TWM651434U - Early warning system for autonomic dysfunction - Google Patents

Abstract

The present invention provides an early warning system for autonomic nervous system dysfunction comprising a housing, an input unit, a processing unit and a display unit. The housing forms a first component and a second component, the first component and/or the second component having at least one sensor. The input unit is separately attached to the at least one sensor to receive physiological characteristic data related to the degree of change in heart rate sensed by the sensor, and is also attached to the second component. The processing unit is coupled to the input unit. The processing unit executes an application program to generate an analytical parameter model with at least one median from the plurality of physiological characteristic data. The application program calculates parameters from the physiological characteristic data, such as at least one of the standard deviation of heartbeat intervals (SDNN), low frequency (LF), high frequency (HF), or very low frequency (VLF). The application program uses the data of at least one of the standard deviation of heartbeat intervals, low frequency, high frequency, and very low frequency to generate corresponding complex indices. Among them, the analytical parameter model is based on the physiological characteristic data of a specific population. The display unit is connected to the processing unit and is set on the first component to display project indicators related to the above-mentioned parameters.

Description

Autonomic nervous system early warning system

This creation is about the technical field of autonomic nervous system. It is an early warning system for autonomic nervous system disorder based on interactive heart rate variability analysis parameter model and indicator generation system to assist doctors (or experts) in establishing analysis parameter models of different countries or different races. Assist doctors (or experts) to clinically inquire about the autonomic nervous system-related indicators customized by the clinic.

Modern people's lives are too stressful. Long-term accumulation of stress will cause the body to release too much steroids and adrenaline, thus damaging the autonomic nervous system (ANS), causing an imbalance of the sympathetic and parasympathetic nerves in the system, leading to dizziness and chest tightness. Symptoms such as heart palpitations, headaches, irritability, excessive tension and anxiety are medically called "autonomic nervous system disorders"; autonomic nervous system disorders are used to describe physical symptoms that are difficult to explain by physiological reasons. According to the current medical definition, autonomic nervous system disorders Disorder is a mental illness with relatively mild symptoms. According to recent medical research, about 30% of the population in Europe and the United States and more than 20% of the population in Taiwan have suffered from the pain caused by autonomic nervous system disorder. It is currently a common term It is called "sub-health"; sub-health refers to the physical or psychological state that is in the fuzzy zone between health and disease. It is a dynamic change. If ignored, it may develop into a disease. If it is improved in time, it can be restored to a healthy state.

Traditionally, heart rate variability (HRV) analysis is a method of measuring the degree of changes in continuous heart rate. HRV measurement is a common method to evaluate whether the autonomic nervous system function is normal or not due to its non-invasive, fast and convenient advantages. . This measurement method is also widely used in the assessment of psychological or physiological stress. The most commonly used calculation method is that each heart cycle (heart cycle) in the electrocardiogram (ECG) can be divided into the sum of multiple waves. namely P, Q, R, S and T. Another important feature of the ECG is the duration of the cardiac cycle. These are measured based on the length of the RR interval (i.e. the distance between consecutive R peaks) and are usually measured by measuring the individual heart ( The variables of heart rate (HR) and variability are summarized into a set of numbers for further calculation and analysis. The autonomic nervous system detector currently used clinically uses heart rate variability to analyze the state of autonomic nervous system balance.

The method and equipment for determining a fatigue index disclosed in the Republic of China Patent Publication No. TW202211868A mainly include receiving physiological signals; generating a plurality of heart rate variability parameters based on the physiological signals; and based on the plurality of heart rate variability parameters. Parameters can be used to determine the fatigue index, but it can only display the fatigue index, and does not allow experts to use relevant parameters to customize the index through this patent.

