Device for estimating biological information

Abstract

A device for estimating bio-information is provided. The device includes: a sensor configured to obtain a bio-signal of a subject; and a processor configured to estimate a change in a feature related to cardiac output (CO) from the bio-signal and obtain a progressive wave component associated with a feature related to total peripheral resistance (TPR) from the bio-signal based on an estimation result of the change.

Description

[0001] This application claims priority from Korean Patent Application No. 10-2020-0029652 filed on March 10, 2020, in the Korean Intellectual Property Office, the disclosure of which is incorporated herein in its entirety for all purposes by reference. Technical Field

[0002] Example embodiments relate to apparatus and methods for non-invasively estimating biological information. Background Art

[0003] With the aging population, rising medical costs, and a lack of medical personnel for specialized medical services, research on IT-medical fusion technology that combines information technology (IT) and medical technology is being actively conducted. Specifically, the monitoring of the health status of the human body is not limited to medical institutions, but is expanding to the field of mobile healthcare that can monitor the health status of users anytime and anywhere in daily life at home or in the office. Typical examples of biosignals indicating the health status of an individual include electrocardiogram (ECG) signals, photoplethysmography (PPG) signals, electromyography (EMG) signals, etc., and various biosignal sensors have been developed to measure these signals in daily life. Specifically, a PPG sensor can estimate the blood pressure of a human body by analyzing the shape of a pulse wave that reflects cardiovascular status, etc.

[0004] According to research on PPG signals, a complete PPG signal is a superposition of a propagation wave moving from the heart to the distal part of the body and a reflected wave returning from the distal part. In addition, it is known that information for estimating blood pressure can be obtained by extracting various features associated with the propagation wave or the reflected wave. Summary of the invention

[0005] According to one aspect of the disclosure, an apparatus for estimating biological information includes: a sensor configured to obtain a biological signal of an object; and a processor configured to estimate a change in a cardiac output (CO)-related characteristic from the biological signal, and obtain a forward wave component associated with a total peripheral resistance (TPR)-related characteristic from the biological signal based on an estimation result of the estimated change.

[0006] The sensor may include a pulse wave sensor, and the pulse wave sensor may include a light source configured to emit light onto the object and a detector configured to detect light reflected or scattered from the object.

[0007] The processor may obtain a heart rate from the bio-signal and estimate a change in the CO-related characteristic based on a change in the heart rate compared to a reference heart rate obtained at a reference time.

[0008] The processor may estimate a large change in the CO-related characteristic in response to a change in the heart rate exceeding a predetermined threshold, and may obtain, from the biosignal, a progressive wave component having a relatively large change compared to a progressive wave component obtained at a reference time.

[0009] The processor may obtain a second order differential signal of the biosignal, and obtain the forward wave component based on a first local minimum point of the second order differential signal.

[0010] The processor can obtain one of the following items as a forward wave component: a first amplitude of the biological signal corresponding to the time of the first local minimum point, and a second amplitude of the biological signal corresponding to an internal dividing point between the time of the first local minimum point and the time of the second local maximum point of the second-order differential signal.

[0011] The processor may detect an inflection point in a detection period of the second-order differential signal, and when the inflection point is detected, obtain the forward wave component based on the inflection point.

[0012] The detection period may include a time interval between a first local maximum point and a first local minimum point of the second-order differential signal.

[0013] The processor may estimate that the change in the CO-related characteristic is small based on the change in the heart rate being less than or equal to a predetermined threshold, and may obtain a progressive wave component having a relatively small change compared to a reference time from the biosignal.

[0014] The processor may obtain the forward wave component based on a maximum amplitude point in a systolic period of the biosignal.

[0015] The processor may obtain one of the following as the forward wave component: a first amplitude of a maximum amplitude point, and a second amplitude of the biosignal corresponding to an internal division point between the time of a first local minimum point and the time of a maximum amplitude point of a second-order differential signal of the biosignal.

[0016] The processor may estimate the change in the CO-related characteristic based on the shape of the waveform of the biosignal obtained at the estimated time.

[0017] The processor may estimate that the change in the CO-related characteristic is large based on the rise in the waveform of the biosignal from the systolic period to the diastolic period, and estimate that the change in the CO-related characteristic is small based on the rise in the waveform of the biosignal from the diastolic period to the systolic period.

[0018] The processor may obtain a CO-related feature from the biosignal, obtain a TPR-related feature based on the forward wave component, and estimate bio-information based on the CO-related feature and the TPR-related feature.

[0019] The biological information may include at least one of blood pressure, blood vessel age, arterial stiffness, aortic pressure waveform, stress index, and fatigue level.

[0020] According to another aspect of the disclosure, a method for estimating biological information includes: obtaining a biological signal of a subject; estimating a change in a characteristic related to cardiac output (CO) from the biological signal; and obtaining a forward wave component associated with a characteristic related to total peripheral resistance (TPR) from the biological signal based on an estimation result of the estimated change.

[0021] The step of estimating the change of the CO-related characteristic may include obtaining a heart rate from the biosignal, and estimating the change of the CO-related characteristic based on a change of the obtained heart rate compared to a reference heart rate obtained at a reference time.

[0022] The step of estimating the change of CO-related characteristics may include: estimating that the change of CO-related characteristics is large based on the change of heart rate exceeding a predetermined threshold, and the step of obtaining the forward wave component may include: obtaining a forward wave component having a relatively large change compared with the forward wave component obtained at a reference time from the biological signal.

[0023] The step of obtaining the progressive wave component may include obtaining a second order differential signal of the biosignal, and obtaining the progressive wave component based on a first local minimum point of the obtained second order differential signal.

[0024] The step of obtaining a forward wave component may include obtaining one of the following items as a forward wave component: a first amplitude of the biological signal corresponding to the time of a first local minimum point, and a second amplitude of the biological signal corresponding to an internal dividing point between the time of the first local minimum point and the time of a second local maximum point of a second-order differential signal.

[0025] The step of obtaining the progressive wave component may include detecting an inflection point in a detection period of the second-order differential signal, and obtaining the progressive wave component based on the inflection point when the inflection point is detected.

