Body composition analysis method using electroencephalography and analysis apparatus therefor

Abstract

The present invention relates to a body composition analysis method using electroencephalography and an analysis apparatus therefor, wherein information about body composition, such as body water content, protein mass, mineral mass, skeletal muscle mass, body fat percentage, body fat mass, or body mass index (BMI), can be analyzed through electroencephalography signals from a scalp region or an ear region in a stable state with eyes open (EO) or eyes closed (EC). Thus, the present invention can be effectively used for body composition analysis without spatial or temporal constraints.

Description

Body composition analysis method and analysis device using brain waves

[0001] The present invention relates to a body composition analysis method and analysis device using brain waves.

[0002]

[0003] With the recent increase in interest in health management, research into devices that can measure and analyze health functions is actively underway. One type of health function measurement and analysis device is a body composition analyzer. The commercially available body composition analyzer (Inbody) can estimate body water, protein, minerals, body fat mass, and skeletal muscle mass. This uses bioelectric impedance analysis (BIA), a technique that passes a weak current through the legs and arms to measure body water content through electrical resistance and thereby predict body fat. This allows for the diagnosis of obesity, nutritional assessment, and the identification of visceral fat, enabling the prevention of various chronic diseases.

[0004] However, existing body composition analyzers require separate equipment, are limited by their high cost and limited portability, and require conscious and periodic measurement and recording by the user. Furthermore, they often have numerous restrictions, such as fasting, exercise, and showering before measurement, and require a specific arm-outstretched posture during measurement, creating inconveniences for users.

[0005] Accordingly, there has been an increasing trend in the release of simplified wearable devices that can collect biosignal data without spatial or temporal constraints and obtain high-quality biosignals.

[0006] Accordingly, the inventors of the present invention have completed the present invention by developing a method and device for analyzing body composition using electroencephalography (EEG) signals in a stable state with eyes closed and open.

[0007]

[0008] An object of the present invention is to provide a method for analyzing body composition using brain waves, comprising the steps of: (a) obtaining brain wave signals from a subject; (b) removing noise from the brain wave signals to obtain preprocessed data; and (c) providing a body composition analysis result of the subject through data analysis from the preprocessed data.

[0009] Another object of the present invention is to provide a body composition analysis device using brain waves, including: an brain wave measurement unit for obtaining brain wave signals from a subject; a data processing unit for removing noise from the brain wave signals measured by the brain wave measurement unit to obtain preprocessed data; a data analysis unit for analyzing the data obtained by the data processing unit; and an analysis result providing unit for providing body composition analysis results analyzed by the data analysis unit.

[0010] Another object of the present invention is to provide a computer-readable recording medium storing a computer program for executing the body composition analysis method using brain waves.

[0011]

[0012] In order to achieve the above object, the present invention provides a method for analyzing body composition using brain waves, comprising the steps of: (a) obtaining brain wave signals from a subject; (b) removing noise from the brain wave signals to obtain preprocessed data; and (c) providing a body composition analysis result of the subject through data analysis from the preprocessed data.

[0013] In one embodiment of the present invention, the body composition may be selected from the group consisting of body water content, protein content, mineral content, skeletal muscle content, body fat percentage, body fat content, and body mass index (BMI).

[0014] In one embodiment of the present invention, the brain wave signal of step (a) may be repeatedly measured in a state of open eyes (eye open, EO) or closed eyes (eye close, EC).

[0015] In one embodiment of the present invention, the brain wave signal of step (a) may be measured in a scalp region or an ear region, and the scalp region may be selected from the group consisting of the entire scalp region, the frontal lobe region, the central region, the temporal lobe region, the parietal lobe region, and the occipital lobe region.

[0016] In one embodiment of the present invention, the brain wave signal of step (a) may be selected from the group consisting of delta waves, theta waves, alpha waves, beta waves, and gamma waves. The delta waves may include a frequency band of 1 to 3 Hz, theta waves may include a frequency band of 4 to 7 Hz, alpha waves may include a frequency band of 8 to 13 Hz, beta waves may include a frequency band of 14 to 30 Hz, and gamma waves may include a frequency band of 31 to 50 Hz.

