Hormone rhythm diagnosis method using biomarkers and apparatus using same

Abstract

Disclosed are a hormone rhythm diagnosis method using biomarkers and an apparatus using same. A hormone rhythm diagnosis method using biomarkers, according to one aspect of the technical idea of the present disclosure, comprises the steps of: receiving biomarker values of a user; correcting the biomarker values by performing a moving average on the biomarker values with a predetermined value; fitting the corrected biomarker values to a function having a 24-hour period; obtaining a time (t) corresponding to a minimum value of the function; and diagnosing hormone rhythm of the user on the basis of the time (t).

Description

Method for diagnosing hormonal rhythm using biomarkers and device using the same

[0001] Cross-reference to related applications

[0002] This disclosure claims priority to patent applications Nos. 10-2024-0100670 and 10-2024-0100671, filed July 30, 2024, the entire contents of each of which are incorporated herein by reference.

[0003] Technology field

[0004] The technical idea of ​​the present disclosure relates to a method for diagnosing a hormone rhythm and a device using the same, and more specifically, to a method for diagnosing a hormone rhythm using a biomarker and a device using the same.

[0005] Human sleep and wakefulness follow a regular cycle. This regular cycle is called the circadian rhythm (also known as the "biorhythm" or "circadian rhythm"), and according to this, we fall asleep and wake at consistent times each day. In addition to the sleep-wake cycle, other factors that follow this circadian rhythm include our basal body temperature, hormones, and melatonin secretion. Therefore, the circadian rhythm can also be considered a hormonal rhythm.

[0006] Providing individualized information on the hormonal rhythms of users exposed to different light environments each day could contribute to improving their health. Therefore, research is needed on technologies that can diagnose hormonal rhythms using personalized biomarkers.

[0007] The above-described content merely provides background information on the present invention and does not correspond to previously disclosed technology.

[0008] The technical idea of ​​the present disclosure is to provide a technology for diagnosing a user's hormonal rhythm by correcting biomarker values ​​measured from a person, fitting them to a 24-hour cycle function, and obtaining the time (t) at which the function has a minimum value.

[0009] Another problem that the technical idea of ​​the present disclosure seeks to solve is to provide a technology for diagnosing a user's hormonal rhythm by calculating a biomarker score that scores the daily variability and daily stability of biomarkers measured from a person.

[0010] The tasks of the present disclosure are not limited to the tasks mentioned above, and other tasks not mentioned will be clearly understood by those skilled in the art from the description below.

[0011] In order to solve the aforementioned problem, a method for diagnosing a hormone rhythm using a biomarker according to one aspect of the technical idea of ​​the present disclosure comprises the steps of: receiving biomarker values ​​from a user; correcting the biomarker values ​​by moving average to a predetermined value;

[0012] It is configured to include a step of fitting the corrected biomarker values ​​to a function having a cycle of 24 hours, a step of obtaining a time (t) corresponding to the minimum value of the function, and a step of diagnosing the user's hormonal rhythm based on the time (t).

[0013] According to a feature of the present invention, the biomarker may be a heart rate, and the function may be a function representing the user's heart rate over time.

[0014] According to a feature of the present invention, based on the biomarker values, the method further includes a step of calculating (R^2), wherein the (R^2) can be calculated by a biomarker fitting value at a specific time, a corrected biomarker value at the specific time, and an average value of all biomarkers measured during the day on a specific date.

[0015] According to a feature of the present invention, the (R^2) may be a parameter indicating the degree of agreement between the corrected biomarker values ​​and the function.

[0016] According to a feature of the present invention, the average value of the time (t) and the morning-evening questionnaire values ​​are inversely proportional, and the average value of the time (t) may be the average of the biomarker values ​​having (R^2) greater than or equal to a threshold value among the measured biomarker values.

[0017] In order to solve the above-described problem, a device for diagnosing a hormone rhythm using a biomarker according to one aspect of the technical idea of ​​the present disclosure is provided. The device is configured to include a memory, a transceiver configured to receive a user's biomarker value, and a processor configured to correct the biomarker values ​​by moving average to a predetermined value, fit the corrected biomarker values ​​to a function having a cycle of 24 hours, obtain a time (t) corresponding to a minimum value of the function, and diagnose the user's hormone rhythm based on the time (t).

[0018] In order to solve the above-described problem, a method for diagnosing a hormone rhythm using a biomarker according to an aspect of the technical idea of ​​the present disclosure is configured to include a step of receiving a biomarker value of a user, a step of calculating a first parameter value representing daily stability of the biomarker value, a step of calculating a second parameter value representing daily variability of the biomarker value, a step of calculating a biomarker score based on the first parameter value and the second parameter value, and a step of diagnosing a hormone rhythm of the user based on the biomarker score.

[0019] According to a feature of the present invention, the first parameter can be calculated based on an average value of biomarkers measured at a specific time during a predetermined X number of days (X is a natural number greater than or equal to 2), an average value of all biomarkers measured on a specific day during the day, an average value of biomarkers measured at an hour h (h is a natural number from 1 to 24) during the X number of days, and an average value of all biomarkers measured during the X number of days.

[0020] According to a feature of the present invention, the first parameter is calculated as a value within a specific range,

[0021] The greater the value of the first parameter within the above range, the higher the daily stability of the biomarker value.

[0022] According to a feature of the present invention, the second parameter can be calculated based on the average value of biomarkers measured at a specific time during a predetermined X number of days (X is a natural number greater than or equal to 2), and the average value of all biomarkers measured on a specific day among the X days.

[0023] According to a feature of the present invention, the second parameter is calculated as a value greater than or equal to 0,

[0024] The larger the value of the second parameter, the higher the daily variability of the biomarker value.

[0025] In order to solve the above-described problem, a device for diagnosing a hormone rhythm using a biomarker according to an aspect of the technical idea of ​​the present disclosure is provided. The device is configured to include a memory, a transceiver configured to receive a biomarker value of a user, and a processor configured to calculate a first parameter value representing daily stability of the biomarker value, calculate a second parameter value representing daily variability of the biomarker value, calculate a biomarker score based on the first parameter and the second parameter value, and diagnose a hormonal rhythm of the user based on the biomarker score.

[0026] Specific details of other embodiments are included in the description and drawings of the invention.

[0027] According to the method for diagnosing a hormone rhythm using a biomarker of the technical idea of ​​the present disclosure and the device using the same, the user's hormone rhythm can be diagnosed more accurately by correcting a biomarker measured from a person, fitting it to a 24-hour cycle function, and calculating the time (t) at which the function has a minimum value.

[0028] Meanwhile, according to the method for diagnosing a hormone rhythm using a biomarker of the technical idea of ​​the present disclosure and the device using the same, the user's hormone rhythm can be diagnosed more accurately by calculating a biomarker score that scores the daily variability and daily stability of a biomarker measured from a person.

[0029] The effects that can be obtained from the present disclosure are not limited to the effects mentioned above, and other effects not mentioned will be clearly understood by those skilled in the art to which the present invention pertains from the description below.

