A method of blood testing

Abstract

The blood detection method provided by the embodiments of the present disclosure comprises: performing multiple-label fluorescence staining on a blood sample to be detected; collecting a fluorescence signal of the multiple labels by a fluorescence microscope; processing the fluorescence signal based on a signal separation algorithm to reduce background noise; and analyzing the processed fluorescence signal to obtain a detection result. The embodiments of the present disclosure can solve the problem of background noise caused by signal cross interference in multiple-label fluorescence detection.

Description

Technical Field

[0001] The present application relates to biomedical engineering, and in particular to a blood testing method. Background Art

[0002] Blood tests analyze specific biomarkers in the blood to diagnose diseases or assess health status, typically with high sensitivity and accuracy. However, when using multiplexed fluorescence detection technology, crosstalk between different fluorescent markers often causes background noise, which not only reduces the reliability of test results but can also affect the ultimate diagnostic accuracy. Summary of the Invention

[0003] In view of this, an embodiment of the present disclosure provides a blood testing method that at least partially solves the problems existing in the prior art.

[0004] A blood testing method comprising:

[0005] Performing multiple-labeled fluorescent staining on the blood sample to be tested;

[0006] The fluorescence signals of multiple labels were collected by fluorescence microscopy;

[0007] processing the fluorescent signal based on a signal separation algorithm to reduce background noise;

[0008] Analyze the processed fluorescence signal to obtain the detection result.

[0009] According to one embodiment, processing the fluorescence signal based on a signal separation algorithm to reduce background noise includes:

[0010] De-noising the fluorescence signal based on the preprocessing step to reduce environmental interference;

[0011] The main spectrum components of each fluorescent marker were extracted using spectrum analysis method;

[0012] The principal component analysis method was applied to further separate the cross-interference signals;

[0013] The low-intensity background signal is removed by the filter, and the signal with high signal-to-noise ratio is retained.

[0014] According to one embodiment, the denoising of the fluorescence signal based on the preprocessing step includes:

[0015] Smoothing the original fluorescence signal;

[0016] Detect and remove spike noise based on sliding window method;

[0017] Use wavelet transform technology to perform multi-scale denoising on the signal;

[0018] If the noise intensity exceeds the threshold A, deep denoising is performed;

[0019] Where A represents the set noise intensity threshold, which is used to determine whether deep denoising is required.

[0020] According to one embodiment, detecting and removing spike noise based on a sliding window method includes:

[0021] Set the initial sliding window size B;

[0022] Calculate the mean C and standard deviation D of the signal within the sliding window;

[0023] Compare the current point with the average value C within the window. If the deviation exceeds K×D, it is marked as spike noise.

[0024] Move the sliding window to continue processing subsequent data;

[0025] Where B represents the size of the sliding window, C represents the average value of the signal within the sliding window, D represents the standard deviation of the signal within the sliding window, and K represents the deviation coefficient.

[0026] According to one embodiment, comparing the current point with the average value C in the window and marking it as spike noise if the deviation exceeds K×D further comprises:

[0027] If the signal strength E of the current point exceeds the window average C plus K×D or is lower than the window average C minus K×D, it is marked as spike noise;

[0028] Use interpolation to repair points marked as spike noise;

[0029] Update the mean C and standard deviation D within the sliding window;

[0030] Repeat the steps until all data points are processed;

[0031] Where E represents the signal strength at the current point, C represents the average signal value within the sliding window, D represents the standard deviation of the signal within the sliding window, and K represents the deviation coefficient.

[0032] According to one embodiment, updating the mean value C and the standard deviation D within the sliding window includes:

[0033] Whenever the sliding window moves forward, the data point at the beginning of the window is removed and a new data point F is added;

[0034] Recalculate the mean C and standard deviation D within the sliding window;

[0035] Check whether the new data point F is spike noise, if so, continue repairing;

[0036] Record the processing status of all data points within the window;

[0037] Among them, F represents the data point of the newly added sliding window, C represents the average value of the signal in the sliding window, and D represents the standard deviation of the signal in the sliding window.

[0038] According to one embodiment, extracting the main spectrum components of each fluorescent marker using a spectrum analysis method includes:

[0039] Perform Fourier transform on the fluorescence signal to obtain the frequency domain map;

[0040] According to the fluorescence frequency characteristic G of the known marker, the spectrum peak position H is located;

[0041] The height I of each peak is calculated to quantify the signal intensity of the marker;

[0042] If the peak height I exceeds the set threshold J, the spectrum component is confirmed to be the main component;

[0043] Wherein, G represents the fluorescence frequency characteristic of the known marker, H represents the position of the spectrum peak, I represents the height of the spectrum peak, and J represents the threshold of the signal intensity.

