KR101628262B1 - Method and apparatus for heart rate calculation using frequency analysis - Google Patents

Abstract

The present invention relates to a technique for calculating the heart rate per minute using frequency analysis. The heart rate signal is measured and collected using a piezoelectric sensor provided under a mattress of a bed, a heart rate signal is detected from the collected heart ballistic signal And a method and an apparatus for converting a detected heartbeat signal into a frequency domain and calculating a frequency component having a peak value in the frequency domain as a heartbeat rate per minute.

Description

[0001] METHOD AND APPARATUS FOR HEAT RATE CALCULATION [0002] USING FREQUENCY ANALYSIS [0003]

More particularly, the present invention relates to a heart rate calculation method using frequency analysis and a technique related to the heart rate calculation method using frequency analysis. More specifically, the present invention relates to a heart rate measurement method using a piezoelectric sensor from a user lying on a bed, Quot;). ≪ / RTI >

The present invention was derived from research carried out as part of the knowledge economy technology innovation project of the future creation science department and the information and communication technology promotion center [task control number: R0101-14-0305, project name: smart healthcare based on non- Bed development].

Ballistocardiogram (BCG) is a signal that measures the trajectory of a heart as a result of blood flow changes in the heart and blood vessels as a result of contraction and relaxation of the heart. Usually, the range of the signal is 0 to 7 mg and the frequency range is 0 to 40 Hz .

Such a ballistocardiogram (BCG) signal, together with an electrocardiogram (ECG) signal, represents the information about the user's heartbeat as an electrical signal.

 Electrocardiogram (ECG) signals attach electrodes to parts of the body close to the heart and measure minute electrical signals from the muscles during heartbeat. The ECG signal is a form that can be analyzed for various heart diseases and is a signal that is mainly measured and analyzed for medical use.

 On the other hand, the BCG signal is a signal that measures the minute vibration of the heartbeat. It can not express the movement of the heart muscle as an electrical signal, but it contains information on the heart rate per minute. In addition, the BCG signal is advantageous in that the user can measure the heartbeat signal of the user without restriction, because it is unnecessary to attach a special electrode or sensor to the body.

The BCG signal is measured mainly when the user is lying on the bed. It can detect the load cell of the entire bed under the bed and measure it. The pressure sensor, the piezoelectric sensor ) Can also be measured. In this case, respiration, movement, and noise signals are measured together. In the measurement, factors affecting the signal include the user's lying position, the material and thickness of the clothes, and the type of the mattress.

 A method of detecting BCG signals without restraint has been actively performed recently. A method of arranging a piezoelectric sensor on a bottom of a bed in a matrix form and a method of changing a weight cell (load cell) And a method of measuring minute vibrations using the above method. The method of using the weight cell (load cell) can be measured when sleeping in the bed, and there is a method of measuring while measuring the weight with a commercial scale.

In the conventional technique of measuring the heart ballistic signal, the intensity of the heart ballistic signal measured by the sensor may vary depending on the weight, key, BMI, age, and gender of the user (subject). Because the fat is more effective than the muscle, the pressure due to heartbeat and respiration cushions the change, and when the muscle is more muscular, the buffer effect becomes larger than when the muscle is not muscle. .

Also, since a general amplifier circuit has a characteristic in which the measurement range (range) becomes smaller as the gain becomes higher, the heart ballistic signal has a higher gain than a general sensor. Therefore, It is easy to see the saturation signal even in small movements. Therefore, the respiration and heart rate waveforms are not observed in the signal including the motion, which can not be restored by the signal processing technology.

In order to reduce the error that occurs when measuring the cardiac tropon signal, a conventional technique for measuring when a subject is under water is disclosed in Korean Patent Registration No. 10-0712198 entitled " .

The prior art suggests a device for measuring the cardiac trajectory during a subject's sleep using a load cell in a bed. Such a device includes a load cell that is mounted at a predetermined position of the bed and senses the weight of the subject, a low-pass filter that outputs a low-band signal from the output of the weight cell, A high-pass filter for outputting a high-band signal, an output of a low-pass filter and a high-pass filter, a low-pass filter which is an output of a low-pass filter or a high-pass filter input through the multiplexer, And a controller for determining the sleep structure of the subject by detecting the sleeping information of the subject from the high-band signal, and a display unit for displaying the determination result of the controller. The measurement of the weight of the subject and the change of the center of gravity in the bed are judged, and the change / intensity measurement of the sleeping person, the attitude change and the heartbeat And to analyze the sleeping structure of the sleeping person.

