CN117717332A - Oximeter data processing method, device, medium and equipment - Google Patents
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- 238000003672 processing method Methods 0.000 title claims description 8
- 238000000034 method Methods 0.000 claims abstract description 38
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- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 claims description 19
- 238000004364 calculation method Methods 0.000 claims description 19
- 229910052760 oxygen Inorganic materials 0.000 claims description 19
- 239000001301 oxygen Substances 0.000 claims description 19
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- 210000004369 blood Anatomy 0.000 claims description 12
- 238000005259 measurement Methods 0.000 abstract description 17
- 229910003798 SPO2 Inorganic materials 0.000 description 11
- 101100478210 Schizosaccharomyces pombe (strain 972 / ATCC 24843) spo2 gene Proteins 0.000 description 11
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Abstract
The invention relates to the technical field of signal processing, and provides a method, a device, a medium and equipment for processing data of an oximeter. The invention is suitable for the situation that continuous and large-amplitude motion does not occur at the input signal end of the measurement frame, and can obviously improve the accuracy of the final output result.
Description
Technical Field
The invention relates to the technical field of signal processing, and is characterized by relating to a method, a device, a medium and equipment for processing oximeter data.
Background
When the blood oxygen saturation measuring instrument is used, the finger grip is reliably fixed on the fingertip, the instrument continuously and alternately emits infrared light and red light at the finger grip, and weak signals scattered by the fingertip are received at the other side of the emitting end. The value of the blood oxygen saturation is calculated indirectly by measuring the change in the absorption of the finger to light of both wavelengths.
However, the movement of the subject is easily disturbed during the measurement. Therefore, it is necessary to remove the interference of the motion noise. In the prior art, when the motion noise is processed, the red light original signal and the infrared light original signal are directly filtered in the continuous measurement process of equipment, so that the noise interference caused by the motion is reduced; filtering the measured value signals obtained after operation, and reducing interference of abnormal values in the measured value signals on a display result; and judging whether the signal is interfered by motion noise or not according to the characteristics of the red light original signal and the infrared light original signal.
In the scheme in the prior art, in the process of processing an original signal, interference noise caused by motion is diversified, and most algorithms are difficult to filter with high robustness, so that the noise generated by measurement errors can only be reduced in the pretreatment of the original signal, but the noise generated by motion is difficult to play an effective role; the conventional digital filtering methods, such as an FIR filter and an IIR filter, have difficulty in adaptively adjusting the weight of an input signal, so that it is difficult to thoroughly eliminate the influence of an error signal on a result, thereby causing inaccuracy of the result.
Therefore, finding an adaptive blood oxygen saturation test method is a problem to be solved by those skilled in the art.
Disclosure of Invention
Based on the above, it is necessary to provide a method, a device, a medium and a device for processing data of an oximeter, so as to solve the technical problem that in the prior art, when the oxygen saturation is measured, the output result is inaccurate due to the influence of movement.
In a first aspect, the present invention provides a method for processing oximeter data, comprising:
acquiring frame input signals, wherein each frame input signal comprises a frame input signal of each discrete frame from a starting moment to a target moment;
calculating the confidence coefficient of the frame input signal to obtain a frame confidence coefficient, superposing the frame input signal to an output signal according to the frame confidence coefficient, and estimating a next frame input signal according to the frame input signal to obtain a next frame estimated value;
and calculating the confidence coefficient of the next frame for the next frame input signal which is actually acquired based on the estimated value of the next frame, and superposing the next frame input signal according to the confidence coefficient of the next frame until the output signal of the target moment is obtained through calculation.