The Republic of China Patent Publication Announcement No. TWI670046B discloses a measuring device and method for both psychological stress index examination and blood pressure examination. When the measuring device enters the psychological pressure measurement mode, the pump unit performs variable-speed pressurization of the air bag unit, and when the pressure signal of the pressure sensing unit is determined to be a pulse signal, the microprocessor unit will control the pump unit to stop pressurizing and measure the pulse signal to calculate the psychological pressure. Stress index; and based on the psychological stress index, based on the data of each pulse interval within a period of time, calculate the ratio of the standard deviation of normal and normal RR intervals (SDNN) to the root mean square difference (RMSSD) of consecutive RR intervals. It can easily judge the balance of autonomic nervous system, but it cannot collect population data to generate statistics for each age group. Analyzing the parameter model allows the subject to know his or her comparative status among the same age and gender, for example, whether the subject's SDNN is above or below the median.

In view of this, this invention provides an early warning system for autonomic nervous system disorders based on an interactive heart rate variability analysis parameter model and an indicator generation system to solve the deficiencies of previous technologies.

The first purpose of this creation is to provide an early warning system for autonomic nervous system disorders based on an interactive heart rate variability analysis parameter model and an indicator generation system. The indicators and their indicator ranges can be customized according to parameters. It is a system based on the physiological characteristic data of a specific group. Quantify heart rhythm variability analysis values to build parametric models and define relevant health indicators through an interactive interface. Among them, the specific group may be sub-healthy people, teenagers, etc., and is not limited thereto. As far as sub-healthy people are concerned, sub-healthy people can use this creation to improve the SDNN (Standard deviation of NN intervals) generated from the long-term exercise process of sub-healthy people, and make the corresponding indicators further reach the goal. Improvement; and, as far as teenagers are concerned, teenagers can use this invention to detect depression early and achieve preventive and treatment effects.

In order to achieve the above purpose or other purposes, this invention provides an early warning system for autonomic nervous system disorder based on an interactive heart rate variability analysis parameter model and an indicator generation system. The early warning system for autonomic nervous system disorders includes a housing, an input unit, a processing unit and a display unit. The housing forms a first component and a second component, and the first component and/or the second component have at least one sensor. The input unit is respectively disposed on the at least one sensor to receive physiological characteristic data related to a heart rate change degree and is disposed on the second component. The processing unit is connected to the input unit. The processing unit executes an application program to generate an analysis parameter model with at least a median from the physiological characteristic data, and the application program calculates the parameters from the physiological characteristic data, such as a standard deviation of heartbeat intervals (SDNN), a low frequency (LF), a high frequency (HF) or a very low frequency (VLF) at least one. The application compares the data of at least one of the parameters to the median of the parameter model to generate the corresponding complex indicator. Among them, the analysis parameter model is established based on the physiological characteristic data of a specific group; the display unit has a touch function and is connected to the processing unit and is provided in the first component to display project indicators related to the above parameters.

Furthermore, it further includes an acquisition unit connected to the input unit, and the acquisition unit is used to acquire the physiological characteristic data of the heart, wherein the acquisition unit acquires the heart rate change degree within a predetermined time.

Further, the degree of change in the heart rate is obtained based on a heart rate variability analysis method.

Further, the application provides a plurality of the analysis parameter models, and the analysis parameter models have corresponding medians, and the corresponding project indicators are generated based on at least one of the parameters, and the project indicators are based on the threshold value at the and other project indicators to define their indicator ranges.

Furthermore, the application also includes a user interface through which doctors can select individual or multiple project indicators through the user interface and touch-sensitive display unit. The project indicators are mental status, physical condition, emotional stability, and stress. At least one of degree, mental fatigue and physical fatigue, stress accumulation, long-term stress, day and night sleep status, dream quality and sleep depth, or the doctor can customize the index and its index range based on the combination of parameters.

Furthermore, the application further includes a custom indicator module, which selects one or more of the parameters to create a custom indicator, and the custom indicator also provides a custom threshold to define its indicator. interval.

Further, the application further includes an adjustment module to adjust the threshold, and a warning module. In the application, the standard deviation of the heartbeat interval, the low frequency, the high frequency and the ultra-low frequency are calculated. After the parameter is compared with the median of the analysis parameter model, when the parameter is different from the median, the warning module generates a warning message.

