System for monitoring anxiety disorder and providing biofeedback

Abstract

The present invention relates to a system for monitoring an anxiety disorder and providing biofeedback, comprising: an electrocardiogram (ECG) measurement device for measuring the ECG of a user; a user terminal, which is used by the user, can download, from a server, and install a dedicated application including a function for monitoring anxiety disorder and receiving biofeedback, and, if an ECG signal is received from the ECG measurement device while the dedicated application is executed, transmits the received ECG signal to the server; and the server for providing the dedicated application to the user terminal, analyzing the ECG signal received from the user terminal, and transmitting the analyzed information to the user terminal. The user terminal diagnoses, through the dedicated application, anxiety disorder by using the information analyzed by the server, and provides biofeedback-related content for treating anxiety disorder when anxiety disorder is diagnosed. According to the present invention, anxiety disorder can be more easily and accurately determined using heart rate variability.

Description

Anxiety Disorder Monitoring and Biofeedback System

[0001] The present invention relates to a technology for monitoring anxiety disorders in real time and providing biofeedback to reduce anxiety disorders.

[0002] The number of patients treated for anxiety disorders has reached 3.54 million over the past five years. Patients in their 20s saw the largest increase, at 80%, followed by those in their teens (47%) and those in their 30s (40%). Those aged 60 and older accounted for 1.43 million. Per 100,000 people, those in their 60s and older represented the largest group at 15%, with a total of 6,963 patients, indicating that 7% of the population suffers from anxiety disorders. However, only a small percentage of patients with anxiety disorders actually receive treatment, necessitating urgent measures to increase the rate of psychiatric treatment.

[0003] Traditionally, diagnosing anxiety disorders involves basic physical examinations (e.g., electrocardiogram, blood and urine tests, thyroid tests), psychological testing, and counseling. Anxiety disorders are chronic conditions requiring ongoing management and are often associated with complications such as depression.

[0004] Treatment methods for anxiety disorders include medication, psychotherapy, cognitive behavioral therapy (CBT), relaxation techniques, and biofeedback therapy. Psychotherapy involves psychiatric counseling, while relaxation techniques include breathing and muscle relaxation, meditation, imagery, hypnosis, biofeedback, music, yoga, massage, and humor. Biofeedback therapy involves regulating one's physiological state by providing feedback on one's own bio-information, allowing one to directly check physiological states such as breathing, muscle relaxation, and brain waves in real time through numbers and pictures. Cognitive behavioral therapy is rational emotive behavioral therapy, and relaxation techniques include breathing, muscle relaxation, meditation, etc. Biofeedback involves regulating one's physiological state and achieving a relaxation effect by providing feedback on one's own bio-information.

[0005] Although medication is effective in treating panic and anxiety disorders, it does not respond well to all patients, and some patients do not want to take medication due to side effects, and there are reports that the relapse rate after discontinuing medication is as high as 25-80%.

[0006] A post hoc analysis of the effects of medication, cognitive behavioral therapy, and a combination of the two treatments for panic disorder found that cognitive behavioral therapy was the most effective.

[0007] An electrocardiogram (ECG) is an interpretation of electrical activity that occurs during the depolarization and repolarization processes of the heart muscle. It is a biosignal that is widely used in clinical settings because it contains biological information such as heart rate, size and location of the heart, and presence or absence of heart damage.

[0008] In clinical settings, electrocardiograms, which are easier to measure than echocardiography or other invasive tests, are widely used to predict or diagnose heart disease in patients with mental illness.

[0009] Heart rate variability (HRV) is a type of biometric data obtained by analyzing electrocardiograms. It refers to the periodic changes in the intervals between heartbeats. In other words, the interval between heartbeats (RR interval) constantly changes even when the heart is at rest, and this is called heart rate variability.

[0010] In general, these changes between heartbeats are larger and more complex when the heart is at rest, and become more regular and consistent when exercising or under stress.

[0011] Abnormalities in heart rate variability (HRV) in patients with mental illness have been found in a variety of psychiatric conditions, including schizophrenia, bipolar disorder, anxiety disorders, and addiction. In particular, abnormalities are observed in mood disorders such as depression and bipolar disorder, as well as anxiety disorders, through HRV testing. These disorders are closely related to the involvement of the sympathetic and parasympathetic nervous systems in HRV. Some studies have shown decreased HRV in patients with schizophrenia, and research on the relationship between HRV and depression is ongoing. Recently, there has been a growing number of reports linking HRV to major depression and its symptoms and treatment outcomes.

[0012] 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 changes in the autonomic nervous system can be influenced by stress and psychological factors. Therefore, more accurately measuring autonomic nervous system biosignals in daily life in patients with anxiety disorders is crucial for diagnosis and treatment, allowing for predicting the severity of the condition.

[0013] Anxiety disorders share many similarities with panic disorder, with many of their characteristic physical symptoms often stemming from physiological changes caused by excessive sympathetic nervous system stimulation. Numerous studies have examined heart rate variability in panic disorder, revealing decreased cholinergic activity and relatively increased adrenergic activity. Consequently, compared to healthy individuals, patients with panic disorder exhibit lower heart rate variability, decreased high-frequency (HF) power, and increased low-frequency (LF) power.