Such a technique is described as a main purpose of analyzing the sleep structure by using one or more weight cells (Load Cell) in the bed, and it is mentioned that it is possible to additionally measure the cardiac trajectory. However, according to the related art, there is a problem that it is difficult to reduce errors due to respiration and movement of the testee in addition to the heart ballistic signal.

Therefore, it is required to minimize the inconvenience that occurs when the examinee takes the sleeping state in an unfamiliar state and to reduce the error of the cardiac trajectory signal measured at the time of sleeping, thereby improving the reliability of the test result.

Korean Patent No. 10-0712198 (Registered on Apr. 20, 2007)

SUMMARY OF THE INVENTION It is an object of the present invention to provide a method and apparatus for calculating a heart rate per minute using frequency analysis.

More particularly, the present invention relates to a technique for calculating the heart rate per minute using frequency analysis, wherein a heart ballistic signal is measured and collected using a piezoelectric sensor provided under a mattress of a bed, And a method and an apparatus for converting a detected heartbeat signal into a frequency domain and calculating a frequency component having a peak value in the frequency domain as a heartbeat rate per minute.

According to an aspect of the present invention, there is provided a heart rate calculation method comprising: collecting a ballistocardiogram (BCG) signal of a user measured using a piezoelectric sensor; Detecting a heartbeat signal of the user from the heart balloon signal; converting the detected heartbeat signal into a frequency domain using a Fourier transform; calculating a frequency peak of the heartbeat signal converted into the frequency domain; And calculating a heart rate per minute using a peak.

The detecting step may detect the heartbeat signal from the heart ballistic signal using a moving standard deviation filter (MSDF). The moving standard deviation filter may be used to detect a noise component So that the heartbeat signal can be detected.

Calculating a moving standard deviation of the cardiac trajectory signal and the average value using a predetermined window size and an average value of the cardiac traction signals in the window size, The heartbeat signal can be detected from the heart ballistic signal.

And the calculating step may calculate the heart rate per minute using the frequency peak value in a predetermined frequency range from among the heart rate signals converted into the frequency domain. At this time, the predetermined frequency range is 0.67 Hz to 3.33 Hz.

The heart rate calculation apparatus includes a heart ballistic signal collection unit for collecting a user's ballistocardiogram (BCG) signal measured using a piezoelectric sensor, a heart rate signal acquisition unit for detecting the heart rate signal of the user from the collected heart- A frequency domain transformer for transforming the detected heartbeat signal into a frequency domain using a Fourier transform, a frequency domain converter for converting the frequency domain of the heartbeat signal into a frequency domain signal, And a heartbeat rate calculating unit for calculating a heartbeat rate.

The present invention relates to a technique for calculating the heart rate per minute by using frequency analysis. By using a piezoelectric sensor, it is possible to simplify the experiment by minimizing the inconvenience of the examinee by not attaching various test equipment directly to the testee's body .

In addition, a heart valve signal is collected from a subject through a piezoelectric sensor, a heartbeat signal is detected from a collected heart balloon signal, a detected heartbeat signal is converted into a frequency domain using Fourier transform, a frequency component having a peak value in a frequency domain Is calculated as the heart rate per minute, the reliability of the test result can be increased by reducing the error of the measured value.

In addition, since the frequency of the heartbeat signal for the BCG signal is calculated using the frequency analysis of the present invention, the heartbeat period can be calculated more accurately than the actual observation result.

In addition, since the present invention detects the heart rate through the frequency analysis of the piezoelectric sensor, it is possible to increase the reliability of the test result using non-invasive and non-invasive methods, u-Healthcare) can provide a basis for a technique that can automatically detect symptoms related to the Heart Rate Variability during sleep.

