KR102271792B1 - A system for measuring heart rate variability using a wearable ecg patch and mehtod for measuring heart rate variability using thereof - Google Patents

Abstract

The present invention relates to a heart rate variability measurement system using a wearable ECG patch and a heart rate variability measurement method using the same, which monitor a user's heart rate variability and status in real time regardless of time and place through the wearable ECG patch which can be conveniently worn on a user's body, thereby confirming a health status of the user with little restrictions in time and space.

Description

A SYSTEM FOR MEASURING HEART RATE VARIABILITY USING A WEARABLE ECG PATCH AND MEHTOD FOR MEASURING HEART RATE VARIABILITY USING THEREOF

The present invention relates to a system for measuring heart rate variability using a wearable electrocardiogram patch and a method for measuring heart rate variability using the same, and more particularly, to a wearable electrocardiogram patch that can be conveniently worn on the user's body. The present invention relates to a heart rate variability measuring system using a wearable electrocardiogram patch, which monitors the heart rate variability and status in real time, so that a user's health status can be checked with little time and space constraints, and a heart rate variability measuring method using the same.

In general, since stress is regarded as a health-threatening factor, a measure for determining the stress is very important in judging a patient's health status.

For a patient's heart rate variability (HRV) test, the patient visits the hospital directly and measures the patient's heart rate variability using a dedicated heart rate variability measurement device. , ECG sensors are attached to the right wrist, left wrist, and right ankle to measure the ECG for 5 minutes.

However, this method has disadvantages in that the measurement time is relatively short, the patient must visit the hospital directly, and the patient's compliance is poor in that the procedure is performed in a limited posture.

In particular, since the existing heart rate variability test excludes the daily living environment and patterns, which are the main causes of patient stress, and is measured in a short time in a limited space such as a hospital, it is difficult to judge stress in daily life based on this.

Therefore, there is a need for a technology that allows a patient or medical staff to measure an electrocardiogram in real time at a desired time and desired place, regardless of time and space, and make an accurate diagnosis of the patient based thereon.

Korean Patent Publication No. 10-2020-0125256

The present invention was derived to solve the above problems, and by monitoring the heart rate variability and status of the user in real time regardless of time and place through a wearable electrocardiogram patch that can be easily worn on the user's body, there is little restriction in space and time The purpose of this study is to provide a heart rate variability measurement system using a wearable electrocardiogram patch that enables users to check the user's health condition and a heart rate variability measurement method using the same.

A system for measuring heart rate variability through a wearable ECG patch according to an embodiment of the present invention includes a wearable ECG patch that acquires myocardial activity current data by contacting the user's body and a user through a user terminal connected to the wearable ECG patch in a wireless network manner. Analysis of acquiring the myocardial activity current data of , and providing the result data of measuring and analyzing heart rate variability (HRV) based on the myocardial activity current data to the user terminal so that the result data is output through the user terminal It may be characterized in that it includes a server.

In an embodiment, the wearable electrocardiogram patch includes an inertial measurement sensor (IMU sensor) that generates activity data by measuring an activity amount according to a change in a user's time, and after including the activity amount data in the myocardial activity current data, the It may be characterized in that it is provided to the analysis server through a user terminal.

In one embodiment, based on the activity amount data, the user's activity amount is determined to be in a static state at a point in time when the value for the user's movement becomes less than or equal to a preset threshold value, and the ECG waveform for the corresponding time point is analyzed to obtain a P wave, It may be characterized in that it is classified into a Q wave, an R wave, an S wave, and a T wave, and the R peak value for the R wave is measured.

In an embodiment, the analysis server is configured to include at least one of a heart rate variation parameter, a heart rate variability parameter, a detrended fluctuation analysis (DFA) parameter for myocardial activity current, and a frequency domain parameter for myocardial activity current, based on the heart rate variability. After analyzing the parameters, it may be characterized in that the analysis result data is provided to the user terminal.

The method for measuring heart rate variability using a heart rate variability measuring system using a wearable electrocardiogram patch according to another embodiment of the present invention includes the steps of: acquiring the user's myocardial activity current data through the wearable electrocardiogram patch in contact with the user's body; The user's myocardial activity current data is acquired through a user terminal connected to the wearable electrocardiogram patch in a wireless network manner, and heart rate variability (HRV) is measured and analyzed based on the myocardial activity current data, and then the analyzed result data is transmitted to the user. It may be provided to the terminal, and the analysis server may include the step of outputting the result data through the user terminal.

