A biosensor-based device and method for monitoring the healing of a burn wound

Abstract

The application provides a kind of based on biological sensor's burn wound recovery monitoring device and method, it is related to biological sensor technical field, the diffuse reflection spectrum signal of burn wound base is collected by biological sensor;According to the characteristic absorption wavelength of hemoglobin in deep tissue of burn wound, the blood oxygen saturation and total hemoglobin concentration index of deep tissue of burn wound are separated from the diffuse reflection spectrum signal;Synchronously through biological sensor, the electrical activity signal of shallow muscle tissue under resting state is collected, the electrical activity signal is analyzed by high frequency band power spectral density, and the electromyographic activity index reflecting the degree of painful muscle spasm of burn wound is obtained;When electromyographic activity index is lower than preset stimulation threshold, the microenvironment of deep tissue of burn wound is judged to enter steady healing period according to blood oxygen saturation and total hemoglobin concentration index.The application can realize the non-invasive detection of the state of deep tissue of burn wound, so as to give consideration to monitoring safety and information integrity.

Description

Technical Field

[0001] This application relates to the field of biosensor technology, and more specifically, to a biosensor-based burn wound recovery monitoring device and method. Background Technology

[0002] With the continuous advancement of clinical medicine, biosensors, as a core technology for accurately capturing human physiological signals, are increasingly demonstrating their value in the field of burn wound monitoring. They possess core advantages such as non-invasive detection, high sensitivity, and real-time response. They can acquire relevant physiological information without invasively accessing the wound, effectively avoiding the damage to the wound caused by traditional monitoring methods. This provides reliable technical support for burn wound recovery assessment, promotes the development of burn care towards precision and non-invasiveness, and is of great significance for improving treatment outcomes and ensuring the safety of wound healing.

[0003] Current burn wound recovery monitoring technologies face numerous challenges. Traditional invasive monitoring methods directly damage wound tissue, increasing the risk of infection and severely impacting the normal healing process. Conventional non-invasive monitoring methods are relatively limited, only able to observe and assess the surface state of the wound, failing to capture core physiological indicators of deep tissues and thus failing to accurately reflect the recovery status of deep tissues. Furthermore, existing monitoring protocols cannot simultaneously consider various physiological signals related to wound recovery, resulting in incomplete monitoring information that cannot provide a comprehensive and accurate basis for adjusting clinical treatment plans, thus hindering the precision of burn diagnosis and treatment. Therefore, how to achieve non-invasive detection of the deep tissue state of burn wounds while ensuring both monitoring safety and information completeness has become a challenge for the industry. Summary of the Invention

[0004] This application provides a burn wound recovery monitoring device and method based on biosensors, which can realize non-invasive detection of the deep tissue state of burn wounds, thereby taking into account both monitoring safety and information integrity.

[0005] In a first aspect, this application provides a method for monitoring burn wound recovery based on biosensors, the method comprising the following steps: The diffuse reflectance spectral signal of the burn wound substrate is collected by a biosensor on the inside of the burn wound dressing. Based on the characteristic absorption wavelength of hemoglobin in the deep tissues of the burn wound, the oxygen saturation and total hemoglobin concentration index of the deep tissues of the burn wound are separated from the diffuse reflectance spectral signal. Simultaneously, electrical activity signals of superficial muscle tissue under resting conditions are collected by biosensors attached to the muscles around the burn wound. High-frequency power spectral density analysis is performed on the electrical activity signals to obtain the electromyographic activity index, which reflects the degree of painful muscle spasm in the burn wound. When the electromyographic activity index is lower than the preset stimulation threshold, the microenvironment of the deep tissue of the burn wound is determined to have entered a steady-state healing period based on the blood oxygen saturation and the total hemoglobin concentration index.

[0006] In this embodiment, separating the oxygen saturation and total hemoglobin concentration index of the deep tissue of the burn wound from the diffuse reflectance spectral signal based on the characteristic absorption wavelength of hemoglobin in the deep tissue of the burn wound specifically includes: The characteristic absorption wavelengths of oxyhemoglobin and deoxyhemoglobin in the near-infrared band of deep tissues of burn wounds are obtained, and then the spectral sampling window corresponding to the characteristic absorption wavelengths is determined. Extract the diffuse reflectance intensity sequence within the spectral sampling window from the diffuse reflectance spectral signal; The diffuse reflection light intensity sequence is subjected to tissue background absorption subtraction and baseline drift correction to obtain the corrected characteristic wavelength light intensity value; Based on the corrected characteristic wavelength light intensity value, combined with the optical path length and scattering compensation factor of the deep tissue of the burn wound, the concentration contribution values ​​of oxyhemoglobin and deoxyhemoglobin are calculated by inversion respectively. The oxygen saturation and total hemoglobin concentration index of the deep tissues of the burn wound are determined by the concentration contribution values ​​of the oxyhemoglobin and the deoxyhemoglobin.

[0007] In this embodiment, performing tissue background absorption subtraction and baseline drift correction on the diffuse reflection light intensity sequence to obtain the corrected characteristic wavelength light intensity values ​​specifically includes: Based on the characteristic absorption wavelength of hemoglobin in the deep tissue of the burn wound, the light intensity value of the non-absorption reference band in the neighborhood of the characteristic absorption wavelength is extracted from the diffuse reflection light intensity sequence and used as the benchmark reference quantity for background absorption of the burn tissue. The light intensity value of each sampling point in the diffuse reflection light intensity sequence is differentially calculated with the reference value of background absorption of burn tissue. The background absorption contribution of non-hemoglobin components at the characteristic absorption wavelength is removed to obtain the differential light intensity sequence after background subtraction. Polynomial baseline fitting is performed on the differential light intensity sequence to identify and remove slowly changing baseline components caused by scattering from burn tissue, thereby obtaining the corrected characteristic wavelength light intensity values.

[0008] In this embodiment, based on the corrected characteristic wavelength light intensity value, combined with the optical path length and scattering compensation factor of the deep tissue of the burn wound, the concentration contribution values ​​of oxyhemoglobin and deoxyhemoglobin are calculated by inversion, specifically including: Based on the optical path length of the deep tissue of the burn wound, the corrected characteristic wavelength light intensity value is normalized by the path length to obtain the characteristic wavelength absorbance per unit path length. Based on the scattering compensation factor of the deep tissue of the burn wound, the scattering attenuation compensation correction is performed on the characteristic wavelength absorbance per unit path length to obtain the scattering corrected absorbance value. The concentration contribution values ​​of oxyhemoglobin and deoxyhemoglobin were determined from the scatter-corrected absorbance values ​​based on the specific absorption coefficients of oxyhemoglobin and deoxyhemoglobin at characteristic wavelengths.

[0009] In this embodiment, high-frequency power spectral density analysis is performed on the electrical activity signal to obtain the electromyographic activity index, which reflects the degree of painful muscle spasms in burn wounds. Specifically, this includes: The electrical activity signal is bandpass filtered to remove low-frequency motion artifacts, resulting in the processed superficial muscle electrical activity signal. The electrical activity signals of the superficial muscles were analyzed to detect active segments and extract the electrical activity segments without active contraction interference in the resting state. Power spectral density transformation is performed on each electrical activity segment in the electrical activity segment sequence, and the characteristic frequency band of the specific response to painful muscle spasm is determined. The concentration of peak power frequency and the variability of average power energy of each electrical activity segment within the characteristic frequency band are extracted. The electromyographic activity index, which reflects the degree of painful muscle spasm in burn wounds, is determined based on the concentration of peak power frequency and the variability of average power energy within the characteristic frequency band of the specific response to painful muscle spasm for each electrical activity segment.

[0010] In this embodiment, the detection of active segments in the superficial muscle electrical activity signal and the extraction of electrical activity segment sequences without active contraction interference in the resting state specifically include: Calculate the short-time energy envelope of the superficial muscle electrical activity signal and construct an activity detection threshold. The continuous intervals where the short-term energy envelope exceeds the activity detection threshold are marked as active contraction activity segments, and the continuous intervals where the energy envelope is below the activity detection threshold are marked as resting candidate segments. The boundary transition region adjacent to the active contraction segment is removed from the resting candidate segment, and the internal interval in a stable low-level state is retained to obtain the electrical activity segment sequence without active contraction interference in the resting state.