Further, the confidence coefficient of the frame input signal is calculated to obtain a frame confidence coefficient, the frame input signal is overlapped to an output signal according to the frame confidence coefficient, and the next frame input signal is estimated according to the frame input signal to obtain a next frame estimated value; calculating a next frame confidence coefficient for a next frame input signal which is actually acquired based on a next frame estimated value, and superposing the next frame input signal according to the next frame confidence coefficient until a target moment output signal is calculated, wherein the method comprises the following steps:
collecting a frame input signal describing blood oxygen saturation; the first frame and the second frame are two adjacent frames from the starting time to the target time, and the time of the second frame is after the time of the first frame;
estimating the frame input signal of the second frame according to the second frame input signal and the first frame estimated value to obtain a second frame estimated value;
calculating a second frame standard deviation based on the second frame input signal, the first frame estimated value and the first frame standard deviation;
calculating the confidence coefficient of the actually input second frame input signal according to the second frame estimated value, the second frame standard deviation and the second frame input signal to obtain a second frame confidence coefficient;
superposing the second frame input signal to the first moment output signal based on the second frame confidence level to calculate a second moment output signal;
and repeatedly acquiring the input signal of the next frame and calculating until the output signal of the target moment is obtained.
Further, the second frame input signal is overlapped to the first time output signal based on the second frame confidence level to calculate a second time output signal; repeatedly acquiring the input signal of the next frame and calculating until the output signal of the target moment is obtained; comprising the following steps:
calculating a target time output value by using the following formula, wherein the target time output value describes the blood oxygen saturation measured in a time period from the starting time to the target time;
Y t ＝[1-a t (1-γ)]Y t - 1 +a t ((1-γ)x t
wherein x is t Representing a t-th frame input signal; a, a t Representing the confidence of the t frame; y is Y t - 1 Indicating the output signal at time t-1, i.e. the time immediately preceding time compared to time t; y is Y t Output signal at t time; gamma represents a forgetting coefficient, and a typical value of gamma is 0.4 to 0.6.
Further, the frame input signal of the second frame is estimated according to the second frame input signal and the first frame estimated value, so as to obtain a second frame estimated value; comprising the following steps:
acquiring a second frame input signal, and adding the second frame input signal to the first frame estimated value according to low-pass filtering to estimate the frame input signal of the second frame to obtain a second frame estimated value; repeating the calculation until the target frame estimated value is obtained by calculation:
if the second frame estimated value is the target frame estimated value to be calculated;
the target frame estimate is calculated using the following formula:
wherein,representing the t frame estimate,/->Representing the t-1 frame estimate, b 1 Is typically 0.7 to 0.9;
and when the second frame input signal is acquired, estimating the frame input signal of the second frame according to the second frame input signal and the first frame estimated value, and obtaining the second frame estimated value, clearing the first frame estimated value.
Further, the second frame standard deviation is calculated based on the second frame input signal, the first frame estimated value and the first frame standard deviation; comprising the following steps:
if the second frame standard deviation is the target frame standard deviation to be calculated;
calculating a target frame standard deviation according to the unbiased estimation standard deviation of the sample variance by using the following formula:
wherein sigma t 2 Represents the standard deviation of the t-th frame,represents the standard deviation of the t-1 th frame; x is x t Representing the t-th frame input signal, ">Representing a t-1 frame estimate;
and when the second frame standard deviation is calculated based on the second frame input signal, the first frame estimated value and the first frame standard deviation, clearing the second frame input signal, the first frame estimated value and the first frame standard deviation data.
Further, the confidence coefficient of the actually input second frame input signal is calculated according to the second frame estimated value, the second frame standard deviation and the second frame input signal, so as to obtain a second frame confidence coefficient; comprising the following steps:
if the second frame confidence coefficient is the target frame confidence coefficient to be calculated;
the frame input signal obeys a gaussian distributionCalculating the confidence coefficient of the target frame by using the CDF;
calculating a target frame confidence using the formula:
where abs represents absolute value, CDF (N, x t ) X or less in the representation distribution N t Occurring at the same timeProbability; representing less than or equal to +.>Probability of occurrence; />Representing the distribution N at x t And->Probability of occurrence in between.