[0044] According to one embodiment, locating the spectrum peak position H based on the fluorescence frequency characteristic G of the known marker includes:

[0045] Set the search range K and search around the preset marked frequency point;

[0046] Use the peak detection algorithm to find the spectrum peak H;

[0047] Verify whether the peak position H is within the theoretical spectrum range L of the marker;

[0048] If position H is within L, it is confirmed to be the spectrum peak of the marker;

[0049] Wherein, K represents the search range, H represents the position of the spectrum peak, and L represents the theoretical spectrum range of the marker.

[0050] According to one embodiment, verifying whether the peak position H is within the theoretical spectrum range L of the marker further comprises:

[0051] Calculate the deviation N between the peak position H and the theoretical spectrum position M of the marker;

[0052] If the deviation N is less than the set threshold O, it is confirmed as a valid spectrum peak;

[0053] Record the information of spectrum peak H;

[0054] Continue searching for the next possible peak;

[0055] Wherein, H represents the position of the spectrum peak, M represents the theoretical spectrum position of the marker, N represents the deviation value, and O represents the threshold value.

[0056] According to the blood testing method of claim 8, the information based on recording the spectrum peak H further includes:

[0057] Record the intensity P of the spectrum peak H into the data table;

[0058] Check whether there are multiple spectrum peaks Q that meet the conditions;

[0059] If there are multiple spectrum peaks Q that meet the criteria, further verification is performed;

[0060] Determine the final effective spectrum peak H and analyze it;

[0061] Wherein, H represents the position of the spectrum peak, P represents the intensity of the spectrum peak, and Q represents multiple spectrum peaks that meet the standard.

[0062] The disclosed embodiments provide a blood testing method, comprising: performing multi-label fluorescent staining on a blood sample to be tested; acquiring multi-label fluorescent signals using a fluorescence microscope; processing the fluorescent signals based on a signal separation algorithm to reduce background noise; and analyzing the processed fluorescent signals to obtain a test result. The disclosed embodiments can address the background noise problem caused by signal crosstalk in multi-label fluorescent testing. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] In the accompanying drawings, unless otherwise specified, the same reference numerals throughout the multiple drawings represent the same or similar components or elements. These drawings are not necessarily drawn to scale. It should be understood that these drawings only depict some embodiments disclosed in this application and should not be construed as limiting the scope of this application.

[0064] Figure 1 This is a flow chart of a blood testing method of the present application;

[0065] Figure 2 It is a flow chart of processing fluorescence signals based on signal separation algorithm to reduce background noise;

[0066] Figure 3 It is a flow chart of denoising the fluorescence signal based on the preprocessing steps;

[0067] Figure 4 It is a flow chart for detecting and removing spike noise based on the sliding window method;

[0068] Figure 5 This is a flow chart of marking spike noise according to one embodiment of the present application;

[0069] Figure 6 It is a flowchart for updating the mean value C and standard deviation D in the sliding window;

[0070] Figure 7 This is a flow chart for extracting the main spectrum components of each fluorescent marker using spectrum analysis method;

[0071] Figure 8 It is a flow chart for locating the spectrum peak position H based on the fluorescence frequency characteristic G of a known marker;

[0072] Figure 9 This is a flow chart of searching for peak positions according to one embodiment of the present application;

[0073] Figure 10 This is a flow chart for recording information of the spectrum peak H. DETAILED DESCRIPTION

[0074] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising a..." does not exclude the presence of additional identical elements in the process, method, article, or device comprising the element.

[0075] Next, refer to Figure 1 , describing a blood testing method of the present invention.

[0076] S101: Perform multi-label fluorescent staining on the blood sample to be tested. In this step, specific fluorescent markers need to be selected. These markers can specifically bind to different target molecules. Specifically, for example, when detecting specific white blood cell subsets, antibodies such as CD3, CD4, and CD8 can be selected and labeled with fluorescent dyes of different colors, such as Alexa Fluor 488, Alexa Fluor 555, and Alexa Fluor 647. This multi-label fluorescent staining allows researchers to observe multiple different target molecules simultaneously in the same field of view, greatly improving the efficiency and accuracy of detection.

[0077] S102: Collecting multi-labeled fluorescent signals using a fluorescence microscope. In this step, a high-resolution fluorescence microscope or flow cytometer is used to capture the fluorescent signals emitted by the stained sample. These signals contain a wealth of information, such as the expression level, location, and distribution of the target molecules. For example, in one embodiment, an inverted fluorescence microscope is used to photograph the stained blood sample, generating a series of high-quality fluorescence images. After preliminary processing, these images can display the location and expression intensity of the different fluorescently labeled target molecules in the cell.

[0078] S103: Processing the fluorescent signal based on a signal separation algorithm to reduce background noise. Signal cross-interference is a common problem in multi-label fluorescence detection. When multiple fluorescent markers coexist in the same sample, the fluorescent signals of different markers may overlap with each other, resulting in an increase in background noise and affecting the final detection results. To this end, the present application uses a signal separation algorithm to process the original fluorescent signal. Specifically, the algorithm analyzes fluorescent signals of different wavelengths through a mathematical model and performs deconvolution processing on them, thereby effectively removing cross-interference and background noise. For example, using blind source separation technology (such as independent component analysis) or a multimodal fusion method based on machine learning, it is possible to effectively separate and enhance different fluorescent signals, making the signal of each fluorescent marker clearer and improving the accuracy and sensitivity of detection.

