Method and apparatus for calculating blood pressure value using characteristic values of photoplethysmogram

Abstract

The present disclosure relates to a method and an apparatus for calculating blood pressure values by using characteristic values of photoplethysmogram. The method according to an embodiment of the present disclosure receives photoplethysmogram data including a photoplethysmogram waveform, normalizes the photoplethysmogram waveform included in the photoplethysmogram data, uses the normalized photoplethysmogram waveform to detect characteristic values for calculating blood pressure values, and can calculate blood pressure values based on the photoplethysmogram data and the detected characteristic values.

Description

Technical Field

[0001] The present invention relates to a method and an apparatus for calculating blood pressure values by using characteristic values of photoplethysmograms.

Background Art

[0002] Each time the heart contracts, blood is supplied from the heart to the whole body through the aorta. At this time, pressure fluctuations occur in the aorta. This pressure fluctuation is transmitted to the peripheral arterioles of the hands and feet, and the photoplethysmogram is a waveform representing the volume fluctuation of the peripheral blood vessels due to the pressure fluctuation of the artery.

[0003] Due to such pulsation, the volume of the blood vessel fluctuates. When light having a predetermined wavelength such as infrared light or visible light is applied to the blood vessel, the amount of light absorbed changes as the volume of the blood vessel increases or decreases. For example, when 100% of the light is emitted, the amount of light reflected without being absorbed changes due to pulsation.

[0004] After light is emitted through a light emitting unit by using this principle, the speed or amount of the reflected infrared light is input to a light receiving unit, and the photoplethysmogram can be measured by using the characteristic that the current and voltage appear to be different depending on the speed or amount of the input infrared light.

[0005] However, the photoplethysmogram may change its result depending on the physical condition of the subject, the environmental condition at the time of measurement, etc. When measuring blood pressure by using such a photoplethysmogram, there is a problem that inaccurate results may be derived.

[0006] The above-mentioned background art is technical information that the inventor possessed in order to derive the present invention or acquired in the process of deriving the present invention, and it cannot necessarily be regarded as publicly known technology publicly disclosed to the general public before the filing of the present invention.

Summary of the Invention

Problems to be Solved by the Invention

[0007] An object of the present invention is to provide a method and an apparatus for calculating a blood pressure value by using characteristic values of photoplethysmogram. Another object is to provide a computer-readable recording medium recording a program for causing a computer to execute the above method.

[0008] The problems to be solved by the present invention are not limited to the problems mentioned above, and other problems and advantages of the present invention not mentioned can be understood from the following description and can be more clearly understood from the embodiments of the present invention. It should also be understood that the problems and advantages to be solved by the present invention can be realized by the means and combinations thereof shown in the claims.

Means for Solving the Problems

[0009] As a technical means for achieving the above-described technical problems, a first aspect of the present disclosure is a method for calculating a blood pressure value by using characteristic values of a photoplethysmogram, the method including: receiving photoplethysmogram data including a photoplethysmogram waveform; normalizing the photoplethysmogram waveform included in the photoplethysmogram data; detecting a characteristic value for calculating a blood pressure value by using the normalized photoplethysmogram waveform; and calculating a blood pressure value based on the photoplethysmogram data and the detected characteristic value.

[0010] A second aspect of the present disclosure is an apparatus for calculating a blood pressure value by using photoplethysmogram data, the apparatus including at least one memory and at least one processor, where the at least one processor receives photoplethysmogram data including a photoplethysmogram waveform, normalizes the photoplethysmogram waveform included in the photoplethysmogram data, detects a characteristic value for calculating a blood pressure value by using the normalized photoplethysmogram waveform, and calculates a blood pressure value based on the photoplethysmogram data and the detected characteristic value.

[0011] A third aspect of the present disclosure can provide a computer-readable recording medium storing a program for causing a computer to execute the method according to the first aspect.

[0012] In addition, other methods for implementing the present invention, other systems, and a computer-readable recording medium storing a computer program for executing the method can be further provided.

