Method and apparatus for diagnosing dementia of patient on basis of olfactory bulb electrical signal

Abstract

A method and an apparatus for diagnosing dementia of a patient on the basis of an olfactory bulb electrical signal are disclosed. A method according to one embodiment of the present disclosure comprises the steps of: acquiring a feature map corresponding to an electrical signal for an olfactory bulb of a patient; extracting feature information corresponding to an element corresponding to a normal patient on the basis of the acquired feature map; and diagnosing dementia of the patient on the basis of the extracted feature information.

Description

Method and device for diagnosing dementia in a patient based on electrical signals of the olfactory bulb

[0001] The present disclosure relates to a medical device, and specifically to a method and device for diagnosing dementia in a patient based on electrical signals from the olfactory bulb.

[0002] Dementia diagnosis is an important medical process requiring scientific accuracy and clinical practicality, yet existing diagnostic methods primarily rely on neuropsychological testing. These tests diagnose the presence of dementia by analyzing the subject's subjective responses. Consequently, as they depend on the subject's subjective responses, test results can vary depending on the subject's temporary condition, and there can be significant variability in results depending on the examiner. In other words, existing neuropsychological tests suffer from a lack of objectivity. To address these issues, imaging approaches such as amyloid PET, cerebrospinal fluid analysis, and MRI have been introduced; however, these methods require high costs and long examination times, which significantly limits their medical utility.

[0003] In addition, while olfactory testing tools are also used, they have limitations as they are based on the subject's subjective responses and lack objectivity. This is further problematic in that it is difficult to clearly distinguish between abnormalities in olfactory ability and abnormalities in cognitive function. Currently used olfactory tests primarily express results based only on the number of correct answers, which makes it difficult to accurately evaluate olfactory ability. Ultimately, existing dementia diagnostic methods lack objectivity, which consequently leads to problems with reduced diagnostic accuracy and efficiency.

[0004] The present disclosure aims to solve the aforementioned problems and provides a method and apparatus for diagnosing dementia in a patient based on electrical signals from the olfactory bulb. However, the problems to be solved by the present disclosure are not limited to those mentioned above.

[0005] A method for diagnosing dementia in a patient based on electrical signals of an olfactory bulb according to one embodiment of the present disclosure comprises the steps of: acquiring a feature map corresponding to electrical signals of the patient's olfactory bulb; extracting feature information corresponding to elements corresponding to a normal patient based on the acquired feature map; and diagnosing dementia in the patient based on the extracted feature information.

[0006] Additionally, the step of acquiring the feature map includes acquiring a plurality of first feature maps corresponding to a plurality of electrodes that applied an electrical signal to the patient's olfactory bulb, and combining the acquired plurality of first feature maps to acquire a feature map corresponding to the electrical signal to the patient's olfactory bulb.

[0007] Additionally, the step of extracting the feature information includes converting the acquired feature map into a feature matrix and calculating an average value of the feature matrix in the frequency direction to extract feature information corresponding to the element corresponding to the normal patient.

[0008] Additionally, the step of diagnosing dementia in the patient includes the step of analyzing the gradient of feature information extracted in correspondence with elements corresponding to the normal patient to calculate a feature value regarding the feature information, and the step of inputting the feature value into a machine learning model to diagnose dementia in the patient.

[0009] Additionally, the step of extracting the feature information includes converting the acquired feature map into a feature matrix, applying a PCA technique to extract key feature information corresponding to elements corresponding to the normal patient in the feature matrix, and diagnosing the patient's dementia based on the extracted key feature information.

[0010] In addition, the method includes the step of comparing a plurality of feature maps corresponding to a normal patient and a plurality of feature maps corresponding to a dementia patient, clustering pixels on the feature maps where the conductivity of the normal patient is greater than the conductivity of the dementia patient to identify a plurality of clusters, and the step of identifying an element corresponding to the normal patient based on the plurality of clusters.

[0011] In addition, the method includes the step of identifying each of the aforementioned multiple clusters as an element corresponding to the normal patient.

[0012] An electronic device for diagnosing dementia in a patient based on an electrical signal of an olfactory bulb according to one embodiment of the present disclosure includes a memory comprising at least one instruction and a processor that acquires a feature map corresponding to an electrical signal of the patient's olfactory bulb by executing the at least one instruction, extracts feature information corresponding to an element corresponding to a normal patient based on the acquired feature map, and diagnoses dementia in the patient based on the extracted feature information.

[0013] The preprocessing method and dementia diagnosis method according to one embodiment of the present disclosure are based on electrical signals of the olfactory bulb, thereby improving the accuracy of the signal by utilizing objective data, unlike existing subjective examination methods. In addition, artifact removal is performed during the preprocessing process, and the precision of the diagnosis is enhanced by generating a feature map through a multi-taper sliding window and a wavelet transform.

[0014] FIG. 1 is an example illustration of a user wearing a wearable device according to one embodiment of the present disclosure.

[0015] FIG. 2a is a front view of a wearable device according to one embodiment of the present disclosure, FIG. 2b is a side view of a wearable device according to one embodiment of the present disclosure, and FIG. 2c is a rear view of a wearable device according to one embodiment of the present disclosure.

[0016] FIG. 3a is a perspective view of the front portion of a wearable device according to one embodiment of the present disclosure, and FIG. 3b is a front view of the front portion of a wearable device according to one embodiment of the present disclosure.

[0017] FIG. 4 is an exemplary diagram of a plurality of electrodes according to one embodiment of the present disclosure.

[0018] FIG. 5 is a block diagram of a configuration for controlling the function of a wearable device according to one embodiment of the present disclosure.

[0019] FIG. 6 is an exemplary diagram showing a change in conductivity occurring in the olfactory bulb when an electrical signal is applied to the frontal portion of a user through a plurality of electrodes of a wearable device according to one embodiment of the present disclosure.