[0030] FIG. 1 is a block diagram illustrating an electronic device within a network environment according to various embodiments according to one embodiment of the present disclosure.

[0031] FIG. 2 is a block diagram schematically illustrating the configuration of a hormone rhythm diagnosis system using a biomarker according to one embodiment of the present disclosure.

[0032] FIG. 3 is a schematic block diagram of a hormone rhythm diagnosis device according to one embodiment of the present disclosure.

[0033] FIG. 4 is a flowchart showing a hormone rhythm diagnosis method of a hormone rhythm diagnosis device according to one embodiment of the present disclosure.

[0034] FIG. 5 is a graph showing biomarker scores measured on an hourly basis according to one embodiment of the present disclosure.

[0035] FIG. 6 is a graph showing biomarker values ​​measured every minute for one day according to one embodiment of the present disclosure.

[0036] FIG. 7 is a graph showing the correlation between MEQ score and (t_min,avg) according to one embodiment of the present disclosure.

[0037] FIG. 8 is a schematic block diagram of a hormone rhythm diagnosis device according to one embodiment of the present disclosure.

[0038] FIG. 9 is a flowchart showing a hormone rhythm diagnosis method of a hormone rhythm diagnosis device according to one embodiment of the present disclosure.

[0039] FIG. 10 is a graph showing biomarker scores measured on an hourly basis according to one embodiment of the present disclosure.

[0040] FIG. 11 is a graph showing biomarker values ​​measured every minute throughout the day according to one embodiment of the present disclosure.

[0041] Figure 12 is a graph showing the biomarker values ​​measured in Figure 10 processed using a moving average.

[0042] FIG. 13 is a graph showing the measurement and moving average processing of biomarker values ​​when (R^2) = 0.76 according to one embodiment of the present disclosure.

[0043] FIG. 14 is a graph showing the measurement and moving average processing of biomarker values ​​when (R^2) = 0.11 according to one embodiment of the present disclosure.

[0044] FIG. 15 is a graph showing the correlation between (R^2) and biomarker scores according to one embodiment of the present disclosure.

[0045] For clarity in the interpretation of this specification, the terms used in this specification are defined below.

[0046] The term 'hormone rhythm' used in this specification refers to a biological phenomenon that exhibits circadian rhythm, such as hormone secretion patterns that appear in a cycle of approximately 24 hours.

[0047] The term 'biomarker score (BM_score)' used in this specification refers to a parameter calculated by a hormone rhythm diagnosis method and a device using the same according to an embodiment of the present disclosure, and is a parameter defined and used to diagnose a hormone rhythm using a biomarker measured from a human.

[0048] Hereinafter, the present disclosure will be described in detail by describing embodiments of the present disclosure with reference to the attached drawings.

[0049] FIG. 1 is a block diagram illustrating an electronic device within a network environment according to various embodiments according to one embodiment of the present disclosure.

[0050] Referring to FIG. 1, in a network environment (100), an electronic device (101) may communicate with an electronic device (102) via a first network (198) (e.g., a short-range wireless communication network), or may communicate with an electronic device (104) or a server (108) via a second network (199) (e.g., a long-range wireless communication network). According to one embodiment, the electronic device (101) may communicate with the electronic device (104) via the server (108). According to one embodiment, the electronic device (101) may include a processor (120), a memory (130), an input module (150), an audio output module (155), a display module (160), an audio module (170), a sensor module (176), an interface (177), a connection terminal (178), a haptic module (179), a camera module (180), a power management module (188), a battery (189), a communication module (190), a subscriber identification module (196), or an antenna module (197). In some embodiments, the electronic device (101) may omit at least one of these components (e.g., the connection terminal (178)), or may have one or more other components added. In some embodiments, some of these components (e.g., the sensor module (176), the camera module (180), or the antenna module (197)) may be integrated into one component (e.g., the display module (160)).

[0051] The processor (120) may, for example, execute software (e.g., a program (140)) to control at least one other component (e.g., a hardware component or a software component) of the electronic device (101) connected to the processor (120) and perform various data processing or operations. According to one embodiment, as at least a part of the data processing or operations, the processor (120) may store commands or data received from other components (e.g., a sensor module (176) or a communication module (190)) in a volatile memory (132), process the commands or data stored in the volatile memory (132), and store result data in a non-volatile memory (134). According to one embodiment, the processor (120) may include a main processor (121) (e.g., a central processing unit or an application processor), or an auxiliary processor (123) (e.g., a graphics processing unit, a neural processing unit (NPU), an image signal processor, a sensor hub processor, or a communication processor)) that can operate independently or together therewith. For example, if the electronic device (101) includes a main processor (121) and an auxiliary processor (123), the auxiliary processor (123) may be configured to use less power than the main processor (121) or to be specialized for a specified function. The auxiliary processor (123) may be implemented separately from the main processor (121) or as a part thereof.

[0052] The auxiliary processor (123) may control at least a portion of functions or states associated with at least one component (e.g., a display module (160), a sensor module (176), or a communication module (190)) of the electronic device (101), for example, on behalf of the main processor (121) while the main processor (121) is in an inactive (e.g., sleep or idle) state, or together with the main processor (121) while the main processor (121) is in an active (e.g., application execution) state. In one embodiment, the auxiliary processor (123) (e.g., an image signal processor or a communication processor) may be implemented as a part of another functionally related component (e.g., a camera module (180) or a communication module (190)). In one embodiment, the auxiliary processor (123) (e.g., a neural network processing unit) may include a hardware structure specialized for processing artificial intelligence models. The artificial intelligence models may be generated through machine learning. This learning can be performed, for example, in the electronic device (101) itself where artificial intelligence is performed, or can be performed through a separate server (e.g., server (108)). The learning algorithm can include, for example, supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning, but is not limited to the examples described above. The artificial intelligence model can include multiple artificial neural network layers.The artificial neural network may be one of a deep neural network (DNN), a convolutional neural network (CNN), a recurrent neural network (RNN), a restricted Boltzmann machine (RBM), a deep belief network (DBN), a bidirectional recurrent deep neural network (BRDNN), a deep Q-network, or a combination of two or more of the above, but is not limited to the examples described above. In addition to, or alternatively to, a hardware structure, an artificial intelligence model may include a software structure.

[0053] The memory (130) can store various data used by at least one component (e.g., processor (120) or sensor module (176)) of the electronic device (101). The data can include, for example, input data or output data for software (e.g., program (140)) and commands related thereto. The memory (130) can include volatile memory (132) or non-volatile memory (134).

[0054] The program (140) may be stored as software in memory (130) and may include, for example, an operating system (142), middleware (144), or an application (146).

[0055] The input module (150) can receive commands or data to be used in a component of the electronic device (101) (e.g., a processor (120)) from an external source (e.g., a user) of the electronic device (101). The input module (150) can include, for example, a microphone, a mouse, a keyboard, a key (e.g., a button), or a digital pen (e.g., a stylus pen).