[0014] Many mental disorders and psychological stress are accompanied by cardiovascular symptoms. Specifically, symptoms of excitement, aggression, anxiety, and nervousness are closely related to heart rate and blood pressure. This psychological stress increases sympathetic nervous system activity and decreases parasympathetic nervous system activity.

[0015] When the sympathetic nervous system is activated in stressful situations, catecholamines are released, accelerating the heart rate. Acute stress has been shown to cause a decrease in heart rate variability and an increase in the LH / HF ratio. Therefore, heart rate variability serves as a link between stress and mental disorders, as well as between mental and physical symptoms.

[0016] Traditionally, hospital-based treatment for anxiety disorders typically involves initial biopsy and psychological testing, with relatively short consultations and medication prescriptions. Furthermore, assessing the condition of anxiety patients in their daily lives remains challenging. Furthermore, immediate response to anxiety disorders is impossible, biometric data collection and analysis is impractical, and the provision of readily usable content based on biometric data is challenging. Furthermore, uninterrupted mental and physical care remains a challenge. Furthermore, ongoing diagnosis and treatment, with a chronic disease management perspective, are essential for anxiety disorders.

[0017] The present invention has been devised to solve the above problems, and the purpose of the present invention is to provide an anxiety disorder monitoring and biofeedback providing system that can extract a heart rate variability signal from an electrocardiogram signal, diagnose an anxiety disorder using the extracted heart rate variability, and provide non-face-to-face content for cognitive behavioral therapy through biofeedback for anxiety disorder patients.

[0018] The purpose of the present invention is not limited to the purposes mentioned above, and other purposes not mentioned will be clearly understood by those skilled in the art from the description below.

[0019] To achieve the above object, the present invention relates to a system for monitoring and providing biofeedback for anxiety disorders, comprising: an electrocardiogram (ECG) measuring device for measuring a user's electrocardiogram; a terminal used by a user, wherein a dedicated application including a function for monitoring anxiety disorders and providing biofeedback can be downloaded and installed from a server; and when an ECG signal is received from the ECG measuring device while the dedicated application is running, the user terminal transmits the received ECG signal to the server; and a server that provides the dedicated application to the user terminal, analyzes the ECG signal received from the user terminal, and transmits analyzed information to the user terminal.

[0020] The above user terminal diagnoses anxiety disorder using information analyzed by the server through the above dedicated application, and if diagnosed with anxiety disorder, provides biofeedback-related content for treating anxiety disorder.

[0021] The server may extract a heart rate variability (HRV) signal using an electrocardiogram signal received from the user terminal, and provide information obtained by analyzing the extracted heart rate variability signal to the user terminal.

[0022] LF curve data representing the average value of LF (Low Frequency) signals according to each age, and HR curve data representing the average value of HR (Heart Rate) signals 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 the 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, and transmits the measured LF signal and the HR signal to the user terminal, and the user terminal compares the LF signal and the LF curve and if the LF signal is higher than the LF curve, or compares the HR signal and the HR curve and if the HR signal is lower than the HR curve, it diagnoses an anxiety disorder, and provides the biofeedback-related content.

[0023] 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.

[0024] According to the present invention, there is an effect that can more easily and accurately diagnose anxiety disorder using heart rate variability.

[0025] In addition, the present invention has the effect of enabling immediate diagnosis of anxiety disorder, more accurate assessment of anxiety disorder, provision of appropriate content according to the degree of anxiety disorder, and provision of continuous mental and physical monitoring of anxiety disorder and biofeedback.

[0026] 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.

[0027] Figure 2 illustrates the shape of a double breathing wear according to one embodiment of the present invention.

[0028] Figure 3 is a block diagram showing the internal configuration of a double breathing wear according to one embodiment of the present invention.

[0029] FIG. 4 is a flowchart showing a method for determining anxiety disorder in an anxiety disorder monitoring and biofeedback provision system according to one embodiment of the present invention.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] Figure 20 is a graph illustrating the process of extracting an HRV signal from an electrocardiogram signal.

[0036] Figure 21 is a graph illustrating an HRV signal.

[0037] Figure 22 is a graph showing the power spectrum of an HRV signal.

[0038] Figure 23 is a graph showing the NN or RR interval in an electrocardiogram signal.

[0039] Figure 24 is a graph illustrating the power spectrum of an HRV signal.

[0040] Figure 25 is a graph illustrating power spectral density estimates obtained from a full 24-hour interval of a long-term Holter recording.

[0041] Figure 26 shows interval tachograms of 256 consecutive RR values ​​of a normal subject in supine rest (A, C, E) and head-up tilt (B, D, F).

[0042] Figures 27 to 29 are examples of screens displaying heart rate variability analysis results.

[0043] 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.

[0044] Referring to FIG. 1, an anxiety disorder monitoring and biofeedback provision system according to one embodiment of the present invention includes an electrocardiogram measuring device (100), a user terminal (200), and a server (300).

[0045] An electrocardiogram measuring device (100) is a device that measures a user's electrocardiogram (ECG).

[0046] In one embodiment of the present invention, the electrocardiogram measuring device (100) may be implemented in various forms, each having the function of measuring the user's electrocardiogram. For example, it may be implemented in a watch-type form that is worn by the user as a wearable device, a patch-type form that is attached to the user as a medical device, and a portable form that is easy to carry.