[0020] FIG. 7 is a block diagram of a system for diagnosing dementia in a patient based on electrical signals from an olfactory bulb according to one embodiment of the present disclosure.

[0021] FIG. 8 is a block diagram of a computing device according to one embodiment of the present disclosure.

[0022] FIG. 9 is a schematic diagram illustrating a method for determining whether dementia is present by preprocessing electrical signals for an olfactory bulb according to one embodiment of the present disclosure.

[0023] FIG. 10 is a diagram showing a feature map corresponding to the electrical signals of the olfactory bulb when a normal patient and a dementia patient smell various objects according to one embodiment of the present disclosure.

[0024] FIG. 11 is a flowchart relating to a method for diagnosing whether a patient has dementia based on electrical signals of an olfactory bulb according to one embodiment of the present disclosure.

[0025] FIG. 12 is an illustrative diagram identifying elements corresponding to a normal patient according to one embodiment of the present disclosure.

[0026] FIG. 13 is an example diagram illustrating a method for determining whether dementia is present by analyzing the gradient of feature information according to one embodiment of the present disclosure.

[0027] FIG. 14 is an exemplary diagram illustrating a method for determining whether dementia is present by extracting key feature information based on a PCA technique according to one embodiment of the present disclosure.

[0028] Throughout the specification, the same reference numerals refer to the same components. This specification does not describe all elements of the embodiments, and general content in the art to which the invention pertains or content that overlaps between embodiments is omitted. The terms 'part, module, component, block' used in the specification may be implemented in software or hardware, and depending on the embodiments, a plurality of 'parts, modules, components, blocks' may be implemented as a single component, or a single 'part, module, component, block' may include a plurality of components.

[0029] Throughout the specification, when a part is described as being "connected" to another part, this includes not only cases where they are directly connected but also cases where they are indirectly connected, and indirect connections include connections made via a wireless communication network.

[0030] Furthermore, when it is stated that a part "includes" a certain component, this means that, unless specifically stated otherwise, it does not exclude other components but may include additional components.

[0031] Throughout the specification, when it is stated that a component is located "on" another component, this includes not only cases where a component is in contact with another component, but also cases where another component exists between the two components.

[0032] Terms such as "first," "second," etc., are used to distinguish one component from another, and the components are not limited by the aforementioned terms.

[0033] Singular expressions include plural expressions unless there is an obvious exception in the context.

[0034] The operating principle and embodiments of the present invention will be described below with reference to the attached drawings.

[0035] FIG. 1 is an example illustration of a user wearing a wearable device (100) according to one embodiment of the present disclosure.

[0036] Referring to FIG. 1, a wearable device (100) according to one embodiment of the present disclosure may have a plurality of electrode interfaces (111-1 to 111-6, hereinafter 111) formed in which a plurality of electrodes (130) are respectively inserted into a front portion (110) (front case, front housing, etc.).

[0037] A plurality of electrode interfaces (111) may have holes (11) formed that penetrate the front and rear. Here, the holes (11) formed in each electrode interface (111) provide a space in which an electrode (130) can be coupled or placed.

[0038] Meanwhile, the front portion (110) to which a plurality of electrodes (130) of a wearable device (100) according to one embodiment of the present disclosure are coupled may have both ends coupled to an angle adjustment knob (hereinafter, a first angle adjustment knob (140-1 and 140-2, hereinafter 140)). Accordingly, the angle of the front portion (110) can be adjusted in the up and down direction. That is, the position of the plurality of electrodes (130) can be appropriately adjusted in the up and down direction according to the shape of the user's head. By appropriately arranging the plurality of electrodes (130) according to the shape of the user's head, the quality of the electrical signal from the user's olfactory bulb obtained through the plurality of electrodes (130) can be improved.

[0039] The olfactory bulb is a part of the brain located at the top of the nose and is a bodily organ that processes olfactory information; electrical signals from the olfactory bulb can be electroencephalogram (EEG) signals.

[0040] Hereinafter, embodiments of the present disclosure related thereto will be described in detail.

[0041] FIG. 2a is a front view of a wearable device (100) according to one embodiment of the present disclosure, FIG. 2b is a side view of a wearable device (100) according to one embodiment of the present disclosure, and FIG. 2c is a rear view of a wearable device (100) according to one embodiment of the present disclosure. FIG. 3a is a perspective view of a front portion (110) of a wearable device (100) according to one embodiment of the present disclosure, and FIG. 3b is a front view of a front portion (110) of a wearable device (100) according to one embodiment of the present disclosure.

[0042] Referring to FIG. 2a, the front portion (110) includes a plurality of electrode interfaces (111) to which a plurality of electrodes (130) are coupled. The front portion (110) is positioned on the frontal and temporal portions, including the user's forehead, and has a concave or curved shape to surround the user's frontal portion (e.g., forehead) and some temporal portions. Correspondingly, the plurality of electrode interfaces (111) included in the front portion (110) can also be connected to adjacent electrode interfaces (111) in a concave or curved shape.

[0043] Referring to FIG. 3a, a hole (11) penetrating the front and rear of the electrode interface (111) may be formed in the electrode interface (111). For example, the electrode interface (111) may have an annular shape with a central part open. At this time, screw threads may be formed on the inner circumference of the electrode interface (111) so that an electrode (130) can be coupled. Meanwhile, FIG. 2 and 3 show that six electrode interfaces (111) are formed on the front part (110), but this is not limited thereto, and the number of electrode interfaces (111) can be set in various ways.

[0044] In particular, sliding connecting members capable of being combined with other electrode interfaces (111) are configured on both sides of the electrode interface (111), so that electrode interfaces (111) can be added or unconnected as needed by the user.