Compared with the conventional technology, this creation provides an early warning system for autonomic nervous system disorders based on an interactive heart rate variability analysis parameter model and an indicator generation system. The indicators and their indicator ranges can be customized, allowing experts to collect heart rate variability of specific groups of people. , you can define the median of each parameter (excluding extreme values) and further customize the indicator based on the combination of parameters.

10: Early warning system for autonomic nervous system disorders

11: Shell

12:Input unit

15: Processing unit

16:Display unit

17:Electrode acquisition unit

18: Blood oxygen measurement unit

20: Blood pressure measurement unit

22: Body temperature sensing unit

24: Identity verification unit

28: Hijacking parts

30: Foot pads

APP: application

BD: physiological characteristic data

MD: median

AIM: Analytical Parametric Model

PT: parameters

IDX: project indicator

VL: threshold

UI: User interface

M1: Custom indicator module

M2: Adjustment module

M3: Alert module

Figure 1 is a block diagram of an early warning system for autonomic nervous system disorder based on an interactive heart rate variability analysis parameter model and an indicator generation system according to one embodiment of this invention; Figure 2 is a block diagram of an interactive heart rate variability analysis parameter model and an indicator generation system according to another embodiment of this invention. A block diagram of the indicator generation system; Figure 3 is a schematic diagram of the interactive heart rate variability analysis parameter model and the threshold value of the indicator generation system according to another embodiment of the present invention.

Figure 4 is a threshold diagram of an early warning system for autonomic nervous system disorder based on an interactive heart rate variability analysis parameter model and an indicator generation system according to an embodiment of this invention.

In order to fully understand the purpose, characteristics and effects of this invention, this invention is described in detail through the following specific embodiments and the attached drawings, as follows: In this work, “a” or “an” is used to describe the units, elements and components described herein. This is only for convenience of explanation and to provide a general sense of the scope of this creation. Accordingly, unless it is obvious otherwise, such description should be understood to include one, at least one, and the singular also includes the plural.

As used herein, the terms “includes,” “includes,” “has,” “contains,” or any other similar term are intended to cover a non-exclusive inclusion. For example, an element, structure, article or device containing plural elements is not limited to the elements listed herein, but may include elements not expressly listed but that are generally inherent to the element, structure, article or device. Other requirements. Otherwise, unless expressly stated to the contrary, the term "or" means an inclusive "or" and not an exclusive "or".

Please refer to FIG. 1 , which is a three-dimensional schematic diagram of an early warning system 10 for autonomic nervous system disorders according to an embodiment of the present invention. In Figure 1, the electrode acquisition unit 17, the blood oxygen measurement unit 18, the blood pressure measurement unit 20 and the body temperature sensing unit 22 are all used as sensors, allowing the subject to measure physiological characteristics. For example, the physiological characteristics include heart rhythm, Heartbeat, blood pressure, blood oxygen, body temperature, etc. The early warning system 10 for autonomic nervous system disorder of this invention includes a housing 11, a sensing unit, a processing unit 15 and a display unit 16 with a touch function. Here, the sensing unit is explained by taking the combination of the electrode acquisition unit 17, the blood oxygen measurement unit 18, the blood pressure measurement unit 20 and the body temperature sensing unit 22 as an example.

The housing 11 provides a first component 121 and a second component 123 . Here, the first component 121 and the second component 123 are arranged at an angle to form an L-shaped structure as an example. In other embodiments, the angle arrangement is not limited. The blood oxygen measurement unit 18 of the sensing unit is provided in the first component 121, and the electrode acquisition unit 17, blood pressure measurement unit 20 and body temperature sensing unit 22 of the sensing unit are provided in the second component 123. In other embodiments, The installation location of the sensing unit is not limited. In this embodiment, the number of electrode capture units 17 is two as an example.

In another embodiment, the autonomic nervous system early warning system 10 further includes a blood oxygen measurement unit 18 disposed in the housing 11 . The blood oxygen measurement unit 18 is capable of measuring physiological characteristics of the subject's blood oxygen concentration. In this embodiment, the blood oxygen measurement unit 18 is provided (for example, attached to the housing 11).