[0079] S104: Analyze the processed fluorescent signal and obtain the test results. After the above steps, the fluorescent signal has significantly reduced the background noise and improved the signal-to-noise ratio. At this point, computer software tools can be used to further analyze the image, such as quantitatively measuring the expression level of each target molecule, counting the proportion of positive cells, drawing flow cytometry graphs, etc. For example, in a specific application, professional software can be used to calculate the specific expression levels of marker molecules such as CD3, CD4 and CD8 in a certain type of cells, and compare them with the normal range values ​​to evaluate the patient's immune status or disease progression. The entire process not only improves the accuracy of the test results, but also greatly simplifies the experimental operation process, providing a strong scientific basis and technical support for clinical diagnosis.

[0080] Next, refer to Figure 2 , specifically describing the steps of processing fluorescence signals based on signal separation algorithms to reduce background noise of the present invention.

[0081] S201: Based on the preprocessing steps, the fluorescence signal is de-noised to reduce environmental interference. This step typically involves smoothing the raw fluorescence signal data to remove random perturbations that may be introduced by external light sources or sensor noise. For example, in one embodiment, median filtering or Gaussian filtering techniques can be used to reduce short-term fluctuations, thereby obtaining a more stable fluorescence signal.

[0082] S202: Use spectrum analysis method to extract the main spectral components of each fluorescent marker. Spectral analysis converts the time domain signal into a frequency domain signal through Fourier transform, thereby separating the different frequency characteristics of each fluorescent marker. The key to this step is to identify the frequency peaks unique to the fluorescent marker and exclude irrelevant frequency bands. Specifically, the fast Fourier transform (FFT) of the fluorescent signal can be calculated, and the low-amplitude spectral components can be filtered by setting a threshold. Fourier transform is a commonly used signal processing method and will not be described in detail here. In this way, the spectral components of the background noise and the target fluorescent signal can be effectively distinguished.

[0083] S203: Apply principal component analysis (PCA) to further separate cross-interference signals. PCA is a linear transformation technique used to reduce the dimensionality of high-dimensional data and extract the main components, thereby reducing the mutual interference between signals. In this process, the frequency components of the fluorescence signal are taken as input, and the principal component directions are obtained by calculating the covariance matrix and performing eigenvalue decomposition. These principal components represent the most important features of the data. For example, in a specific embodiment, C=X T The covariance matrix C is calculated from X, where X is the matrix of spectral data. Next, eigenvalue decomposition is performed on C, and the vector with the largest eigenvalue is selected as the principal component direction to reconstruct the fluorescence signal.

[0084] S204: Filter out low-intensity background signals through a filter and retain signals with a high signal-to-noise ratio. The goal of this step is to remove low-intensity signals in the frequency domain or time domain and retain only those with a higher signal-to-noise ratio. Specifically, an adaptive filter, such as a wavelet transform or a multi-bandpass filter, can be designed to dynamically adjust the filter parameters to ensure the best denoising effect. The design of the filter depends on the specific circumstances of the experimental data, but in general, it can be achieved by setting appropriate filter frequency ranges f_low and f_high. For example, when processing a certain fluorescent marker in blood, the frequency range f_low = 50 Hz and f_high = 300 Hz can be selected to retain most of the effective fluorescent signals and filter out low-frequency noise.

[0085] In summary, the present invention processes the fluorescence signal step by step through multiple steps, ultimately achieving effective suppression of background noise and improving the accuracy of the detection results.

[0086] Next, refer to Figure 3 , describing the process of denoising the fluorescence signal based on the preprocessing step of the present invention.

[0087] S301: Smoothing the raw fluorescence signal. This step uses mathematical filters to reduce high-frequency noise in the signal. Common smoothing methods include moving average and Gaussian filtering. For example, in one embodiment, a moving average filter of length N (e.g., N = 5 or 7) is used to process the raw fluorescence signal to reduce the impact of local fluctuations. Here, N represents the width of the sliding window, typically selected between 3 and 9, with an optimal value of 5, which effectively balances smoothing with the preservation of signal detail.

[0088] S302: Detect and remove spike noise based on the sliding window method. This step sets a sliding window and compares the difference between the data point in the window and its adjacent data points to identify and remove spike noise. Specifically, if the difference between a data point in the window and the K data points before and after it is greater than a preset threshold B, the point is considered to be spike noise. For example, K = 2 or 3, and the optimal value is 2, because a K that is too large may misjudge non-noise points, and a K that is too small may not effectively detect noise points. Threshold B can be adjusted according to actual conditions and is generally set to 2 times the standard deviation of the signal mean.