[0013] Other aspects, features, and advantages other than those described above can become clear from the following drawings, claims, and detailed description of the invention.

Effect of the Invention

[0014] According to the problem-solving means of the present disclosure described above, in the present disclosure, photoplethysmogram data including a photoplethysmogram waveform is received, the photoplethysmogram waveform is standardized, a feature value is detected using the standardized photoplethysmogram waveform, and a blood pressure value is calculated based on the photoplethysmogram data and the feature value, so that the blood pressure value can be calculated from the photoplethysmogram waveform with higher accuracy.

[0015] By correcting the blood pressure value based on personal information and environmental information related to the photoplethysmogram data and the blood pressure value, the blood pressure value can be calculated from the photoplethysmogram waveform with higher accuracy.

[0016] The effects of the present invention are not limited to those mentioned above, and other effects not mentioned can be clearly understood by those skilled in the art from the following description.

Brief Description of the Drawings

[0017]

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6

Mode for Carrying Out the Invention

[0018] The method according to an embodiment of the present disclosure receives photoplethysmogram data including a photoplethysmogram waveform, standardizes the photoplethysmogram waveform included in the photoplethysmogram data, and uses the standardized photoplethysmogram waveform to detect characteristic values for calculating blood pressure values, and based on the photoplethysmogram data and the detected characteristic values, blood pressure values can be calculated.

[0019] Embodiment Hereinafter, various embodiments of the present disclosure will be described in connection with the accompanying drawings. Various embodiments of the present disclosure can be subjected to various changes and can have various embodiments, and specific embodiments are illustrated in the drawings and detailed descriptions thereof are described. However, this is not intended to limit the various embodiments of the present disclosure to specific embodiments, and it should be understood to include all changes and / or equivalents or alternatives included in the spirit and technical scope of the various embodiments of the present disclosure. When explaining the drawings, similar reference numerals are used for similar components.

[0020] Expressions such as "comprising" or "can comprise" used in various embodiments of the present disclosure indicate the existence of the disclosed functions, operations, or components, etc., and do not limit one or more additional functions, operations, or components, etc. Also, in various embodiments of the present disclosure, terms such as "comprising" or "having" are intended to specify the existence of the features, numbers, steps, operations, components, parts, or combinations thereof described in the specification, and it should be understood that the existence or possibility of addition of one or more other features, numbers, steps, operations, components, parts, or combinations thereof, etc. is not precluded in advance.

[0021] In various embodiments of the present disclosure, expressions such as "or" include any and all combinations of the words listed together. For example, "A or B" may include A, may include B, or may include both A and B.

[0022] Expressions such as "first", "second", "the first", or "the second" used in various embodiments of the present disclosure can modify various components of the various embodiments, but do not limit the components. For example, the above expressions do not limit the order and / or importance of the components, etc. The above expressions can be used to distinguish one component from another. For example, the first user device and the second user device are both user devices and indicate different user devices from each other. For example, without departing from the scope of the rights of various embodiments of the present disclosure, the first component may be named the second component, and similarly, the second component may also be named the first component.

[0023] When it is mentioned that a certain component is "connected" or "attached" to another component, it should be understood that the certain component may be directly connected or attached to the other component, or there may be another new component between the certain component and the other component. On the other hand, when it is mentioned that a certain component is "directly connected" or "directly attached" to another component, it should be understood that there is no new component between the certain component and the other component.

[0024] In embodiments of the present disclosure, terms such as "module", "unit", "part", etc. are terms used to refer to components that perform at least one function or operation, and such components may be embodied in hardware or software, or may be embodied in a combination of hardware and software. Also, multiple "modules", "units", "parts", etc. may be integrated into at least one module or chip and embodied by at least one processor, unless each needs to be embodied in separate specific hardware.

[0025] The terms used in various embodiments of the present disclosure are only used to describe specific embodiments and are not intended to limit the various embodiments of the present disclosure. The singular form includes the plural form unless the context clearly dictates otherwise.

