KR101410989B1 - Methode for ECG and Stress Detection - Google Patents

Abstract

The present invention relates to an apparatus and method for obtaining long-term electrocardiogram (ECG) and analyzing stress, and a method for measuring and recording electrocardiogram and motion monitoring apparatus of a subject for 168 hours (7 days) or more in continuous real- So as to acquire only the image data.
SUMMARY OF THE INVENTION An object of the present invention is to provide an electrocardiogram sensor which makes contact with the skin of a subject, a signal processing device for continuously storing ECG signals sensed by the ECG sensor in real time, A long-term electrocardiogram acquisition and stress analyzer comprising a stress analyzer, the signal processor comprising: a filtering and amplifying unit for filtering and analog-amplifying an electrocardiogram signal sensed by an electrocardiogram sensor; An acceleration sensor for sensing three-axis acceleration data according to movement of the subject; The electrocardiogram signal is input through the connection terminal and converted into a digital signal. The electrocardiogram data is acquired from the electrocardiogram signal and stored. The 3-axis acceleration data input through the acceleration sensor is input to the electrocardiogram signal A microcontroller for storing data in a real time synchronous manner and transmitting the data to a stress analysis device; And a memory unit for storing the electrocardiogram data and the three-axis acceleration data synchronously in the time domain under the control of the microcontroller.

Description

[0001] METHOD FOR FORMING ECG AND STRESS DETECTION [0002]

The present invention relates to a long-term stress monitoring system in a non-restrained state, and more particularly, to a long-term stress monitoring system in which an electrocardiogram signal measured by a body- And obtaining only a pure mental stress signal from which the stress signal due to the motion is removed, and a stress analysis method for obtaining the long time electrocardiogram.

In general, mental stress increases the blood stress hormones or increases the sympathetic nerves, resulting in various symptoms and consequences. Emotional states such as stress and anxiety, depression, and hostility affect the hormones in the body, To secrete hormones such as chronic diseases not only worsen, but also increase the incidence of heart disease. In other words, mental stress is a cause of various chronic diseases such as diabetes, hypertension, hyperlipidemia, cardiovascular disease and depression.

Various bioinformation such as electrocardiogram, electromyogram, pulse, skin temperature, skin electrical resistance and respiration rate obtained from the subject are used as important parameters for analyzing the mental stress and health condition of the subject.

Here, electrocardiogram (ECG) is recorded on the surface of the body by a change in dislocation that occurs when excitation occurs in the heart and disappears. It has unique waveform information such as P-QRS-T and is called ECG or EKG.

Pulse waves are acquired using a photoplethysmograph (PPG), and are used as a signal for estimating the state of the heartbeat activity by measuring the blood flow flowing through the blood vessel using the optical characteristics of the living tissue.

In this way, the heart rate and heartbeat interval (RRI: R-R interbals) are extracted from the obtained electrocardiogram or pulse wave, and heart rate variability (HRV) is calculated from the RRI.

The HRV has been actively studied as a bio-signal information analysis technique for health maintenance and disease prevention. Particularly, it is possible to determine the degree of action of the autonomic nervous system (sympathetic and parasympathetic nerves) of the body by analyzing the heart rate change amount, The psychological stress of the subject is quantified by the parameter.

Recently, the electrocardiogram measuring device is provided in the electrocardiogram examination room of the hospital, so that there is a fixed type which is measured when the patient is lying on the bed, and it is made portable so that the patient can carry it easily and can easily measure the electrocardiogram There is a portable type, which is attached to the chest in the form of a pace, so that remotely measured electrocardiogram signals can be transmitted to a mobile phone or a computer as a wireless signal.

FIG. 1 is a block diagram of a stress monitoring apparatus using electrocardiogram measurement according to the related art, and is disclosed in Korean Patent Registration No. 10-1006534.

That is, as shown in FIG. 1, the stress monitoring apparatus 10 includes an electrode 11, and the electrode 11 is attached to the user's body.

In addition, the stress monitoring device 10 is manufactured in a size that is easy to carry. The electrocardiogram data collected through the electrode 11 is recorded in real time to analyze the HRV signal, and the stress index is calculated, Through the display window.

