of factors such as unusual waveforms that in calculating process, is difficult to suppress to exist in the time-domain signal and baseline drift can't be carried out effective real-time assessment to the data quality in calculating process;
2, the single extraction method of clapping of time domain has tentatively solved the difficulty that the dynamic spectrum time domain is extracted; Realized the direct extraction of logarithm pulse wave peak-to-peak value and can better suppress in the photoelectricity volume pulsation wave unusual waveforms the influence of dynamic spectrum precision; Data processing speed promotes to some extent, however this method fail experimental data is made full use of, on pulse wave peak value location, still exist than mistake; The lengthy and tedious complicacy of Data Processing in Experiment program, monitoring capability is relatively poor in real time.
Summary of the invention
For solve in the present dynamic spectrum frequency domain extraction method operation efficiency low with computing in can't effectively assess and overcome deficiency such as unusual waveforms influence; And problem such as pulse wave location difficulty and computing complicacy in the single bat of the time domain extraction method; The invention provides a kind of method for processing dynamic spectral data that extracts based on difference, said method comprising the steps of:
A kind of method for processing dynamic spectral data that extracts based on difference said method comprising the steps of:
(1) the photoelectricity volume pulsation wave under all band N wavelength of synchronous acquisition detected part is provided with the scope S that counts at interval;
(2) adopt the difference extraction method to extract the said interval corresponding difference dynamic spectrum group of each numerical value SA in the scope S of counting;
(3) the corresponding difference dynamic spectrum group of said each numerical value SA is compared, one group that chooses the dispersion degree minimum is carried out superposed average as final dynamic spectrum result.
Said employing difference extraction method in the step (2) is extracted the said interval difference dynamic spectrum group that each numerical value is corresponding in the scope S of counting and is specifically comprised:
1) all band photoelectricity volume pulsation wave is taken the logarithm; Obtain all band logarithm pulse wave, the selected said interval any said numerical value SA in the scope S that counts, in chronological sequence the order computation absolute value of two sampled point differences of said numerical value SA of being separated by; Obtain all band sequence of differences; Wherein, the length of sequence of differences is M-SA under each wavelength, and M is the sampled point number;
2) same position difference in the said all band sequence of differences is carried out superposed average and obtain the mean difference sequence;
3) to all difference D in the said mean difference sequence
i, ask mean difference
According to said mean difference
The difference threshold scope is set, difference in the said mean difference sequence is screened, obtain L the difference in screening back through said difference threshold scope, wherein, i=1,2,3..., M-SA, the value of L is smaller or equal to M-SA;
4), press L initial difference dynamic spectrum of difference composition that the wavelength size order extracts same position in all band sequence of differences according to the position of L the difference in said screening back;
5) said L initial difference dynamic spectrum carried out normalization, obtain normalization difference dynamic spectrum X
j, wherein, j=1,2,3 ..., L;
6) with said normalization difference dynamic spectrum X
jCarry out superposed average and obtain difference dynamic spectrum template
7) with Euclidean distance each normalization difference dynamic spectrum X is described
jWith said difference dynamic spectrum template
Similarity degree;
8), judge said each normalization difference dynamic spectrum X according to 3 σ criterions and said similarity degree
jWhether there is gross error,, rejects corresponding normalization difference dynamic spectrum X if exist
jIf do not exist, then screening finishes, and finally obtains the corresponding difference dynamic spectrum group with said numerical value SA;
9) circulation execution in step 2)～8), extract the said interval corresponding difference dynamic spectrum group of other numerical value in the scope of counting successively.
Said said L initial difference dynamic spectrum carried out normalization, obtain normalization difference dynamic spectrum X
jSpecifically comprise:
Said L initial difference dynamic spectrum carried out superposed average obtain an average optical length difference dynamic spectrum;
The spectral value of each wavelength in the initial difference dynamic spectrum divided by the corresponding spectral value of said average optical length difference dynamic spectrum, is obtained one group of proportionality coefficient K
λ, λ=1,2 wherein, 3..., N;
To all proportions COEFFICIENT K
λCarry out superposed average and obtain an average light path normalization coefficient
Spectral value with each wavelength in the said initial difference dynamic spectrum multiply by
Obtain said normalization difference dynamic spectrum X
j
The value that said dispersion degree minimum in the step (3) is a standard deviation sigma is minimum.