[0089] S303: Multi-scale denoising of the signal is performed using wavelet transform technology. This step utilizes wavelet transforms to decompose the signal at different scales, separating noise and useful signal components. Thresholding is then used to remove the noise component and reconstruct the signal. The choice of wavelet basis significantly influences the denoising effect. Commonly used wavelet bases include Db4 and Sym4. For example, the Sym4 wavelet basis is used to perform a three-level decomposition of the fluorescence signal. The decomposition threshold C for each level is the signal standard deviation multiplied by a coefficient α, with α ranging from 0.7 to 0.9, with an optimal value of 0.8. This setting ensures effective denoising while preserving the subtle features of the signal as much as possible.

[0090] S304: If the noise intensity exceeds threshold A, deep denoising is performed. This step is to re-evaluate the signal after denoising in the previous steps. If the noise intensity is still higher than threshold A, a more complex or deeper denoising algorithm is further applied. Threshold A is usually set to 1.5 to 2.0 times the standard deviation of the original signal mean, and the optimal value is 1.8. This is because a lower threshold may lead to excessive denoising, while a higher threshold may not be able to completely remove the noise. In one embodiment, if the signal after processing in the above steps still contains significant noise (for example, the peak-to-peak value exceeds threshold A), adaptive filtering or iterative denoising algorithm is further applied to ensure the accuracy and reliability of the signal.

[0091] Next, refer to Figure 4 , describing the steps of detecting and removing spike noise based on the sliding window method of the present invention.

[0092] S401: Set the initial sliding window size B. The sliding window size B determines the number of data points used as a reference when detecting noise. It typically ranges from 3 to 10, with the specific value depending on the application scenario. In one embodiment, the sliding window size B can be set to 5, which provides a good balance between computational complexity and detection accuracy.

[0093] S401: Calculate the signal's mean C and standard deviation D within each sliding window. Mean C reflects the general signal level within the current window, while standard deviation D reflects the signal's volatility. These two values ​​are calculated using the following formulas: C = (x1 + x2 + ... + xB) / B and D = sqrt(Σ(xC)^2 / B), where x1, x2, ..., xB represent the data points within the sliding window. These calculation steps ensure a comprehensive understanding of the signal characteristics within the current window.

[0094] S403: Compare the current point with the average value C in the window. If the deviation exceeds K×D, it is marked as spike noise. K is a pre-set deviation coefficient, usually between 2 and 3, and the specific value can be optimized based on experimental results. The meaning of this formula is that when judging whether a data point is spike noise, not only the difference between the point and the average value in the window is considered, but also the standard deviation of the signal is combined to more accurately identify abnormal points. For example, when K is set to 2, it means that if the deviation between the current data point and the average value in its window exceeds 2 times the standard deviation, the point is considered to be spike noise.

[0095] S404: The sliding window is moved to continue processing subsequent data. This is a cyclic step in the method. The sliding step size of each window is typically 1, ensuring that all data points are evaluated. In a specific blood testing application, suppose that some sudden changes in values ​​occur during the test. These changes may be caused by instrument error or other external factors. Through the above steps, the system can effectively identify these sudden changes and mark them as noise.

[0096] Through the above steps, the present invention provides an effective means of detecting and removing spike noise based on a sliding window method, which is particularly suitable for scenarios requiring high-precision data analysis, such as blood testing.

[0097] Next, refer to Figure 5 , describing the steps of comparing the current point with the average value C in the window and marking it as spike noise if the deviation exceeds K×D:

[0098] First, obtain the signal data within the sliding window. Specifically, the sliding window contains a certain number of signal strength values. Assuming the window size is N, C represents the signal average value within the sliding window, which is calculated by dividing the sum of all signal strength values ​​within the window by N; D represents the signal standard deviation within the sliding window, which is used to quantify the degree of signal fluctuation, and is calculated by taking the square root of the average of the squares of the deviations between each signal strength value within the window and C. K is a predetermined constant, usually between 1 and 3, and the optimal value depends on the specific application. On this basis, the method of the present application also includes:

[0099] S501: If the signal strength E of the current point exceeds the window average value C plus K×D or is lower than the window average value C minus K×D, it is marked as spike noise. Specifically, the signal strength E of the current point is compared with the average value C in the window. If E exceeds C plus K×D or is lower than C minus K×D, it is considered that there is spike noise at this point and it is marked. The core of this step is to identify outliers through statistical methods. K×D sets a reasonable threshold to determine whether E exceeds the normal range. Specifically, K×D represents K times the standard deviation, which indicates the maximum allowable deviation. The value between 1 and 3 is selected to avoid misjudging normal data while identifying noise.

[0100] S502: Use interpolation to repair points marked as spike noise. Interpolation methods typically select nearby normal values ​​and perform linear or polynomial fitting to fill or correct spike noise points. For example, in blood test data, if a point has an abnormally high or low value due to external interference, interpolation can restore the point to a reasonable value, thereby maintaining data continuity and accuracy.

