Anxiety disorder monitoring and biofeedback provision system

Abstract

The present invention relates to an anxiety disorder monitoring and biofeedback provision system, comprising: an electrocardiogram (ECG) measuring device for measuring a user's electrocardiogram; a user terminal that is capable of downloading and installing a dedicated application from a server that includes functions for monitoring anxiety disorders and providing biofeedback, and transmits the received ECG signal to the server when the dedicated application is running and the ECG signal is received from the ECG measuring device; and a server that provides the dedicated application to the user terminal, analyzes the ECG signal received from the user terminal, and transmits the analyzed information to the user terminal. The user terminal diagnoses an anxiety disorder using the information analyzed by the server through the dedicated application, and if an anxiety disorder is diagnosed, provides biofeedback-related content for treating the anxiety disorder. According to the present invention, there is an effect of being able to determine anxiety disorders more easily and accurately using heart rate variability.

Description

Technology Field

[0001] The present invention relates to a biofeedback provision technology for monitoring anxiety disorders in real time and reducing anxiety disorders. Background Technology

[0003] Over the past five years, 3.54 million patients received treatment for anxiety disorders. It was found that the number of patients in their 20s increased the most at 80%, followed by those in their teens at 47% and those in their 30s at 40%. Additionally, 1.43 million patients were aged 60 or older. The number of patients receiving treatment per 100,000 people was highest among those aged 60 or older at 15%, with the total being 6.963, indicating that 7% of the population suffers from anxiety disorders. However, only a small fraction of patients with anxiety disorders actually received treatment, making it urgent to devise measures to increase the rate of psychiatric treatment.

[0004] Traditionally, the diagnosis of anxiety disorders involves basic biopsies (ECG, blood and urine tests, thyroid tests, etc.), psychological testing, and counseling. As a chronic condition requiring consistent management, anxiety disorders are characterized by frequent complications, such as depression.

[0005] Treatment methods for anxiety disorders include medication, as well as psychotherapy, Cognitive Behavioral Therapy (CBT), relaxation techniques, and biofeedback therapy. Psychotherapy involves treatment through psychiatric counseling; relaxation techniques utilize breathing and muscle relaxation, meditation, imagery, hypnosis, biofeedback, music, yoga, massage, and humor; and biofeedback therapy achieves relaxation effects by regulating physiological states through self-feedback of one's own biological information, allowing for real-time monitoring of physiological states such as breathing, muscle relaxation, and brainwaves through numerical data and visualizations. Cognitive Behavioral Therapy is Rational Emotive Behavior Therapy; relaxation techniques refer to breathing and muscle relaxation and meditation; and biofeedback involves regulating physiological states and achieving relaxation effects through self-feedback of one's own biological information.

[0006] Although drug treatment is effective in treating panic and anxiety disorders, it does not respond well to all patients, and there are patients who do not want to take the medication due to side effects, and there are reports that the relapse rate after discontinuing the medication reaches 25 to 80 percent.

[0007] According to a retrospective analysis of the effectiveness of pharmacotherapy, cognitive behavioral therapy, and a combination of the two for panic disorder, cognitive behavioral therapy was found to be the most effective treatment.

[0008] An electrocardiogram (ECG) is a representation of the electrical activity undergoing depolarization and repolarization processes during heart muscle activity. It is a biosignal widely used in clinical practice because it contains biological information such as heart rate, heart size and location, and the presence of heart damage.

[0009] Electrocardiograms, which are easier to measure than echocardiograms or other invasive tests in clinical settings, are widely used to predict or diagnose heart disease in patients with mental illnesses.

[0010] Heart rate variability (HRV) is one of the biological data that can be obtained by analyzing an electrocardiogram, and refers to the periodic change in the interval between heartbeats. In other words, the interval between heartbeats (RR interval) is always changing even when at rest, and this is called heart rate variability.

[0011] Generally, these changes in heartbeats appear larger and more complex in a resting state, while they appear regular and constant when exercising or under stress.

[0012] Abnormalities in heart rate variability (HVVA) are observed in a wide range of psychiatric conditions, including schizophrenia, bipolar disorder, anxiety disorders, and addiction disorders. In particular, HVVA testing reveals abnormal findings in mood disorders such as depression and bipolar disorder, as well as anxiety disorders; it is reported that abnormalities in the sympathetic and parasympathetic nervous systems in these conditions are highly correlated with their role in HVVA. Some studies have indicated a decrease in HVVA in schizophrenic patients, and research on the relationship between HVVA and depression is continuously being conducted. Recently, there has been an increasing number of reports linking major depression with HVVA and its association with symptoms and treatment efficacy.

[0013] Anxiety disorders are common psychiatric conditions, and a link with cardiovascular disease has been reported. Anxiety disorders are thought to be caused by dysregulation of the heart's autonomic nervous system, and stress or psychological factors can influence changes in this system. Therefore, accurately measuring autonomic nervous system biosignals in patients with anxiety disorders during daily life to predict the severity of the disease is necessary for diagnosis and treatment.

[0014] Anxiety disorders are similar to panic disorders in that many of their characteristic physical symptoms are physiological changes resulting from excessive excitation of the sympathetic nervous system. Many studies on heart rate variability have been conducted on panic disorders, and in panic disorders, cholinergic activity is reduced while adrenergic activity is relatively increased. Consequently, lower heart rate variability is observed in patients with panic disorders compared to normal individuals, and High Frequency (HF) power decreases while Low Frequency (LF) power increases.

[0015] Various mental disorders and psychological stress are accompanied by cardiovascular symptoms. In other words, symptoms such as excitement, aggression, anxiety, and restlessness are closely linked to heart rate and blood pressure. Such psychological stress increases the activity of the sympathetic nervous system and decreases the activity of the parasympathetic nervous system.

[0016] When the sympathetic nervous system is activated under stressful situations, catecholamines are released, causing the heart rate to accelerate. It has been revealed that acute stress causes a decrease in heart rate variability and an increase in the LH / HF ratio. Therefore, heart rate variability serves as a link between the relationship between stress and mental disorders, as well as between psychiatric and somatic symptoms.