1 is a view showing a configuration for collecting a cardiac tidal signal according to an embodiment of the present invention.
2 is a flowchart illustrating a heart rate calculation method according to an embodiment of the present invention.
FIG. 3 is a diagram illustrating a heart beat, a heart rate, and a heart rate variance in an electrocardiogram (ECG) signal according to an embodiment of the present invention.
FIG. 4 is a view showing a heart ballistic signal measured using a piezoelectric sensor according to an embodiment of the present invention.
5 is a diagram illustrating a heart ballistic signal using a moving standard deviation filter according to an embodiment of the present invention.
FIG. 6 is a diagram illustrating a heartbeat signal converted into a frequency domain using Fourier transform according to an embodiment of the present invention. Referring to FIG.
7 is a view illustrating a heart rate calculation apparatus according to an embodiment of the present invention.
FIG. 8 is a graph illustrating a comparison between a cardiac troposphere signal and an electrocardiogram signal according to an embodiment of the present invention.

Other objects and features of the present invention will become apparent from the following description of embodiments with reference to the accompanying drawings.

Preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings. In the following description of the present invention, a detailed description of known functions and configurations incorporated herein will be omitted when it may make the subject matter of the present invention rather unclear.

However, the present invention is not limited to or limited by the embodiments. Like reference symbols in the drawings denote like elements.

1 is a view showing a configuration for collecting a cardiac tidal signal according to an embodiment of the present invention.

A piezoelectric sensor 110 for measuring a cardiac trajectory signal, a piezoelectric sensor 110 for measuring a cardiac tilt signal, a bed sensor L-4060SL for measuring a heart balloonsignal (BCG) A data acquisition board that samples the analog signal of the user's heart ballistic signal measured by the user and transmits it to the PC and a PC that stores the user's heart ballistic signal transmitted from the data acquisition board in a form that can be processed, And can be configured as an application (Acquisition Application).

At this time, the device that stores the user's heart ballistic signal in a machinable form can include not only a PC, a collection application, but also all displayable electronic devices such as a smart phone, a tablet PC, and the like.

The piezoelectric sensor 110 is disposed between the floor of the hospital bed and the mattress, and is disposed so that the user is positioned on the back surface having the largest surface area in the lying position. Thereafter, the data acquisition board amplifies the user's heart ballistic signal generated by the piezoelectric sensor 110 through an analog amplifier 120, and the amplified heart ballistic signal is converted into an analog digital signal (0 to 4095) resolution and 100 Samples / sec. Then, the data acquisition board transmits the user's heart ballistic signal measured by the analog digital converter to the PC via RS232 using serial communication.

At this time, the piezoelectric sensor 110 can be applied to a hospital bed, but it can be used not only for a hospital bed, but also for a thing that can be measured by touching with a user such as a cushion under a wheelchair chair.

Since the piezo-electric sensor 110 is designed to detect minute changes in a person's body weight, the resolution is generally very high. The piezo-electric sensor 110 measures the amount of change in pressure, Depending on the thickness and material of the mattress, the reliability of the measurement results may vary when the mattress is inspected. More specifically, the reliability of the measurement result increases as the material that does not buffer the impact force and the material that can cushion the impact force do not buffer the impact force. For example, a spring mattress made of metal rather than a sponge mattress can provide a clearer heartbeat waveform.

On a similar principle, the fat of the human body will have a buffering effect than the muscles, so it will have the effect of buffering changes in pressure due to heartbeat and respiration. Because muscle mass is also more effective in the case of many muscles than in the absence of muscles, the heart rate and breathing signals will be measured weakly even when the muscle mass is high.

Since the reliability of the measurement result depends on the measurement environment and the body composition of the subject (subject), the piezoelectric sensor 110 intends to propose an algorithm for increasing the reliability of the measurement result through the present invention. have. In addition, not only the subject's weight, height, BMI, but also the age or gender of the subject may influence the reliability of the cardiac trajectory measurement results. The expected result is that as the obesity degree increases, the intensity of the signal measured by the sensor decreases. The present invention will propose an algorithm for improving reliability by coping with various variables according to the contents described later.

As the piezoelectric sensor 110 is installed close to the heart, the signal is clearly and largely measured. Because of the time it takes for the heartbeat signal from the heart to be delivered to the entire body, the heartbeat signal may be less pronounced than at a narrow area when measured over a large area.