Further, the method further comprises the following steps:
the calculated target frame confidence will be approximated as the following:
in a second aspect, the present invention provides an oximeter data processing device comprising:
an acquisition module, configured to acquire frame input signals, where each frame input signal includes a frame input signal of each discrete frame from a start time to a target time;
the calculation module calculates the confidence coefficient of the frame input signal to obtain a frame confidence coefficient, the frame input signal is overlapped to an output signal according to the frame confidence coefficient, and the next frame input signal is estimated according to the frame input signal to obtain a next frame estimated value;
and the output module is used for calculating the confidence coefficient of the next frame for the next frame input signal which is actually acquired based on the estimated value of the next frame, and superposing the next frame input signal according to the confidence coefficient of the next frame until the output signal of the target moment is obtained through calculation.
In a third aspect, a computer-readable storage medium is provided, storing a computer program, characterized in that the computer program, when executed by a processor, implements the steps of the method of any of the first aspects.
In a fourth aspect, a computer device is provided, comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any of the first aspects when executing the computer program.
The oximeter data processing method provided by the invention has the beneficial effects that: because the oximeter can calculate and obtain the measured value with relatively high frequency during measurement, namely a frame input signal; the pulse rate, SPO2 (percutaneous arterial oxygen saturation, percutaneous arterial oxygen saturation, oxygen saturation measured by non-invasive pulse oximetry) and perfusion index can be obtained in a theoretically complete cardiac cycle, and in most cases these measurements will not jump significantly, but will transition from state a to state B at a certain rate, e.g. the SPO2 index will drop from about 97% to less than 90% or even less than 80% of normal due to respiratory obstruction at a very slow rate. Only in a small fraction of cases, rapid changes in state occur, such as rapid increase in heart rate under the action of epinephrine or rapid increase in SPO2 after re-normalization of ventilation. The method is mainly used for reducing the disturbance of movement in the slow change process of the SPO2 index for the slow change of the SPO2 index. The invention provides a motion noise filtering algorithm based on an assumption verification method, and provides a oximeter data processing method. The invention is suitable for the situation that continuous and large-amplitude motion does not occur at the input signal end of the measurement frame, and can obviously improve the accuracy of the final output result.
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In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method for processing oximeter data according to one embodiment;
FIG. 2 is a schematic diagram of a frame of an oximeter data processing device, according to one embodiment.
Detailed Description
In order to better understand the technical solutions in the present application, the following description will clearly and completely describe the technical solutions in the embodiments of the present application in conjunction with the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only some embodiments of the present application, not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are within the scope of the present disclosure.
When the blood oxygen saturation measuring instrument is used, the finger grip is reliably fixed on the fingertip, the instrument continuously and alternately emits infrared light and red light at the finger grip, and weak signals scattered by the fingertip are received at the other side of the emitting end. The value of the blood oxygen saturation is calculated indirectly by measuring the change in the absorption of the finger to light of both wavelengths.
However, the movement of the subject is easily disturbed during the measurement. Therefore, it is necessary to remove the interference of the motion noise. In the prior art, when the motion noise is processed, the red light original signal and the infrared light original signal are directly filtered in the continuous measurement process of equipment, so that the noise interference caused by the motion is reduced; filtering the measured value signals obtained after operation, and reducing interference of abnormal values in the measured value signals on a display result; and judging whether the signal is interfered by motion noise or not according to the characteristics of the red light original signal and the infrared light original signal. If the signal is not interfered by the motion noise, the signal is adopted in the subsequent processing process, and otherwise, the signal is abandoned.
In the above-described prior art solution, however,
1. in the process of processing the original signal, as the interference noise caused by the motion is diversified, most algorithms are difficult to filter with high robustness, so that the noise generated by measurement errors can only be reduced in the pretreatment of the original signal, but the noise generated by the motion is difficult to play an effective role;
2. the measured result obtained by calculation is filtered, so that the interference caused by movement during measurement can be greatly reduced, and the result is closer to an actual value. However, if the conventional digital filtering method is used, such as an FIR filter and an IIR filter, it is difficult to adaptively adjust the weight of the input signal, so it is still difficult to thoroughly eliminate the influence of the error signal on the result, thereby resulting in inaccuracy of the result.