[0026] The step of estimating the change of CO-related characteristics may include: estimating that the change of CO-related characteristics is small based on the change of heart rate being less than or equal to a predetermined threshold, and the step of obtaining the forward wave component may include: obtaining the forward wave component having a relatively small change compared to a reference time from the biological signal.

[0027] The step of obtaining the progressive wave component may include detecting a maximum amplitude point in a contraction period of the biosignal, and obtaining the progressive wave component based on the maximum amplitude point.

[0028] The step of obtaining a forward wave component may include obtaining one of the following items as the forward wave component: a first amplitude of a maximum amplitude point, and a second amplitude of the biological signal corresponding to an internal division point between the time of a first local minimum point of a second-order differential signal of the biological signal and the time of a maximum amplitude point.

[0029] The estimating the change of the CO-related feature may include estimating the change of the CO-related feature based on a shape of a waveform of a biosignal obtained at an estimation time.

[0030] The step of estimating the change of CO-related characteristics may include: estimating that the change of CO-related characteristics is large based on the waveform of the biological signal obtained at the estimated time rising from the systole to the diastole, and estimating that the change of CO-related characteristics is small based on the waveform of the biological signal obtained at the estimated time rising from the diastole to the systole.

[0031] The method of estimating bio-information may include: obtaining a CO-related feature from a bio-signal; obtaining a TPR-related feature based on the obtained forward wave component; and estimating the bio-information based on the CO-related feature and the TPR-related feature. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] The above and other aspects, features and advantages of certain example embodiments of the present disclosure will become more apparent from the following description taken in conjunction with the accompanying drawings, in which:

[0033] Figure 1 is a block diagram illustrating an apparatus for estimating bio-information according to example embodiments.

[0034] Figure 2 is a block diagram illustrating an apparatus for estimating bio-information according to another example embodiment.

[0035] Figure 3 It shows that according to Figure 1 and Figure 2 A block diagram of a processor of an example embodiment of the present invention is provided.

[0036] 4A to 4F is a diagram showing an example of extracting a progressive wave component from a biological signal.

[0037] Figure 5 is a flowchart illustrating a method of estimating bio-information according to example embodiments.

[0038] Figure 6 is a diagram illustrating a wearable device according to example embodiments.

[0039] Figure 7 is a diagram illustrating a smart device according to an example embodiment. DETAILED DESCRIPTION

[0040] Details of example embodiments are included in the following detailed description and drawings. Advantages and features of the present invention and methods of implementing the present invention will be more clearly understood from the following example embodiments described in detail with reference to the drawings. Throughout the drawings and detailed description, unless otherwise described, the same figure reference numerals will be understood to represent the same elements, features and structures. The relative sizes and depictions of these elements are not necessarily to scale and may be exaggerated for clarity, illustration and convenience.

[0041] Although the terms first, second, etc. can be used to describe various elements here, these elements should not be limited by these terms. These terms are only used to distinguish one element from another element. Unless otherwise clearly stated, any reference to the singular may include plural. In addition, unless clearly described to the contrary, statements such as "including" or "comprising" will be understood to indicate that the elements stated are included but do not exclude any other elements. In addition, terms such as "components" or "modules" should be understood as units for performing at least one function or operation and can be implemented as hardware, software or their combination. As used herein, statements such as "at least one of ......" are modified after a column of elements, without modifying the individual elements in the column. For example, the statement "at least one of a, b and c" should be understood as: only including a, only including b, only including c, including both a and b, including both a and c, including both b and c, or including all of a, b and c.

[0042] Hereinafter, exemplary embodiments of an apparatus and method for estimating bio-information will be described in detail with reference to the accompanying drawings.

[0043] Figure 1 is a block diagram showing an apparatus for estimating bio-information according to an example embodiment. The apparatus 100 for estimating bio-information may be embedded in a terminal (such as a smart phone, a tablet personal computer (PC), a desktop computer, a laptop computer, etc.), or may be manufactured as an independent hardware device. If the apparatus 100 for estimating bio-information is manufactured as an independent hardware device, the device may be a wearable device that is worn by a user so that the user can easily measure bio-information while wearing the device. Examples of wearable devices may include watch-type wearable devices, bracelet-type wearable devices, wristband-type wearable devices, ring-type wearable devices, glasses-type wearable devices, headband-type wearable devices, etc., but wearable devices are not limited thereto and may be modified for various purposes (such as fixed devices used in medical institutions for measuring and analyzing bio-information, etc.).

[0044] Reference Figure 1 , an apparatus 100 for estimating bio-information includes a sensor 110 and a processor 120 .

[0045] like Figure 1 As shown in , the sensor 110 may obtain a biosignal from the subject, and may send the obtained biosignal to the processor 120. The biosignal may include a photoplethysmography (PPG) signal (also referred to as a "pulse wave signal"). However, the biosignal is not limited thereto, and may include various biosignals that can be modeled by the sum of a plurality of waveform components (such as an electrocardiogram (ECG) signal, a photoplethysmography (PPG) signal, an electromyogram (EMG) signal, etc.).

[0046] For example, the sensor 110 may include a PPG sensor for measuring a PPG signal. The PPG sensor may include a light source for emitting light onto an object and a detector for measuring the PPG signal by detecting light emitted from the object when the light emitted by the light source to the object is scattered or reflected from the body tissue of the object. In this case, the light source may include at least one of a light emitting diode (LED), a laser diode (LD), and a phosphor, but is not limited thereto. The detector may include a photodiode.

[0047] Upon receiving a control signal from the processor 120, the sensor 110 may direct the PPG sensor to obtain a pulse wave signal from the object. In this case, the object may be a body part that is in contact with or adjacent to the PPG sensor, and may be a body part where the pulse wave can be easily measured using photoplethysmography. For example, the object may be an area on the wrist adjacent to the radial artery, and may include an upper portion where veins or capillaries of the wrist are located. In the case of measuring the pulse wave on the area of ​​the skin where the radial artery passes, the measurement may be relatively less affected by external factors (such as the thickness of the skin tissue in the wrist, etc.) that may cause errors in the measurement. However, the skin area is not limited thereto, and may be a distal portion of the body where blood vessels are densely located (such as fingers, toes, etc.).