[0047] The user terminal (200) is a terminal used by a user and is a terminal capable of communicating with an electrocardiogram measuring device (100) and a 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.

[0048] The user terminal (200) receives an electrocardiogram signal measured by an electrocardiogram measuring device (100) and transmits the received electrocardiogram signal to the server (300).

[0049] The server (300) analyzes the electrocardiogram signal received from the user terminal (200) to extract a heart rate variability (HRV) signal, and determines anxiety disorder using the extracted heart rate variability signal.

[0050] And, the server (300) provides information on the anxiety disorder diagnosis result to the user terminal (200).

[0051] In the present invention, the user terminal (200) is a terminal used by the user, and can download and install a dedicated application including a function for monitoring anxiety disorders and receiving biofeedback from the server (300), and when the dedicated application receives an electrocardiogram signal from the electrocardiogram measuring device (100) while running, the received electrocardiogram signal is transmitted to the server (300).

[0052] 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).

[0053] The user terminal (200) diagnoses anxiety disorder using information analyzed by the server (300) through a dedicated application, and if diagnosed with anxiety disorder, provides biofeedback-related content for treating anxiety disorder.

[0054] The server (300) can extract a heart rate variability (HRV) signal using an electrocardiogram signal received from a user terminal (200) and provide information obtained by analyzing the extracted heart rate variability signal to the user terminal (200).

[0055] In one embodiment of the present invention, LF curve data indicating an average value of an LF (Low Frequency) signal according to each age and HR curve data indicating an average value of an HR (Heart Rate) signal according to each age may be stored in advance in a database.

[0056] 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 an LF signal from the power spectrum of the extracted heart rate variability signal, measures an HR signal using an electrocardiogram signal, and transmits the measured LF signal and HR signal to the user terminal (200). In addition, the user terminal (200) can compare the LF signal and the LF curve and diagnose an anxiety disorder if the LF signal is higher than the LF curve, or compare the HR signal and the HR curve and diagnose an anxiety disorder if the HR signal is lower than the HR curve, and provide biofeedback-related content.

[0057] The user terminal (200) can provide biofeedback-related content including abdominal breathing content for inducing abdominal breathing, muscle relaxation content for muscle relaxation, and meditation content.

[0058] 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 anxiety disorder based on the ratio of the calculated LF area and HF area.

[0059] In the present invention, the server (300) can provide information on the results of anxiety disorder diagnosis to the user terminal (200) in a web viewer format. Figures 27 to 29 are examples of screens displaying the results of heart rate variability analysis.

[0060] The user terminal (200) displays anxiety disorder diagnosis results information in a web viewer format provided by the server (300). Furthermore, the user terminal (200) may provide content related to cognitive behavioral therapy (CBT) for treating anxiety disorders. For example, the user terminal (200) may provide content related to cognitive behavioral therapy (CBT) including imagery relaxation training, anxiety coping training, biofeedback, breathing techniques, muscle relaxation techniques, etc.

[0061] In the anxiety disorder determination method in the anxiety disorder monitoring and biofeedback provision system of the present invention, the server (300) includes a step of receiving an electrocardiogram (ECG) signal from a user terminal (200), a step of analyzing the ECG signal and extracting a heart rate variability (HRV) signal, and a step of determining an anxiety disorder using the extracted HRV signal.

[0062] 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 can determine anxiety disorder based on the ratio of the calculated LF area and HF area.

[0063] The present invention is susceptible to various modifications and embodiments. Specific embodiments are illustrated and described in detail in the drawings. However, this is not intended to limit the present invention to specific embodiments, but rather to encompass all modifications, equivalents, and alternatives falling within the spirit and technical scope of the present invention.

[0064] The terminology used in this application is only used to describe specific embodiments and is not intended to limit the present invention. The singular expression includes the plural expression unless the context clearly indicates otherwise. In this application, it should be understood that the terms "comprise" or "have" indicate the presence of a feature, number, step, operation, component, part, or combination thereof described in the specification, but do not preclude the possibility of the presence or addition of one or more other features, numbers, steps, operations, components, parts, or combinations thereof.

[0065] Unless otherwise defined, all terms used herein, including technical or scientific terms, have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. Terms defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the relevant technology, and will not be interpreted in an idealized or overly formal sense unless explicitly defined herein.

[0066] In addition, when describing with reference to the attached drawings, identical components will be assigned the same reference numerals regardless of the drawing numbers, and redundant descriptions thereof will be omitted. When describing the present invention, if a detailed description of a related known technology is judged to unnecessarily obscure the gist of the present invention, the detailed description will be omitted.

[0067] The present invention proposes a method for assessing anxiety disorders using heart rate variability. Analysis of heart rate variability can quantify the degree of autonomic nervous system activity, and this quantified heart rate variability analysis can be very useful in assessing cardiovascular disease. Heart rate variability research has been used to quantify various autonomic dysfunction findings in conditions such as dementia, syncope, Parkinson's disease, motor neuron disease, stroke, and epilepsy. It has also been reported to be closely associated with cardiovascular abnormalities or clinical prognosis. Therefore, assessment using heart rate variability analysis can be a useful assessment tool for all diseases accompanied by autonomic nervous system dysfunction.

