JP7317486B2 - Blood pressure estimation device and method and wearable device - Google Patents

Description

TECHNICAL FIELD The present invention relates to a blood pressure estimation device and method, and more particularly to a blood pressure estimation device and method and a wearable device for independently estimating systolic blood pressure and diastolic blood pressure non-invasively.

Recently, due to the aging population structure, rapid increase in medical expenses, shortage of specialized medical service personnel, etc., active research is being conducted on IT-medical convergence technology in which IT technology and medical technology are combined. In particular, the monitoring of the health status of the human body is not limited to hospitals, but is expanding into the mobile healthcare field, which monitors the health status of users who move around in their daily lives, such as homes and offices, anytime and anywhere. Typical types of biomedical signals that indicate an individual's health condition include ECG (electrocardiography), PPG (photoplethysmogram), and EMG (electromyography) signals. Various biosignal sensors have been developed to measure the signals. In particular, in the case of the PPG sensor, it is possible to estimate the blood pressure of the human body by analyzing the pulse wave morphology that reflects the cardio-blood related condition.

A general biosignal-based indirect blood pressure estimation method extracts various features correlated with blood pressure from a biosignal and estimates a blood pressure value based on these features. Blood pressure changes due to various mechanisms in daily life, and in some cases, systolic blood pressure (SBP) and diastolic blood pressure (DBP) have different trends of change. Most indirect methods for estimating blood pressure cannot accurately estimate systolic blood pressure and diastolic blood pressure independently when a decouple phenomenon occurs in which the systolic blood pressure and the diastolic blood pressure change in different trends. A problem exists. In the case of the indirect blood pressure estimation method, in which it is difficult to independently estimate the systolic blood pressure and the diastolic blood pressure, there is a risk of providing the user with erroneous blood pressure information.

JP 2016-214736 A

The present invention has been made in view of the conventional problems described above, and an object of the present invention is to provide a blood pressure estimation apparatus and method, and a wearable device.

A blood pressure estimating apparatus according to one aspect of the present invention, which has been made to achieve the above object, comprises a sensor that acquires a biomedical signal of a subject, and extracts a first cardiovascular feature and a second cardiovascular feature based on the biomedical signal. and estimating blood pressure based on a first trend of the first cardiovascular feature relative to a first reference cardiovascular feature and a second trend of the second cardiovascular feature relative to a second reference cardiovascular feature. and a processor for performing the same, wherein the first trend and the second trend are independent of each other.

The biosignal may include one or more of a photoplethysmogram (PPG), an electrocardiography (ECG), an electromyography (EMG), and a ballistocardiogram (BCG). .
The sensors may include multiple sensors that acquire the biosignals.
The blood pressure estimation device may further include a communication unit that receives a biosignal from an external device.
The first cardiovascular characteristic may be cardiac output (CO) and the second cardiovascular characteristic may be total peripheral resistance (TPR).
The processor extracts heartbeat information from the biosignal, the waveform form of the biosignal, the time and amplitude of the maximum point of the biosignal, the time and amplitude of the minimum point of the biosignal, the area of the waveform of the biosignal, and The first cardiovascular feature and the second cardiovascular feature may be extracted based on feature points including one or more of amplitude and time information of constituent pulse waveforms forming the biosignal.
The processor may independently estimate systolic blood pressure (SBP) and diastolic blood pressure (DBP) based on the first trend and the second trend.
The processor scales the first cardiovascular feature to a third cardiovascular feature, scales the second cardiovascular feature to a fourth cardiovascular feature, and calculates the systolic blood pressure based on the third cardiovascular feature. and estimate the diastolic blood pressure based on the fourth cardiovascular characteristic.
The first cardiovascular characteristic may be cardiac output and the second cardiovascular characteristic may be total vascular resistance.
The processor scales the cardiac output to a first cardiac output and a second cardiac output; scales the total vascular resistance to a first total vascular resistance and a second total vascular resistance; A linear combination of the cardiac output and the first total vascular resistance may be used to estimate systolic blood pressure, and a linear combination of the second cardiac output and the second total vascular resistance may be used to estimate diastolic blood pressure.
The processor linearly combines the first cardiac output and the first total vascular resistance after giving a first cardiac output weight and a first total vascular resistance weight, respectively, to the second cardiac output The volume and the second total vascular resistance may be linearly combined after applying a second cardiac output weighting and a second total vascular resistance weighting, respectively.
The processor applies a predetermined first scaling factor to the result of linearly combining the first cardiac output and the first total vascular resistance to estimate systolic blood pressure, the second cardiac output and the A predetermined second scaling factor may be applied to the linearly combined result of the second total vascular resistance to estimate the diastolic blood pressure.
The processor may determine a scaling degree for the first cardiovascular feature and the second cardiovascular feature based on the first trend and the second trend, respectively.
The processor equalizes the rate of increase of the second cardiac output with respect to the rate of increase of the first cardiac output by increasing the cardiac output when the first trend increases. or can be reduced.
The processor equalizes or increases the rate of decrease of the second total vascular resistance with respect to the rate of decrease of the first total vascular resistance by decreasing the total vascular resistance when the second trend of change decreases. can let
The blood pressure estimation device may further include an output unit for outputting the blood pressure estimation result of the processor.

