KR20140114181A - Method and Apparatus for Stress Analysis and Estimation based on Electrocardiogram Signal - Google Patents

Abstract

Suggested are a method and apparatus for analyzing and estimating stress by extracting an electrocardiogram waveform when a wireless electrocardiogram measuring device is attached. The method for analyzing and estimating the stress using a wireless communications electrocardiogram measurement obtains an inactive electrocardiogram data signal by attaching an electrocardiogram measuring device to a body of a user, and transmits the obtained electrocardiogram data signal to the apparatus for analyzing and estimating the stress using wireless communications. A preprocess for analyzing the obtained electrocardiogram data signal is performed. The preprocessed data signal is normalized. The feature of the normalized electrocardiogram data signal is stored. And, a stress degree is calculated by analyzing the stored electrocardiogram data signal. The calculated stress degree is quantified and displayed.

Description

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a method and apparatus for analyzing and estimating stress based on electrocardiographic signals,

The present invention relates to a method and apparatus for extracting an electrocardiogram waveform in a state of attaching a wireless electrocardiogram measuring apparatus and analyzing and estimating stress using the same. More particularly, the present invention relates to a method and apparatus for measuring an electrocardiogram in a cuff, a leg, and a chest, transmitting the data to a PC using a wireless communication method such as Bluetooth, analyzing and estimating the degree of stress through a preprocessing process and normalization of an ECG data signal .

As the general public's interest in health increases, the desire to check their stress is increasing. Accordingly, there is a need to accurately measure the stress state of the user, and an electrocardiogram is used as a method for this. Korean Unexamined Patent Application Publication No. 10-2000-0063267 discloses an electrocardiogram measuring apparatus for measuring such electrocardiogram and describes a device for easily measuring an electrocardiogram by a patient alone without a measurement assistant.

Electrocardiogram (ECG) is a graphical representation of the change in dislocations generated in the heart from the surface of the body. The heartbeat is constantly changing to maintain homeostasis in the body.

We found that the interval between heartbeats, that is, the R-R wave interval, was not constant when a continuous ECG was measured in healthy adults. The change in the R-R wave interval over time is referred to as heart rate variability (HRV). Although the morphology of each waveform on ECG is useful for the diagnosis of various cardiac diseases due to the basal lesion of the heart, information on the time interval (R-R interval) between consecutive R-R waves may be useful in evaluating the autonomic nervous system function.

Based on the fact that such heart-beat variability is highly dependent on the autonomic nervous system activity pattern, the pattern of R-R wave interval variation can be used in basic autonomic nerve abnormalities and stress tests. However, in general, distortion due to low-frequency noise caused by breathing and power supplies can cause serious errors in accuracy.

The present invention provides a method for improving the accuracy of heart beat variability by analyzing a waveform of an electrocardiogram measured in a user's crotch, a leg, and a chest, analyzing a user's current stress level through an arithmetic step using a stress index And to provide a method and apparatus for estimation.

Further, the present invention provides a method and an apparatus for analyzing and estimating stress that informs or alerts the patient to take precautions for preventive measures for patients who have heart disease or susceptible to shock due to excessive stress have.

In one aspect, the stress analysis and estimation method using the wireless communication electrocardiogram measurement proposed in the present invention measures an inactivity electrocardiogram data signal by attaching an electrocardiogram measurement device to a user's body, The method comprising the steps of: obtaining an electrocardiogram data signal transmitted to a stress analysis and estimation device using the electrocardiogram data signal, performing a preprocessing process for analyzing the acquired electrocardiogram data signal, normalizing the preprocessed data signal, Calculating the degree of stress by analyzing the stored electrocardiogram data signal, and displaying the calculated degree of stress numerically.

Inactivity electrocardiogram (ECG) is measured using a 3-channel electrocardiogram (ECG) device that attaches electrodes to the wrists, legs, and chest of a human body, and the measured ECG data signals can be transmitted to a stress analysis and estimation device using wireless communication.

The step of performing the preprocessing step includes the steps of removing the baseline variation and the power noise to analyze the acquired ECG data signal, differentiating the ECG data signal, squaring each data of the differentiated ECG data signal, Extracting the R-wave minutiae points by reflecting on the actual ECG data signal, and extracting P, QRS, T minutiae points using the extracted R-wave minutiae points can do.

