Patent Description:
The metabolic process of the human body is a biological oxidation process, and the oxygen required for the metabolic process enters the human blood through the respiratory system, and is combined with reduced hemoglobin (Hb) in the red blood cells of the blood to form oxyhemoglobin (HbO2), which is then transported to tissue cells of various parts of the human body. When there is an imbalance between oxygen supply and oxygen consumption in the body, it means that hypoxic conditions occur. Hypoxia has a huge impact on the body. Therefore, a real-time monitoring of arterial blood oxygen concentration is very important in clinical care.

Blood oxygen saturation (SpO2) is the percentage of oxyhemoglobin (HbO2) capacity to the total hemoglobin (Hb + HbO2) capacity in the blood, i.e., the concentration of blood oxygen in the blood. As an indirect reflection of arterial blood oxygen saturation (SaO2), the blood oxygen saturation (SpO2) is quickly adopted and used by various application fields because of its non-invasive, simple, accurate and rapid characteristics.

The application of pulse oximetry is so extensive that the requirements for the accuracy of pulse oximeters are also rising. As will be appreciated by those skilled in the art, there are two key factors in evaluating the pros and cons of the performance of the pulse oximetry: weak perfusion performance and movement performance. If the patient under test has a poor weak perfusion performance or a poor movement performance, the accuracy and stability requirements for the performance of the pulse oximetry are more demanding.

Masimo has quickly established its leading position in the blood oxygen industry since the introduction of Masimo's "a technique for an accurate measurement of patient blood oxygen saturation under movement and weak perfusion" in <NUM>. The core idea of the technique is to establish coefficients Ra and Rv, where Ra is related to an arterial blood oxygen saturation and Rv is related to a venous blood oxygen saturation. By means of the R coefficients, infrared light and red light, a reference signal may be constructed. The energy of the reference signal is related to the coefficients. At the coefficients Ra and Rv, the maximum energy may be reached. Therefore, the coefficient of blood oxygen saturation from <NUM> to <NUM>% is traversed, and the coefficients Ra and Rv are found to calculate the arterial blood oxygen saturation. This method requires a high amount of computation, and the hardware cost of implementing same is relatively high.

It may be understood that, based on Lambert - Bear's law, the oxyhemoglobin (HbO2) and deoxyhemoglobin (HB) have different absorption characteristics in different light bands, as shown in <FIG>, which is an oxyhemoglobin and reduced hemoglobin absorption spectrum curve; and the blood oxygen saturation parameter may be further evaluated by using the absorption characteristics.

As shown in <FIG>, which is a working diagram of blood oxygen measurement in the prior art, a light emitting tube is configured to emit two beams of light (for example, red light and near-infrared light) for transmitting through body tissues, and a receiving tube is configured to receive light signals transmitted through the body tissues, so as to obtain the amount of blood pulsating component alternating current (AC) (for example, arterial blood) and the amount of non-pulsating component direct current (DC) (for example, venous blood, muscle, bone, skin, etc.). By using the amount of alternating current (AC) and the amount of direct current (DC) of red and near-infrared light respectively, the ratio of the mapping curve to the arterial blood oxygen saturation (SaO2), that is, a R coefficient table (as shown in Equation <NUM>) may be obtained. The infinite approximation pulse oxygen saturation (SpO2) of arterial blood oxygen saturation (SaO2) may then be obtained based on a look-up table method. <MAT> where ACRed = the amount of red light detected, i.e., the amount of alternating current; DCRed = the maximum transmission amount of red light detected, i.e., the amount of direct current; ACIred = the amount of infrared light detected; and DCIred = the amount of direct current of infrared light detected.

However, how to accurately identify the AC and DC components in an acquired signal and obtain the accurate R values are not so easy. There are two technologies in the prior art: time domain technology and frequency domain technology.

The time domain technology has the characteristics of fast response speed and clear phase information. Since the time domain signal is a mixture of the useful signal and the noise signal, the noise outside a physiological bandwidth may be easily filtered by a high pass/low pass filter; whereas when the noise within the physiological bandwidth occurs, due to the variability of the noise and lack of prior knowledge, the time domain method has almost no way to perform filtering processing on out this part of the noise. Therefore, the time domain technology has natural defects in anti-movement performance.

