Apparatus and method for estimating oxygen saturation

Abstract

According to an embodiment of the present disclosure, a method for measuring oxygen saturation in a person may include the steps of: acquiring a radar complex signal reflected from a person by a radar sensor; deriving information on micro-movement displacement of the person's chest region based on the radar complex signal by a processor; deriving information on intra-lung pressure of the person based on micro-movement displacement of the chest region by a processor; deriving information on the person's respiratory volume based on intra-lung pressure by a processor; deriving information on the person's minute ventilation volume based on the respiratory volume information by a processor; deriving information on the person's carbon dioxide partial pressure based on the minute ventilation volume information by a processor; deriving information on the person's oxygen partial pressure based on the carbon dioxide partial pressure information by a processor; and deriving information on the person's oxygen saturation based on the oxygen partial pressure information by a processor.

Description

Technology Field

[0001] The present disclosure relates to an apparatus and method for estimating oxygen saturation, and more specifically, to an apparatus and method for estimating the oxygen saturation of a randomly moving person using a moving average filter and a Butterworth bandpass filter. Background Technology

[0002] As smart devices advance, the market for healthcare-related sensing devices is growing. Accordingly, various devices for measuring oxygen saturation are being developed.

[0003] Conventional techniques utilize micro-movement displacements of the upper chest, respiratory rate, heart rate, height, and body weight as inputs to estimate human oxygen saturation as an output value. However, according to conventional techniques, the accuracy of oxygen saturation estimation plummets because unnecessary, random human movements cause significant changes in pulmonary ventilation and distort the estimated tidal volume. Consequently, in the post-processing stage, additional identification of machine learning-based estimation results is performed using the estimated oxygen saturation and micro-movement displacements of the upper chest as inputs. The problem to be solved

[0004] The object of the present disclosure is to provide an apparatus and method for estimating the oxygen saturation of a randomly moving person using a moving average filter and a Butterworth bandpass filter. means of solving the problem

[0005] According to an embodiment of the present disclosure, a method for measuring oxygen saturation in a person may include the steps of: acquiring a radar complex signal reflected from a person by a radar sensor; deriving information on micro-movement displacement of the person's chest region based on the radar complex signal by a processor; deriving information on intra-lung pressure of the person based on micro-movement displacement of the chest region by a processor; deriving information on the person's respiratory volume based on intra-lung pressure by a processor; deriving information on the person's minute ventilation volume based on the respiratory volume information by a processor; deriving information on the person's carbon dioxide partial pressure based on the minute ventilation volume information by a processor; deriving information on the person's oxygen partial pressure based on the carbon dioxide partial pressure information by a processor; and deriving information on the person's oxygen saturation based on the oxygen partial pressure information by a processor.

[0006] According to an embodiment of the present disclosure, a device for measuring a person's oxygen saturation may include a radar sensor configured to acquire a radar complex signal reflected from a person, a processor that derives information on the person's oxygen saturation based on the radar complex signal, and a memory that stores command information instructing the operation of the processor. In one example, based on the command information, the processor may derive information on the minute movement displacement of the person's chest region based on the radar complex signal, derive information on the person's intra-lung pressure based on the information on the minute movement displacement of the chest region, derive information on the person's respiratory volume based on the intra-lung pressure, derive information on the person's minute ventilation volume based on the respiratory volume information, derive information on the person's carbon dioxide partial pressure based on the minute ventilation volume information, derive information on the person's oxygen partial pressure based on the carbon dioxide partial pressure information, and derive information on the person's oxygen saturation based on the oxygen partial pressure information. Effects of the invention

[0007] According to the present disclosure, the accuracy of oxygen saturation estimation can be improved through a simple signal processing process while reducing the influence of random movement by utilizing a moving average filter and a second-order Butterworth bandpass filter. Brief explanation of the drawing

[0008] FIG. 1 is a block diagram showing an apparatus for estimating coral saturation according to one embodiment of the present disclosure. FIG. 2 is a graph showing the change over time of the phase modulation component of a radar complex signal reflected from a person, according to one embodiment of the present disclosure. FIG. 3 is a diagram illustrating micro-movement displacement of a human chest area in which arbitrary movement is removed through a moving average filter and a second-order Butterworth bandpass filter according to one embodiment of the present disclosure. FIG. 4 is a drawing for illustrating the pressure inside the lungs estimated according to one embodiment of the present disclosure. FIG. 5 is a graph showing the change in average breathing volume over time according to one embodiment of the present disclosure. FIG. 6 is a flowchart illustrating a method for estimating oxygen saturation according to one embodiment of the present disclosure. Specific details for implementing the invention

[0009] In the following, embodiments of the present invention will be described clearly and in detail so that a person skilled in the art can easily practice the present invention.