[0056] The input module (150) can receive commands or data to be used in a component of the electronic device (101) (e.g., a processor (120)) from an external source (e.g., a user) of the electronic device (101). The input module (150) can include, for example, a microphone, a mouse, a keyboard, a key (e.g., a button), or a digital pen (e.g., a stylus pen).

[0057] The audio output module (155) can output audio signals to the outside of the electronic device (101). The audio output module (155) can include, for example, a speaker or a receiver. The speaker can be used for general purposes, such as multimedia playback or recording playback. The receiver can be used to receive incoming calls. In one embodiment, the receiver can be implemented separately from the speaker or as part of the speaker.

[0058] The display module (160) can visually provide information to an external party (e.g., a user) of the electronic device (101). The display module (160) may include, for example, a display, a holographic device, or a projector and a control circuit for controlling the device. In one embodiment, the display module (160) may include a touch sensor configured to detect a touch, or a pressure sensor configured to measure the intensity of a force generated by the touch.

[0059] The audio module (170) can convert sound into an electrical signal, or vice versa, convert an electrical signal into sound. According to one embodiment, the audio module (170) can acquire sound through the input module (150), or output sound through an audio output module (155), or an external electronic device (e.g., electronic device (102)) (e.g., speaker or headphone) directly or wirelessly connected to the electronic device (101).

[0060] The sensor module (176) can detect the operating status (e.g., power or temperature) of the electronic device (101) or the external environmental status (e.g., user status) and generate an electrical signal or data value corresponding to the detected status. According to one embodiment, the sensor module (176) can include, for example, a gesture sensor, a gyro sensor, a barometric pressure sensor, a magnetic sensor, an acceleration sensor, a grip sensor, a proximity sensor, a color sensor, an IR (infrared) sensor, a biometric sensor, a temperature sensor, and a humidity sensor. According to one embodiment, the sensor module (176) can include a sensor that measures a biomarker.

[0061] The interface (177) may support one or more designated protocols that may be used to directly or wirelessly connect the electronic device (101) with an external electronic device (e.g., the electronic device (102)). According to one embodiment, the interface (177) may include, for example, a high definition multimedia interface (HDMI), a universal serial bus (USB) interface, an SD card interface, an audio interface, and an Inter-Integrated Circuit (I²C) interface.

[0062] The connection terminal (178) may include a connector through which the electronic device (101) may be physically connected to an external electronic device (e.g., electronic device (102)). According to one embodiment, the connection terminal (178) may include, for example, an HDMI connector, a USB connector, an SD card connector, or an audio connector (e.g., a headphone connector).

[0063] A haptic module (179) can convert electrical signals into mechanical stimuli (e.g., vibration or movement) or electrical stimuli that a user can perceive through tactile or kinesthetic sensations. In one embodiment, the haptic module (179) can include, for example, a motor, a piezoelectric element, or an electrical stimulation device.

[0064] The camera module (180) can capture still images and videos. According to one embodiment, the camera module (180) may include one or more lenses, image sensors, image signal processors, or flashes.

[0065] The power management module (188) can manage power supplied to the electronic device (101). According to one embodiment, the power management module (188) can be implemented, for example, as at least a part of a power management integrated circuit (PMIC).

[0066] A battery (189) may power at least one component of the electronic device (101). In one embodiment, the battery (189) may include, for example, a non-rechargeable primary battery, a rechargeable secondary battery, or a fuel cell.

[0067] The communication module (190) may support the establishment of a direct (e.g., wired) communication channel or a wireless communication channel between the electronic device (101) and an external electronic device (e.g., electronic device (102), electronic device (104), or server (108)), and the performance of communication through the established communication channel. The communication module (190) may operate independently from the processor (120) (e.g., application processor) and may include one or more communication processors that support direct (e.g., wired) communication or wireless communication. According to one embodiment, the communication module (190) may include a wireless communication module (192) (e.g., a cellular communication module, a short-range wireless communication module, or a global navigation satellite system (GNSS) communication module) or a wired communication module (194) (e.g., a local area network (LAN) communication module, or a power line communication module). Among these communication modules, the corresponding communication module can communicate with an external electronic device via a first network (198) (e.g., a short-range communication network such as Bluetooth, wireless fidelity (WiFi) direct, or infrared data association (IrDA)) or a second network (199) (e.g., a long-range communication network such as a legacy cellular network, a 5G network, a next-generation communication network, the Internet, or a computer network (e.g., a LAN or WAN)). These various types of communication modules can be integrated into a single component (e.g., a single chip) or implemented as multiple separate components (e.g., multiple chips). The wireless communication module (192) can verify or authenticate the electronic device (101) within a communication network such as the first network (198) or the second network (199) by using subscriber information (e.g., an international mobile subscriber identity (IMSI)) stored in the subscriber identification module (196).

[0068] The wireless communication module (192) can support 5G networks and next-generation communication technologies following the 4G network, such as NR access technology (new radio access technology). The NR access technology can support high-speed transmission of high-capacity data (eMBB (enhanced mobile broadband)), minimization of terminal power and connection of multiple terminals (mMTC (massive machine type communications)), or high reliability and low latency (URLLC (ultra-reliable and low-latency communications)). The wireless communication module (192) can support, for example, a high-frequency band (e.g., mmWave band) to achieve a high data transmission rate. The wireless communication module (192) can support various technologies for securing performance in a high-frequency band, such as beamforming, massive multiple-input and multiple-output (MIMO), full dimensional MIMO (FD-MIMO), array antenna, analog beam-forming, or large scale antenna. The wireless communication module (192) can support various requirements specified in the electronic device (101), an external electronic device (e.g., the electronic device (104)), or a network system (e.g., the second network (199)). According to one embodiment, the wireless communication module (192) can support a peak data rate (e.g., 20 Gbps or more) for eMBB realization, a loss coverage (e.g., 164 dB or less) for mMTC realization, or a U-plane latency (e.g., 0.5 ms or less for downlink (DL) and uplink (UL), or 1 ms or less for round trip) for URLLC realization.

[0069] The antenna module (197) can transmit or receive signals or power to or from an external device (e.g., an external electronic device). In one embodiment, the antenna module may include an antenna including a radiator formed of a conductor or a conductive pattern formed on a substrate (e.g., a PCB). In one embodiment, the antenna module (197) may include a plurality of antennas (e.g., an array antenna). In this case, at least one antenna suitable for a communication method used in a communication network, such as the first network (198) or the second network (199), may be selected from the plurality of antennas by, for example, the communication module (190). A signal or power may be transmitted or received between the communication module (190) and an external electronic device via the at least one selected antenna. In some embodiments, in addition to the radiator, another component (e.g., a radio frequency integrated circuit (RFIC)) may be additionally formed as a part of the antenna module (197).

[0070] According to various embodiments, the antenna module (197) may form a mmWave antenna module. According to one embodiment, the mmWave antenna module may include a printed circuit board, an RFIC disposed on or adjacent a first side (e.g., a bottom side) of the printed circuit board and capable of supporting a designated high frequency band (e.g., a mmWave band), and a plurality of antennas (e.g., an array antenna) disposed on or adjacent a second side (e.g., a top side or a side side) of the printed circuit board and capable of transmitting or receiving signals in the designated high frequency band.