In another embodiment, the autonomic nervous system early warning system 10 further includes a blood pressure measurement unit 20 placed on a hanger, capable of measuring physiological characteristics of the subject's blood pressure.

In another embodiment, the autonomic nervous system early warning system 10 further includes a body temperature sensing unit 22 disposed in the second component 123 capable of measuring at least one of the physiological characteristics of the subject's body temperature, forehead temperature, and ear temperature. Here, the second component 123 further includes a holding member 28 to fix the body temperature sensing unit 22 .

In another embodiment, the autonomic nervous system early warning system 10 further includes an identity verification unit 24 to identify the identity of the subject. For example, the identity verification unit 24 may use RFID that complies with the near field communication protocol to identify the identity. Here, the application APP stores or obtains the subject's sensed physiological characteristic data based on the identified subject's identity.

In another embodiment, the autonomic nervous system early warning system 10 further includes a button 26 to activate or deactivate the processing unit 15 and the display unit 16 .

Please refer to Figure 2, which is a block diagram of an early warning system for autonomic nervous system disorder based on an interactive heart rate variability analysis parameter model and an indicator generation system according to an embodiment of this invention. In FIG. 2 , an early warning system 10 for autonomic nervous system disorders based on an interactive heart rate variability analysis parameter model and an index generation system includes a housing (not shown), an input unit 12 , a processing unit 15 and a display unit 16 .

Please refer to Figure 3, which is a block diagram of an early warning system for autonomic nervous system disorder based on an interactive heart rate variability analysis parameter model and an index generation system according to another embodiment of the present invention; the input unit 12 receives information related to a heart rate change degree. A physiological characteristic data BD; here, the heart rate change degree is obtained based on the heart rate variability analysis method.

In another embodiment, the input unit 12 can acquire the physiological characteristic data BD of the subject's heart through the electrode acquisition unit 17. For example, the electrode acquisition unit 17 may include an electrode sheet (or electrode grip) and an amplification circuit. , signal processing circuit, analog-to-digital conversion circuit, etc., among which the electrode acquisition unit 17 can obtain the corresponding myoelectric signal of the subject through the electrode sheet or electrode grip; it is worth noting that the degree of change in heart rate can also be based on heart rate variability. Obtained by analysis methods, such as heart rate variability analysis calculation method mainly analyzes the time series of heartbeats and heartbeat intervals obtained by electrocardiogram or pulse measurement, such as by measuring the R wave peak in the electrocardiogram, by measuring the difference between the two R wave peaks. The time interval between heartbeat intervals is then used to convert the time series of heartbeat intervals into the frequency domain using discrete Fourier transform, which is expressed in the form of power spectral density or spectral distribution. Generally, spectrum analysis of heart rate variability signals requires 200 to 500 times. Stable recording performance of continuous heartbeat intervals requires a recording time of, for example, 2 minutes; the frequency of the heartbeat interval spectrum appears below 1Hz, and several peaks can mainly be found in the range of 0 to 0.4Hz.