[0017] In one embodiment of the present invention, the preprocessing data of step (b) may be obtained by applying a band-pass filter to a frequency band of 1 to 50 Hz, applying a notch filter to a frequency band of 59 to 61 Hz, applying down-sampling to 200 Hz to reduce the amount of calculation, and applying a common average reference (CAR), contralateral average reference, and independent component analysis (ICA) to remove noise introduced into the brain wave signal.

[0018] In particular, the re-referencing of the common average reference (CAR) and contralateral average reference may be applied to remove noise commonly introduced into the EEG signals. The common average reference (CAR) may be applied to scalp EEG, and the contralateral average reference may be applied to ear EEG. In addition, the independent component analysis (ICA) may be applied to remove noise such as electroocular electromyography and electromyography introduced into the EEG signals.

[0019] In one embodiment of the present invention, the data analysis in step (c) may extract brain wave features by analyzing the average power spectral density (PSD) by brain wave frequency band according to the eyes-open state (EO) and the eyes-closed state (EC), and by scalp region and ear region. Thereafter, the body composition analysis results may be provided based on the results of the correlation analysis between the brain wave features and body composition. Statistical significance was verified by deriving the Pearson correlation coefficient.

[0020] In one embodiment of the present invention, the body composition analysis results may be provided to the subject's terminal while electrically connected to the body composition measurement device. That is, the body composition measurement results generated by the body composition measurement device may be provided to the subject's terminal in response to a request from the subject's terminal accessing a body composition measurement application or body composition measurement site.

[0021] In one embodiment of the present invention, the body composition measuring device may be a wearable device or device.

[0022] In addition, the present invention provides a body composition analysis device using brain waves, including: an brain wave measurement unit that obtains brain wave signals from a subject; a data processing unit that removes noise from the brain wave signals measured by the brain wave measurement unit to obtain preprocessed data; a data analysis unit that analyzes the data obtained by the data processing unit; and an analysis result providing unit that provides body composition analysis results analyzed by the data analysis unit.

[0023] In one embodiment of the present invention, the body composition may be selected from the group consisting of body water content, protein content, mineral content, skeletal muscle content, body fat percentage, body fat content, and body mass index (BMI).

[0024] In one embodiment of the present invention, the brain wave measurement unit may repeatedly measure the brain wave signal in a state of open eyes (eye open, EO) or closed eyes (eye close, EC).

[0025] In one embodiment of the present invention, the brain wave signal in the brain wave measurement unit may be measured in a scalp region or an ear region, and the scalp region may be selected from the group consisting of the entire scalp region, the frontal lobe region, the central region, the temporal lobe region, the parietal lobe region, and the occipital lobe region.

[0026] In one embodiment of the present invention, the brain wave signal may be selected from the group consisting of delta wave, theta wave, alpha wave, beta wave, and gamma wave.

[0027] In one embodiment of the present invention, the preprocessing data of the data processing unit may be obtained by applying a band-pass filter to a frequency band of 1 to 50 Hz, applying a notch filter to a frequency band of 59 to 61 Hz, applying down-sampling to 200 Hz to reduce the amount of calculation, and applying a common average reference (CAR), a contralateral average reference, and an independent component analysis (ICA) to remove noise introduced into the brain wave signal.

[0028] In particular, the re-referencing of the common average reference (CAR) and contralateral average reference may be applied to remove noise commonly introduced into the EEG signals. The common average reference (CAR) may be applied to scalp EEG, and the contralateral average reference may be applied to ear EEG. In addition, the independent component analysis (ICA) may be applied to remove noise such as electroocular electromyography and electromyography introduced into the EEG signals.

[0029] In one embodiment of the present invention, the data analysis unit may extract brain wave features by analyzing the average power spectral density (PSD) by brain wave frequency band according to the eyes-open state (EO) and the eyes-closed state (EC), and by scalp region and ear region.

[0030] In one embodiment of the present invention, the analysis result providing unit may provide a body composition analysis result from a correlation analysis result between brain wave features analyzed by the data analysis unit and body composition.

[0031] In one embodiment of the present invention, the body composition analysis results may be provided to the subject's terminal while electrically connected to the body composition measurement device. That is, the body composition measurement results generated by the body composition measurement device may be provided to the subject's terminal in response to a request from the subject's terminal accessing a body composition measurement application or body composition measurement site.