[0045] Referring to FIGS. 3a and 3b, a plurality of electrode interfaces (111) may be arranged and connected on the same horizontal axis and may include a plurality of electrode interfaces (111-2 to 111-5) (hereinafter referred to as first electrode interfaces) formed to be placed on the frontal portion of a user and a plurality of electrode interfaces (111-1 and 111-6) (hereinafter referred to as second electrode interfaces) formed to be placed on the temporal portion of a user relatively more than the first electrode interface (111), each connected to a plurality of first electrode interfaces (111-2 and 111-5) arranged at both ends among the plurality of first electrode interfaces (111-2 to 111-5).

[0046] Referring to FIGS. 3a and 3b, according to one embodiment of the present disclosure, the spacing between a plurality of first electrode interfaces (111) is a first value, and the spacing between the first electrode interfaces (111-2 and 111-5) and the second electrode interfaces (111-1 and 111-6) located at both ends of the plurality of first electrode interfaces (111-2 to 111-5) may be a second value. For example, the second value may be set to be larger than the first value. For example, the ratio of the first value to the second value may be 3:5. This is to set the spacing between the first electrode interfaces (111-2 and 111-5) and the second electrode interfaces (111-1 and 111-6) located at both ends of the plurality of first electrode interfaces (111-2 to 111-5) wide so as to prevent the electrode interfaces (111) from being placed closely on the user's head at the boundary between the frontal and temporal regions.

[0047] Meanwhile, although not illustrated in FIG. 3a and 3b, according to another embodiment of the present disclosure, a plurality of electrode interfaces (111) can be extended and shortened and connected through a rail that adjusts the spacing between the plurality of electrode interfaces (111). A connecting member connecting each electrode interface (111) is implemented as a rail, a sliding member, a telescopic member, etc., so that the spacing between each electrode interface (111) can be adjusted according to the shape of the user's head.

[0048] Additionally, according to one embodiment of the present disclosure, a plurality of electrode interfaces (111) may be connected so as to be bent according to the shape of the user's frontal and temporal regions. For example, the plurality of electrode interfaces (111) may be connected via hinges, or the connecting member connecting the plurality of electrode interfaces (111) may be implemented with an elastic material. Through this, the plurality of electrode interfaces (111) may be arranged in various shapes rather than in a straight line on the same horizontal axis.

[0049] FIG. 4 is an exemplary diagram of a plurality of electrodes (130) according to one embodiment of the present disclosure.

[0050] Meanwhile, a plurality of electrodes (130) may be coupled to each hole (11) formed in a plurality of electrode interfaces (111). At this time, the plurality of electrodes (130) coupled to the plurality of electrode interfaces (111) may apply an electrical signal by contacting the user's head. At this time, referring to FIG. 4, the plurality of electrodes (130) may be either a cap-shaped hole (11) type or a scroll type. In the case of the cap-shaped hole (11) type, the electrode (130) is designed to be fixed to a cap-shaped hole (11) to stably contact the user's scalp, which allows the electrode (130) to maintain the same pressure on the user's scalp while being easily detachable as needed. This structure enables more accurate signal measurement by optimizing the electrical contact between the electrode (130) and the scalp while maintaining the position of the electrode (130) accurately. In the case of a scroll type, the diameter may be 13 mm and the height may be 30 mm, and it may be coupled to an electrode interface (111) through a screw thread formed in a hole (11).

[0051] Referring again to FIG. 2b, according to one embodiment of the present disclosure, both ends of the front portion (110) may be connected to a plurality of first angle adjustment knobs (140) for adjusting the angle of the front portion (110). More specifically, both ends of the front portion (110) and the plurality of first angle adjustment knobs (140) may each be connected through connecting portions (150-1 and 150-2, hereinafter 150). At this time, the front portion (110) and the first angle adjustment knobs (140) are connected on one end area of ​​the plurality of connecting portions (150), and the other end of the plurality of connecting portions (150) may be formed to extend forward but be inclined upward at a preset angle.

[0052] Meanwhile, according to one embodiment of the present disclosure, the wearable device (100) may include a rear portion (120) (rear case, rear housing, etc.) positioned on the back of the user's head. At this time, the rear portion (120) may include a strap (121) extending forward from both ends of the rear portion (120) and a length adjustment knob (122) for adjusting the length of the strap (121). The length adjustment knob (122) may be implemented as a compression rotary latch. Meanwhile, the rear portion (120) may also have a curved shape or a bent shape so as to stably surround the back of the user's head, similar to the front portion (110). Additionally, a pad may be formed at the portion where the rear portion (120) contacts the back of the user's head.

[0053] Additionally, although not clearly illustrated in the drawing, the rear portion (120) may include a power supply unit (e.g., a battery, etc.) that supplies power to the wearable device (100).

[0054] Both ends of the strap (121) of the rear portion (120) can be connected to an angle adjustment knob (second angle adjustment knob (160-1 and 160-2, hereinafter 160)). At this time, the second angle adjustment knob (160) can adjust the angle of the rear portion (120) in the up and down direction.

[0055] Meanwhile, the wearable device (100) may include a first band portion (or, first case, etc.) (170) formed to be seated on the head portion of a user by being connected to a plurality of connecting portions (150) and a plurality of first angle adjustment knobs (140), and a second band portion (180) formed to be seated on the head portion of a user by intersecting the first band portion (170), with both ends connected to a plurality of second angle adjustment knobs (160). Although both ends of the second band portion (180) are located relatively to the back of the user's head compared to both ends of the first band portion (170), as the second band portion (180) and the first band portion (170) intersect, the upper surface may be located further forward than the first band portion (170).

[0056] Meanwhile, the wearable device (100) may further include a strap (190) connecting the central part of the second band and the upper part of the rear part (120). At this time, a control unit (and a biosignal measuring unit) may be included on the strap placed on the head of the user.