[0017] Currently, medical treatment for anxiety disorders involves initial diagnosis through biopsy and psychological testing; however, consultations are relatively short, and treatment primarily consists of prescribing medication. Furthermore, there is a problem in that it is difficult to assess the condition of patients with anxiety disorders in their daily lives. Additionally, immediate response is impossible upon an onset, and the collection and analysis of biometric data cannot be performed at that time. It is also difficult to provide immediately usable content based on biometric data, and uninterrupted mental and physical management is challenging. Moreover, continuous diagnosis and treatment of anxiety disorders are required from the perspective of chronic disease management. Prior art literature

[0018] Republic of Korea Registered Patent 10-2097246 The problem to be solved

[0019] The present invention has been devised to solve the aforementioned problems, and aims to provide an anxiety disorder monitoring and biofeedback provision system capable of extracting heart rate variability signals from an electrocardiogram signal, diagnosing anxiety disorders using the extracted heart rate variability, and providing non-face-to-face content for cognitive behavioral therapy through biofeedback for patients with anxiety disorders.

[0020] The objectives of the present invention are not limited to those mentioned above, and other unmentioned objectives will be clearly understood by a person skilled in the art from the description below. means of solving the problem

[0022] The present invention, for achieving the above purpose, relates to an anxiety disorder monitoring and biofeedback provision system, comprising: an electrocardiogram (ECG) measuring device for measuring a user's electrocardiogram; a user terminal used by the user, which can download and install a dedicated application from a server that includes a function for receiving anxiety disorder monitoring and biofeedback, and which transmits the received ECG signal to the server when the dedicated application is executed and the ECG signal is received from the ECG measuring device; and a server that provides the dedicated application to the user terminal, analyzes the ECG signal received from the user terminal, and transmits the analyzed information to the user terminal.

[0023] The user terminal diagnoses an anxiety disorder using information analyzed by the server through the dedicated application, and if an anxiety disorder is diagnosed, provides biofeedback-related content for treating the anxiety disorder.

[0024] The server can extract a heart rate variability (HRV) signal using an electrocardiogram signal received from the user terminal and provide information analyzing the extracted heart rate variability signal to the user terminal.

[0025] LF curve data representing the average value of the LF (Low Frequency) signal according to each age and HR curve data representing the average value of the HR (Heart Rate) signal according to each age are stored in advance in a database, and when the server receives user age information from the dedicated application of the user terminal, it reads the LF curve data and HR curve data from the database and transmits them to the user terminal, measures the LF signal in the power spectrum of the extracted heart rate variability signal, measures the HR signal using the electrocardiogram signal, transmits the measured LF signal and HR signal to the user terminal, and the user terminal compares the LF signal with the LF curve and diagnoses an anxiety disorder if the LF signal is higher than the LF curve, or compares the HR signal with the HR curve and if the HR signal is higher than the HR curve, it can provide the biofeedback-related content.

[0026] The above user terminal can provide biofeedback-related content including abdominal breathing content for inducing abdominal breathing, muscle relaxation content for muscle relaxation, and meditation content. Effects of the invention

[0028] According to the present invention, there is an effect of being able to determine anxiety disorders more easily and accurately using heart rate variability.

[0029] In addition, according to the present invention, it is possible to immediately diagnose the presence of an anxiety disorder, determine the anxiety disorder more accurately, provide appropriate content based on the degree of the anxiety disorder, and enable continuous mental and physical monitoring of the anxiety disorder and the provision of biofeedback. Brief explanation of the drawing

[0031] FIG. 1 is a conceptual diagram schematically illustrating the configuration of an anxiety disorder monitoring and biofeedback provision system according to one embodiment of the present invention. FIG. 2 illustrates the form of abdominal breathing wear according to one embodiment of the present invention. FIG. 3 is a block diagram showing the internal configuration of abdominal breathing wear according to one embodiment of the present invention. FIG. 4 is a flowchart showing a method for determining an anxiety disorder in an anxiety disorder monitoring and biofeedback provision system according to one embodiment of the present invention. FIG. 5 is a flowchart showing a specific example of a method for determining anxiety disorder in an anxiety disorder monitoring and biofeedback provision system according to one embodiment of the present invention. FIG. 6 is a flowchart showing a method for providing biofeedback content in an anxiety disorder monitoring and biofeedback provision system according to one embodiment of the present invention. Figure 7 is a graph showing an LF curve representing the average value of the LF signal according to age and the LF signal measured for the user. Figure 8 is a graph showing an HR curve representing the average value of the HR signal according to age and the HR signal measured for the user. FIGS. 9 to 19 are examples of screens of a user terminal in an anxiety disorder monitoring and biofeedback provision system according to one embodiment of the present invention. Figure 20 is a graph illustrating the process of extracting HRV signals from an electrocardiogram signal. Figure 21 is a graph showing the HRV signal. Figure 22 is a graph showing the power spectrum of an HRV signal. Figure 23 is a graph showing the NN or RR interval in an electrocardiogram signal. Figure 24 is a graph illustrating the power spectrum of an HRV signal. Figure 25 is a graph illustrating power spectral density estimates obtained over a full 24-hour interval of a long-term Holter recording. Figure 26 shows the interval tachogram of 256 consecutive RR values ​​of a normal subject at supine rest (A, C, E) and head-up tilt (B, D, F). Figures 27 to 29 are examples of screens displaying the results of heart rate variability analysis. Specific details for implementing the invention

[0032] The present invention is capable of various modifications and may have various embodiments, and specific embodiments are illustrated in the drawings and described in detail. However, this is not intended to limit the present invention to specific embodiments, and it should be understood that it includes all modifications, equivalents, and substitutions that fall within the spirit and scope of the invention.

[0033] The terms used in this application are used merely to describe specific embodiments and are not intended to limit the invention. The singular expression includes the plural expression unless the context clearly indicates otherwise. In this application, terms such as "comprising" or "having" are intended to indicate the presence of the features, numbers, steps, actions, components, parts, or combinations thereof described in the specification, and should be understood as not precluding the existence or addition of one or more other features, numbers, steps, actions, components, parts, or combinations thereof.