The stress index is transmitted to the user's mobile communication terminal 20, that is, the mobile phone, and the mobile phone transmits the received stress index again to the server built in the hospital wirelessly.

The apparatus and method for monitoring a stress using the electrocardiogram according to the related art have the effect of accurately quantifying the degree of stress by extracting the inherent stress index using the existing stress analysis method, By measuring the stress in real time objectively and sharing it with the hospital medical system, it can be applied to various diseases related to stress such as diabetes, myocardial infarction, digestive dysfunction, depression and so on to understand the degree of stress and to prevent disease deterioration There is an effect.

However, due to the limitation of the driving power generated due to the power consumption required for communication with the mobile phone when measuring the electrocardiogram of the subject in order to acquire the electrocardiogram of the non-restrained unconscious state according to the related art, only a short time electrocardiogram And can not acquire a long-lasting ECG for more than a week.

In addition, since the wires for connecting the stress monitoring device 10 and the electrode 11 are worn, they are very inconvenient for daily life, and thus there is a limit in measuring the electrocardiogram in the daily life of the subject.

In addition, when the subject is attached to or worn on a part of the body, the subject can not distinguish the generated stress signal due to the movement or posture change and the pure mental stress, so that it is difficult to calculate only the mental stress.

Accordingly, the present invention provides an electrocardiogram and motion monitoring device capable of continuous measurement and recording for 168 hours (7 days) or more, and is attached to a part of the body in a patch form to provide an ultra-small, And to provide a long-term electrocardiogram acquisition and stress analysis apparatus using an ultra-lightweight autonomic nervous system (ANS) analysis technique.

It is another object of the present invention to provide a patch type long-term electrocardiogram acquiring device which includes an accelerometer to measure the movement or attitude change of a user synchronized with the electrocardiogram in the time domain, and to obtain a pure mental stress state And to provide a long-term ECG acquisition and stress analysis apparatus and method.

In order to accomplish the object of the present invention, there is provided an apparatus for acquiring and analyzing long-term electrocardiogram, comprising: an electrocardiogram sensor for contacting the skin of a subject; a signal processor for continuously storing electrocardiogram signals detected from the electrocardiogram sensor in real time; And a stress analysis device for analyzing the mental stress of the subject from the transmitted electrocardiogram signal, wherein the signal processing device comprises a filtering and amplifying part for filtering and analog-amplifying the electrocardiogram signal detected from the electrocardiogram sensor, ; An acceleration sensor for detecting three-axis acceleration data according to the motion of the subject; The electrocardiogram signal amplified by the filtering and amplifying unit is input through a connection terminal and converted into a digital signal. The electrocardiogram data is acquired and stored from the electrocardiogram signal, and the 3-axis acceleration data input through the acceleration sensor is input A microcontroller for synchronously storing the electrocardiogram signal in real time and transmitting the electrocardiogram signal to the stress analyzer; And a memory unit for synchronously storing the ECG data and the three-axis acceleration data in a time domain under the control of the microcontroller.

Here, the electrocardiogram sensor comprises a three-point electrocardiogram electrode formed in a patch shape attached to the body of the subject in combination with the signal processing apparatus and detecting a minute current signal in contact with the skin of the subject. And a fixing member of a conductor connected to the three-point electrocardiogram electrode by a signal line and electrically connecting the other end of the signal line to the signal processing apparatus and fixing the electrode to the electrocardiogram sensor of the patch type, The electrocardiograph sensor and the signal processing device are detachably attached to the fixing member.

The stress analyzing apparatus includes a smartphone or a personal computer equipped with a program for extracting only mental stress by removing electrocardiographic stress by analyzing electrocardiogram data and triaxial acceleration data of the subject transmitted from the signal processing apparatus do.

The microcontroller includes an analog / digital converter for converting the analog electrocardiogram signal input from the filtering and amplifying unit into a digital signal; A serial communication unit for driving the acceleration sensor and receiving 3-axis acceleration data; A central processing unit for acquiring and storing electrocardiogram data from the electrocardiogram signal through the analog / digital converter, receiving the three-axis acceleration data input through the acceleration sensor, and storing the synchronous data in the time domain in the electrocardiogram signal; A RTC for providing a real time clock to the central processing unit; And an external communication unit for transmitting the electrocardiogram data and the three-axis acceleration data stored in the memory unit to the stress analysis apparatus; A memory controller for storing or accessing data in the connected memory unit under the control of the central processing unit; And an external input / output unit for sensing the on / off operation selection of the signal processing apparatus and communicating an operation and a power status display control signal.