[0071] At least some of the above components can be interconnected and exchange signals (e.g., commands or data) with each other via a communication method between peripheral devices (e.g., a bus, GPIO (general purpose input and output), SPI (serial peripheral interface), or MIPI (mobile industry processor interface)).

[0072] According to one embodiment, commands or data may be transmitted or received between the electronic device (101) and an external electronic device (104) via a server (108) connected to a second network (199). Each of the external electronic devices (102 or 104) may be the same or a different type of device as the electronic device (101). According to one embodiment, all or part of the operations executed in the electronic device (101) may be executed in one or more of the external electronic devices (102, 104, or 108). For example, when the electronic device (101) is to perform a certain function or service automatically or in response to a request from a user or another device, the electronic device (101) may, instead of or in addition to executing the function or service itself, request one or more external electronic devices to perform the function or at least a part of the service. One or more external electronic devices that receive the request may execute at least a portion of the requested function or service, or an additional function or service related to the request, and transmit the result of the execution to the electronic device (101). The electronic device (101) may process the result as is or additionally and provide it as at least a portion of a response to the request. For this purpose, cloud computing, distributed computing, mobile edge computing (MEC), or client-server computing technology may be used, for example. The electronic device (101) may provide an ultra-low latency service by using distributed computing or mobile edge computing, for example. In another embodiment, the external electronic device (104) may include an Internet of Things (IoT) device. The server (108) may be an intelligent server utilizing machine learning and / or a neural network. According to one embodiment, the external electronic device (104) or the server (108) may be included in the second network (199).The electronic device (101) can be applied to intelligent services (e.g., smart home, smart city, smart car, or healthcare) based on 5G communication technology and IoT-related technology.

[0073] Electronic devices according to various embodiments disclosed in this document may take various forms. Electronic devices may include, for example, portable communication devices (e.g., smartphones), computer devices, portable multimedia devices, portable medical devices, cameras, wearable devices (e.g., smart glasses), or home appliances. Electronic devices according to embodiments of this specification are not limited to the aforementioned devices.

[0074] The various embodiments of this document and the terminology used therein are not intended to limit the technical features described in this document to specific embodiments, but should be understood to include various modifications, equivalents, or substitutes of the embodiments. In connection with the description of the drawings, similar reference numerals may be used for similar or related components. The singular form of a noun corresponding to an item may include one or more of the items, unless the context clearly indicates otherwise. In this document, each of the phrases "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" can include any one of the items listed together in the corresponding phrase among those phrases, or all possible combinations thereof. Terms such as "first," "second," or "first" or "second" may be used merely to distinguish one component from another, and do not limit the components in any other respect (e.g., importance or order). When a component (e.g., a first component) is referred to as "coupled" or "connected" to another component (e.g., a second component), with or without the terms "functionally" or "communicatively," it means that the component can be connected to the other component directly (e.g., wired), wirelessly, or through a third component.

[0075] The term "module" used in various embodiments of this document may include a unit implemented in hardware, software, or firmware, and may be used interchangeably with terms such as logic, logic block, component, or circuit. A module may be an integral component, or a minimum unit or part of such a component that performs one or more functions. For example, according to one embodiment, a module may be implemented in the form of an application-specific integrated circuit (ASIC).

[0076] According to various embodiments, each component (e.g., a module or a program) of the above-described components may include one or more entities, and some of the entities may be separated and arranged in other components. According to various embodiments, one or more components or operations of the aforementioned components may be omitted, or one or more other components or operations may be added. Alternatively or additionally, a plurality of components (e.g., a module or a program) may be integrated into a single component. In this case, the integrated component may perform one or more functions of each of the plurality of components identically or similarly to those performed by the corresponding component among the plurality of components prior to the integration. According to various embodiments, the operations performed by a module, program, or other component may be executed sequentially, in parallel, iteratively, or heuristically, or one or more of the operations may be executed in a different order, omitted, or one or more other operations may be added.

[0077] Hereinafter, a hormone rhythm diagnosis system using a biomarker according to one embodiment of the present disclosure will be described with reference to FIG. 2.

[0078] FIG. 2 is a block diagram schematically illustrating the configuration of a hormone rhythm diagnosis system using a biomarker according to one embodiment of the present disclosure.

[0079] A hormone rhythm diagnosis system (200) using a biomarker according to one embodiment of the present disclosure is a system for diagnosing a hormone rhythm from a biomarker measured by a user of the present system, and may include a biomarker measurement device (202) and / or a hormone rhythm diagnosis device (201). In another embodiment, the biomarker measurement device (202) and the hormone rhythm diagnosis device (201) may be integrated into one component (e.g., a biomarker measurement and hormone rhythm diagnosis device).

[0080] Referring to FIGS. 1 and 2, a biomarker measurement device (202, FIG. 2) can be applied to an electronic device (102, FIG. 1) of FIG. 1, and a hormone rhythm diagnosis device (201, FIG. 2) can be applied to an electronic device (101, FIG. 1) of FIG. 1. According to one embodiment, the hormone rhythm diagnosis device (201, FIG. 2) can communicate with the biomarker measurement device (202, FIG. 2) via a first network (198, FIG. 1).

[0081] In one embodiment, the biomarker measurement device (202) can detect a biomarker of a user. The biomarker measurement device (202) can measure a biomarker.

[0082] In one embodiment, the hormone rhythm diagnosis device (201) can receive the user's biomarker values ​​measured by the biomarker measurement device (202). The hormone rhythm diagnosis device (201) can correct the measured biomarker values ​​by moving average to a predetermined value. The hormone rhythm diagnosis device (201) can fit the corrected biomarker values ​​to a function with a cycle of 24 hours. The hormone rhythm diagnosis device (201) can obtain a time (t) corresponding to the minimum value of the function. The hormone rhythm diagnosis device (201) can diagnose the user's hormone rhythm based on the time (t).

[0083] In one embodiment, the hormone rhythm diagnosis device (201) can receive a user's biomarker value measured by a biomarker measurement device (202). The hormone rhythm diagnosis device (201) can calculate a first parameter value representing daily stability of the biomarker value. The hormone rhythm diagnosis device (201) can calculate a second parameter value representing daily variability of the biomarker value. The hormone rhythm diagnosis device (201) can calculate a biomarker score based on the first parameter and the second parameter values. Details regarding the first parameter, the second parameter, and the biomarker value will be described later.

[0084] The hormonal rhythm diagnosis device (201) can diagnose the user's hormonal rhythm based on the biomarker score.

[0085] FIG. 3 is a schematic block diagram of a hormone rhythm diagnosis device according to one embodiment of the present disclosure.

[0086] Referring to FIGS. 2 and 3, the hormone rhythm diagnosis device (300) of FIG. 3 can be applied to the hormone rhythm diagnosis device (300) of FIG. 2.