[0034] Unless otherwise defined, all terms used herein, including technical or scientific terms, have the same meaning as generally understood by those skilled in the art to which the present invention pertains. Terms such as those defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the relevant technology, and should not be interpreted in an ideal or overly formal sense unless explicitly defined in this application.

[0035] Furthermore, in the description referring to the attached drawings, identical components are assigned the same reference numeral regardless of drawing symbols, and redundant descriptions thereof are omitted. In describing the present invention, if it is determined that a detailed description of related prior art could unnecessarily obscure the essence of the present invention, such detailed description is omitted.

[0036] This invention proposes a method for assessing anxiety disorders using heart rate variability (HV). The degree of autonomic nervous system activity can be quantified through the analysis of HV, and the quantified HV analysis can be very useful for evaluating cardiovascular diseases. Research on HV is used as a method to quantify various autonomic nervous system abnormalities in conditions such as dementia, syncope, Parkinson's disease, motor neuron disease, stroke, and epilepsy, and it is reported to have a close correlation with cardiovascular abnormalities or the clinical prognosis of diseases. Therefore, evaluation based on HV analysis can be used as a useful assessment tool for all diseases accompanied by autonomic nervous system abnormalities.

[0037] FIG. 1 is a conceptual diagram schematically illustrating the configuration of an anxiety disorder monitoring and biofeedback provision system according to one embodiment of the present invention.

[0038] Referring to FIG. 1, an anxiety disorder monitoring and biofeedback providing system according to one embodiment of the present invention includes an electrocardiogram measuring device (100), a user terminal (200), and a server (300).

[0039] The electrocardiogram measuring device (100) is a device that measures the user's electrocardiogram (ECG).

[0040] In one embodiment of the present invention, the electrocardiogram measuring device (100) may be implemented in various forms having the function of measuring the user's electrocardiogram. For example, it may be a watch type worn by the user as a wearable device in the form of a watch, a patch type attached to the user as a medical device in the form of a patch, or a portable type that is easy to carry.

[0041] The user terminal (200) is a terminal used by a user and is capable of communicating with the electrocardiogram measuring device (100) and the server (300) via a wired or wireless communication network. In one embodiment of the present invention, the user terminal (200) may be implemented as a mobile device such as a smartphone, a mobile phone, or a tablet PC. For example, the electrocardiogram measuring device (100) and the user terminal (200) may communicate via a Bluetooth communication network, and the user terminal (200) and the server (300) may communicate via a mobile communication network.

[0042] The user terminal (200) receives an electrocardiogram signal measured by the electrocardiogram measuring device (100) and transmits the received electrocardiogram signal to the server (300).

[0043] The server (300) analyzes the electrocardiogram signal received from the user terminal (200) to extract a heart rate variability (HRV) signal, and uses the extracted heart rate variability signal to determine an anxiety disorder.

[0044] And, the server (300) provides anxiety disorder diagnosis result information to the user terminal (200).

[0045] In the present invention, the user terminal (200) is a terminal used by the user, and can download and install a dedicated application from the server (300) that includes functions for monitoring anxiety disorders and receiving biofeedback, and when the dedicated application is running and receives an electrocardiogram signal from the electrocardiogram measuring device (100), the received electrocardiogram signal is transmitted to the server (300).

[0046] The server (300) provides a dedicated application to the user terminal (200), analyzes the electrocardiogram signal received from the user terminal (200), and transmits the analyzed information to the user terminal (200).

[0047] The user terminal (200) diagnoses an anxiety disorder using information analyzed by the server (300) through a dedicated application, and if an anxiety disorder is diagnosed, provides biofeedback-related content for treating the anxiety disorder.

[0048] The server (300) can extract a heart rate variability (HRV) signal using an electrocardiogram signal received from a user terminal (200) and provide information on the analysis of the extracted heart rate variability signal to the user terminal (200).

[0049] In one embodiment of the present invention, LF curve data representing the average value of the LF (Low Frequency) signal according to each age and HR curve data representing the average value of the HR (Heart Rate) signal according to each age may be stored in advance in a database.

[0050] When the server (300) receives user age information from a dedicated application of the user terminal (200), it reads LF curve data and HR curve data from a database and transmits them to the user terminal (200), measures the LF signal in the power spectrum of the extracted heart rate variability signal, measures the HR signal using the electrocardiogram signal, and transmits the measured LF signal and HR signal to the user terminal (200). Then, the user terminal (200) compares the LF signal with the LF curve and if the LF signal is higher than the LF curve, or compares the HR signal with the HR curve and if the HR signal is higher than the HR curve, it diagnoses the condition as an anxiety disorder and can provide biofeedback-related content.

[0051] The user terminal (200) can provide biofeedback-related content including abdominal breathing content to induce abdominal breathing, muscle relaxation content for muscle relaxation, and meditation content.

[0052] In one embodiment of the present invention, the server (300) calculates the LF area, which is the area of ​​the LF (Low Frequency) signal, and the HF area, which is the area of ​​the HF (High Frequency) signal, in the power spectrum of the heart rate variability signal, and can determine an anxiety disorder based on the ratio of the calculated LF area and HF area.

[0053] In the present invention, the server (300) can provide information on the results of an anxiety disorder determination to a user terminal (200) in the form of a web viewer. FIGS. 27 to 29 are examples of screens displaying the results of heart rate variability analysis.

[0054] The user terminal (200) displays anxiety disorder diagnosis result information in the form of a web viewer received from the server (300). Additionally, the user terminal (200) can provide content related to cognitive behavioral therapy (CBT) for treating anxiety disorders. For example, the user terminal (200) can provide content related to cognitive behavioral therapy (CBT) including imagery relaxation training, anxiety coping training, biofeedback, breathing techniques, muscle relaxation techniques, etc.