In one embodiment, the step of acquiring the user's myocardial activity current data through the wearable electrocardiogram patch in contact with the user's body includes the user's time change through an inertial measurement sensor (IMU sensor) included in the wearable electrocardiogram patch. generating activity data by measuring the amount of activity according to the present invention; and in the wearable electrocardiogram patch, including the activity data in the myocardial activity current data, and then providing the data to the analysis server through the user terminal. can

In an embodiment, the step of outputting the result data from the analysis server through the user terminal may include, in the analysis server, a point in time when the numerical value of the user's movement becomes less than or equal to a preset threshold value based on the activity amount data. Determining the amount of activity in a static state, analyzing the ECG waveform for the time point, classifying it into P-wave, Q-wave, R-wave, S-wave and T-wave, and measuring the R peak value for the R-wave It can be characterized as

In an embodiment, the step of outputting the result data from the analysis server through the user terminal may include, in the analysis server, based on the heart rate variability, a heart rate variability parameter, a heart rate variability parameter, and a DFA for myocardial activity current. (Detrended fluctuation analysis) further comprising the step of analyzing one or more parameters of a frequency domain parameter for myocardial activity current and providing the analyzed result data to the user terminal to be output through the user terminal can be done with

According to one aspect of the present invention, by monitoring the heart rate variability and health state of the user in real time regardless of time and place, it is possible to check the health state of the user with little limitation in time and space.

In particular, according to one aspect of the present invention, it is possible to more efficiently set a time for measuring heart rate variability by referring to activity level data indicating the degree of movement of the user, and to select and measure a section in which the amount of activity is static. , it has the advantage of being able to continuously measure the patient's condition for more than 24 hours by organically setting the time when the medical staff and the user want to analyze the autonomic nerve.

In addition, according to an aspect of the present invention, in addition to the wearable ECG patch, it has versatility that can be used in both medical and wellness and fitness devices, such as an oximeter for measuring R peak.

In addition, according to one aspect of the present invention, it is possible to make useful diagnoses such as stress index, sleep quality (whether parasympathetic nerve is active during sleep), exercise effect (sympathetic nerve activation), etc. through the ratio of the autonomic nervous system in daily life, It has the advantage of providing a basis for judging abnormal conditions such as arrhythmias, heart failure, and syncope through the variability of the NN interval.

1 is a diagram schematically illustrating a heart rate variability measuring system 100 through a wearable electrocardiogram patch according to an embodiment of the present invention.
2 is a diagram illustrating activity amount data generated by measuring an activity amount according to time change.
3 is a diagram illustrating an electrocardiogram waveform analyzed by the analysis server 120 .
4 is a diagram illustrating types of one or more analysis parameters analyzed based on heart rate variability.
5 is a diagram illustrating a screen on which one or more analysis parameters shown in FIG. 4 are output through a user terminal.
6 is a diagram illustrating a screen in which one or more analysis parameters shown in FIG. 5 are extracted and classified according to a time series and output through a user terminal.
FIG. 7 is a view sequentially illustrating a process of measuring heart rate variability through the heart rate variability measuring system 100 through the wearable electrocardiogram patch shown in FIG. 1 .

Hereinafter, preferred examples are presented to help the understanding of the present invention. However, the following examples are only provided for easier understanding of the present invention, and the content of the present invention is not limited by the examples.

1 is a diagram schematically illustrating a heart rate variability measurement system 100 through a wearable electrocardiogram patch according to an embodiment of the present invention, and FIG. 2 is a diagram illustrating activity data generated by measuring the amount of activity according to time change. .

Referring to FIG. 1 , a system 100 for measuring heart rate variability through a wearable ECG patch according to an embodiment of the present invention largely includes a wearable ECG patch 110 and an analysis server 120 .

First, the wearable ECG patch 110 is in contact with the user's body to acquire myocardial activity current data (including myocardial activity current) necessary for measuring heart rate variability, and then a user terminal (smartphone, tablet) serving as a kind of gateway It is provided to the analysis server 120 through a PC, notebook computer, PC, etc.).

The wearable electrocardiogram patch 110 may be in a form that can be worn as an accessory, or may be in a form of a patch directly attached to the body surface of the user. Meanwhile, the shape of the wearable ECG patch 110 is not limited as long as it is possible to measure the myocardial activity current required to measure the user's heart rate variability.