[0101] S503: Update the mean C and standard deviation D within the sliding window. After processing each point, C and D within the window need to be recalculated to reflect the latest data status. Specifically, when a new data point is added to the window and the oldest data point is removed from the window, C and D should be updated immediately to ensure that subsequent comparisons are accurate and effective.

[0102] S504: Repeat the above steps until all data points have been processed. For example, during a complete blood test, from the beginning to the end of the data sequence, each data point is checked for spike noise, and the corresponding processing and updates are performed until all data points are analyzed. This ensures the consistency and reliability of the entire data set.

[0103] Next, refer to Figure 6 , describing the steps of updating the mean value C and standard deviation D within the sliding window of the present invention.

[0104] S601: Each time the sliding window moves forward, it removes the leading data point and adds a new data point F. This operation ensures that the data in the sliding window is always the latest continuous data segment, so that changes in the test signal are reflected promptly. For example, during a blood test, each time a new blood data point F is collected, the older data point is removed from the window.

[0105] S602: The average value C and standard deviation D in the sliding window need to be recalculated. The average value C refers to the arithmetic mean of all data points in the sliding window, while the standard deviation D describes the degree of dispersion of the data points. The purpose of recalculating these two values ​​is to accurately reflect the state of the data in the current window. For example, in one embodiment, if the first N-1 data points in the sliding window are X1, X2, ..., X(N-1), after adding the new data point F, the data points of the new window are X2, X3, ..., XN = F. At this time, the average value C = (X2+X3+...+XN) / N, and the standard deviation D = sqrt{[Σ(Xi-C) 2 ] / N}, where Xi represents the i-th data point in the window, and i ranges from 2 to N. This formula is chosen because it can effectively reflect the overall level of the data and its fluctuations.

[0106] S603: Check whether the newly added data point F is spike noise. Spike noise generally refers to abnormal data points that suddenly increase or decrease in a short period of time, which may affect the accuracy of the mean value C and standard deviation D. If F is identified as spike noise, it is necessary to continue to repair it, that is, to eliminate the outlier to avoid interference with subsequent analysis. For example, in one embodiment, a threshold value, such as 3 standard deviations, can be set. That is, when the value of F exceeds the mean value C plus or minus 3 times the standard deviation D, F is determined to be spike noise and corrected or eliminated.

[0107] S604: Record the processing status of all data points within the window. This includes marking which data points are normal, which are identified as spike noise, and how these data points were processed. This record is very useful for subsequent data analysis and review of abnormalities. For example, in practical applications, the status of each data point can be saved in a log file for subsequent review and reference.

[0108] Through the above steps, blood test data can be effectively monitored and analyzed in real time, and the reliability and accuracy of the test results can be improved.

[0109] Next, refer to Figure 7 , describing the steps of extracting the main spectrum components of each fluorescent marker using the spectrum analysis method of the present invention:

[0110] S701: Perform Fourier transform on the fluorescence signal to obtain a frequency domain image;

[0111] S702: Locate the spectrum peak position H based on the fluorescence frequency characteristic G of the known marker;

[0112] S703: Calculate the height I of each peak to quantify the signal intensity of the marker;

[0113] S704: If the peak height I exceeds the set threshold J, the spectrum component is confirmed to be the main component.

[0114] In practice, the fluorescence signal must first be Fourier transformed to convert the time-domain signal into a frequency-domain signal. The frequency-domain plot reveals the frequency composition of the signal, facilitating subsequent processing. This step filters out background noise and nonspecific signals, improving the signal-to-noise ratio. For example, in a blood test, different types of fluorescent markers may appear as a complex mixed signal in the time-domain plot. The Fourier transform can decompose this mixed signal into specific frequency-domain components, more clearly displaying the information of each marker.

[0115] Next, the location of the spectrum peak is determined based on the known fluorescence frequency characteristics of the marker. The fluorescence frequency characteristics of the marker are typically determined through laboratory testing and recorded as a set of standard frequency values. By comparing the peak positions in the frequency domain plot with the known frequency characteristics, the characteristic frequencies of different markers can be accurately identified. For example, in a blood sample, fluorescent markers associated with specific diseases may have specific frequency characteristics. By analyzing the spectrum plot, these frequency characteristics can be precisely located, thereby preliminarily identifying potential markers.

[0116] Next, the height of each peak is calculated to quantify the signal strength of the marker. The peak height reflects the strength of the signal at that frequency and can therefore serve as an important basis for determining the presence and concentration of the marker. A larger peak height indicates a higher concentration of the marker. Specifically, the peak height can be determined by taking the maximum value of the peaks in the spectrum. For example, if one peak in the frequency domain graph has a height of 200 and another has a height of 300, by comparing these two values, one can preliminarily determine that the concentration of marker B is higher than that of marker A.