[0087] In one embodiment, the hormone rhythm diagnosis device (300) may include a transceiver (301), a processor (302), or a memory (303). This is merely an example, and the technical contents of the present disclosure are not limited thereto. In other embodiments, the hormone rhythm diagnosis device (300) may additionally or alternatively include other components.

[0088] The transceiver (301) can receive a user's biomarker value (BM) from an external source. Referring to FIG. 2, for example, the biomarker value can be received from a biomarker measurement device (202, FIG. 2). In one embodiment, the biomarker may be a heart rate.

[0089] The processor (302) may control at least one other component of the hormone rhythm diagnosis device (300). For example, the processor (302) may control the operation of the transceiver (301) or the memory (303).

[0090] The processor (302) can process or calculate a value corrected by moving average of the received biomarker value (BM), a function with a cycle of 24 hours, and a time (t_min) corresponding to the minimum value of the function.

[0091] The processor (302) can correct the biomarker values ​​by moving average to a predetermined value, thereby calculating the corrected biomarker values. According to one embodiment, the moving average can be processed as 10.

[0092] The processor (302) can fit the corrected biomarker values ​​to a function with a 24-hour period. Details of the function are described below.

[0093] The processor (302) can obtain the time (t_min) corresponding to the minimum value of the above function.

[0094] The processor (302) can diagnose the user's hormonal rhythm based on the biomarker score.

[0095] The memory (303) can store various data used in the hormone rhythm diagnosis device (300). For example, the memory (303) can store a biomarker value (BM) and / or a time (t_min) corresponding to the minimum value of the function. The memory (303) can store an (R^2) value. The memory (303) can store a biomarker fitting value at a specific time, a corrected biomarker value at the specific time, and / or an average value of all biomarkers measured during the day on a specific date. The memory (303) can store an average value at the time (t_min) and / or a morning-evening questionnaire value. The morning-evening questionnaire value (Morningness-Eveningness Questionnaire; MEQ) is a self-assessment questionnaire developed by researchers James A. Horne and Olov Ostberg in 1976, and refers to a questionnaire for measuring whether a person's circadian rhythm produces peak alertness in the morning, evening, or in between.

[0096] The memory (303) may include, but is not limited to, volatile memory such as DRAM or SRAM, nonvolatile memory such as PRAM, MRAM, ReRAM, or NAND flash memory, or a hard disk drive (HDD) or a solid state drive (SSD). In addition, the memory (303) may include, but is not limited to, a cache, a buffer, a main memory, an auxiliary memory, or a separately provided storage system depending on its use / location.

[0097] FIG. 4 is a flowchart showing a hormone rhythm diagnosis method of a hormone rhythm diagnosis device according to one embodiment of the present disclosure.

[0098] The hormone rhythm diagnostic device can receive biomarker values ​​(BM) from the user from an external source (S402). For example, the biomarker value can be received from a biomarker measurement device. The biomarker may be a heart rate.

[0099] The hormone rhythm diagnosis device can correct biomarker values ​​by moving average to a predetermined value (S404). Here, the predetermined value may be 10.

[0100] The hormone rhythm diagnostic device can fit the corrected biomarker values ​​to a function with a 24-hour cycle (S406). Here, the function may be a function representing the user's heart rate over time.

[0101] The hormone rhythm diagnosis device can calculate (R^2) based on the biomarker values. (R^2) is a biomarker fitting value at a specific time (BM_fit(t)), a corrected biomarker value at the specific time (BM_ma(t)), and an average value of all biomarkers measured during the day on a specific date ( ) can be calculated. (R^2) may be a parameter indicating the degree of agreement between the corrected biomarker values ​​and the function.

[0102] Referring to FIGS. 5 and 6, FIG. 5 is a graph showing biomarker values ​​measured every minute for a day according to one embodiment of the present disclosure, and FIG. 6 is a graph showing biomarker values ​​measured in FIG. 5 processed by a moving average.

[0103] Figures 5 and 6 are graphs showing the process of extracting (R^2). In order to extract (R^2), biomarker values ​​measured for 24 hours are required, and the graph of Figure 5 shows the biomarker values ​​of a specific user measured every minute for a day. The circles in the graph of Figure 5 represent data measuring the biomarker values ​​of a specific user every minute for a day. The x-axis represents time {hour}, and the y-axis represents the biomarker value (BM). The circles in the graph of Figure 6 represent data obtained by calculating the moving average of Figure 5. In one embodiment, the moving average can be processed as 10.

[0104] The curve (702) illustrated in Fig. 6 represents the circles in the graph of Fig. 7 fitted with a sine function. The sine function used in the fitting can be defined by the following mathematical equation.

[0105] [Mathematical Formula 1-1]

[0106]

[0107] In Equation 1-1, y represents the biomarker value (BM), x represents time (time), and x_c, A, and y_0 represent constants. Since the biomarker values ​​of a person with a healthy hormonal rhythm have a period of approximately 24 hours, they can be fitted as a sine function with a period of 24, as in Equation 1-1.

[0108] Fitting can be performed using the Levenberg-Marquardt method for 1000 iterations. During the fitting process, x_c, A, and y_0 can be determined to appropriate values.

[0109] After fitting as described above, (R^2) can be calculated. (R^2) can be defined as follows:

[0110] [Equation 2-1]

[0111]

[0112]

[0113] In mathematical expression 2-1, BM_fit(t) means the biomarker fitting value (602) at time t, and BM_ma(t) means the biomarker value (circles in Fig. 6) processed by the moving average 10 at time t. can be defined by the following mathematical formula.

[0114] [Equation 3-1]

[0115]

[0116] According to mathematical formula 3-1, refers to the average value of all heart rates measured on a specific date (D). BM(t) refers to the biomarker value at time t (t-1,…,24), and N refers to the number of biomarker measurements per day. In the embodiments, it can be assumed that the measurement cycle of the biomarker is constant.

[0117] According to an embodiment of the present disclosure, the (R^2) value is at most 1, and the closer the (R^2) value is to 1, the higher the agreement between the sine function and the biomarker for 24 hours a day.

[0118] Referring again to FIG. 4, the hormone rhythm diagnosis device can obtain the time (t_min) corresponding to the minimum value of the above function (S408).

[0119] Referring to Fig. 6, the time (t_min) corresponding to the minimum value of the function means t at which BM_fit(t) becomes minimum.

[0120] The average value of the time (t_min) corresponding to the minimum value of the above function and the morning-evening questionnaire values ​​are inversely proportional, and the average value of the time (t_min) corresponding to the minimum value of the above function may be the average of the biomarker values ​​having the (R^2) exceeding a threshold value among the measured biomarker values. Here, the threshold value may be 0.25.

[0121] Referring to FIG. 7, FIG. 7 is a graph showing the correlation between MEQ score and (t_min,avg) according to one embodiment of the present disclosure. The x-axis represents the MEQ score, and the y-axis represents the average value of the time (t_min) corresponding to the minimum value of the function, i.e., t_min,avg.