[0032] In one embodiment of the present invention, the body composition measuring device may be a wearable device or device.

[0033] In addition, the present invention provides a computer-readable recording medium storing a computer program for executing the body composition analysis method using the brain waves.

[0034]

[0035] The method according to the present invention can analyze information on body composition such as body water content, protein content, mineral mass, skeletal muscle mass, body fat percentage, body fat content, or body mass index (BMI) through brain wave signals in the scalp area or ear area in a stable state with eyes open (EO) or eyes closed (EC), and thus can be effectively used for body composition analysis without time and space constraints.

[0036]

[0037] Figure 1 shows the locations of electrode attachment for scalp or ear EEG measurement.

[0038] Figure 2 shows the experimental paradigm in which the eyes were closed (EC) and the eyes were open (EO) for 1 minute each, repeated 3 times.

[0039] Figures 3 and 4 show the results of confirming the correlation between the EEG signal in a stable state and body composition in the entire scalp area (All channels) and the results of confirming the power spectral density (PSD) according to the body composition index.

[0040] Figure 5 shows the results of confirming the correlation between the resting brain wave signal and body composition in the frontal region.

[0041] Figure 6 shows the results of confirming the correlation between the stable brain wave signal and body composition in the central region.

[0042] Figure 7 shows the results of confirming the correlation between the steady-state EEG signal and body composition in the parietal region.

[0043] Figure 8 shows the results of confirming the correlation between the steady-state EEG signal and body composition in the occipital region.

[0044] Figure 9 illustrates a body composition analysis device according to the present invention.

[0045]

[0046] Hereinafter, the present invention will be described in detail.

[0047] The terms used in this invention have been selected from widely used, common terms, taking into account the functionality of the invention. However, these terms may vary depending on the intentions of those skilled in the art or the emergence of new technologies. Furthermore, in certain cases, terms may be arbitrarily selected, and in such cases, their meanings will be described in detail in the description of the relevant embodiments. Therefore, the terms used in this invention should not be defined simply as names, but rather based on their meanings and the overall content of the invention.

[0048] When the present invention is said to “include” a certain component or a certain step, this does not mean that other components or other steps are excluded, but rather that other components or other steps may be further included, unless specifically stated otherwise.

[0049] Hereinafter, preferred embodiments of the present invention, which can specifically achieve the above objectives, will be described with reference to the attached drawings. In describing the embodiments of the present invention, the same names and symbols will be used for identical components, and additional and redundant explanations thereof will be omitted.

[0050]

[0051] The present invention provides a method for analyzing body composition using brain waves, comprising the steps of: (a) obtaining brain wave signals from a subject; (b) removing noise from the brain wave signals to obtain preprocessed data; and (c) providing a body composition analysis result of the subject through data analysis from the preprocessed data.

[0052] First, a body composition analysis method using brain waves is described with reference to FIGS. 1 to 8.

[0053] Referring to Fig. 1, Fig. 1 shows the locations of electrode attachment for scalp or ear EEG measurement. In the present invention, 32 scalp EEG electrodes and 6 ear electrodes were attached to measure EEG in a resting state with eyes closed (EC) and eyes open (EO) of 22 subjects. Fig. 1 shows the frontal regions of AF3, AF4, Fz, F3, F4, F7, and F8; the central regions of FC1, FC2, FC5, FC6, Cz, C3, C4, T7, and T8; the parietal regions of CP1, CP2, CP5, CP6, Pz, P3, P4, P7, and P8; It represents the entire scalp area (All channel) including the occipital areas of P03, P04, Oz, O1, and O2, and the ear areas (Ear) of L1~L3, R1~R3.

[0054] Referring to Fig. 2, Fig. 2 illustrates an experimental paradigm in which the eyes-closed state (EC) and the eyes-open state (EO) were repeated three times for one minute each. It shows that the brain wave signals were measured by attaching electrodes to the scalp area and ear area shown in Fig. 1 and then repeating them three times for one minute each in the eyes-closed state (EC) and the eyes-open state (EO).