The processing unit 15 is connected to the input unit 12; the processing unit 15 executes an application program APP to calculate the physiological characteristic data BD of each age group of a specific group by removing extreme values and generate an analytical parameter model AIM with at least a median MD. . The application APP calculates complex parameters from the physiological characteristic data BD, such as a standard deviation of heartbeat intervals (SDNN), a low frequency (LF), a high frequency (HF) or a very low frequency (VLF) or a combination of the above data, such as low frequency The ratio of (LF) to high frequency (HF) (LF/HF), the percentage of low frequency (LF) in high frequency (HF) plus low frequency (LF) (LF%), etc., where the standard deviation of the heartbeat interval is The clinical significance can be represented by the overall activity of the autonomic nervous system. The low-frequency band can range from 0.04Hz to 0.15Hz. The clinical significance can be represented by the activity of the sympathetic and parasympathetic nerves. The high-frequency band can range from 0.15 Hz to 0.4Hz and can represent the activity of parasympathetic nerves in clinical significance. The frequency used in the ultra-low frequency range is ≦0.04Hz, and the ratio of low frequency (LF) to high frequency (HF) (LF/HF) Its clinical significance can be represented by the balance of autonomic nervous system activity; it is worth noting It is noted that the number of analysis parameter models AIM and median MD can be one or more, and the application APP can select one or more models from multiple analysis parameter models AIM, and each analysis parameter model AIM have their corresponding median MD, and the median MD can also be different according to the parameters PT; here, the analysis parameter model AIM is established based on the physiological characteristic data BD of a specific group, where the specific group It can be 100 athletes, 50 elderly people over 65 years old, 20 young adults aged 30 to 50, 30 primary school students, etc. For example, LF of 50 18-year-old boys is taken, and the results are combined according to the clinical diagnosis by experts. A user interface UI is used to remove extreme values and calculate to help doctors generate the analytical parameter model AIM of these 18-year-old boys, the most important of which is the median MD.

In another embodiment, the processing unit 15 executes the application APP, and compares the analysis parameter model AIM with at least one of the parameters PT to generate the corresponding project indicator IDX and its indicator range, and the doctor can use the user interface UI to select Individual or multiple project indicators IDX or customized indicators and their indicator ranges. For example, project indicators IDX can be mental status, physical status, mood stability, stress tension, mental fatigue and physical fatigue, stress accumulation, long-term stress, At least one of day and night sleep state, dream quality and sleep depth, or the doctor can customize the indicator and its indicator range according to the parameter combination; the project indicator IDX uses the threshold VL to define its indicator range in the project indicator IDX, and illustrates it with a scenario , one of the project indicators IDX "cardiac status" adopts the standard deviation of the heartbeat interval (SDNN). If the median of the analysis parameter model AIM is defined as "normal", it will be about 10 MD higher than the median. A percentage above the threshold VL can be displayed as "upset and irritable", and a percentage below about 10 percentage points below the median MD can be displayed as "frustrated" on the threshold VL.

In another embodiment, the application APP further includes a custom indicator module M1. One or more are selected from the parameters PT to create the custom indicator module M1, and the custom indicator Module M1 also provides a custom threshold to define the indicator range of the custom indicator. Doctors can use the user interface UI to customize the indicator and the indicator range. For example, in the custom indicator module M1, set the custom indicator to "mental status" , and select the standard deviation of the heartbeat interval (SDNN) from these parameters PT. If the median of the analysis parameter model AIM is defined as "normal", then the value higher than the median MD by about 15 percentage points can be set in the custom valve The value VL is set to display as "upset and irritable". If the value is less than about 15 percentage points below the median MD, the value can be set to display as "disheartened" on the customized threshold VL.

In another embodiment, the application APP further includes an adjustment module M2 to adjust the threshold VL, and a warning module M3 to adjust the heartbeat interval standard deviation, the low frequency, the high frequency or the After the ultra-low frequency parameters are compared with the median MD of the analysis parameter model AIM, when the parameter is different from the median MD, the warning module M3 generates a warning message.

The display unit 16 is connected to the processing unit 15 to display the project index IDX related to the parameters PT.

Please refer to Figure 4, which is a schematic diagram of the threshold of the autonomic nervous system early warning system based on an interactive heart rate variability analysis parameter model and an indicator generation system according to an embodiment of this invention, in which the threshold VL can be represented graphically and displayed on the display unit 16 (not shown), such as fan, circle, triangle, etc., and can distinguish the indicator range defined by the project indicator IDX. For example, "mental state" is the project indicator IDX. When the threshold VL displays "upset and irritable" ”, the pointer will be in the red range.