[0048] Upon receiving a request for estimating bio-information from the user, the processor 120 may generate a control signal for controlling the sensor 110, and may transmit the control signal to the sensor 110. In addition, the processor 120 may receive a bio-signal from the sensor 110 and may estimate the bio-information by analyzing the received bio-signal. In this case, the bio-information may include blood pressure, vascular age, arterial stiffness, aortic pressure waveform, stress index, fatigue level, etc., but is not limited thereto.

[0049] Upon receiving the biosignal from the sensor 110 , the processor 120 may perform pre-processing such as filtering for removing noise, amplifying the biosignal, converting the signal into a digital signal, etc.

[0050] The processor 120 may extract features required for estimating bio-information by analyzing the waveform of the received bio-signal, and may estimate the bio-information by using the extracted features. The bio-information may include blood pressure, vascular age, arterial stiffness, aortic pressure waveform, pressure index, fatigue level, etc., but is not limited thereto. For ease of explanation, the following description will be made using blood pressure as an example of bio-information.

[0051] For example, it is known that changes in mean arterial pressure (MAP) are proportional to cardiac output (CO) and total peripheral resistance (TPR) as shown in the following equation (1).

[0052] ΔMAP=CO x TPR (1)

[0053] Here, ΔMAP represents the difference in MAP between the left ventricle and the right atrium, wherein the MAP of the right atrium is generally in the range of 3 mmHg to 5 mmHg, so that the MAP in the right atrium is similar to the MAP in the left ventricle or the MAP of the upper arm. If the absolute actual CO and TPR values ​​are known, the MAP of the aorta or the upper arm can be obtained. However, it may be difficult to estimate the absolute CO and TPR values ​​based on biosignals. Under normal circumstances, the human body can regulate blood pressure. For example, when blood pressure rises due to a sharp increase in cardiac output, the blood vessel diameter is relaxed so that the total peripheral resistance can be reduced, thereby restoring the blood pressure to a normal level.

[0054] The processor 120 may obtain a feature related to cardiac output (CO) (hereinafter referred to as a CO-related feature) and a feature related to total peripheral resistance (TPR) (hereinafter referred to as a TPR-related feature) from the pulse wave signal, and may estimate the blood pressure by using the CO-related feature and the TPR-related feature. The CO-related feature may be a feature value showing an increasing / decreasing trend associated with a relatively increasing / decreasing actual CO value when the actual TPR value does not change significantly compared to a resting state. In addition, the TPR-related feature may be a feature value showing an increasing / decreasing trend associated with a relatively increasing / decreasing actual TPR value when the actual CO value does not change significantly compared to a resting state.

[0055] According to an exemplary embodiment of the present disclosure, the processor 120 may adaptively obtain a CO-related feature and a TPR-related feature from a biosignal by using a complementary relationship between CO and TPR. For example, the processor 120 may estimate a change in the CO-related feature at the time of blood pressure estimation based on the heart rate (HR) and stroke volume (SV) characteristics constituting the cardiac output. In addition, the processor 120 may adaptively obtain a progressive wave component associated with the TPR-related feature based on the change in the estimated CO-related feature. When it is estimated that the change in the CO-related feature at the time of blood pressure estimation is relatively large compared to the time when the user performs calibration at rest (hereinafter referred to as the "reference time"), the processor 120 may select a TPR-related feature value that also has a large change. On the contrary, when it is estimated that the change in the CO-related feature is relatively small, the processor 120 may select a TPR-related feature value that also has a small change.

[0056] When obtaining the CO-related features and the TPR-related features, the processor 120 may estimate the blood pressure by applying a predefined blood pressure estimation model. The blood pressure estimation model may be represented in the form of a linear function or a nonlinear function defining the correlation between the CO-related features and the TPR-related features and the blood pressure.

[0057] Figure 2 is a block diagram illustrating an apparatus for estimating bio-information according to another example embodiment.

[0058] Reference Figure 2 , the apparatus 200 for estimating biological information includes a sensor 110 , a processor 120 , an output interface 210 , a storage device 220 , and a communication interface 230 .

[0059] The sensor 110 may measure a biosignal from a subject, and the processor 120 may estimate bioinformation by using the biosignal information obtained by the sensor 110 .

[0060] The output interface 210 may output the bio-signal information obtained by the sensor 110 and various processing results of the processor 120, and may provide the output information to the user. The output interface 210 may provide information through various visual methods / non-visual methods using a display module, a speaker, a tactile device, etc. installed in the apparatus 200 for estimating bio-information.

[0061] For example, once the user's blood pressure is estimated, the output interface 210 may output the estimated blood pressure by using various visual methods (such as by changing the color, line thickness, font, etc.) based on whether the estimated blood pressure value falls within the normal range or falls outside the normal range. Optionally, the output interface 210 may output the estimated blood pressure by voice, or may output the estimated blood pressure using a non-visual method such as providing different vibrations or tactile sensations corresponding to abnormal blood pressure levels. In addition, when comparing the measured blood pressure with the previous measurement history, if it is determined that the measured blood pressure is abnormal, the output interface 210 may warn the user, or may provide information for guiding the user's behavior (such as food information that the user should pay attention to, information for booking a hospital appointment, etc.).

[0062] The storage device 220 may store reference information, bio-signals, acquired features, bio-information estimation results, etc. The reference information may include user information (such as the user's age, gender, occupation, current health status, etc.) and / or information about a bio-information estimation model, etc., but the reference information is not limited thereto. The storage device 220 may include at least one of the following storage media: a flash memory, a hard disk memory, a multimedia card micro memory, a card-type memory (e.g., an SD memory, an XD memory, etc.), a random access memory (RAM), a static random access memory (SRAM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), a programmable read-only memory (PROM), a magnetic memory, a magnetic disk, an optical disk, etc., but is not limited thereto.