In the step of normalizing and storing the characteristics of the data, the normalizing method can normalize the denormalized one cycle ECG data signal using the Fourier series and Fourier interpolation algorithm.

The normalized ECG data signal may include the location of the R waveform, the R-R interval, the QRS interval, and the T waveform.

The step of calculating the degree of stress can calculate the degree of stress using the position of the normalized P-waveform, the R-R interval, the interval of the QRS group, and the characteristics of the T wave.

In the step of displaying the numerical value, the degree of the calculated stress can be expressed in the form of YES / NO and the predetermined step.

According to an aspect of the present invention, there is provided a stress analysis and estimation apparatus using wireless communication electrocardiogram measurement, which comprises: an electrocardiogram signal receiver for acquiring an electrocardiogram data signal from a user and receiving an electrocardiogram data signal obtained using wireless communication; A normalization and data storage unit for normalizing the one-cycle ECG data signal denormalized using the preprocessed data signal and storing the characteristics of the normalized ECG data signal, a pre-processing unit for performing a preprocessing process for analyzing the ECG data signal, A signal analyzer for analyzing the electrocardiogram data signal and calculating the degree of stress using the positions of the normalized and stored P waveforms, the RR intervals, the intervals of the QRS complexes, and the T waveforms, and a display for displaying the calculated degree of stress numerically Section.

According to embodiments of the present invention, the accuracy of the stress index measurement can be improved by acquiring the electrocardiogram of the user.

Also, according to embodiments of the present invention, graphs and numerical values can be shown through the user's stress index. Therefore, the health state of the user can be easily grasped and the health care can be made easier.

1 is a flowchart of a stress analysis and estimation method according to an embodiment of the present invention.
2 is a diagram for explaining a preprocessing process of an ECG data signal according to an embodiment of the present invention.
FIG. 3 is an explanatory view showing an electrocardiogram measuring apparatus according to an embodiment of the present invention.
4 is a control instruction chart for controlling the operation of the electrocardiogram measuring apparatus according to an embodiment of the present invention.
FIG. 5 is a graph showing a result of normal ECG co-ordination according to an embodiment of the present invention.
FIG. 6 is a graph showing a result of quantifying the degree of stress according to an embodiment of the present invention.
7 is a diagram illustrating a configuration of a stress analysis and estimation apparatus according to an embodiment of the present invention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings.

1 is a flowchart of a stress analysis and estimation method according to an embodiment of the present invention.

Referring to FIG. 1, a stress analysis and estimation method according to an exemplary embodiment of the present invention is a method for acquiring an electrocardiogram data signal and effectively grasping the degree of stress of a user through a preprocessing process and normalization, A step 120 of performing a preprocessing process, a step 130 of storing a normalization and a data signal, a step 140 of calculating a degree of stress, and a step 150 of digitizing and displaying the data.

The electrocardiogram measuring device may be attached to the user's body to measure an inactive ECG data signal (110). At this time, the inactivity electrocardiogram can be measured by using a 3-channel electrocardiogram measuring device attaching an electrode to the cuff, leg and chest of a human body. The measured electrocardiogram data signal can then be transmitted to the stress analysis and estimation device using wireless communication.

For example, the measured ECG data signal may be transmitted to a stress analysis and estimation device using Bluetooth or other wireless communication. Therefore, the stress analysis and estimation apparatus can acquire the inactive electrocardiogram of the user through the process of transmitting the user's inactivity electrocardiogram data signal using the wireless communication.

The stress analysis and estimation device that acquires the electrocardiogram data signal from the electrocardiogram measuring device may perform a preprocessing process for analyzing the acquired electrocardiogram data signal (120). Upon completion of the preprocessing process, the stress analysis and estimation apparatus can use a differential-based QRS detection method for P-wave, QRS complex, and T-wave minutiae point extraction of the ECG data signal. In addition, the stress analysis and estimation apparatus can extract the R-wave minutiae points by using the differential-based QRS detection method. A detailed description of the execution of the preprocessing process will be given later with reference to FIG.

The stress analysis and estimation device may normalize the preprocessed data signal and store the characteristics of the normalized ECG data signal (130). For example, the stress analysis and estimation apparatus can normalize an irregularized one-cycle ECG data signal using a Fourier series and a Fourier interpolation algorithm.