In one embodiment, referring to FIG. 1, there is provided an oximeter data processing method comprising:
acquiring frame input signals, wherein each frame input signal comprises a frame input signal of each discrete frame from a starting moment to a target moment;
specifically, the oximeter is fixed on the fingertip, the device continuously and alternately emits infrared light and red light at the finger grip, receives weak signals scattered by the fingertip at the other side of the emitting end, and indirectly calculates the value of the blood oxygen saturation by measuring the change of the absorption rate of the finger to the light with two wavelengths. The frame input signal is received at a preset frequency for a period of time. The sequence of frame input signals is acquired, illustratively, sequentially at a frequency of 60 to 70hz per minute.
Calculating the confidence coefficient of the frame input signal to obtain a frame confidence coefficient, superposing the frame input signal to an output signal according to the frame confidence coefficient, and estimating a next frame input signal according to the frame input signal to obtain a next frame estimated value;
specifically, in the prior art, when filtering motion noise by using an IIR filter (Infinite Impulse Response, IIR, infinite impulse response filter), the calculated measurement result is filtered, and each input signal is superimposed on the output signal, but it is difficult to adaptively adjust the weight of the input signal. Thus, the present invention adjusts the weight of the input signal by calculating the confidence level of the input signal for each frame.
And calculating the confidence coefficient of the next frame for the next frame input signal which is actually acquired based on the estimated value of the next frame, and superposing the next frame input signal according to the confidence coefficient of the next frame until the output signal of the target moment is obtained through calculation.
Specifically, when calculating the confidence coefficient of the frame, the frame estimation value is obtained by estimating the next frame, and the confidence coefficient of the input signal of the next frame is estimated according to the frame estimation value and the actual input signal of the frame.
The beneficial effects that the scheme that according to this embodiment provided can reach lie in: because the oximeter can calculate and obtain the measured value with relatively high frequency during measurement, namely a frame input signal; the pulse rate, SPO2 (percutaneous arterial oxygen saturation, percutaneous arterial oxygen saturation, oxygen saturation measured by non-invasive pulse oximetry) and perfusion index can be obtained in a theoretically complete cardiac cycle, and in most cases these measurements will not jump significantly, but will transition from state a to state B at a certain rate, e.g. the SPO2 index will drop from about 97% to less than 90% or even less than 80% of normal due to respiratory obstruction at a very slow rate. Only in a small fraction of cases, rapid changes in state occur, such as rapid increase in heart rate under the action of epinephrine or rapid increase in SPO2 after re-normalization of ventilation. The method is mainly used for reducing the disturbance of movement in the slow change process of the SPO2 index for the slow change of the SPO2 index. The invention provides a motion noise filtering algorithm based on an assumption verification method, which is characterized in that the confidence coefficient of a frame input signal is calculated, the frame input signal is superimposed on a moment output signal according to the confidence coefficient, the next frame input signal is estimated, a frame estimated value is used as a main basis for calculating the confidence coefficient of the next frame input signal, whether a specific frame input signal is subjected to motion interference can be adaptively judged, the influence of the frame input signal on the moment output signal is reduced or inhibited through the frame confidence coefficient, and the accuracy of measuring SPO2 is remarkably improved. The invention is suitable for the situation that continuous and large-amplitude motion does not occur at the input signal end of the measurement frame, and can obviously improve the accuracy of the final output result.
It is to be noted that in the case where no continuous and large-amplitude motion occurs, it is assumed that the frame input signal follows a gaussian distribution, i.eObeying a gaussian distribution, wherein σ t 2 Representing the standard deviation of the target frame>Representing the target frame estimate, when x is acquired t And then calculating the confidence coefficient of the target frame according to the Gaussian distribution and the cumulative distribution function, and calculating a target moment output value corresponding to the target frame according to a preset formula.