In order to achieve the object of the present invention, a long-term ECG acquisition and stress analysis process is performed by acquiring a continuous electrocardiogram signal from a subject for a predetermined period of time, calculating RRI from the electrocardiogram signal, And a stress analysis method for analyzing a mental stress of a subject by receiving the electrocardiogram data and the three-axis acceleration data stored in the signal processing device in the stress analysis device, the method comprising the steps of: A first step of transmitting the electrocardiogram data and the three-axis acceleration data stored in the memory to the stress analysis device; A second step of calculating a stress index for each time domain from the electrocardiogram data received in the first step; A third step of determining a degree of stress of the subject in the stress index calculated through the second process; A fourth step of analyzing the motion and posture of the subject in the time domain from the three-axis acceleration data transmitted in the first step; A fifth step of measuring a time domain in which there is no movement according to the posture of the subject analyzed through the fourth process; And a sixth step of comparing the time domain of the posture without motion of the subject calculated in the fifth step with the stress index of the time domain of the third process to determine a state of mental stress according to posture without movement of the subject; And a control unit.

The long-term electrocardiogram acquisition and stress analysis method according to the present invention is a method for obtaining a long-term electrocardiogram (ECG) and analyzing the stress using a patch-type electrode, 7 days) or more, and an acceleration sensor is provided in the signal processor to measure the movement and posture of the user synchronized with the electrocardiogram in the time domain to measure the stress signal due to the motion, It is possible to measure only pure mental stress by analyzing the stress in the fixed posture excluding the stress due to the motion.

1 is a block diagram of a stress monitoring apparatus using electrocardiogram measurement according to the prior art,
FIG. 2 is a view showing an embodiment in which a long-term ECG acquisition and stress analysis apparatus according to an embodiment of the present invention is worn.
FIG. 3 is a schematic configuration diagram of an apparatus for obtaining long-term electrocardiogram and a stress analyzer according to an embodiment of the present invention,
4 is a block diagram of a signal processing apparatus according to an embodiment of the present invention,
FIG. 5 is a block diagram of a microcontroller in FIG. 4,
FIG. 6 is a flowchart of the operation of the analog / digital converter in FIG. 5,
7 is a flowchart illustrating an operation of a signal processing apparatus according to an embodiment of the present invention,
FIG. 8 is a flowchart of a long-term stress analysis process according to an embodiment of the present invention,
9 is a graph showing a process for calculating a stress index in a stress analysis apparatus,
FIG. 10 is a graph illustrating a 3-axis set value according to a position of a signal processing apparatus incorporating an acceleration sensor according to an embodiment of the present invention,
FIG. 11 is a graphical representation of an analysis and analysis of movement and posture of a subject by analyzing 3-axis acceleration data calculated from an acceleration sensor according to an embodiment of the present invention,
12 is an x-axis acceleration graph and a stress index graph according to an embodiment of the present invention.

The construction and operation of an apparatus and method for obtaining long-term electrocardiogram and stress analysis according to an embodiment of the present invention will be described in detail with reference to the accompanying drawings.

FIG. 3 is an overall block diagram of an apparatus and method for obtaining long-term electrocardiogram according to an embodiment of the present invention. The apparatus includes a patch-type electrocardiogram sensor 100 attached to a body of a subject, Acquires an ECG by amplifying and filtering an electric signal sensed by the electrocardiogram sensor 100, calculates and stores an RRI from the electrocardiogram, acquires 3-axis acceleration data from the built-in acceleration sensor, And the three-axis acceleration data is received from the signal processing device 200 to analyze the entire stress of the subject, and the stress due to the movement of the subject is removed to obtain purely mental stress And a stress analysis device 300 for analyzing the stress.