[0057] A control unit (220) according to one embodiment of the present disclosure can apply an electrical signal to a user through a plurality of electrodes (130) and measure the user's olfactory bulb potential signal through the plurality of electrodes (130). Here, the olfactory bulb potential signal may be a signal regarding the amount of potential change measured in the user's olfactory bulb as an electrical signal is applied to the user's face through the plurality of electrodes (130).

[0058] Additionally, the control unit (220) can identify the angle of the front part (110) for the user when the plurality of first angle adjustment knobs (140) are adjusted, identify the length of the strap for the user when the length adjustment knob is adjusted, and store the identified angle and the identified strap length in memory by matching them with the user. Through this, the control unit can identify the shape of the wearable device (100) optimized for each user.

[0059] FIG. 5 is a block diagram of a configuration for controlling the function of a wearable device (100) according to one embodiment of the present disclosure. Referring to FIG. 5, the wearable device (100) includes a biosignal measuring unit (210) and a control unit (220). However, at least one component may be added or removed in accordance with the performance of the components of the wearable device (100) shown in FIG. 5. Furthermore, it will be readily understood by those skilled in the art that the relative positions of the components may be changed in accordance with the performance or structure of the system. Meanwhile, the plurality of electrodes (212) shown in FIG. 5 are a configuration corresponding to the plurality of electrodes (130) shown in FIG. 1 to FIG. 4.

[0060] Meanwhile, each component illustrated in Fig. 5 refers to a software and / or hardware component such as a Field Programmable Gate Array (FPGA) and an Application Specific Integrated Circuit (ASIC).

[0061] FIG. 6 is an example diagram showing a change in conductivity occurring in the olfactory bulb when an electrical signal is applied to the frontal portion of a user through a plurality of electrodes (130) of a wearable device (100) according to one embodiment of the present disclosure.

[0062] The biosignal measuring unit (210) measures the user's biosignal. More specifically, referring to FIG. 6, the biosignal measuring unit (210) can apply electrical stimulation to the user's olfactory bulb through a plurality of electrodes (130) in contact with the user's head through a plurality of electrode interfaces (111) placed on the user's head (particularly, the frontal and temporal regions), and measure the electrical signal in the olfactory bulb. Here, the electrical signal in the olfactory bulb may be a conductive signal identified by detecting the potential difference in the olfactory bulb.

[0063] To this end, the biosignal measuring unit (210) may include a controller (211) that controls the plurality of electrodes (130) electrically connected to the plurality of electrodes (130) attached to the user's body. At this time, the plurality of electrodes (130) may include a ground electrode, a reference electrode, and an active electrode. The ground electrode is attached to an inactive area such as the user's forehead or behind the ear, the reference electrode is attached to the mastoid, and the active electrode is an electrode that measures the electrical signal of the user's olfactory bulb and may be attached to the user's head via the electrode interface (111) as described above.

[0064] The control unit (220) may be implemented as a memory that stores data for an algorithm or a program that reproduces the algorithm for controlling the operation of components within the wearable device (100), and a processor (221) that performs the aforementioned operation using the data stored in the memory. In this case, the memory and the processor (221) may each be implemented as separate chips. Alternatively, the memory and the processor (221) may be implemented as a single chip.

[0065] A processor (221) may refer to a data processing device embedded in hardware having a physically structured circuit to perform a function expressed by code or instructions included in a program. Examples of such data processing devices embedded in hardware may include, but are not limited to, processing devices such as a microprocessor, a central processing unit (CPU), a processor (221) core, a multiprocessor, an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA), a Graphics Processing Unit (GPU), and a neural Processing Unit (NPU). A processor (221) may include one or more processors (221).

[0066] The communication interface (222) may include one or more components that enable communication with an external device, and may include, for example, at least one of a short-range communication module, a wired communication module, and a wireless communication module.

[0067] The short-range communication module may include various short-range communication modules that transmit and receive signals using a wireless communication network at short range, such as a Bluetooth module, an infrared communication module, an RFID (Radio Frequency Identification) communication module, a WLAN (Wireless Local Access Network) communication module, an NFC communication module, and a Zigbee communication module.

[0068] Wired communication modules may include various wired communication modules such as Local Area Network (LAN) modules, Wide Area Network (WAN) modules, or Value Added Network (VAN) modules, as well as various cable communication modules such as USB (Universal Serial Bus), HDMI (High Definition Multimedia Interface), DVI (Digital Visual Interface), RS-232 (recommended standard 232), power line communication, or POTS (plain old telephone service).

[0069] In addition to Wi-Fi modules and WiBro (Wireless broadband) modules, the wireless communication module may include wireless communication modules that support various wireless communication methods such as GSM (global System for Mobile Communication), CDMA (Code Division Multiple Access), WCDMA (Wideband Code Division Multiple Access), UMTS (universal mobile telecommunications system), TDMA (Time Division Multiple Access), and LTE (Long Term Evolution).

[0070] The wireless communication module may include a wireless communication interface comprising an antenna and a transmitter that transmits an electrical signal of an olfactory bulb obtained through a biosignal measurement unit (210). Additionally, the wireless communication module may further include a conversion module for the electrical signal of an olfactory bulb that modulates a digital control signal output from the control unit (220) through the wireless communication interface into an analog wireless signal under the control of the control unit (220).

[0071] The memory (223) may be implemented as at least one of a non-volatile memory device such as a cache, ROM (Read Only Memory), PROM (Programmable ROM), EPROM (Erasable Programmable ROM), EEPROM (Electrically Erasable Programmable ROM), and flash memory, a volatile memory device such as RAM (Random Access Memory), or a storage medium such as a hard disk drive (HDD) and CD-ROM, but is not limited thereto. The memory (223) may be a memory implemented as a separate chip from the processor (221) described above in relation to the control unit (220), or it may be implemented as a single chip with the processor (221).

