Near-infrared signal quality optimization method, system and device and storage medium

Abstract

The invention discloses a near-infrared signal quality optimization method, system and device and a storage medium, and the method comprises the steps: collecting a near-infrared light intensity signal, carrying out the filtering processing of the near-infrared light intensity signal, extracting a heart rate frequency band signal, calculating the power spectrum energy of the heart rate frequency band signal, judging whether the power spectrum energy is lower than a preset threshold value or not, and if the power spectrum energy is lower than the preset threshold value, carrying out the filtering processing of the near-infrared light intensity signal; if yes, increasing the sampling rate of the near-infrared light intensity signal, and if not, calculating a dynamic amplitude ratio and a correlation coefficient between an oxygen-containing hemoglobin signal and a deoxidized hemoglobin signal in the near-infrared light intensity signal based on a preset first algorithm; the near-infrared light intensity signal with the dynamic amplitude ratio and the correlation coefficient not meeting the preset range value is corrected to obtain a corrected signal, quality evaluation is performed on the corrected signal, and the corrected signal with the quality evaluation score exceeding the preset score value is output, so that automatic processing and quality analysis are performed on the collected signal, manual intervention is reduced, and the detection efficiency is improved. Real-time feedback is improved, and invalid experiments are avoided.

Description

[0001] Technology Neighborhood

[0002] This invention relates to the field of near-infrared imaging technology, and in particular to methods, systems, devices and storage media for optimizing near-infrared signal quality. Background Technology

[0003] Functional near-infrared spectroscopy (fNIRS) is widely used for brain function testing. Its basic principle is to use near-infrared light of different wavelengths to penetrate the scalp and skull, measuring changes in the concentrations of oxyhemoglobin (HbO) and deoxyhemoglobin (HbR) in brain tissue. Current near-infrared brain imaging systems typically use a fixed sampling rate, such as the common 10Hz or 20Hz, with some high-speed devices using 50Hz or 100Hz. Changes in hemoglobin concentration are calculated using a modified Beer-Lambert law. Common indicators for signal quality assessment include light intensity stability, coupling index (SCI), and signal-to-noise ratio (SNR). However, existing methods are mainly based on the low-frequency characteristics of light intensity signals and lack real-time optimization mechanisms for heart rate signals and hemoglobin characteristics. This leads to the following problems: a fixed sampling rate may not fully preserve high-frequency signal components such as heart rate (0.8–1.5Hz), affecting signal interpretation; signal quality optimization usually relies on manual intervention and lacks real-time automated adjustment strategies. Summary of the Invention

[0004] The purpose of this invention is to address the technical problems existing in the background art by proposing a method, system, device, and storage medium for optimizing near-infrared signal quality.

[0005] To achieve the above-mentioned technical objectives, the technical solution adopted by the present invention is as follows:

[0006] A first implementation of the first aspect of the present invention provides a near-infrared signal quality optimization method, comprising:

[0007] S101. Acquire near-infrared light intensity signals, wherein the near-infrared light intensity signals include light intensity signals of two different wavelengths;

[0008] S102. Filter the near-infrared light intensity signal to extract the heart rate frequency band signal and calculate the power spectral energy of the heart rate frequency band signal.

[0009] S103. Determine whether the power spectrum energy is lower than the preset threshold.

[0010] S104. If so, increase the sampling rate of the near-infrared light intensity signal and return to step S102.

[0011] S105. If not, then based on the preset first algorithm, calculate the dynamic amplitude ratio and correlation coefficient between the oxygenated hemoglobin signal and the deoxygenated hemoglobin signal in the near-infrared light intensity signal.

[0012] S106. Correct the near-infrared light intensity signal that does not conform to the preset range value in terms of dynamic amplitude ratio and correlation coefficient to obtain the corrected signal;

[0013] S107. Perform quality assessment on the correction signal and output the correction signal whose quality assessment score exceeds the preset score value.

[0014] Optionally, in a second implementation of the first aspect of the present invention, acquiring near-infrared light intensity signals includes: emitting two light intensity signals of different wavelengths using a preset light source module, and acquiring the two light intensity signals using a preset detector, wherein the detector gain is a preset initial value.

[0015] Optionally, in a third implementation of the first aspect of the present invention, calculating the power spectral energy of the heart rate frequency band signal includes:

[0016] The power spectral energy of the heart rate frequency band signal is calculated using the Fast Fourier Transform (FFT), where the calculation formula is: Among them, E HR denoted as power spectral energy, and f as the sampling rate.