[0055] In a method for determining anxiety disorder in an anxiety disorder monitoring and biofeedback provision system of the present invention, the server (300) includes the step of receiving an electrocardiogram (ECG) signal from a user terminal (200); the server (300) includes the step of analyzing the electrocardiogram signal to extract a heart rate variability (HRV) signal; and the server (300) includes the step of determining anxiety disorder using the extracted heart rate variability signal.

[0056] The server (300) can calculate the LF area, which is the area of ​​the LF (Low Frequency) signal, and the HF area, which is the area of ​​the HF (High Frequency) signal, in the power spectrum of the heart rate variability signal, and determine the anxiety disorder based on the ratio of the calculated LF area and HF area.

[0057] FIG. 2 illustrates the form of abdominal breathing wear according to one embodiment of the present invention, and FIG. 3 is a block diagram showing the internal configuration of abdominal breathing wear according to one embodiment of the present invention.

[0058] Referring to FIGS. 2 and FIGS. 3, the abdominal breathing wear (500) is implemented in a form that can be worn on the upper body of a user and is worn on the upper body of a user to detect abdominal breathing and to stimulate abdominal breathing to occur.

[0059] The abdominal breathing wear (500) comprises a first sensor unit (510), a second sensor unit (520), a stimulation unit (530), and a communication unit (540).

[0060] The first sensor unit (510) plays the role of generating a first detection signal by measuring the degree of contraction of the breathing muscles in order to detect whether abdominal breathing is taking place in response to the user's breathing cycle.

[0061] The second sensor unit (520) plays the role of generating a second detection signal by measuring the degree of contraction of the breathing muscles to detect whether abdominal breathing is taking place in response to the user's breathing cycle.

[0062] The stimulating part (530) serves to output a stimulating signal to stimulate the breathing muscles so that the user performs abdominal breathing.

[0063] The communication unit (540) serves to communicate with the user terminal (200). In one embodiment of the present invention, the communication unit (540) can communicate with the user terminal (200) using a short-range wireless communication network communication method including a Bluetooth communication method.

[0064] The communication unit (540) can transmit the detection signal detected by the first sensor unit (510) and the second sensor unit (520) to the user terminal (200), and receive a control signal from the user terminal (200) and transmit it to the stimulation unit (530).

[0065] As shown in FIG. 2, the abdominal breathing wear (500) is implemented to be worn on the upper body including the user's abdomen, with the upper part open to allow the head to enter and the lower part also open. In one embodiment of the present invention, the abdominal breathing wear (500) may be made of a synthetic resin fabric. The abdominal breathing wear (500) may be implemented in the form of innerwear.

[0066] The first sensor unit (510) may be configured to generate a first detection signal by measuring the degree of contraction of the breathing muscles to detect whether abdominal breathing is taking place in response to the user's breathing cycle.

[0067] The first sensor unit (510) can measure the degree of contraction of the user's breathing muscles and generate an inhalation start signal and an exhalation start signal, respectively.

[0068] The first sensor unit (510) may comprise an inhalation sensing pad attached to the user's inhalation muscle to measure the degree of contraction of the inhalation muscle, and an exhalation sensing pad attached to the user's exhalation muscle to measure the degree of contraction of the exhalation muscle. Here, the inhalation start signal and the exhalation start signal may be the user's breathing signals generated by the respective inhalation sensing pad and exhalation sensing pad at the time of the user's inhalation start and the time of the user's exhalation start, corresponding to the user's breathing cycle.

[0069] In one embodiment of the present invention, the first detection signal may include a user's inhalation start signal and an exhalation start signal.

[0070] Specifically, the inhalation start signal and exhalation start signal generated by the first sensor unit (510) can be transmitted to the user terminal (200) through the communication unit (540). That is, the signal generated by the first sensor unit (510) is transmitted to the user terminal (200), and the user terminal (200) can control the operation of the stimulation unit (530) using the received signal.

[0071] The second sensor unit (520) may be configured to generate a second detection signal by measuring the degree of contraction of the breathing muscles to detect whether breathing other than abdominal breathing is taking place in response to the user's breathing cycle.

[0072] Here, respiratory muscles are divided into inspiratory muscles and expiratory muscles. Inspiratory muscles include superficial muscles such as the pectoralis major, pectoralis minor, trapezius, and erector spinae in addition to the diaphragm, which is the primary inspiratory muscle. Expiratory muscles include the rectus abdominis, external oblique, internal oblique, and transversus abdominis.

[0073] In particular, the first sensor unit (510) is attached to the abdominal muscles (also called "abdominal breathing muscles") where the user's abdominal breathing takes place, and can generate a first detection signal by measuring the degree of contraction of the breathing muscles during inhalation and exhalation.

[0074] Additionally, the second sensor unit (520) is attached to the muscles used for thoracic breathing or the auxiliary breathing muscles (also referred to as "breathing muscles other than abdominal breathing muscles") to detect whether the user is performing thoracic breathing or breathing using auxiliary breathing muscles (such as breathing with force applied to the shoulders and neck), and can generate a second detection signal by measuring the degree of contraction of the breathing muscles during inhalation and exhalation.

[0075] In one embodiment of the present invention, the first sensor part (510) may be attached to the abdomen, and the second sensor part (520) may be attached to at least one of the chest, shoulder, and neck.

[0076] For example, the first sensor unit (510) may be configured to generate a first detection signal by the contraction of at least one of the diaphragm, rectus abdominis, external oblique, internal oblique, and transversus abdominis muscles, and the second sensor unit (520) may be configured to generate a second detection signal by the contraction of at least one of the external intercostal muscles, internal intercostal muscles, and accessory respiratory muscles.

[0077] Here, accessory respiratory muscles may include the sternocleidomastoid muscle, pectoralis muscle, deltoid muscle (or trapezius), scalene muscle, etc.

[0078] In the present invention, the first sensor part (510) and the second sensor part (520) can be attached to each of the aforementioned respiratory muscles.

[0079] In one embodiment of the present invention, a plurality of first sensor units (510) and second sensor units (520) may each be provided and attached to one or more of the user's respiratory muscles to measure the degree of contraction of the respiratory muscles.