[0011] In this embodiment, power spectral density transformation is performed on each electrical activity segment in the electrical activity segment sequence, and the characteristic frequency band of the specific response to painful muscle spasms is determined. Extracting the concentration of peak power frequency and the variability of average power energy for each electrical activity segment within the characteristic frequency band specifically includes: Power spectral density transformation is performed on each electrical activity segment in the electrical activity segment sequence to obtain the power spectral curve of each electrical activity segment; The characteristic frequency band of the specific response to painful muscle spasm is determined based on the power spectrum curve of each electrical activity segment, and then the peak power frequency within the characteristic frequency band is extracted. The peak power frequencies of all electrical activity segments are then used to form a frequency distribution set. The concentration of peak power frequencies in the characteristic frequency band for each segment of electrical activity is calculated based on the frequency distribution set. The variability of the average power energy of each electrical activity segment within the characteristic frequency band is calculated based on the dispersion of the average power energy of each segment within the characteristic frequency band.

[0012] In this embodiment, when the electromyographic activity index is lower than a preset stimulation threshold, determining whether the deep tissue microenvironment of the burn wound has entered a steady-state healing period based on the blood oxygen saturation and the total hemoglobin concentration index specifically includes: When the electromyographic activity index remains below the preset stimulation threshold for a continuous time window, it is determined that the painful muscle spasm of the burn wound has been relieved. Obtain the blood oxygen saturation sequence and total hemoglobin concentration index sequence within the forward backtracking time window at the current moment; The blood oxygen saturation sequence was subjected to fluctuation analysis to obtain the fluctuation amplitude and frequency of blood oxygen saturation, and the total hemoglobin concentration index sequence was subjected to stability analysis to obtain the drift amount and drift rate of the total hemoglobin concentration index. Steady-state healing period similarity matching was performed on the fluctuation amplitude and frequency of blood oxygen saturation and the drift amount and drift rate of total hemoglobin concentration index to obtain the healing period matching confidence of the deep tissue microenvironment of burn wound. When the confidence level of the healing period exceeds the preset confidence level threshold, it is determined that the deep tissue microenvironment of the burn wound has entered the steady-state healing period, and the determination result with healing period stage label and confidence level is output.

[0013] In this embodiment, the electromyographic activity index represents an index used to characterize the severity of painful muscle spasms in burn wounds.

[0014] Secondly, this application provides a burn wound recovery monitoring device based on a biosensor. The burn wound recovery monitoring device includes a burn wound recovery monitoring unit, which comprises: The spectral signal acquisition module is used to acquire diffuse reflectance spectral signals of the burn wound substrate through a biosensor on the inside of the burn wound dressing; The deep tissue analysis module for burn wounds is used to separate the blood oxygen saturation and total hemoglobin concentration index of the deep tissues of burn wounds from the diffuse reflectance spectral signal based on the characteristic absorption wavelength of hemoglobin in the deep tissues of burn wounds. The superficial muscle tissue analysis module is used to simultaneously collect electrical activity signals of superficial muscle tissue in a resting state through biosensors attached to the muscles around the wound, and perform high-frequency power spectral density analysis on the electrical activity signals to obtain the electromyographic activity index reflecting the degree of painful muscle spasm in the burn wound. The burn wound recovery monitoring module is used to determine, based on the blood oxygen saturation and the total hemoglobin concentration index, that the deep tissue microenvironment of the burn wound has entered a steady-state healing period when the electromyographic activity index is lower than a preset stimulation threshold.

[0015] The technical solutions provided by the embodiments disclosed in this application have the following beneficial effects: A biosensor on the inner side of the burn wound dressing collects diffuse reflectance spectral signals of the burn wound base. Based on the characteristic absorption wavelength of hemoglobin in the deep tissues of the burn wound, the oxygen saturation and total hemoglobin concentration index of the deep tissues of the burn wound are separated from the diffuse reflectance spectral signals. Simultaneously, a biosensor attached to the muscles around the wound collects electrical activity signals of the superficial muscle tissue in a resting state. High-frequency power spectral density analysis is performed on the electrical activity signals to obtain an electromyographic activity index reflecting the degree of painful muscle spasms in the burn wound. When the electromyographic activity index is lower than a preset stimulation threshold, the microenvironment of the deep tissues of the burn wound is determined to have entered a steady-state healing period based on the oxygen saturation and the total hemoglobin concentration index.

[0016] Therefore, this application demonstrates that when the electromyographic activity index is lower than a preset stimulation threshold, the microenvironment of the deep tissue of the burn wound can be determined to have entered a steady-state healing period based on the blood oxygen saturation and the total hemoglobin concentration index. Firstly, a biosensor on the inner side of the burn wound dressing collects the diffuse reflectance spectral signal of the burn wound base. This non-invasive application method avoids direct damage to the wound tissue, reducing the risk of infection and preventing interference with the normal healing process. Secondly, by acquiring the characteristic absorption wavelength of hemoglobin in the deep tissue of the burn wound, the light intensity sequence of the corresponding spectral sampling window is extracted. After precise processing including tissue background absorption subtraction, baseline drift correction, and scattering compensation, the blood oxygen saturation and total hemoglobin concentration index of the deep tissue are calculated. This overcomes the technical bottleneck of conventional non-invasive monitoring, which can only observe the surface state of the wound, and accurately captures the core physiological indicators of the deep tissue. This method accurately reflects the recovery status of deep tissues, filling the information gap in non-invasive monitoring of deep tissues. Furthermore, it simultaneously collects muscle electrical activity signals at rest through biosensors attached to the muscles around the wound. After processing through bandpass filtering, resting activity segment screening, and high-frequency power spectral density analysis, an electromyographic activity index reflecting the degree of painful muscle spasms is obtained. This achieves simultaneous monitoring of deep physiological signals and pain-related signals, enriching the monitoring dimensions, solving the problem of incomplete information in existing monitoring schemes, and ensuring the integrity of monitoring information. Finally, when the electromyographic activity index remains below a preset stimulation threshold, by analyzing the fluctuations, stability, and similarity matching of blood oxygen saturation and total hemoglobin concentration indices, it accurately determines whether the deep tissue microenvironment has entered the steady-state healing period and outputs results with confidence. This provides a comprehensive and accurate basis for adjusting clinical treatment plans, promoting the development of burn care towards precision and non-invasive methods.

[0017] In summary, the technical solution adopted in this application can achieve non-invasive detection of the deep tissue condition of burn wounds, thus balancing monitoring safety and information integrity. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only for this embodiment of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is an exemplary flowchart of a biosensor-based method for monitoring burn wound recovery provided in this application; Figure 2 This is an application scenario diagram of the burn wound recovery monitoring device provided in this application; Figure 3 This is a trend diagram of the spectral inversion index as a function of the recovery process, provided in this application; Figure 4 It is based on the confidence curve of electromyographic activity index and steady-state healing period provided in this application; Figure 5 This is a module structure diagram of a burn wound recovery monitoring device based on a biosensor, as provided in this application. Detailed Implementation

[0020] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0021] This application provides a biosensor-based burn wound recovery monitoring device and method. The core of this method involves using a biosensor on the inner side of the burn wound dressing to collect diffuse reflectance spectral signals from the burn wound base. Based on the characteristic absorption wavelength of hemoglobin in the deep tissues of the burn wound, the oxygen saturation and total hemoglobin concentration index of the deep tissues are separated from the diffuse reflectance spectral signals. Simultaneously, a biosensor attached to the surrounding muscles collects electrical activity signals from the superficial muscle tissue at rest. High-frequency power spectral density analysis is performed on these electrical activity signals to obtain an electromyographic activity index reflecting the degree of painful muscle spasms in the burn wound. When the electromyographic activity index is lower than a preset stimulation threshold, the microenvironment of the deep tissues of the burn wound is determined to have entered a steady-state healing period based on the oxygen saturation and total hemoglobin concentration index.