[0063] Upon receiving a control signal including access information of the external device 250 from the processor 120, the communication interface 230 may access the communication network using a communication technology connected to the external device 250. When connected to the external device 250, the communication interface 230 may receive various information related to estimating bio-information from the external device 250, and may transmit bio-signal information obtained by the sensor 110, bio-information estimated by the processor 120, and the like to the external device 250. Examples of the external device 250 may include other equipment for estimating bio-information, a cuff pressure meter for measuring cuff blood pressure, etc., a smart phone, a tablet PC, a desktop computer, a laptop computer, and the like, but the external device 250 is not limited thereto.

[0064] Examples of communication technologies may include: Bluetooth communication, Bluetooth low energy (BLE) communication, near field communication (NFC), WLAN communication, Zigbee communication, infrared data association (IrDA) communication, Wi-Fi direct (WFD) communication, ultra-wideband (UWB) communication, Ant+ communication, WIFI communication, and mobile communication. However, these are exemplary only and are not intended to be limiting.

[0065] Figure 3It shows that according to Figure 1 and Figure 2 A block diagram of a processor of an example embodiment of the present invention is provided. 4A to 4F is a diagram illustrating an example of extracting a forward wave component from a biological signal.

[0066] Reference Figure 3 , the processor 300 includes a variability estimator 310, a feature obtainer 320 and a biological information estimator 330.

[0067] The variability estimator 310 may estimate variability (ie, variation) of the CO-related feature by using a biosignal acquired from a subject.

[0068] For example, when a biosignal is acquired from an object for estimating bioinformation, the variability estimator 310 may obtain a heart rate as an element constituting cardiac output. The heart rate may be obtained by using one cycle of the biosignal. In addition, the variability estimator 310 may obtain a change in the heart rate compared to a reference heart rate, and may estimate the variability of the CO-related feature based on the heart rate change. In this case, the reference heart rate is a heart rate measured when the user is resting, and may be obtained from a biosignal obtained at a reference time or obtained by an external device for measuring the heart rate.

[0069] When the heart rate variation is obtained, the variability estimator 310 can estimate the variability of the CO-related feature by comparing the obtained heart rate variation with a predetermined threshold value. If the heart rate variation exceeds the predetermined threshold value, the variability estimator 310 can estimate that the variability of the CO-related feature is large. On the contrary, if the heart rate variation is less than or equal to the predetermined threshold value, the variability estimator 310 can estimate that the variability of the CO-related feature is relatively small. The predetermined threshold value can be defined as a value personalized according to the type of biometric information, the user's gender, age, health status, blood vessel specificity, etc., and can be adjusted by calibration. However, the threshold value is not limited thereto and can be defined as a general fixed value obtained from multiple users.

[0070] In another example, the variability estimator 310 may predict variability of stroke volume (SV) indicating cardiac output by analyzing the shape of a waveform of a measured biosignal measured at an estimated time, and may estimate a variation in a CO-related feature based on the prediction result.

[0071] For example, if the waveform of the reference biosignal measured at the reference time rises from the systolic period to the diastolic period, the variability estimator 310 may predict that the variability of the stroke volume (SV) is high at the estimated time of the biosignal, and may estimate that the change of the CO-related feature is also large, wherein the CO-related feature is extracted from the biosignal at the estimated time of the biosignal. On the contrary, if the waveform of the reference biosignal at the reference time rises from the diastolic period to the systolic period, the variability estimator 310 may predict that the variability of the stroke volume (SV) is low at the estimated time of the biosignal, and may estimate that the change of the CO-related feature is also small. The systolic period may represent an interval from the starting point of the biosignal to the dicrotic notch point, and the diastolic period may represent a time interval after the dicrotic notch.

[0072] Figure 4A 4 is a diagram showing a waveform of a pulse wave signal 40, which is a superposition of five component pulses 41, 42, 43, 44, and 45. By using information associated with each of the component pulses 41, 42, 43, 44, and 45 of the pulse wave signal 40, a feature having a high correlation with blood pressure can be obtained. Generally, pulses up to the third component pulse are mainly used to estimate blood pressure. In some cases, depending on the individual, the pulses after the third pulse may not be observed and may be difficult to be found due to noise or have a low correlation with the estimation of blood pressure.

[0073] For example, refer to Figure 4B , the variability estimator 310 may detect a first local minimum point T1, a second local minimum point T2, and a third local minimum point T3 from the second order differential signal of the measured biosignal. In addition, the variability determiner 310 may extract a first amplitude P1, a second amplitude P2, and a third amplitude P3 corresponding to the first local minimum point T1, the second local minimum point T2, and the third local minimum point T3, respectively, from the measured biosignal, and may analyze the shape of the waveform of the measured biosignal by using the extracted amplitudes P1, P2, and P3. For example, if the amplitude P2 is larger than the value obtained by multiplying the amplitude P1 by a predetermined first value and the amplitude P3 is larger than the value obtained by multiplying the amplitude P2 by a predetermined second value, the variability estimator 310 may determine that the waveform of the measured biosignal rises from the systolic period to the diastolic period, and if not, the variability estimator 310 may determine that the waveform of the measured biosignal rises from the diastolic period to the systolic period.

[0074] The feature obtainer 320 may obtain a CO-related feature and a TPR-related feature from the measured biosignal based on the estimation result of the variability estimator 310 .

[0075] The feature obtainer 320 may obtain a heart rate (HR) from the measured biosignal, and may set the obtained HR as a CO-related feature. However, the feature obtainer 320 is not limited thereto, and may obtain other additional information and combine the additional information to obtain the CO-related feature. For example, the additional information may include the waveform of the measured biosignal, the time Tmax and / or the amplitude Pmax at the maximum point in the systolic period of the biosignal, the time Tmin and / or the amplitude Pmin at the minimum point of the biosignal, the total area or partial area PPGarea of ​​the waveform of the biosignal, the duration PPGdur of the biosignal, the amplitudes P1, P2, and P3 and / or the times T1, T2, and T3 of the constituent pulse waveforms constituting the biosignal, and the like, and, for example, PPGarea, P3 / Pmax, Pmax / PPGarea, 1 / PPGdur, and the like may be obtained as CO-related features.

