Apparatus and method for generating acute cardiac arrest prediction information based on heart rate and respiration rate

Abstract

The present invention relates to a system for monitoring a risk state of a target structure by using artificial intelligence, and a method therefor. The system comprises: a wireless sensing device provided at the target structure so as to measure structure information and structure-adjacent ground information about the target structure, thereby transmitting sensing data according to a pre-set communication cycle; a smart gateway which transmits the sensing data transmitted from the wireless sensing device according to the pre-set communication cycle; a worker terminal for collecting and transmitting field data about the target structure; a weather sensing device for collecting and transmitting weather data about a region where the target structure is located; and a monitoring server, which receives each of the sensing data, the field data and the weather data and inputs same into an artificial intelligence model, and then evaluates and predicts a risk state of the target structure by means of the output of the artificial intelligence model, and thus quantitative risk with respect to the collapse risk of a construction structure can be effectively assessed and predicted.

Description

Device and method for generating acute cardiac arrest prediction information based on heart rate and respiratory rate

[0001] The present invention relates to a device and method for generating acute cardiac arrest prediction information based on heart rate and respiratory rate.

[0002]

[0003] Unless otherwise indicated herein, the materials described in this section are not prior art to the claims of this application, and their inclusion in this section is not intended to be admitted as prior art.

[0004] In critically ill patients, such as cancer patients, sudden cardiac arrest can occur, requiring 24-hour monitoring by medical personnel. However, limited staff can make it difficult to provide adequate patient care.

[0005] Accordingly, the present invention proposes an AI-based acute cardiac arrest prediction technology for safe cancer patient care that can be utilized in institutions such as cancer treatment centers or hospices where 24-hour monitoring by medical professionals is difficult, by predicting the occurrence of cardiac arrest within 24 hours and allowing medical personnel to remotely take measures for the patient in advance through decision-making based on the patient's condition.

[0006] [Prior Art Literature]

[0007] 1. Patent Publication No. 10-1744691 (June 1, 2017)

[0008]

[0009] The disclosed embodiment provides a device and method for generating acute cardiac arrest prediction information based on heart rate and respiratory rate.

[0010] The technical problems to be achieved in the present invention are not limited to the technical problems mentioned above, and other technical problems not mentioned can be clearly understood by a person having ordinary skill in the technical field to which the present invention belongs from the description below.

[0011]

[0012] In order to achieve the above-described purpose, a building underground drainage control device according to one embodiment of the present invention includes a memory and a processor connected to the memory, and the processor receives a first heart rate and respiratory rate from a bio-signal measuring sensor installed on a patient's bed and generating a first heart rate and respiratory rate for the patient, analyzes the first bio-signal through an artificial intelligence module, detects abnormal symptoms of cardiac arrest, and when the abnormal symptoms of cardiac arrest are detected, transmits a warning notification to the patient to a medical diagnosis terminal.

[0013] At this time, the first bio-signal refers to a graph showing changes in heart rate and respiration rate over time, and the processor can distinguish a first heart rate signal and a first respiration signal from the first bio-signal, and detect the cardiac arrest abnormality symptoms based on the first heart rate signal and the first respiration signal.

[0014] At this time, the processor, through the artificial intelligence module, derives a first correlation score between the first heartbeat signal and the first respiratory signal, and can detect a case in which the first correlation score falls within a preset threshold correlation score and a preset error range as a cardiac arrest abnormality symptom.

[0015] At this time, the processor divides the first heart rate signal, which represents a change in value over time, into a plurality of sections with a preset period time, retroactively based on the current time, derives a preset number of judgments from the sections and sets them as first evaluation sections, derives a first average evaluation graph by averaging the values ​​for the plurality of first evaluation sections, divides the first respiratory signal, which represents a change in value over time, into a plurality of sections with the period time, retroactively based on the current time, derives a preset number of judgments from the sections and sets them as second evaluation sections, derives a second average evaluation graph by averaging the values ​​for the plurality of second evaluation sections, normalizes the values ​​of the first average evaluation graph and the second average evaluation graph to a preset identical range, derives a first normalized graph and a second normalized graph, respectively, and derives the first correlation score representing a correlation between the first normalized graph and the second normalized graph through the artificial intelligence module.