A method for estimating blood pressure according to one aspect of the present invention to achieve the above object comprises the steps of obtaining a biomedical signal of a subject, and extracting a first cardiovascular feature and a second cardiovascular feature based on the biomedical signal. and blood pressure based on a first trend of change of said first cardiovascular characteristic compared to a first reference cardiovascular characteristic and a second trend of change of said second cardiovascular characteristic compared to a second reference cardiovascular characteristic. and estimating , wherein the first trend and the second trend are independent of each other.

The biosignals may include one or more of photoplethysmogram (PPG), electrocardiogram (ECG), electromyogram (EMG), and ballistocardiogram (BCG).
The first cardiovascular characteristic may be cardiac output and the second cardiovascular characteristic may be total vascular resistance.
The step of extracting the first cardiovascular feature and the second cardiovascular feature includes heartbeat information from the biosignal, waveform form of the biosignal, time and amplitude of the maximum point of the biosignal, and minimum point of the biosignal. , the area of the waveform of the biosignal, the passage of time of the biosignal, the amplitude and time information of the constituent pulse waveforms constituting the biosignal, and the internal division between two or more extracted feature points The first cardiovascular feature and the second cardiovascular feature may be extracted based on feature points comprising one or more of the points.
Estimating the blood pressure may independently estimate systolic blood pressure (SBP) and diastolic blood pressure (DBP) based on the first trend and the second trend.
The blood pressure estimation method comprises scaling the cardiac output to a first cardiac output and a second cardiac output and scaling the total vascular resistance to a first total vascular resistance and a second total vascular resistance. further comprising estimating the blood pressure linearly combining the first cardiac output and the first total vascular resistance to estimate a systolic blood pressure; A linear combination of the resistances can be used to estimate the diastolic pressure.
Scaling the cardiac output and the total vascular resistance comprises determining scaling degrees for the first cardiovascular feature and the second cardiovascular feature based on the first trend and the second trend, respectively. can.
In the step of scaling, when the first change trend increases, the increase rate of the second cardiac output is the same as the increase rate of the first cardiac output according to the increase in the cardiac output. can be made or reduced.
In the scaling step, if the second trend of change decreases, the decrease in the total vascular resistance causes the decrease rate of the second total vascular resistance to be the same as the decrease rate of the first total vascular resistance. or may be increased.
The blood pressure estimation method may further comprise outputting the estimated blood pressure result.

A wearable device according to one aspect of the present invention, which has been made to achieve the above object, includes a main body worn by a subject, connected to both ends of the main body, and fixing the main body to the subject while surrounding the subject. a strap, a sensor attached to the main body to acquire a biomedical signal from a subject, a first cardiovascular feature and a second cardiovascular feature extracted based on the measured biomedical signal, and a first reference cardiovascular feature. a processor for estimating blood pressure based on a first trend of the first cardiovascular feature compared to and a second trend of the second cardiovascular feature compared to a second reference cardiovascular feature; The first trend and the second trend are independent of each other.

The sensors may include one or more of a photoplethysmogram (PPG) sensor, an electrocardiogram (ECG) sensor, an electromyogram (EMG) sensor, and a ballistocardiogram (BCG) sensor.
The first cardiovascular characteristic may be cardiac output and the second cardiovascular characteristic may be total vascular resistance.
The processor may independently estimate systolic blood pressure (SBP) and diastolic blood pressure (DBP) based on the first trend and the second trend.
The processor scales the first cardiovascular feature to a third cardiovascular feature, scales the second cardiovascular feature to a fourth cardiovascular feature, and calculates the systolic blood pressure based on the third cardiovascular feature. and estimate the diastolic blood pressure based on the fourth cardiovascular characteristic.
The wearable device may further include a display for outputting blood pressure estimation results of the processor.
The wearable device may further include a communication unit that transmits the blood pressure estimation result of the processor to an external device.