[0072] A device-readable storage medium may be provided in the form of a non-transitory storage medium. Here, 'non-transitory storage medium' simply means that it is a tangible device and does not contain a signal (e.g., electromagnetic waves), and the term does not distinguish between cases where data is stored semi-permanently and cases where it is stored temporarily. For example, a 'non-transitory storage medium' may include a buffer in which data is stored temporarily.

[0073] Meanwhile, although not illustrated in FIG. 5, the wearable device (100) may further include a power supply unit that supplies power to a biosignal measuring unit (210) and a control unit (220). Additionally, the wearable device (100) may further include a camera that acquires an image of the user's face, a microphone that acquires sound around the wearable device (100), and a GPS sensor that detects the location of the wearable device (100).

[0074] FIG. 7 is a block diagram of a system (1000) for diagnosing dementia in a patient based on electrical signals from an olfactory bulb according to one embodiment of the present disclosure.

[0075] Referring to FIG. 7, a system (1000) for diagnosing a patient's dementia based on electrical signals from an olfactory bulb may include a wearable device (100) and a computing device (300). However, although the wearable device (100) and the computing device (300) are depicted as separate devices in FIG. 7, the computing device (300) may be a component provided on one side of the wearable device (100).

[0076] The computing device (300) analyzes the electrical signal of the olfactory bulb acquired by the wearable device (100) to determine whether the patient has dementia. Below, the computing device (300) related thereto will be described in detail.

[0077] FIG. 8 is a block diagram of a computing device (300) according to an embodiment of the present disclosure. Referring to FIG. 8, the computing device (300) includes a memory (310), a communication interface (320), and a processor (330). However, at least one component may be added or removed in accordance with the performance of the components of the computing device (300) shown in FIG. 8. Furthermore, it will be readily understood by those skilled in the art that the relative positions of the components may be changed in accordance with the performance or structure of the system.

[0078] The memory (310) stores various programs or data temporarily or non-temporarily and transmits the stored information to the processor (330) upon the call of the processor (330). Additionally, the memory (310) can store various information required for the operation, processing, or control operation of the processor (330) in an electronic format.

[0079] The memory (310) may include, for example, at least one of a main memory and an auxiliary memory. The main memory may be implemented using a semiconductor storage medium such as ROM and / or RAM. The ROM may include, for example, a conventional ROM, EPROM, EEPROM and / or MASK-ROM. The RAM may include, for example, a DRAM and / or SRAM. The auxiliary memory may be implemented using at least one storage medium capable of storing data permanently or semi-permanently, such as a flash memory device, an SD (Secure Digital) card, a solid state drive (SSD), a hard disk drive (HDD), an optical recording medium such as a magnetic drum, a compact disc (CD), a DVD, or a laser disc, a magnetic tape, a magneto-optical disc and / or a floppy disk.

[0080] The communication interface (320) may include a wireless communication interface, a wired communication interface, or an input interface. The wireless communication interface may communicate with various external devices using wireless communication technology or mobile communication technology. Such wireless communication technologies may include, for example, Bluetooth, Bluetooth Low Energy, CAN communication, Wi-Fi, Wi-Fi Direct, ultrawide band (UWB), Zigbee, infrared data association (IrDA), or near field communication (NFC), and mobile communication technologies may include 3GPP, WiMAX, LTE (Long Term Evolution), 5G, etc.

[0081] A wireless communication interface can be implemented using an antenna, a communication chip, a substrate, etc., capable of transmitting electromagnetic waves to the outside or receiving electromagnetic waves transmitted from the outside.

[0082] A wired communication interface can communicate with various devices based on a wired communication network. Here, the wired communication network can be implemented using physical cables, such as, for example, pair cables, coaxial cables, fiber optic cables, or Ethernet cables.

[0083] Depending on the embodiment, either the wireless communication interface or the wired communication interface may be omitted. Accordingly, the computing device (300) may include only a wireless communication interface or only a wired communication interface. In addition, the computing device (300) may be equipped with an integrated communication interface (320) that supports both wireless connection via the wireless communication interface and wired connection via the wired communication interface.

[0084] The computing device (300) is not limited to having one communication interface (320) that performs a communication connection in one manner, but may include multiple communication interfaces (320) that perform communication connections in multiple manners.

[0085] The processor (330) can transmit or receive various information by establishing a communication connection with an external server, external device, etc., through the communication interface (320).

[0086] For example, the processor (330) can receive electrical signals of the olfactory bulb from the wearable device (100) via the communication interface (320).

[0087] The processor (330) controls the overall operation of the computing device (300). Specifically, the processor (330) is connected to the configuration of the computing device (300) including the memory (310) as described above, and can control the overall operation of the computing device (300) by executing at least one instruction stored in the memory (310) as described above. In particular, the processor (330) can be implemented as a single processor as well as as a plurality of processors.

[0088] The processor (330) may be implemented in various ways. For example, one or more processors (330) may include one or more of a CPU (Central Processing Unit), GPU (Graphics Processing Unit), APU (Accelerated Processing Unit), MIC (Many Integrated Core), DSP (Digital Signal Processor), NPU (Neural Processing Unit), hardware accelerator, or machine learning accelerator. One or more processors (130) may control one or any combination of other components of the computing device (300) and may perform operations or data processing related to communication. One or more processors (330) may execute one or more programs or instructions stored in memory (310). For example, one or more processors (330) may perform a method according to one embodiment of the present disclosure by executing one or more instructions stored in memory (120).

[0089] FIG. 9 is a schematic diagram illustrating a method for determining whether dementia is present by preprocessing electrical signals for an olfactory bulb according to one embodiment of the present disclosure.