[0026] Some embodiments of the present disclosure are also shown by functional block configurations and various processing stages. Some or all of such functional blocks may also be embodied by various numbers of hardware and / or software configurations that execute specific functions. For example, the functional blocks of the present disclosure may be embodied by one or more microprocessors, or by a circuit configuration for a predetermined function. Also, for example, the functional blocks of the present disclosure may be embodied by various programming languages or scripting languages. The functional blocks may be embodied by algorithms executed on one or more processors. Also, the present disclosure may employ conventional techniques for electronic environment settings, signal processing, and / or data processing. Terms such as "mechanism", "element", "means", and "configuration" are used in a broad sense and are not limited to mechanical and physical configurations.

[0027] Also, the connection lines or connection members between the components shown in the drawings merely exemplarily show functional connections and / or physical or circuit connections. In an actual device, the connections between components can be shown by replaceable or additional various functional connections, physical connections, or circuit connections.

[0028] Unless otherwise defined, all terms used herein, including technical or scientific terms, have the same meaning as commonly understood by one of ordinary skill in the technical field to which the various embodiments of the present disclosure belong.

[0029] Terms defined in commonly used dictionaries should be interpreted to have a meaning consistent with the meaning in the context of the related art, and should not be interpreted in an ideal or overly formal sense unless clearly defined in the various embodiments of the present disclosure.

[0030] Hereinafter, various embodiments of the present invention will be specifically described with reference to the accompanying drawings.

[0031] FIG. 1 is a drawing for explaining an example of a system that calculates blood pressure values using photoplethysmogram data according to an embodiment.

[0032] Referring to FIG. 1, system 1 includes user terminal 10 and server 20. For example, user terminal 10 and server 20 may be connected by a wired or wireless communication method to transmit and receive data (e.g., photoplethysmogram data including a photoplethysmogram waveform) to and from each other.

[0033] For convenience of explanation, FIG. 1 shows that system 1 includes user terminal 10 and server 20, but is not limited thereto. For example, system 1 may include other external devices (not shown), and the operations of user terminal 10 and server 20 described below may also be implemented by a single device (e.g., user terminal 10 or server 20).

[0034] User terminal 10 may be a computing device including a display device and a device for receiving user input (e.g., a keyboard, a mouse, etc.), and including a memory and a processor. For example, user terminal 10 may correspond to, but is not limited to, a notebook PC, a desktop PC, a laptop, a tablet computer, a smartphone, etc.

[0035] Server 20 may be a device that communicates with external devices (not shown) including user terminal 10. For example, server 20 may be a device that stores various data including photoplethysmogram data including a photoplethysmogram waveform, and may, in some cases, be a device having autonomous computing ability. For example, server 20 may be a cloud server, but is not limited thereto.

[0036] User terminal 10 calculates blood pressure values using photoplethysmogram data. Then, user terminal 10 corrects the blood pressure values based on personal information and environmental information regarding the calculated blood pressure values.

[0037] System 1 according to one embodiment calculates blood pressure values using photoplethysmogram data. Specifically, the user terminal 10 receives photoplethysmogram data including a photoplethysmogram waveform, normalizes the photoplethysmogram waveform included in the photoplethysmogram data, detects characteristic values for calculating blood pressure values using the normalized photoplethysmogram waveform, and calculates blood pressure values based on the photoplethysmogram data and the detected characteristic values. The user 30 can use the photoplethysmogram data and the characteristic values to confirm the more accurately calculated blood pressure values.

[0038] Hereinafter, with reference to FIGS. 2 to 6, an example in which the user terminal 10 calculates blood pressure values using photoplethysmogram data will be described. On the other hand, as described above with reference to FIG. 1, the operations described below with reference to FIGS. 2 to 6 may be executed by the server 20.

[0039] FIG. 2 is a configuration diagram showing an example of a user terminal according to one embodiment.