The frequency domain technology theoretically may separate the frequency bands of noise and useful signals, thereby achieving the purpose of distinguishing and identifying real signals. According to the definition of digital signal processing, any waveform is composed of multiple sinusoidal waves, and the pulse wave of a physiological parameter is no exception. Therefore, when the physiological pulse wave signal is converted into a frequency domain signal, the physiological parameter exhibits the fundamental and multiplied frequency characteristics. When interference occurs, the interference frequency spectrum and the fundamental and multiplied frequency spectrum of the physiological parameter are aliased together, and it is very difficult to identify which one is the true frequency spectrum. Therefore, although the frequency domain technology has congenital advantages, it is also very difficult to accurately calculate the blood oxygen related parameter under an interference condition.

According to Parseval's theorem, the total energy of the time domain is equal to the total energy of the frequency domain of the signal. Therefore, Equation <NUM> is also applicable to the frequency domain signal, wherein the AC amount corresponds to the energy change at each frequency point. Under an ideal condition, after normalization, the red and infrared frequency spectrum of the pulse wave is as shown in <FIG>, and the ratio of the main frequency band and various multiplied frequency bands may be regarded as the corresponding energy ratio in this frequency band. The meaning of the energy ratio is completely consistent with the meaning of the corresponding R values in Equation <NUM>, that is, it corresponds uniquely to the blood oxygen saturation. In an ideal state without considering noise, the ratio of each frequency band is the same, which corresponds uniquely to the current blood oxygen saturation. In theory, the blood oxygen saturation parameter may be obtained in any frequency band.

Similarly, according to the composition theory of the signal, it may be seen that the frequency at which the fundamental frequency peak is located in <FIG> is the pulse rate value. The method of routinely detecting the fundamental frequency peak frequency may be obtained by identifying and screening the fundamental and multiplied frequency peaks, based on the theory that the fundamental frequency peak and the harmonic peak have a proportional relationship between energy and frequency. The physiological pulse rate value may be identified by means of this proportional relationship.

In summary, the pulse rate parameter and the blood oxygen parameter may be obtained by retrieving the position information about the fundamental and multiplied frequency peaks in the frequency domain signal and the energy ratios of the red light and the infrared light. However, in the case of interference, the information about the frequency spectrum peaks of the red light and the infrared light may be confused and annihilated by the noise, and the pulse rate may be abnormally calculated due to the unrecognizable or misidentified fundamental frequency peak frequency information; and at the same time, the energy ratios of the red light and the infrared light also result in that the blood oxygen calculation deviation is relatively large due to the mixing of noise. As shown in <FIG>, an illustration of the frequency spectrum distribution under interference is given. It may be seen from <FIG> that the frequency spectrum peak energy ratios of the infrared light and the red light are suddenly large and small, and the fundamental frequency peak is almost annihilated. In this case, it is almost impossible to correctly identify the frequency spectrum peak by using the existing time-frequency domain technology and calculate the accurate blood oxygen and pulse rate parameters Relevant prior art is disclosed in <CIT>.

The technical problem to be solved by the embodiments of the present disclosure is to provide a method for calculating a physiological parameter and corresponding medical equipment, which may improve the accuracy of calculating the physiological parameter (such as the pulse rate value and the blood oxygen parameter) under weak irrigation and movement conditions, and has a low computational complexity and a low demand for computing resources.

In order to solve the above technical problem, an embodiment of the present disclosure provides a method for calculating a physiological parameter, the method comprising :.

Correspondingly, in another aspect of the present disclosure, further provided is medical equipment for calculating a physiological parameter, comprising:.

Correspondingly, in another aspect of the present disclosure, further provided is a method for calculating a physiological parameter, the method comprising:.

Correspondingly, in another aspect of the present disclosure, further provided is a medical equipment for calculating a physiological parameter, comprising:.

The embodiments of the present disclosure have the following beneficial effects:.

In addition, the methods provided in the embodiments of the present disclosure have low computational complexity and low demand for computing resources.

In order to more clearly illustrate the embodiments of the present disclosure or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below.

In order to make the purposes, technical solutions and advantages of the present disclosure clearer and more comprehensible, the present disclosure is further illustrated in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the particular embodiments described herein are only used for illustrating the present disclosure and not intended to limit the present disclosure.

As shown in <FIG>, which shows a schematic diagram of a main process of an embodiment of a method for calculating the accuracy of a physiological parameter provided by the present disclosure, in this embodiment, the compensation process of a venous oxygen compensation (VOC) method is shown, and specifically, the method comprises the following steps:.