The piezoelectric sensor 110 is an electron charging / discharging type device, and charging and discharging of electrons alternately occur in a certain period. However, as the sensor is used for a long time, the charge and discharge are not made on time, which reduces the sensitivity. In general, it is known that a widely used piezoelectric sensor sets its life to about 5 years. In fact, it has been reported that the sensitivity is lowered after about 2 years.

2 is a flowchart illustrating a heart rate calculation method according to an embodiment of the present invention.

The method for calculating the heart rate from the heart trajectory signal of the user (subject) includes collecting the ballistocardiogram (BCG) signal of the user measured using a piezoelectric sensor (S210) The heartbeat signal is detected (S220). At this time, a heart rate signal can be detected from a user's heart ballistic signal using a moving standard deviation filter (MSDF).

In addition, it is also possible to detect a heartbeat signal by removing a noise component of a user's heart ballistic signal using a moving standard deviation filter. At this time, the noise component includes a user's respiration signal, a signal due to the user's movement or movement, and noise.

Also, the moving standard deviation of the heartbeat signal and the mean value is calculated using a predetermined sliding window size and the average value of the heart ballistic signal at the window size, and the calculated moving standard deviation is calculated The heartbeat signal can be detected from the user's heart ballistic signal.

Thereafter, the detected heartbeat signal is converted into a frequency spectrum using a Fourier transform (S230), and the heartbeat signal is converted into a frequency domain using the frequency peak value Peak of the user's heartbeat signal, (S240).

The method of finding the representative frequency of the heart rate from the frequency spectrum is to find the maximum value or to find the intermediate frequency. The method of finding the intermediate frequency means a method of finding a frequency that divides the frequency spectrum section into two halves. When the heart rate frequency is found by the above method, the influence of the noise component can be largely reflected. Therefore, in the present invention, a method of finding the maximum value is used to find the representative frequency of the heart rate. The method of finding the maximum value is more effective than the method of finding the intermediate frequency because it reduces the influence of the high frequency noise or the low frequency noise component.

At this time, the heart rate per minute may be calculated using the frequency peak value in a predetermined frequency range among the heart rate signals converted into the frequency spectrum (Frequency Spectrum), and the predetermined frequency range may be 0.67 Hz to 3.33 Hz have.

For example, the heart rate per minute can be calculated by counting only the frequency peak in a predetermined frequency range for one minute. In addition to the predetermined frequency range, the frequency range may be set differently depending on the case.

Conventionally, a peak detection method has been used to accurately locate the position of the peak of the heartbeat. For this purpose, a number of feature signals (usually three) that can represent the position of the heartbeat peak from the original signal are calculated to form a position candidate of the heartbeat peak, and the position of the heartbeat peak is determined from the formed candidates. The heartbeat period information and the probability density information are utilized to recursively correct the heartbeat. This method has a disadvantage that it is difficult to implement in real time because of high computation amount.

In the present invention, since only one characteristic signal is calculated (moving standard deviation signal), the signal of the heartbeat in contrast to the noise is extracted, and the frequency of the indirect heartbeat is calculated through the frequency analysis, (Less than 300ms, 16Mhz processor).

FIG. 8 is a graph illustrating a comparison between a cardiac troposphere signal and an electrocardiogram signal according to an embodiment of the present invention.

Referring to FIG. 8, in the heart ballistic signal, the heartbeat pattern is represented by H, I, J, and K peak values, and the part recognized as the actual heartbeat is the J peak.

In the heart ballistic signal, the heartbeat pattern appears in various forms due to noise, environment, and personal effects. Usually, the I peak has a noticeable difference depending on environment, measurement conditions, and individual differences, and the H and J peak sizes are not constant. When the I peak is small, only one of the H or J peak appears in a large form. In each of these cases, it is possible to process it in an adaptive manner, but in the present invention, common features of each form are extracted and calculated as one feature signal.

In other words, the characteristics that the amount of change is larger than the adjacent signal are common in various measurement examples considering environment, measurement condition, and individual difference, and standard deviation can be applied to track the amount of change. The standard deviation is a value that can measure the degree of variance of the variance of the population. The larger the degree of variance, the greater the variation.

FIG. 3 is a diagram illustrating a heart beat, a heart rate, and a heart rate variance in an electrocardiogram (ECG) signal according to an embodiment of the present invention.