The stress analysis and estimation device can determine the ECG normal coordination rate based on the heart rate, ECG rhythm, R-R wave interval, and QRS group interval through the normalized ECG data signal. For example, the normalization data can be stored as 200 pieces of data per second, and the position of the R waveform, the R-R wave interval, and the QRS group interval characteristics can be stored and stored during the storing process. Thus, the normalized ECG data signal may include the location of the R waveform, the R-R wave interval, the QRS complex interval, and the T waveform.

The stress analysis and estimation apparatus can calculate the degree of stress by analyzing the stored electrocardiogram data signal (140). At this time, the degree of stress can be calculated using the position of the normalized and stored P waveform, the R-R interval, the interval of the QRS group, and the characteristics of the T waveform.

For example, in the case of the R waveform, a normal person can show a P wave, a QRS group, and a T wave in a period of 0.75 second. With this feature, it is possible to detect the maximum R waveform in 0.75 seconds except for the first value and the last value, and the detection equation can be expressed by the following equation (1).

[Equation 1]

By storing the characteristics of the R waveform, the R-R wave interval can be obtained due to the difference between the R waveform and the next R waveform. For example, the R-R wave interval is in the normal range of 0.6-1.0 seconds and can occur at regular intervals. Therefore, if the interval is irregular, it can be seen that the electrocardiogram rhythm is irregular.

In the case of the QRS group, the lowest value that occurs in the left and right based on the previously detected R waveform can be stored as Q wave and S wave, respectively, in the R waveform, 0.25 second before and after.

At this time, if the stored Q-wave and S-wave values correspond to ± 0.25, that is, the maximum value of the search range, it can be regarded as a user's abnormal signal or a bad signal of the measurement signal. The stored R wave, R-R wave interval, and QRS group interval can determine whether or not it is in the normal range according to the ECG normal cochlea. In addition, the R-R wave interval and the QRS group interval can be used as an important index for detecting various heart diseases such as arrhythmia. The error value detected in the QRS complex waveform can be expressed by the reliability of the measurement signal.

The stress analysis and estimation apparatus can display the calculated stress level numerically (150). For example, the calculated degree of stress can be quantified and displayed in the form of YES / NO and predetermined steps. For example, the calculated stress level can be numerically expressed as 0 to 100, and the user can check the degree of stress and the degree of the stress through the monitor or the corresponding display device.

2 is a diagram for explaining a preprocessing process of an ECG data signal according to an embodiment of the present invention.

The pre-processing of the electrocardiogram data signal includes a step 210 of removing baseline variation and power noise, a step 220 of differentiating the electrocardiogram data signal, a step 230 of squaring each data of the electrocardiogram data signal, A step 250 of extracting R-wave minutiae points, and a step 260 of extracting P, QRS, T minutiae points.

The stress analysis and estimation device may remove baseline variation and power supply noise for analysis of the acquired ECG data signal (210).

Electrocardiogram data signals can generally be distorted by low-frequency noise due to breathing and power supplies. Therefore, a preprocessing process may be required to remove the distortion phenomenon in advance. The stress analysis and estimation method proposed in the present invention can use a method of removing baseline variation and power supply noise in an electrocardiogram data signal transmitted from an electrocardiogram measuring apparatus.

The stress analysis and estimation device can differentiate the electrocardiogram data signal from the baseline variation and the power noise (220). It is possible to obtain a section having a large change amount of the gradient through the differential of the electrocardiogram data signal.

The stress analysis and estimation device may square each data of the differentiated ECG data signal (230). The stress analysis and estimation device may squared each data of the differentiated ECG data signal after eliminating the DC component using a baseline variation and a power noise canceling method for the ECG data signal. Therefore, the process of squaring can invert a negative value to a positive value through a squaring operation.

The stress analysis and estimation device may emphasize the R-wave feature points in each cycle of the ECG data signal (240). The R-wave minutiae points in each period of the electrocardiogram can be emphasized by using a moving average filter on the squared ECG data signal.