In an example, the following method may be used to construct the compensation coefficient:.

Step S16 of compensating for at least one of the time domain signal and the frequency domain signal by using the compensation coefficient, and calculating the physiological parameter based on at least one of the compensated time domain signal and frequency domain signal, wherein the physiological parameter is at least one of blood oxygen, pulse rate, waveform area, and perfusion index.

Taking the physiological parameter being the blood oxygen saturation parameter as an example below, a further detailed flowchart in <FIG> is illustrated.

Firstly, in step S10, a section of a time domain signal is selected, which corresponds to at least one signal of red light and infrared light obtained by sampling, and a time-to-frequency domain transformation is performed on the section of the time domain signal to obtain a corresponding frequency domain signal;.

Specifically, in some embodiments, the process of constructing the compensation coefficient may be obtained by the following method:.

The step of obtaining the repetition factor in a predetermined manner is specifically:.

As shown in <FIG>, which shows a schematic diagram of a main process of another embodiment of a method for calculating the accuracy of a physiological parameter provided by the present disclosure, in this embodiment, the compensation process of a power spectrum array (PSA) method is shown, and specifically, the method comprises the following steps:.

As shown in <FIG>, which shows a schematic diagram of a main process of another embodiment of a method for calculating the accuracy of a physiological parameter provided by the present disclosure, in this embodiment, a venous oxygen compensation (VOC) method and a power spectrum array (PSA) method are combined in the compensation process, and specifically, the method comprises the following steps:.

The specific process of constructing the stability coefficient according to the frequency spectrum peak energy ratio sequence, and constructing the compensation coefficient may be referred to the foregoing description of step S14 in <FIG>;
and the specific process of constructing the stability coefficient according to the frequency spectrum peak position sequence, and constructing the compensation coefficient may be referred to the foregoing description of step S24 in <FIG>, so that they will not be described in detail herein.

Step S36 of compensating for at least one of the time domain signal and the frequency domain signal by using the compensation coefficient, and further calculating the physiological parameter based on at least one of the compensated time domain signal and frequency domain signal, wherein the physiological parameter is at least one or more of blood oxygen, pulse rate, waveform area, and perfusion index.

Specifically, the step comprises at least one of the following: using the compensation coefficient to perform a gain processing on the selected time domain signal to achieve compensation; or
using the compensation coefficient to perform filtering processing on the selected frequency domain signal to achieve compensation.

Correspondingly, the present disclosure further provides a medical equipment for calculating a physiological parameter.

It will be understood that the method for calculating a physiological parameter disclosed by the present disclosure may be implemented by means of functional modularization, and integrated as a plug-in into other auxiliary diagnostic devices (such as a monitoring device, a defibrillator, an AED, an automatic resuscitation instrument, an electrocardiograph, etc.), or may be made as a single-parameter medical apparatus for monitoring a related physiological parameter, wherein the physiological parameter comprises at least one of a blood oxygen parameter, a pulse rate parameter, a waveform area parameter, and a perfusion index parameter.

As shown in <FIG>, which shows a schematic structural diagram of medical equipment disclosed in the present disclosure, in which a single-parameter medical apparatus is shown; and in this embodiment, the medical equipment comprises:.

Further, the medical equipment comprises: a display unit connected to the digital processor to display the physiological parameter calculated by the digital processor; and/or
a communication unit connected to the digital processor to send the physiological parameter calculated by the digital processor.

The blood oxygen sensor includes two types of transmissive and reflective, and may be generally worn on a part of the human subject, such as the finger, the forehead, the earlobe, the toe and the sole, for measuring a physiological parameter comprising at least one of a blood oxygen parameter, a pulse rate parameter, a waveform area parameter, and a perfusion index parameter. The display unit may be a local display unit, or may be remotely communicated to a remote display unit in a wired/wireless manner. The display unit provides the user with a perceptible parameter representation by means of characters, values, waveforms, bar graphs, voice prompts, etc..

In one embodiment of the present disclosure, the at least some of the characteristics of the physiological tissue is one or more of the optical characteristics of oxygenated hemoglobin, deoxyhemoglobin, methemoglobin, total hemoglobin and carbon monoxide in the blood.

In one embodiment of the present disclosure, the rational frequency spectrum peaks satisfy at least one of a frequency spectrum energy relationship, a frequency spectrum amplitude relationship, a frequency spectrum positional relationship, and a frequency spectrum peak morphological relationship.