According to another aspect of the present invention, which has been made to achieve the above object, a wearable device worn by a subject and estimating blood pressure from the subject comprises: a sensor for acquiring a biosignal from the subject; and determining a first different scaling degree for the first cardiovascular feature and the second cardiovascular feature for estimating systolic blood pressure. determining a second scaling degree different from each other for the first cardiovascular feature and the second cardiovascular feature for estimating diastolic blood pressure; and determining the first cardiovascular feature to which the first scaling degree is applied. and a linear combination of a second cardiovascular feature to which the second degree of scaling is applied, estimating the systolic blood pressure and the diastolic blood pressure, respectively, the systolic blood pressure and the diastolic blood pressure The first scaling degree and the second scaling degree for the first cardiovascular feature and the second cardiovascular feature for estimating are based on the change trend of the first cardiovascular feature and the second cardiovascular feature, respectively. determined independently based on

The first cardiovascular characteristic may be cardiac output and the second cardiovascular characteristic may be total vascular resistance.
The processor determines that a first scaling degree of cardiac output for estimating the diastolic blood pressure is a cardiac output for estimating the systolic blood pressure based on the cardiac output having an increasing trend compared to a steady state. It may be determined to be even less than the first scaling degree of stroke volume.
The processor determines that a second scaling degree of total vascular resistance for estimating the diastolic blood pressure is based on total vascular resistance having a decreasing trend compared to a steady state. may be determined to be even greater than the second scaling degree of .

According to the present invention, cardiovascular features such as cardiac output and total vascular resistance are extracted from various biological signals, and the extracted cardiovascular features are compared with the reference cardiovascular features in the stable period to grasp the trend of change. However, the accuracy of blood pressure estimation can be improved by independently estimating systolic and diastolic blood pressure based on trends in cardiovascular characteristics.

1 is a block diagram of a blood pressure estimator according to one embodiment; FIG. FIG. 3 is a block diagram of a blood pressure estimation device according to another embodiment; FIG. 4 is a diagram for explaining the correlation between the change tendency of cardiovascular characteristics and blood pressure; FIG. 4 is a diagram for explaining the correlation between the change tendency of cardiovascular characteristics and blood pressure; FIG. 4 is a diagram for explaining the correlation between the change tendency of cardiovascular characteristics and blood pressure; FIG. 4 is a diagram for explaining the correlation between the change tendency of cardiovascular characteristics and blood pressure; 3 is a block diagram of the processor configuration shown in FIGS. 1 and 2; FIG. FIG. 3 illustrates the extraction and scaling of cardiovascular features; FIG. 3 illustrates the extraction and scaling of cardiovascular features; FIG. 3 illustrates the extraction and scaling of cardiovascular features; FIG. 3 illustrates the extraction and scaling of cardiovascular features; 4 is a flowchart of a blood pressure estimation method according to one embodiment; 1 illustrates a wearable device according to one embodiment; FIG. 1 illustrates a wearable device according to one embodiment; FIG.

Specifics of embodiments of the invention are included in the detailed description and drawings. Advantages and features of the techniques described herein, as well as the manner in which they are carried out, will become apparent with reference to the embodiments described in detail below in conjunction with the drawings. Like reference numerals refer to like elements throughout the specification.

Although the terms first, second, etc. are used to describe various components, the components are not limited by the terms. Terms are only used to distinguish one component from another. Singular references include plural references unless the context clearly dictates otherwise. Also, when a part "includes" a component, it does not exclude other components, but means that it may further include other components, unless specifically stated otherwise. In addition, terms such as "... unit" and "module" described in the specification mean a unit for processing at least one function or operation, which is embodied as hardware or software, or and software.

Hereinafter, specific examples of embodiments for implementing the blood pressure estimation apparatus and method of the present invention will be described in detail with reference to the drawings.