[0090] Referring to FIG. 9, the processor (330) acquires an electrical signal for the patient's olfactory bulb (S910). Here, the electrical signal for the olfactory bulb may be a signal acquired by a wearable device using a plurality of electrodes as described above.

[0091] At this time, according to one embodiment of the present disclosure, the processor (330) can obtain a first electrical signal for the patient's olfactory bulb by measuring the change in potential of the patient's olfactory bulb before applying an electrical signal to the patient. Then, the processor (330) can obtain an electrical signal for the patient's olfactory bulb to be analyzed by extracting a preset interval from the first electrical signal. For example, the processor (330) can obtain an electrical signal for the patient's olfactory bulb to be analyzed by extracting an interval from 1200ms before the electrical signal is applied to 1800ms after the electrical signal is applied from the first electrical signal.

[0092] Additionally, according to one embodiment of the present disclosure, the processor (330) can adjust the electrical signal obtained based on the average value of the electrodes of the left and right mastoid protrusions and remove power line noise from the adjusted electrical signal. Specifically, the processor (330) can average the signals of the left and right mastoid protrusion electrodes and set them as reference electrodes, and use this average value to redefine the reference line of the entire electrical signal. Then, the processor (330) can remove noise caused by power lines by applying a 60Hz notch filter to the adjusted electrical signal.

[0093] Referring again to FIG. 9, the processor (330) can remove artifacts from the electrical signal (S920). The processor (330) can detect and remove distortions or deformations of the electrical signal, i.e., artifacts, caused by non-neurological factors such as the patient's facial muscle movements or eye movements.

[0094] In this regard, according to one embodiment of the present disclosure, the processor (330) may identify a signal in which the Z score is greater than or equal to a first value based on the amplitude value of the acquired electrical signal as a first artifact generated by the patient's facial muscles, and remove the identified first artifact.

[0095] Specifically, the processor (330) can apply an 8th-order Butterworth bandpass filter to the electrical signal of the acquired olfactory bulb. The processor (330) can extract the amplitude value of the signal by applying a Hilbert transform. The Hilbert transform is a technique used to analyze the amplitude and phase information of a signal, through which the processor (330) can precisely analyze the temporal change of the electrical signal of the olfactory bulb. The processor (330) can apply Z-score standardization to the extracted amplitude value and remove from the electrical signal any signal where the Z-score exceeds a first value by determining it as an artifact (first artifact) generated by facial muscle movement. For example, the first value can be set to 6.

[0096] Meanwhile, according to one embodiment of the present disclosure, when a first artifact generated by the patient's facial muscles is removed from the electrical signal of the olfactory bulb, the processor (330) may identify a signal with a Z-score greater than or equal to a second value that is smaller than the first value as a second artifact due to the patient's eye movement based on the amplitude value of the acquired electrical signal, and remove the second artifact from the electrical signal of the olfactory bulb. Specifically, the processor (330) may determine a signal portion with a Z-score of 4 or more as an artifact (second artifact) and remove it from the electrical signal of the acquired olfactory bulb in order to detect electrical signal fluctuations that may occur due to blinking and rapid eye movements.

[0097] When the artifact is removed, the processor (330) can obtain a feature map corresponding to the electrical signal from which the artifact has been removed (S930). Here, the feature map may be a spectrum (or scalegram) in which the intensity of the electrical signal is determined in the time-frequency domain.

[0098] According to one embodiment of the present disclosure, the processor (330) can obtain a patent map corresponding to an electrical signal from which artifacts have been removed based on a multi-taper sliding window method. The processor (330) can analyze an electrical signal from which artifacts have been removed using a multi-taper sliding window method and thereby obtain a patent map corresponding to the signal. For example, the processor (330) can analyze the spectrum of a signal at intervals of 0.5 Hz within a frequency range of 0.5-100 Hz and at intervals of 0.01 sec between -2 sec and 2 sec using two tapers based on DPSS (discrete prolate spheroidal sequences).

[0099] In addition, according to one embodiment of the present disclosure, the processor (330) may apply a multi-taper sliding window method to the electrical signal from which artifacts have been removed and perform a Fourier transform on the electrical signal over time. At this time, the processor may obtain a first time-frequency map corresponding to the electrical signal of the olfactory bulb using the multi-taper sliding window method and obtain a feature map corresponding to the first time-frequency map based on a wavelet transform.

[0100] The processor (330) can perform a wavelet transform on a first time-frequency map corresponding to an electrical signal obtained by applying a multitaper sliding window method using a wavelet function. Specifically, the processor (330) can combine two sets of first time-frequency maps derived from the DPSS taper and then perform a wavelet transform to finally obtain a feature map corresponding to the electrical signal of the olfactory bulb. The processor can obtain a feature map having a resolution corresponding to the intensity, features, strength, etc. of the electrical signal of the olfactory bulb in the time-frequency domain.

[0101] At this time, the processor (330) performs a wavelet transform by multiplying the Fourier coefficients and wavelet coefficients of the signal in the frequency domain together, and by setting the frequency smoothing parameter to 80% of each frequency interval, it can reduce the variability of the signal and derive stable analysis results. The processor (330) analyzes the frequency configuration at each time point through the wavelet transform, and thereby can generate a more sophisticated feature map of the electrical signal of the olfactory bulb.

[0102] Meanwhile, according to one embodiment of the present disclosure, the electrical signal of the patient's olfactory bulb acquired by the processor (330) may be acquired by applying an electrical signal to the patient's olfactory bulb while the patient smells a specific object.