The beneficial effect of a kind of method for processing dynamic spectral data that extracts based on difference provided by the invention is:
Method provided by the invention is compared with time domain difference extraction method with existing frequency domain extraction method, can obtain a large amount of difference dynamic spectrums through the difference computing, has realized experimental data is utilized more fully, has improved computational efficiency, has reduced the complexity of test; In processing procedure, at first utilize the average effect of sequence of differences template to realize to effective rejecting of low and unusual difference dynamic spectrum of signal to noise ratio; Secondly in gross error rejecting process, utilize the average effect of difference dynamic spectrum that the dynamic spectrum that contains gross error is rejected; Greatly improve the signal to noise ratio of dynamic spectrum, improved the precision that dynamic spectrum noinvasive blood constituent detects.
Description of drawings
Fig. 1 is the flow chart of a kind of method for processing dynamic spectral data that extracts based on difference provided by the invention;
Fig. 2 is the flow chart that extracts the difference dynamic spectrum group that each numerical value is corresponding in the scope S that counts at interval provided by the invention.
The specific embodiment
For making the object of the invention, technical scheme and advantage clearer, will combine accompanying drawing that embodiment of the present invention is done to describe in detail further below.
For solve in the present dynamic spectrum frequency domain extraction method operation efficiency low with computing in can't effectively assess and overcome deficiency such as unusual waveforms influence; And problem such as pulse wave location difficulty and computing complicacy in the single bat of the time domain extraction method; The embodiment of the invention provides a kind of method for processing dynamic spectral data that extracts based on difference; Referring to Fig. 1 and Fig. 2, see hereinafter for details and describe:
101: the photoelectricity volume pulsation wave under all band N wavelength of synchronous acquisition detected part is provided with the scope S that counts at interval;
Wherein, the sampled point number of each photoelectricity volume pulsation wave is M, and detected part can be positions such as finger and ear-lobe, and the embodiment of the invention does not limit this when specifically realizing;
Sample rate and precision according to photoelectricity volume pulsation wave data acquisition unit; Combine the characteristic of human pulse ripple that the scope S that counts at interval is set simultaneously; As long as photoelectricity pulse wave data acquisition unit adopts the general device that can realize synchronous acquisition in the prior art, the embodiment of the invention does not limit this when specifically realizing;
102: adopt the difference extraction method to extract the corresponding difference dynamic spectrum group of each numerical value SA in the scope S that counts at interval;
Wherein, this step specifically comprises step 1021-1029, sees hereinafter for details and describes:
1021: all band photoelectricity volume pulsation wave is taken the logarithm, obtain all band logarithm pulse wave, the selected interval any number SA in the scope S that counts, in chronological sequence the order computation absolute value of two sampled point differences of SA of being separated by obtains all band sequence of differences;
Wherein, this step is specially: according to the Lambert-Beer's law of revising, the photoelectricity volume pulsation wave under all wavelengths of gathering is carried out logarithmic transformation obtain all band logarithm pulse wave, the length of sequence of differences is M-SA under each wavelength.
Wherein, numerical value SA can select the interior any number of scope S of counting at interval, and for example: the scope S that counts at interval is 1 to 5, and numerical value SA can value be 1,2,3,4 or 5, and when specifically realizing, the embodiment of the invention does not limit this.
1022: same position difference in all band sequence of differences is carried out superposed average obtain the mean difference sequence;
Wherein since the photoelectricity volume pulsation wave under each wavelength synchronous acquisition arrives at same position, thereby they have stric consistency in time, have similarity on the figure.The all band sequence of differences that obtains through logarithm and difference computing has the concordance of time and the concordance of figure equally, thereby can carry out superposed average to same position difference in the sequence of differences of each wavelength and obtain the mean difference sequence.