[0080] The stimulation unit (530) can output a stimulation signal to stimulate the user's breathing muscles according to a control signal transmitted from the user terminal (200). In one embodiment of the present invention, the stimulation unit (530) may be implemented as a neuromuscular electrical stimulation (NMES).

[0081] The stimulating part (530) can be installed by attaching it to the aforementioned respiratory muscle and can provide electrical stimulation to the respiratory muscle corresponding to a breathing signal generated according to the user's breathing cycle.

[0082] In one embodiment of the present invention, the stimulating part (530) may include an inspiratory muscle pad provided to stimulate the inspiratory muscle and an expiratory muscle pad provided to stimulate the expiratory muscle.

[0083] In one embodiment of the present invention, a plurality of stimulating parts (530) may be provided, and a plurality of inhaling muscle pads and exhaling muscle pads may each be provided and provided at a part corresponding to the corresponding user's muscle.

[0084] In one embodiment of the present invention, the stimulating portion (530) may be provided in the form of a band or belt that has a predetermined width, wraps around the abdomen, and is adjustable in length so as to be worn on the abdomen. For example, the stimulating portion (530) may be provided to be worn while wrapping around the abdomen, and may be formed to completely wrap around the abdomen, i.e., the abdominal muscles.

[0085] In another embodiment of the present invention, the stimulating part (530) may include an air tube that can be filled with air. Here, the air tube may be configured to allow for an adjustable internal air volume. For example, the air tube may be configured to inject a predetermined air pressure into it, thereby being formed to compress the entire abdominal muscle. That is, by compressing the entire abdominal muscle rather than stimulating only a portion of the abdominal muscle, it can be implemented to facilitate abdominal breathing more easily. Additionally, by adjusting the air pressure inside the air tube according to the user's condition, it can help the user train their abdominal breathing more easily.

[0086] In the present invention, the user terminal (200) receives a first detection signal and a second detection signal from the first sensor unit (510) and the second sensor unit (520) of the abdominal breathing wear (500), and can determine whether the user is in an abdominal breathing state or a non-abdominal breathing state using the received first detection signal and second detection signal. For example, when the first detection signal is received at the user terminal (200), it can be determined to be in an abdominal breathing state, and when the second detection signal is received, it can be determined to be in a non-abdominal breathing state.

[0088] FIG. 4 is a flowchart showing a method for determining an anxiety disorder in an anxiety disorder monitoring and biofeedback provision system according to one embodiment of the present invention.

[0089] Referring to FIG. 4, when the server (300) receives an electrocardiogram signal (S110), it analyzes the received electrocardiogram signal to extract a heart rate variability (HRV) signal (S120).

[0090] Then, the server (300) calculates the LF (Low Frequency) signal area and the HF (High Frequency) signal area in the power spectrum of the HRV signal (S130).

[0091] Then, the server (300) calculates the ratio of the LF area to the HF area (LF area / HF area) (S140).

[0092] And, the server (300) determines the anxiety disorder based on the ratio of the LF area and the HF area (S150).

[0093] FIG. 6 is a flowchart showing a method for providing biofeedback content in an anxiety disorder monitoring and biofeedback provision system according to one embodiment of the present invention.

[0094] Referring to FIG. 6, a screen is provided on the user terminal (200) that allows user age information to be entered through a dedicated application (S301).

[0095] When user age information is entered at the user terminal (200), the server (300) reads the LF curve and HR curve representing the average value of the signal according to the user age from the database and transmits the read LF curve and HR curve data to the user terminal (200) (S303). At this time, the database has LF curve and HR curve data representing the average value of the LF signal according to each age stored in advance through clinical trials.

[0096] Next, the server (300) measures an LF signal and a HR (Heart Rate) signal using the user's electrocardiogram signal measured through the electrocardiogram measuring device (100), and transmits the measured LF signal and HR signal to the user terminal (200) (S305, S309).

[0097] The user terminal (200) compares the LF signal with the LF curve (S307) and compares the HR signal with the HR curve (S311). If the LF signal is higher than the LF curve or the HR signal is higher than the HR curve, it displays a message instructing the user to wear abdominal breathing wear (500) (S313). Conversely, if the LF signal is lower than the LF curve and the HR signal is lower than the HR curve, it diagnoses the user as normal and terminates the process.

[0098] Then, the user terminal (200) receives a breathing signal detected from the first sensor unit (510) and the second sensor unit (520) of the abdominal breathing wear (500) (S315).

[0099] When the user terminal (200) detects that the user is breathing other than abdominal breathing through a breathing signal (S317), a stimulation signal to stimulate abdominal breathing is output from the stimulation part (530) of the abdominal breathing wear (500) (S319).

[0100] Then, the user terminal (200) periodically compares the LF signal and the LF curve (S321) and compares the HR signal and the HR curve (S323) while the user is wearing the abdominal breathing wear (500), and outputs muscle relaxation content if the LF signal is higher than the LF curve or if the HR signal is higher than the HR curve (S325). Conversely, if the LF signal is lower than the LF curve and the HR signal is lower than the HR curve, it is diagnosed as normal and the process is terminated.

[0101] After outputting muscle relaxation content, periodically compare the LF signal with the LF curve (S327) and compare the HR signal with the HR curve (S329). If the LF signal is higher than the LF curve or the HR signal is higher than the HR curve, output meditation content (S331). Conversely, if the LF signal is lower than the LF curve and the HR signal is lower than the HR curve, diagnose as normal and terminate the process.

[0102] Figure 7 is a graph showing an LF curve representing the average value of the LF signal according to age and the LF signal measured for the user.

[0103] In FIG. 7, an LF curve (710) (Hz) representing the average value of the LF signal according to age is shown as a solid line, and an LF signal (720) (Hz) measured for the user is shown as a dot.

[0104] Figure 8 is a graph showing an HR curve representing the average value of the HR signal according to age and the HR signal measured for the user.