[0016] At this time, the processor can derive the critical correlation score based on a learning DB including information on a second bio-signal including the second heart rate signal and the second respiratory signal of an emergency critically ill patient.

[0017] At this time, the processor divides the second heart rate signal, which represents a change in value over time, into a plurality of sections based on the period time, derives a third average evaluation graph as an average of the values ​​for the entire section, divides the second respiratory signal, which represents a change in value over time, into a plurality of sections based on the period time, derives a fourth average evaluation graph as an average of the values ​​for the entire section, normalizes the values ​​of the third average evaluation graph and the fourth average evaluation graph to the same range, derives a third normalized graph and a fourth normalized graph, respectively, derives a second correlation score representing a correlation between the third normalized graph and the fourth normalized graph through the artificial intelligence module, and sets the second correlation score as the critical correlation score.

[0018] At this time, the first correlation score may be the similarity between vectors of the vectorized first normalized graph and the vectorized second normalized graph obtained by vectorizing the first normalized graph and the second normalized graph through the artificial intelligence module, and the second correlation score may be the similarity between vectors of the vectorized third normalized graph and the vectorized fourth normalized graph obtained by vectorizing the third normalized graph and the vectorized fourth normalized graph through the artificial intelligence module.

[0019]

[0020] According to the disclosed embodiment, a device and method for generating acute cardiac arrest prediction information based on heart rate and respiratory rate can be provided.

[0021] The effects that can be obtained from the present invention are not limited to the effects mentioned above, and other effects not mentioned can be clearly understood by a person having ordinary skill in the art to which the present invention belongs from the description below.

[0022]

[0023] Other aspects, features and advantages of the above-described specific preferred embodiments of the present invention will become more apparent from the following description taken in conjunction with the accompanying drawings.

[0024] FIG. 1 is a schematic diagram of an apparatus and method for generating acute cardiac arrest prediction information based on heart rate and respiratory rate according to one embodiment of the present invention.

[0025] FIG. 2 is a diagram illustrating a device for generating acute cardiac arrest prediction information based on heart rate and respiratory rate according to one embodiment of the present invention.

[0026] FIG. 3 and FIG. 4 are diagrams showing a process for deriving a first correlation score according to one embodiment of the present invention.

[0027] Figure 5 is an exemplary diagram showing a cycle time and section according to one embodiment of the present invention.

[0028] FIG. 6 is a diagram showing the derivation of a critical correlation score according to one embodiment of the present invention.

[0029] Figure 7 is a flowchart of a method for generating acute cardiac arrest prediction information based on heart rate and respiratory rate according to one embodiment of the present invention.

[0030] It should be noted that throughout the drawings, like reference numerals are used to illustrate identical or similar elements, features and structures.

[0031]

[0032] The advantages and features of the present invention, and the methods for achieving them, will become clearer with reference to the embodiments described in detail below together with the accompanying drawings. However, the present invention is not limited to the embodiments disclosed below and may be implemented in various different forms. These embodiments are provided only to ensure that the disclosure of the present invention is complete and to fully inform those skilled in the art of the scope of the invention, and the present invention is defined only by the scope of the claims. Like reference numerals refer to like elements throughout the specification.

[0033] When describing embodiments of the present invention, detailed descriptions of known functions or configurations will be omitted if they are deemed to unnecessarily obscure the gist of the invention. Furthermore, the terms described below are defined based on their functions in the embodiments of the present invention and may vary depending on the intent or custom of the user or operator. Therefore, their definitions should be based on the overall content of this specification.

[0034] The advantages and features of the present invention, and the methods for achieving them, will become clearer with reference to the embodiments described in detail below together with the accompanying drawings. However, the present invention is not limited to the embodiments disclosed below and may be implemented in various different forms. These embodiments are provided only to ensure that the disclosure of the present invention is complete and to fully inform those skilled in the art of the scope of the invention, and the present invention is defined only by the scope of the claims. Like reference numerals designate like elements throughout the specification.