Here, the electrocardiogram sensor 100 is electrically coupled with and fixed to the signal processing device 200 as shown in FIGS. 2 and 3. The electrocardiogram sensor 100 is provided with a patch shape Point electrocardiogram electrodes 101 to 103 for sensing a minute current signal in contact with the skin of the subject and a signal line 104 connected to the three-point electrocardiogram electrodes 101 to 103, And a conductive member (105) for electrically connecting the other end of the line (104) to the signal processing device (200) and for fixing the electrocardiograph sensor to the patch type, wherein the fixing member (105) The electrocardiogram sensor 100 and the signal processing device 200 are preferably configured as snap buttons to facilitate detachment.

As shown in FIG. 3, the outside of the signal processing apparatus 200 includes a three-point connection terminal 201 for electrically conducting and fixing the respective fixation members 105 of the electrocardiogram sensor 100 of the patch type And a connector 202 for wired connection between the signal processing device and the stress analysis device, each of which is provided with respective display portions 225 for indicating a power state and a power on / off state.

FIG. 4 is a detailed block diagram of a signal processing apparatus according to an embodiment of the present invention. The signal processing apparatus includes a filtering and amplifying unit 210 for filtering the electrocardiogram sensed by the electrocardiogram sensor 100 to a predetermined band, An acceleration sensor 221 built in the signal processing apparatus 200 for measuring a 3-axis acceleration signal according to the motion of the examinee, and an electrocardiograph signal amplified by the filtering and amplifying unit 210 through a connection terminal Acquires and stores the electrocardiogram data from the electrocardiogram signal, receives the 3-axis acceleration data input through the acceleration sensor 221, stores the 3-axis acceleration data in synchronism with the electrocardiogram signal in real time, A microcontroller 230 for transmitting the electrocardiogram data and the three-axis acceleration data to the time domain under the control of the microcontroller 230; A button unit 224 for on / off control of the signal processing apparatus 200 by the user, and a control unit 223 for controlling the operation state of the signal processing apparatus 200 and the battery charging state And a battery unit 222 for supplying driving power to the respective units of the signal processing apparatus 200. The display unit 225 displays the display unit 225,

The filtering and amplifying unit 210 includes a patch connector 211 for detecting an electrocardiogram signal input from the electrocardiogram sensor 100 and a controller 211 for controlling the electrocardiogram signal input through the patch connector 211 to a primary amplification A first band-pass filter and an amplifier 213 for performing filtering and second-order amplification in a band set after the first amplification in the precision measurement amplifier 212, and a second band- A notch filter 214 for removing power supply noise, a second band filter for filtering and thirdly amplifying the electrocardiogram signal passed through the notch filter 214 to a predetermined band and then transmitting the amplified signal to the signal processor 200, (215).

The memory unit 223 may include a flash memory or an external detachable micro SD memory card. Preferably, the memory unit 223 is embedded in the signal processing apparatus 200 as a flash memory.

The memory unit 223 stores ECG or RRI data. Preferably, the central processing unit 250 acquires and stores only the RRI in the ECG, thereby reducing the memory capacity.

The battery unit 222 may be built in the signal processing apparatus 200 or may be attached to the patch electrocardiogram sensor 100 in the form of an external paper battery. In the case of an external paper, 200 can be reduced in size.

5 is a detailed block diagram of the microcontroller 230. The microcontroller 230 includes an analog / digital converter (ADC) 231 for converting an analog electrocardiogram signal input from the filtering and amplifying unit 210 into digital data, A serial communication unit 240 for driving the acceleration sensor 221 and receiving the 3-axis acceleration data, and a controller 240 for calculating and storing the RRI data from the electrocardiogram input through the analog / digital converter 231, A central processing unit 250 for receiving the 3-axis acceleration data input through the central processing unit 250 and storing the 3-axis acceleration data in the time domain in synchronism with the ECG signal, and a RTC An external communication unit 234 for transmitting the electrocardiogram data and the three-axis acceleration data stored in the memory unit 223 to the stress analysis apparatus 300; In control A memory controller 235 for storing or accessing data in the memory unit 223 connected to the memory unit 223, and a memory controller 235 for detecting the on / off operation selection of the signal processing unit 230, An external input / output unit 238, a ROM 236 on which an operating program, a communication program, a program for acquiring RRI from the electrocardiogram of the signal processing apparatus 200, and the like are stored, and a RAM (237).