[0117] Finally, determine whether it exceeds the set threshold. If the peak height exceeds the preset threshold, the spectral component is considered to be the main component, otherwise it is not. The setting of the threshold is usually based on a large amount of experimental data and statistical analysis to ensure high accuracy. For example, the threshold can be set to 150. If the height of a peak in the frequency domain graph is 200, which exceeds 150, the marker is confirmed to be the main component; if the height of another peak is 100, it is not considered to be the main component. In this way, noise and weak signals can be effectively filtered out, improving the reliability and accuracy of the detection results.

[0118] Through spectral analysis and signal processing, the above steps can effectively identify and quantify different types of fluorescent markers in complex blood samples, providing important reference for disease diagnosis and research. In this process, G represents the fluorescence frequency characteristic of a known marker, typically measured using a standard solution; H represents the location of the spectral peak, that is, the coordinates of a specific frequency point on the frequency domain graph; I represents the height of the spectral peak, reflecting the strength of the signal; and J represents the signal intensity threshold, an optimal value determined based on extensive experimental data. The effective utilization of these parameters makes spectral analysis methods play a vital role in blood testing.

[0119] Next, refer to Figure 8 , describes the present invention's method of locating the spectrum peak position H based on the fluorescence frequency characteristic G of a known marker. This process includes four main steps.

[0120] S801: Set the search range K to search around the preset marker frequency. This means selecting a specific frequency range K on the fluorescence spectrum that encompasses the expected frequency of the known marker. For example, if the expected frequency of the known marker is around 650 nanometers, the search range K could be set to 640 to 660 nanometers. This way, the algorithm will search for spectral peaks within this range, improving detection accuracy and efficiency.

[0121] S802: Use a peak detection algorithm to find the spectrum peak H. This step uses mathematical methods or signal processing techniques to find the point with the highest signal strength within the set search range K, thereby determining the location of the spectrum peak H. Specifically, methods such as Gaussian fitting or Fourier transform can be used to identify and accurately locate the spectrum peak. These algorithms can effectively eliminate interference from background noise and improve the accuracy of spectrum peak identification. For example, a Fourier transform can be used to convert the time domain signal into a frequency domain signal, and then the peak is searched within a specified frequency range.

[0122] S803: Verify that the peak position H is within the theoretical spectral range L of the marker. This is a critical step to ensure that the found spectral peak truly belongs to the target marker. Known markers typically have a theoretical spectral range L, for example, 645 to 655 nanometers. During the verification process, the peak position H is compared with this theoretical range L. If H falls within L, the found peak is considered to be the true spectral peak of the marker. Otherwise, the data needs to be rechecked or the search range needs to be adjusted.

[0123] S804: If position H is within L, it is confirmed as the spectral peak of the marker. Once the spectral peak position is confirmed, it can be used for subsequent analysis and calculations, such as concentration determination or disease diagnosis. For example, in one embodiment, a known marker is added to a blood sample, and the theoretical spectral range L of the marker is 645 to 655 nanometers. Through the above steps, the spectral peak position H is determined to be 650 nanometers, and H is verified to be within the range L, ultimately confirming 650 nanometers as the spectral peak position of the marker.

[0124] In this process, the purpose of setting the search range K is to reduce the amount of calculation and improve detection accuracy. Generally speaking, the search range K should be as narrow as possible, but sufficient to cover all possible peak positions of the marker. In theory, the narrower K, the faster the calculation, but too narrow a range may result in missing true peaks. The selection of the optimal value needs to be combined with the actual application scenario. For example, in an environment with high background noise, the range of K can be appropriately relaxed to increase the robustness of the detection.

[0125] Overall, these steps ensure the accurate localization of the marker's fluorescence frequency through rigorous search, verification, and confirmation, thereby improving the reliability and accuracy of blood testing.

[0126] Next, refer to Figure 9 , describing the steps of the present invention for verifying whether the peak position H is within the theoretical spectrum range L of the marker:

[0127] S901: Calculate the deviation N between the peak position H and the theoretical spectrum position M of the marker;

[0128] S902: If the deviation N is less than the set threshold O, it is confirmed as a valid spectrum peak;

[0129] S903: Record information of spectrum peak H;

[0130] S904: Continue searching for the next possible peak.

[0131] First, the deviation N between the peak position H and the theoretical spectral position M of the marker is calculated. Specifically, the deviation N is calculated using the formula N = |HM|, where H represents the actual detected spectral peak position and M represents the theoretical spectral position of the marker. The unit of deviation N is usually wavelength or frequency unit, such as nanometers or hertz. The purpose of this formula is to quantify the difference between the actual detected spectral peak and the theoretical expectation. Usually, the theoretical spectral position M of the marker is determined by literature data or known chemical properties, while the actual measured spectral peak position H is obtained by instrument measurement. To ensure measurement accuracy, M can range from a few nanometers to hundreds of nanometers, and the maximum acceptable value of deviation N is generally within a few wavelength units.

[0132] For example, in one embodiment, assuming the theoretical spectral position M of the marker is 530 nm, and the actual detected spectral peak position H is 529 nm, the deviation N = |529 nm - 530 nm| = 1 nm. Based on laboratory experience, the threshold O for the deviation N is set at 2 nm, so the deviation N is less than the threshold O.