[0035] At this time, it will be understood that each block of the processing flowchart drawings and combinations of the flowchart drawings can be performed by computer program instructions. These computer program instructions can be installed in a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing equipment, so that the instructions executed by the processor of the computer or other programmable data processing equipment create a means for performing the functions described in the flowchart block(s). These computer program instructions can also be stored in a computer-available or computer-readable memory that can direct a computer or other programmable data processing equipment to implement the functions in a specific manner, so that the instructions stored in the computer-available or computer-readable memory can also produce a manufactured item that includes an instruction means for performing the functions described in the flowchart block(s). Since the computer program instructions may be installed on a computer or other programmable data processing device, a series of operational steps may be performed on the computer or other programmable data processing device to create a computer-executable process, and the instructions that cause the computer or other programmable data processing device to perform the steps for performing the functions described in the flowchart block(s) may also provide steps for performing the functions described in the flowchart block(s).

[0036] Additionally, each block may represent a module, segment, or portion of code that contains one or more executable instructions for performing a specific logical function(s). It should also be noted that in some alternative implementation examples, the functions described in the blocks may occur out of order. For example, two blocks depicted in succession may actually be executed substantially concurrently, or the blocks may sometimes be executed in reverse order, depending on their respective functions.

[0037] Here, the term '~ part' used in the present embodiment means software or hardware components such as FPGA (field-programmable gate array) or ASIC (application specific integrated circuit), and the '~ part' performs certain roles. However, the '~ part' is not limited to software or hardware. The '~ part' may be configured to be on an addressable storage medium and may be configured to play one or more processors. Therefore, as an example, the '~ part' includes components such as software components, object-oriented software components, class components, and task components, processes, functions, properties, procedures, subroutines, segments of program code, drivers, firmware, microcode, circuits, data, databases, data structures, tables, arrays, and variables. The functions provided within the components and '~ parts' may be combined into a smaller number of components and '~ parts' or further separated into additional components and '~ parts'. Additionally, components and '~parts' may be implemented to regenerate one or more CPUs within a device or secure multimedia card.

[0038] In specifically describing embodiments of the present invention, examples of specific systems will be primarily used, but the main points claimed in this specification can be applied to other communication systems and services having similar technical backgrounds without significantly departing from the scope disclosed in this specification, and this can be done at the discretion of a person skilled in the relevant technical field.

[0039]

[0040] Hereinafter, with reference to the attached drawings, a device and method for generating acute cardiac arrest prediction information based on heart rate and respiratory rate according to one embodiment of the present invention will be described in detail.

[0041] FIG. 1 is a schematic diagram of an apparatus and method for generating acute cardiac arrest prediction information based on heart rate and respiratory rate according to one embodiment of the present invention.

[0042] Referring to FIG. 1, a device and method for generating prediction information on sudden cardiac arrest based on heart rate and respiratory rate according to one embodiment of the present invention can predict that a patient will experience sudden cardiac arrest through a bio-signal measuring sensor installed on a bed used by the patient, and if it is determined that cardiac arrest will occur, a warning notification is transmitted to a medical diagnosis terminal in advance, thereby enabling efficient use of limited medical personnel and immediate treatment of a patient's emergency situation.

[0043]

[0044] At this time, the medical diagnosis terminal may include a communication-capable desktop computer, laptop computer, notebook, smart phone, tablet PC, mobile phone, smart watch, smart glass, e-book reader, portable multimedia player (PMP), portable game console, navigation device, digital camera, digital multimedia broadcasting (DMB) player, digital audio recorder, digital audio player, digital video recorder, digital video player, PDA (Personal Digital Assistant), etc.

[0045]

[0046] FIG. 2 is a drawing showing an acute cardiac arrest prediction information generation device based on heart rate and respiratory rate according to one embodiment of the present invention.

[0047] According to one embodiment, a device for generating prediction information for acute cardiac arrest based on heart rate and respiratory rate comprises a processor and memory. The processor can perform at least one of the methods described above. The memory can store information related to the method described above or a program implementing the method described above. The memory may be volatile or non-volatile memory. The memory may be referred to as a "database," a "storage unit," or the like.

[0048] At this time, the processor receives the first bio-signal from a bio-signal measuring sensor installed on the patient's bed and generating the first bio-signal for the patient, analyzes the first bio-signal through an artificial intelligence module, detects abnormal symptoms of cardiac arrest, and when abnormal symptoms of cardiac arrest are detected, transmits a warning notification to the patient through a medical diagnosis terminal.

[0049] At this time, the artificial intelligence module can utilize deep learning, a field of machine learning.