The electrocardiogram data and the three-axis acceleration data stored in the signal processing device 200 are subjected to the stress analysis using USB or a wireless communication method such as USB, UART, RF, Bluetooth, Zigbee, NFC To be transmitted to the device 300, and preferably to be transmitted through the USB communication method.

The stress analysis device 300 receives the 3-axis acceleration data synchronized with the electrocardiogram data from the signal processing device 200, analyzes the total stress state of the subject by time, and calculates the motion of the subject determined by the 3-axis acceleration data, The electrocardiogram data is an electrocardiogram and an RRI (RR interval) or a heart rate variability (HRV) converted into a digital signal by the electrocardiogram signal, and preferably the heart rate variability The central processing unit 250 acquires and stores the RRI data and transmits the RRI data to the stress analysis device 300.

The stress analysis device 300 calculates the parameters (HR, SDNN, LF, HF, LF / HF) for stress analysis from the RRI received from the signal processing device 200, A smart pad, or a personal computer equipped with an algorithm for calculating the real-time motion and attitude data from the three-axis acceleration data and acquiring only the stress in the motionless state.

Hereinafter, the operation of the present invention will be described in detail with reference to FIGS. 2 to 12. FIG.

First, the present invention is characterized in that the fixation member 105 of the patch-type electrocardiogram electrode 100 is fastened to the connection terminal 201 of the signal processing apparatus 200, and the button unit is turned on at the side of the signal processing apparatus 200 The signal processing apparatus 200 operates normally to acquire an electrocardiogram signal while acquiring three-axis acceleration data through the built-in acceleration sensor 221. The three-

FIG. 6 is a flowchart illustrating an operation of the apparatus for obtaining long-term electrocardiogram and analyzing stress according to an embodiment of the present invention. When the signal analysis apparatus 300 is connected with the signal processing apparatus 200 turned on, The central processing unit 250 detects the connection through the external communication unit 234 and controls the memory controller 235 to transmit the electrocardiogram data and the triaxial acceleration data stored in the memory unit 223 to the stress analysis apparatus 300).

 When the external communication unit 234 is a USB communication module, the external communication unit 234 transmits electrocardiogram data and three-axis acceleration data to the stress analysis apparatus 300 through a USB communication method. The external communication unit 234 transmits data, such as Bluetooth, The data is transmitted to the stress analysis device 300 using the wireless communication method.

In addition, the central processing unit 250 checks the state of charge of the battery unit 222 through the power management unit 239, and turns off the power when the battery unit 222 is in the discharge state.

If the state of charge of the battery 222 is normal, the central processing unit 250 starts acquiring the electrocardiogram signal through the A / D converter 231.

That is, the electric signal sensed by the electrocardiogram sensor 100 is input to the filtering and amplifying unit 210, amplified to a predetermined level, and filtered to obtain a stable electrocardiogram signal by filtering external noise such as motion noise.

The electrocardiogram signal input through the patch connector 211 is amplified to a predetermined gain (Gain = 10) through the precision measurement amplifier 212 and then amplified by the first band filter / amplifier 213 Gain = 2 ~ 68), and filtering is performed in the set band (0.36 ~ 48Hz).

The ECG signal passed through the first bandpass filter / amplifier 214 is extracted through a notch filter 214 to a 60 Hz power supply noise.

After power supply noise is removed through the notch filter 214, the signal is subjected to third-order amplification with a gain (DC Gain = 22) set through the second band-pass filter / amplifier 215 and secondary filtering is performed with the set band (5.9-48 Hz) .

 Thus, the second band-pass filter / amplifier 215 removes external noise at the signal input terminal and prevents the base line from being shaken, thereby obtaining a stable signal. Thus, the micro- 230 to detect undistorted RRIs.

FIG. 5 is a detailed block diagram of a microcontroller according to an embodiment of the present invention. The electrocardiogram signal output from the filtering and amplifying unit 210 is input to an analog / digital converter 231 of the microcontroller 230 And then the valid R-peak and RRI are obtained through the algorithm. Meanwhile, the three-axis acceleration data input through the acceleration sensor 221 is stored in synchronism with the electrocardiogram in the time domain.