[0010] Terms such as "unit" and "module" used below, or functional blocks illustrated in the drawings, may be implemented in the form of software configurations, hardware configurations, or combinations thereof. In order to clearly explain the technical concept of the present invention, detailed descriptions of redundant components are omitted below.

[0011] In this document, each of the phrases such as "A or B", "at least one of A and B", "at least one of A or B", "A, B or C", "at least one of A, B and C", and "at least one of A, B, or C" may include any one of the items listed together in the corresponding phrase, or all possible combinations thereof.

[0012] The embodiments according to the following descriptions relate to a technique for estimating the oxygen saturation of a randomly moving person using a non-contact radar sensor, and more specifically, to a radar signal processing algorithm for more accurately estimating oxygen saturation under the influence of random motion interference and thermal noise using a moving average filter and a Butterworth bandpass filter. These will be described in detail together with the drawings.

[0013] FIG. 1 is a block diagram illustrating an apparatus for estimating coral saturation according to one embodiment of the present disclosure. Referring to FIG. 1, the apparatus (100) for estimating coral saturation may include a radar sensor (110), a processor (120), a network interface (130), and a memory (140).

[0014] The radar sensor (110) of FIG. 1 may be a non-contact radar sensor, for example, a Doppler radar sensor. The oxygen saturation estimation device (100) may estimate oxygen saturation from a radar complex signal reflected from a human upper body area. Here, data regarding one-dimensional upper body chest area micro-movement displacement, respiratory rate, and / or heart rate may be extracted from the reflected radar complex signal. The oxygen saturation estimation device (100) may obtain pulmonary ventilation, tidal volume, and minute ventilation using the one-dimensional upper body chest area micro-movement displacement and respiratory rate.

[0015] According to one embodiment, carbon dioxide partial pressure based on minute ventilation, heart rate, height, and body weight ( )) and oxygen partial pressure ( ) can be obtained, and carbon dioxide partial pressure ( ) and oxygen partial pressure ( Oxygen saturation based on ) )) can be estimated.

[0016] The processor (120) can function as a central processing unit of the device (100). At least one of the processors (120) may include at least one general-purpose processor, such as a central processing unit (CPU), an application processor (AP), etc. The processor (120) may also include at least one special-purpose processor, such as a neural processing unit, a neuromorphic processor, a graphics processing unit (GPU), etc. The processor (120) may include two or more processors of the same type. As another example, at least one of the processors (120) may be manufactured to implement various machine learning or deep learning modules.

[0017] The network interface (130) can provide remote communication with an external device. The network interface (130) can communicate wirelessly or wired with an external device. The network interface (130) can communicate with an external device through at least one of various communication forms such as Ethernet, Wi-Fi, LTE, 5G mobile communication, etc. For example, the network interface (130) can communicate with an external device of the device (100).

[0018] The network interface (130) can receive computational data to be processed by the device (100) from an external device. The network interface (130) can output result data (information) generated (derived) by the device (100) to an external device.

[0019] The memory (140) can store command information, data, and process codes that are processed (executed) or are scheduled to be processed (executed) by the processor (120).

[0020] The memory (140) can be used as the main memory of the device (100). The memory (140) may include DRAM (Dynamic RAM), SRAM (Static RAM), PRAM (Phase-change RAM), MRAM (Magnetic RAM), FeRAM (Ferroelectric RAM), RRAM (Resistive RAM), etc.

[0021] According to one embodiment of the present disclosure, the operation and method of estimating a person's oxygen saturation by means of a device (100) and components included therein will be described in detail together with the drawings below.

[0022] FIG. 2 is a graph showing the change over time of the phase modulation component of a radar complex signal reflected from a person, according to one embodiment of the present disclosure. FIG. 2 may show a phase modulation component distorted by any movement.