[0122] According to Figure 7, the correlation between the average value of the time (t_min) corresponding to the minimum value of the function extracted from the biomarkers measured for 7 days in 17 clinical subjects and the morning-evening questionnaire values ​​is shown.

[0123] In one embodiment, t_min,avg is the average value obtained by extracting only the t_min of days satisfying (R^2)>0.25 from the biomarkers measured for 7 days per person.

[0124] Referring to Figure 7, the larger the morning-evening questionnaire value, the smaller the t_min,avg value, and the smaller the morning-evening questionnaire value, the larger the t_min,avg value.

[0125] Referring to FIG. 4, the hormone rhythm diagnosis device can diagnose the user's hormone rhythm based on the time (t_min) (S410).

[0126] Accordingly, the time (t_min) corresponding to the minimum value of the function according to the embodiment of the present disclosure can be utilized as a measure of hormonal rhythm health. In addition, according to the method for diagnosing hormonal rhythm using a biomarker according to the embodiment of the present disclosure and the device using the same, the user's hormonal rhythm can be diagnosed more accurately by correcting the biomarker measured from a person, fitting it to a 24-hour cycle function, and calculating the time (t) at which the function has the minimum value.

[0127] FIG. 8 is a schematic block diagram of a hormone rhythm diagnosis device according to one embodiment of the present disclosure.

[0128] Referring to FIG. 2 and FIG. 8, the hormone rhythm diagnosis device (300) of FIG. 8 can be applied to the hormone rhythm diagnosis device (300) of FIG. 2.

[0129] In one embodiment, the hormone rhythm diagnosis device (300) may include a transceiver (301), a processor (302), or a memory (303). This is merely an example, and the technical contents of the present disclosure are not limited thereto. In other embodiments, the hormone rhythm diagnosis device (300) may additionally or alternatively include other components.

[0130] The transmitter / receiver (301) can receive a user's biomarker value (BM) from an external source. Referring to FIG. 2, for example, the biomarker value can be received from a biomarker measurement device (202, FIG. 2).

[0131] The processor (302) may control at least one other component of the hormone rhythm diagnosis device (300). For example, the processor (302) may control the operation of the transceiver (301) or the memory (303).

[0132] The processor (302) can perform processing or calculation of the first parameter, the second parameter, and / or the biomarker score (BM_score) based on the received biomarker value (BM).

[0133] In one embodiment, the processor (302) may calculate the first parameter using the first mathematical equation according to one embodiment of the present disclosure. The first mathematical equation may be composed of an average value of biomarkers measured at a specific time during a predetermined X days (where X is a natural number greater than or equal to 2), an average value of all biomarkers measured on a specific day among X days, an average value of biomarkers measured at h hours (where h is a natural number from 1 to 24) during X days, and an average value of all biomarkers measured during X days. As an example, the first parameter may be a value within the range of 0 to 1, and a larger value within the range of 0 to 1 may indicate higher daily stability of the user's biomarker value.

[0134] In one embodiment, the processor (302) may calculate the second parameter using the second mathematical formula according to one embodiment of the present disclosure. The second mathematical formula may be composed of an average value of biomarkers measured at a specific time during a predetermined X days (where X is a natural number greater than or equal to 2), and an average value of all biomarkers measured on a specific day among X days. As an example, the second parameter may be a value greater than or equal to 0, and a larger value may indicate a higher daily variability of the user's biomarker value.

[0135] The processor (302) can calculate a biomarker score (BM_score) based on the first parameter value and the second parameter value. The biomarker score refers to the daily variability and daily stability of the biomarker measured by the user.

[0136] The processor (302) can diagnose the user's hormonal rhythm based on the biomarker score.

[0137] The memory (303) can store various data used in the hormone rhythm diagnosis device (300). For example, the memory (303) can store a biomarker value (BM) and / or a biomarker score (BM_score). The memory (303) can store a first parameter value and / or a second parameter value. The memory (303) can store various mathematical formulas for calculating the first parameter value and / or the second parameter value. The memory (303) can store an average value of biomarkers measured at a specific time during a predetermined X (X is a natural number greater than or equal to 2) days, an average value of all biomarkers measured on a specific day among X days, an average value of biomarkers measured at h (h is a natural number from 1 to 24) hours during X days, and / or an average value of all biomarkers measured during X days.

[0138] The memory (303) may include, but is not limited to, volatile memory such as DRAM or SRAM, nonvolatile memory such as PRAM, MRAM, ReRAM, or NAND flash memory, or a hard disk drive (HDD) or a solid state drive (SSD). In addition, the memory (303) may include, but is not limited to, a cache, a buffer, a main memory, an auxiliary memory, or a separately provided storage system depending on its use / location.

[0139] FIG. 9 is a flowchart showing a hormone rhythm diagnosis method of a hormone rhythm diagnosis device according to one embodiment of the present disclosure.

[0140] The hormone rhythm diagnosis device can receive a user's biomarker value (BM) from an external source (S402). For example, the biomarker value can be received from a biomarker measurement device.

[0141] The hormone rhythm diagnosis device can calculate a first parameter value indicating the stability of a biomarker value during a day (S404). Here, the first parameter can be calculated based on an average value of biomarkers measured at a specific time during a predetermined X days (X is a natural number greater than or equal to 2), an average value of all biomarkers measured on a specific day among X days, an average value of biomarkers measured at h hours (h is a natural number from 1 to 24) during X days, and an average value of all biomarkers measured during X days. In addition, the first parameter is calculated as a value within a specific range, and the larger the first parameter has within the range, the higher the daily stability of the biomarker value can be.

[0142] The hormone rhythm diagnosis device can calculate a second parameter value representing the daily variability of the biomarker value (S406). Here, the second parameter can be calculated based on the average value of the biomarkers measured at a specific time during a predetermined X days (X is a natural number greater than or equal to 2), and the average value of all biomarkers measured on a specific day among X days. In addition, the second parameter is calculated as a value greater than or equal to 0, and the larger the second parameter value, the higher the daily variability of the biomarker value.

[0143] The hormone rhythm diagnosis device can calculate a biomarker score based on the first parameter value and the second parameter value (S408).

[0144] The biomarker score (BM_score) can be defined by the following mathematical formula.

[0145] [Mathematical Formula 1]

[0146]

[0147] According to Equation 1, the biomarker score is , a, and b can be defined as a non-linear function determined by. In one embodiment, a=2.5, b=0.3. a is an indicator that determines the threshold of the biomarker score, and b can be defined as an indicator that determines the linearity of the biomarker score.

[0148] can be defined by the following mathematical formula.

[0149] [Equation 2]

[0150]

[0151] According to mathematical formula 2, is a value determined by α and β.

[0152] α is an indicator representing the daily stability of the biomarker, and may be a value determined using the date (D) as a variable. α may be referred to as the first parameter in this specification. β is an indicator representing the daily variability of the biomarker, and may be a value determined using the date (D) as a variable. β may be referred to as the second parameter in this specification. Here, the date (D) refers to the date for which α and / or β are to be calculated.