[0105] In FIG. 8, the HR (Heart Rate) curve (810) (beat / min) representing the average value of the HR signal according to age is shown as a solid line, and the LF signal (820) (beat / min) measured for the user is shown as a dot.

[0106] As shown in FIGS. 7 and 8, the user terminal (200) of the present invention compares an LF curve (710) and an LF signal (720) according to age, and compares an HR curve (810) and an HR signal (820) according to age. If the LF signal (720) is higher than the LF curve (710) or the HR signal (820) is higher than the HR curve (810), it can be diagnosed as an anxiety disorder and provide biofeedback-related content.

[0107] FIGS. 9 to 19 are examples of screens of a user terminal in an anxiety disorder monitoring and biofeedback provisioning system according to an embodiment of the present invention, and are examples of screens in which a dedicated application is executed on the user terminal.

[0108] Figure 9 illustrates a home screen in a dedicated application, showing an electrocardiogram measurement menu, a biofeedback menu, and a screen for viewing recent electrocardiogram records.

[0109] FIGS. 10 and FIGS. 11 are examples of push / alarm setting screens, FIG. 10 is an example of a multi-alarm setting screen where alarms can be set for various times, and FIG. 11 is an example of a detailed alarm setting screen where alarm time, whether to repeat by day of the week, and the push / alarm display format can be set.

[0110] FIGS. 12 to 14 are examples of screens related to biofeedback content, FIG. 12 is an example of a screen for muscle relaxation content among biofeedback, FIG. 13 is an example of a screen for ending content, and FIG. 14 is an example of a screen that allows selection of abdominal breathing, muscle relaxation, and meditation content.

[0111] Figure 15 is an example of a screen showing animation effects in content. In a format where the content transitions to the next image after a specified time has passed through a delay, GIFs can be added to express movements such as arrows so that the user can follow along.

[0112] Fig. 16 is an example of an electrocardiogram measurement screen, and Fig. 17 is an example of a screen where an electrocardiogram measurement record can be viewed.

[0113] FIGS. 18 and 19 are examples of screens showing results when measuring an electrocardiogram using biofeedback. When the 'Measure' button provided at the bottom of FIG. 18 is selected, the electrocardiogram is measured by measuring HR and LF, and FIG. 19 shows an example of the electrocardiogram measurement result screen using biofeedback.

[0115] FIG. 5 is a flowchart showing a specific example of a method for determining anxiety disorder in an anxiety disorder monitoring and biofeedback providing system according to an embodiment of the present invention, FIG. 20 is a graph illustrating the process of extracting an HRV signal from an electrocardiogram signal, FIG. 21 is a graph illustrating an HRV signal, FIG. 22 is a graph illustrating the power spectrum of an HRV signal, and FIG. 23 is a graph indicating NN or RR intervals in an electrocardiogram signal.

[0116] Referring to FIGS. 5, 20 to 23, when the electrocardiogram (ECG) signal of a user is measured by the electrocardiogram measuring device (100), the measured electrocardiogram signal is transmitted to the user terminal (200) (S201). Then, the user terminal (200) transmits the electrocardiogram signal to the server (300) (S203).

[0117] The server (300) detects the R peak and RR interval of the received electrocardiogram signal (S205). The RR interval in the electrocardiogram signal is shown in FIG. 23. The RR interval refers to the interval between the R peak of the electrocardiogram signal and the next R that follows.

[0118] Next, the server (300) converts the RR interval into a time series signal and arranges it on the time axis. At this time, the interval start time t1 and interval end time t2 can be input to set the time interval of interest.

[0119] Figure 20 illustrates the process of extracting an HRV signal from an electrocardiogram signal, wherein an R peak is detected from the electrocardiogram signal. Then, by converting the detected RR intervals into a time series signal and rearranging them on the time axis, changes in heart rate over time can be identified, and this is the HRV signal.

[0120] In the example of FIG. 20, the process of detecting the R peak in an electrocardiogram signal is illustrated, and a graph is shown in which the RR intervals in the time interval of interest t1 to t2 are converted into a time series signal and arranged on the time axis.

[0121] The server (300) converts the RR interval, which is converted into a time series signal, into a heart rate (beat / sec) to generate an HRV signal (S209). The graph in FIG. 21 is a graph where the x-axis is time (time(sec)) and the y-axis is heart rate (Heart rate(beat / sec)), and the HRV signal is shown. FIG. 21 shows the HRV signal over 400 seconds, and it can be seen that the heart rate changes every moment even in a stable state.

[0122] Then, the server (300) converts the HRV signal into a frequency analysis signal of frequency and power spectrum density (PSD) (S211).

[0123] Frequency analysis of the HRV signal yields results as shown in Fig. 22. As seen in Fig. 22, blood pressure and heart rate can be defined by two periodic components. The first is the HF component, which is associated with respiratory activity between 0.15 Hz and 0.4 Hz, and the second is the LF component, which is associated with the blood pressure regulation mechanism and is known as the 'Mayer wave', between 0.05 Hz and 0.15 Hz.

[0124] FIG. 22 is an example graphing the power spectrum of an HRV signal, where the x-axis represents frequency (Hz) and the y-axis represents PSD (ms 2 It is (Hz). In Fig. 22, the area between the power spectrum graph of the HRV signal and the x-axis represents the power carried by the HRV signal.

[0125] Frequency domain analysis is a method of analyzing HRV waveforms to see the relative intensity of each frequency component signal, and the intensity of the LF band relative to the HF band can be interpreted as the balance between the sympathetic and parasympathetic nervous systems.

[0126] In frequency domain analysis methods, HF is relatively well-established in relation to physiological significance, and since HF mainly reflects the activity of the vagus nerve branching to the heart, it can be interpreted as a representative measurement of the activity of the parasympathetic nervous system.

[0127] It is known that mental stress increases LF activity and decreases HF activity in heart rate variability under stressful situations. Additionally, it is known that there is an association between symptoms and autonomic nervous system activity, as the more severe the phobia symptoms, the lower the heart rate variability.