[0133] Next, determine whether the deviation N is less than the set threshold O. If the deviation N is less than the threshold O, the spectrum peak is confirmed to be a valid spectrum peak. In this step, the threshold O is a pre-set value used to distinguish valid peaks from noise. The choice of threshold O is based on experimental experience and the accuracy of the equipment. For blood testing applications, the common threshold range is 1nm to 5nm, with a preferred value of 2nm. This is because a threshold that is too small may miss the detection of real peaks, while a threshold that is too large may misjudge background noise.

[0134] Next, record the information about the spectrum peak H. This information includes, but is not limited to, the location, intensity, and width of the spectrum peak. Specifically, during the recording process, the value of H, peak intensity, and other characteristic parameters are saved in a database or result report for subsequent analysis and verification. For example, the location of the spectrum peak H is 529 nm, the peak intensity is 250 arbitrary units, and the peak width is 15 nm.

[0135] Finally, the search continues for the next possible peak. After confirming a spectrum peak as valid, the system rescans the spectrum to find the next possible peak and repeats the above verification steps. This process continues until all peaks in the spectrum have been verified.

[0136] Throughout the validation process, each step is designed to ensure the accuracy and reliability of the test results, thereby providing credible data support in blood testing methods.

[0137] Next, refer to Figure 10 , describing the information steps of the present invention based on recording the spectrum peak H.

[0138] S1001: Record the intensity P of the spectrum peak H in the data table. This means that during the spectral analysis process, the detected spectrum peaks and their corresponding intensities are accurately recorded. This ensures that all possible signals are recorded in detail for subsequent analysis. For example, in one embodiment, if the spectrum analyzer detects an absorbance value of 1.2 at a certain wavelength, this value is recorded as the intensity P of the spectrum peak H.

[0139] S1002: Check whether there are multiple spectral peaks Q that meet the criteria. This means that after recording all spectral peaks H, these peaks are further screened to determine whether multiple peaks meet the pre-set criteria. For example, a threshold of 0.8 or above is set to define peaks that meet the criteria. Specifically, during the analysis of a blood sample, absorbance at two or more wavelengths may exceed 0.8. In this case, these peaks require further processing.

[0140] S1003: If there are multiple spectral peaks Q that meet the criteria, further verification is performed. When multiple spectral peaks that meet the criteria are found, a more rigorous verification method is required to distinguish the validity of these peaks. For example, different solvents can be added to the same wavelength range, and the test can be repeated to observe the changes in the spectrum. Specifically, if the presence of a certain solvent causes a spectral peak to disappear or weaken, while other peaks do not change significantly, then this step can eliminate interfering peaks and determine the valid spectral peaks.

[0141] S1004: Determine the final valid spectral peak H and perform analysis. After the above verification, the peaks are finally determined to be truly meaningful, and then these peaks are analyzed in detail. For example, a spectral peak can be determined to be caused by the presence of a specific protein, thereby inferring the protein content or other important information in the blood sample.

[0142] In this process, the parameter H represents the position of the spectral peak, P represents the intensity of the spectral peak, and Q represents the number of spectral peaks that meet the standard. The spectral peak position H is typically measured at a specific wavelength, and its range depends on the measurement range of the spectral analyzer. The spectral peak intensity P is typically the absorbance or fluorescence intensity value detected at that wavelength, and its range is also determined by the sensitivity of the instrument. The optimal spectral peak H and intensity P need to be determined based on the specific application scenario and background, but generally speaking, high accuracy and high sensitivity are the primary optimization goals.

[0143] For example, in a blood test method used to detect a specific protein, if the standard threshold is set at 0.8, the instrument detects an absorbance value of 1.2 at a wavelength of 650nm. After recording this peak and its intensity, the instrument then detects absorbance values ​​of 1.0 and 0.9 at wavelengths of 700nm and 750nm, respectively. Because all three values ​​exceed 0.8, they are preliminarily identified as spectral peak Q. Further verification, such as adding a specific solvent and retesting, reveals that only the peaks at 650nm and 700nm remain unchanged, while the peak at 750nm is significantly weakened. Ultimately, the effective spectral peaks H are determined to be 650nm and 700nm. Further analysis can provide information about the specific protein in the blood sample.

[0144] Through the above steps, the present invention not only realizes an efficient multi-target blood detection method, but also effectively solves the background noise problem commonly found in multi-labeled fluorescence detection, thereby greatly improving the reliability and accuracy of the detection results.

[0145] The specific implementation methods described above further illustrate the purpose, technical solutions and beneficial effects of the embodiments of the present disclosure. It should be understood that the above description is only a specific implementation method of the embodiments of the present disclosure and is not intended to limit the scope of protection of the embodiments of the present disclosure. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the embodiments of the present disclosure should be included in the scope of protection of the embodiments of the present disclosure.