The stress analysis and estimation apparatus can extract the R-wave minutiae from the actual electrocardiogram data signal (250). The method of extracting the R-wave minutiae points can use a method of finding the highest point in the moving window using a moving average filter. Then, the R-wave minutiae point can be extracted by reflecting the peak to the actual ECG data signal.

The stress analysis and estimation apparatus can extract P, QRS, and T feature points using the extracted R wave feature points (260). The stress analysis and estimation apparatus can extract P, QRS, and T feature points through Webster's electrocardiographic feature point extraction algorithm using the extracted R wave feature points.

FIG. 3 is an explanatory view showing an electrocardiogram measuring apparatus according to an embodiment of the present invention.

FIG. 3 is a diagram showing an electrocardiogram measuring apparatus 320 attached to a user's body 310 to perform the stress analysis and estimation method proposed in the present invention. At this time, the electrocardiogram measuring apparatus 320 may be attached to the user's body and measure the inactivity electrocardiogram data signal. Inactivity electrocardiogram can be measured using a 3-channel electrocardiograph. The 3-channel electrocardiogram measuring device can be measured using a 3-channel electrocardiogram measuring device which attaches an electrode to the cuff, leg and chest of the user's body. The measured electrocardiogram data signal can then be transmitted to the stress analysis and estimation device using wireless communication.

For example, the measured ECG data signal may be transmitted to a stress analysis and estimation device using Bluetooth or equivalent wireless communication. Therefore, the stress analysis and estimation apparatus can acquire the inactive electrocardiogram of the user through the process of transmitting the user's inactivity electrocardiogram data signal using the wireless communication.

4 is a control instruction chart for controlling the operation of the electrocardiogram measuring apparatus according to an embodiment of the present invention.

The electrocardiogram measuring apparatus 320 illustrated in FIG. 3 may operate to perform a stress analysis and estimation method according to a command of a control command diagram.

The ECG measuring apparatus 320 can turn on the power according to the sensor on command and turn off the power according to the sensor off command. The electrocardiogram measuring apparatus 320 can set the electrocardiogram measuring apparatus 320 to the normal mode according to the normal mode command and can set the stream mode according to the stream mode command. In addition, the state of the battery can be indicated by a battery status command.

FIG. 5 is a graph showing a result of normal ECG co-ordination according to an embodiment of the present invention.

The Fourier series and the Fourier interpolation algorithm can be used to normalize the normalized one cycle ECG data signal and to store the characteristics of the normalized ECG data signal. Normalized electrocardiogram data signals can be used to determine the ECG normal coordination rate based on heart rate, electrocardiogram rhythm, RR wave interval, and QRS interval. Therefore, the degree of stress can be derived from the stress index after being stored in the database through the normalization process. The stress indicators can be shown in Table 1.

<Table 1>

FIG. 6 is a graph showing a result of quantifying the degree of stress according to an embodiment of the present invention.

The degree of stress calculated as shown in Fig. 6 can be numerically displayed. The degree of stress can be displayed in the form of YES / NO and a predetermined level. For example, the calculated stress level can be numerically expressed as 0 to 100, and the user can check the degree of stress and the degree of the stress through the monitor or the corresponding display device.

7 is a diagram illustrating a configuration of a stress analysis and estimation apparatus according to an embodiment of the present invention.

The stress analysis and estimation apparatus 700 proposed in the present invention includes an electrocardiogram signal receiving unit 710, a preprocessing unit 720, a normalization and data storing unit 730, a signal analyzing unit 740, and a display unit 750 can do.

 The electrocardiogram signal receiving unit 710 may measure the electrocardiogram data signal from the user through the electrocardiogram measuring device and receive the measured electrocardiogram data signal using the wireless communication.

The electrocardiogram measuring device may be attached to the user's body to measure an inactive ECG data signal. At this time, the inactivity electrocardiogram can be measured by using a three-channel electrocardiogram measuring device which attaches an electrode to the cuff, leg and chest of a human body. The measured electrocardiogram data signal may be transmitted to the electrocardiogram signal receiving unit 710 using wireless communication. The electrocardiogram signal receiving unit 710 may receive the measured electrocardiogram data signal and transmit the same to the preprocessing unit 720 for performing the preprocessing process.

The preprocessing unit 720 may perform a preprocessing process for analyzing the received electrocardiogram data signal.