In one embodiment, the calculating calculates the confidence coefficient of the frame input signal to obtain a frame confidence coefficient, and superimposes the frame input signal on an output signal according to the frame confidence coefficient, and estimates a next frame input signal according to the frame input signal to obtain a next frame estimated value; calculating a next frame confidence coefficient for a next frame input signal which is actually acquired based on a next frame estimated value, and superposing the next frame input signal according to the next frame confidence coefficient until a target moment output signal is calculated, wherein the method comprises the following steps:
collecting a frame input signal describing blood oxygen saturation; the first frame and the second frame are two adjacent frames from the starting time to the target time, and the time of the second frame is after the time of the first frame;
estimating the frame input signal of the second frame according to the second frame input signal and the first frame estimated value to obtain a second frame estimated value;
calculating a second frame standard deviation based on the second frame input signal, the first frame estimated value and the first frame standard deviation;
calculating the confidence coefficient of the actually input second frame input signal according to the second frame estimated value, the second frame standard deviation and the second frame input signal to obtain a second frame confidence coefficient;
superposing the second frame input signal to the first moment output signal based on the second frame confidence level to calculate a second moment output signal;
and repeatedly acquiring the input signal of the next frame and calculating until the output signal of the target moment is obtained.
Illustratively, from the start time 0s to the target time 60s, one frame of input signal is acquired every 1s, and by accumulating and overlapping each frame of input signal acquired in the past 60s with the output signal according to the confidence level thereof, the output signal at the 60 s-th time is obtained.
In one embodiment, the second frame input signal is added to the first time output signal based on the second frame confidence level to calculate a second time output signal; repeatedly acquiring the input signal of the next frame and calculating until the output signal of the target moment is obtained; comprising the following steps:
calculating a target time output value by using the following formula, wherein the target time output value describes the blood oxygen saturation measured in a time period from the starting time to the target time;
Y t ＝[1-a t (1-γ)]Y t - 1 +a t ((1-γ)x t (1)
Wherein x is t Representing a t-th frame input signal; a, a t Representing the confidence of the t frame; y is Y t - 1 Indicating the output signal at time t-1, i.e. the time immediately preceding time compared to time t; y is Y t Output signal at t time; gamma represents a forgetting coefficient, and a typical value of gamma is 0.4 to 0.6.
According to the scheme in the embodiment, the frame confidence of each frame is superimposed on the output signal, and the influence degree of the frame input signal on the time-dependent output signal is adaptively adjusted, so that the interference of motion measurement is reduced, and the measurement accuracy is improved.
Specifically, in using Y t Prior to the formula calculation, a target frame confidence is calculated,
in one embodiment, the estimating the frame input signal of the second frame according to the second frame input signal and the first frame estimated value to obtain the second frame estimated value; comprising the following steps:
acquiring a second frame input signal, and adding the second frame input signal to the first frame estimated value according to low-pass filtering to estimate the frame input signal of the second frame to obtain a second frame estimated value; repeating the calculation until the target frame estimated value is obtained by calculation:
if the second frame estimated value is the target frame estimated value to be calculated;
the target frame estimate is calculated using the following formula:
wherein,representing the t frame estimate,/->Representing the t-1 frame estimate, b 1 Is typically 0.7 to 0.9;
and when the second frame input signal is acquired, estimating the frame input signal of the second frame according to the second frame input signal and the first frame estimated value, and obtaining the second frame estimated value, clearing the first frame estimated value.