[0055] To analyze the correlation between EEG signals and body composition data, body composition data was acquired using a commercial body composition analyzer (Inbody 770), and the following precautions were observed for accurate body composition measurement: no food or drink, including water, after midnight on the day of the experiment; no strenuous exercise or showering on the day of the experiment; for women, measurements were taken during the menstrual cycle; and body composition of all subjects was measured between 9:00 AM and 12:00 PM.

[0056] The measured EEG signals were bandpass filtered for the 1 to 50 Hz frequency band typically used in EEG analysis, notch filtered for the 59 to 61 Hz frequency band, and then downsampled to 200 Hz to reduce the amount of calculation. In addition, to remove noise commonly introduced into EEG signals, a common average reference (CAR) was applied to the scalp EEG, and a contralateral average reference was applied to the ear EEG. In addition, independent component analysis (ICA) was applied to remove noise introduced into EEG signals such as electroocular and electromyographic signals.

[0057] Afterwards, the average power spectral density (PSD) was analyzed for each EEG frequency band, including the delta frequency band of 1 to 3 Hz, the theta frequency band of 4 to 7 Hz, the alpha frequency band of 8 to 13 Hz, the beta frequency band of 14 to 30 Hz, and the gamma frequency band of 31 to 50 Hz; and for each scalp region and ear region, to extract EEG features. In addition, the Pearson correlation coefficient was derived through correlation analysis between the EEG features and body composition, and statistical significance was verified.

[0058] In the present invention, the body composition may be selected from the group consisting of body water content, protein content, mineral mass, skeletal muscle mass, body fat percentage, body fat content, and body mass index (BMI).

[0059] Referring to FIGS. 3 and 4, FIGS. 3 and 4 show the results of confirming the correlation between the EEG signal in a resting state and body composition in the entire scalp area (All channels) and the results of confirming the power spectral density (PSD) according to the body composition index. In the entire scalp area (All channels), it was confirmed that body water, protein, and skeletal muscle mass all had a significant negative correlation with the beta frequency band in the eyes-closed state (EC) (r = -0.45, p< 0.05; r = -0.45, p< 0.05; and r = -0.46, p< 0.05, respectively). In addition, it was confirmed that body fat mass had a significant positive correlation with the beta frequency band and the gamma frequency band (both r = 0.42, p< 0.05), and body fat percentage had a significant positive correlation with the beta frequency band and the gamma frequency band (r = 0.59, p< 0.01; r = 0.45, p< 0.05, respectively). In addition, in the eyes-open state (EO), body fat mass and body fat percentage had a significant positive correlation with the theta frequency band (r = 0.43, r = 0.49, p< 0.05, respectively), body fat mass and body fat percentage had a significant positive correlation with the beta frequency band (r = 0.72, 0.76, p< 0.001, respectively), and body fat mass, BMI, and body fat percentage had a significant positive correlation with the gamma frequency band (r = 0.84, 0.69, 0.72, p< 0.001, respectively).

[0060] Referring to Figure 5, Figure 5 shows the results of confirming the correlation between the resting EEG signal and body composition in the frontal region. In the frontal region (Frontal channel), it was confirmed that body water, protein, and skeletal muscle mass had a negative correlation with the beta frequency band in the eyes-closed state (EC) (r = -0.44, -0.43, -0.44, p< 0.05, respectively), and body fat percentage had a significant positive correlation with the beta frequency band (r= 0.57, p< 0.01). In addition, in the eyes-open state (EO), body fat mass and body fat percentage were significantly positively correlated with the theta frequency band (r = 0.44, 0.51, p< 0.05, respectively), body fat mass, BMI, and body fat percentage were significantly positively correlated with the beta frequency band (r = 0.71, p< 0.001;, r = 0.47, p< 0.05; r = 0.68, p< 0.001, respectively), and body fat mass, BMI, and body fat percentage were significantly positively correlated with the gamma frequency band (r = 0.72, p< 0.001; r = 0.55, p< 0.01; r = 0.64, p< 0.01, respectively).