[0076] In addition, the feature obtainer 320 may obtain a TPR-related feature from the measured biosignal based on the change in the CO-related feature estimated by the variability estimator 310. The TPR-related feature may be defined as, for example, a ratio P2 / P1 between the amplitude P2 of the first reflected wave component and the amplitude P1 of the forward wave component constituting the pulse waveform of the measured biosignal. However, the TPR-related feature is not limited thereto, and for example, the feature obtainer 320 may obtain 1 / (T3-T1), 1 / (T3-Tmax), 1 / (T2-T1), P3 / Pmax, P3 / P1, etc. as the TPR-related feature by combining the above additional information.

[0077] Based on the estimated change of the CO-related feature, the feature obtainer 320 may adaptively obtain the amplitude of the forward wave component associated with the TPR-related feature. Once the variability estimator 310 estimates that the change of the CO-related feature is large, the feature obtainer 320 may obtain the forward wave component having a relatively large change compared to the reference time as an element of the TPR-related feature. In a non-limiting example, when the variability estimator 310 estimates that the change of the CO-related feature exceeds a predetermined threshold, the feature obtainer 320 may obtain the forward wave component having a first change compared to the reference forward wave component obtained at the reference time as an element of the TPR-related feature, wherein the first change is greater than a predetermined first threshold.

[0078] For example, refer to Figure 4C, the feature acquirer 320 may acquire the second-order differential signal of the measured biosignal, and may acquire the amplitude P1 of the biosignal corresponding to the time T1 of the first local minimum point MP as a forward wave component having a large change. However, the feature acquirer 320 is not limited thereto, and may also acquire the amplitude corresponding to the internally dividing point between the time of the first local minimum point MP and the time of the second local maximum point of the measured biosignal as a forward wave component. The internal dividing point may be acquired by applying a predetermined weight to each of the time of the first local minimum point and the time of the second local maximum point.

[0079] In another example, if Figure 4D As shown in FIG, the feature acquirer 320 may detect an inflection point IP in a detection period of the second-order differential signal. When the inflection point IP is detected, the feature acquirer 320 may obtain a forward wave component having a relatively large change based on the detected inflection point IP. For example, the feature acquirer 320 may obtain the amplitude Pip of the biological signal corresponding to the time Tip of the inflection point IP as a forward wave component. In this case, the inflection point may be a point at which the waveform of the second-order differential signal changes from a "downward" convexity to an "upward" convexity over time in the detection period of the second-order differential signal; and the detection period may include a time interval between the time Ta of the first local maximum point and the time Tb of the first local minimum point of the second-order differential signal.

[0080] In addition, once the variability estimator 310 estimates that the change in the CO-related feature is relatively small, the feature obtainer 320 may obtain a forward wave component having a relatively small change compared to the reference time as an element of the TPR-related feature. In a non-limiting example, when the variability estimator 310 estimates that the change in the CO-related feature is less than or equal to a predetermined threshold, the feature obtainer 320 may obtain a forward wave component having a second change compared to a reference forward wave component obtained at the reference time as an element of the TPR-related feature, wherein the second change is less than or equal to a predetermined second threshold. Here, the second threshold may be less than or equal to the above-mentioned first threshold.

[0081] For example, refer to Figure 4E , the feature obtainer 320 may obtain the maximum amplitude Pmax corresponding to the time Tmax in the systolic period of the measured biosignal as a progressive wave component with a small change. The systolic period may represent an interval from the starting point of the biosignal to the dicrotic notch point, and the dicrotic notch point may represent a time point of the third local maximum point of the second-order differential signal.

[0082] In another example, if Figure 4FAs shown in the figure, the feature acquirer 320 can obtain a second-order differential signal, detect the time T1 of the first local minimum point and the time Tmax of the maximum amplitude point in the contraction period of the biological signal from the second-order differential signal, and obtain the amplitude Psys of the internal dividing point between the time T1 of the first local minimum point and the time Tmax of the maximum amplitude point as a forward wave component with small changes.

[0083] When the forward wave component is obtained based on the change of the CO-related feature, the feature obtainer 320 may obtain the TPR-related feature based on the forward wave component. For example, the feature obtainer 320 may obtain the amplitude of the biosignal corresponding to the time of the second local minimum point of the second-order differential signal as the amplitude component of the first reflected wave, and may obtain the TPR-related feature by obtaining the ratio between the amplitude of the forward wave component and the amplitude component of the first reflected wave. However, the TPR-related feature is not limited thereto, and the feature obtainer 320 may obtain the above-mentioned other additional information in addition to the first reflected wave component, and may also obtain the TPR-related feature by combining two or more of the forward wave, the first reflected wave, and the other additional information.

[0084] When the CO-related feature and the TPR-related feature are obtained from the measured biosignal, the bio-information estimator 330 may estimate the bio-information by applying a bio-information estimation model that defines correlations between the CO-related feature, the TPR-related feature, and the bio-information.

[0085] Figure 5 is a flowchart illustrating a method of estimating bio-information according to example embodiments.

[0086] Figure 5 The method is an example of a method of estimating bio-information performed by the apparatuses 100 and 200 and will be briefly described below to avoid redundancy.

[0087] Upon receiving a request for estimating bio-information, the apparatuses 100 and 200 for estimating bio-information may measure a bio-signal from a user in operation 510 .

[0088] The apparatuses 100 and 200 for estimating biometric information may provide an interface for various interactions with a user, and may receive a request for estimating biometric information from a user through the provided interface. Alternatively, the apparatuses 100 and 200 may receive a request for estimating biometric information from an external device. In this case, the request for estimating biometric information from the external device may include a request for providing a biometric information estimation result. In the case where the external device includes a biometric information estimation algorithm, the request for estimating biometric information may also include a request for providing feature information. The external device may be a smart phone, a tablet PC, etc. that can be carried by a user.

[0089] Then, the apparatuses 100 and 200 for estimating bio-information may estimate a change in the CO-related feature in operation 520 , and may estimate whether the estimated change is large or small in operation 530 .