In addition, although not shown in the drawings, the wearable ECG patch 110 may be operated by receiving external power, and may include a separate communication device for wireless network communication (eg, Bluetooth) with the user terminal.

In addition, the wearable electrocardiogram patch 110 may include an inertial measurement sensor (IMU sensor, 110a) configured to generate activity amount data by measuring the user's activity amount according to time change.

The inertial measurement sensor 110a measures and quantifies the activity level indicating the degree of movement of the user, thereby allowing the analysis server 120 to determine whether the user is currently actively moving or resting stably. to judge In general, since the accuracy of the heart rate variability measurement result may decrease when the user moves a lot, the analysis server 120 analyzes the user's ECG waveform based on the activity data measured through the inertial measurement sensor 110a as shown in FIG. 2 . to determine the optimal time for In this case, the wearable electrocardiogram patch 110 includes the activity amount data generated through the inertial measurement sensor 110a in the myocardial activity current data, and then provides it to the analysis server 120 through the user terminal.

The analysis server 120 receives myocardial activity current data including activity amount data from the wearable electrocardiogram patch 110 through the user terminal, and measures and analyzes the user's heart rate variability (HRV) based on the myocardial activity current data and the activity amount data. And, by providing the analyzed result data back to the user terminal, the user can check the heart rate variability measurement result through his or her user terminal.

At this time, after analyzing the heart rate variability, the analysis server 120 outputs analysis results according to various analysis parameters. This will be described in more detail with reference to FIGS. 3 to 5 .

3 is a diagram illustrating an electrocardiogram waveform analyzed through the analysis server 120, FIG. 4 is a diagram illustrating the types of one or more analysis parameters analyzed based on a heart rate variability diagram, and FIG. 5 is one or more analysis parameters shown in FIG. It is a diagram illustrating a screen on which analysis parameters are output through a user terminal.

First, referring to FIG. 3( a ), the analysis server 120 analyzes the electrocardiogram waveform of the myocardial activity current data acquired through the wearable electrocardiogram patch 110 and classifies it into P wave, Q wave, R wave, S wave and T wave. will do

The P wave represents the depolarization of the right atrium to the depolarization of the left atrium, the QRS wave represents the depolarization of the ventricle, and the T wave represents the normal repolarization of the ventricle. In particular, the R peak value is a bipolar wave that appears for the first time in the QRS complex, and when the voltage is high, the time exhaust change rate is large, so it is a reference point for determining the cycle of the ECG signal and for ECG detection and division.

In addition, the interval between the R peak value and the R peak value is a cycle in which the ventricles of the atrium contract and relax once, and means one cardiac cycle.

Accordingly, when these ECG waveforms appear continuously, they appear at Normal to Normal (NN) intervals as shown in FIG. 3(b). The NN interval represents the RR interval of the normal waveform in a different way.

For example, when a V (Ventricular) waveform appears as shown in FIG. 3(c), it is indicated as a CI (Coupling Interval) rather than an NN interval. HRV is measured by extracting only the interval between normal waveforms.

Meanwhile, one or more types of parameters analyzed based on heart rate variability are as follows.

Referring to FIG. 4 , in the analysis server 120 , based on the heart rate variability, a heart rate change parameter (a), a heart rate variability parameter (b), a detrended fluctuation analysis (DFA) parameter for myocardial activity current (c), and myocardial activity current The frequency domain parameter (d) for , etc. will be analyzed.

Heart rate variability measurement in the heart rate variation parameter (a) Start Time is the start time, End Time is the measurement end time, Mean HR is the average heart rate [bpm], AVNN is the average NN interval [ms], SDNN is the standard deviation of the NN interval It means [ms].

In the heart rate variability parameter (b), SDANN is the standard deviation of the 5-minute mean of NN intervals [ms], RMSSD is the square root of the mean of the squared values of the NN interval differences [ms], NN50 is the number of NN intervals of 50 ms or more [ dog], pNN50 is the percentage of NN50 to the total number of beats [%], ApEn is the quantification of the complexity of HRV, SD1 is the short-term heart rate variability of the Poincarι plot, and SD2 is the long-term heart rate variability of the Poincarι plot.

In the DFA parameter (C) for myocardial activity current, alpha1 means short-term fluctuation in DFA (detrended fluctuation analysis), and alpha2 means long-term fluctuation in DFA (detrended fluctuation analysis).