[0061] Referring to Fig. 6, Fig. 6 shows the results of confirming the correlation between the resting EEG signal and body composition in the central region (Central). In the central region (Central channel), in the eyes-closed state (EC), body water, protein, and skeletal muscle mass were negatively correlated with the delta frequency band (all r = -0.43, p < 0.05), body fat percentage was significantly positively correlated with the delta frequency band (r = 0.47, p < 0.01), body fat mass and body fat percentage were significantly positively correlated with the theta frequency band (r = 0.45, 0.51, p < 0.05, respectively), and body fat mass and body fat percentage were significantly positively correlated with the beta frequency band (r = 0.58, 0.64, p < 0.01, respectively). In addition, in the eyes-open state (EO), body water and protein content were negatively correlated with the delta frequency band (both r = -0.43, p < 0.05), body fat mass and body fat percentage were significantly positively correlated with the delta frequency band (r = 0.53, p < 0.05; r = 0.64, p < 0.01, respectively), body fat mass and body fat percentage were significantly positively correlated with the cerebrospinal fluid frequency band (r = 0.56, p < 0.05; r = 0.59, p < 0.01, respectively), body fat mass, BMI, and body fat percentage were significantly positively correlated with the beta frequency band (r = 0.83, p < 0.001; r = 0.64, p < 0.01; r = 0.76, p < 0.001, respectively), and body fat mass, BMI, and body fat percentage were significantly positively correlated with the gamma frequency band. It was confirmed (r = 0.83, p < 0.001; r = 0.75, p < 0.01; r = 0.64, p < 0.01, respectively).

[0062] Referring to Figure 7, Figure 7 shows the results of confirming the correlation between the resting EEG signal and body composition in the parietal region. In the parietal region (Parietal channel), it was confirmed that in the eyes-closed state (EC), body water, protein, mineral mass, and skeletal muscle mass had a negative correlation with the beta frequency band (all r = -0.48, p < 0.05), and body fat percentage had a significant positive correlation with the beta frequency band (r = 0.54, p < 0.01). In addition, in the eyes-open state (EO), body fat mass and body fat percentage were significantly positively correlated with the beta frequency band (r = 0.47, p< 0.05; r = 0.61, p< 0.01, respectively), and body fat mass, BMI, and body fat percentage were significantly positively correlated with the gamma frequency band (r = 0.64, 0.55, 0.54, p< 0.01, respectively).

[0063] Referring to Figure 8, Figure 8 shows the results of confirming the correlation between the resting EEG signal and body composition in the occipital region. In the occipital region (Occipital channel), it was confirmed that in the eyes-closed state (EC), body water, protein, and skeletal muscle mass had a negative correlation with the beta frequency band (all r = -0.48, p < 0.05), and body fat percentage had a significant positive correlation with the beta frequency band (r = 0.55, p < 0.01). In addition, in the eyes-open state (EO), body water, protein, and skeletal muscle mass were negatively correlated with the beta frequency band (all r = -0.45, p < 0.05), body fat mass and body fat percentage were significantly positively correlated with the beta frequency band (r = 0.45, p < 0.05; r = 0.62, p < 0.01), and body fat mass and body fat percentage were significantly positively correlated with the gamma frequency band (r = 0.54, 0.55, p < 0.01, respectively).

[0064] According to the results of the above Figures 3 to 8, it was confirmed that the present invention can provide body composition analysis results from the correlation analysis results between brain wave characteristics and body composition.

[0065] In the present invention, the body composition analysis results may be provided to the subject's terminal while electrically connected to the body composition measurement device. That is, the body composition measurement results generated by the body composition measurement device may be provided to the subject's terminal in response to a request from the subject's terminal accessing a body composition measurement application or body composition measurement site.

[0066] In the present invention, the body composition measuring device may be a wearable device or apparatus.

[0067] Referring to FIG. 9, FIG. 9 illustrates a body composition analysis device (100) according to the present invention. The body composition analysis device of the present invention includes an EEG measurement unit (110) that obtains EEG signals from a subject; a data processing unit (120) that removes noise from EEG signals measured by the EEG measurement unit to obtain preprocessed data; a data analysis unit (130) that analyzes data obtained by the data processing unit; and an analysis result provision unit (140) that provides body composition analysis results analyzed by the data analysis unit.

[0068] In the present invention, the analysis result provision unit (140) of the body composition analysis device may provide body composition analysis results based on the correlation analysis results between brain wave characteristics and body composition analyzed by the data analysis unit. Preferably, the body composition analysis results may be provided to the subject's terminal while electrically connected to the body composition analysis device.