[0023] In one embodiment, the radar complex signal s(t) can be expressed as shown in Equation 1 below.

[0024]

[0025] In mathematical formula 1, ε is the received power, λ is the wavelength of the radar signal, is fine movement displacement of the upper body chest area, is a randomly moving movement, It can be a phase component of thermal noise.

[0026] of mathematical formula 2 below may be a phase modulation component of the radar complex signal (s(t)) expressed by applying the inverse tangent function to the radar complex signal according to the above mathematical formula 1.

[0027]

[0028] FIG. 3 is a diagram illustrating micro-movement displacement of a human chest area in which arbitrary movement is removed through a moving average filter and a second-order Butterworth bandpass filter according to one embodiment of the present disclosure.

[0029] In one embodiment, of mathematical formula 2 to When applying a recursive moving average filter using samples, the low-frequency phase modulation component for arbitrary movement It can be defined as shown in mathematical formula 3 below.

[0030]

[0031] In one embodiment, low-frequency phase modulation component By subtracting, the phase modulation component for arbitrary motion can be removed. For example, low-frequency phase modulation component Result after subtracting It can be expressed as shown in mathematical equation 4 below.

[0032]

[0033] In one embodiment, When a second-order Butterworth bandpass filter is applied, residual components of random motion and thermal noise can be removed. For example, cutoff frequency The result of applying a second-order Butterworth bandpass filter can be expressed as Equation 5 below.

[0034]

[0035] In one example, the transfer function of a second-order Butterworth bandpass filter can be constructed in the Laplace s-domain as shown in Equation 6 below. In Equation 6, can be the cutoff frequency.

[0036]

[0037] FIG. 4 is a diagram illustrating the estimated intrapulmonary pressure according to one embodiment of the present disclosure. Specifically, FIG. 4 shows a micro-movement displacement of a human chest area with any movement removed. and This is a graph showing the change over time of the estimated intrapulmonary pressure using the respiratory rate estimated from.

[0038] In one embodiment, the device and / or method for estimating oxygen saturation is a one-dimensional upper body chest region micro-movement displacement and The respiratory rate, which is the frequency having the maximum value. Using intrapulmonary pressure Calculate. For example, of mathematical formula 7 is a micro-movement displacement of the human chest area with arbitrary movement removed, and can be derived from Equation 5 based on Equations 1 to 4, which is based on the measured radar complex signal.

[0039]

[0040] In the above mathematical formula 7, Is The frequency having the maximum value of ( is the respiratory rate corresponding to the maximum frequency, g is the acceleration due to gravity, and BMI is the body mass index.

[0041] In one embodiment, the device and / or method for estimating oxygen saturation pressure in the lungs The rate of change of (e.g., derived based on Equation 7) Respiratory volume based on ) It can be calculated, for example, based on mathematical formula 8 below. Here, is the total respiratory volume (e.g., It could be).

[0042]

[0043] Mathematical Equation 9 below refers to the breathing volume in a device and / or method for estimating oxygen saturation. Upper boundary value of change and lower boundary values Used to derive It illustrates exemplarily. is the absolute value of the upper boundary value and the lower boundary value (hereinafter, boundary value).

[0044]

[0045] In the above mathematical formula 9, is the body weight of a person.

[0046] In one example, the breathing volume within the boundary value based on Equation 9 It can be derived based on the following mathematical formula 10.

[0047]

[0048] FIG. 5 is a graph showing the change in average breathing volume over time according to one embodiment of the present disclosure.

[0049] Referring to FIG. 5, in one embodiment of the present disclosure, an apparatus and method for estimating oxygen saturation is based on a breathing volume Respiratory rate explained with mathematical formula 7 Low-pass filtering can be performed with the extremes, and then the average of the absolute values ​​of the maximum and minimum values ​​is added to obtain the average respiration volume. It can be estimated. Estimated average respiratory volume Minute ventilation rate based on can be derived.

[0050] For example, a device and method for estimating oxygen saturation is average breathing volume and the breathing rate explained with mathematical formula 7 Minute ventilation rate based on It can be estimated, for example, based on Equation 11 below, the ventilation rate per minute It can be estimated.