Specifically, the frame estimate needs to be calculated before the frame confidence is calculated. Calculating a frame input signal sequence according to low-pass filtering by using the following formula to obtain a low-pass filtering output value;
wherein y [ t ]]Representing the output value of the t-th frame low-pass filtering, where m=0, n=1 first order low-pass filtering is used, b 1 +c 0 ＝1，b 1 Is typically 0.7 to 0.9, p=0, q=1;
obtaining y [ t ]]＝((1-b 1 )x[t]+b 1 y[t-1]Wherein y [ t ]]Representing the output value at time t, y [ t-1 ] under low pass filtering]An output value at time t-1 in low-pass filtering;
taking the output value under low-pass filtering as a frame estimation value
In one embodiment, the calculating is based on the second frame input signal, the first frame estimation value, and the first frame standard deviation to obtain a second frame standard deviation; comprising the following steps:
if the second frame standard deviation is the target frame standard deviation to be calculated;
calculating a target frame standard deviation according to the unbiased estimation standard deviation of the sample variance by using the following formula:
wherein sigma t 2 Represents the standard deviation of the t-th frame,represents the standard deviation of the t-1 th frame; x is x t Representing the t-th frame input signal, ">Representing a t-1 frame estimate;
and when the second frame standard deviation is calculated based on the second frame input signal, the first frame estimated value and the first frame standard deviation, clearing the second frame input signal, the first frame estimated value and the first frame standard deviation data.
Specifically, sigma t 2 Is the standard deviation of the Gaussian distribution, unbiased by sample variance using the following formulaEstimating standard deviation;
wherein S is t 2 Representing the sample variance of the t-th frame, and sigma t 2 Approximately S t 2 ，Representing the sample mean. Samples are the individual frame input signals from the start time to the target time.
Wherein,representing the sample mean.
From the following components
It is known that the number of the components,
it is known that the number of the components,
according toCalculation of sigma t 2 When only store +.>S and S t-1 2 Without requiring the preservation of the framed input signal or the traversal of the framed input signal during the computation. The method is convenient to use on an embedded platform with low power consumption, and has low requirement on operation performance.
In order to properly reduce the weight of the frame input signal at the early time to the target frame estimated valueWill beAfter rewriting due to sigma t 2 ＝S t 2 ；
It can be seen thatIs->
According toIs->
It can be seen that
The frame input signal may be estimated by calculating a sample mean.
In one example, the calculating the confidence of the actually input second frame input signal according to the second frame estimation value, the second frame standard deviation and the second frame input signal obtains a second frame confidence; comprising the following steps:
if the second frame confidence coefficient is the target frame confidence coefficient to be calculated;
the frame input signal obeys a gaussian distributionCalculating the confidence coefficient of the target frame by using the CDF;
calculating a target frame confidence using the formula:
wherein abs representsAbsolute value, CDF (N, x t ) X or less in the representation distribution N t Probability of occurrence; representing less than or equal to +.>Probability of occurrence;representing the distribution N at x t And->Probability of occurrence in between.
Specifically, when the frame estimation value is calculated according to the formula 6, the standard deviation is calculated according to the formula 4, whether the frame input signal accords with the estimation is judged according to the frame estimation value, the standard deviation and the actual frame input signal value, and the frame confidence of the frame input signal is calculated.
According to the CDF function of the cumulative distribution, a value is taken from a certain distribution N, which falls within a certain interval [ g, h ] in the distribution]Is a probability of (2). If the probability is P, p=cdf (N, g) -CDF (N, h). According to this characteristic, we can apply a certain value x t Confidence that falls on the Gaussian distribution N is calculated as any value in the distribution that does not fall in the intervalOr interval-> Probability on (i). Calculating the probability with the CDF function can then be expressed as:
in the distribution N, the distribution N does not fall in the intervalOr interval->Probability P on is target frame confidence a t 。
In one embodiment, the method further comprises:
approximating the calculated target frame confidence to the following formula:
specifically, when the frame estimation value is calculated according to equation 6Calculating according to the standard deviation of 4And judging whether the frame input signal accords with the estimation according to the frame estimation value, the standard deviation and the actual frame input signal value, and obtaining the frame confidence coefficient of the frame input signal by using the approximate calculation of 7.