In one embodiment of the present disclosure, the stability coefficient is constructed based on deviation statistics for energy ratios of the red light and the infrared light, or is an empirical coefficient.

In one embodiment of the present disclosure, the stability factor adjusts a stability weight thereof over time according to at least one of the characteristics of a fundamental and multiplied frequency group and a frequency spectrum peak morphological rationality.

In one embodiment of the present disclosure, the process of constructing, by the digital processor, the compensation coefficient by using the frequency spectrum peak energy ratio sequence is specifically:
selecting the mean value and converting same by means of a coefficient table to a denominator of a compensation coefficient calculation formula, and selecting (the mean value + the standard deviation) or (the mean value - the standard deviation) and converting same by means of a coefficient table to a numerator of the compensation coefficient calculation formula, and calculating a ratio of the numerator to the denominator to obtain the compensation coefficient.

In one embodiment of the present disclosure, the physiological parameter is at least one of blood oxygen, pulse rate, waveform area, perfusion index, etc..

In order to better understand the function and working principle of the digital processor in this embodiment, the following will be described in conjunction with a specific example.

As shown in <FIG>, which shows a schematic structural diagram of an embodiment of a digital processor used in medical equipment for calculating a physiological parameter provided by the present disclosure, the digital processor uses a venous oxygen compensation (VOC) method for compensation. Reference is also made to <FIG>, specifically, the digital processor comprises:.

The compensation coefficient construction unit <NUM> further comprises:.

For more details, reference may be made to the aforementioned description of <FIG>.

Correspondingly, the present disclosure further provides a medical equipment for calculating a physiological parameter, in this embodiment, the medical equipment comprises:.

Further, the medical equipment comprises: a display unit connected to the digital processor to display the physiological parameter calculatied by the digital processor; and/or
a communication unit connected to the digital processor to send the physiological parameter calculated by the digital processor.

In one embodiment of the present disclosure, the two optical signals are red light and infrared light.

In one embodiment of the present disclosure, the rational frequency spectrum peaks satisfy at least one or more of a frequency spectrum energy relationship, a frequency spectrum amplitude relationship, a frequency spectrum positional relationship, and a frequency spectrum peak morphological relationship.

In one embodiment of the present disclosure, the stability coefficient is constructed based on at least one of a frequency spectrum energy ratio deviation, a frequency spectrum blood oxygen deviation, a state of a fundamental and multiplied frequency group, a number of rational frequency spectrum peaks, and a frequency spectrum peak morphological rationality.

In one embodiment of the present disclosure, whether the stability coefficient is stable is determined according to at least one of a number of stability factors and weight values.

For more details, reference may be made to the aforementioned description of <FIG>; and at the same time, in order to better understand the function and working principle of the digital processor in this embodiment, the following will be described in conjunction with a specific example.

As shown in <FIG>, which shows a schematic structural diagram of a further embodiment of a digital processor used in medical equipment for calculating a physiological parameter provided by the present disclosure, the digital processor uses a power spectrum array method (PSA) for compensation. Reference is also made to <FIG>, specifically, the digital processor comprises:.

The compensation coefficient construction unit <NUM> comprises:.

In one embodiment of the present disclosure, the stability coefficient is constructed based on at least one of a frequency spectrum energy ratio deviation, a frequency spectrum blood oxygen deviation, a state of a fundamental and multiplied frequency group, and a number of rational frequency spectrum peaks.

In one embodiment of the present disclosure, the compensation coefficient is an energy ratio deviation coefficient calculated based on the frequency spectrum peak energy ratio sequence, or a position coefficient statistically obtained over time based on the frequency spectrum peak position sequence.

In one embodiment of the present disclosure, the process of compensating for at least one of the time domain signal and the frequency domain signal by using the compensation coefficient is specifically:.

As shown in <FIG>, which shows a schematic structural diagram of a further embodiment of a digital processor used in medical equipment for calculating a physiological parameter provided by the present disclosure, the digital processor uses a combined venous oxygen compensation (VOC) method and power spectrum array method (PSA) for compensation. Reference is also made to <FIG>, specifically, the digital processor comprises:.

The compensation processing unit <NUM> further comprises:.