[0128] In people who complained of two or more anxiety symptoms, all heart rate variability indices were evaluated as lower compared to the control group, and in people who were evaluated as having the highest score on the anxiety scale, a significantly higher reference LF / HF ratio was observed, and there was also a correlation between the LF / HF ratio and the anxiety scale score.

[0129] In the present invention, the LF value was significantly higher in the group of patients with generalized anxiety disorder than in the group of patients with major depressive disorder. This result implies that patients with depression feel more loss of energy, fatigue, sleep deprivation, lethargy, and drowsiness than patients with generalized anxiety disorder. Additionally, the LF / HF ratio, which indicates the balance of the autonomic nervous system, shows that autonomic imbalance is more severely exacerbated in patients with generalized anxiety disorder than in patients with major depressive disorder.

[0130] The server (300) defines the frequency as LF region if it is 0.05~0.15 Hz and calculates the LF area of ​​the HRV signal (S213, S215, S217). Then, if the frequency is 0.15~0.4 Hz, it defines the frequency as HF region and calculates the HF area of ​​the HRV signal (S213, S215, S219).

[0131] Referring to Fig. 22, f represents the HRV signal in the LF region as a function. LF Let be denoted as , and f is the HRV signal in the HF region represented as a function. HF It is called [this].

[0132] Then, the LF area is ∫f LF It can be expressed as df, and the HF area is ∫f HF It can be represented as df.

[0133] More specifically,

[0134]

[0135]

[0136] It can be represented as.

[0137] The server (300) calculates the LF area / HF area ratio, which is the ratio of the LF area to the HF area (S221).

[0138] As a result of the calculation, if the area ratio is 3.3 or higher, it is determined to be severe anxiety disorder, if it is less than 2.3, it is determined to be normal, and if it is 2.3 or higher and less than 3.3 (2.3~3.3), it is determined to be anxiety disorder caution (S223~S231).

[0139] When an area ratio of 2.3 or higher and less than 3.3 is determined as an anxiety disorder, the level of attention can be determined by specifying the level of attention according to a specific numerical range. For example, if it is 2.3 or higher and less than 2.5, it can be determined as level 1 of attention; if it is 2.5 or higher and less than 2.7, it can be determined as level 2 of attention; if it is 2.7 or higher and less than 2.9, it can be determined as level 3 of attention; if it is 2.9 or higher and less than 3.1, it can be determined as level 4 of attention; and if it is 3.1 or higher and less than 3.3, it can be determined as level 5 of attention (S231). In the present invention, when determining an anxiety disorder in this way, the level of attention can be specifically classified into 5 levels, and a higher level number indicates a higher level of anxiety disorder.

[0140] As such, in the frequency domain analysis of the HRV signal in the present invention, the heart rate can be described as a rhythm that changes at various frequencies without interruption for 24 hours, and the characteristics of the rhythm can be determined by calculating what frequency waveforms this rhythm (tachogram) is composed of and what the contribution of each of those frequencies is, which is called spectral analysis.

[0141] Recently, as the correlation between specific periodic components and HRV and autonomic nervous activity has become known, active attempts are being made to interpret HRV signals in the frequency domain.

[0142] FIG. 24 is a graph illustrating the power spectrum of an HRV signal, FIG. 25 is a graph illustrating power spectral density estimates obtained over a full 24-hour interval of a long-term Holter recording, and FIG. 26 is an interval tachogram of 256 consecutive RR values ​​of a normal subject at supine rest (A, C, E) and head-up tilt (B, D, F).

[0143] As shown in Figures 24 to 26, the high frequency component (HF component) is between 0.15 and 0.4 Hz and is associated with respiration and is widely used as an indicator of the activity of the parasympathetic nervous system. In particular, the HF component is known to be closely related to the electrical stability of the heart. In patients with aged cardiopulmonary function or those who died of sudden cardiac death, the HF component before death is significantly reduced, and factors such as chronic stress, fear, and anxiety were also found to reduce HF.

[0144] The low-frequency component (LF component) reflects changes in heart rate caused by baroreceptor reflexes or blood pressure regulation, and it can be seen that patients exhibiting excessive sympathetic nervous activity have increased LF variations compared to healthy individuals.

[0145] In addition, a decrease in LF and HF has been frequently reported in most patients complaining of significant stress and fatigue, and it has been shown that the persistent maintenance of anxiety and depressive states leads to a decrease in parasympathetic nervous system activity. While increased parasympathetic nervous system activity plays a role in stabilizing and protecting the heart, increased sympathetic nervous system activity lowers the ventricular fibrillation induction threshold and increases the risk of ventricular fibrillation.

[0146] In patients with chronic fatigue syndrome, an excessive increase in LF and an excessive increase in the LF / HF ratio are observed upon standing, indicating that the sympathetic nervous system is overactive under stressful situations. In normal individuals, the LF / HF ratio is approximately 2.3:1, and if this ratio increases or decreases excessively, it can be said that the autonomic nervous system has lost its balance.

[0147] The heart rate cycle changes with respiration, generally increasing during inspiration and decreasing during expiration; this phenomenon is called RSA (Respiratory Sinus Arrhythmia). RSA is a natural arrhythmia cycle that occurs when respiration affects sympathetic and vagal nerve impulses leading to the sinoatrial node.

[0148] HRV biofeedback, also known as RSA biofeedback, involves training individuals to increase the amplitude of RSA by autonomously regulating their respiratory rate through visual feedback of heart rate oscillations that change with respiration. In the case of anxiety disorders, anxiety responses decreased following repeated HRV biofeedback training, and there were significant differences in changes in mood subjectively reported by the patients.