[0068] 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.

[0069] Referring to FIG. 1, an anxiety disorder monitoring and biofeedback provision system according to one embodiment of the present invention includes an electrocardiogram measuring device (100), a user terminal (200), and a server (300).

[0070] An electrocardiogram measuring device (100) is a device that measures a user's electrocardiogram (ECG).

[0071] In one embodiment of the present invention, the electrocardiogram measuring device (100) may be implemented in various forms, each having the function of measuring the user's electrocardiogram. For example, it may be implemented in a watch-type form that is worn by the user as a wearable device, a patch-type form that is attached to the user as a medical device, and a portable form that is easy to carry.

[0072] The user terminal (200) is a terminal used by a user and is a terminal capable of communicating with an electrocardiogram measuring device (100) and a 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.

[0073] The user terminal (200) receives an electrocardiogram signal measured by an electrocardiogram measuring device (100) and transmits the received electrocardiogram signal to the server (300).

[0074] The server (300) analyzes the electrocardiogram signal received from the user terminal (200) to extract a heart rate variability (HRV) signal, and determines anxiety disorder using the extracted heart rate variability signal.

[0075] And, the server (300) provides information on the anxiety disorder diagnosis result to the user terminal (200).

[0076] In the present invention, the user terminal (200) is a terminal used by the user, and can download and install a dedicated application including a function for monitoring anxiety disorders and receiving biofeedback from the server (300), and when the dedicated application receives an electrocardiogram signal from the electrocardiogram measuring device (100) while running, the received electrocardiogram signal is transmitted to the server (300).

[0077] 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).

[0078] The user terminal (200) diagnoses anxiety disorder using information analyzed by the server (300) through a dedicated application, and if diagnosed with anxiety disorder, provides biofeedback-related content for treating anxiety disorder.

[0079] The server (300) can extract a heart rate variability (HRV) signal using an electrocardiogram signal received from a user terminal (200) and provide information obtained by analyzing the extracted heart rate variability signal to the user terminal (200).

[0080] In one embodiment of the present invention, LF curve data indicating an average value of an LF (Low Frequency) signal according to each age and HR curve data indicating an average value of an HR (Heart Rate) signal according to each age may be stored in advance in a database.

[0081] 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 an LF signal from the power spectrum of the extracted heart rate variability signal, measures an HR signal using an electrocardiogram signal, and transmits the measured LF signal and HR signal to the user terminal (200). In addition, the user terminal (200) can compare the LF signal and the LF curve and diagnose an anxiety disorder if the LF signal is higher than the LF curve, or compare the HR signal and the HR curve and diagnose an anxiety disorder if the HR signal is lower than the HR curve, and provide biofeedback-related content.

[0082] The user terminal (200) can provide biofeedback-related content including abdominal breathing content for inducing abdominal breathing, muscle relaxation content for muscle relaxation, and meditation content.

[0083] 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 anxiety disorder based on the ratio of the calculated LF area and HF area.

[0084] In the present invention, the server (300) can provide information on the results of anxiety disorder diagnosis to the user terminal (200) in a web viewer format. Figures 27 to 29 are examples of screens displaying the results of heart rate variability analysis.

[0085] The user terminal (200) displays anxiety disorder diagnosis results information in a web viewer format provided by the server (300). Furthermore, the user terminal (200) may provide content related to cognitive behavioral therapy (CBT) for treating anxiety disorders. For example, the user terminal (200) may provide content related to cognitive behavioral therapy (CBT) including imagery relaxation training, anxiety coping training, biofeedback, breathing techniques, muscle relaxation techniques, etc.

[0086] In the anxiety disorder determination method in the anxiety disorder monitoring and biofeedback provision system of the present invention, the server (300) includes a step of receiving an electrocardiogram (ECG) signal from a user terminal (200), a step of analyzing the ECG signal and extracting a heart rate variability (HRV) signal, and a step of determining an anxiety disorder using the extracted HRV signal.

[0087] 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 can determine anxiety disorder based on the ratio of the calculated LF area and HF area.

[0088] FIG. 2 is an example of a 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.

[0089] Referring to FIGS. 2 and 3, the abdominal breathing wear (500) is implemented in a form that can be worn on the user's upper body, and is worn on the user's upper body to detect abdominal breathing and provide stimulation to cause abdominal breathing.

[0090] The double breathing wear (500) comprises a first sensor unit (510), a second sensor unit (520), a stimulation unit (530), and a communication unit (540).

[0091] The first sensor unit (510) detects whether abdominal breathing is taking place in response to the user's breathing cycle by measuring the degree of contraction of the respiratory muscles and generating a first detection signal.

[0092] The second sensor unit (520) measures the degree of contraction of respiratory muscles to detect whether abdominal breathing is taking place in response to the user's breathing cycle, thereby generating a second detection signal.

[0093] The stimulation unit (530) outputs a stimulation signal to stimulate the respiratory muscles so that the user can perform abdominal breathing.

[0094] 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.

[0095] 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).

[0096] As shown in Fig. 2, the abdominal breathing wear (500) is designed to be worn on the user's upper body, including the abdomen, and has an upper portion that is open to accommodate the head, and a lower portion that is also open. In one embodiment of the present invention, the abdominal breathing wear (500) may be manufactured from a synthetic resin material. The abdominal breathing wear (500) may be implemented in the form of innerwear.