FIG. 1 is a block diagram of a blood pressure estimation device according to one embodiment. The blood pressure estimation device 100 of this embodiment is installed in terminals such as smart phones, tablet PCs, desktop PCs, and notebook computers, or is manufactured as an independent hardware device. The independent hardware device is a wearable device that can be worn by the subject, such as a wristwatch type, bracelet type, wrist band type, ring type, glasses type, and hair band type, but is not limited to these.

Referring to FIG. 1, blood pressure estimation device 100 includes acquisition unit 110 and processor 120 .

The acquisition unit 110 includes one or more sensors, and measures various biological signals from the subject through the sensors. The one or more sensors include, but are not limited to, sensors that measure photoplethysmogram (PPG), electrocardiogram (ECG), electromyogram (EMG), ballistocardiogram (BCG), and the like. do not have.

Processor 120 estimates blood pressure based on the biosignals measured by acquisition unit 110 . Processor 120 extracts cardiovascular features affecting blood pressure from the biosignal when the biosignal is received from acquisition unit 110 . Cardiovascular characteristics include, but are not limited to, cardiac output (CO), total vascular resistance (TPR), and the like.

When the cardiovascular features are extracted, the processor 120 comprehends a change tendency or a change pattern of the extracted cardiovascular features compared to a reference cardiovascular feature, for example, a reference value of the cardiovascular features, and determines the change tendency of the cardiovascular features. Estimate blood pressure based on Processor 120 independently estimates systolic blood pressure (SBP) and diastolic blood pressure (DBP). The cardiovascular feature reference value is the value of the cardiovascular feature extracted from the steady state. Also, the change tendency of the cardiovascular characteristics indicates the tendency of the cardiovascular characteristics to change in general compared to the reference value of the cardiovascular characteristics.

For example, the processor 120 scales each cardiovascular feature with a systolic blood pressure feature and a diastolic blood pressure cardiovascular feature based on the change trend of each cardiovascular feature, and scales each cardiovascular feature with the scaled systolic blood pressure feature. Cardiovascular features for diastolic pressure are used to independently estimate systolic and diastolic pressure. For example, processor 120 may apply weights to each of the scaled cardiovascular features for systolic and diastolic pressure, respectively, and then combine the scaled cardiovascular features to obtain systolic and diastolic pressure. Estimate period blood pressure. For example, processor 120 linearly combines the scaled cardiovascular features and estimates systolic and diastolic blood pressure, respectively, based on the linear results. A scaling factor is then applied to the linear combination results to adjust the scaling of the overall cardiovascular features, and the results are used to estimate the systolic and diastolic pressures.

FIG. 2 is a block diagram of a blood pressure estimation device according to another embodiment.

Referring to FIG. 2 , the blood pressure estimation device 200 according to this embodiment includes an acquisition unit 110 , a processor 120 , a communication unit 210 , an output unit 220 and a storage unit 230 . Since the acquisition unit 110 and the processor 120 have been described with reference to FIG. 1, the rest of the configuration will be mainly described below.

The communication unit 210 communicates with the external device 250 under the control of the processor 120 to cooperate in estimating the blood pressure. For example, the communication unit 210 receives a biosignal from the external device 250 and transfers it to the acquisition unit 110 . External device 250 includes sensors that measure photoplethysmogram (PPG), electrocardiogram (ECG), electromyogram (EMG), ballistocardiogram (BCG), and the like. Through this, the acquiring unit 110 is equipped with a minimum number of sensors, for example, only a PPG sensor for measuring a photoelectric volume pulse wave signal, thereby reducing the size of the main body. One or more of the configurations shown in FIG. 2 can be implemented in one or more pieces of hardware.

In addition, under the control of the processor 120, the communication unit 210 transmits the biological signal measurement result, the blood pressure estimation result, and the like to the external device 250, and causes the external device 250 to perform blood pressure history management and health condition monitoring. Here, the external device 250 includes, but is not limited to, smart phones, tablet PCs, desktop PCs, notebook computers, devices of medical institutions, and the like.

The communication unit 210 supports Bluetooth (registered trademark) communication, BLE (Bluetooth (registered trademark) Low Energy) communication, near field communication (NFC), WLAN communication, and ZigBee (registered trademark). ) (Zigbee (registered trademark)) communication, infrared (Infrared Data Association: IrDA) communication, WFD (Wi-Fi Direct) communication, UWB (ultra-wideband) communication, Ant+ communication, Wi-Fi communication, RFID (Radio Frequency Identification ) communication, 3G communication, 4G communication, 5G communication, etc., to communicate with an external device. However, this is only an example and is not limited to these.