1023: to all difference D in the mean difference sequence
i(i=1,2,3... M-SA) asks mean difference
According to mean difference
The difference threshold scope is set, difference in the mean difference sequence is screened, obtain L the difference in screening back through the difference threshold scope;
Wherein, this step is specially: it is unusual the difference size that difference is too small or unusual waveforms causes in the difference calculating process, can to occur, and these all have a strong impact on the signal to noise ratio of difference dynamic spectrum, need reject.In processing procedure, to all difference D in the mean difference sequence
i(i=1,2,3... M-SA) asks mean difference
According to mean difference
The difference threshold scope is set, and screening obtains L difference in the difference range, and the value of L is smaller or equal to M-SA.Wherein, Difference threshold scope in the embodiment of the invention is chosen for
when specifically realizing; Can also be set to other scope, the embodiment of the invention does not limit this.
1024:, press L initial difference dynamic spectrum of difference composition that the wavelength size order extracts same position in all band sequence of differences according to the position of L the difference in screening back;
Wherein, theoretical according to dynamic spectrum, the difference of same position can be formed a difference dynamic spectrum in all band sequence of differences; Because the mean difference sequence is " ideal sequence " of each wavelength difference value sequence of all band, to the selection of difference in the mean difference sequence, its essence is preferred to same position difference in all band sequence of differences, promptly to the selection of difference dynamic spectrum; The position of L the difference that obtains according to screening obtains L corresponding initial difference dynamic spectrum respectively.
1025: L initial difference dynamic spectrum carried out normalization, obtain normalization difference dynamic spectrum X
j(j=1,2,3 ..., L);
Owing to have optical length difference between the initial difference dynamic spectrum, thereby need carry out normalization to the initial difference dynamic spectrum and handle.Because different difference dynamic spectrums constantly have similarity but optical length there are differences, and L initial difference dynamic spectrum carried out superposed average can obtain an average optical length difference dynamic spectrum.
Because average optical length difference dynamic spectrum has very high signal to noise ratio, with this as standard to each initial difference dynamic spectrum normalization, finally make each difference dynamic spectrum have identical optical length with average optical length difference dynamic spectrum.
With a certain difference dynamic spectrum is example, and its normalization concrete steps are following: 1. with the spectral value of each wavelength in the initial difference dynamic spectrum divided by the corresponding spectral value of average optical length difference dynamic spectrum, obtain one group of proportionality coefficient K
λ(λ=1,2,3..., N); 2. to all proportions COEFFICIENT K
λCarry out superposed average and obtain an average light path normalization coefficient
3. the spectral value with each wavelength in the initial difference dynamic spectrum multiply by
Obtain normalization difference dynamic spectrum X
j
1026: with normalization difference dynamic spectrum X
jCarry out superposed average and obtain difference dynamic spectrum template
1027: describe each normalization difference dynamic spectrum X with Euclidean distance
jWith difference dynamic spectrum template
Similarity degree;
Wherein, this step is specially: according to the definition of Euclidean distance, and each normalization difference dynamic spectrum X
jWith difference dynamic spectrum template
Between distance do
With
The similarity of the two is described,
More little, show that then the similarity of the two is high more.
Wherein,
X
J, λ,
Be respectively X
j,
At the corresponding spectral value of wavelength X, λ=1,2,3 ..., N.
1028:, judge each normalization difference dynamic spectrum X according to 3 σ criterion and similarity degrees
jWhether there is gross error,, rejects corresponding normalization difference dynamic spectrum X if exist
jIf do not exist, then screening finishes, and finally obtains the corresponding difference dynamic spectrum group with numerical value SA;
In measuring process owing to there are interference such as outside noise or baseline drift, thereby these factors can produce the precision that gross error influences dynamic spectrum.Thereby need reject the signal to noise ratio that improves dynamic spectrum to the normalization difference dynamic spectrum that contains gross error.
Wherein, gross error rejecting step is specially: calculate each normalization difference dynamic spectrum X
jWith difference dynamic spectrum template
Between average Euclidean distance
Residual error v
j, standard deviation sigma; If the residual error of a certain normalization difference dynamic spectrum is greater than 3 σ, promptly | v
j|＞3 σ, think that then this normalization difference dynamic spectrum contains gross error and rejects, otherwise keep; All normalization difference dynamic spectrums are accomplished one under the said dynamic spectrum template takes turns after gross error rejects; To screening residue normalization difference dynamic spectrum execution in step 1026～step 1028 again, regain difference dynamic spectrum template and come to reject containing the gross error dynamic spectrum; The every wheel through one screened the corresponding minimizing of back normalization difference spectrum quantity L meeting, and the normalization difference dynamic spectrum that contains gross error until all is disallowable; Finally obtain one group and the corresponding difference dynamic spectrum group of numerical value SA.