[0097] The first sensor unit (510) may be configured to generate a first detection signal by measuring the degree of contraction of respiratory muscles in response to the user's breathing cycle to detect whether diaphragmatic breathing is taking place.

[0098] The first sensor unit (510) can measure the degree of contraction of the user's respiratory muscles and generate an inhalation start signal and an exhalation start signal, respectively.

[0099] The first sensor unit (510) may include an inhalation sensing pad that is attached to the user's inhalation muscle to measure the degree of contraction of the inhalation muscle, and an expiratory sensing pad that is attached to the user's expiration muscle to measure the degree of contraction of the expiration muscle. Here, the inhalation start signal and the expiry start signal may be user breathing signals generated by the inhalation sensing pad and the expiry sensing pad, respectively, at the time of the user's inhalation start and the time of the exhalation start in response to the user's breathing cycle.

[0100] In one embodiment of the present invention, the first detection signal may include a user's inhalation start signal and an exhalation start signal.

[0101] Specifically, the inhalation start signal and the exhalation start signal generated from the first sensor unit (510) can be transmitted to the user terminal (200) via the communication unit (540). That is, the signal generated from 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.

[0102] The second sensor unit (520) may be provided to measure the degree of contraction of respiratory muscles and generate a second detection signal in response to the user's breathing cycle to detect whether breathing other than abdominal breathing is occurring.

[0103] Here, the respiratory muscles are divided into inspiratory muscles and expiratory muscles. Inspiratory muscles include the diaphragm, which is the main inspiratory muscle, as well as the superficial muscles such as the pectoralis major, pectoralis minor, trapezius, and erector spinae, and expiratory muscles include the rectus abdominis, external oblique, internal oblique, and transverse abdominis.

[0104] In particular, the first sensor unit (510) is attached to the abdominal muscles (also called 'abdominal respiratory muscles') through which the user breathes diaphragmatically, and can generate a first detection signal by measuring the degree of contraction of the respiratory muscles during inhalation and exhalation.

[0105] In addition, the second sensor unit (520) can detect whether the user is breathing using thoracic breathing or accessory respiratory muscles (such as breathing by applying force to the shoulders and neck), by being attached to a muscle used for thoracic breathing or an accessory respiratory muscle (also called a “respiratory muscle other than abdominal respiratory muscle”), and can generate a second detection signal by measuring the degree of contraction of the respiratory muscle during inhalation and exhalation.

[0106] In one embodiment of the present invention, the first sensor unit (510) may be attached to the abdomen, and the second sensor unit (520) may be attached to at least one of the chest, shoulder, and neck.

[0107] For example, the first sensor unit (510) may be configured to generate a first detection signal by contraction of at least one muscle among the diaphragm, rectus abdominis, external oblique muscle, internal oblique muscle, and transverse abdominis muscle, and the second sensor unit (520) may be configured to generate a second detection signal by contraction of at least one muscle among the external intercostal muscle, internal intercostal muscle, and accessory respiratory muscle.

[0108] Here, accessory respiratory muscles may include the sternocleidomastoid muscle, pectoralis, deltoid (or trapezius), and scalene.

[0109] In the present invention, the first sensor unit (510) and the second sensor unit (520) can be attached to each of the respiratory muscles described above.

[0110] In one embodiment of the present invention, a plurality of first sensor units (510) and second sensor units (520) may be provided, and one or more may be attached to the user's respiratory muscles to measure the degree of contraction of the respiratory muscles.

[0111] The stimulation unit (530) can output a stimulation signal to stimulate the user's respiratory muscles according to a control signal transmitted from the user terminal (200). In one embodiment of the present invention, the stimulation unit (530) can be implemented as a neuromuscular electrical stimulation (NMES).

[0112] The stimulation unit (530) can be installed by being attached to the aforementioned respiratory muscles, and can provide electrical stimulation corresponding to a respiratory signal generated according to the user's breathing cycle to the respiratory muscles.

[0113] In one embodiment of the present invention, the stimulation unit (530) may include an inspiratory muscle pad designed to stimulate inspiratory muscles and an expiratory muscle pad designed to stimulate expiratory muscles.

[0114] In one embodiment of the present invention, a plurality of stimulation units (530) may be provided, and a plurality of inhalation muscle pads and expiratory muscle pads may be provided and provided in areas corresponding to the corresponding user's muscles.

[0115] In one embodiment of the present invention, the stimulation unit (530) may be provided in the form of a band or belt having a predetermined width, encircling the abdomen, and having an adjustable length so that it can be worn on the abdomen. For example, the stimulation unit (530) may be provided so that it can be worn around the abdomen, thereby completely enclosing the abdomen, i.e., the abdominal muscles.

[0116] In another embodiment of the present invention, the stimulation unit (530) may include an air tube that can be filled with air. Here, the air tube may be provided with an adjustable internal air volume. For example, the air tube may be provided with a predetermined air pressure injected therein, thereby being configured to compress the entire abdominal muscles. In other words, by compressing the entire abdominal muscles rather than stimulating only a portion of the abdominal muscles, the stimulation unit may be implemented to facilitate abdominal breathing. Furthermore, by adjusting the air pressure within the air tube according to the user's condition, the user's abdominal breathing may be more easily trained.