In the frequency domain parameter (d) for myocardial activity current, SRD is the reliability of the measurement result, TF is the log value of the total power in the frequency domain band, VLF is the log value of the power in the frequency band from 0.003 to 0.04 Hz, and LF is 0.04. The log value of power in the frequency band of ~0.15 Hz, HF is the log value of the power in the frequency band of 0.15 ~ 0.4 Hz, LF/HF is the ratio between LF and HF, and PSI (Physical Stress Index) is The stress measurement value, MSI (Mental Stress Index; Mental Stress Index), refers to the relative ratio of sympathetic and parasympathetic nerves.

The analysis server 120 generates HRV report data as shown in FIG. 5 as HRV report data as shown in FIG. 5 and transmits it to the user terminal, and the user checks the HRV report output to his/her user terminal to perform various analysis according to heart rate variability. Results can be checked in real time.

6 is a diagram illustrating a screen in which one or more analysis parameters shown in FIG. 5 are extracted and classified according to a time series and output through a user terminal.

Referring to FIG. 6 , the analysis server 120 may extract and classify one or more analysis parameters described above according to a time series by date and time, and then transmit them to the user terminal. Accordingly, the user can check how his/her heart rate variability changes every day based on the time series analysis data output to his/her user terminal.

Next, a process of measuring a user's heart rate variability using the heart rate variability measuring system 100 through a wearable electrocardiogram patch and a process of analyzing the measurement result and outputting it through the user terminal will be described in order.

FIG. 7 is a view sequentially illustrating a process of measuring heart rate variability through the heart rate variability measuring system 100 through the wearable electrocardiogram patch shown in FIG. 1 .

Referring to FIG. 7 , first, as the user attaches the wearable ECG patch to his/her body part, the user's myocardial activity current data and activity amount data are acquired through the wearable ECG patch ( S701 ). It may be in a form that can be worn as an accessory, or may be in the form of a patch directly attached to the body surface of the user.

Next, the myocardial activity current data and activity amount data are transmitted to the analysis server through the wearable ECG patch and the user terminal connected via Bluetooth (S702).

The analysis server determines whether the current user is actively moving or resting stably based on the activity data, and if it is determined that the ECG is in a stable state capable of measuring the heart rate, the user's heart rate variability (HRV) is measured and Analyze (S703).

Next, the analysis server generates HRV report data according to the analysis result and transmits it to the user terminal, so that the corresponding HRV report is output through the screen of the user terminal (S704). Through this, the user can check his/her heart rate variability and health status in real time regardless of time and place.

Although the above has been described with reference to the preferred embodiments of the present invention, those skilled in the art can variously modify and change the present invention without departing from the spirit and scope of the present invention as set forth in the claims below. You will understand that you can.

100: Heart rate variability measurement system through wearable ECG patch
110: wearable electrocardiogram patch
110a: inertial measurement sensor
120: analysis server

Claims (8)