[0040] Referring to FIG. 2, the user terminal 100 includes a processor 110 and a memory 120. For convenience of explanation, only the components related to the present invention are shown in FIG. 2. In addition to the components shown in FIG. 2, other general-purpose components may be further included in the user terminal 100. As an example, the user terminal 100 may include an input / output interface (not shown) and / or a communication module (not shown). Also, it is obvious to those having ordinary knowledge in the technical field related to the present invention that the processor 110 and the memory 120 shown in FIG. 2 may be implemented as independent devices.

[0041] The processor 110 can process the instructions of a computer program by performing basic arithmetic, logic, and input / output operations. Here, the instructions can be provided from the memory 120 or an external device (for example, the server 20, etc.). Also, the processor 110 can generally control the operations of other components included in the user terminal 100.

[0042] In particular, the processor 110 calculates blood pressure values using photoplethysmogram data. Specifically, the processor 110 receives photoplethysmogram data including a photoplethysmogram waveform. Then, the processor 110 normalizes the photoplethysmogram waveform included in the photoplethysmogram data. Then, the processor 110 detects characteristic values for calculating blood pressure values using the normalized photoplethysmogram waveform. Then, the processor 110 calculates blood pressure values based on the photoplethysmogram data and the detected characteristic values.

[0043] A specific example of the operation of the processor 110 according to an embodiment will be described with reference to FIGS. 2 to 6.

[0044] The processor 110 may be embodied as an array of a number of logic gates, or may be embodied as a combination of a general-purpose microprocessor and a memory storing a program executable by this microprocessor. For example, the processor 110 may include a general-purpose processor, a central processing unit (CPU), a microprocessor, a digital signal processor (DSP), a controller, a microcontroller, a state machine, etc. In some environments, the processor 110 may include an application-specific semiconductor (ASIC), a programmable logic device (PLD), a field programmable gate array (FPGA), etc. For example, the processor 110 may refer to a combination of a digital signal processor (DSP) and a microprocessor, a combination of multiple microprocessors, a combination of one or more microprocessors coupled to a digital signal processor (DSP) core, or any other combination of processing devices such as a combination of configurations.

[0045] Memory 120 can include any non-transitory computer-readable recording medium. As an example, memory 120 may include a non-volatile mass storage device such as a RAM (random access memory), ROM (read only memory), disk drive, SSD, flash memory, etc. As another example, non-volatile mass storage devices such as ROM, SSD, flash memory, disk drive, etc. may be separate permanent storage devices distinct from the memory. Also, the operating system (OS) and at least one program code (for example, the code for the processor 110 to execute the operations described later with reference to FIGS. 2 to 6) can be stored in the memory 210.

[0046] Such software components can be loaded from a computer-readable recording medium separate from the memory 120. Such a separate computer-readable recording medium may be a recording medium that can be directly connected to the user terminal 100, and may include, for example, computer-readable recording media such as a floppy drive, disk, tape, DVD / CD-ROM drive, memory card, etc. Or, the software components can also be loaded into the memory 120 via a communication module (not shown) that is not a computer-readable recording medium. For example, at least one program can be a computer program (such as a computer program for the processor 110 to execute the operations described later with reference to FIGS. 2 to 6) installed by a file provided by a file distribution system that distributes the developer or application installation files via a communication module (not shown), and can be loaded into the memory 120 based on this.

[0047] The input / output interface (not shown) may be a means for interfacing with a user terminal 100 or a device for input or output (e.g., keyboard, mouse, etc.) that can be included in the user terminal 100. The input / output interface (not shown) may be configured separately from the processor 110, but is not limited thereto, and may be configured to be included in the processor 110.

[0048] The communication module (not shown) can provide a configuration or function for the server 20 and the user terminal 100 to communicate with each other via a network. Also, the communication module (not shown) can provide a configuration or function for the user terminal 100 to communicate with other external devices. For example, control signals, instructions, data, etc. provided under the control of the processor 110 can be transmitted to the server 20 and / or external devices via the communication module (not shown) and the network.