[0022] Example 1: To better understand the above technical solution, the following will provide a detailed description of the technical solution in conjunction with the accompanying drawings and specific implementation methods. (Refer to...) Figure 1 As shown in the figure, this is an exemplary flowchart of a burn wound recovery monitoring method based on a biosensor according to this embodiment of the present application. The burn wound recovery monitoring method includes the following steps: In step S1, the diffuse reflectance spectral signal of the burn wound substrate is collected by a biosensor on the inside of the burn wound dressing.

[0023] It should be noted that the biosensor described in this application is a sensor capable of collecting near-infrared diffuse reflectance spectra from the inner side of burn wound dressings; the burn wound substrate refers to the surface of deep tissues below the surface of the burn wound, which is the core area reflecting the wound's recovery status; the diffuse reflectance spectral signal refers to the spectral signal reflected back to the biosensor after near-infrared light irradiates the burn wound substrate, is scattered and absorbed by the deep tissues, and contains characteristic information of the physiological components of the deep tissues of the wound.

[0024] In practice, a burn wound dressing integrating a near-infrared diffuse reflectance spectroscopy biosensor is flatly attached to the burn wound surface, ensuring that the biosensor probe is in close contact with the wound substrate without air bubbles or foreign objects obstructing it. The biosensor is used to collect spectral data from the burn wound substrate, and the light intensity information corresponding to different wavelengths at each sampling time is recorded simultaneously. These continuously collected raw spectral data containing light intensity data of each wavelength are used as the diffuse reflectance spectral signal of the burn wound substrate.

[0025] It should also be noted that in this application, references Figure 2 As shown in the figure, this is an application scenario diagram of the burn wound recovery monitoring device provided in this application embodiment. In the figure, the forearm 1 is the location of the burn wound, and the wound surface is covered with a monitoring dressing 2. The monitoring dressing 2 is used to stably cover the wound area without damaging the wound tissue. An inner biosensor 3 is provided on the inner side of the monitoring dressing 2, facing the base and deep tissue of the burn wound, and acquiring the diffuse reflectance spectral signal of the wound base through near-infrared diffuse reflectance. Biosensors 4 are also attached to the skin surface around the wound to collect the electrical activity signal of the superficial muscle tissue around the wound in a resting state. Each biosensor is connected to a monitoring terminal 5 through wires. The monitoring terminal 5 analyzes and processes the collected diffuse reflectance spectral signal and electromyographic activity signal to determine whether the microenvironment of the deep tissue of the burn wound has entered the steady-state healing stage. The enlarged view further illustrates the relative positional relationship between the monitoring dressing 2, the biosensor 3 inside the dressing, and the burn wound base / deep tissue 6, highlighting the application method of this application to non-invasively collect deep tissue status information of the wound and simultaneously combine it with peri-wound electromyographic signals for recovery monitoring.

[0026] In step S2, the oxygen saturation and total hemoglobin concentration index of the deep tissue of the burn wound are separated from the diffuse reflectance spectral signal based on the characteristic absorption wavelength of hemoglobin in the deep tissue of the burn wound.

[0027] In this embodiment, the oxygen saturation and total hemoglobin concentration index of the deep tissue of the burn wound can be separated from the diffuse reflectance spectral signal based on the characteristic absorption wavelength of hemoglobin in the deep tissue of the burn wound using the following steps: The characteristic absorption wavelengths of oxyhemoglobin and deoxyhemoglobin in the near-infrared band of deep tissues of burn wounds are obtained, and then the spectral sampling window corresponding to the characteristic absorption wavelengths is determined. Extract the diffuse reflectance intensity sequence within the spectral sampling window from the diffuse reflectance spectral signal; The diffuse reflection light intensity sequence is subjected to tissue background absorption subtraction and baseline drift correction to obtain the corrected characteristic wavelength light intensity value; Based on the corrected characteristic wavelength light intensity value, combined with the optical path length and scattering compensation factor of the deep tissue of the burn wound, the concentration contribution values ​​of oxyhemoglobin and deoxyhemoglobin are calculated by inversion respectively. The oxygen saturation and total hemoglobin concentration index of the deep tissues of the burn wound are determined by the concentration contribution values ​​of the oxyhemoglobin and the deoxyhemoglobin.

[0028] It should be noted that the characteristic absorption wavelength mentioned in this application refers to a fixed wavelength in the near-infrared band where oxyhemoglobin and deoxyhemoglobin exhibit specific differences in light absorption, and is the core wavelength basis for distinguishing the two types of hemoglobin; the spectral sampling window refers to the wavelength acquisition interval defined around the characteristic absorption wavelength to eliminate invalid spectral interference; the diffuse reflectance intensity sequence refers to the dataset of diffuse reflectance intensity values ​​corresponding to each wavelength within the spectral sampling window arranged in the sampling order; the corrected characteristic wavelength intensity value refers to the pure intensity value of the characteristic wavelength after eliminating tissue background absorption interference and baseline drift interference; and the optical path length refers to the actual effective path of near-infrared light transmission within the deep tissue of the burn wound. The length is set using empirical optical parameters of skin tissue; the scattering compensation factor is a parameter set based on the scattering characteristics of deep tissues in superficial second-degree burn wounds, and is set to 0.85 in this application to correct the interference of light intensity attenuation caused by tissue scattering on absorbance; the concentration contribution value of oxyhemoglobin refers to the numerical component of light intensity at a characteristic wavelength produced by the absorption of oxyhemoglobin; the concentration contribution value of deoxyhemoglobin refers to the numerical component of light intensity at a characteristic wavelength produced by the absorption of deoxyhemoglobin; the blood oxygen saturation refers to a physiological indicator reflecting the oxygenation level of deep tissues in burn wounds; the total hemoglobin concentration index refers to a quantitative indicator reflecting the total hemoglobin content of deep tissues in burn wounds.

[0029] In specific implementation, firstly, the characteristic absorption wavelengths of oxyhemoglobin and deoxyhemoglobin in the near-infrared band at 805nm and 880nm are retrieved using a lookup table method for the characteristic bands of near-infrared hemoglobin. The wavelength range is then extended by 20nm before and after each characteristic absorption wavelength, and this wavelength range is used as the spectral sampling window corresponding to the characteristic absorption wavelength. Secondly, a spectral window truncation algorithm is used to extract all light intensity data within the spectral sampling window from the previously acquired diffuse reflectance spectral signal, and this light intensity data, arranged sequentially according to sampling time, is used as the diffuse reflectance light intensity sequence. Next, tissue background absorption subtraction and baseline shifting are performed on the diffuse reflectance light intensity sequence. First, the characteristic wavelength light intensity value is corrected. Then, based on the corrected characteristic wavelength light intensity value, combined with the optical path length and scattering compensation factor of the deep tissue of the burn wound, the concentration contribution values ​​of oxyhemoglobin and deoxyhemoglobin are calculated respectively. Finally, the ratio of the oxyhemoglobin concentration contribution value to the sum of the concentration contribution values ​​of the two types of hemoglobin is calculated, and the calculated value is used as the blood oxygen saturation of the deep tissue of the burn wound. The concentration contribution values ​​of oxyhemoglobin and deoxyhemoglobin are superimposed, and the superimposed value is used as the total hemoglobin concentration index of the deep tissue of the burn wound.

[0030] In this embodiment, the tissue background absorption subtraction and baseline drift correction are performed on the diffuse reflection light intensity sequence to obtain the corrected characteristic wavelength light intensity value, which can be achieved by the following steps: Based on the characteristic absorption wavelength of hemoglobin in the deep tissue of the burn wound, the light intensity value of the non-absorption reference band in the neighborhood of the characteristic absorption wavelength is extracted from the diffuse reflection light intensity sequence and used as the benchmark reference quantity for background absorption of the burn tissue. The light intensity value of each sampling point in the diffuse reflection light intensity sequence is differentially calculated with the reference value of background absorption of burn tissue. The background absorption contribution of non-hemoglobin components at the characteristic absorption wavelength is removed to obtain the differential light intensity sequence after background subtraction. Polynomial baseline fitting is performed on the differential light intensity sequence to identify and remove slowly changing baseline components caused by scattering from burn tissue, thereby obtaining the corrected characteristic wavelength light intensity values.