[0017] Optionally, in a fourth implementation of the first aspect of the present invention, step S104 further includes: increasing the sampling rate and adjusting the gain of the detector to ensure the signal-to-noise ratio.

[0018] Optionally, in a fifth implementation of the first aspect of the present invention, step S105 further includes:

[0019] The concentration changes of oxyhemoglobin and deoxyhemoglobin are calculated using a modified Lambert-Beer law algorithm. The calculation formula is as follows:

[0020] Where ε is the molar extinction coefficient, L is the optical path length, and DPF is the differential path length factor.

[0021] Optionally, in the sixth implementation of the first aspect of the present invention, the formula for calculating the dynamic amplitude ratio is:

[0022]

[0023] The formula for calculating the correlation coefficient is:

[0024]

[0025] Where R is the dynamic amplitude ratio, ρ is the correlation coefficient, HbO is oxyhemoglobin, and HbR is deoxyhemoglobin.

[0026] Optionally, in the seventh implementation of the first aspect of the present invention, the calculation formula for quality assessment is:

[0027] Q=ω1*SCI+ω2*SNR+ω3*HR+ω4*R+……+ω i *ρ; where ω i , where SCI is the coupling index, SNR is the signal-to-noise ratio, HR is the heart rate energy determination, and Q is the quality assessment score.

[0028] A first implementation of the second aspect of the present invention provides a near-infrared signal quality optimization system, comprising:

[0029] The acquisition module is used to acquire near-infrared light intensity signals, which include light intensity signals of two different wavelengths.

[0030] The filtering module is used to filter the near-infrared light intensity signal to extract the heart rate frequency band signal and calculate the power spectral energy of the heart rate frequency band signal.

[0031] The judgment module is used to determine whether the power spectrum energy is lower than a preset threshold.

[0032] The sampling rate adaptive module is used to increase the sampling rate of the near-infrared light intensity signal if the condition is met, and then return the result to the filtering module.

[0033] The calculation module is used to calculate the dynamic amplitude ratio and correlation coefficient between the oxygenated hemoglobin signal and the deoxygenated hemoglobin signal in the near-infrared light intensity signal, based on the preset first algorithm, if not.

[0034] The correction module is used to correct near-infrared light intensity signals whose dynamic amplitude ratio and correlation coefficient do not conform to the preset range, and obtain the corrected signal.

[0035] The evaluation module is used to evaluate the quality of the correction signal and output the correction signal whose quality evaluation score exceeds the preset score value.

[0036] A first implementation of the third aspect of the present invention provides a near-infrared signal quality optimization device, the near-infrared signal quality optimization device comprising: a memory and at least one processor, the memory storing instructions, and the memory and the at least one processor being interconnected via a circuit;

[0037] The at least one processor invokes the instructions in the memory to cause the near-infrared signal quality optimization device to perform the near-infrared signal quality optimization method as described in any one of the first aspects of the present invention.

[0038] A first implementation of the fourth aspect of the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the near-infrared signal quality optimization method as described in any one of the first aspects of the present invention.

[0039] Compared with the prior art, the present invention has the following beneficial technical effects:

[0040] By acquiring near-infrared light intensity signals and filtering them, heart rate frequency band signals are extracted. The power spectral energy of the heart rate frequency band signals is calculated, and it is determined whether the power spectral energy is lower than a preset threshold. If it is lower than the preset threshold, the sampling rate of the near-infrared light intensity signals is increased. If it is higher than the preset threshold, the dynamic amplitude ratio and correlation coefficient between oxyhemoglobin and deoxyhemoglobin signals in the near-infrared light intensity signals are calculated based on a preset first algorithm. Near-infrared light intensity signals whose dynamic amplitude ratio and correlation coefficient do not meet the preset range are corrected to obtain a corrected signal. The quality of the corrected signal is evaluated, and the corrected signal with a quality evaluation score exceeding the preset score is output. This achieves automatic processing and quality analysis of the acquired signals, reduces manual intervention, improves real-time feedback, and avoids invalid experiments. Attached Figure Description

[0041] Figure 1 This is a schematic diagram of the first embodiment of the near-infrared signal quality optimization method in this invention;

[0042] Figure 2 This is a schematic diagram of one embodiment of the near-infrared signal quality optimization system of the present invention;

[0043] Figure 3 This is a schematic diagram of one embodiment of the near-infrared signal quality optimization device in this invention.