[0049] On the other hand, although not shown in FIG. 2, the user terminal 100 may further include a display device. Alternatively, the user terminal 100 can be connected to an independent display device by a wired or wireless communication method to transmit and receive data to and from each other.

[0050] FIG. 3 is a flowchart for explaining an example of a method for calculating a blood pressure value using characteristic values of a photoplethysmogram according to an embodiment.

[0051] Referring to FIG. 3, the method for calculating a blood pressure value using characteristic values of a photoplethysmogram is composed of steps that are processed in time series by the user terminals 10, 100 or the processor 110 shown in FIGS. 1 and 2. Therefore, even for the content omitted below, the content described above for the user terminals 10, 100 or the processor 110 shown in FIGS. 1 and 2 can also be applied to the method for calculating a blood pressure value using characteristic values of the photoplethysmogram in FIG. 3.

[0052] At stage 310, the processor receives photoplethysmogram data including a photoplethysmogram waveform.

[0053] The photoplethysmogram data can be data including a color difference signal based on PPG detected in the user's face region. And the photoplethysmogram data can be data including one waveform of an rPPG waveform or a cPPG waveform. And the photoplethysmogram data can be data including a pulse wave measured from the user through a PPG measuring device, but is not limited thereto.

[0054] At stage 320, the processor normalizes the photoplethysmogram waveform included in the photoplethysmogram data.

[0055] First, the processor calculates an average value and a standard deviation corresponding to the photoplethysmogram waveform. The processor calculates the average value and the standard deviation using the photoplethysmogram waveform corresponding to three cycles from the photoplethysmogram data.

[0056] Then, the processor normalizes the photoplethysmogram waveform using the average value and the standard deviation.

[0057] As an example, the processor can normalize the photoplethysmogram waveform using the following mathematical formula 1. The photoplethysmogram waveform included in the photoplethysmogram data can be [x1, x2, x3, …, xn] and can be composed of n numerical data.

[0058] [Mathematical formula 1] TIFF2025523177000002.tif13138

[0059] Referring to Mathematical formula 1, the processor can obtain a normalized photoplethysmogram waveform data value (z(x)) using the photoplethysmogram waveform data value (x), the average value (m), and the standard deviation (σ).

[0060] At stage 330, the processor detects a characteristic value for calculating a blood pressure value using the normalized photoplethysmogram waveform.

[0061] The processor uses a standardized photoplethysmogram waveform to detect a first eigenvalue corresponding to the amplitude associated with the systolic phase, a second eigenvalue corresponding to the amplitude associated with the diastolic phase, and a third eigenvalue corresponding to the interval associated with the time of the waveform reflected by the blood vessel wall.

[0062] Hereinafter, with reference to FIG. 4, an example of the processor detecting eigenvalues using a standardized photoplethysmogram waveform will be described.

[0063] FIG. 4 is a drawing for explaining an example in which a processor according to an embodiment detects eigenvalues using a standardized photoplethysmogram waveform. In FIG. 4, the systolic phase (SP) refers to the interval from the start time (P0) of the cycle to the time having the maximum blood pressure value M2 of the cycle. In FIG. 4, the diastolic phase (DP) refers to the interval from the time having the maximum blood pressure value M2 of the cycle to the time of the end point of the cycle.

[0064] The processor uses a standardized photoplethysmogram waveform to detect a first eigenvalue 410 corresponding to the amplitude associated with the systolic phase SP, a second eigenvalue 420 corresponding to the amplitude associated with the diastolic phase DP, and a third eigenvalue 430 corresponding to the interval associated with the time of the waveform reflected by the blood vessel wall.