1029: circulation execution in step 1022～1028, extract the corresponding difference dynamic spectrum group of other numerical value in the scope S that counts at interval successively.
103: the difference dynamic spectrum group corresponding to each numerical value compares, and one group that chooses the dispersion degree minimum is carried out superposed average as final dynamic spectrum result.
Wherein, the dispersion degree minimum is the value minimum of σ.
The 3 σ decision criterias that are applied in the embodiment of the invention method are the known technology in the data processing method, and engineers and technicians are known for this area.
In sum; The embodiment of the invention provides a kind of method for processing dynamic spectral data that extracts based on difference; This method is compared with time domain difference extraction method with existing frequency domain extraction method, can obtain a large amount of difference dynamic spectrums through the difference computing, has realized experimental data is utilized more fully; Improve computational efficiency, reduced the complexity of test; In processing procedure, at first utilize the average effect of sequence of differences template to realize to effective rejecting of low and unusual difference dynamic spectrum of signal to noise ratio; Secondly in gross error rejecting process, utilize the average effect of difference dynamic spectrum that the dynamic spectrum that contains gross error is rejected; Greatly improve the signal to noise ratio of dynamic spectrum, improved the precision that dynamic spectrum noinvasive blood constituent detects.
It will be appreciated by those skilled in the art that accompanying drawing is the sketch map of a preferred embodiment, the invention described above embodiment sequence number is not represented the quality of embodiment just to description.
The above is merely preferred embodiment of the present invention, and is in order to restriction the present invention, not all within spirit of the present invention and principle, any modification of being done, is equal to replacement, improvement etc., all should be included within protection scope of the present invention.
Claims (4)
1. a method for processing dynamic spectral data that extracts based on difference is characterized in that, said method comprising the steps of:
(1) the photoelectricity volume pulsation wave under all band N wavelength of synchronous acquisition detected part is provided with the scope S that counts at interval;
(2) adopt the difference extraction method to extract the said interval corresponding difference dynamic spectrum group of each numerical value SA in the scope S of counting;
(3) the corresponding difference dynamic spectrum group of said each numerical value SA is compared, one group that chooses the dispersion degree minimum is carried out superposed average as final dynamic spectrum result.
2. a kind of method for processing dynamic spectral data that extracts based on difference according to claim 1 is characterized in that, the said employing difference extraction method in the step (2) is extracted said interval and counted that the corresponding difference dynamic spectrum group of each numerical value specifically comprises in the scope S:
1) all band photoelectricity volume pulsation wave is taken the logarithm; Obtain all band logarithm pulse wave, the selected said interval any said numerical value SA in the scope S that counts, in chronological sequence the order computation absolute value of two sampled point differences of said numerical value SA of being separated by; Obtain all band sequence of differences; Wherein, the length of sequence of differences is M-SA under each wavelength, and M is the sampled point number;
2) same position difference in the said all band sequence of differences is carried out superposed average and obtain the mean difference sequence;
3) to all difference D in the said mean difference sequence
i, ask mean difference
According to said mean difference
The difference threshold scope is set, difference in the said mean difference sequence is screened, obtain L the difference in screening back through said difference threshold scope, wherein, i=1,2,3..., M-SA, the value of L is smaller or equal to M-SA;
4), press L initial difference dynamic spectrum of difference composition that the wavelength size order extracts same position in all band sequence of differences according to the position of L the difference in said screening back;
5) said L initial difference dynamic spectrum carried out normalization, obtain normalization difference dynamic spectrum X
j, wherein, j=1,2,3 ..., L;
6) with said normalization difference dynamic spectrum X
jCarry out superposed average and obtain difference dynamic spectrum template
7) with Euclidean distance each normalization difference dynamic spectrum X is described
jWith said difference dynamic spectrum template
Similarity degree;
8), judge said each normalization difference dynamic spectrum X according to 3 σ criterions and said similarity degree
jWhether there is gross error,, rejects corresponding normalization difference dynamic spectrum X if exist
jIf do not exist, then screening finishes, and finally obtains the corresponding difference dynamic spectrum group with said numerical value SA;
9) circulation execution in step 2)～8), extract the said interval corresponding difference dynamic spectrum group of other numerical value in the scope of counting successively.