The heart rate per minute represents the number of heart beats per unit time, and is basically measured in units of 1 minute and uses BPM (Beat per Minute) units. Heart Rate Variability (HRV) refers to the interval between heartbeats in a heartbeat signal, usually in seconds (seconds) or milliseconds.

These heart rates (HR) and heart rate variability (HRV) during sleep can have medical implications. (HRV), blood pressure (Blood Pressure), and Muscle Sympathetic Nerve Activity (HRV) are known to change drastically when the level of sleep is changed The characteristics vary depending on the disorder or insomnia. Therefore, the heart rate (HR) and heart rate variability (HRV) per minute indicate changes in the cardiovascular system and are used as a major feature in the change of the body condition, the presence of the disease, and the type of the disease.

FIG. 4 is a view showing a heart ballistic signal measured using a piezoelectric sensor according to an embodiment of the present invention.

The BCG signal measured through the piezoelectric sensor 110 includes the user's respiration, motion signal, and noise in addition to the heartbeat signal. Figure 4 depicts the peak waveforms of various forms of cardiac balloons (BCG) and actual heartbeat signals in red.

The data of FIG. 4 shows that the BCG signal is not measured in a uniform form even when the same user (examinee) measures the same posture. In addition, low frequency components caused by respiration are different according to the user 's sleep posture.

'a) Single-peak shape' waveform is a single-peak waveform when heartbeat signal is detected. It is easy to detect because the amplitude of heartbeat signal is larger than that of respiratory component. '(b) Multi-Peak Shape' waveform is a waveform in which all heartbeat signals have the form of a multi-peak and the peak value of the heartbeat signal has risen upward. '(c) Complex-Peak Shape' waveform is a waveform when multi-peak and single-peak are irregular. '(d) Ripple Shape' refers to the shape of a heartbeat when it appears as a ripple rather than as a peak. In this case, (a) waveform is the best example to apply a general peak detection method among the waveforms from (a) to (d).

(A) and (b) are the signals measured from the same user (testee). This means that the BCG signal for one person can be changed according to the posture or breathing state of the user (subject). (b) Peak detection is a peak detection method. However, additional multi-peak processing is required. Unlike the peak type as in (a), (b), and (c), the ripple type heartbeat signal as in (d) .

 In addition, the range of the overall signal may vary depending on the signal from the respiration to the user. For this purpose, a high pass filter for eliminating direct current (DC) components should be applied. However, for a signal sampled at 100 Hz, a filter that divides high frequency and low frequency must have a high coefficient. It is also necessary to perform resizing processing on the sampled signal, but most of the waveforms from (a) to (d) through the adaptive threshold are used.

5 is a diagram illustrating a heart ballistic signal using a moving standard deviation filter according to an embodiment of the present invention.

The heartbeat signal shown in FIG. 4 is difficult to detect by a simple peak detection method (Peak Detection), and it is difficult to guarantee its performance in the case of a ripple type heartbeat signal.

A common feature of many heartbeat signals is that the amount of change is greater than other noise. The amount of change may be a momentary change in the form of a peak, or a change in duration, such as a ripple form.

Accordingly, a moving standard deviation filter (MSDF) can be used to effectively find the peak value of the heartbeat signal.

The equation for obtaining the standard deviation of the past signals by the number of sliding window sizes w in the input BCG signal can be expressed by Equation (1).

[Equation 1]

는 해당 신호 범위의 평균을 나타내며 w는 슬라이딩 윈도우 크기(Sliding window Size)를 나타낸다.

Denotes an average of the signal range, and w denotes a sliding window size.

FIG. 5 is a graph showing the measured cardiac trajectory (BCG) signal as a result of a heartbeat signal passed through a mobile standard deviation filter (MSDF), a sliding window size of 0.24 sec (24 samples) to be.

Accordingly, a waveform similar to a heartbeat cycle is calculated for a peak-like heartbeat signal that has undergone a mobile standard deviation filter (MSDF), and a standard deviation value for a corresponding heartbeat signal is also increased in a ripple- .