[0050] Additionally, various models such as RNN (Recurrent Neural Network), DNN (Deep Neural Network), and DRNN (Dynamic Recurrent Neural Network) can be utilized as artificial intelligence network models for learning in the artificial intelligence module.

[0051] Here, RNN is a deep learning technique that considers current and past data simultaneously, and a recurrent neural network (RNN) refers to a neural network in which the connections between the units that make up the artificial neural network form a directed cycle. Furthermore, various methods can be used to construct a recurrent neural network (RNN), for example, a fully recurrent network (FRN), a Hopfield network, an Elman network, an Echo state network (ESN), a Long short term memory network (LSTM), a Bi-directional RNN, a Continuous-time RNN (CTRNN), a Hierarchical RNN, and a Secondary RNN are representative examples. In addition, methods such as gradient descent, Hessian Free Optimization, and the Global Optimization Method can be used to train a recurrent neural network (RNN).

[0052]

[0053] The biosignal measuring sensor according to an embodiment of the present invention can be installed on a bed and recognize the user's biosignal without the user being aware of it.

[0054] At this time, the first bio-signal refers to a graph showing changes in heart rate and respiration rate over time, and the processor can distinguish a first heart rate signal and a first respiration signal from the first bio-signal, and detect the cardiac arrest abnormality symptoms based on the first heart rate signal and the first respiration signal.

[0055] As described below, based on the second bio-signal of an emergency critically ill patient, the relationship between the heart rate signal and the respiratory signal before cardiac arrest is derived, and if the relationship between the heart rate signal and the respiratory signal in the first bio-signal is similar to that of an emergency critically ill patient, it can be seen that cardiac arrest is likely to occur.

[0056] Accordingly, referring to FIGS. 3 to 5, the processor can derive a first correlation score between the first heartbeat signal and the first respiratory signal through the artificial intelligence module, and detect a case in which the first correlation score falls within a preset threshold correlation score and a preset error range as the cardiac arrest abnormality symptom.

[0057] In more detail, the processor divides the first heartbeat signal, which indicates a change in value over time, into a plurality of sections with a preset period time, retroactively based on the current time, derives a preset number of judgments from the sections and sets them as first evaluation sections, derives a first average evaluation graph by averaging the values ​​for the plurality of first evaluation sections, divides the first respiration signal, which indicates a change in value over time, into a plurality of sections with the preset period time, retroactively based on the current time, derives a preset number of judgments from the sections and sets them as second evaluation sections, derives a second average evaluation graph by averaging the values ​​for the plurality of second evaluation sections, normalizes the values ​​of the first average evaluation graph and the second average evaluation graph to a preset identical range, derives a first normalized graph and a second normalized graph, respectively, and derives the first correlation score representing a correlation between the first normalized graph and the second normalized graph through the artificial intelligence module.

[0058] At this time, the cycle time can be arbitrarily set by the operator of the present invention, and can be set to, for example, 1 minute or 10 minutes.

[0059] At this time, the number of judgments can be arbitrarily set by the operator of the present invention, and can be set to, for example, 100, 1000, etc.

[0060] In addition, the above normalization is intended to more clearly derive correlations by organizing the values ​​of heart rate signals and respiratory signals, which have different units, into the same order.

[0061]

[0062] FIG. 6 is a diagram showing the derivation of a critical correlation score according to one embodiment of the present invention.

[0063] Referring to FIG. 6, the processor can derive the critical correlation score based on a learning DB including information on a second bio-signal including the second heart rate signal and the second respiratory signal of an emergency critically ill patient.

[0064] This establishes criteria for determining whether there is a possibility of cardiac arrest based on the second vital signs of an emergency critically ill patient at risk of cardiac arrest.

[0065] In more detail, the processor divides the second heart rate signal, which represents a change in value over time, into a plurality of sections based on the period time, derives a third average evaluation graph as an average of the values ​​for the entire section, divides the second respiration signal, which represents a change in value over time, into a plurality of sections based on the period time, derives a fourth average evaluation graph as an average of the values ​​for the entire section, normalizes the values ​​of the third average evaluation graph and the fourth average evaluation graph to the same range, derives a third normalized graph and a fourth normalized graph, and derives a second correlation score representing a correlation between the third normalized graph and the fourth normalized graph through the artificial intelligence module, and sets the second correlation score as the critical correlation score.