[0065] The processor uses the maximum blood pressure value M2 and the minimum blood pressure value M0 corresponding to the peak of the photoplethysmogram waveform 41 to detect a first eigenvalue 410 corresponding to the amplitude associated with the systolic phase SP. The first eigenvalue 410 corresponds to the absolute value of the difference between the maximum blood pressure value M2 of the photoplethysmogram waveform 41 and the minimum blood pressure value M0 of the photoplethysmogram waveform 41. On the other hand, the first eigenvalue 410 can be the average value corresponding to all the cycles included in the photoplethysmogram waveform.

[0066] Then, when the processor sets the time corresponding to the inflection point having the minimum value in the photoplethysmogram waveform 42 to which differentiation is applied as P2, the processor detects a second eigenvalue 420 corresponding to the amplitude related to the diastolic period DP by using the amplitude at P2 in the photoplethysmogram waveform 41. The second eigenvalue 420 corresponds to the absolute value of the amplitude at P2 in the photoplethysmogram waveform 41. On the other hand, the second eigenvalue 420 can be an average value corresponding to all the periods included in the photoplethysmogram waveform.

[0067] Then, when the processor sets the time corresponding to the point where the absolute value of the gradient after the maximum value in the differentiated photoplethysmogram waveform 42 becomes small and then large as P1, the processor detects a third eigenvalue 430 corresponding to the section related to the time of the waveform reflected by the blood vessel wall by using the time from the start time P0 to P1 of the cycle in the photoplethysmogram waveform 41. The third eigenvalue 430 corresponds to the absolute value of the difference in time from the start time P0 to P1 of the cycle in the photoplethysmogram waveform 41. On the other hand, the third eigenvalue 430 can be an average value corresponding to all the periods included in the photoplethysmogram waveform.

[0068] Also, referring to FIG. 3, at step 340, blood pressure values are calculated based on the photoplethysmogram data and the detected eigenvalues.

[0069] The processor calculates systolic blood pressure values and diastolic blood pressure values based on the detected eigenvalues.

[0070] On the other hand, the processor calculates blood pressure values based on the photoplethysmogram data, the first eigenvalue, the second eigenvalue, and the third eigenvalue.

[0071] On the other hand, the processor can calculate blood pressure values by using a first neural network model. Specifically, the processor inputs the photoplethysmogram data and the detected eigenvalues as input data of the first neural network model. Then, the processor obtains blood pressure values as output data of the first neural network model.

[0072] Hereinafter, with reference to FIG. 5, an example of a processor obtaining a blood pressure value using a first neural network model will be described.

[0073] FIG. 5 is a drawing for explaining an example of a processor obtaining a blood pressure value using a first neural network model according to an embodiment.

[0074] The first neural network model 52 may be any form of deep learning model for obtaining a blood pressure value. As an example, the first neural network model 52 includes a deep learning model of the RNN series that processes time-series data. In FIG. 5, the first neural network model 52 is described as an LSTM deep learning model, but it is not limited thereto. The first neural network model 52 includes a plurality of LSTM layers.

[0075] The deep learning model can include a neural network for obtaining a blood pressure value. In this specification, neural network, network function, and neural network can be used interchangeably. A neural network can generally be composed of a set of interconnected computing units, which are generally referred to as nodes. Such nodes can be referred to as neurons. A neural network is composed of at least one or more nodes. The nodes (or neurons) constituting the neural network can be interconnected by one or more links.

[0076] The processor inputs the photoplethysmogram data and the detected feature values as the input data 51 of the first neural network model 52. As an example, the processor inputs the photoplethysmogram data, the first feature value, the second feature value, and the third feature value as the input data 51 of the first neural network model 52.

[0077] The input data 51 is a vector composed of photoplethysmogram data, a first eigenvalue, a second eigenvalue, and a third eigenvalue. As an example, the input data 51 may be a vector composed of 878 values. The 878 values include 875 values corresponding to the photoplethysmogram waveform included in the photoplethysmogram data, the first eigenvalue, the second eigenvalue, and the third eigenvalue.