[0103] At this time, the processor (330) can obtain a correction feature map corresponding to the electrical signal for the patient's olfactory bulb obtained by applying an electrical signal to the patient's olfactory bulb while the patient is simply inhaling through the respiratory tract rather than smelling a specific object. The method for obtaining the correction feature map may be applied in the same way as the process of steps S910 to S940 described above. At this time, the processor (330) can obtain a final feature map corresponding to the electrical signal for the patient's olfactory bulb by comparing the correction feature map with the obtained feature map. Here, comparing the obtained feature map with the correction feature map may involve subtracting the pixel value of the correction feature tab from the pixel value of the feature map for pixels at the same location.

[0104] FIG. 10 is a diagram showing a feature map corresponding to the electrical signals of the olfactory bulb when a normal patient and a dementia patient smell various objects according to one embodiment of the present disclosure.

[0105] According to one embodiment of the present disclosure, the processor (330) can diagnose dementia in a patient based on the acquired final feature map (S950). The processor (330) can determine whether the patient has dementia by comparing the feature maps of a normal patient and a dementia patient, respectively, based on the acquired feature maps. Referring to FIG. 10, even if a normal patient and a dementia patient smell the same object, the high-resolution areas on the feature maps are different. Therefore, after acquiring the feature map for the patient, the processor (330) can determine whether the patient has dementia by extracting the feature maps of a normal patient and a dementia patient who smelled the same object from among a plurality of feature maps stored in memory and comparing them.

[0106] FIG. 11 is a flowchart relating to a method for diagnosing whether a patient has dementia based on electrical signals of an olfactory bulb according to one embodiment of the present disclosure.

[0107] FIG. 12 is an illustrative diagram identifying elements corresponding to a normal patient according to one embodiment of the present disclosure.

[0108] FIG. 13 is an example diagram illustrating a method for determining whether dementia is present by analyzing the gradient of feature information according to one embodiment of the present disclosure.

[0109] FIG. 14 is an exemplary diagram illustrating a method for determining whether dementia is present by extracting key feature information based on a PCA technique according to one embodiment of the present disclosure.

[0110] Referring to FIG. 11, the processor (330) can acquire a feature map corresponding to an electrical signal for the patient's olfactory bulb (S1110). In particular, the processor (330) can acquire a feature map based on an electrical signal acquired through the preprocessing process described in FIG. 9 and FIG. 10.

[0111] At this time, according to one embodiment of the present disclosure, the processor (330) may obtain a plurality of first feature maps corresponding to a plurality of electrodes (130) that apply an electrical signal to the patient's olfactory bulb, and may combine the obtained plurality of first feature maps to obtain a feature map (10) corresponding to the electrical signal for the patient's olfactory bulb. Specifically, referring to FIG. 12, the processor (330) may preprocess the electrical signal obtained through each electrode (130), obtain a plurality of feature maps (10) corresponding to each electrode (130), and combine the obtained plurality of feature maps (10) to finally obtain a feature map corresponding to the electrical signal for the patient's olfactory bulb. At this time, the processor (330) may average the pixel values ​​of the same pixel included in each feature map to finally obtain a feature map corresponding to the electrical signal for the patient's olfactory bulb.

[0112] According to one embodiment of the present disclosure, the processor (330) can extract feature information corresponding to an element corresponding to a normal patient based on the acquired feature map (S1120). Here, the feature information may be in the form of a continuous value along a time axis or a frequency axis.

[0113] Meanwhile, the processor (330) can identify multiple clusters by comparing multiple feature maps corresponding to normal patients and multiple feature maps corresponding to dementia patients, and by clustering pixels on the feature maps where the conductivity intensity of normal patients is greater than that of dementia patients. Specifically, the processor (330) can extract multiple feature maps corresponding to normal patients and multiple feature maps corresponding to dementia patients from among multiple feature maps corresponding to multiple objects stored in memory (310) that correspond to the same object smelled by the patient. At this time, the processor (330) can identify olfactory activation areas identified in the multiple feature maps corresponding to normal patients. This can be identified by identifying pixels in the feature maps of normal patients where the resolution or conductivity intensity is relatively larger than that of dementia patients, and by clustering the identified pixels. Referring to FIG. 12, the processor (330) can identify five clusters in the feature maps. Here, the five clusters may be areas where the olfactory sense of normal patients is relatively more activated than that of dementia patients.

[0114] Meanwhile, the processor (330) can verify the statistical significance of the difference between multiple feature maps corresponding to multiple normal patients and multiple feature maps corresponding to multiple dementia patients through a Monte Carlo permutation verification method to identify clusters. At this time, the processor (330) can repeat the verification process (e.g., 1000 times) and the P Value can be set to 0.05 or less.

[0115] The processor (330) can identify elements corresponding to normal patients based on multiple clusters.

[0116] For example, the processor (330) can identify multiple clusters as elements corresponding to a normal patient. Referring again to FIG. 13, the processor (330) can identify five clusters as elements corresponding to a normal patient. Alternatively, the processor (330) may classify multiple clusters into beta bands and gamma bands, and identify elements corresponding to a normal patient by separating multiple clusters by frequency band.

[0117] Meanwhile, according to one embodiment of the present disclosure, the processor (330) can convert the acquired feature map into a feature matrix and calculate an average value of the feature matrix in the frequency axis direction to extract feature information corresponding to an element corresponding to a normal patient. As an example, the processor (330) may convert the feature map (10) corresponding to each electrode (130) into a matrix and then calculate an average value of the matrix to obtain a feature matrix corresponding to a feature map corresponding to an electrical signal for the patient's olfactory bulb.

[0118] At this time, the processor (330) can calculate an average value of the feature matrix in the frequency axis direction and extract feature information corresponding to the element corresponding to a normal patient. Referring to FIG. 13, the processor (330) can calculate an average value of the feature matrix in the frequency axis direction (i.e., in the vertical direction). By doing so, the processor (330) can reduce the dimensionality of the feature matrix. Then, the processor (330) can extract feature information corresponding to the element corresponding to a normal patient based on the feature matrix with reduced dimensionality.