(d) If we look at the signal, it can be seen that multi-peak can occur in the signal that has been passed through the MSDF by the noise component. However, it is possible to identify the heartbeat period that is markedly higher than the heartbeat (BCG) signal as the high and low of the signal, and remove the low frequency respiration signal and the high frequency noise component together.

FIG. 6 is a diagram illustrating a heartbeat signal converted into a frequency domain using Fourier transform according to an embodiment of the present invention. Referring to FIG.

Referring to the graphs shown in FIG. 5, it can be seen that a noise signal having a value similar to that of a heartbeat signal remains even in a signal passed through a mobile standard deviation filter (MSDF). However, changes in the heartbeat cycle do not change significantly during one or two heartbeats, nor do they change by half or even twice. When the period changes, it changes markedly for about 30 seconds and the change is about 1.3 times. It is not uncommon for the period to change in a lying state, and it appears even after changing the posture.

According to the characteristic of the heartbeat period, if the sinusoidal signal cycle having the largest component in the signal passed through the MSDF is calculated, it can be predicted that the cycle is closest to the heartbeat cycle.

 Therefore, the frequency of the sinusoidal signal can be found from the frequency analysis results of the signal passed through the MSDF using the Fourier transform (Fourier transform). As a result of the frequency analysis on the actual one minute BCG signal, a very low frequency DC offset signal based on the average of the signals passed through the standard deviation filter (MSDF) was detected. Generally, in the case of a normal person, since the heart rate per minute is generally in the range of 40 to 200 in the lying position, it can be assumed that the largest value in the frequency signal between 0.67 Hz and 3.33 Hz And the inverse number of this frequency can be calculated as the heart rate per minute. Here, if the possibility of measurement error is further considered, the upper limit of the heart rate per minute may be increased or the lower limit may be lowered to adjust the detection frequency region.

FIG. 6 is a graph showing a frequency analysis result of a signal passed through the moving standard deviation filter (MSDF) shown in FIG. 5 in a frequency spectrum between 0.67 Hz and 3.33 Hz. Therefore, the reciprocal of the values of each frequency can be confirmed as (a) 2, (b) 2, (c) 1, (d) 0 errors with respect to the heart rate per minute of the corresponding signal.

In practice, the frequency spectrum is set from 0.67 Hz to 3.33 Hz, but the set value can be changed flexibly.

7 is a view illustrating a heart rate calculation apparatus according to an embodiment of the present invention.

The apparatus 700 for calculating the heart rate per minute includes a heart ballistic signal collecting unit 710 for collecting a user's ballistocardiogram (BCG) signal measured using a piezoelectric sensor, A heartbeat signal detecting unit 720 for detecting a heartbeat signal of a user from a heart ballistic signal of a user collected at the heartbeat signal detecting unit 710, a heartbeat signal detecting unit 720 for detecting a heartbeat signal of the user detected by the heartbeat signal detecting unit 720, Frequency domain transforming unit 730 for transforming the frequency domain converted frequency domain signal into a frequency domain transformed frequency domain transformed by the frequency domain transforming unit 730, ).

At this time, the heartbeat signal detector 720 may detect a heartbeat signal from a heart rate signal (BCG) signal measured by a user using a moving standard deviation filter (MSDF).

In addition, the moving standard deviation filter (MSDF) may be used to detect the heartbeat signal by removing the noise component of the cardiac trajectory (BCG) signal measured by the user. In this case, the noise signal refers to a noise including a user's respiration signal, a motion signal, and the like.

Also, a moving standard deviation of a heart ballistic signal and an average value is calculated using a predetermined sliding window size and an average value of a cardiac traction signal in a window size, and the calculated moving standard The heartbeat signal can be detected from the heart ballistic signal using the deviation.

The heartbeat rate calculating unit 740 may calculate the heartbeat rate per minute using the frequency peak value (Peak) in a predetermined frequency range from among the heartbeat signals converted into the frequency domain. At this time, the predetermined frequency range is 0.67 Hz To 3.33 Hz.

However, the frequency range is not limited from 0.67 Hz to 3.33 Hz, and the set value for the frequency range may be changed by the type of noise, measurement method, error, and the like.

The heart rate calculation method according to an embodiment of the present invention 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 recorded on the medium may be those specially designed and constructed for the present invention 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 present invention, and vice versa.