[0044] Figure 4 This is a schematic diagram illustrating the execution flow of the near-infrared signal quality optimization method in an embodiment of the present invention. Detailed Implementation

[0045] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other.

[0046] For ease of understanding, the specific process of the embodiments of the present invention is described below. Please refer to [link / reference]. Figure 1 as well as Figure 4 The near-infrared signal quality optimization method in this embodiment of the invention includes:

[0047] S101. Acquire near-infrared light intensity signals, wherein the near-infrared light intensity signals include light intensity signals of two different wavelengths;

[0048] It should be noted that the two light intensity signals of different wavelengths are light intensity signals of 760±10nm and 850±10nm respectively. The acquisition of near-infrared light intensity signals includes: emitting two light intensity signals of different wavelengths using a preset light source module, and acquiring the two light intensity signals using a preset detector. The detector gain is a preset initial value.

[0049] The specific implementation involves first emitting two light intensity signals of different wavelengths using a pre-set light source module. This module is a pre-configured device that emits near-infrared light of a specific wavelength, much like a flashlight emits visible light. Then, a pre-set detector collects these two light intensity signals. The detector captures the light signals and converts them into electrical signals or other forms that can be analyzed and processed later. It is specifically mentioned that the detector gain is a preset initial value. Gain, simply put, is the factor by which the signal received by the detector is amplified. The preset initial value is a fixed amplification factor set from the beginning. This ensures the consistency of signal amplification during the acquisition process, making the acquired data more accurate and comparable, and avoiding deviations in the acquired light intensity signal data due to arbitrary changes in gain, which could affect subsequent analysis.

[0050] S102. Filter the near-infrared light intensity signal to extract the heart rate frequency band signal and calculate the power spectral energy of the heart rate frequency band signal.

[0051] It's important to note that filtering the acquired near-infrared light intensity signal is a crucial step. In real-world environments, the acquired light intensity signal often contains various noise and interference components, such as interference from other light sources in the surrounding environment and electromagnetic interference from electronic devices. These additional interference signals can obscure the valuable information we want to obtain. Filtering acts like a sieve, filtering out unwanted frequency components based on specific frequency characteristics, retaining only the near-infrared light intensity signal in the heart rate-related frequency band that we are interested in. This allows subsequent analysis to focus on the valuable signals.

[0052] After filtering, the signal within the heart rate frequency band is extracted from the complex signal reflected by near-infrared light intensity changes. The power spectral density reflects the energy distribution of the signal at different frequencies. Calculating the power spectral density of the extracted heart rate frequency band signal provides a direct understanding of the signal's energy strength.

[0053] The power spectral energy of the heart rate frequency band signal is calculated as follows:

[0054] The power spectral energy of the heart rate frequency band signal is calculated using the Fast Fourier Transform (FFT), where the calculation formula is: Among them, E HR denoted as power spectral energy, and f as the sampling rate.

[0055] S103. Determine whether the power spectrum energy is lower than the preset threshold.

[0056] It should be noted that after filtering the near-infrared light intensity signal, extracting the heart rate frequency band signal, and calculating its power spectral energy, the step of determining whether the power spectral energy is lower than a preset threshold is of great significance. The preset threshold is a critical value pre-set based on a large amount of power spectral energy data under normal heart rate conditions and relevant medical standards.

[0057] If the power spectral energy is lower than the preset threshold, it likely indicates an abnormality in the heart rate signal. For example, a weakened heartbeat could lead to a decrease in the power spectral energy of the near-infrared light intensity signal that reflects this characteristic. This judgment can help detect heart rate-related abnormalities in a timely manner, providing crucial reference for further diagnosis and appropriate medical intervention, thus assisting in safeguarding human health.

[0058] S104. If so, increase the sampling rate of the near-infrared light intensity signal and return to step S102.

[0059] It should be noted that the sampling rate is increased and the detector gain is adjusted to ensure the signal-to-noise ratio.

[0060] S105. If not, then based on the preset first algorithm, calculate the dynamic amplitude ratio and correlation coefficient between the oxygenated hemoglobin signal and the deoxygenated hemoglobin signal in the near-infrared light intensity signal.

[0061] It should be noted that...

[0062] Furthermore, step S105 can also be performed as follows:

[0063] The concentration changes of oxyhemoglobin and deoxyhemoglobin are calculated using a modified Lambert-Beer law algorithm. The calculation formula is as follows:

[0064] Where ε is the molar extinction coefficient, L is the optical path length, and DPF is the differential path length factor.