[0069]

[0070] The embodiments of the present invention described above may be implemented in the form of program commands that can be executed by various computer components and recorded on a computer-readable recording medium. The computer-readable recording medium may include program commands, data files, data structures, etc., either singly or in combination. The program commands recorded on the computer-readable recording medium may be those specifically designed and constructed for the present invention or may be known and usable by those skilled in the art of computer software.

[0071] Examples of computer-readable recording media include magnetic media such as hard disks, floppy disks, and magnetic tapes, optical recording media such as CD-ROMs and DVDs, magneto-optical media such as floptical disks, and hardware devices specifically configured to store and execute program instructions, such as ROMs, RAMs, and flash memories. Examples of program instructions include not only machine language codes such as those generated by a compiler, but also high-level language codes that can be executed by a computer using an interpreter or the like. Hardware devices may be modified with one or more software modules to perform processing according to the present invention, and vice versa.

[0072]

[0073] The foregoing description of the present invention is for illustrative purposes only, and those skilled in the art will readily appreciate that the present invention can be readily modified into other specific forms without altering the technical spirit or essential characteristics of the present invention. Therefore, the embodiments described above should be understood as illustrative in all respects and not restrictive. For example, each component described as a single entity may be implemented in a distributed manner, and similarly, components described as distributed may be implemented in a combined manner.

[0074] The scope of the present invention is indicated by the claims set forth below, and all changes or modifications derived from the meaning and scope of the claims and their equivalent concepts should be interpreted as being included in the scope of the present invention.

Claims

1. (a) A step of obtaining brain wave signals from a subject; (b) a step of obtaining preprocessed data by removing noise from the brain wave signal; and (c) A method for analyzing body composition using brain waves, including a step of providing a body composition analysis result of a subject through data analysis from the above preprocessing data.

2. In paragraph 1, A body composition analysis method, wherein the above body composition is selected from the group consisting of body water content, protein content, mineral mass, skeletal muscle mass, body fat percentage, body fat content, and body mass index (BMI).

3. In paragraph 1, A body composition analysis method wherein the brain wave signal of step (a) is repeatedly measured in a state of open eyes (eye open, EO) or closed eyes (eye closed, EC).

4. In paragraph 1, A body composition analysis method, wherein the brain wave signal of step (a) is measured in the scalp area or the ear area.

5. In paragraph 4, A body composition analysis method, wherein the above scalp region is selected from the group consisting of the entire scalp region, the frontal lobe region, the central region, the temporal lobe region, the parietal lobe region, and the occipital lobe region.

6. In paragraph 1, A body composition analysis method, wherein the brain wave signal of step (a) is selected from the group consisting of delta wave, theta wave, alpha wave, beta wave, and gamma wave.

7. In paragraph 1, A body composition analysis method, wherein the preprocessing data of the above step (b) is obtained by applying a band-pass filter to a frequency band of 1 to 50 Hz, applying a notch filter to a frequency band of 59 to 61 Hz, applying down-sampling to 200 Hz to reduce the amount of calculation, and applying a common average reference (CAR), contralateral average reference, and independent component analysis (ICA) to remove noise introduced into the brain wave signal.

8. In paragraph 1, A body composition analysis method in which data analysis of step (c) extracts brain wave features by analyzing the average power spectral density (PSD) by brain wave frequency band according to the eyes-open state (EO) and the eyes-closed state (EC) and by scalp region and ear region.

9. In paragraph 8, A body composition analysis method that provides a body composition analysis result from the correlation analysis result between the above brain wave characteristics and body composition.

10. In paragraph 9, A body composition analysis method, wherein the above body composition analysis results are provided to a subject's terminal while being electrically connected to a body composition measurement device.

11. An electroencephalogram measuring unit that obtains electroencephalogram signals from the subject; A data processing unit that obtains preprocessed data by removing noise from the brain wave signal measured by the brain wave measurement unit; A data analysis unit that analyzes the data obtained from the above data processing unit; and A body composition analysis device using brain waves, including an analysis result providing unit that provides body composition analysis results analyzed from the above data analysis unit.

12. A computer-readable recording medium storing a computer program for executing a body composition analysis method using brain waves according to any one of paragraphs 1 to 10.

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