[0066] Through this, it is possible to determine whether there is a possibility of cardiac arrest.

[0067] As another example, the first correlation score may be a vector similarity between the vectorized first normalized graph and the vectorized second normalized graph by vectorizing the first normalized graph and the second normalized graph through the artificial intelligence module, and the second correlation score may be a vector similarity between the vectorized third normalized graph and the vectorized fourth normalized graph by vectorizing the third normalized graph and the vectorized fourth normalized graph through the artificial intelligence module.

[0068] At this time, the similarity between vectors may mean any one of cosine similarity, Euclidean distance similarity, Jaccard similarity, and Levenshtein distance similarity.

[0069]

[0070] Figure 7 is a flowchart of a method for generating acute cardiac arrest prediction information based on heart rate and respiratory rate according to one embodiment of the present invention.

[0071] Referring to FIG. 7, a method for generating acute cardiac arrest prediction information based on heart rate and respiratory rate according to one embodiment of the present invention can receive a first bio-signal from a bio-signal measuring sensor installed on a patient's bed and generating a first bio-signal for the patient (S100).

[0072] In addition, a method for generating acute cardiac arrest prediction information based on heart rate and respiratory rate according to one embodiment of the present invention can detect abnormal signs of cardiac arrest by analyzing the first bio-signal through an artificial intelligence module (S200).

[0073] In addition, the method for generating acute cardiac arrest prediction information based on heart rate and respiratory rate according to one embodiment of the present invention can transmit a warning notification to the patient through a medical diagnosis terminal when detecting the above-mentioned abnormal symptoms of cardiac arrest (S300).

[0074] In addition, a method for generating acute cardiac arrest prediction information based on heart rate and respiratory rate according to one embodiment of the present invention can be configured in the same manner as the device for generating acute cardiac arrest prediction information based on heart rate and respiratory rate disclosed in FIGS. 1 to 6.

[0075] Furthermore, the embodiments of the present invention disclosed in this specification and drawings are merely specific examples to easily explain the technical content of the present invention and facilitate understanding of the present invention, and are not intended to limit the scope of the present invention. In other words, it will be apparent to those skilled in the art that other modifications based on the technical concept of the present invention are possible. Furthermore, the above-described embodiments may be combined and operated as needed.

[0076] In addition, the device and method for generating acute cardiac arrest prediction information based on heart rate and respiratory rate according to one embodiment of the present invention may be implemented in the form of program commands that can be executed through various computer means and recorded on a computer-readable medium.

[0077] Various embodiments of the present invention can be implemented as computer-readable code on a computer-readable recording medium in certain aspects. A computer-readable recording medium is any data storage device capable of storing data that can be read by a computer system. Examples of computer-readable recording media can include read-only memories (ROMs), random access memories (RAMs), compact disk-read-only memories (CD-ROMs), magnetic tapes, floppy disks, optical data storage devices, and carrier waves (such as data transmission over the Internet). A computer-readable recording medium can also be distributed across network-connected computer systems, so that the computer-readable code is stored and executed in a distributed manner. Additionally, functional programs, codes, and code segments for achieving various embodiments of the present invention can be easily interpreted by programmers skilled in the field to which the present invention is applied.

[0078] It will also be appreciated that devices and methods according to various embodiments of the present invention can be implemented in the form of hardware, software, or a combination of hardware and software. Such software may be stored in a volatile or non-volatile storage device, such as a ROM, for example, regardless of whether it is erasable or rewritable, or in a memory, such as a RAM, a memory chip, a device, or an integrated circuit, or in a storage medium that is optically or magnetically recordable and simultaneously machine-readable (e.g., a computer), such as a compact disk (CD), a DVD, a magnetic disk, or a magnetic tape. It will be appreciated that methods according to various embodiments of the present invention can be implemented by a computer or a portable terminal including a control unit and a memory, and such a memory is an example of a machine-readable storage medium suitable for storing a program or programs including commands for implementing embodiments of the present invention.

[0079] Accordingly, the present invention encompasses a program containing code for implementing the devices or methods described in the claims of this specification, and a machine-readable storage medium (e.g., a computer) storing such a program. Furthermore, such a program may be transmitted electronically via any medium, such as a communication signal transmitted via a wired or wireless connection, and the present invention appropriately encompasses equivalents thereof.