[0031] It should be noted that, in this application, the characteristic wavelength neighborhood refers to the wavelength range defined around the previously determined characteristic absorption wavelengths (805nm and 880nm) of oxyhemoglobin and deoxyhemoglobin, used to screen reference bands without hemoglobin absorption interference; the non-absorption reference band refers to the wavelength range within the characteristic wavelength neighborhood that is not absorbed by hemoglobin and only reflects the basic scattering of tissue, and its light intensity value is not affected by hemoglobin concentration; the baseline reference quantity of burn tissue background absorption refers to the background absorption intensity generated by non-hemoglobin components at the characteristic wavelength; the differential light intensity sequence after background subtraction refers to the light intensity data sequence that contains only the hemoglobin absorption contribution and tissue scattering interference after removing non-hemoglobin background absorption; the slowly changing baseline component refers to the light intensity component that slowly changes over time due to the fluctuation of the scattering characteristics of deep tissue in the burn wound; and the corrected characteristic wavelength light intensity value refers to the pure light intensity value that only reflects the absorption characteristics of hemoglobin.

[0032] In practice, firstly, a 50nm range before and after each characteristic absorption wavelength is defined based on the characteristic absorption wavelengths of oxyhemoglobin and deoxyhemoglobin, and this range is taken as the neighborhood of the characteristic absorption wavelength. Two non-absorption reference bands, 750nm and 950nm, are extracted from this neighborhood from the diffuse reflectance intensity sequence (these two wavelengths are known non-hemoglobin absorption wavelengths in the near-infrared band, effectively avoiding hemoglobin absorption interference). The arithmetic mean of all intensity values ​​within the two non-absorption reference bands is then calculated, and the resulting average is used as the baseline reference for background absorption in burn tissue. Secondly, point-by-point difference calculation is employed. The method involves subtracting the reference value from the light intensity value at each sampling point in the diffuse reflection light intensity sequence. This operation eliminates the background absorption contribution from non-hemoglobin components at the characteristic absorption wavelength. The resulting light intensity data sequence is then used as the differential light intensity sequence after background subtraction. Next, a quadratic polynomial baseline fitting algorithm is used to fit the differential light intensity sequence. Each value on the fitted curve is used as a baseline component reflecting the slow change in tissue scattering interference. The light intensity value at each sampling point in the differential light intensity sequence is subtracted from the baseline component at the corresponding time, and the resulting light intensity value is used as the corrected characteristic wavelength light intensity value.

[0033] In this embodiment, the concentration contribution values ​​of oxyhemoglobin and deoxyhemoglobin can be calculated by inverting the optical path length and scattering compensation factor of the deep tissue of the burn wound based on the corrected characteristic wavelength light intensity value. This can be achieved through the following steps: Based on the optical path length of the deep tissue of the burn wound, the corrected characteristic wavelength light intensity value is normalized by the path length to obtain the characteristic wavelength absorbance per unit path length. Based on the scattering compensation factor of the deep tissue of the burn wound, the scattering attenuation compensation correction is performed on the characteristic wavelength absorbance per unit path length to obtain the scattering corrected absorbance value. The concentration contribution values ​​of oxyhemoglobin and deoxyhemoglobin were determined from the scatter-corrected absorbance values ​​based on the specific absorption coefficients of oxyhemoglobin and deoxyhemoglobin at characteristic wavelengths.

[0034] It should be noted that the characteristic wavelength absorbance per unit path length mentioned in this application refers to the characteristic wavelength light intensity absorption value corresponding to each millimeter of deep tissue in the wound; the scattering attenuation compensation correction refers to the process of offsetting the influence of tissue scattering on absorbance and restoring the true absorption level of hemoglobin; the scattering-corrected absorbance value refers to the pure absorbance value that reflects only the absorption effect of hemoglobin after eliminating scattering interference; the specific absorption coefficient refers to the inherent light absorption coefficient of oxyhemoglobin and deoxyhemoglobin at the characteristic wavelength, which is the core basis for distinguishing the absorption contribution of the two types of hemoglobin.

[0035] In practice, firstly, a preset optical path length of 1.2 mm for the deep tissue of the burn wound is retrieved (this value is set based on the average thickness and optical transmission characteristics of the deep tissue of the superficial second-degree burn wound). The previously obtained corrected characteristic wavelength light intensity value is then divided point by this optical path length of 1.2 mm. This calculation eliminates the path differences caused by different wound tissue thicknesses, and the calculated value is used as the characteristic wavelength absorbance per unit path length. Secondly, a preset scattering compensation factor is retrieved (this factor is set based on the scattering coefficient test data of the deep tissue of the superficial second-degree burn wound, which can effectively counteract the attenuation effect of tissue scattering on absorbance). The characteristic wavelength absorbance per unit path length is then calculated. The absorption is multiplied point by point by the scattering compensation factor. This multiplication operation corrects the absorption deviation caused by tissue scattering, and the calculated value is used as the scatter-corrected absorption metric. Then, the modified Beer-Lambert light absorption algorithm is used to retrieve the specific absorption coefficients of oxyhemoglobin and deoxyhemoglobin at characteristic wavelengths of 805nm and 880nm, which are known in the art. The scatter-corrected absorption metric is substituted into the algorithm. By separating the specific absorption components of the two types of hemoglobin, the absorption values ​​of oxyhemoglobin and deoxyhemoglobin are extracted respectively. These two absorption values ​​are used as the concentration contribution values ​​of oxyhemoglobin and deoxyhemoglobin, respectively.

[0036] Furthermore, to verify the effectiveness of the diffuse reflectance spectral signal in separating blood oxygen saturation and total hemoglobin concentration index, this embodiment sets up a spectral inversion verification experiment. The experimental subjects are 30 groups of burn wound recovery monitoring samples, and the monitoring time points are day 1, day 3, day 5, day 7, day 9, day 11, and day 14 post-injury. During each monitoring, the biosensor inside the dressing is attached to the wound surface, and the diffuse reflectance spectral signal is collected three times consecutively and the average value is taken. At the same time, the wound status observation results at the same sampling time are recorded. After removing abnormal data caused by probe offset, dressing exudate obstruction, and obvious limb movement, the following experimental data are obtained: refer to Figure 3 As shown in the figure, this graph illustrates the trend of spectral inversion indices provided in this embodiment of the application during the recovery process. As the wound heals, the blood oxygen saturation gradually increases from 54.8% on day 1 to 77.8% on day 14, while the total hemoglobin concentration index gradually decreases from 1.42 on day 1 to approximately 1.05. This indicates that the deep tissues of the wound gradually transition from a state of significant early hypoxia, hyperemia, and exudation to a state of improved blood oxygen supply and reduced microcirculatory load. The standard deviation of blood oxygen saturation decreases from 4.8 to 2.2, and the standard deviation of the total hemoglobin concentration index decreases from 0.18 to 0.08. This demonstrates that the tissue background absorption subtraction, baseline drift correction, and scattering compensation correction can reduce the impact of wound exudate and tissue scattering differences on the spectral inversion results, enabling the obtained blood oxygen saturation and total hemoglobin concentration index to exhibit good temporal continuity and consistency in recovery trends. Therefore, the diffuse reflectance spectral signal can not only reflect the gradual improvement of oxygenation level in the deep tissues of the burn wound, but also reflect the stable changes in hemoglobin concentration load in the deep tissues, thus supporting the non-invasive and continuous monitoring of the microenvironment of the deep tissues of the burn wound using biosensors in this application.

[0037] In step S3, the electrical activity signals of the superficial muscle tissue at rest are collected simultaneously by a biosensor attached to the muscle around the burn site. The electrical activity signals are analyzed by high-frequency power spectral density analysis to obtain the electromyographic activity index, which reflects the degree of painful muscle spasm in the burn wound.

[0038] In practice, the simultaneous acquisition of electrical activity signals of superficial muscle tissue at rest using a biosensor attached to the surrounding muscle can be achieved in the following way: First, the surface electromyography (SEMG) biosensor is flatly attached to the superficial muscle skin surface within a 2cm radius around the burn wound using an integrated medical non-invasive conductive gel. The position of the biosensor electrode is adjusted so that the electrode axis is consistent with the direction of the muscle fibers, ensuring that the electrode is tightly attached to the skin without gaps or loosening. Second, the subject is guided to keep the limb fixed and in a resting state without any active muscle contraction. Muscle electrophysiological voltage waveform data is continuously acquired for 30 seconds. Abnormal waveform segments caused by slight limb shaking during the acquisition process are simultaneously removed. Finally, the selected continuous and stable voltage waveform data is used as the electrical activity signal of the superficial muscle tissue at rest.