In order to facilitate a better understanding of the present disclosure, the principles and implementation processes of the venous oxygen compensation (VOC) and power spectrum array (PSA) methods referred to in the foregoing will be further explained in conjunction with practical examples.

The applicants believe that under a non-interference condition, venous blood flows relatively slowly due to its physiological characteristics and may be considered as part of the direct current (DC) amount, and the venous blood oxygen saturation does not have any effect on the normal blood oxygen measurement. Under an interference condition, the venous blood is affected by the interference, a venous pulsation is generated, and the alternating current (AC) amount formed by the venous pulsation will be mixed into the AC amount formed by the arterial blood pulsation. According to Equation <NUM> described above, the oxygen saturation calculated at this point must deviate from the true value; from the physiological point of view, it will be understood that the venous blood oxygen saturation is mixed in the arterial blood oxygen saturation, resulting in that the final blood oxygen saturation deviates from a blood gas value.

As shown in <FIG>, a schematic diagram of a venous blood pulsation interfered with an arterial blood pulsation is given. In the case where random interference source characteristics may not be obtained, the time domain algorithm has almost no way to accurately calculate the blood oxygen saturation value; in addition, it is very difficult to obtain the random interference source characteristics.

As mentioned in the background art, each frequency spectrum band in the frequency domain signal uniquely corresponds to the blood oxygen saturation. Theoretically, the blood oxygen saturation may be calculated for each frequency spectrum band; and due to the randomness of the venous interference, interferences superimposed on different frequency bands also have different weights. The venous oxygen compensation (VOC) method proposed in the present application is constructed based on the two hypothetical models. When the blood oxygen value deviation between the frequency spectrum bands is relatively large, it indicates that the venous pulsation has interfered with the real blood oxygen result, and a difference distribution feature is obtained by collecting and analyzing the change in the blood oxygen value deviations of the frequency spectrum bands, and based on this, the compensation coefficient is constructed to eliminate the interference in a blood oxygen sampling signal, and finally the parameter result infinitely approximating the true physiological blood oxygen may be obtained by recalculating the sampling signal with the interference eliminated.

Therefore, the process of the venous oxygen compensation (VOC) method is as follows:.

As shown in <FIG>, the calculation process of the compensation coefficient is given.

In a first step, the difference between vMax and vMin is compared with a first threshold (Threshold1), and the vStd value is compared with a second threshold (Threshold2) to determine whether a normal mode or an interference mode is selected. The first threshold and the second threshold are selected to be empirical coefficients obtained according to the characteristics of the physiological parameter and the characteristics of a blood oxygen system. For example, in an example, the first threshold (Threshold1) may be <NUM>% of a blood oxygen deviation, and the second threshold (Threshold2) may be <NUM>% of the blood oxygen deviation.

In a second step, if the normal mode is selected, it indicates that the fluctuation caused by the venous oxygen is relatively small, and the mean value and the standard deviation may be selected to be the input source of the compensation coefficient calculation formula. The mean value is used as the denominator input source, if the number of blood oxygens exceeding the mean value in the blood oxygen sequence accounts for at least half of the total number of the sequence, vMean + vStd is used as the numerator, otherwise, vMean - vStd is used as the numerator.

In a third step, which is alternative to the second step, if the abnormal mode is selected, it is necessary to introduce a repetition factor. There are two sources of repetition factors: <NUM>) the maximum blood oxygen set, which satisfies a certain value (e.g., ± <NUM>%), in a blood oxygen sequence is collected and analyzed, and the mean value of the set is taken as the repetition factor, wherein ± <NUM>% here is an empirical coefficient which may be adjusted according to the actual change; and <NUM>) a stable segment of the historical trend of the blood oxygen is selected, for example, the stable trend of <NUM> - <NUM>, the mean value of the set is calculated as the repetition factor, and the time period of <NUM> - <NUM> is also an empirical coefficient which may be adjusted according to the actual change. This repetition factor is used as the numerator input source, and the mean value is used as the denominator input source.

In a fourth step, the numerator and denominator parameters are input into Formula <NUM> below to calculate the compensation coefficient. The calculation formula is as follows, where tabR is a mapping table of blood oxygen and R curve coefficients (as described above), and the corresponding R coefficient values may be obtained by means of inverse look-up according to the input blood oxygen value. The compensation coefficient is the ratio of the R coefficient value of the numerator blood oxygen to the R coefficient value of the denominator blood oxygen.