As shown in FIG. 7, the analog-to-digital conversion unit 231 calculates a slope using linear regression to obtain valid electrocardiogram data through a skip step (Skip) 231a, If it does not become flat, it skips, and when it becomes flat, it is decided by effective electrocardiogram.

The ECG block is processed for 2 seconds through the gain control step 231b and the maximum value MAX is positioned at 1,000 from the Y coordinate (-2,048-2,2048), and the gain is adjusted up or down And a standardized signal is obtained.

The R-peak is obtained through the following normal process (Normal) 231c. For this, the maximum (Max) and minimum (Min) values are calculated through a moving window. Here, the size of the window is experimentally calculated, and the R-peak is determined with the maximum / minimum value and the slope.

The signal and noise classification refers to the current value and the previous value (to store a certain number of values), and if it is within ± 30% of the R-peak reference value using the median, it is determined as R-peak.

On the other hand, the R-peak value predicts the time to be calculated, and the R-peak value is regarded as ± 50% in the subsequent section. Here, the R-peak ratio of ± 30% and ± 50% can be changed through many experiments.

The central processing unit 250 buffers the electrocardiogram data sampled at 250 Hz into an electrocardiogram buffer to determine the R-peak, calculates an RRI value from the determined R-peaks, and outputs the RRI value to the memory unit 223, 233 in accordance with the time zone.

Meanwhile, the central processing unit 250 acquires three-axis acceleration data in units of 0.5 Hz according to the movement and attitude of the subject from the acceleration sensor 221 through the serial communication unit 240, and transmits the three-axis acceleration data to the memory controller 235 And stored in the memory unit 223 according to the time zone provided from the RTC 233. [

That is, the memory unit 223 stores the RRI value and the three-axis acceleration data acquired from the subject in real time in synchronism with the time domain.

If the state of charge of the battery unit 222 is discharged (Low) through the power management unit 239 in the central processing unit 250 or when power off is detected through the button unit 244, The operation of the processing apparatus 200 is stopped.

The central processing unit 250 detects that the stress analyzer 300 is connected to the connector 202 through the external communication unit 234 and transmits the RRI stored in the memory unit 223, And transmits the axial acceleration data through the connector 202.

For example, when the computer 301 is connected to the external connector 202 of the signal processing apparatus 200 by the USB communication method, the central processing unit 250 transmits, via the memory controller 235, And accesses the data stored in the external storage unit 223 and is transmitted to the computer 301 through the external communication unit 234. [

The computer 301 acquires only mental stress by a built-in stress analysis algorithm.

8 is a flowchart of a long-term stress analysis process according to an embodiment of the present invention. When the computer 301 has completed transmission of RRI and three-axis acceleration data from the signal processing apparatus 200, parameters for stress analysis (HR, SDNN, LF, HF) through time series analysis and frequency analysis.

The stress index (LF / HF) is calculated from the calculated LF and HF, and the degree of stress in the time domain is analyzed / judged.

FIG. 9 is a graph showing a process for calculating the stress index in the stress analysis apparatus. FIG. 9 (a) is a graph showing an average heart rate (HR) value per minute in the RRI received from the signal processing apparatus 200, (RLRI) is a graph showing SDNN values calculated by time domain analysis of RRI, (c) is a graph showing RRI, LF (Low Frequency) and parasympathetic activity (HF: (LF / HF) calculated from the sympathetic activity (LF) and the parasympathetic activity (HF). FIG.

Thus, the total stress index (LF / HF) in the time domain of the subject is calculated.

10 is a graph showing a relationship between a set value of three axes (x, y, z) according to the position of a signal processing apparatus 200 having an acceleration sensor 221 according to an embodiment of the present invention, The subject is standing upside down with x = +1, y = 0 and z = 0, (b) the subject is standing with x = -1, y = 0, z = 0, (C) is a state in which the subject is lying to the left with x = 0, y = -1, z = 0, and (d) (F) is a state in which x = 0, y = 0, z = +1, and the subject is lying on the right side. Is set to be in a prone state.

The attitude and motion state of the subject can be determined according to the change of the three-axis acceleration value when the subject is wearing a daily life with the signal processing device 200 set as described above.