[0039] It should be noted that the peri-wound muscle mentioned in this application refers to the superficial skeletal muscle tissue surrounding the burn wound that is susceptible to spasms caused by pain stimulation from the wound; the biosensor attached to the peri-wound muscle is a medical non-invasive surface electromyography sensor, used to collect electrophysiological activity signals of muscle fibers without causing secondary stimulation to the burn wound; the resting state refers to the physiological state in which the subject keeps the limb still, without active muscle contraction, and only maintains natural muscle relaxation; the electrical activity signal of the superficial muscle tissue refers to the spontaneously generated physiological voltage waveform signal of the peri-wound muscle collected by the surface electromyography sensor, which includes high-frequency electrical activity characteristics corresponding to painful muscle spasms.

[0040] Preferably, in this embodiment, the electromyographic activity index reflecting the degree of painful muscle spasms in burn wounds can be obtained by performing high-frequency power spectral density analysis on the electrical activity signal using the following steps: The electrical activity signal is bandpass filtered to remove low-frequency motion artifacts, resulting in the processed superficial muscle electrical activity signal. The electrical activity signals of the superficial muscles were analyzed to detect active segments and extract the electrical activity segments without active contraction interference in the resting state. Power spectral density transformation is performed on each electrical activity segment in the electrical activity segment sequence, and the characteristic frequency band of the specific response to painful muscle spasm is determined. The concentration of peak power frequency and the variability of average power energy of each electrical activity segment within the characteristic frequency band are extracted. The electromyographic activity index, which reflects the degree of painful muscle spasm in burn wounds, is determined based on the concentration of peak power frequency and the variability of average power energy within the characteristic frequency band of the specific response to painful muscle spasm for each electrical activity segment.

[0041] It should be noted that the bandpass filtering process described in this application refers to a signal processing method that filters out high-frequency bands related to painful muscle spasms in electromyography (EMG) signals and removes interference from irrelevant frequency bands; the low-frequency motion artifacts refer to low-frequency signals generated by slight limb swaying or breathing interference of the subject; the processed superficial muscle electrical activity signal refers to a pure electrical signal containing only spontaneous electrical activity of the superficial muscles around the wound after filtering out all interference; and the EMG activity index represents an index used to characterize the severity of painful muscle spasms in burn wounds.

[0042] In specific implementation, firstly, a Butterworth bandpass filter is used to perform secondary filtering on the electrical activity signal, and the filtered electrical signal is used as the processed superficial muscle electrical activity signal. Secondly, the superficial muscle electrical activity signal is subjected to activity segment detection to extract the electrical activity segment sequence without active contraction interference in the resting state. Then, power spectral density transformation is performed on each electrical activity segment in the electrical activity segment sequence, and the characteristic frequency band of the specific response to painful muscle spasm is determined. The concentration of peak power frequency and the variability of average power energy of each electrical activity segment within the characteristic frequency band are extracted. Finally, a weighted summation algorithm is used to assign weights of 0.4 and 0.6 to the concentration of peak power frequency and the variability of average power energy, respectively (these weights are set according to their influence on the degree of muscle spasm, with spasm stability and attack frequency accounting for 40% and 60% of the influence on the degree of spasm, respectively). The value obtained by weighted summation of the two is used as the electromyographic activity index reflecting the degree of painful muscle spasm in burn wounds.

[0043] In this embodiment, the detection of active segments in the superficial muscle electrical activity signal and the extraction of electrical activity segment sequences without active contraction interference in the resting state can be achieved by the following steps: Calculate the short-time energy envelope of the superficial muscle electrical activity signal and construct an activity detection threshold. The continuous intervals where the short-term energy envelope exceeds the activity detection threshold are marked as active contraction activity segments, and the continuous intervals where the energy envelope is below the activity detection threshold are marked as resting candidate segments. The boundary transition region adjacent to the active contraction segment is removed from the resting candidate segment, and the internal interval in a stable low-level state is retained to obtain the electrical activity segment sequence without active contraction interference in the resting state.

[0044] It should be noted that, in this application, the short-time energy envelope refers to the energy curve obtained by calculating the energy of superficial muscle electrical activity signals using a short-time window, which is used to intuitively reflect the changes in the strength of muscle electrical activity; the activity detection threshold refers to the energy critical value used to distinguish between active muscle contraction and resting state; the active contraction activity segment refers to the continuous signal interval where the short-time energy envelope exceeds the threshold, within which active muscle contraction occurs, the electrical activity signal intensity is high, and it will interfere with the detection of painful muscle spasm electrical activity; the resting candidate segment refers to the continuous signal interval where the short-time energy envelope is below the threshold. Within this interval, there is no obvious active muscle contraction, but it may contain boundary interference signals adjacent to the active contraction segment; the boundary transition zone refers to the signal interval in the resting candidate segment adjacent to the active contraction segment. The electrical activity signal in this interval is affected by active contraction and has weak fluctuations, and does not belong to the pure resting state signal; the internal interval of the stable low-level state refers to the signal interval in the resting candidate segment where the electrical activity energy is stable and has no obvious fluctuations after removing the boundary transition zone; the electrical activity segment sequence refers to the continuous signal segment sequence containing only the spontaneous weak electrical activity of the muscle in the resting state after removing the strong electrical signal segments generated by the active muscle contraction.

[0045] In specific implementation, firstly, the short-time energy method is used to calculate the short-time energy envelope of superficial muscle electrical activity signals. The short-time window length is set to 200ms and the window overlap rate to 50%. Energy calculation is performed window by window on the electrical activity signal, and the energy values ​​of each window are arranged in chronological order to form a short-time energy envelope that changes over time. Simultaneously, the average energy of the electrical activity signal in the resting state is calculated, and three times this average energy is used as the activity detection threshold (this threshold is set based on the energy range of superficial muscle electrical activity in the human body in the resting state, which can effectively distinguish between active contraction and the resting state, avoiding misjudgment). Secondly, a segmented labeling method is used to traverse the short-time energy envelope, identifying intervals where the energy values ​​of three or more consecutive sampling windows exceed the activity detection threshold. The active contraction segment is marked as an active contraction segment. Intervals with energy values ​​below the threshold for three or more consecutive sampling windows are marked as resting candidate segments (setting three consecutive windows can avoid mismarking caused by a single abnormal sampling point and improve the accuracy of marking). Next, a boundary transition area elimination algorithm is used, with the boundary transition area duration set to 50ms. The boundary transition area adjacent to the active contraction segment is eliminated from each resting candidate segment. Then, through signal level stability detection, stable low-level internal intervals in the resting candidate segments with electrical activity energy fluctuation amplitude not exceeding 10% of the average energy are retained. All retained stable low-level internal intervals are arranged in chronological order, and the arranged signal set is taken as the electrical activity segment sequence without active contraction interference in the resting state.

[0046] In this embodiment, the power spectral density transformation is performed on each electrical activity segment in the electrical activity segment sequence, and the characteristic frequency band of the specific response to painful muscle spasm is determined. The concentration of peak power frequency and the variability of average power energy of each electrical activity segment within the characteristic frequency band can be extracted by the following steps: Power spectral density transformation is performed on each electrical activity segment in the electrical activity segment sequence to obtain the power spectral curve of each electrical activity segment; The characteristic frequency band of the specific response to painful muscle spasm is determined based on the power spectrum curve of each electrical activity segment, and then the peak power frequency within the characteristic frequency band is extracted. The peak power frequencies of all electrical activity segments are then used to form a frequency distribution set. The concentration of peak power frequencies in the characteristic frequency band for each segment of electrical activity is calculated based on the frequency distribution set. The variability of the average power energy of each electrical activity segment within the characteristic frequency band is calculated based on the dispersion of the average power energy of each segment within the characteristic frequency band.