[0150] Although the present invention has been described above using several preferred embodiments, these embodiments are illustrative and not limiting. Those skilled in the art will understand that various changes and modifications can be made without departing from the spirit of the invention and the scope of rights set forth in the appended claims. Explanation of the symbols

[0152] 100 ECG measuring devices 200 user terminals 300 Server 500 Abdominal Breathing Wear 510 1st sensor unit 520 2nd sensor unit 530 Stimulation unit 540 Communication unit

Claims

Claim 1 An electrocardiogram (ECG) measuring device for measuring a user's electrocardiogram; a user terminal used by a user, capable of downloading and installing a dedicated application from a server that includes functions for monitoring anxiety disorders and receiving biofeedback, and transmitting the received electrocardiogram signal to the server when the dedicated application is running and the electrocardiogram signal is received from the ECG measuring device; The system includes a server that provides the dedicated application to the user terminal, analyzes an electrocardiogram signal received from the user terminal, and transmits the analyzed information to the user terminal; the user terminal diagnoses an anxiety disorder using the information analyzed by the server through the dedicated application, and if diagnosed with an anxiety disorder, provides biofeedback-related content for treating the anxiety disorder; the server extracts a heart rate variability (HRV) signal using the electrocardiogram signal received from the user terminal, provides information analyzing the extracted HRV signal to the user terminal, and LF curve data representing the average value of the LF (Low Frequency) signal according to each age and HR curve data representing the average value of the HR (Heart Rate) signal according to each age are pre-stored in a database; and when the server receives user age information from the dedicated application of the user terminal, it reads the LF curve data and HR curve data from the database and transmits them to the user terminal, measures the LF signal in the power spectrum of the extracted HRV signal, measures the HR signal using the electrocardiogram signal, and the measured LF signal and The HR signal is transmitted to the user terminal, and the user terminal compares the LF signal with the LF curve and diagnoses an anxiety disorder if the LF signal is higher than the LF curve, or if the HR signal is higher than the HR curve and the HR signal is higher than the HR curve.The above-mentioned biofeedback-related content is provided, and the user terminal provides biofeedback-related content including abdominal breathing content for inducing abdominal breathing, muscle relaxation content for muscle relaxation, and meditation content; is implemented in a form that can be worn on the upper body of a user, and further includes abdominal breathing wear for detecting abdominal breathing and applying stimulation to cause abdominal breathing to occur, wherein the abdominal breathing wear comprises a first sensor unit for generating a first detection signal by measuring the degree of contraction of respiratory muscles to detect whether abdominal breathing occurs in response to the user's breathing cycle, a second sensor unit for generating a second detection signal by measuring the degree of contraction of respiratory muscles to detect whether abdominal breathing occurs in response to the user's breathing cycle, a stimulation unit for outputting a stimulation signal to stimulate respiratory muscles to cause the user to perform abdominal breathing, and a communication unit for communicating with the user terminal, wherein the communication unit transmits the detection signals detected by the first sensor unit and the second sensor unit to the user terminal, receives a control signal from the user terminal, and [transmits] to the stimulation unit The first sensor unit is configured to generate a first detection signal by measuring the degree of contraction of respiratory muscles to detect whether abdominal breathing is occurring in response to the user's breathing cycle, and to generate an inhalation start signal and an exhalation start signal, respectively, by measuring the degree of contraction of the user's respiratory muscles, and comprises an inhalation sensing pad attached to the user's inhalation muscles to measure the degree of contraction of the inhalation muscles and an exhalation sensing pad attached to the user's exhalation muscles to measure the degree of contraction of the exhalation muscles, and the first sensor unit is attached to the abdominal muscles where the user's abdominal breathing occurs to generate a first detection signal by measuring the degree of contraction of the respiratory muscles during inhalation and exhalation, and the second sensor unit is configured to detect whether breathing other than abdominal breathing is occurring in response to the user's breathing cycle,It is configured to generate a second detection signal by measuring the degree of contraction of the respiratory muscles, and the second sensor unit is attached to the muscles used for thoracic breathing or the accessory respiratory muscles to detect whether the user is performing thoracic breathing or breathing using accessory respiratory muscles, and measures the degree of contraction of the respiratory muscles during inhalation and exhalation to generate a second detection signal, and the stimulation unit outputs a stimulation signal to stimulate the user's respiratory muscles according to a control signal transmitted from the user terminal, and is implemented as a Neuromuscular Electrical Stimulation (NMES), and the stimulation unit is installed attached to the respiratory muscles and applies electrical stimulation to the respiratory muscles corresponding to the breathing signal generated according to the user's breathing cycle, and the user terminal receives a first detection signal and a second detection signal from the first sensor unit and the second sensor unit of the abdominal breathing wear, and uses the received first detection signal and second detection signal to determine whether it is an abdominal breathing state or a breathing state other than abdominal breathing, wherein if the first detection signal is received, it is determined to be an abdominal breathing state, and if the second detection signal is received, it is determined to be a breathing state other than abdominal breathing. In determining and providing biofeedback-related content for treating anxiety disorders on the user terminal, the user terminal provides a screen for inputting user age information through a dedicated application; when user age information is input, the server reads an LF curve and an HR curve representing the average value of the signal according to the user age from a database, transmits the read LF curve and HR curve data to the user terminal, the server measures the LF signal and HR (Heart Rate) signal using the user's electrocardiogram signal measured through the electrocardiogram measuring device, transmits the measured LF signal and HR signal to the user terminal, and the user terminal compares the LF signal with the LF curve and compares the HR signal with the HR curve,Anxiety disorder monitoring and biofeedback provision system characterized by: displaying a message guiding the wearing of the abdominal breathing wear when the LF signal is higher than the LF curve or the HR signal is higher than the HR curve; the user terminal receives a breathing signal detected from the first sensor unit and the second sensor unit of the abdominal breathing wear; if the user terminal detects that breathing other than abdominal breathing is occurring through the breathing signal, a stimulation signal to stimulate abdominal breathing is output from the stimulation unit of the abdominal breathing wear; the user terminal periodically compares the LF signal and the LF curve and the HR signal and the HR curve while the user is wearing the abdominal breathing wear, and outputs muscle relaxation content if the LF signal is higher than the LF curve or the HR signal is higher than the HR curve; and after outputting the muscle relaxation content, the user terminal periodically compares the LF signal and the LF curve and the HR signal and the HR curve, and outputs meditation content if the LF signal is higher than the LF curve or the HR signal is higher than the HR curve. Claim 2 delete Claim 3 delete Claim 4 delete

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