[0117] 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 user terminal (200) receives the first detection signal, it can determine that the user is in an abdominal breathing state, and when the second detection signal is received, it can determine that the user is in a non-abdominal breathing state.

[0118]

[0119] FIG. 4 is a flowchart showing a method for determining anxiety disorder in an anxiety disorder monitoring and biofeedback provision system according to one embodiment of the present invention.

[0120] Referring to FIG. 4, when the server (300) receives an electrocardiogram signal (S110), it analyzes the received electrocardiogram signal and extracts a heart rate variability (HRV) signal (S120).

[0121] Then, the server (300) calculates the LF (Low Frequency) signal area and the HF (High Frequency) signal area from the power spectrum of the HRV signal (S130).

[0122] And, the server (300) calculates the ratio of the LF area and the HF area (LF area / HF area) (S140).

[0123] And, the server (300) determines anxiety disorder based on the ratio of the LF area and the HF area (S150).

[0124] 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.

[0125] Referring to FIG. 6, a screen is provided on the user terminal (200) through which user age information can be input through a dedicated application (S301).

[0126] When user age information is input into 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's 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.

[0127] Next, the server (300) measures the LF signal and the 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).

[0128] The user terminal (200) compares the LF signal and the LF curve (S307), and compares the HR signal and the HR curve (S311). If the LF signal is higher than the LF curve or the HR signal is lower than the HR curve, a message guiding the wearing of abdominal breathing wear (500) is displayed (S313). Conversely, if the LF signal is lower than the LF curve and the HR signal is higher than the HR curve, it is diagnosed as normal and the process is terminated.

[0129] And, 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).

[0130] When the user terminal (200) detects that breathing other than abdominal breathing is in progress through a breathing signal (S317), the stimulation unit (530) of the abdominal breathing wear (500) outputs a stimulation signal to stimulate abdominal breathing (S319).

[0131] In addition, the user terminal (200) periodically compares the LF signal and the LF curve while the user is wearing the abdominal breathing wear (500) (S321), compares the HR signal and the HR curve (S323), and outputs muscle relaxation content if the LF signal is higher than the LF curve or the HR signal is lower than the HR curve (S325). Conversely, if the LF signal is lower than the LF curve and the HR signal is higher than the HR curve, it is diagnosed as normal and the process is terminated.

[0132] After outputting muscle relaxation content, the LF signal and the LF curve are periodically compared (S327), and the HR signal and the HR curve are compared (S329). If the LF signal is higher than the LF curve or the HR signal is lower than the HR curve, the meditation content is output (S331). Conversely, if the LF signal is lower than the LF curve and the HR signal is higher than the HR curve, the result is diagnosed as normal and the process is terminated.

[0133] 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.

[0134] In Fig. 7, an LF curve (710) (Hz) representing the average value of the LF signal according to age is indicated by a solid line, and an LF signal (720) (Hz) measured for the user is indicated by a dot.

[0135] 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.

[0136] In Fig. 8, the HR (Heart Rate) curve (810) (beats / min) representing the average value of the HR signal according to age is indicated by a solid line, and the LF signal (820) (beats / min) measured for the user is indicated by dots.

[0137] As shown in FIGS. 7 and 8, the user terminal (200) of the present invention compares the LF curve (710) and the LF signal (720) according to age, and compares the HR curve (810) and the HR signal (820) according to age, and if the LF signal (720) is higher than the LF curve (710) or the HR signal (820) is lower than the HR curve (810), an anxiety disorder can be diagnosed, and biofeedback-related content can be provided.

[0138] 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, and are examples of screens in which a dedicated application is executed on the user terminal.

[0139] Figure 9 is an example of a home screen in a dedicated application, showing an electrocardiogram measurement menu, a biofeedback menu, and a recent electrocardiogram record inquiry screen.

[0140] Figures 10 and 11 are examples of push / alarm setting screens. Figure 10 is an example of an alarm multi-setting screen that can set alarms for various times, and Figure 11 is an example of an alarm detail setting screen that can set alarm time, whether to repeat by day of the week, push / alarm display format, etc.

[0141] Figures 12 to 14 are examples of screens related to biofeedback content. Figure 12 is an example of a screen for muscle relaxation content among biofeedback, Figure 13 is an example of a screen for ending content, and Figure 14 is an example of a screen for selecting abdominal breathing, muscle relaxation, and meditation content.

[0142] Figure 15 is an example screen showing an animation effect in content. In the format where the content transitions to the next image after a specified time has elapsed through a delay, a GIF can be added to express movement using arrows, etc. so that the user can follow along.

[0143] Figure 16 is an example of an electrocardiogram measurement screen, and Figure 17 is an example of a screen that can view electrocardiogram measurement records.

[0144] Figures 18 and 19 are examples of screens showing the results of electrocardiogram measurement using biofeedback. When the 'Measure' button provided at the bottom of Figure 18 is selected, the electrocardiogram is measured by measuring HR and LF, and Figure 19 shows an example screen showing the results of electrocardiogram measurement using biofeedback.

[0145]

[0146] 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 one embodiment of the present invention, FIG. 20 is a graph illustrating a 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 a power spectrum of an HRV signal, and FIG. 23 is a graph indicating an NN or RR interval in an electrocardiogram signal.