Factorcompensation is the compensation coefficient, tabR(Numerator) is the R coefficient value obtained by means of look-up after the numerator is used as the input source and substituted into the mapping table of blood oxygen and R curve coefficients, and tabR(Denominator) is the R coefficient value obtained by means of look-up after the denominator is used as the input source and substituted into the mapping table of blood oxygen and R curve coefficients.

The following will be combined with a practical example to illustrate how to calculate a compensation coefficient in the venous oxygen compensation (VOC) method. As shown in <FIG>, a schematic diagram of blood oxygen distribution in a frequency spectrum band under an interference condition is given, assuming that the frequency spectrum signal has four frequency spectrum peaks A, B, C and D in a physiological bandwidth of <NUM> - <NUM>, and assuming that the frequency spectrum signal is affected by a random pulsation of venous oxygen, and the oxygen saturation valuesof the frequency spectrum peaks obtained by converting the ratio of infrared light to red light are <NUM>%, <NUM>%, <NUM>%, <NUM>%, respectively. There is a deviation between the calculated valuesof the four blood oxygen saturation values. By collecting and analyzing the deviation distribution feature, the following may be respectively obtained: vMean = <NUM>%, vStd = <NUM>%, vMax = <NUM>%, and vMin = <NUM>%. It is determined that the deviation of vMax from vMin is less than the first threshold (e.g., <NUM>%), and vStd is less than the second threshold (e.g., <NUM>%), so the normal mode is selected. At this point, the number of blood oxygens exceeding the mean value is <NUM>, accounting for <NUM>% of the total blood oxygen sequence, so the numerator is selected as vMean + vStd = <NUM>%, and the denominator is vMean = <NUM>%. The R values corresponding to the numerator and the denominator are respectively obtained by looking up the mapping table of blood oxygen and R curve coefficients, and are substituted into Formula <NUM>, so that a correction factor of about <NUM> may be calculated.

Again, the compensation coefficient is used to compensate for the loss of the time domain signal due to interference (the frequency domain signal is obtained by transforming this signal). That is, the time domain signal used for the frequency domain transformation is multiplied by the compensation coefficient to obtain a compensation signal. In an example of the present application, compensation is only made for the red light signal, as an example. However, in practical applications, the compensation coefficient may also be divided to achieve compensation for each signal.

Finally, the time domain signal that completes the compensation is used to be transformed to the frequency domain signal again, and then the frequency domain signal is used to calculate the accurate parameter such as blood oxygen. The embodiment of the present disclosure is an example given based on a frequency domain algorithm. In the actual application, the frequency domain method may also be omitted, and a time domain algorithm is used to obtain the accurate parameter such as blood oxygen based on the compensated time domain signal.

As shown in <FIG>, a schematic diagram of a framework process of a venous oxygen compensation (VOC) method is given. The entire process is: selecting a section of a time domain signal, and performing a time-to-frequency domain transformation on same to transform the time domain signal to a frequency domain signal. The characteristics of the physiological signal and the basic knowledge of digital signal processing (e.g., a physiological pulse wave frequency range, a fundamental and multiplied frequency principle, morphological features, etc.) are used, a rational frequency spectrum peak is selected and the blood oxygen saturation parameter of the frequency spectrum peak is calculated. According to the foregoing steps, a series of characteristic information is obtained by statistically analyzing a series of blood oxygen distribution, and according to the characteristic information, whether the blood oxygen saturations have a deviation (or a small deviation) is determined. If not, the result of the blood oxygen parameter is output; otherwise, the compensation coefficient is calculated based on the characteristic information and is compensated into the time domain signal. Finally, the compensated time domain signal is then used to re-perform a time-to-frequency domain transformation, the rational frequency spectrum peak is selected based on the new frequency domain signal, and the final blood oxygen saturation parameter is calculated and output.

It may be seen that the venous oxygen compensation (VOC) method may eliminate the measurement deviation of blood oxygenation caused by interference, infinitely approach the true physiological blood oxygen value, and greatly provide the accuracy of the calculation of the blood oxygen parameter under the interference condition.