[0119] At this time, the processor (330) can extract the element corresponding to a normal patient, that is, the part corresponding to a cluster, from the dimensionally reduced feature matrix as feature information, and analyze the gradient of the extracted feature information to calculate a feature value regarding the feature information. Here, the feature value may be at least one of a maximum value, a minimum value, a median value, and an average value.

[0120] The processor (330) can diagnose dementia in a patient based on extracted feature information (S340). In particular, the processor (330) can diagnose dementia in a patient by inputting feature values ​​into a machine learning model. The machine learning model may include a linear regression model, a logistic regression model, etc. In particular, the machine learning model may be trained in advance based on feature values ​​of normal patients and feature values ​​of dementia patients.

[0121] Meanwhile, according to one embodiment of the present disclosure, the processor (330) may convert the acquired feature map into a feature matrix, apply a PCA technique to extract key feature information corresponding to elements corresponding to normal patients in the feature matrix, and diagnose dementia in the patient based on the extracted key feature information. Specifically, referring to FIG. 14, the PCA technique may be applied to the feature matrix to identify elements corresponding to normal patients, i.e., directions with high variability in clusters, thereby extracting dimensionally reduced key feature information. In particular, key feature information may be extracted based on the time range and frequency range of the clusters. For example, if the processor (330) sets the clusters in FIG. 12 as elements corresponding to normal patients, it may extract key feature information (e.g., vector values, matrices, etc.) corresponding to five clusters. Meanwhile, the processor (330) may apply a weighted mean to the extracted multiple key feature information to extract linearly independent key feature information among the key feature information.

[0122] In this way, the processor (330) can determine whether a patient has dementia based on the extracted key feature information. For example, the processor (330) can determine whether a patient has dementia by converting the extracted key feature information and the key feature information of a normal patient or the key feature information of a dementia patient into vector values ​​in a pre-set coordinate space to calculate a similarity (e.g., Euclidean distance, etc.).

[0123] Meanwhile, the method for determining whether there is dementia described above is not performed selectively but is performed in combination, so that if the patient is determined to have dementia according to both methods, the processor (330) can diagnose the patient as having dementia.

[0124] In each step, identification codes are used for convenience of explanation and do not describe the order of the steps; the steps may be performed differently from the specified order unless a specific order is clearly indicated in the context.

[0125] Meanwhile, the disclosed embodiments may be implemented in the form of a recording medium that stores instructions executable by a computer. The instructions may be stored in the form of program code and, when executed by a processor, may generate a program module to perform the operation of the disclosed embodiments. The recording medium may be implemented as a computer-readable recording medium.

[0126] Computer-readable recording media include all types of recording media that store instructions that can be decoded by a computer. Examples include ROM (Read Only Memory), RAM (Random Access Memory), magnetic tape, magnetic disk, flash memory, optical data storage devices, etc.

[0127] As described above, the disclosed embodiments have been explained with reference to the attached drawings. Those skilled in the art will understand that the present invention may be practiced in forms different from the disclosed embodiments without changing the technical spirit or essential features of the invention. The disclosed embodiments are illustrative and should not be interpreted restrictively.

Claims

1. In a method for diagnosing dementia in a patient based on electrical signals of the olfactory bulb, A step of acquiring a feature map corresponding to electrical signals for the patient's olfactory bulb; Based on the above-mentioned acquired feature map, a step of extracting feature information corresponding to elements corresponding to normal patients; and A step of diagnosing dementia in the patient based on the extracted feature information; comprising method.

2. In Paragraph 1, The step of acquiring the above feature map is, A step comprising: acquiring a plurality of first feature maps corresponding to a plurality of electrodes to which an electrical signal is applied to the olfactory bulb of the patient, and combining the acquired plurality of first feature maps to acquire a feature map corresponding to an electrical signal to the olfactory bulb of the patient; method.

3. In Paragraph 1, The step of extracting the above feature information is, The method comprises the step of converting the above-mentioned acquired feature map into a feature matrix, calculating an average value of the above-mentioned feature matrix in the frequency direction, and extracting feature information corresponding to an element corresponding to the above-mentioned normal patient. method.

4. In Paragraph 3, The step of diagnosing dementia in the above patient is, A step of analyzing the gradient of feature information extracted in correspondence with the element corresponding to the normal patient to calculate a feature value regarding the feature information; and A step of diagnosing dementia in the patient by inputting the above feature values ​​into a machine learning model; comprising method.

5. In Paragraph 3, The step of extracting the above feature information is, The method comprises the step of converting the above-mentioned acquired feature map into a feature matrix, applying a PCA technique to extract key feature information corresponding to elements corresponding to the normal patient in the feature matrix, and diagnosing the patient's dementia based on the extracted key feature information. method.

6. In Paragraph 1, A step of comparing a plurality of feature maps corresponding to a normal patient and a plurality of feature maps corresponding to a dementia patient, and identifying a plurality of clusters by clustering pixels on the feature maps where the conduction strength of the normal patient is greater than the conduction strength of the dementia patient; and A step of identifying an element corresponding to the normal patient based on the plurality of clusters above; comprising method.

7. In Paragraph 6, A step of identifying each of the above plurality of clusters as an element corresponding to the above normal patient; comprising method.

8. An electronic device for diagnosing dementia in a patient based on electrical signals from the olfactory bulb, Memory containing at least one instruction; and A processor comprising, by executing at least one of the above instructions, acquiring a feature map corresponding to an electrical signal for a patient's olfactory bulb, extracting feature information corresponding to an element corresponding to a normal patient based on the acquired feature map, and diagnosing dementia in the patient based on the extracted feature information. Electronic device.

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