[0051]

[0052] In one embodiment, the apparatus and method for estimating oxygen saturation is a ventilation rate per minute. (For example, of mathematical formula 11 partial pressure of carbon dioxide using ). It can be estimated. For example, a device and method for estimating oxygen saturation carbon dioxide partial pressure It can be estimated based on the following mathematical formula 13.

[0053]

[0054] In mathematical formula 12, M is the body weight, is heart rate, RQ is respiratory quotient, and H is height.

[0055] In one embodiment, the apparatus and method for estimating oxygen saturation is carbon dioxide partial pressure (For example, of mathematical formula 12 oxygen partial pressure using ). It can be estimated. For example, a device and method for estimating oxygen saturation based on the oxygen partial pressure in Equation 13 below. It can be estimated.

[0056]

[0057] In mathematical formula 13, is the amount of oxygen in the air (for example, usually 0.21 on Earth), is atmospheric pressure (for example, 760 mmHg at sea level), is the vapor pressure inside the alveoli (for example, usually 47 mmHg).

[0058] In one embodiment, the apparatus and method for estimating oxygen saturation is carbon dioxide partial pressure (For example, of mathematical formula 12 ) and / or oxygen partial pressure (For example, of mathematical formula 13 Oxygen saturation using ) It can be estimated, for example, oxygen saturation based on Equation 14 below It can be estimated.

[0059]

[0060] In one example, of the above mathematical formula 14 It can be derived based on the following mathematical formula 15.

[0061]

[0062] In one example, of the above mathematical formula 14 It can be derived based on the following mathematical formula 16.

[0063]

[0064] In one example, of the above mathematical formula 14 can be derived based on the following mathematical formula 17. For example, of mathematical formula 17 It can be based on mathematical formula 13.

[0065]

[0066] In one example, the mathematical formulas 15 through 17 used can be derived based on the following mathematical formula 18, and of mathematical formula 18 It can be derived based on the above mathematical formula 12.

[0067]

[0068] In one example, the mathematical formulas 15 through 17 used can be derived based on the following mathematical formula 19, and of mathematical formula 19 It can be derived based on the above mathematical formula 12.

[0069]

[0070] In one example, the mathematical formula 15 used can be derived based on the following mathematical formula 20, and of mathematical formula 20 can be derived based on the above mathematical formula 12, and can be based on mathematical formula 19, and / or It can be based on mathematical formula 18.

[0071]

[0072] In one example, the mathematical formula 15 used can be derived based on the following mathematical formula 21, and of mathematical formula 21 can be based on mathematical formula 19, and / or It can be based on mathematical formula 18.

[0073]

[0074] In one example, the mathematical formula 15 used can be derived based on the following mathematical formula 22, and of mathematical formula 22 can be based on mathematical formula 19, and / or It can be based on mathematical formula 18.

[0075]

[0076] In one example, the mathematical formula 16 used can be derived based on the following mathematical formula 23, and of mathematical formula 22 can be based on mathematical formula 19, and / or It can be based on mathematical formula 18.

[0077]

[0078] In one example, the mathematical formula 17 used It can be based on mathematical formula 24, and of mathematical formula 24 can be based on mathematical formula 20, and can be based on mathematical formula 13, and / or It can be based on mathematical formula 18.

[0079]

[0080] In one example, the mathematical formula 17 used It can be based on mathematical formula 25, and of mathematical formula 24 can be based on mathematical formula 21, and can be based on mathematical formula 22, and It can be based on mathematical formula 13.

[0081]

[0082] FIG. 6 is a flowchart illustrating a method for estimating oxygen saturation according to one embodiment of the present disclosure. FIG. 6 will be described together with FIG. 1 to 5 and the mathematical formulas described above. The method according to FIG. 6 may include steps S600 to S6 and may be performed by the apparatus (100) of FIG. 1.

[0083] In S600, the radar sensor (110) can acquire a radar complex signal reflected from a person (e.g., s(t) of Equation 1).

[0084] In S610, the processor (120) can derive information on the minute movement displacement of a person's chest area based on the radar complex signal acquired in step S600. In one example, the processor (120) can remove components corresponding to arbitrary movements of a person from the radar complex signal by applying a moving average filter and a Butterworth filter.