3. a kind of method for processing dynamic spectral data that extracts based on difference according to claim 2 is characterized in that, said said L initial difference dynamic spectrum is carried out normalization, obtains normalization difference dynamic spectrum X
jSpecifically comprise:
Said L initial difference dynamic spectrum carried out superposed average obtain an average optical length difference dynamic spectrum;
The spectral value of each wavelength in the initial difference dynamic spectrum divided by the corresponding spectral value of said average optical length difference dynamic spectrum, is obtained one group of proportionality coefficient K
λ, λ=1,2 wherein, 3..., N;
To all proportions COEFFICIENT K
λCarry out superposed average and obtain an average light path normalization coefficient
Spectral value with each wavelength in the said initial difference dynamic spectrum multiply by
Obtain said normalization difference dynamic spectrum X
j
4. a kind of method for processing dynamic spectral data that extracts based on difference according to claim 1 is characterized in that the value that the said dispersion degree minimum in the step (3) is a standard deviation sigma is minimum.
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Cited By (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103027692A (en) * | 2012-12-27 | 2013-04-10 | 天津大学 | Dynamic spectrum data processing method based on uncertainty |
| CN103239240A (en) * | 2013-04-23 | 2013-08-14 | 天津大学 | Oxygen saturation extraction method for eliminating optical path length differences |
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101507607A (en) * | 2009-03-27 | 2009-08-19 | 天津大学 | No-wound blood spectrum and component measurement method |
| CN101912256A (en) * | 2010-08-13 | 2010-12-15 | 天津大学 | Method for processing dynamic spectral data based on single-edge extraction |
| CN101933809A (en) * | 2010-08-31 | 2011-01-05 | 天津大学 | Multiband reflection spectrum noninvasive blood component measuring device and method |
| WO2011040599A1 (en) * | 2009-10-02 | 2011-04-07 | シャープ株式会社 | Device for monitoring blood vessel conditions and method for monitoring same |
| CN102258365A (en) * | 2011-08-17 | 2011-11-30 | 天津大学 | Sine-wave modulation photo plethysmo graphy measuring device and method |
| CN102389313A (en) * | 2011-08-17 | 2012-03-28 | 天津大学 | Device and method for measuring square wave modulated photoelectric volume pulse wave |
-
2012
- 2012-04-20 CN CN 201210118409 patent/CN102631198B/en not_active Expired - Fee Related
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101507607A (en) * | 2009-03-27 | 2009-08-19 | 天津大学 | No-wound blood spectrum and component measurement method |
| WO2011040599A1 (en) * | 2009-10-02 | 2011-04-07 | シャープ株式会社 | Device for monitoring blood vessel conditions and method for monitoring same |
| CN101912256A (en) * | 2010-08-13 | 2010-12-15 | 天津大学 | Method for processing dynamic spectral data based on single-edge extraction |
| CN101933809A (en) * | 2010-08-31 | 2011-01-05 | 天津大学 | Multiband reflection spectrum noninvasive blood component measuring device and method |
| CN102258365A (en) * | 2011-08-17 | 2011-11-30 | 天津大学 | Sine-wave modulation photo plethysmo graphy measuring device and method |
| CN102389313A (en) * | 2011-08-17 | 2012-03-28 | 天津大学 | Device and method for measuring square wave modulated photoelectric volume pulse wave |
Non-Patent Citations (1)
| Title |
|---|
| 李刚: "无创血液成分检测全波段信号信噪比均衡", 《光谱学与光谱分析》 * |
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| CN112183273A (en) * | 2020-09-18 | 2021-01-05 | 广州地理研究所 | Wheat stripe rust monitoring method based on spectral information and meteorological data |
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