In one embodiment, referring to FIG. 2, the present invention provides an oximeter data processing device, comprising:
an acquisition module, configured to acquire frame input signals, where each frame input signal includes a frame input signal of each discrete frame from a start time to a target time;
the calculation module calculates the confidence coefficient of the frame input signal to obtain a frame confidence coefficient, the frame input signal is overlapped to an output signal according to the frame confidence coefficient, and the next frame input signal is estimated according to the frame input signal to obtain a next frame estimated value;
and the output module is used for calculating the confidence coefficient of the next frame for the next frame input signal which is actually acquired based on the estimated value of the next frame, and superposing the next frame input signal according to the confidence coefficient of the next frame until the output signal of the target moment is obtained through calculation.
In one embodiment, a computer-readable storage medium is provided, storing a computer program, characterized in that the computer program, when executed by a processor, implements the steps of the method of any of the first aspects.
In one embodiment, a computer device is provided, comprising a memory storing a computer program and a processor, characterized in that the processor implements the steps of the method of any of the first aspects when executing the computer program.
Those skilled in the art will appreciate that all or part of the processes in the methods of the above embodiments may be implemented by a computer program for instructing relevant hardware, where the program may be stored in a non-volatile computer readable storage medium, and where the program, when executed, may include processes in the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the various embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), memory bus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.
It is noted that when an element is referred to as being "fixed" or "disposed on" another element, it can be directly on the other element or be indirectly disposed on the other element; when an element is referred to as being "connected to" another element, it can be directly connected to the other element or be indirectly connected to the other element.
Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include one or more such feature. In the description of the present application, the meaning of "a plurality" or "a number" is two or more, unless explicitly defined otherwise.
It should be understood that the structures, proportions, sizes, etc. shown in the drawings are for illustration purposes only and should not be construed as limiting the scope of the present disclosure, since any structural modifications, proportional changes, or dimensional adjustments made by those skilled in the art should not be made in the present disclosure without affecting the efficacy or achievement of the present disclosure.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The above examples only represent a few embodiments of the present application, which are described in more detail and are not to be construed as limiting the scope of the present application. It should be noted that it would be apparent to those skilled in the art that various modifications and improvements could be made without departing from the spirit of the present application, which would be within the scope of the present application. Accordingly, the scope of protection of the present application is to be determined by the claims appended hereto.
Claims (10)
1. An oximeter data processing method comprising:
acquiring frame input signals, wherein each frame input signal comprises a frame input signal of each discrete frame from a starting moment to a target moment;
calculating the confidence coefficient of the frame input signal to obtain a frame confidence coefficient, superposing the frame input signal to an output signal according to the frame confidence coefficient, and estimating a next frame input signal according to the frame input signal to obtain a next frame estimated value;
and calculating the confidence coefficient of the next frame for the next frame input signal which is actually acquired based on the estimated value of the next frame, and superposing the next frame input signal according to the confidence coefficient of the next frame until the output signal of the target moment is obtained through calculation.
2. The method according to claim 1, wherein the calculating the confidence level of the frame input signal obtains a frame confidence level, the frame input signal is superimposed to an output signal according to the frame confidence level, and a next frame input signal is estimated according to the frame input signal to obtain a next frame estimated value; calculating a next frame confidence coefficient for a next frame input signal which is actually acquired based on a next frame estimated value, and superposing the next frame input signal according to the next frame confidence coefficient until a target moment output signal is calculated, wherein the method comprises the following steps:
collecting a frame input signal describing blood oxygen saturation; the first frame and the second frame are two adjacent frames from the starting time to the target time, and the time of the second frame is after the time of the first frame;
estimating the frame input signal of the second frame according to the second frame input signal and the first frame estimated value to obtain a second frame estimated value;
calculating a second frame standard deviation based on the second frame input signal, the first frame estimated value and the first frame standard deviation;
calculating the confidence coefficient of the actually input second frame input signal according to the second frame estimated value, the second frame standard deviation and the second frame input signal to obtain a second frame confidence coefficient;
superposing the second frame input signal to the first moment output signal based on the second frame confidence level to calculate a second moment output signal;
and repeatedly acquiring the input signal of the next frame and calculating until the output signal of the target moment is obtained.