[0080] The embodiments of the present invention disclosed in this specification and drawings are intended to provide a simple explanation of the technical content of the present invention and provide specific examples to aid understanding of the present invention, and are not intended to limit the scope of the present invention. Furthermore, the embodiments of the present invention described above are merely exemplary, and those skilled in the art will understand that various modifications and equivalent embodiments are possible. Therefore, the true technical protection scope of the present invention should be defined by the following claims.

Claims

1. In a device for generating acute cardiac arrest prediction information based on heart rate and respiratory rate, comprising a memory and a processor connected to the memory; The above processor, Receives the first bio-signal from a bio-signal measuring sensor installed on the patient's bed and generating the first bio-signal for the patient, Through the artificial intelligence module, the first bio-signal is analyzed to detect abnormal signs of cardiac arrest, A device for generating prediction information on acute cardiac arrest based on heart rate and respiratory rate, characterized in that when the above abnormal signs of cardiac arrest are detected, a warning notification is transmitted to the patient through a medical diagnosis terminal.

2. In claim 1, The above first biosignal is, It refers to a graph that shows changes in heart rate and breathing rate over time. The above processor, Distinguish the first heart rate signal and the first respiratory signal from the above first biosignal, A device for generating prediction information for acute cardiac arrest based on heart rate and respiratory rate, characterized by detecting abnormal symptoms of cardiac arrest based on the first heart rate signal and the first respiratory signal.

3. In claim 2, The above processor, Through the above artificial intelligence module, a first correlation score between the first heart rate signal and the first respiratory signal is derived, A device for generating prediction information for acute cardiac arrest based on heart rate and respiratory rate, characterized in that it detects a case in which the first correlation score is within a preset threshold correlation score and a preset error range as an abnormal symptom of cardiac arrest.

4. In claim 3, The above processor, The first heart rate signal, which represents a change in value over time, is divided into multiple sections with a preset cycle time, Based on the present time, the above section is derived as the preset number of judgments and set as the first evaluation section. A first average evaluation graph is derived by averaging the values ​​for multiple first evaluation intervals, The first respiratory signal, which represents a change in value over time, is divided into multiple sections based on the periodic time, Based on the present time, the above section is derived as the above judgment number and set as the second evaluation section. A second average evaluation graph is derived by averaging the values ​​for multiple second evaluation intervals, The values ​​of the first average evaluation graph and the second average evaluation graph are normalized to a preset identical range, thereby deriving a first normalized graph and a second normalized graph, respectively. A device for generating prediction information on acute cardiac arrest based on heart rate and respiratory rate, characterized in that the first correlation score representing the correlation between the first normalized graph and the second normalized graph is derived through the artificial intelligence module.

5. In claim 4, The above processor, Based on a learning DB that includes information on the second bio-signal including the second heart rate signal and the second respiratory signal of an emergency critically ill patient, A device for generating prediction information for acute cardiac arrest based on heart rate and respiratory rate, characterized by deriving the above-mentioned critical correlation score.

6. In claim 5, The above processor, The second heart rate signal, which represents a change in value over time, is divided into multiple sections based on the periodic time, A third average evaluation graph is derived by averaging the values ​​for the entire section, The second respiratory signal, which represents the change in value over time, is divided into multiple sections based on the periodic time, The fourth average evaluation graph is derived as the average of the values ​​for the entire section, By normalizing the values ​​of the third average evaluation graph and the fourth average evaluation graph to the same range, a third normalized graph and a fourth normalized graph are derived, respectively. Through the above artificial intelligence module, a second correlation score representing the correlation between the third normalized graph and the fourth normalized graph is derived, A device for generating prediction information for acute cardiac arrest based on heart rate and respiratory rate, characterized in that the second correlation score is set to the critical correlation score.

7. In claim 6, The above first correlation score is, Through the above artificial intelligence module, the first normalized graph and the second normalized graph are vectorized, and the vector similarity between the vectorized first normalized graph and the vectorized second normalized graph is The above second correlation score is, A device for generating prediction information for acute cardiac arrest based on heart rate and respiratory rate, characterized in that the third normalized graph and the fourth normalized graph are vectorized through the artificial intelligence module, and the vector similarity between the vectorized third normalized graph and the vectorized fourth normalized graph is obtained.

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