11A and 11B are graphical representations of analysis and determination of the motion and attitude of the subject by analyzing the 3-axis acceleration data calculated from the acceleration sensor according to the embodiment of the present invention. FIGS. 11A, y, and z-axis acceleration data.

In the graphs (a) and (b), the x, y, and z values are referenced to determine the subject's movement and posture such as whether the subject is lying down, sitting, walking, lying down or the like.

That is, when x = 0, y = 0 and z = -1, the subject is lying, x = -1, y = 0, z = 0, .

12 is a graph in which the stress index (a) and the x-axis acceleration data (b) according to the embodiment of the present invention are synchronized in the time domain.

As shown in the figure, since the x-axis value is substantially '0' in the interval 1 of the graph (a), it is determined that the subject is lying down, and it is known that the person is sleeping in time.

The masked stress index graph (b) of section 1 can be used to determine the stress state of the subject during sleep.

In the stress index graph (b), the subject's stress state is determined based on the minimum value (min) of the stress index and the average value (B) of the maximum value (C). That is, a stress index higher than the average value (B) is a stress condition in which the subject is judged to be under stress.

In other words, there are areas where stress index is high during sleep, and the stress index is judged to be "mental stress interval during sleep".

As another example of setting the reference value for determining the stress state, the stress index of a large number of subjects classified into age, male, female, and health condition that can affect the stress index is calculated through clinical evaluation and the average standard distribution A database is used to provide the reference value according to the subject's condition.

In the time period (0:00 to 11:20 am), the subject stands and moves while the interval 1 of the x-axis acceleration graph (a) changes beyond the constant amplitude (A) at 'x = -1' It is judged that there is.

In section 2 and section 3, the x-axis acceleration graph (a) is determined to be in a state in which the subject is not sitting or standing with a variation amount within a certain amplitude (A).

When the same time zone of the stress index graph (b) at this time is masked, it is judged that the stress index is less than the stress reference value (B)

However, in section 3, the subject is sitting or standing and does not move. The stress index is higher than the set baseline stress index (B), indicating that the subject is under stress.

In section 4, when the x-axis acceleration graph (a) is examined, it is judged that the subject is walking or running with a large change in the amount of change over the previous amplitude A. In this case, the stress index graph (b) (B), but it is not the mental stress state of the subject, but the stress index according to the exercise.

In section 5, the x-axis acceleration graph (a) is changed from '0' to '-1' and changed to '0' again.

In conclusion, when masking the motion-free lying section 1 (excluding section 5) and the standing and standing sections 2 and 3 in the same time domain graph (b) as shown in the x-axis acceleration graph (a) Can be judged in terms of pure mental stress.

That is, it is judged that the subject is under mental stress in the time domain in which the stress index above the reference value is obtained in the interval 1 (excluding the interval 5) and the interval 3.

The x-axis acceleration graph and the stress index graph (a) (b) shown in FIG. 12 are shown from 0:00 to 23:00, but it is preferable to obtain the x-axis acceleration and stress index lasting 168 hours And it is possible to determine various postures or motions due to the acquisition of the y and z axis acceleration data, and to determine the corresponding stress state.

The graphs shown in FIGS. 9, 11, and 12 can be displayed on the screen of the stress analysis device so that they can be checked and printed, and the state and stress of the subject in each time domain Can be automatically converted to letters or numbers and output.

As described above, according to the embodiment of the present invention, it is possible to acquire the stress state of the subject for 168 hours or longer through the acquisition of the long-term electrocardiogram and the stress analysis process. In addition to the physical stress in the activity of the subject, Mental stress can also be measured and separated.

Although specific embodiments of the present invention have been described and illustrated above, various sensors such as a gyro sensor as well as an acceleration sensor for acquiring the three-axis acceleration data of the present invention are possible, and in addition to the electrocardiogram sensor, It is obvious that various modifications can be made by those skilled in the art such as using a pulse wave sensor.

It should be understood, however, that such modified embodiments are not to be understood individually from the spirit and scope of the invention, and such modified embodiments are intended to be included within the scope of the appended claims.