As described in the background art, when an interference exists and severely disturbs the frequency spectrum signal, it is difficult to identify physiological frequency spectrum information from the frequency spectrum. For example, in the frequency spectrum diagram shown in <FIG>, there is no way to identify the information about the fundamental peak based merely on the energy and frequency relationships of the fundamental and multiplied frequency principle. How to identify the physiological frequency spectrum peak under an interference condition? The present application proposes a hypothesis theory based on the characteristics of random distribution of noise. Any type of interference, such as weak perfusion, limb rubbing, finger shaking, etc., is presented as a random interference component in a blood oxygen sampling signal, and the intensity of the interference is positively correlated with the intensity of movement of the measuring end. Considering the state of the physiological characteristics, most of the interferences are presented as random white noise distribution; and a few thereof are relatively regular interferences (e.g., Parkinson's disease), because of the relatively low amplitude of vibration, the vibration frequency is relatively high, but has no substantial effect on the blood oxygen sampling signal, that is, does not affect the measurement of the blood oxygen parameter. Although the interference signal disturbs the frequency spectrum signal such that the physiological frequency spectrum may not be recognized, no matter how the interference signal changes, the characteristics of the physiological frequency spectrum peak always exist at a certain frequency point and will not change or change slowly in a certain period of time (the physiological characteristics); and at the same time, the interference noise is randomly distributed, and the characteristics of the frequency spectrum change over time. By arranging the frequency spectrum at each moment in chronological order and observing the characteristics of the frequency spectrum signal longitudinally, it may be found that when the amplitudes and positions of most of the frequency spectrum peaks change, there is always at least one frequency spectrum peak, the position of which is relatively stable and does not shift. This frequency spectrum peak is the physiological frequency spectrum peak (fundamental frequency peak). If an identification algorithm may be used to find the frequency spectrum peak, it means that the correct pulse rate value is found. This is the core idea of the power spectrum array method.

In the present application, the general process of the power spectrum array (PSA) method is as follows:.

Again, the true physiological frequency spectrum peak is identified according to the information about the cache. According to the fundamental and multiplied frequency principle, the multiplied frequency peaks in the PeakArray are eliminated; at the same time, the weight coefficients of the multiplied frequency peaks are converted according to the frequency multipliers and added to the weight coefficient of the fundamental frequency peak (for example, the fundamental frequency peak weight coefficient is <NUM>, the weight coefficient of a <NUM>-multiplied frequency peak is <NUM>, the weight coefficient of <NUM> is divided by the frequency multiplier of <NUM> to obtain the adjusted weight coefficient of <NUM>, which is added to the weight coefficient of the fundamental frequency peak, that is, the weight coefficient of the fundamental frequency peak is adjusted to <NUM>), and the information about the multiplied frequency stored in the PeakArray and WeigtedArray is eliminated, i.e., is initialized to <NUM>. The detailed process is as shown in <FIG>, at this point, it may be assumed that each frequency spectrum peak is the fundamental frequency peak, all the other frequency spectrum peaks are traversed, and it is determined whether the other frequency spectrum peaks and the assumed fundamental frequency peak satisfy a frequency multiplication relationship, and if so, the information about the multiplied frequency peaks is eliminated; otherwise, remaining the information. According to the characteristics of the energy attenuation of the fundamental and multiplied frequencies, and the physiological characteristics, in general, the influence of the multiplied frequency having a multiplier more than <NUM> is relatively small, which may be selected and set according to the requirements of the system in practical applications. Following a similar operation, each frequency spectrum peak is traversed, to complete the elimination of the information about the multiplied frequency peaks. Finally, the peaks with the first three largest weight coefficients are selected as the input information for the next step.

Finally, it is determined whether the maximum weight coefficient of the frequency spectrum peak is greater than or equal to a set threshold (for example, the threshold may be set to be <NUM>, indicating that there is a stable frequency spectrum peak for <NUM> consecutively, which may be selected and set according to the requirements of the system in practical applications). If two or more frequency spectrum peaks satisfy the weight coefficient greater than or equal to the set threshold, there is a need to further make a determination based on the physiological characteristics. For example, under an interference condition, the physiological pulse rate may not be too high and may not be relatively low. The most rational frequency spectrum peak that satisfies the set threshold is selected, the pulse rate parameter is calculated and output, and the weight coefficients of the other frequency spectrum peaks are optimized according to the state at the same time.