The preprocessing unit 720 can remove baseline variation and power supply noise for analysis of the acquired electrocardiogram data signal. Electrocardiogram data signals can generally be distorted by low-frequency noise due to breathing and power supplies. Therefore, a preprocessing process may be required to remove the distortion phenomenon in advance.

The preprocessing unit 720 may use a method of removing baseline variation and power supply noise in the electrocardiogram data signal transmitted from the electrocardiogram measuring apparatus. Thereafter, the electrocardiogram data signal from which the baseline variation and the power supply noise are removed is differentiated to obtain a section in which the variation of the gradient is large. Then, each data of the differentiated ECG data signal can be squared. The negative value can be inverted to a positive value by squaring each data of the differentiated ECG data signal. The R-wave minutiae points in each period of the electrocardiogram can be emphasized by using a moving average filter on the squared ECG data signal. By using the moving average filter, the peak in the moving window can be found, and the peak can be reflected in the actual electrocardiogram data signal to extract the R-wave minutiae. Then, using the extracted R-wave minutiae points, all the P, QRS, and T minutiae points can be extracted through Webster's electrocardiogram minutiae extraction algorithm.

The normalization and data storage unit 730 may normalize the normalized one-cycle electrocardiogram data signal using the preprocessed data signal and store the characteristic of the normalized electrocardiogram data signal.

The normalization and data storage 730 may normalize the preprocessed data signal and store the characteristics of the normalized ECG data signal. For example, the normalization and data storage unit 730 may normalize one cycle of the ECG data signal denormalized using a Fourier series and Fourier interpolation algorithm. Thus, normalized ECG data signals can be used to determine ECG normal coordination rates based on heart rate, electrocardiogram rhythm, R-R wave interval, and QRS group interval.

The signal analyzing unit 740 can analyze the electrocardiogram data signal and calculate the degree of stress using the positions of the normalized and stored P waveform, the R-R interval, the interval of the QRS group, and the characteristics of the T waveform.

The display unit 750 can display the calculated degree of stress numerically.

The display unit 750 can display the degree of stress calculated by the signal analyzer 740 in the form of YES / NO and a predetermined level. For example, the calculated degree of stress can be expressed as a numerical value ranging from 0 to 100.

The apparatus described above may be implemented as a hardware component, a software component, and / or a combination of hardware components and software components. For example, the apparatus and components described in the embodiments may be implemented within a computer system, such as, for example, a processor, a controller, an arithmetic logic unit (ALU), a digital signal processor, a microcomputer, a field programmable array (FPA) A programmable logic unit (PLU), a microprocessor, or any other device capable of executing and responding to instructions. The processing device may execute an operating system (OS) and one or more software applications running on the operating system. The processing device may also access, store, manipulate, process, and generate data in response to execution of the software. For ease of understanding, the processing apparatus may be described as being used singly, but those skilled in the art will recognize that the processing apparatus may have a plurality of processing elements and / As shown in FIG. For example, the processing unit may comprise a plurality of processors or one processor and one controller. Other processing configurations are also possible, such as a parallel processor.

The software may include a computer program, code, instructions, or a combination of one or more of the foregoing, and may be configured to configure the processing device to operate as desired or to process it collectively or collectively Device can be commanded. The software and / or data may be in the form of any type of machine, component, physical device, virtual equipment, computer storage media, or device , Or may be permanently or temporarily embodied in a transmitted signal wave. The software may be distributed over a networked computer system and stored or executed in a distributed manner. The software and data may be stored on one or more computer readable recording media.

The method according to an embodiment may be implemented in the form of a program command that can be executed through various computer means and recorded in a computer-readable medium. The computer-readable medium may include program instructions, data files, data structures, and the like, alone or in combination. The program instructions to be recorded on the medium may be those specially designed and configured for the embodiments or may be available to those skilled in the art of computer software. Examples of computer-readable media include magnetic media such as hard disks, floppy disks and magnetic tape; optical media such as CD-ROMs and DVDs; magnetic media such as floppy disks; Magneto-optical media, and hardware devices specifically configured to store and execute program instructions such as ROM, RAM, flash memory, and the like. Examples of program instructions include machine language code such as those produced by a compiler, as well as high-level language code that can be executed by a computer using an interpreter or the like. The hardware devices described above may be configured to operate as one or more software modules to perform the operations of the embodiments, and vice versa.