[0065] It should be added that the formula for calculating the dynamic amplitude ratio is:

[0066]

[0067] The formula for calculating the correlation coefficient is:

[0068]

[0069] Where R is the dynamic amplitude ratio, ρ is the correlation coefficient, HbO is oxyhemoglobin, and HbR is deoxyhemoglobin.

[0070] S106. Correct the near-infrared light intensity signal that does not conform to the preset range value in terms of dynamic amplitude ratio and correlation coefficient to obtain the corrected signal;

[0071] It should be noted that the ratio determination is as follows: if R is not within a reasonable range, signal correction will be performed.

[0072] Correlation determination: If ρ does not meet the preset standard (such as a negative correlation trend), then it is determined that the two do not meet the negative correlation relationship, and signal correction is performed.

[0073] Signal correction strategy: Adjust the light source power and detector gain; prompt the user to check the probe position; call the short-channel regression algorithm to eliminate superficial blood flow interference.

[0074] S107. Perform quality assessment on the correction signal and output the correction signal whose quality assessment score exceeds the preset score value.

[0075] It should be noted that the formula for calculating the quality assessment is as follows:

[0076] Q=ω1*SCI+ω2*SNR+ω3*HR+ω4*R+……+ω i *ρ; where ω i , where SCI is the coupling index, SNR is the signal-to-noise ratio, HR is the heart rate energy determination, and Q is the quality assessment score.

[0077] When the score is lower than the preset threshold (e.g., 70 / 100), the user is prompted to reposition the probe or optimize the parameters, and the optimized signal is output for subsequent brain function analysis.

[0078] In this embodiment, near-infrared light intensity signals are collected and filtered to extract heart rate frequency band signals. The power spectral energy of the heart rate frequency band signals is calculated, and it is determined whether the power spectral energy is lower than a preset threshold. If it is lower than the preset threshold, the sampling rate of the near-infrared light intensity signals is increased. If it is higher than the preset threshold, the dynamic amplitude ratio and correlation coefficient between oxyhemoglobin and deoxyhemoglobin signals in the near-infrared light intensity signals are calculated based on a preset first algorithm. Near-infrared light intensity signals whose dynamic amplitude ratio and correlation coefficient do not meet the preset range are corrected to obtain corrected signals. The quality of the corrected signals is evaluated, and corrected signals with quality evaluation scores exceeding the preset score are output. This realizes automatic processing and quality analysis of the collected signals, reduces manual intervention, improves real-time feedback, and avoids invalid experiments.

[0079] The near-infrared signal quality optimization method in the embodiments of the present invention has been described above. The near-infrared signal quality optimization system in the embodiments of the present invention is described below. Please refer to [link / reference]. Figure 2 The near-infrared signal quality optimization system includes:

[0080] The acquisition module 201 is used to acquire near-infrared light intensity signals, wherein the near-infrared light intensity signals include light intensity signals of two different wavelengths;

[0081] The filtering module 202 is used to filter the near-infrared light intensity signal to extract the heart rate frequency band signal and calculate the power spectral energy of the heart rate frequency band signal.

[0082] The judgment module 203 is used to determine whether the power spectrum energy is lower than a preset threshold.

[0083] The sampling rate adaptive module 204 is used to increase the sampling rate of the near-infrared light intensity signal if the condition is met, and then return the result to the filtering module.

[0084] The calculation module 205 is used to calculate the dynamic amplitude ratio and correlation coefficient between the oxygenated hemoglobin signal and the deoxygenated hemoglobin signal in the near-infrared light intensity signal, based on the preset first algorithm if the condition is not met.

[0085] Correction module 206 is used to correct near-infrared light intensity signals whose dynamic amplitude ratio and correlation coefficient do not meet the preset range values, and obtain a corrected signal;

[0086] Evaluation module 207 is used to evaluate the quality of the correction signal and output the correction signal whose quality evaluation score exceeds a preset score value.

[0087] The "acquisition of near-infrared light intensity signals" in acquisition module 201 includes:

[0088] Two light intensity signals of different wavelengths are emitted using a preset light source module, and the two light intensity signals are collected using a preset detector. The detector gain is a preset initial value.