[0078] The first neural network model 52 inputs the input data 51 into the first LSTM layer (LSTM_1). The resultant values derived by the LSTM layer (LSTM_1) are sequentially input into the next LSTM layer, input into the nth LSTM layer (LSTM_n), and input into the fully connected layer.

[0079] When the input data 51 is input into the first neural network model 52, the first neural network model 52 outputs a systolic blood pressure value (SBP) and a diastolic blood pressure value (DBP) as output data.

[0080] On the other hand, the processor corrects the blood pressure value based on personal information related to the blood pressure value and environmental information related to the blood pressure value. As an example, the processor can correct the blood pressure value calculated using the Kohort model.

[0081] Personal information related to the blood pressure value includes at least one of gender information, age information, body information, and disease information of the subject related to the blood pressure value. The personal information related to the blood pressure value can be obtained from an existing database or from the user's input, and is not limited thereto.

[0082] Environmental information related to the blood pressure value includes at least one of temperature information and pressure information at the time of measuring the photoplethysmogram waveform.

[0083] The processor analyzes the correlation between personal information related to blood pressure values, environmental information related to blood pressure values, and the blood pressure values to calculate a weighting value. As an example, the processor uses gender information among the personal information to analyze the correlation with the blood pressure values and calculates a weighting value corresponding to the gender information. As another example, the processor uses age information among the personal information to group age groups, analyzes the correlation between the grouping result and the blood pressure values, and calculates a weighting value corresponding to the age information. As still another example, the processor uses body information among the personal information to analyze the correlation between the BMI index and the blood pressure values and calculates a weighting value corresponding to the body information. As yet another example, the processor uses temperature information among the environmental information to analyze the correlation between the temperature and the systolic blood pressure value and the diastolic blood pressure value and calculates a weighting value corresponding to the temperature information. For example, the correlation can include a relationship where the systolic blood pressure value increases by 1.3 mmHg and the diastolic blood pressure value increases by 0.6 mmHg in inverse proportion to a 1-degree decrease in temperature.

[0084] On the other hand, the processor can correct the blood pressure values using the correction operation model 62. Specifically, the processor calculates the weighting values corresponding to the personal information and the environmental information. Then, the processor inputs the blood pressure values, personal information, environmental information, and the weighting values as input data 61 into the correction operation model 62. Then, the processor obtains the corrected blood pressure values as output data 63 from the correction operation model 62.

[0085] Hereinafter, with reference to FIG. 6, an example of the processor correcting the blood pressure values using the correction operation model will be described.

[0086] FIG. 6 is a drawing for explaining an example of the processor correcting the blood pressure values using the correction operation model according to an embodiment.

[0087] The correction operation model 62 may be any form of machine learning model or deep learning model that obtains blood pressure values.

[0088] The processor inputs the calculated blood pressure value, personal information related to the blood pressure value, environmental information related to the blood pressure value, and the calculated weighted value as input data 61 into the correction calculation model 62.

[0089] As an example, the correction calculation model 62 applies the input data 61 to the following mathematical formulas 2 and 3.

[0090] [Mathematical formula 2] Corrected SBP = β0 + β1sSBP + β2sage + β3sgender + β4sBMI

[0091] The processor can obtain the corrected systolic blood pressure value (corrected SBP) by using the weighted value (β0), systolic blood pressure value (SBP), weighted value corresponding to the systolic blood pressure value (β1), value corresponding to age (age), weighted value corresponding to age (β2), value corresponding to gender (gender), weighted value corresponding to gender (β3), value corresponding to BMI (BMI), and weighted value corresponding to BMI (β4).

[0092] [Mathematical formula 3] Corrected DBP = β5 + β6sDBP + β7sage + β8sgender + β9sBMI

[0093] The processor can obtain the corrected diastolic blood pressure value (corrected DBP) by using the weighted value (β5), diastolic blood pressure value (DBP), weighted value corresponding to the diastolic blood pressure value (β6), value corresponding to age (age), weighted value corresponding to age (β7), value corresponding to gender (gender), weighted value corresponding to gender (β8), value corresponding to BMI (BMI), and weighted value corresponding to BMI (β9).