[0090] For example, the apparatus 100 and 200 may obtain a heart rate as an element of cardiac output from the biosignal obtained in operation 510. In addition, the apparatus 100 and 200 may detect a change in heart rate compared to a reference heart rate measured in a resting state at a reference time, and may estimate a change in a CO-related feature based on the heart rate change. For example, if the heart rate change exceeds a predetermined threshold, the apparatus 100 and 200 may estimate that the change in the CO-related feature is large, and if the heart rate change is less than or equal to the predetermined threshold, the apparatus 100 and 200 may estimate that the change in the CO-related feature is small.

[0091] In another example, the apparatuses 100 and 200 may predict a change in stroke volume, which is an element of cardiac output, based on the shape of a waveform of a reference biosignal measured at a reference time, and may estimate a change in a CO-related feature based on the prediction result. For example, if the waveform of the measured reference biosignal rises from the systolic period to the diastolic period, the apparatuses 100 and 200 may predict that a change in stroke volume at the estimated time of the bioinformation is large and may estimate that a change in the CO-related feature is large, and in the opposite case, the apparatuses 100 and 200 may predict that a change in stroke volume is small and may estimate that a change in the CO-related feature is small.

[0092] Then, when it is estimated that the change of the CO-related feature is large, the apparatuses 100 and 200 for estimating bio-information may obtain a forward wave component having a relatively large change compared with the reference time as an element of the TPR-related feature in operation 540 .

[0093] For example, the apparatus 100 and 200 for estimating bio-information may obtain a second-order differential signal of a measured bio-signal, and may obtain an amplitude of the measured bio-signal corresponding to the time of a first local minimum point of the second-order differential signal as a forward wave component having a large change. Alternatively, the apparatus 100 and 200 may obtain an amplitude of the measured bio-signal corresponding to an internal division point between the time of a first local minimum point and the time of a second local maximum point as a forward wave component. In this case, the internal division point may be obtained by applying a predetermined weight to each of the time of the first local minimum point and the time of the second local maximum point. Alternatively, the apparatus 100 and 200 may detect an inflection point in a detection period of the second-order differential signal, and when the inflection point is detected, the apparatus 100 and 200 may obtain an amplitude of the measured bio-signal corresponding to the detected inflection point as a forward wave component having a relatively large change.

[0094] Then, when it is estimated that the change in the CO-related feature is small, the apparatus 100 and 200 may obtain a progressive wave component having a relatively small change compared to the reference time in operation 550. For example, the apparatus 100 and 200 may obtain the amplitude of the measured biosignal corresponding to the maximum amplitude point in the systolic period of the biosignal as the progressive wave component having a small change. Alternatively, the apparatus 100 and 200 may detect a first local minimum point from a second-order differential signal of the measured biosignal, and may obtain the amplitude of an inner division point between the first local minimum point and the maximum amplitude point as the progressive wave component having a small change.

[0095] Next, the apparatuses 100 and 200 may obtain a CO-related feature based on the biosignal obtained in operation 510 , and may obtain a TPR-related feature based on the forward wave component obtained in operation 540 or operation 550 in operation 560 .

[0096] Then, the devices 100 and 200 may estimate the bio-information based on the obtained CO-related features and TPR-related features in operation 570. When estimating the bio-information, the devices 100 and 200 may provide the user with the estimation result. In this case, the devices 100 and 200 may provide the user with the estimated bio-information through various visual methods / non-visual methods. In addition, the devices 100 and 200 may determine the health status of the user based on the estimated bio-information, and may provide the user with a warning or a response action based on the determination.

[0097] Figure 6 1 is a diagram showing a wearable device according to an example embodiment. The aforementioned example embodiments of the apparatuses 100 and 200 may be installed in a Figure 6 The device shown in the figure may be a smart watch or a smart band-type wearable device that can be worn on a wrist, but is not limited thereto.

[0098] Reference Figure 6 , the wearable device 600 may include a main body 610 and a band 630.

[0099] The body 610 may be formed to have various shapes and may include modules installed inside or outside the body 610 to perform the aforementioned function of estimating bio-information and various other functions. A battery may be embedded in the body 610 or the band 630 to power various modules of the wearable device 600.

[0100] The band 630 may be connected to the body 610. The band 630 may be flexible so as to be bent around the user's wrist. The band 630 may be bent in a manner that allows the band 630 to be detached from the user's wrist or may be formed as a non-detachable band. Air may be injected into the band 630 or an air bag may be included in the band 630 so that the band 630 may have elasticity according to changes in pressure applied to the wrist, and the band 630 may transmit changes in pressure of the wrist to the body 610.

[0101] The body 610 may include a sensor 620 for measuring a biosignal. The sensor 620 may be mounted on a rear surface of the body 610 that may contact an upper portion of a user's wrist. The sensor 620 may include a light source for emitting light onto the skin of the user's wrist and a detector for detecting light scattered or reflected from tissue of the user's wrist. The sensor 620 may also include a contact pressure sensor for measuring a contact pressure applied by the user's wrist.

[0102] The processor may be installed in the body 610. The processor may be electrically connected to various modules installed in the wearable device 600 to control operations of the various modules. In addition, the processor may estimate bio-information by using a bio-signal measured by the sensor 620.

[0103] For example, the processor may obtain CO-related features and TPR-related features associated with blood pressure from the biosignal, and may estimate the blood pressure by using the obtained CO-related features and TPR-related features. When worn on the user's wrist, the wearable device 600 may measure the biosignal from the capillary portion on the upper part of the wrist, thereby acquiring a biosignal mainly having a low-frequency component. Therefore, in order to stably extract the forward wave component associated with the TPR-related feature even when a biosignal mainly having a low-frequency component is acquired, the processor may estimate the change in the CO-related feature based on the heart rate or the waveform shape of the measured biosignal, and may adaptively obtain the forward wave component based on the change in the estimated CO-related feature.