As described above, the present invention has been described with reference to particular embodiments, such as specific elements, and specific embodiments and drawings. However, it should be understood that the present invention is not limited to the above- And various modifications and changes may be made thereto by those skilled in the art to which the present invention pertains.

Accordingly, the spirit of the present invention should not be construed as being limited to the embodiments described, and all of the equivalents or equivalents of the claims, as well as the following claims, belong to the scope of the present invention .

700: Device for calculating heart rate per minute
710: heart ballistic signal collecting unit
720: heart beat signal detection unit
730: Frequency domain transform unit
740: Heart rate calculation unit per minute

Claims (13)

Collecting a user's measured ballistocardiogram (BCG) signal using a piezoelectric sensor;
Detecting heartbeat signals of the user from the collected cardiovascular signals;
Converting the detected heartbeat signal into a frequency domain using a Fourier transform; And
Calculating a heart rate per minute by using a frequency peak value (Peak) of the heartbeat signal converted into the frequency domain;
Including the
The detecting step
Wherein the heart rate signal is detected from the heart ballistic signal using a moving standard deviation filter (MSDF). delete The method according to claim 1,
The detecting step
Wherein the heart rate signal is detected by removing a noise component of the heart valve signal using the moving standard deviation filter. Collecting a user's measured ballistocardiogram (BCG) signal using a piezoelectric sensor;
Detecting heartbeat signals of the user from the collected cardiovascular signals;
Converting the detected heartbeat signal into a frequency domain using a Fourier transform; And
Calculating a heart rate per minute by using a frequency peak value (Peak) of the heartbeat signal converted into the frequency domain;
Including the
The detecting step
Calculating a moving standard deviation of the cardiac trajectory signal and the average value using a predetermined window size and an average value of the cardiac traction signals in the window size, and using the calculated moving standard deviation And the heartbeat signal is detected from the heart ballistic signal. The method according to claim 1,
The calculating step
Wherein the heartbeat rate per minute is calculated using the frequency peak value in a predetermined frequency range among the heartbeat signals converted into the frequency domain. 6. The method of claim 5,
The predetermined frequency range is
Wherein the heart rate is 0.67 Hz to 3.33 Hz. A computer-readable recording medium having recorded thereon a program for executing the method according to any one of claims 1 to 6. A heart ballistic signal collector for collecting a user's ballistocardiogram (BCG) signal measured using a piezoelectric sensor;
A heartbeat signal detector for detecting a heartbeat signal of the user from the collected cardiovascular signals;
A frequency domain transformer for transforming the detected heartbeat signal into a frequency domain using a Fourier transform; And
A heartbeat rate calculating unit for calculating a heartbeat rate per minute by using the frequency peak value (Peak) of the heartbeat signal converted into the frequency domain;
, And the heartbeat signal detecting unit
Wherein the heart rate signal is detected from the heart ballistic signal using a moving standard deviation filter (MSDF). delete 9. The method of claim 8,
The heartbeat signal detection unit
Wherein the heart rate signal is detected by removing a noise component of the heart ballistic signal using the moving standard deviation filter. A heart ballistic signal collector for collecting a user's ballistocardiogram (BCG) signal measured using a piezoelectric sensor;
A heartbeat signal detector for detecting a heartbeat signal of the user from the collected cardiovascular signals;
A frequency domain transformer for transforming the detected heartbeat signal into a frequency domain using a Fourier transform; And
A heartbeat rate calculating unit for calculating a heartbeat rate per minute by using the frequency peak value (Peak) of the heartbeat signal converted into the frequency domain;
Lt; / RTI >
The heartbeat signal detection unit
Calculating a moving standard deviation of the cardiac trajectory signal and the average value using a predetermined window size and an average value of the cardiac traction signals in the window size, and using the calculated moving standard deviation And detects the heartbeat signal from the heart ballistic signal. 9. The method of claim 8,
The per minute heart rate calculation unit
Wherein the heart rate calculating unit calculates the heart rate per minute using the frequency peak value in a predetermined frequency range from among the heart rate signals converted into the frequency domain. 13. The method of claim 12,
The predetermined frequency range is
Wherein the heart rate is 0.67 Hz to 3.33 Hz.

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