[0153] In one embodiment, α(D) can be defined according to the following mathematical formula:

[0154] [Equation 3]

[0155]

[0156] According to mathematical expression 3, α(D) is the average value of biomarkers measured at a specific time (i) for a predetermined number X (X is a natural number greater than or equal to 2). ), the average value of all biomarkers measured on a specific day (D) out of X days ( ), the average values ​​of biomarkers measured at h (h is a natural number from 1 to 24) hours over X days ( ), the average value of all biomarkers measured over X days ( ) can be calculated.

[0157] In one embodiment, β(D) can be defined according to the following mathematical formula:

[0158] [Equation 4]

[0159]

[0160] According to the mathematical formula, β(D) is the average value of biomarkers measured at a specific time during a predetermined X (X is a natural number greater than or equal to 2) days. ), the average value of all biomarkers measured on a specific day during X days ( ) can be calculated.

[0161] In one embodiment, the predetermined X days refer to days from D to (D-X+1). Day_window refers to the sum of days for which α is to be calculated, and has the same value and definition as X. For example, if D=20 and Day_window=6, α can be calculated for a total of 6 days, from 15 (20-6+1) to 20 (D).

[0162] The hormonal rhythm diagnosis device can diagnose the user's hormonal rhythm based on the biomarker score (S410).

[0163] FIG. 10 is a graph showing biomarker scores measured hourly throughout the day according to one embodiment of the present disclosure.

[0164] Referring to Figure 10, the X-axis represents hours from 1:00 to 24:00. 1:00 represents 1:00 AM, and 12:00 represents 12:00 PM. The Y-axis represents days and can range from day D to (D-Day_window+1). The graph in Figure 10 represents biomarker scores measured every hour for a predetermined X number of days.

[0165] For example, BM1 refers to the biomarker score measured at 1:00 AM on Day D, and BM25 refers to the biomarker score measured at 1:00 AM on Day (D-1). BM_1 refers to the average value of BM1, BM25, …, and BM(24x(Day_window-1)+1)(502).

[0166] The average values ​​of biomarkers measured at a specific time (i) for a predetermined number of X days (X is a natural number greater than or equal to 2) ) can be defined according to the following mathematical formula.

[0167] [Equation 5]

[0168]

[0169] In mathematical expression 5, N represents the number of biomarker measurements per day. In the embodiments, it can be assumed that the biomarker measurement cycle is constant.

[0170] The average value of all biomarkers measured on a specific day (D) during X days ( ) can be defined according to the following mathematical formula.

[0171] [Equation 6]

[0172]

[0173] The average values ​​of biomarkers measured at h hours (where h is a natural number from 1 to 24) over X days ( ) can be defined according to the following mathematical formula.

[0174] [Equation 7]

[0175]

[0176] In mathematical expression 7, refers to the average value of all biomarkers measured at h hours on D-(D-Day_window+1).

[0177] The average value of all biomarkers measured over X days ( ) can be defined according to the following mathematical formula.

[0178] [Equation 8]

[0179]

[0180] FIG. 11 is a graph showing biomarker values ​​measured every minute throughout the day according to one embodiment of the present disclosure.

[0181] Figure 12 is a graph showing the biomarker values ​​measured in Figure 11 processed using a moving average.

[0182] Figures 11 and 12 are graphs illustrating the process of extracting (R^2). In order to extract (R^2), biomarker values ​​measured over 24 hours are required. The graph in Figure 11 shows the biomarker values ​​of a specific user measured every minute throughout the day. The circles in the graph in Figure 11 represent data measuring the biomarker values ​​of a specific user every minute throughout the day. The x-axis represents time {hour}, and the y-axis represents the biomarker value (BM).

[0183] The circles in the graph of Figure 12 represent data obtained by moving average of Figure 11. In one embodiment, the moving average can be processed as 10.

[0184] The curve (702) illustrated in Fig. 12 represents the circles in the graph of Fig. 12 fitted with a sine function. The sine function used in the fitting can be defined by the following mathematical equation.

[0185] [Equation 9]

[0186]

[0187] In Equation 9, y represents the biomarker value (BM), x represents time (time), and x_c, A, and y_0 represent constants. Since the biomarker values ​​of a person with a healthy hormonal rhythm have a period of approximately 24 hours, fitting can be performed using a sine function with a period of 24, as in Equation 9.

[0188] Fitting can be performed using the Levenberg-Marquardt method for 1000 iterations. During the fitting process, x_c, A, and y_0 can be determined to appropriate values.

[0189] After fitting as described above, (R^2) can be calculated. (R^2) can be defined as follows:

[0190] [Equation 10]

[0191]

[0192] In mathematical expression 10, BM_fit(t) refers to the biomarker fitting value (702) at time t, and BM_ma(t) refers to the biomarker value (circles in Figure 12) processed by the moving average 10 at time t. As described above in mathematical expression 6, refers to the average value of all biomarkers measured on a specific date (D) for one day.

[0193] According to an embodiment of the present disclosure, the (R^2) value is at most 1, and the closer the (R^2) value is to 1, the higher the agreement between the sine function and the biomarker for 24 hours a day.

[0194] FIG. 13 is a graph showing the measurement and moving average processing of biomarker values ​​when (R^2) = 0.76 according to one embodiment of the present disclosure.

[0195] FIG. 14 is a graph showing the measurement and moving average processing of biomarker values ​​when (R^2) = 0.11 according to one embodiment of the present disclosure.

[0196] The fitting method and sine function used in the graphs of Figs. 13 and 14 are the same as those described above in Figs. 11 and 12, and therefore are omitted below.

[0197] The graph in Fig. 13 is a graph using biomarker values ​​of a user with a relatively healthy hormonal rhythm, whereas the graph in Fig. 14 is a graph using biomarker values ​​of a user with a relatively unhealthy hormonal rhythm.

[0198] Referring to Figure 13, it can be confirmed that the biomarker values ​​for a day, processed through a moving average, closely follow the curve of a sine function. In particular, it can be clearly seen that the biomarker values ​​during sleep (802) are relatively lower than those during wakefulness. Depending on the biomarker used, the trends or patterns of a user's biomarker values ​​over a 24-hour period may vary.

[0199] On the other hand, referring to Figure 14, it can be confirmed that the biomarker values ​​for a day, processed by moving average, do not follow the curve of a sine function well. In particular, it can be confirmed that the biomarker values ​​during sleep time (902) do not differ significantly compared to those during wakefulness, and the biomarker values ​​during sleep and awake times are not clearly distinguished.

[0200] According to an embodiment of the present disclosure, it can be seen that a person with a healthy hormonal rhythm has a relatively high value of (R^2), and a person with an unhealthy hormonal rhythm has a relatively low value of (R^2).

[0201] FIG. 15 is a graph showing the correlation between (R^2) and biomarker scores according to one embodiment of the present disclosure.

[0202] Referring to the graph in Fig. 15, the correlation between the biomarker score (BM_score) calculated using the biomarker measured on a total of 17 clinicians and (R^2) is shown. It can be confirmed that clinicians with high biomarker scores also have high (R^2) values. In other words, a high biomarker score means that the biomarker's daily periodicity is clearly visible. According to an embodiment of the present disclosure, a clear daily periodicity means that the user's biomarker has a low value during sleep and a high value during activity.