[0085] In one embodiment, the processor (120) has a phase modulation component of the radar complex signal (e.g., of Equation 2). ) derive, and based on the phase modulation component of the radar complex signal, the low-frequency phase modulation component according to arbitrary human movement (e.g., of Equation 3 ) derive, and the difference between the phase modulation component and the low-frequency phase modulation component (e.g., of Equation 4 ...can be derived. For example, the processor (120) can perform filtering by applying a moving average filter to derive a low-frequency phase modulation component corresponding to an arbitrary movement of a person based on the phase modulation component of a radar complex signal, based on command information. Micro-movement displacement information of the chest area of ​​a person (e.g., ) can be derived based on the result of filtering (e.g., the result of Equation 5) resulting from the application of a Butterworth filter (by the processor (120)) to the difference between the phase modulation component and the low-frequency phase modulation component.

[0086] In S620, the processor (120) obtains the human lung pressure information (e.g., of Equation 4) based on the chest area micro-movement displacement information. ) can be derived. In one example, the processor (120) derives information on the amount of change in pressure within a person's lungs (e.g., of Equation 7) based on micro-movement displacement information of the chest area. ) can be derived.

[0087] In S630, the processor (120) can derive information on a person's breathing volume based on intra-lung pressure. In one example, the processor (120) derives information on the change in a person's intra-lung pressure (e.g., of Equation 7). Based on ) human respiration volume information (e.g., of mathematical formula 8 ) can be derived.

[0088] In S640, the processor (120) can derive information on a person's minute ventilation rate based on breathing volume information. In one example, the processor (120), based on command information, derives information on a person's minute ventilation rate based on breathing volume information by using a boundary value of the person's breathing volume change (e.g., of Equation 9). ) derive, and the maximum frequency value of the boundary value and the fine movement displacement information of the chest area (e.g., the one described above Based on ), information on a person's average breathing volume (e.g., the aforementioned ) derive, and based on average respiratory volume, minute ventilation information (e.g., of Equation 11 ) can be derived. For example, the threshold value is a person's body weight (as described above It can be derived based on ).

[0089] In S650, the processor (120) can derive carbon dioxide partial pressure information based on the ventilation rate information per minute. The derivation of carbon dioxide partial pressure information according to step S650 may be based on the carbon dioxide partial pressure estimation according to the mathematical formula 12 described above.

[0090] In S660, the processor (120) can derive oxygen partial pressure information of a person based on carbon dioxide partial pressure information. The derivation of oxygen partial pressure information according to step S660 may be based on the estimation of oxygen partial pressure according to the above-described mathematical formula 13.

[0091] In S670, the processor (120) can derive oxygen saturation information of a person based on oxygen partial pressure information. The derivation of oxygen saturation information according to step S670 may be based on the estimation of oxygen saturation according to the mathematical formulas 14 to 25 described above.

[0092] Oxygen saturation estimation based on the mathematical formulas of the present disclosure may be based on an algorithm performed by a processor (120) based on command information stored in memory (140).

[0093] In an embodiment according to the descriptions above, an apparatus and method for acquiring oxygen saturation using a non-contact Doppler radar sensor on a randomly moving person may be provided, and an apparatus and method for extracting fine movement displacement of the upper body chest area by compensating for phase components distorted by random movement through a moving average filter may be provided. Additionally, an apparatus and method for extracting fine movement displacement of the upper body chest area by compensating for residual random movement and thermal noise effects through a second-order Butterworth bandpass filter may be provided.

[0094] In the embodiments described above, components according to the technical concept of the present invention have been described using terms such as first, second, third, etc. However, terms such as first, second, third, etc. are used to distinguish the components from one another and do not limit the present invention. For example, terms such as first, second, third, etc. do not imply a sequential order or any numerical meaning.

[0095] The description above describes specific examples for implementing the present invention. The present invention will include not only the embodiments described above, but also embodiments that can be easily modified or simply changed. Furthermore, the present invention will include technologies that can be easily modified and implemented in the future using the embodiments described above.