[0089] The "calculation of the power spectral energy of the heart rate frequency band signal" in the filtering module 202 includes:

[0090] The power spectral energy of the heart rate frequency band signal is calculated using the Fast Fourier Transform (FFT), where the calculation formula is: Among them, E HR denoted as power spectral energy, and f as the sampling rate.

[0091] The sampling rate adaptive module 204 also includes increasing the sampling rate and adjusting the detector gain to ensure the signal-to-noise ratio.

[0092] The calculation module 205 also includes:

[0093] The concentration changes of oxyhemoglobin and deoxyhemoglobin are calculated using a modified Lambert-Beer law algorithm. The calculation formula is as follows:

[0094] Where ε is the molar extinction coefficient, L is the optical path length, and DPF is the differential path length factor.

[0095] The calculation formula for "dynamic amplitude ratio" in calculation module 205 is as follows:

[0096]

[0097] The formula for calculating the "correlation coefficient" in calculation module 205 is as follows:

[0098]

[0099] Where R is the dynamic amplitude ratio, ρ is the correlation coefficient, HbO is oxyhemoglobin, and HbR is deoxyhemoglobin.

[0100] The calculation formula for "Quality Assessment" in assessment module 207 is as follows:

[0101] Q=ω1*SCI+ω2*SNR+ω3*HR+ω4*R+……+ω i *ρ; where ω i , where SCI is the coupling index, SNR is the signal-to-noise ratio, HR is the heart rate energy determination, and Q is the quality assessment score.

[0102] In this embodiment, near-infrared light intensity signals are collected and filtered to extract heart rate frequency band signals. The power spectral energy of the heart rate frequency band signals is calculated, and it is determined whether the power spectral energy is lower than a preset threshold. If it is lower than the preset threshold, the sampling rate of the near-infrared light intensity signals is increased. If it is higher than the preset threshold, the dynamic amplitude ratio and correlation coefficient between oxyhemoglobin and deoxyhemoglobin signals in the near-infrared light intensity signals are calculated based on a preset first algorithm. Near-infrared light intensity signals whose dynamic amplitude ratio and correlation coefficient do not meet the preset range are corrected to obtain corrected signals. The quality of the corrected signals is evaluated, and corrected signals with quality evaluation scores exceeding the preset score are output. This realizes automatic processing and quality analysis of the collected signals, reduces manual intervention, improves real-time feedback, and avoids invalid experiments.

[0103] The above is attached Figure 2The near-infrared signal quality optimization method in this embodiment of the invention will be described in detail from the perspective of unitized functional entities. The near-infrared signal quality optimization device in this embodiment of the invention will be described in detail from the perspective of hardware processing.

[0104] Figure 3 This is a schematic diagram of a near-infrared signal quality optimization device 300 provided in an embodiment of the present invention. The near-infrared signal quality optimization device 300 can vary significantly due to different configurations or performance. It may include one or more central processing units (CPUs) 310 (e.g., one or more processors) and a memory 320, and one or more storage media 330 (e.g., one or more mass storage devices) storing application programs 333 or data 332. The memory 320 and storage media 330 can be temporary or persistent storage. The program stored in the storage media 330 may include one or more units (not shown in the diagram), each unit may include a series of instruction operations on the near-infrared signal quality optimization device 300. Furthermore, the processor 310 may be configured to communicate with the storage media 330 and execute the series of instruction operations in the storage media 330 on the near-infrared signal quality optimization device 300.

[0105] The near-infrared signal quality optimization device 300 may also include one or more power supplies 340, one or more wired or wireless network interfaces 350, one or more input / output interfaces 360, and / or one or more operating systems 331, such as Windows Server, Mac OS X, Unix, Linux, FreeBSD, etc. Those skilled in the art will understand that... Figure 3 The near-infrared signal quality optimization device structure shown does not constitute a limitation on communication protocol devices based on local area network projection. It may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0106] The present invention also provides a computer-readable storage medium, which can be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium, wherein the computer-readable storage medium stores instructions that, when the instructions are executed on a computer, cause the computer to perform the steps of the near-infrared signal quality optimization method.

[0107] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0108] The above describes one or more near-infrared signal quality optimization methods or implementations in conjunction with specific content, and does not imply that the specific implementation of the present invention is limited to these descriptions. Any methods or structures that are similar to or identical to those of the present invention, or any technical deductions or substitutions made under the premise of the present invention, should be considered within the scope of protection of the present invention.