[0147] Referring to FIG. 5, FIG. 20 to FIG. 23, when an electrocardiogram (ECG) signal of a user is measured by an electrocardiogram measuring device (100), the measured ECG signal is transmitted to a user terminal (200) (S201). Then, the user terminal (200) transmits the ECG signal to a server (300) (S203).

[0148] The server (300) detects the R peak and RR interval of the received electrocardiogram signal (S205). In Fig. 23, the RR interval is displayed in the electrocardiogram signal. The RR interval refers to the interval between the R peak and the next consecutive R of the electrocardiogram signal.

[0149] Next, the server (300) converts the RR interval into a time series signal and arranges it on the time axis. At this time, an interval start time t1 and an interval end time t2 can be input to set the time interval of interest.

[0150] Figure 20 illustrates the process of extracting an HRV signal from an electrocardiogram signal. First, an R peak is detected from the electrocardiogram signal. Then, by converting the detected RR interval into a time series signal and rearranging it on the time axis, the heartbeat changes that change over time can be identified, which is the HRV signal.

[0151] In the example of Fig. 20, the process of detecting R peaks 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.

[0152] The server (300) converts the RR interval converted into a time series signal into a heart rate (beats / sec) to generate an HRV signal (S209). The graph of Fig. 21 is a graph in which the x-axis represents time (time (sec)) and the y-axis represents the heart rate (beats / sec), and an HRV signal is depicted. Fig. 21 shows an HRV signal for 400 seconds, and it can be seen that the heart rate changes every moment even in a stable state.

[0153] Then, the server (300) converts the HRV signal into a frequency analysis signal of frequency and power spectrum density (PSD) (S211).

[0154] Frequency analysis of HRV signals yields results similar to those in Figure 22. As shown in Figure 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, known as the "Mayer wave," between 0.05 Hz and 0.15 Hz, which is associated with the blood pressure regulation mechanism.

[0155] Figure 22 is an example of a graph showing the power spectrum of an HRV signal, where the x-axis is frequency (Frequency (Hz)) and the y-axis is PSD (ms). 2 / 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.

[0156] The frequency domain analysis method analyzes the HRV waveform to see the relative strength of each frequency component signal, and the strength of the LF band relative to the HF band can be interpreted as the balance between the sympathetic and parasympathetic nerves.

[0157] In frequency domain analysis, HF is relatively well-related to physiological meaning, and since HF mainly reflects the vagus nerve activity branching into the heart, it can be interpreted as a measurement representing the activity of the parasympathetic nervous system.

[0158] In heart rate variability measurements under stressful situations, mental stress is known to increase LF activity and decrease HF activity. Furthermore, the more severe the phobic symptoms, the lower the heart rate variability, suggesting a link between symptoms and autonomic nervous system activity.

[0159] In those who complained of two or more anxiety symptoms, all heart rate variability indices were evaluated as lower than in the control group, and a significantly higher baseline LF / HF ratio was observed in those who were evaluated as having the highest score on the anxiety scale, and there was also a correlation between the LF / HF ratio and the anxiety scale score.

[0160] In the present invention, the LF value was significantly higher in the group of patients with generalized anxiety disorder than in the group with major depressive disorder. This result means that patients with depression feel more loss of energy, fatigue, sleep deprivation, lethargy, and languidness than patients with generalized anxiety disorder. In addition, the LF / HF ratio, which indicates the balance of the autonomic nervous system, shows that the autonomic imbalance is more severely worse in patients with generalized anxiety disorder than in patients with major depression.

[0161] The server (300) defines the frequency as LF region when it is 0.05 to 0.15 Hz and calculates the LF area of ​​the HRV signal (S213, S215, S217). Then, if the frequency is 0.15 to 0.4 Hz, it defines the frequency as HF region and calculates the HF area of ​​the HRV signal (S213, S215, S219).

[0162] Referring to Figure 22, the HRV signal in the LF region is expressed as a function f LF And, the HRV signal in the HF region is expressed as a function f HF It is said.

[0163] Then, the area of ​​LF is ∫f LF It can be expressed as df, and the HF area is ∫f HF It can be expressed as df.

[0164] More specifically,

[0165]

[0166]

[0167] can be expressed as

[0168] The server (300) calculates the ratio of the LF area and the HF area, LF area / HF area (S221).

[0169] As a result of the calculation, if the area ratio is 3.3 or higher, it is judged as severe anxiety disorder, if it is less than 2.3, it is judged as normal, and if it is 2.3 or higher but less than 3.3 (2.3~3.3), it is judged as caution anxiety disorder (S223~S231).

[0170] When the area ratio is judged to be an anxiety disorder caution level of 2.3 or more and less than 3.3, the caution level can be judged by specifying the specific numerical range. For example, when the area ratio is 2.3 or more and less than 2.5, the caution level can be judged as 1, when the area ratio is 2.5 or more and less than 2.7, the caution level can be judged as 2, when the area ratio is 2.7 or more and less than 2.9, the caution level can be judged as 3, when the area ratio is 2.9 or more and less than 3.1, the caution level can be judged as 4, and when the area ratio is 3.1 or more and less than 3.3, the caution level can be judged as 5 (S231). In the present invention, when the anxiety disorder caution level is judged in this way, the caution level can be specifically divided into 5 levels, and a higher level number indicates a higher level of anxiety disorder.