3. The oximeter data processing method according to claim 2, wherein said second frame input signal is superimposed to a first time output signal based on said second frame confidence level to calculate a second time output signal; repeatedly acquiring the input signal of the next frame and calculating until the output signal of the target moment is obtained; comprising the following steps:
calculating a target time output value by using the following formula, wherein the target time output value describes the blood oxygen saturation measured in a time period from the starting time to the target time;
Y t ＝[1-a t (1-γ)]Y t-1 +a t ((1-γ)x t
wherein x is t Representing a t-th frame input signal; a, a t Representing the confidence of the t frame; y is Y t-1 Indicating the output signal at time t-1, i.e. the time immediately preceding time compared to time t; y is Y t Output signal at t time; gamma represents a forgetting coefficient, and a typical value of gamma is 0.4 to 0.6.
4. The method according to claim 2, wherein the estimating the frame input signal of the second frame according to the frame input signal of the second frame and the first frame estimated value obtains the second frame estimated value; comprising the following steps:
acquiring a second frame input signal, and adding the second frame input signal to the first frame estimated value according to low-pass filtering to estimate the frame input signal of the second frame to obtain a second frame estimated value; repeating the calculation until the target frame estimated value is obtained by calculation:
if the second frame estimated value is the target frame estimated value to be calculated;
the target frame estimate is calculated using the following formula:
wherein,representing the t frame estimate,/->Representing the t-1 frame estimate, b 1 Is typically 0.7 to 0.9;
and when the second frame input signal is acquired, estimating the frame input signal of the second frame according to the second frame input signal and the first frame estimated value, and obtaining the second frame estimated value, clearing the first frame estimated value.
5. The method according to claim 4, wherein the second frame standard deviation is calculated based on the second frame input signal, the first frame estimated value, and the first frame standard deviation; comprising the following steps:
if the second frame standard deviation is the target frame standard deviation to be calculated;
calculating a target frame standard deviation according to the unbiased estimation standard deviation of the sample variance by using the following formula:
wherein sigma t 2 Represents the standard deviation of the t-th frame,represents the standard deviation of the t-1 th frame; x is x t Representing the t-th frame input signal, ">Representing a t-1 frame estimate;
and when the second frame standard deviation is calculated based on the second frame input signal, the first frame estimated value and the first frame standard deviation, clearing the second frame input signal, the first frame estimated value and the first frame standard deviation data.
6. The method according to claim 5, wherein the calculating the confidence level of the actually inputted second frame input signal according to the second frame estimation value, the second frame standard deviation, and the second frame input signal obtains a second frame confidence level; comprising the following steps:
if the second frame confidence coefficient is the target frame confidence coefficient to be calculated;
the frame input signal obeys a gaussian distributionCalculating the confidence coefficient of the target frame by using the CDF;
calculating a target frame confidence using the formula:
where abs represents absolute value, CDF (N, x t ) X or less in the representation distribution N t Probability of occurrence; representing less than or equal to +.>Probability of occurrence; />Representing the distribution N at x t And->Probability of occurrence in between.
7. The oximeter data processing method of claim 6, further comprising:
approximating the calculated target frame confidence to the following formula:
8. oximeter data processing device, characterized by comprising:
an acquisition module, configured to acquire frame input signals, where each frame input signal includes a frame input signal of each discrete frame from a start time to a target time;
the calculation module calculates the confidence coefficient of the frame input signal to obtain a frame confidence coefficient, the frame input signal is overlapped to an output signal according to the frame confidence coefficient, and the next frame input signal is estimated according to the frame input signal to obtain a next frame estimated value;
and the output module is used for calculating the confidence coefficient of the next frame for the next frame input signal which is actually acquired based on the estimated value of the next frame, and superposing the next frame input signal according to the confidence coefficient of the next frame until the output signal of the target moment is obtained through calculation.
9. Computer readable storage medium storing a computer program, characterized in that the computer program when executed by a processor implements the steps of the method according to any one of claims 1 to 7.
10. Computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any of claims 1 to 7 when the computer program is executed.
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