Claims

Claim 1 A method for measuring oxygen saturation in a person comprises: acquiring a radar complex signal reflected from the person by means of a radar sensor; deriving information on micro-movement displacement of the chest area of ​​the person by means of the radar complex signal by means of a processor; deriving information on intra-lung pressure of the person by means of the micro-movement displacement of the chest area by means of the processor; deriving information on respiratory volume of the person by means of the intra-lung pressure by means of the processor; deriving information on ventilation volume per minute of the person by means of the respiratory volume information by means of the processor; deriving information on carbon dioxide partial pressure of the person by means of the ventilation volume per minute by means of the processor; deriving information on oxygen partial pressure of the person by means of the carbon dioxide partial pressure information by means of the processor; and deriving information on oxygen saturation of the person by means of the oxygen partial pressure information by means of the processor, wherein the step of deriving information on micro-movement displacement of the chest area of ​​the person based on the radar complex signal includes the step of removing a component corresponding to any movement of the person from the radar complex signal by means of the application of a moving average filter and a Butterworth filter. Claim 2 In claim 1, the step of deriving micro-movement displacement information of the chest area of ​​the person based on the radar complex signal comprises: a step of deriving a phase modulation component of the radar complex signal; a step of deriving a low-frequency phase modulation component according to the arbitrary movement of the person based on the phase modulation component of the radar complex signal; and a step of deriving the difference between the phase modulation component and the low-frequency phase modulation component. Claim 3 In claim 2, the step of deriving the low-frequency phase modulation component according to the arbitrary movement of the person based on the phase modulation component of the radar complex signal is characterized by being based on filtering according to the application of the moving average filter. Claim 4 A method according to claim 2, characterized in that the micro-movement displacement information of the human chest area is derived based on the result of filtering according to the application of a Butterworth filter to the difference between the phase modulation component and the low-frequency phase modulation component. Claim 5 A method according to claim 1, wherein the step of deriving the minute ventilation information of the person based on the respiratory volume information comprises: a step of deriving a boundary value of the change in the person's respiratory volume; a step of deriving the average respiratory volume information of the person based on the boundary value and the maximum frequency value of the chest area fine movement displacement information; and a step of deriving the minute ventilation information based on the average respiratory volume, wherein the boundary value is derived based on the person's body weight. Claim 6 A device for measuring oxygen saturation of a person, comprising: a radar sensor configured to acquire a radar complex signal reflected from the person; a processor for deriving oxygen saturation information of the person based on the radar complex signal; and a memory for storing command information that directs the operation of the processor, wherein, based on the command information, the processor derives micro-movement displacement information of the chest area of ​​the person based on the radar complex signal, derives intra-lung pressure information of the person based on the micro-movement displacement information of the chest area, derives respiratory volume information of the person based on the intra-lung pressure, derives minute ventilation volume information of the person based on the respiratory volume information, derives carbon dioxide partial pressure information of the person based on the minute ventilation volume information, derives oxygen partial pressure information of the person based on the carbon dioxide partial pressure information, and derives oxygen saturation information of the person based on the oxygen partial pressure information, wherein, based on the command information, the processor removes components corresponding to arbitrary movements of the person from the radar complex signal by applying a moving average filter and a Butterworth filter in order to derive micro-movement displacement information of the chest area of ​​the person based on the radar complex signal. Claim 7 In claim 6, the processor, based on the command information, derives micro-movement displacement information of the chest area of ​​the person based on the radar complex signal: a phase modulation component of the radar complex signal, a low-frequency phase modulation component according to the arbitrary movement of the person based on the phase modulation component of the radar complex signal, and a device for deriving the difference between the phase modulation component and the low-frequency phase modulation component. Claim 8 A method according to claim 7, wherein the processor performs filtering by applying the moving average filter to derive the low-frequency phase modulation component according to the arbitrary movement of the person based on the phase modulation component of the radar complex signal based on the command information. Claim 9 A device according to claim 7, characterized in that the micro-movement displacement information of the human chest area is derived based on the result of filtering according to the application of a Butterworth filter to the difference between the phase modulation component and the low-frequency phase modulation component. Claim 10 In claim 6, the processor, in order to derive the person’s minute ventilation information based on the command information and the breathing volume information: derives a boundary value of the person’s breathing volume change, derives the person’s average breathing volume information based on the boundary value and the maximum frequency value of the chest area fine movement displacement information, and derives the minute ventilation information based on the average breathing volume, wherein the boundary value is derived based on the person’s body weight.

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