The output unit 220 outputs additional information such as the acquired biosignal, the blood pressure estimation result, and a warning based on the blood pressure estimation result. For example, the output unit 220 visually provides various information to the user through a display module (eg, display device). For example, when the blood pressure estimation result is displayed, if the estimated blood pressure is out of the normal range, the warning information is displayed to the user by emphasizing it by displaying it in red. As another example, a speaker module (eg, speaker) or a haptic module (eg, vibrator or haptic motor) provides various information to the user in a non-visual manner such as sound, vibration, and touch.

For example, systolic blood pressure and diastolic blood pressure are guided by voice, vibration and/or touch. If the estimated blood pressure is out of the normal range, the user is informed of the health condition through voice, vibration, and tactile sensation. For example, the output unit 220 informs the user of systolic blood pressure and diastolic blood pressure within the normal range through voice. In addition, it guides the systolic and diastolic blood pressures that are out of the normal range through vibration and touch. However, this is a simple example and is not intended to be limiting.

Output unit 220 can be embodied as one or more hardware networks.

The storage unit 230 stores reference information, acquired biosignals, blood pressure estimation results, and the like. The reference information includes user information such as user's age, gender, and health condition, or an estimation model such as a blood pressure estimation formula.

The storage unit 230 may include flash memory type, hard disk type, multimedia card micro type, card type memory (eg, SD or XD memory), RAM ( Random Access Memory), SRAM (Static Random Access Memory), ROM (Read-Only Memory), EEPROM (Electrically Erasable Programmable Read-Only Memory), PROM (Programmable Memory) read-only memory), magnetic memory, magnetic disk, optical disk, etc. Including, but not limited to, recording media.

3A to 3D are diagrams for explaining the correlation between changes in cardiovascular characteristics and blood pressure.

In general, mean blood pressure (MBP) is proportional to cardiac output and total vascular resistance as shown in Equation 1 below.

Here, ΔMBP indicates the mean blood pressure difference between the left ventricle and the right atrium, and generally does not exceed 3-5 mmHg for right atrial mean blood pressure, and is similar to left ventricular mean blood pressure or brachial mean blood pressure. have.

Generally, systolic blood pressure and diastolic blood pressure use values within a range of 0.5 to 0.7 left and right of the mean blood pressure thus calculated. However, the systolic blood pressure and the diastolic blood pressure may be separated from each other due to the change mechanism of the mean blood pressure. Therefore, in order to improve the accuracy of blood pressure estimation, it is necessary to estimate blood pressure in consideration of the influence of the blood pressure change mechanism.

FIGS. 3A and 3B illustrate the tendency of systolic blood pressure and diastolic blood pressure to change according to various blood pressure change mechanisms.

Referring to FIGS. 3A and 3B, the trends in cardiac output, total vascular resistance, systolic blood pressure, and diastolic blood pressure are estimated by a variety of mechanisms that affect changes in blood pressure.

As an example, if the blood pressure change mechanism is anaerobic exercise or isometric exercise, compared to the plateau, cardiac output (CO), total resistance vascular (TPR), systolic blood pressure (SBP), diastolic Both blood pressure (DBP) show a tendency to increase. Therefore, when estimating blood pressure by combining the cardiac output feature (f1) and the total vascular resistance feature (f2), the cardiac output feature (f1) increases and the total vascular resistance feature (f2) similarly increases. Processor 120 determines an anaerobic or isometric exercise state when showing a tendency to be sustained or increased insignificantly, and a combination of cardiac output feature (f1) and total vascular resistance feature (f2). Estimate the systolic and diastolic blood pressures proportional to the combined result.

As another example, when the blood pressure change mechanism is aerobic exercise, cardiac output (CO) and systolic blood pressure (SBP) tend to increase compared to the stable period, but total vascular resistance (TPR ) and diastolic blood pressure (DBP) show a decreasing trend. Therefore, when estimating blood pressure by combining the cardiac output feature (f1) and the total vascular resistance feature (f2), the cardiac output feature (f1) increases significantly, and the total vascular resistance feature ( When f2) shows a decreasing trend, the systolic blood pressure increases in a somewhat large range, but the diastolic blood pressure increases relatively less than the increase in the cardiac output characteristic (f1).