[0104] In the case where the sensor 620 includes a contact pressure sensor, the processor may monitor the contact state of the user based on the contact pressure between the wrist and the sensor 620, and may provide the user with guidance information on the contact position and / or contact state through the display.

[0105] In addition, the body 610 may include a memory for storing the processing results of the processor and various information. In this case, the various information may include reference information related to the estimated bio-information and information associated with the function of the wearable device 600.

[0106] In addition, the body 610 may further include a controller 640 that receives a control command from a user and transmits the received control command to the processor. The controller 640 may include a power button for inputting a command for turning on / off the wearable device 600.

[0107] The display 614 may be mounted on the front surface of the body 610 and may include a touch panel for receiving a touch input. The display 614 may receive a touch input from a user, may send the received touch input to the processor, and may display a processing result of the processor. For example, the display 614 may display an estimated biometric information value and warning / alarm information.

[0108] A communication interface configured to communicate with an external device (such as a user's mobile terminal) may be installed in the main body 610. The communication interface may send the biometric information estimation result to the external device (e.g., the user's smartphone) to display the result to the user. However, the communication interface is not limited thereto, and may send and receive various necessary information.

[0109] Figure 7 is a diagram illustrating a smart device according to an exemplary embodiment. The smart device may be a smart phone, a tablet PC, etc., and may include the aforementioned apparatuses 100 and 200 for estimating bio-information.

[0110] Reference Figure 7 , the smart device 700 may include a body 710 and a sensor 730 mounted on one surface of the body 710. The sensor 730 may include a pulse wave sensor including at least one light source 731 and a detector 732. Figure 7 As shown in , the sensor 730 may be mounted on the rear surface of the body 710 , but is not limited thereto and may be configured to be combined with a fingerprint sensor or a touch panel mounted on the front surface of the body 710 .

[0111] In addition, a display may be installed on the front surface of the body 710. The display may visually display the bio-information estimation result, etc. The display may include a touch panel, and may receive various information input through the touch panel and transmit the received information to the processor.

[0112] The image sensor 720 may be installed in the body 710. When the user's finger approaches the sensor 730 to measure the pulse wave signal, the image sensor 720 may capture an image of the finger and may transmit the captured image to the processor. In this case, based on the image of the finger, the processor may recognize the relative position of the finger relative to the actual position of the sensor 730, and may provide the user with the relative position of the finger through the display, so as to guide the measurement of the pulse wave signal with improved accuracy.

[0113] As described above, the processor may estimate bio-information based on the bio-signal measured by the sensor 730. In this case, as described above, the processor may adaptively obtain the progressive wave component associated with the TPR-related feature based on the change of the CO-related feature, thereby improving the accuracy of estimating the bio-information.

[0114] In an exemplary embodiment, the method for estimating biometric information may be implemented as computer-readable codes written on a computer-readable recording medium. The computer-readable recording medium may be any type of recording device that stores data in a computer-readable manner.

[0115] Examples of computer-readable recording media include ROM, RAM, CD-ROM, magnetic tape, floppy disk, optical data storage device, and carrier wave (e.g., data transmission via the Internet). Computer-readable recording media can be distributed on multiple computer systems connected to a network so that computer-readable code is written to and executed from the computer-readable recording media in a decentralized manner. Functional programs, codes, and code segments required to implement the present invention can be easily derived by ordinary programmers in the field to which the present invention belongs.

[0116] Although example embodiments have been described, it will be appreciated by those skilled in the art that various changes and modifications may be made without changing the technical ideas and features of the present disclosure. Therefore, it is clear that the above embodiments are illustrative in all aspects and are not intended to limit the present disclosure.

Claims

1. A device for estimating biological information, the device comprising: a sensor configured to obtain a biological signal of a subject; and The processor is configured as: estimating changes in cardiac output-related characteristics from biosignals, and obtaining a progressive wave component associated with a total peripheral resistance-related feature from the biosignal based on an estimation result of the estimated change, The processor is configured to obtain a progressive wave component associated with a total peripheral resistance-related feature from the biosignal based on an estimation result of the estimated change by the following operations: obtaining a progressive wave component associated with a characteristic related to total peripheral resistance having a relatively large change compared to a reference time based on estimating that the change in the characteristic related to cardiac output is large; as well as Based on estimating that the change in the cardiac output-related characteristic is small, a progressive wave component associated with the total peripheral resistance-related characteristic having a relatively small change compared to a reference time is obtained.

2. The device according to claim 1, wherein: The sensor includes a pulse wave sensor, and The pulse wave sensor includes a light source configured to emit light onto an object and a detector configured to detect light reflected or scattered from the object.

3. The device according to claim 1 or claim 2, wherein: The processor is also configured to: obtain heart rate from biosignals, and A change in a cardiac output-related characteristic is estimated based on a change in the obtained heart rate compared to a reference heart rate obtained at a reference time.

4. The device according to claim 3, wherein: The processor is further configured to, in response to a change in the heart rate exceeding a predetermined threshold: The estimated variability in cardiac output-related characteristics is large, and A progressive wave component having a first change compared to a reference progressive wave component obtained at a reference time is obtained from the biosignal, wherein the first change is greater than a predetermined first threshold value.

5. The device according to claim 4, wherein: The processor is also configured to: Obtain the second-order differential signal of the biological signal, and The forward wave component is obtained based on the first local minimum point of the second-order differential signal.

6. The device according to claim 5, wherein: The processor is also configured to obtain one of the following items as a forward wave component: a first amplitude of the biological signal corresponding to the time of a first local minimum point, and a second amplitude of the biological signal corresponding to an internal dividing point between the time of the first local minimum point and the time of a second local maximum point of the second-order differential signal.

7. The device according to claim 5, wherein: The processor is also configured to: detecting an inflection point in a detection period of the second-order differential signal, and When the inflection point is detected, the forward wave component is obtained based on the inflection point.

8. The device according to claim 7, wherein: The detection period includes: a time interval between a first local maximum point and a first local minimum point of the second-order differential signal.