[0047] It should be noted that the power spectrum curve mentioned in this application refers to the power distribution of muscle electrical activity at different frequencies after power spectral density transformation; the characteristic frequency band of the specific response to painful muscle spasm refers to the high-frequency band directly related to painful spasm of the muscles around the wound, which can effectively distinguish painful spasm from normal resting electromyographic signals; the peak power frequency refers to the frequency corresponding to the maximum power value within the characteristic frequency band, which is the core parameter reflecting the intensity of spasmodic electrical activity; the frequency distribution set refers to the set formed by summing the peak power frequencies of all electrical activity segments; the concentration of the peak power frequency refers to the degree of concentration of the frequency distribution corresponding to the peak power within the characteristic frequency band, reflecting the stability of spasmodic electrical activity; and the variability of the average power energy refers to the degree of dispersion of the average power energy within the characteristic frequency band, reflecting the frequency of spasmodic attacks. In specific implementation, firstly, the Fast Fourier Transform (FFT) algorithm is used to perform power spectral density transformation on each electrical activity segment in the electrical activity segment sequence. The FFT points are set to 1024, and each segment is transformed segment by segment, converting the time-domain voltage signal of each segment into frequency-domain power distribution data. The curve plotted from this power distribution data is used as the power spectrum curve of each electrical activity segment. Secondly, the power spectrum curve of each electrical activity segment is iterated, and the 50-150Hz frequency band is extracted as the characteristic frequency band of the specific response to painful muscle spasms (this frequency band is a well-known characteristic frequency band of electromyographic signals for painful muscle spasms, which can effectively eliminate interference from normal resting electromyographic signals). Then, the peak detection algorithm is used to extract the frequency point with the highest power value in this characteristic frequency band for each electrical activity segment, and this frequency point is used as the peak value of that segment. The peak power frequencies of all electrical activity segments are then aggregated to form a set containing all peak power frequencies, which is used as the frequency distribution set. Next, the standard deviation algorithm is used to calculate the standard deviation of the frequency distribution set by substituting each peak power frequency value into the set. The reciprocal of the standard deviation is used as the concentration of peak power frequencies of each electrical activity segment within the characteristic frequency band. Finally, the arithmetic mean of all power values ​​of each electrical activity segment within the characteristic frequency band is calculated to obtain the average power energy of each segment. The coefficient of variation of the average power energy of all electrical activity segments (i.e., the ratio of the standard deviation to the average value) is then calculated, and this coefficient of variation is used as the variability of the average power energy of each electrical activity segment within the characteristic frequency band.

[0048] In step S4, when the electromyographic activity index is lower than the preset stimulation threshold, the microenvironment of the deep tissue of the burn wound is determined to have entered a steady-state healing period based on the blood oxygen saturation and the total hemoglobin concentration index.

[0049] In this embodiment, when the electromyographic activity index is lower than a preset stimulation threshold, the determination that the deep tissue microenvironment of the burn wound has entered a steady-state healing period based on the blood oxygen saturation and the total hemoglobin concentration index can be achieved by the following steps: When the electromyographic activity index remains below the preset stimulation threshold for a continuous time window, it is determined that the painful muscle spasm of the burn wound has been relieved. Obtain the blood oxygen saturation sequence and total hemoglobin concentration index sequence within the forward backtracking time window at the current moment; The blood oxygen saturation sequence was subjected to fluctuation analysis to obtain the fluctuation amplitude and frequency of blood oxygen saturation, and the total hemoglobin concentration index sequence was subjected to stability analysis to obtain the drift amount and drift rate of the total hemoglobin concentration index. Steady-state healing period similarity matching was performed on the fluctuation amplitude and frequency of blood oxygen saturation and the drift amount and drift rate of total hemoglobin concentration index to obtain the healing period matching confidence of the deep tissue microenvironment of burn wound. When the confidence level of the healing period exceeds the preset confidence level threshold, it is determined that the deep tissue microenvironment of the burn wound has entered the steady-state healing period, and the determination result with healing period stage label and confidence level is output.

[0050] It should be noted that the preset stimulation threshold mentioned in this application refers to a critical value set based on the electromyographic activity index test data of superficial muscles around the wound in healthy individuals at rest, used to determine whether painful muscle spasms have been relieved (this stimulation threshold is set based on the average resting electromyographic activity index of 300 healthy individuals in clinical practice); the relief of painful muscle spasms means that the spasm response caused by burn pain in the muscles around the wound has disappeared, and the electromyographic activity is at a normal resting level, providing a prerequisite for subsequent determination of the healing period; the forward retrospective time window at the current moment refers to a fixed duration of retrospection from the current moment; the blood oxygen saturation sequence refers to the data of all blood oxygen saturation values ​​arranged in chronological order within the retrospective time window, and the total hemoglobin concentration index sequence refers to the data of all total hemoglobin concentration indices arranged in chronological order within the retrospective time window, both of which are derived from physiological indicators obtained in previous steps; the fluctuation amplitude is The terms refer to the maximum difference in blood oxygen saturation values, the fluctuation frequency (number of fluctuations in blood oxygen saturation per unit time), and both reflect the stability of blood oxygen supply. The drift amount (drift value) refers to the difference between the first and last values ​​of the trend sequence, and the drift rate (drift rate) is the ratio of the drift amount to the backtracking time, both reflecting the stable state of tissue blood supply. The preset confidence threshold is the critical value for determining whether the wound has entered the steady-state healing period, which is 0.8 in this application (the confidence threshold is set based on the physiological characteristics of the steady-state healing period of clinical burn wounds). The steady-state healing period refers to the stage in which the microenvironment of the deep tissues of the burn wound is stable, blood oxygen supply is sufficient, and tissue repair continues to advance. The healing period stage label is a label used to identify the healing stage of the wound. The confidence level is a level divided according to the matching confidence level. The judgment result is an output result containing the healing period stage label and the confidence level, providing a reference for clinical diagnosis and treatment.

[0051] In specific implementation, firstly, a continuous time window of 60 seconds is set (based on the stable duration of electromyographic activity after muscle spasm relief, to avoid misjudgment of single abnormal values). The electromyographic activity index is monitored in real time. When the value of the electromyographic activity index at each sampling point within the 60-second continuous window is lower than the preset stimulation threshold, it is determined that the painful muscle spasm of the burn wound has been relieved. Secondly, a time window backtracking algorithm is used, setting the forward backtracking time window to 5 minutes. All blood oxygen saturation values ​​and total hemoglobin concentration index within the backtracking time window are obtained and arranged in chronological order of sampling time. The arranged blood oxygen saturation dataset is then processed... To analyze the blood oxygen saturation sequence, the total hemoglobin concentration index (THI) dataset was arranged as the THI sequence. Next, a sliding window variance method was used to perform volatility analysis on the blood oxygen saturation sequence. The sliding window was set to 30 seconds, and the variance of the blood oxygen saturation values ​​was calculated window by window. The maximum value among all window variances was taken as the volatility amplitude of blood oxygen saturation. Simultaneously, the number of times the volatility amplitude exceeded a preset threshold per unit time was counted and taken as the volatility frequency of blood oxygen saturation. A linear fitting algorithm was then used to linearly fit the THI sequence, and the difference between the first and last values ​​of the fitted curve was calculated. This difference was taken as the total hemoglobin concentration index value. The drift of the total hemoglobin concentration index (THI) is calculated, and then the drift is divided by the backtracking time of 5 minutes to obtain the drift rate of the THI. A vector is constructed from the fluctuation amplitude and frequency of blood oxygen saturation, along with the drift amount and drift rate of the THI, and this vector is used as the recovery monitoring vector. Clinically known standard parameters for steady-state healing are then obtained. Furthermore, the cosine similarity between the recovery monitoring vector and the standard parameters for steady-state healing is calculated, and this cosine similarity is used as the confidence level for matching the healing period of the deep tissue microenvironment of the burn wound. Finally, a preset confidence threshold is obtained; in this application, it is set to 0.8. The confidence level of the healing period was compared with a confidence threshold. When the confidence level exceeded 0.8, the deep tissue microenvironment of the burn wound was considered to have entered a steady-state healing period. Confidence levels were assigned based on the specific values ​​of the confidence levels. A confidence level between 0.8 and 0.9 was considered good, and a "good" confidence level label was generated, along with corresponding clinical reference recommendations (the deep tissue microenvironment of the wound is basically stable; wound monitoring should be performed at the usual frequency, and blood oxygen and hemoglobin-related indicators should be checked regularly). When the confidence level was less than 0.9...A score of 9 or higher is considered excellent, generating a corresponding "Excellent" confidence level label and indicating relevant clinical reference suggestions (stable deep tissue microenvironment, sufficient blood oxygen supply, good tissue repair progress; monitoring frequency can be appropriately reduced, and the current nursing plan should be maintained). Then, the healing stage labels for the "steady-state healing period," the assigned confidence levels, the specific matching confidence values, and the corresponding clinical reference suggestions are integrated to generate a complete assessment result. This result is presented in text format, clearly annotating each core piece of information for easy reading and reference by clinicians. Finally, the assessment result is output to the clinical monitoring terminal via a data interface for intuitive display on the terminal screen, completing the assessment of the entire steady-state healing period.