[0171] In this way, in the frequency domain analysis of the HRV signal of the present invention, the heart rate can be said to be a rhythm that changes in various frequencies without rest for 24 hours, and the characteristics of the rhythm can be found by calculating what frequency waveform this rhythm (tachogram) is composed of and what the contribution of each frequency is, which is called spectral analysis.

[0172] Recently, as the correlation between specific periodic components of HRV and autonomic nervous system activity has become known, frequency-domain interpretation of HRV signals is being actively attempted.

[0173] Figure 24 is a graph illustrating the power spectrum of the HRV signal, Figure 25 is a graph illustrating the power spectral density estimates obtained from the entire 24-hour interval of a long-term Holter recording, and Figure 26 illustrates interval tachograms of 256 consecutive RR values ​​of a normal subject in supine rest (A, C, E) and head-up tilt (B, D, F).

[0174] As shown in Figures 24 to 26, the high-frequency component (HF component) ranges from 0.15 to 0.4 Hz and is related to respiration and is widely used as an indicator of parasympathetic nervous system activity. In particular, the HF component is known to be closely related to the electrical stability of the heart. In patients with aging cardiopulmonary function or who died from sudden cardiac death, the HF component before death is significantly reduced, and factors such as chronic stress, fear, and anxiety have also been shown to reduce the HF.

[0175] The low-frequency component (LF component) reflects changes in heart rate due to baroreceptor reflexes or blood pressure regulation, and it can be seen that patients with excessive sympathetic nerve activity have increased LF variation compared to healthy individuals.

[0176] Furthermore, a significant decrease in LF and HF has been reported in many patients complaining of significant stress and fatigue. Furthermore, persistent anxiety and depression have been shown to induce decreased parasympathetic nervous system activity. While increased parasympathetic nervous system activity stabilizes and protects the heart, increased sympathetic nervous system activity lowers the ventricular fibrillation threshold and increases the risk of ventricular fibrillation.

[0177] In patients with chronic fatigue syndrome, excessive increases in LF and LF / HF ratio are observed upon standing, indicating that the sympathetic nerves are overactive under stressful situations. In normal people, 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 is out of balance.

[0178] The heart rate changes with breathing, typically increasing with inspiration and decreasing with expiration. This phenomenon is called Respiratory Sinus Arrhythmia (RSA). RSA is a natural arrhythmic cycle that occurs when breathing influences sympathetic and vagal nerve impulses to the sinoatrial node.

[0179] HRV biofeedback, also known as RSA biofeedback, trains patients to increase RSA amplitude by voluntarily adjusting their breathing rate through visual feedback of heart rate oscillations that change with breathing. In patients with anxiety disorders, repeated HRV biofeedback training reduced anxiety responses and significantly improved subjectively reported mood changes.

[0180]

[0181] While the present invention has been described using several preferred embodiments, these embodiments are illustrative and not limiting. Those skilled in the art will appreciate that various changes and modifications can be made without departing from the spirit and scope of the invention as set forth in the appended claims.

Claims

1. An electrocardiogram measuring device for measuring the user's electrocardiogram (ECG); A user terminal capable of downloading and installing a dedicated application from a server that includes a function for monitoring anxiety disorders and providing biofeedback, and transmitting the received electrocardiogram signal to the server when the dedicated application is running and receiving an electrocardiogram signal from the electrocardiogram measuring device; and A server that provides the dedicated application to the user terminal, analyzes the electrocardiogram signal received from the user terminal, and transmits the analyzed information to the user terminal, An anxiety disorder monitoring and biofeedback provision system characterized in that the user terminal diagnoses anxiety disorder using information analyzed by the server through the dedicated application, and, if diagnosed with anxiety disorder, provides biofeedback-related content for treating anxiety disorder.

2. In claim 1, An anxiety disorder monitoring and biofeedback provision system characterized in that the server extracts a heart rate variability (HRV) signal using an electrocardiogram signal received from the user terminal and provides information obtained by analyzing the extracted HRV signal to the user terminal.

3. In claim 2, LF curve data, which represents the average value of LF (Low Frequency) signals for each age, and HR curve data, which represents the average value of HR (Heart Rate) signals for each age, are pre-stored in the database. When the server receives user age information from the dedicated application of the user terminal, it reads LF curve data and HR curve data from the database and transmits them to the user terminal, measures an LF signal from the power spectrum of the extracted heart rate variability signal, measures an HR signal using the electrocardiogram signal, and transmits the measured LF signal and HR signal to the user terminal. An anxiety disorder monitoring and biofeedback provision system characterized in that the user terminal compares an LF signal and an LF curve and diagnoses an anxiety disorder if the LF signal is higher than the LF curve, or compares an HR signal and an HR curve and diagnoses an anxiety disorder if the HR signal is lower than the HR curve, and provides biofeedback-related content.

4. In claim 1, An anxiety disorder monitoring and biofeedback provision system characterized in that the user terminal provides biofeedback-related content including abdominal breathing content for inducing abdominal breathing, muscle relaxation content for muscle relaxation, and meditation content.

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