9. The device according to claim 4, wherein: The processor is also configured to: estimate that the change in cardiac output-related characteristics is small based on the change in heart rate being less than or equal to a predetermined threshold, and obtain a forward wave component from the biological signal having a second change compared to a reference forward wave component obtained at a reference time, wherein the second change is less than or equal to a predetermined second threshold, and the second threshold is less than or equal to the first threshold.

10. The device according to claim 9, wherein: The processor is further configured to obtain a forward wave component based on a maximum amplitude point in a systolic period of the biosignal.

11. The device according to claim 10, wherein: The processor is also configured to obtain one of the following items as a forward wave component: a first amplitude of a maximum amplitude point, and a second amplitude of the biological signal corresponding to an internal division point between the time of a first local minimum point of a second-order differential signal of the biological signal and the time of a maximum amplitude point.

12. The apparatus of claim 1 or claim 2, wherein: The processor is further configured to estimate a change in the cardiac output-related characteristic based on a shape of a waveform of a reference biosignal obtained at a reference time.

13. The device according to claim 12, wherein: The processor is also configured to: Based on the waveform of the reference biosignal obtained at the reference time rising from the systolic period to the diastolic period, it is estimated that the change of the cardiac output-related characteristics is large, and It is estimated that the change in the cardiac output-related characteristic is small based on the rise of the waveform of the reference biosignal obtained at the reference time from the diastole to the systole.

14. The apparatus of claim 1 or claim 2, wherein: The processor is also configured to: Obtain cardiac output-related features from biological signals, Based on the forward wave component, the total peripheral resistance related characteristics are obtained, and The biological information is estimated based on the cardiac output-related features and the total peripheral resistance-related features.

15. The device according to claim 14, wherein: The biological information includes at least one of blood pressure, vascular age, arterial stiffness, aortic pressure waveform, stress index, and fatigue level.

16. A computer-readable storage medium storing a program, wherein when the program is executed by a processor, the processor executes a method for estimating biological information, the method comprising: obtaining biological signals of the subject; Estimating changes in cardiac output-related characteristics from biosignals; and obtaining a progressive wave component associated with a total peripheral resistance-related feature from the biosignal based on an estimation result of the estimated change, The step of obtaining a forward wave component associated with a total peripheral resistance-related feature from a biological signal based on an estimation result of the estimated change comprises: Based on estimating that the change in the cardiac output-related characteristic is large, obtaining a progressive wave component associated with the total peripheral resistance-related characteristic having a relatively large change compared to a reference time; and Based on estimating that the change in the cardiac output-related characteristic is small, a progressive wave component associated with the total peripheral resistance-related characteristic having a relatively small change compared to a reference time is obtained.

17. The computer-readable storage medium of claim 16, wherein: The step of estimating the change in the cardiac output-related characteristic includes obtaining a heart rate from the biosignal, and estimating the change in the cardiac output-related characteristic based on a change in the obtained heart rate compared to a reference heart rate obtained at a reference time.

18. The computer-readable storage medium of claim 17, wherein: The step of estimating the change of the characteristic related to cardiac output comprises: estimating that the change of the characteristic related to cardiac output is large based on the change of the heart rate exceeding a predetermined threshold, and The step of obtaining the progressive wave component includes obtaining, from the biosignal, a progressive wave component having a first change compared to a reference progressive wave component obtained at a reference time, wherein the first change is greater than a predetermined first threshold value.

19. The computer-readable storage medium of claim 18, wherein: The steps to obtain the forward wave component include: Obtain the second-order differential signal of the biological signal, and The forward wave component is obtained based on the first local minimum point of the obtained second-order differential signal.

20. The computer-readable storage medium of claim 19, wherein: The step of obtaining a forward wave component includes obtaining one of the following items as a forward wave component: a first amplitude of the biological signal corresponding to the time of a first local minimum point, and a second amplitude of the biological signal corresponding to an internal dividing point between the time of the first local minimum point and the time of a second local maximum point of a second-order differential signal.

21. The computer-readable storage medium of claim 20, wherein: The steps to obtain the forward wave component include: detecting an inflection point in a detection period of the second-order differential signal, and When the inflection point is detected, the forward wave component is obtained based on the inflection point.

22. The computer-readable storage medium of claim 17, wherein: The step of estimating the change of the characteristic related to cardiac output comprises: estimating that the change of the characteristic related to cardiac output is small based on the change of the heart rate being less than or equal to a predetermined threshold, and The step of obtaining the progressive wave component includes obtaining a progressive wave component having a second change compared to a reference progressive wave component obtained at a reference time from the biosignal, wherein the second change is less than or equal to a predetermined second threshold value, and the second threshold value is less than or equal to the first threshold value.

23. The computer-readable storage medium of claim 22, wherein: The steps to obtain the forward wave component include: Detecting the maximum amplitude point during the systolic period of the biological signal, and The forward wave component is obtained based on the maximum amplitude point.

24. The computer-readable storage medium of claim 23, wherein: The step of obtaining a forward wave component includes obtaining one of the following items as the forward wave component: a first amplitude of a maximum amplitude point, and a second amplitude of the biological signal corresponding to an internal division point between the time of a first local minimum point of a second-order differential signal of the biological signal and the time of a maximum amplitude point.

25. The computer-readable storage medium of claim 17, wherein: The step of estimating the change in the cardiac output-related characteristic includes estimating the change in the cardiac output-related characteristic based on the shape of a waveform of a reference biosignal obtained at a reference time.

26. The computer-readable storage medium of claim 25, wherein: The steps for estimating changes in cardiac output-related characteristics include: Based on the waveform of the reference biosignal obtained at the reference time rising from the systolic period to the diastolic period, it is estimated that the change of the cardiac output-related characteristics is large, and It is estimated that the change in the cardiac output-related characteristic is small based on the rise of the waveform of the reference biosignal obtained at the reference time from the diastole to the systole.

27. The computer-readable storage medium according to claim 16 or 17, wherein: The method further comprises: Obtain cardiac output-related features from biological signals; Obtaining total peripheral resistance related characteristics based on the obtained progressive wave components; and The biological information is estimated based on the cardiac output-related features and the total peripheral resistance-related features.

Images