[0052] Furthermore, to verify the reliability of the combined determination of steady-state healing period using the electromyographic activity index, blood oxygen saturation, and total hemoglobin concentration index, this embodiment further extracts the resting perioperative electromyographic signals at each monitoring time point based on the aforementioned 30 groups of samples. The electromyographic activity index is calculated according to the high-frequency power spectral density analysis method. At the same time, the fluctuation amplitude and frequency of blood oxygen saturation, the drift amount and drift rate of total hemoglobin concentration index are input into the steady-state healing period similarity matching process, resulting in the following experimental data.

[0053] refer to Figure 4 As shown in the figure, this graph represents the change in electromyographic activity index and confidence level during the steady-state healing period provided in this application embodiment. The graph shows that the electromyographic activity index gradually decreased from 0.82 on day 1 to 0.20 on day 14, and fell below the preset stimulation threshold of 0.30 on day 11, indicating that the superficial muscles around the wound gradually transitioned from a state of significant painful spasm response to a low-activity resting state. The confidence level for the healing period gradually increased from 0.36 on day 1 to 0.91 on day 14, reaching 0.86 on day 11, exceeding the preset confidence threshold of 0.80. This result is consistent with the conclusions of "less exudation, stable color, and continuous wound repair" observed in manual observation. Further analysis reveals that relying solely on elevated blood oxygen saturation may be affected by short-term blood flow fluctuations, and relying solely on a decrease in total hemoglobin concentration index may be affected by dressing pressure and changes in local exudate. This application uses electromyographic activity index as a prerequisite for assessing the relief of painful muscle spasms, and combines this with fluctuation and stability analysis of blood oxygen saturation and total hemoglobin concentration index sequences. This reduces misjudgments based on a single indicator and improves the reliability of the results output during the steady-state healing period. Therefore, the above experimental data, from the perspectives of deep tissue oxygenation status, changes in microcirculatory load, and relief of peri-wound muscle electrical activity, demonstrate that the monitoring method described in this application can reliably determine whether the deep tissue microenvironment of a burn wound has entered the steady-state healing period.

[0054] Therefore, this application demonstrates that when the electromyographic activity index is lower than a preset stimulation threshold, the microenvironment of the deep tissue of the burn wound can be determined to have entered a steady-state healing period based on the blood oxygen saturation and the total hemoglobin concentration index. Firstly, a biosensor on the inner side of the burn wound dressing collects the diffuse reflectance spectral signal of the burn wound base. This non-invasive application method avoids direct damage to the wound tissue, reducing the risk of infection and preventing interference with the normal healing process. Secondly, by acquiring the characteristic absorption wavelength of hemoglobin in the deep tissue of the burn wound, the light intensity sequence of the corresponding spectral sampling window is extracted. After precise processing including tissue background absorption subtraction, baseline drift correction, and scattering compensation, the blood oxygen saturation and total hemoglobin concentration index of the deep tissue are calculated. This overcomes the technical bottleneck of conventional non-invasive monitoring, which can only observe the surface state of the wound, and accurately captures the core physiological indicators of the deep tissue. This method accurately reflects the recovery status of deep tissues, filling the information gap in non-invasive monitoring of deep tissues. Furthermore, it simultaneously collects muscle electrical activity signals at rest through biosensors attached to the muscles around the wound. After processing through bandpass filtering, resting activity segment screening, and high-frequency power spectral density analysis, an electromyographic activity index reflecting the degree of painful muscle spasms is obtained. This achieves simultaneous monitoring of deep physiological signals and pain-related signals, enriching the monitoring dimensions, solving the problem of incomplete information in existing monitoring schemes, and ensuring the integrity of monitoring information. Finally, when the electromyographic activity index remains below a preset stimulation threshold, by analyzing the fluctuations, stability, and similarity matching of blood oxygen saturation and total hemoglobin concentration indices, it accurately determines whether the deep tissue microenvironment has entered the steady-state healing period and outputs results with confidence. This provides a comprehensive and accurate basis for adjusting clinical treatment plans, promoting the development of burn care towards precision and non-invasive methods.

[0055] In summary, the technical solution adopted in this application can achieve non-invasive detection of the deep tissue condition of burn wounds, thus balancing monitoring safety and information integrity.

[0056] Example 2: This application provides a burn wound recovery monitoring device based on a biosensor. The burn wound recovery monitoring device includes a burn wound recovery monitoring unit. (Refer to...) Figure 5 As shown in the figure, this is a schematic diagram of exemplary hardware and / or software of a burn wound recovery monitoring unit according to some embodiments of this application. The burn wound recovery monitoring unit includes: a spectral signal acquisition module 100, a deep tissue analysis module for burn wounds 200, a superficial muscle tissue analysis module, and a burn wound recovery monitoring module 400, which are described below: The spectral signal acquisition module 100 is used to acquire the diffuse reflectance spectral signal of the burn wound substrate through the biosensor inside the burn wound dressing. The deep tissue analysis module 200 for burn wounds is used to separate the blood oxygen saturation and total hemoglobin concentration index of the deep tissues of burn wounds from the diffuse reflectance spectral signal based on the characteristic absorption wavelength of hemoglobin in the deep tissues of burn wounds. The superficial muscle tissue analysis module 300 is used to simultaneously collect electrical activity signals of superficial muscle tissue in a resting state through a biosensor attached to the muscle around the wound, and perform high-frequency power spectral density analysis on the electrical activity signals to obtain an electromyographic activity index that reflects the degree of painful muscle spasm in the burn wound. The burn wound recovery monitoring module 400 is used to determine, based on the blood oxygen saturation and the total hemoglobin concentration index, that the deep tissue microenvironment of the burn wound has entered a steady-state healing period when the electromyographic activity index is lower than a preset stimulation threshold.

[0057] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0058] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, including read-only memory (ROM), random access memory (RAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), one-time programmable read-only memory (OTPROM), electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, disk storage, magnetic tape storage, or any other computer-readable medium capable of carrying or storing data.

[0059] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

Claims

1. A method for monitoring burn wound recovery based on biosensors, characterized in that, The method for monitoring burn wound recovery includes the following steps: The diffuse reflectance spectral signal of the burn wound substrate is collected by a biosensor on the inside of the burn wound dressing. Based on the characteristic absorption wavelength of hemoglobin in the deep tissues of the burn wound, the oxygen saturation and total hemoglobin concentration index of the deep tissues of the burn wound are separated from the diffuse reflectance spectral signal. Simultaneously, electrical activity signals of superficial muscle tissue under resting conditions are collected by biosensors attached to the muscles around the burn wound. High-frequency power spectral density analysis is performed on the electrical activity signals to obtain the electromyographic activity index, which reflects the degree of painful muscle spasm in the burn wound. When the electromyographic activity index is lower than the preset stimulation threshold, the microenvironment of the deep tissue of the burn wound is determined to have entered a steady-state healing period based on the blood oxygen saturation and the total hemoglobin concentration index.