In another embodiment, the standard deviation of heartbeat intervals (SDNN) of a young male is displayed as 53.2, and below it is displayed a multiple of the difference between the young male and the SDNN of the specific ethnic group, such as 1.95, and the project indicator IDX is displayed on the display unit 16 And the threshold VL. For example, if the "mental status" of the project indicator IDX shows that the threshold VL is "normal range", the pointer will be in the blue section.

In another embodiment, the autonomic nervous system early warning system 10 further includes foot pads 30 to increase the stability of the housing 11 .

The present invention has been disclosed above with preferred embodiments. However, those familiar with the art should understand that the embodiments are only used to describe the invention and should not be interpreted as limiting the scope of the invention. It should be noted that all changes and substitutions that are equivalent to this embodiment should be deemed to be included within the scope of this invention. Therefore, the scope of protection of this invention shall be subject to the scope of the patent application.

10: Early warning system for autonomic nervous system disorders

11: Shell

15: Processing unit

16:Display unit

17:Electrode acquisition unit

18: Blood oxygen measurement unit

20: Blood pressure measurement unit

22: Body temperature sensing unit

24: Identity verification unit

28: Hijacking parts

30: Foot pads

APP: application

Claims (10)

An early warning system for autonomic nervous system disorders, including: a housing forming a first component and a second component, the first component and/or the second component having at least one sensor; an input unit, respectively The at least one sensor is arranged on the first component and/or the second component to receive the sensed physiological characteristic data; the processing unit is connected to the input unit, and the processing unit Execute an application program to calculate physiological characteristic data of a specific group at each age and generate an analytical parameter model with at least a median. The application program calculates complex parameters from the physiological characteristic data, such as heartbeat interval standards. Difference (SDNN), low frequency (LF) and high frequency (HF), VLF (very low frequency) or at least one of a combination, and the application assists experts to compare the standard deviation of the heartbeat interval, the low frequency, the high frequency and the ultra-low frequency. Parameters of at least one of low frequency and the like are compared with the median of the analysis parameter model to generate corresponding complex indicators and their index intervals, wherein the analysis parameter model is established based on the physiological characteristic data of a specific group; and a display unit, The processing unit is connected to display project indicators related to these parameters. The early warning system for autonomic nervous system disorder as described in claim 1 further includes an electrode acquisition unit connected to the input unit, and the electrode acquisition unit is used to acquire the physiological characteristic data of the heart. The early warning system for autonomic nervous system disorder as described in claim 2, wherein the electrode acquisition unit acquires a heart rate change degree within a predetermined time. The early warning system for autonomic nervous system disorders as described in claim 3, wherein the heart rate change degree is obtained based on a heart rate variability analysis method. The early warning system for autonomic nervous system disorders as described in claim 1, wherein the application provides a plurality of the analysis parameter models, and the analysis parameter models have corresponding medians. The early warning system for autonomic nervous system disorders as described in claim 1, wherein the processing unit executes the application program to allow the analysis parameters to generate corresponding project indicators based on at least one of the parameters, and the project indicators are determined by a threshold Define the indicator range for these project indicators. An early warning system for autonomic nervous system disorders as described in claim 6, wherein the application further includes a user interface through which a doctor can select individual or multiple project indicators, and the doctor can customize indicators based on parameter combinations and its index range, and the project indicators are at least one of mental status, physical condition, mood stability, stress intensity, mental fatigue and physical fatigue, stress accumulation, long-term stress, day and night sleep status, dream quality and sleep depth. One. The early warning system for autonomic nervous system disorder as described in request item 6, wherein the application further includes a custom indicator module, one or more of the parameters are selected to create a custom indicator, and the custom indicator also provides Customize the threshold to define the indicator range for this custom indicator. The early warning system for autonomic nervous system disorders as described in claim 6 or 8, wherein the application further includes an adjustment module to adjust the threshold. The early warning system for autonomic nervous system disorder as described in claim 1, wherein the application further includes a warning module, and the application compares and analyzes the parameters of the standard deviation of the heartbeat interval, the low frequency, the high frequency and the ultra-low frequency. After the median of the parameter model, when the parameter is different from the median, the warning module generates a warning message.