Claims

1. A method for optimizing near-infrared signal quality, characterized in that, include: S101. Acquire near-infrared light intensity signals, wherein the near-infrared light intensity signals include light intensity signals of two different wavelengths; S102. Filter the near-infrared light intensity signal to extract the heart rate frequency band signal and calculate the power spectral energy of the heart rate frequency band signal. S103. Determine whether the power spectrum energy is lower than a preset threshold. S104. If so, increase the sampling rate of the near-infrared light intensity signal and return to step S102. S105. If not, then based on the preset first algorithm, calculate the dynamic amplitude ratio and correlation coefficient between the oxygenated hemoglobin signal and the deoxygenated hemoglobin signal in the near-infrared light intensity signal. S106. Correct the near-infrared light intensity signal if the dynamic amplitude ratio and the correlation coefficient do not meet the preset range value to obtain a corrected signal; S107. Perform a quality assessment on the corrected signal and output the corrected signal whose quality assessment score exceeds a preset score value.

2. The near-infrared signal quality optimization method according to claim 1, characterized in that, The acquisition of near-infrared light intensity signals includes: emitting two light intensity signals of different wavelengths using a preset light source module, and acquiring the two light intensity signals using a preset detector, wherein the gain of the detector is a preset initial value.

3. The near-infrared signal quality optimization method according to claim 2, characterized in that, The calculation of the power spectral energy of the heart rate frequency band signal includes: The power spectral energy of the heart rate frequency band signal is calculated using the Fast Fourier Transform, where the calculation formula is: Wherein, E HR denoted as power spectral energy, and f as the sampling rate.

4. The near-infrared signal quality optimization method according to claim 3, characterized in that, S104 further includes: increasing the sampling rate and adjusting the gain of the detector to ensure the signal-to-noise ratio.

5. The near-infrared signal quality optimization method according to claim 4, characterized in that, The S105 further includes: The concentration changes of oxyhemoglobin and deoxyhemoglobin are calculated using a modified Lambert-Beer law algorithm. The calculation formula is as follows: Wherein, ε is the molar extinction coefficient, L is the optical path length, and DPF is the differential path length factor.

6. The near-infrared signal quality optimization method according to claim 5, characterized in that, The formula for calculating the dynamic amplitude ratio is: The formula for calculating the correlation coefficient is: Wherein, R is the dynamic amplitude ratio, ρ is the correlation coefficient, HbO is oxyhemoglobin, and HbR is deoxyhemoglobin.

7. The near-infrared signal quality optimization method according to claim 6, characterized in that, The formula for calculating the quality assessment is as follows: Q=ω1*SCI+ω2*SNR+ω3*HR+ω4*R+……+ω i *ρ; where Q is the quality assessment score, and ω i The weights are SCI (coupling index), SNR (signal-to-noise ratio), and HR (heart rate energy determination).

8. A near-infrared signal quality optimization system, characterized in that, include: The acquisition module is used to acquire near-infrared light intensity signals, wherein the near-infrared light intensity signals include light intensity signals of two different wavelengths; The filtering module is used to filter the near-infrared light intensity signal to extract the heart rate frequency band signal and calculate the power spectral energy of the heart rate frequency band signal. The judgment module is used to determine whether the power spectrum energy is lower than a preset threshold. A sampling rate adaptive module is used to increase the sampling rate of the near-infrared light intensity signal if the condition is met, and then return the result to the filtering module. The calculation module is used to calculate, if not, the dynamic amplitude ratio and correlation coefficient between the oxygenated hemoglobin signal and the deoxygenated hemoglobin signal in the near-infrared light intensity signal based on a preset first algorithm; The correction module is used to correct the near-infrared light intensity signal where the dynamic amplitude ratio and the correlation coefficient do not conform to the preset range value, and obtain the corrected signal; An evaluation module is used to evaluate the quality of the corrected signal and output the corrected signal whose quality evaluation score exceeds a preset score value.

9. A near-infrared signal quality optimization device, characterized in that, The near-infrared signal quality optimization device includes: a memory and at least one processor, wherein the memory stores instructions, and the memory and the at least one processor are interconnected via a line; The at least one processor invokes the instructions in the memory to cause the near-infrared signal quality optimization device to perform the near-infrared signal quality optimization method as described in any one of claims 1-7.

10. A computer-readable storage medium storing a computer program thereon, characterized in that, When the computer program is executed by the processor, it implements the near-infrared signal quality optimization method as described in any one of claims 1-7.

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