a wearable electrocardiogram patch that contacts the user's body and acquires myocardial activity current data; and
The user's myocardial activity current data is obtained through a user terminal connected to the wearable electrocardiogram patch in a wireless network manner, and heart rate variability (HRV) is measured and analyzed based on the myocardial activity current data, and the result data is provided to the user terminal and an analysis server that outputs the result data through the user terminal.
The analysis server,
Based on the amount of activity data generated by measuring the amount of activity according to the change of time of the user, the point in time when the numerical value of the user's movement becomes less than a preset threshold value is determined as a static state of the user's activity amount, and the ECG waveform for that time point to classify it into P-wave, Q-wave, R-wave, S-wave and T-wave by analyzing
In the case of a normal waveform in which the electrocardiogram waveform continuously appears, the RR interval, which is the interval between the R peak value and the R peak value, is determined as the Normal to Normal (NN) interval of the normal waveform,
If the ECG waveform appears as a V (Ventricular) waveform, it is determined as CI (Coupling Interval),
A system for measuring heart rate variability through a wearable electrocardiogram patch, characterized in that extracting only the Normal to Normal (NN) interval of the normal waveform, measuring and analyzing the heart rate variability (HRV), and providing the result data to the user terminal.
According to claim 1,
The wearable electrocardiogram patch,
Including; an inertial measurement sensor (IMU sensor) for generating activity data by measuring the amount of activity according to the change of time of the user;
A heart rate variability measurement system using a wearable electrocardiogram patch, characterized in that the activity amount data is included in the myocardial activity current data and then provided to the analysis server through the user terminal.
delete According to claim 1,
The analysis server,
Based on the heart rate variability, any one or more parameters among a heart rate variation parameter, a heart rate variability parameter, a Detrended fluctuation analysis (DFA) parameter for myocardial activity current, and a frequency domain parameter for myocardial activity current are analyzed and then analyzed result data provided to the user terminal,
The frequency domain parameters for the myocardial activity current are SRD, which is the reliability of the measurement result, TF, which is the log value of the total power in the frequency domain band, VLF, which is the log value of the power in the frequency band, which is 0.003 to 0.04 Hz, and 0.04 to 0.15 Hz. LF, which is the logarithmic value of power in the frequency band, HF, which is the logarithmic value of power in the frequency band of 0.15 to 0.4Hz, LF/HF, which is the ratio between LF and HF, and PSI (Pressure Stress), a measure of stress with the pressure applied to the heart Index; Physical Stress Index), and MSI (Mental Stress Index; Mental Stress Index), which is a relative ratio of sympathetic nerves and parasympathetic nerves, a heart rate variability measurement system through a wearable electrocardiogram patch.
Acquiring the user's myocardial activity current data through the wearable ECG patch in contact with the user's body; and
The analysis server acquires the user's myocardial activity current data through a user terminal connected to the wearable electrocardiogram patch in a wireless network manner, measures and analyzes heart rate variability (HRV) based on the myocardial activity current data, and then analyzes the result data providing to the user terminal, and allowing the analysis server to output the result data through the user terminal;
The step of outputting the result data through the user terminal in the analysis server,
In the analysis server, based on the activity amount data generated by measuring the amount of activity according to the change of time of the user, the point in time when the numerical value of the user's movement becomes less than a preset threshold value is determined as a static state of the user's activity amount, and the corresponding time point Analyzing the electrocardiogram waveform for , classifying it into P wave, Q wave, R wave, S wave and T wave, and measuring the R peak value for the R wave;
In the step of measuring the R peak value for the R wave,
In the case of a normal waveform in which the electrocardiogram waveform continuously appears, the RR interval, which is the interval between the R peak value and the R peak value, is determined as the Normal to Normal (NN) interval of the normal waveform,
If the ECG waveform appears as a V (Ventricular) waveform, it is determined as CI (Coupling Interval),
Using a heart rate variability measuring system through a wearable electrocardiogram patch, characterized in that only the Normal to Normal (NN) interval of the normal waveform is extracted and the heart rate variability (HRV) is measured and analyzed and the result data is provided to the user terminal How to measure heart rate variability.
6. The method of claim 5,
Acquiring the user's myocardial activity current data through the wearable electrocardiogram patch in contact with the user's body includes:
generating activity amount data by measuring an activity amount according to a time change of a user through an inertial measurement sensor (IMU sensor) included in the wearable ECG patch; and
In the wearable ECG patch, including the activity amount data in the myocardial activity current data and then providing the activity amount data to the analysis server through the user terminal; a heart rate variability measurement system through a wearable ECG patch, comprising: Method for measuring heart rate variability using
delete 6. The method of claim 5,
The step of outputting the result data through the user terminal in the analysis server,
In the analysis server, based on the heart rate variability, any one or more parameters among a heart rate variation parameter, a heart rate variability parameter, a Detrended fluctuation analysis (DFA) parameter for myocardial activity current, and a frequency domain parameter for myocardial activity current are analyzed, , providing the analyzed result data to the user terminal to be output through the user terminal; further comprising,
The frequency domain parameters for the myocardial activity current are SRD, which is the reliability of the measurement result, TF, which is the log value of the total power in the frequency domain band, VLF, which is the log value of the power in the frequency band, which is 0.003 to 0.04 Hz, and 0.04 to 0.15 Hz. LF, which is the logarithmic value of power in the frequency band, HF, which is the logarithmic value of power in the frequency band of 0.15 to 0.4Hz, LF/HF, which is the ratio between LF and HF, and PSI (Pressure Stress), a measure of stress with the pressure applied to the heart Index; physical stress index), and a method for measuring heart rate variability using a heart rate variability measurement system using a wearable electrocardiogram patch, characterized in that it includes MSI (Mental Stress Index), which is a relative ratio of sympathetic and parasympathetic nerves. .

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