Claims

1. A blood testing method, characterized in that: include: Performing multiple-labeled fluorescent staining on the blood sample to be tested; The fluorescence signals of multiple labels were collected by fluorescence microscopy; processing the fluorescent signal based on a signal separation algorithm to reduce background noise; Analyze the processed fluorescence signal to obtain the detection result; in Processing the fluorescence signal based on a signal separation algorithm to reduce background noise includes: De-noising the fluorescence signal based on the preprocessing step to reduce environmental interference; The main spectrum components of each fluorescent marker were extracted using spectrum analysis method; The principal component analysis method is applied to further separate the cross-interference signals, including taking the various frequency components of the fluorescence signal as input, calculating the covariance matrix and performing eigenvalue decomposition to obtain the principal component direction; The low-intensity background signal is removed by the filter, and the signal with high signal-to-noise ratio is retained.

2. A blood testing method according to claim 1, characterized in that: The denoising process of the fluorescence signal based on the preprocessing step includes: Smoothing the original fluorescence signal; Detect and remove spike noise based on sliding window method; Use wavelet transform technology to perform multi-scale denoising on the signal; If the noise intensity exceeds the threshold A, deep denoising is performed; Where A represents the set noise intensity threshold, which is used to determine whether deep denoising is required.

3. A blood testing method according to claim 2, characterized in that: The method of detecting and removing spike noise based on a sliding window method includes: Set the initial sliding window size B; Calculate the mean C and standard deviation D of the signal within the sliding window; Compare the current point with the average value C within the window. If the deviation exceeds K×D, it is marked as spike noise. Move the sliding window to continue processing subsequent data; Where B represents the size of the sliding window, C represents the average value of the signal within the sliding window, D represents the standard deviation of the signal within the sliding window, and K represents the deviation coefficient.

4. A blood testing method according to claim 3, characterized in that: The process of comparing the current point with the average value C within the window and marking it as spike noise if the deviation exceeds K×D also includes: If the signal strength E of the current point exceeds the window average C plus K×D or is lower than the window average C minus K×D, it is marked as spike noise; Use interpolation to repair points marked as spike noise; Update the mean C and standard deviation D within the sliding window; Repeat the steps until all data points are processed; Where E represents the signal strength at the current point, C represents the average signal value within the sliding window, D represents the standard deviation of the signal within the sliding window, and K represents the deviation coefficient.

5. A blood testing method according to claim 4, characterized in that: The updating of the mean value C and standard deviation D within the sliding window includes: Whenever the sliding window moves forward, the data point at the beginning of the window is removed and a new data point F is added; Recalculate the mean C and standard deviation D within the sliding window; Check whether the new data point F is spike noise, if so, continue repairing; Record the processing status of all data points within the window; Among them, F represents the data point of the newly added sliding window, C represents the average value of the signal in the sliding window, and D represents the standard deviation of the signal in the sliding window.

6. A blood testing method according to claim 1, characterized in that: The method of extracting the main spectrum components of each fluorescent marker by using a spectrum analysis method comprises: Perform Fourier transform on the fluorescence signal to obtain the frequency domain map; According to the fluorescence frequency characteristic G of the known marker, the spectrum peak position H is located; The height I of each peak is calculated to quantify the signal intensity of the marker; If the peak height I exceeds the set threshold J, the spectrum component is confirmed to be the main component; Wherein, G represents the fluorescence frequency characteristic of the known marker, H represents the position of the spectrum peak, I represents the height of the spectrum peak, and J represents the threshold of the signal intensity.

7. A blood testing method according to claim 6, characterized in that: The locating of the spectrum peak position H according to the fluorescence frequency characteristic G of the known marker comprises: Set the search range K and search around the preset marked frequency point; Use the peak detection algorithm to find the spectrum peak H; Verify whether the peak position H is within the theoretical spectrum range L of the marker; If position H is within L, it is confirmed to be the spectrum peak of the marker; Wherein, K represents the search range, H represents the position of the spectrum peak, and L represents the theoretical spectrum range of the marker.

8. A blood testing method according to claim 7, characterized in that: The verification of whether the peak position H is within the theoretical spectrum range L of the marker further includes: Calculate the deviation N between the peak position H and the theoretical spectrum position M of the marker; If the deviation N is less than the set threshold O, it is confirmed as a valid spectrum peak; Record the information of spectrum peak H; Continue searching for the next possible peak; Wherein, H represents the position of the spectrum peak, M represents the theoretical spectrum position of the marker, N represents the deviation value, and O represents the threshold value.

9. A blood testing method according to claim 8, characterized in that: The information of recording spectrum peak H also includes: Record the intensity P of the spectrum peak H into the data table; Check whether there are multiple spectrum peaks Q that meet the conditions; If there are multiple spectrum peaks Q that meet the criteria, further verification is performed; Determine the final effective spectrum peak H and analyze it; Wherein, H represents the position of the spectrum peak, P represents the intensity of the spectrum peak, and Q represents multiple spectrum peaks that meet the standard.

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