[0094] When the input data 61 is input into the correction calculation model 62, the correction calculation model 62 outputs the corrected systolic blood pressure value (corrected SBP) and the corrected diastolic blood pressure value (corrected DBP) as output data.

[0095] According to the foregoing, the processor calculates blood pressure values based on the detected characteristic values by using the photoplethysmogram data and the photoplethysmogram data. The user can confirm the blood pressure values calculated from the photoplethysmogram waveform with higher accuracy.

[0096] On the other hand, the above-described method can be created by a program executable on a computer and can be embodied by a general-purpose digital computer that operates the program using a computer-readable recording medium. Also, the data structure used in the above-described method can be recorded on a computer-readable recording medium through various means. The computer-readable recording medium includes storage media such as magnetic storage (e.g., ROM, RAM, USB, floppy disk, hard disk, etc.) and optical reading media (e.g., CD-ROM, DVD, etc.).

[0097] Those having ordinary knowledge in the technical field related to this embodiment should understand that it can be embodied in a modified form without departing from the essential characteristics of the above-described description. Therefore, the disclosed method should be considered from an illustrative rather than a limiting perspective, and the scope of rights is shown not in the above description but in the claims, and should be construed to include all differences within the equivalent scope.

Claims

1. A method for calculating blood pressure values using characteristic values of photoplethysmogram, comprising: receiving photoplethysmogram data including a photoplethysmogram waveform; normalizing the photoplethysmogram waveform included in the photoplethysmogram data; detecting characteristic values for calculating blood pressure values using the normalized photoplethysmogram waveform; and calculating blood pressure values based on the photoplethysmogram data and the detected characteristic values. A method comprising the above steps.

2. The step of normalizing includes: calculating an average value and a standard deviation corresponding to the photoplethysmogram waveform; and normalizing the photoplethysmogram waveform using the average value and the standard deviation. The method according to Claim 1, comprising the above steps.

3. The step of detecting includes: detecting a first characteristic value corresponding to an amplitude related to the systolic phase, a second characteristic value corresponding to an amplitude related to the diastolic phase, and a third characteristic value corresponding to an interval related to the time of the waveform reflected by the blood vessel wall, using the normalized photoplethysmogram waveform; The step of calculating includes: calculating the blood pressure values based on the photoplethysmogram data, the first characteristic value, the second characteristic value, and the third characteristic value. The method according to Claim 1, comprising the above steps.

4. The step of calculating includes: inputting the photoplethysmogram data and the detected characteristic values as input data into a first neural network model; and obtaining the blood pressure values as output data of the first neural network model. The method according to Claim 1, comprising the above steps.

5. The step of calculating includes: calculating systolic blood pressure values and diastolic blood pressure values based on the detected characteristic values. The method according to Claim 1, comprising the above steps.

6. The method according to Claim 1 further comprises: correcting the blood pressure values based on personal information related to the blood pressure values and environmental information related to the blood pressure values.

7. The step of correcting includes: calculating a weighted value corresponding to the personal information and the environmental information; and inputting the blood pressure values, the personal information, the environmental information, and the weighted value as input data into a correction calculation model, and obtaining the corrected blood pressure values as output data from the correction calculation model. The method according to Claim 6, comprising the above steps.

8. A computer-readable recording medium having recorded thereon a program for causing a computer to execute the method according to claim 1.

9. An apparatus for calculating a blood pressure value by using a characteristic value of a photoplethysmogram, comprising: at least one memory; and at least one processor; wherein the at least one processor receives photoplethysmogram data including a photoplethysmogram waveform, normalizes the photoplethysmogram waveform included in the photoplethysmogram data, detects a characteristic value for calculating a blood pressure value by using the normalized photoplethysmogram waveform, and calculates a blood pressure value based on the photoplethysmogram data and the detected characteristic value.

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