[0203] Therefore, the biomarker score according to the embodiment of the present disclosure can be utilized as a measure of hormonal rhythm health. Furthermore, according to the method for diagnosing hormonal rhythms using biomarkers according to the embodiment of the present disclosure and the device using the same, a biomarker score that scores the daily variability and daily stability of biomarkers measured from a person can be calculated, thereby enabling a more accurate diagnosis of the user's hormonal rhythm.

[0204] As described above, exemplary embodiments have been disclosed in the drawings and specifications. Although specific terminology has been used to describe the embodiments herein, it is used solely for the purpose of explaining the technical concept of the present disclosure and is not intended to limit the meaning or scope of the present disclosure as set forth in the claims. Therefore, those skilled in the art will understand that various modifications and equivalent embodiments are possible. Accordingly, the true technical protection scope of the present disclosure should be determined by the technical concept of the appended claims.

Claims

1. A method performed by a processor of a hormone rhythm diagnosis device, A step of receiving biomarker values ​​of a user; A step of correcting the above biomarker values ​​by moving the average to a predetermined value; A step of fitting the corrected biomarker values ​​to a function with a period of 24 hours; A step of obtaining the time (t_min) corresponding to the minimum value of the above function; and A method for diagnosing a hormonal rhythm using a biomarker, comprising: a step of diagnosing the hormonal rhythm of the user based on the above time (t_min); 2. In paragraph 1, The above biomarker is heart rate, The above function is a function that represents the user's heart rate over time. A method for diagnosing hormonal rhythms using biomarkers.

3. In paragraph 2, Further comprising a step of calculating (R^2) based on the above biomarker values; The above (R^2) is, Calculated by the biomarker fitting value at a specific time, the corrected biomarker value at the specific time, and the average value of all biomarkers measured during the day on a specific date. A method for diagnosing hormonal rhythms using biomarkers.

4. In paragraph 3, The above (R^2) is a parameter indicating the consistency between the corrected biomarker values ​​and the function. A method for diagnosing hormonal rhythms using biomarkers.

5. In paragraph 4, The average value of the above time (t_min) corresponding to the minimum value of the above function and the morning-evening questionnaire values ​​are inversely proportional, The average value of the time (t_min) corresponding to the minimum value of the above function is the average of the biomarker values ​​having the (R^2) exceeding the threshold value among the measured biomarker values. A method for diagnosing hormonal rhythms using biomarkers.

6. Memory; A transmitter / receiver configured to receive biomarker values ​​of the user; and A processor configured to correct the biomarker values ​​by moving average to a predetermined value, fit the corrected biomarker values ​​to a function with a cycle of 24 hours, obtain a time (t_min) corresponding to the minimum value of the function, and diagnose the user's hormonal rhythm based on the time (t_min); A device for diagnosing hormonal rhythms using biomarkers.

7. In paragraph 6, The above biomarker is heart rate, The above function is a function that represents the user's heart rate over time. A device for diagnosing hormonal rhythms using biomarkers.

8. In paragraph 7, The above processor, Based on the above biomarker values, it is further configured to calculate (R^2), The above (R^2) is, Calculated by the biomarker fitting value at a specific time, the corrected biomarker value at the specific time, and the average value of all biomarkers measured during the day on a specific date. A device for diagnosing hormonal rhythms using biomarkers.

9. In paragraph 8, The above (R^2) is a parameter indicating the consistency between the corrected biomarker values ​​and the function. A device for diagnosing hormonal rhythms using biomarkers.

10. In paragraph 9, The average value of the above time (t_min) and the morning-evening questionnaire values ​​are inversely proportional, The above average value of the above time (t_min) is the average of the biomarker values ​​having the (R^2) exceeding the threshold value among the measured biomarker values. A device for diagnosing hormonal rhythms using biomarkers.

11. A method performed by a processor of a hormone rhythm diagnosis device, A step of receiving a user's biomarker value; A step of calculating a first parameter value representing the daily stability of the biomarker value; A step of calculating a second parameter value representing the daily variability of the biomarker value; A step of calculating a biomarker score based on the first parameter value and the second parameter value; and A method for diagnosing a hormonal rhythm using a biomarker, comprising: a step of diagnosing a hormonal rhythm of the user based on the biomarker score; 12. In paragraph 11, The above first parameter is, The average value of biomarkers measured at a specific time during a predetermined X (X is a natural number greater than or equal to 2) days, the average value of all biomarkers measured on a specific day during the day, the average value of biomarkers measured at h (h is a natural number from 1 to 24) hours during the X days, and the average value of all biomarkers measured during the X days are calculated based on the A method for diagnosing hormonal rhythms using biomarkers.

13. In paragraph 12, The above first parameter is calculated as a value within a specific range, The greater the value of the first parameter within the range, the higher the daily stability of the biomarker value. A method for diagnosing hormonal rhythms using biomarkers.

14. In paragraph 13, The second parameter above is, The average value of biomarkers measured at a specific time during a predetermined X (X is a natural number greater than or equal to 2) days, calculated based on the average value of all biomarkers measured on a specific day among the X days. A method for diagnosing hormonal rhythms using biomarkers.

15. In paragraph 14, The above second parameter is calculated as a value greater than or equal to 0, The larger the value of the second parameter, the higher the daily variability of the biomarker value during the day. A method for diagnosing hormonal rhythms using biomarkers.

16. Memory; a transmitter / receiver configured to receive a user's biomarker value; and A processor configured to calculate a first parameter value representing daily stability of the biomarker value, calculate a second parameter value representing daily variability of the biomarker value, calculate a biomarker score based on the first parameter and the second parameter value, and diagnose the user's hormonal rhythm based on the biomarker score; A device for diagnosing hormonal rhythms using biomarkers.

17. In paragraph 16, The above first parameter is, The average value of biomarkers measured at a specific time during a predetermined X (X is a natural number greater than or equal to 2) days, the average value of all biomarkers measured on a specific day during the day, the average value of biomarkers measured at h (h is a natural number from 1 to 24) hours during the X days, and the average value of all biomarkers measured during the X days are calculated based on the A device for diagnosing hormonal rhythms using biomarkers.

18. In paragraph 17, The above first parameter is calculated as a value within a specific range, The greater the value of the first parameter within the range, the higher the daily stability of the biomarker value. A device for diagnosing hormonal rhythms using biomarkers.

19. In paragraph 18, The second parameter above is, The average value of biomarkers measured at a specific time during a predetermined X (X is a natural number greater than or equal to 2) days, calculated based on the average value of all biomarkers measured on a specific day among the X days. A device for diagnosing hormonal rhythms using biomarkers.

20. In paragraph 19, The above second parameter is calculated as a value greater than or equal to 0, The larger the value of the second parameter, the higher the daily variability of the biomarker value during the day. A device for diagnosing hormonal rhythms using biomarkers.

Images