100: Electrocardiogram sensor 101 to 103: Electrocardiogram electrode
104: signal line 105: fixing member
200: signal processing device 201: connection terminal
202: Connector 210: Filtering /
211: patch connector 212: precision instrumentation amplifier
213, 215: first and second band filter / amplifier 214: notch filter
221: Accelerometer < RTI ID = 0.0 > 222:
223: memory part 224: button part
225: Display section 230: Microcontroller
231: Analog /
233: RTC 234: External communication unit
235: Memory controller 236: ROM
237: RAM 238: External input / output unit
239: Power management unit 240: Serial communication unit
250: central processing unit

Claims (21)

delete delete delete delete delete delete delete delete delete delete delete Storing the three-axis acceleration data of the subject synchronized with the RRI in the time domain in the signal processor, acquiring the electrocardiogram data stored in the signal processor, acquiring the ECG signal from the subject, And a three-axis acceleration data transmitted from the stress analyzer to analyze the mental stress of the subject, the method comprising:
A first step of transmitting the electrocardiogram data and the three-axis acceleration data stored in the signal processing device to the stress analysis device;
A second step of calculating a stress index for each time domain from the electrocardiogram data received in the first step;
A third step of determining a degree of stress of the subject in the stress index calculated through the second process;
A fourth step of analyzing the motion and posture of the subject in the time domain from the three-axis acceleration data transmitted in the first step;
A fifth step of measuring a time domain in which there is no movement according to the posture of the subject analyzed through the fourth process; And
A sixth step of comparing the time domain of the posture without motion of the subject calculated in the fifth step with the stress index of the time domain of the third process to determine a state of mental stress according to the posture in which the subject does not move; Wherein the long-term electrocardiogram acquisition and stress analysis method comprises the steps of: 13. The method of claim 12,
Wherein the electrocardiogram data stored in the signal processor in the first step is one of ECG raw data, R-peak data, and RRI data. 13. The method of claim 12,
Wherein the data transmission method from the signal processing device to the stress analysis device in the first step is a wireless communication method including a wireless communication method including USB, UART, or RF, Bluetooth, ZigBee, and NFC. Analysis method. 13. The method of claim 12,
The second step is to calculate HR, SDNN, LF, HF and stress index (LF / HF) values from the RRI when RRI and triaxial acceleration data are transmitted to the stress analyzer in the signal processing apparatus Long - term ECG acquisition and stress analysis method. 13. The method of claim 12,
The third step calculates the peak value, the minimum value, and the average value of the calculated total stress index or the set time domain, and determines that the subject is in the stress state in the time domain in which the stress index is obtained larger than the average value on the basis of the average value A method for obtaining long - term electrocardiogram and analyzing stress. 13. The method of claim 12,
The third step compares the calculated stress index with the stress index based on the pre-stored stress average standard distribution to determine that the subject is in the stress state when the stress index is within the normal range of the stress average,
The stress average standard distribution is characterized by calculating the RRI from the electrocardiogram of many people by age, sex, race, height, weight, and body fat, and calculating the average standard distribution of stress from the calculated RRI to set the reference stress index Long - term ECG acquisition and stress analysis method. 13. The method of claim 12,
The fourth step measures the posture and the movement of the subject from the state values of the three-axis directions (x, y, z) and the acceleration data of the acceleration sensor built in the signal processing apparatus attached to the subject,
Wherein the attitude of the subject is a lying posture, a sitting posture, a standing posture, a lying posture, and the movement state of the subject is a state in which x, y, z state values oscillate with a constant amplitude. Stress analysis method. 13. The method of claim 12,
The time domain measurement in which the subject does not move in the fifth process is a time domain excluding the time domain in which the posture change of the subject acquired from the 3-axis acceleration data and the vibration of the constant amplitude determined in the walking or running state are calculated A method for obtaining long - term electrocardiogram and analyzing stress. 13. The method of claim 12,
The sixth step determines the stress in the time domain in which there is no movement of the subject calculated in the fifth step,
Wherein the stress state obtained in the lying, sitting or standing posture or the prone position of the subject analyzed in the fourth step is determined as pure mental stress. 13. The method of claim 12,
Wherein the stress analysis device displays the data of each of the second through sixth processes in a graph through a display unit.

Images