While the present invention has been particularly shown and described with reference to exemplary embodiments thereof, it is to be understood that the invention is not limited to the disclosed exemplary embodiments. For example, it is to be understood that the techniques described may be performed in a different order than the described methods, and / or that components of the described systems, structures, devices, circuits, Lt; / RTI &gt; or equivalents, even if it is replaced or replaced.

Therefore, other implementations, other embodiments, and equivalents to the claims are also within the scope of the following claims.

Claims (10)

Receiving an inactivity electrocardiogram data signal measured by the electrocardiogram measuring device using wireless communication and acquiring an electrocardiogram data signal;
Performing a preprocessing process for analyzing the acquired ECG data signal;
Normalizing the preprocessed data signal and storing a characteristic of the normalized ECG data signal;
Analyzing the stored ECG data signal to calculate a degree of stress; And
The step of numerically displaying the calculated degree of stress
A method for analyzing and estimating stress using wireless communication electrocardiogram measurement. The method according to claim 1,
The inactivity electrocardiogram data signal is measured using a three-channel electrocardiogram measuring device that attaches an electrode to a cuff, a leg, and a chest of a human body, and the measured electrocardiogram data signal is transmitted to a stress analysis and estimation device using wireless communication
Stress Analysis and Estimation Method Using Radio Communication Electrocardiogram Measurement. The method according to claim 1,
The step of performing the pre-
Removing baseline variation and power supply noise for the acquired ECG data signal analysis;
Differentiating the electrocardiogram data signal;
Squaring each data of the differentiated electrocardiogram data signal;
Emphasizing the R-wave characteristic point in each cycle of the electrocardiogram data signal;
Extracting the R-wave minutiae points by reflecting them on an actual electrocardiogram data signal; And
Extracting P, QRS, and T feature points using the extracted R wave feature points
A method for analyzing and estimating stress using wireless communication electrocardiogram measurement. The method according to claim 1,
The storing of the normalization and data characteristics may include normalizing an ECG data signal of one period denormalized using a Fourier series and Fourier interpolation algorithm
Stress Analysis and Estimation Method Using Radio Communication Electrocardiogram Measurement. The method according to claim 1,
The normalized electrocardiogram data signal includes the characteristics of the position of the R waveform, the RR wave interval, the QRS group interval, and the T waveform
Stress Analysis and Estimation Method Using Radio Communication Electrocardiogram Measurement. The method according to claim 1,
The step of calculating the degree of stress includes calculating the degree of stress using the position of the normalized and stored P waveform, the RR interval, the interval of the QRS complex, and the characteristics of the T waveform
Stress Analysis and Estimation Method Using Radio Communication Electrocardiogram Measurement. The method according to claim 1,
In the step of displaying the numerical value, the calculated degree of stress is expressed in a YES / NO type and a predetermined level and displayed
Stress Analysis and Estimation Method Using Radio Communication Electrocardiogram Measurement. An electrocardiogram signal receiving unit for receiving the inactive electrocardiogram data signal measured by the electrocardiogram measuring apparatus using wireless communication;
A preprocessing unit for performing a preprocessing process for analyzing the received ECG data signal;
A normalization and data storage unit for normalizing one cycle of the electrocardiogram data signal denormalized using the preprocessed data signal and storing characteristics of the normalized electrocardiogram data signal;
A signal analyzer for analyzing the electrocardiogram data signal and calculating the degree of stress using the position of the normalized and stored P waveform, the RR interval, the interval of the QRS complex, and the characteristics of the T waveform; And
A display unit for numerically displaying the calculated degree of stress,
A stress analysis and estimation apparatus using wireless communication electrocardiogram measurement. 9. The method of claim 8,
The preprocessor removes baseline variation and power supply noise for analyzing the received ECG data signal and extracts P, QRS, and T feature points using the extracted R wave feature points
Stress Analysis and Estimation System Using Electrocardiographic Measurement of Wireless Communication. 9. The method of claim 8,
The normalization and data storage unit may normalize an ECG data signal of one period denormalized using a Fourier series and a Fourier interpolation algorithm
Stress Analysis and Estimation System Using Electrocardiographic Measurement of Wireless Communication.

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