As shown in <FIG>, a schematic diagram of a process of a power spectrum array (PSA) method is given. It may be seen that the frequency spectrum peak information cache is firstly established, and the cache is filled in chronological order. When the cache is filled up, the stored frequency spectrum peaks are simplified according to the fundamental and multiplied frequency principle, and the frequency spectrum peaks that satisfy the set threshold are selected from the simplified frequency spectrum peaks. If the number of frequency spectrum peaks that satisfy the condition is greater than or equal to two, an exclusion operation is performed on same according to the criterion of converting the physiological characteristics, and finally one rational frequency spectrum peak is selected, and the pulse rate parameter is calculated based on this frequency spectrum peak. The power spectrum array (PSA) method may eliminate the measurement deviation of the pulse rate caused by interference, may accurately identify the physiological frequency spectrum information even under the long-term interference condition, and greatly provides the accuracy of the calculation of the pulse rate parameter under the interference condition.

In summary, the venous oxygen correction (VOC) method may identify the interference of venous oxygen and compensate for the blood oxygen deviation caused by the interference, and the power spectrum array (PSA) method may accurately identify the pulse rate information in a continuous interference. Each of the two methods has the ability to identify and process the interference. Therefore, when the two methods are combined, the identification and suppression of the interference may be significantly improved, thereby obtaining the great improvement in the accuracy of the calculation of the blood oxygen parameter and the accuracy of the pulse rate parameter.

As shown in <FIG>, an example of the combined application of the two solutions in the method provided by the present disclosure is shown. In practical applications, these steps may be adaptively added, deleted, and adjusted according to the requirements of the system. The general process is as follows: selecting a specified section of a time domain signal, performing a time-to-frequency transformation on same, and perform a retrieval and identification on frequency spectrum peaks based on the transformed frequency spectrum signal, and calculating a blood oxygen value of each rational frequency spectrum peak at the same time. The blood oxygen values of the rational peaks are collected and analyzed to obtain relevant statistical information to prepare for the VOC, and the information about all the frequency spectrum peaks obtained by the current calculation is also recorded to prepared for the PSA. If a pair of fundamental and multiplied frequency groups are identified and the relative deviation of the blood oxygen values of the frequency spectrum peaks is relatively small, the fundamental frequency peak is directly selected to calculate the pulse rate, and the finally resulting blood oxygen and pulse rate values are output. If the pair of fundamental and multiplied frequency groups is not satisfied, the PSA method is implemented, and the rational frequency spectrum peak is calculated. If the time domain signal with the noise/compensated venous oxygen being eliminated is based, but the blood oxygen value is unstable (for example, it does not match the historical trend result), only the pulse rate parameter is calculated and output; otherwise, if the blood oxygen value is stable, the pulse rate value is calculated, and the finally resulting blood oxygen and pulse rate values are output; if the first calculation is performed (the noise/compensated venous oxygen is not eliminated from the time domain signal) and the blood oxygen value is unstable, a VOC identification branch is implemented. A specific filter of the frequency spectrum peaks obtained based on the PSA method is added to the VOC identification branch. Combining the compensation coefficient with the specific filter, the time domain signal is compensated and the noise is eliminated, and then the time-to-frequency domain transformation is performed again and the relevant parameter is calculated.

In summary, the methods and the medical equipment provided in the embodiments of the present disclosure have the following beneficial effects:.

Claim 1:
A computer-implemented method for calculating a physiological parameter, comprising:
- selecting a section of a time domain signal (S10, S20, S30) corresponding to at least one signal of red light and infrared light obtained by sampling, and performing a time-to-frequency domain transformation on the section of the time domain signal to obtain at least one frequency domain signal;
- selecting all rational frequency spectrum peaks (S12, S22, S32) from the frequency domain signal, calculating energy and position information of the selected rational frequency spectrum peaks, and forming a frequency spectrum peak energy ratio sequence and a frequency spectrum peak position sequence;
- constructing a stability coefficient (S14, S24, S34) according to the frequency spectrum peak energy ratio sequence and the frequency spectrum peak position sequence, and
- if the stability coefficient is less than or equal to a set threshold, the stability coefficient is determined to be relatively low, and the method further comprises:
(i) constructing a compensation coefficient by using the frequency spectrum peak energy ratio sequence and the frequency spectrum peak position sequence; and
(ii) compensating for at least one of the time domain signal and the frequency domain signal by using the compensation coefficient (S16, S26, S36), and
(iii) calculating the physiological parameter based on at least one of the compensated time domain signal and frequency domain signal;
- if the stability coefficient is higher than the set threshold, the method further comprises: calculating the physiological parameter based on at least one of the time domain signal and frequency domain signal without compensation.