2. The method for monitoring burn wound recovery based on biosensors as described in claim 1, characterized in that, Based on the characteristic absorption wavelength of hemoglobin in the deep tissues of burn wounds, the oxygen saturation and total hemoglobin concentration index of the deep tissues of burn wounds are separated from the diffuse reflectance spectral signal, specifically including: The characteristic absorption wavelengths of oxyhemoglobin and deoxyhemoglobin in the near-infrared band of deep tissues of burn wounds are obtained, and then the spectral sampling window corresponding to the characteristic absorption wavelengths is determined. Extract the diffuse reflectance intensity sequence within the spectral sampling window from the diffuse reflectance spectral signal; The diffuse reflection light intensity sequence is subjected to tissue background absorption subtraction and baseline drift correction to obtain the corrected characteristic wavelength light intensity value; Based on the corrected characteristic wavelength light intensity value, combined with the optical path length and scattering compensation factor of the deep tissue of the burn wound, the concentration contribution values ​​of oxyhemoglobin and deoxyhemoglobin are calculated by inversion respectively. The oxygen saturation and total hemoglobin concentration index of the deep tissues of the burn wound are determined by the concentration contribution values ​​of the oxyhemoglobin and the deoxyhemoglobin.

3. The method for monitoring burn wound recovery based on biosensors as described in claim 2, characterized in that, Performing tissue background absorption subtraction and baseline drift correction on the diffuse reflection light intensity sequence to obtain the corrected characteristic wavelength light intensity values ​​specifically includes: Based on the characteristic absorption wavelength of hemoglobin in the deep tissue of the burn wound, the light intensity value of the non-absorption reference band in the neighborhood of the characteristic absorption wavelength is extracted from the diffuse reflection light intensity sequence and used as the benchmark reference quantity for background absorption of the burn tissue. The light intensity value of each sampling point in the diffuse reflection light intensity sequence is differentially calculated with the reference value of background absorption of burn tissue. The background absorption contribution of non-hemoglobin components at the characteristic absorption wavelength is removed to obtain the differential light intensity sequence after background subtraction. Polynomial baseline fitting is performed on the differential light intensity sequence to identify and remove slowly changing baseline components caused by scattering from burn tissue, thereby obtaining the corrected characteristic wavelength light intensity values.

4. The method for monitoring burn wound recovery based on biosensors as described in claim 2, characterized in that, Based on the corrected characteristic wavelength light intensity value, combined with the optical path length and scattering compensation factor of the deep tissue of the burn wound, the concentration contribution values ​​of oxyhemoglobin and deoxyhemoglobin are calculated separately, including: Based on the optical path length of the deep tissue of the burn wound, the corrected characteristic wavelength light intensity value is normalized by the path length to obtain the characteristic wavelength absorbance per unit path length. Based on the scattering compensation factor of the deep tissue of the burn wound, the scattering attenuation compensation correction is performed on the characteristic wavelength absorbance per unit path length to obtain the scattering corrected absorbance value. The concentration contribution values ​​of oxyhemoglobin and deoxyhemoglobin were determined from the scatter-corrected absorbance values ​​based on the specific absorption coefficients of oxyhemoglobin and deoxyhemoglobin at characteristic wavelengths.

5. The method for monitoring burn wound recovery based on biosensors as described in claim 1, characterized in that, High-frequency power spectral density analysis of the electrical activity signal yields an electromyographic activity index reflecting the degree of painful muscle spasms in burn wounds, specifically including: The electrical activity signal is bandpass filtered to remove low-frequency motion artifacts, resulting in the processed superficial muscle electrical activity signal. The electrical activity signals of the superficial muscles were analyzed to detect active segments and extract the electrical activity segments without active contraction interference in the resting state. Power spectral density transformation is performed on each electrical activity segment in the electrical activity segment sequence, and the characteristic frequency band of the specific response to painful muscle spasm is determined. The concentration of peak power frequency and the variability of average power energy of each electrical activity segment within the characteristic frequency band are extracted. The electromyographic activity index, which reflects the degree of painful muscle spasm in burn wounds, is determined based on the concentration of peak power frequency and the variability of average power energy within the characteristic frequency band of the specific response to painful muscle spasm for each electrical activity segment.

6. The method for monitoring burn wound recovery based on biosensors as described in claim 5, characterized in that, The detection of active segments in the electrical activity signals of the superficial muscles, and the extraction of electrical activity segments without active contraction interference in the resting state, specifically includes: Calculate the short-time energy envelope of the superficial muscle electrical activity signal and construct an activity detection threshold. The continuous intervals where the short-term energy envelope exceeds the activity detection threshold are marked as active contraction activity segments, and the continuous intervals where the energy envelope is below the activity detection threshold are marked as resting candidate segments. The boundary transition region adjacent to the active contraction segment is removed from the resting candidate segment, and the internal interval in a stable low-level state is retained to obtain the electrical activity segment sequence without active contraction interference in the resting state.

7. The method for monitoring burn wound recovery based on biosensors as described in claim 5, characterized in that, The power spectral density transformation is performed on each electrical activity segment in the sequence of electrical activity segments, and the characteristic frequency band of the specific response to painful muscle spasms is determined. The concentration of peak power frequency and the variability of average power energy of each electrical activity segment within the characteristic frequency band are extracted, specifically including: Power spectral density transformation is performed on each electrical activity segment in the electrical activity segment sequence to obtain the power spectral curve of each electrical activity segment; The characteristic frequency band of the specific response to painful muscle spasm is determined based on the power spectrum curve of each electrical activity segment, and then the peak power frequency within the characteristic frequency band is extracted. The peak power frequencies of all electrical activity segments are then used to form a frequency distribution set. The concentration of peak power frequencies in the characteristic frequency band for each segment of electrical activity is calculated based on the frequency distribution set. The variability of the average power energy of each electrical activity segment within the characteristic frequency band is calculated based on the dispersion of the average power energy of each segment within the characteristic frequency band.

8. The method for monitoring burn wound recovery based on biosensors as described in claim 1, characterized in that, When the electromyographic activity index is lower than a preset stimulation threshold, the determination that the deep tissue microenvironment of the burn wound has entered a steady-state healing period based on the blood oxygen saturation and the total hemoglobin concentration index specifically includes: When the electromyographic activity index remains below the preset stimulation threshold for a continuous time window, it is determined that the painful muscle spasm of the burn wound has been relieved. Obtain the blood oxygen saturation sequence and total hemoglobin concentration index sequence within the forward backtracking time window at the current moment; The blood oxygen saturation sequence was subjected to fluctuation analysis to obtain the fluctuation amplitude and frequency of blood oxygen saturation, and the total hemoglobin concentration index sequence was subjected to stability analysis to obtain the drift amount and drift rate of the total hemoglobin concentration index. Steady-state healing period similarity matching was performed on the fluctuation amplitude and frequency of blood oxygen saturation and the drift amount and drift rate of total hemoglobin concentration index to obtain the healing period matching confidence of the deep tissue microenvironment of burn wound. When the confidence level of the healing period exceeds the preset confidence level threshold, it is determined that the deep tissue microenvironment of the burn wound has entered the steady-state healing period, and the determination result with healing period stage label and confidence level is output.

9. The method for monitoring burn wound recovery based on biosensors as described in claim 1, characterized in that, The electromyographic activity index is an index used to characterize the severity of painful muscle spasms in burn wounds.

10. A burn wound recovery monitoring device based on a biosensor, used for monitoring as described in any one of claims 1 to 9, the burn wound recovery monitoring device comprising a burn wound recovery monitoring unit, characterized in that, The burn wound recovery monitoring unit includes: The spectral signal acquisition module is used to acquire diffuse reflectance spectral signals of the burn wound substrate through a biosensor on the inside of the burn wound dressing; The deep tissue analysis module for burn wounds is used to separate the blood oxygen saturation and total hemoglobin concentration index of the deep tissues of burn wounds from the diffuse reflectance spectral signal based on the characteristic absorption wavelength of hemoglobin in the deep tissues of burn wounds. The superficial muscle tissue analysis module is used to simultaneously collect electrical activity signals of superficial muscle tissue in a resting state through biosensors attached to the muscles around the wound, and perform high-frequency power spectral density analysis on the electrical activity signals to obtain the electromyographic activity index reflecting the degree of painful muscle spasm in the burn wound. The burn wound recovery monitoring module is used to determine, based on the blood oxygen saturation and the total hemoglobin concentration index, that the deep tissue microenvironment of the burn wound has entered a steady-state healing period when the electromyographic activity index is lower than a preset stimulation threshold.

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