As another example, if the blood pressure change mechanism is alcohol, cardiac output (CO) increases, but total vascular resistance (TPR), systolic blood pressure (SBP), and diastolic Both blood pressure (DBP) tend to decrease. Therefore, when estimating blood pressure by combining the cardiac output feature (f1) and the total vascular resistance feature (f2), the degree of increase in the cardiac output feature (f1) is the degree of decrease in the total vascular resistance feature (f2). , the systolic and diastolic blood pressure tend to decrease even though the combined results of the cardiac output (f1) and total vascular resistance (f2) characteristics show an increasing trend. Here, the degree of decrease in diastolic blood pressure is relatively greater than that in systolic blood pressure.

As yet another example, when the blood pressure change mechanism is breath holding, when estimating blood pressure by combining the cardiac output feature (f1) and the total vascular resistance feature (f2), the total vascular resistance feature (f2) is increased and the cardiac output feature (f1) is held the same, the systolic and diastolic blood pressures are both proportional to the combined result.

FIG. 3C illustrates changes in systolic blood pressure and diastolic blood pressure due to changes in cardiac output, and FIG. 3D illustrates changes in systolic blood pressure and diastolic blood pressure due to changes in total vascular resistance. It is what I did.

Referring to FIG. 3C, both systolic blood pressure (SBP) and diastolic blood pressure (DBP) show the same change trend when cardiac output (CO) increases or decreases. Systolic and diastolic blood pressure show a downward trend as well when the cardiac output decreases compared to the steady phase. However, when the cardiac output gradually increases compared to the stable period, the degree of increase in diastolic blood pressure tends to become smaller than the degree of increase in systolic blood pressure. That is, when the cardiac output (x-axis) gradually increases, the diastolic blood pressure (y-axis) increases gradually, and when the cardiac output (y-axis) gradually increases, the systolic blood pressure (x axis) shows a tendency to increase gradually.

Referring to FIG. 3D, both systolic blood pressure (SBP) and diastolic blood pressure (DBP) show the same trend when the total vascular resistance (TPR) increases or decreases. However, when the total vascular resistance gradually decreases compared to the stable period, the degree of decrease in diastolic blood pressure tends to be greater than the decrease in systolic blood pressure. That is, when the cardiac output (x-axis) gradually decreases, the diastolic blood pressure (y-axis) decreases gradually, and when the cardiac output (y-axis) gradually decreases, the systolic blood pressure (x axis) shows a tendency to decrease gradually.

Thus, cardiovascular characteristics such as cardiac output and total vascular resistance, and systolic blood pressure and diastolic blood pressure, vary depending on the blood pressure change mechanism. In particular, systolic blood pressure is sensitive to changes in cardiac output, and diastolic blood pressure responds even more sensitively to changes in total vascular resistance. Therefore, according to the present embodiment, cardiovascular features such as cardiac output and total vascular resistance are extracted from various biological signals, and the extracted cardiovascular features are compared with the reference cardiovascular features in the stable period to determine the tendency of change. , and independently estimating systolic and diastolic blood pressure based on trends in cardiovascular characteristics, the accuracy of blood pressure estimation can be improved.

4 is a block diagram of the processor configuration shown in FIGS. 1 and 2. FIG. 5A-5D are diagrams illustrating the extraction and scaling of cardiovascular features. Embodiments in which the processor 400 estimates blood pressure based on biosignals are described with reference to FIGS. 4-5D.

Referring to FIG. 4, processor 400 includes feature point extractor 410 , feature extractor 420 , scaler 430 and blood pressure estimator 440 .

The feature point extraction unit 410 extracts feature points for estimating blood pressure from various biological signals. The feature points include heartbeat information, the shape of the biosignal waveform, the time and amplitude of the maximum point of the biosignal, the time and amplitude of the minimum point of the biosignal, the area of the biosignal waveform, the passage of time of the biosignal, and the biosignal. including, but not limited to, amplitude and time information of constituent pulse waveforms, internal dividing points between two or more feature points.

FIG. 5A illustrates a pulse wave signal among biological signals acquired from a subject. An example in which the feature point extraction unit 410 extracts feature points from the pulse wave signal will be described with reference to FIG. 5A.

In general, a pulse wave signal is composed of a superimposed signal of a propagation wave that starts from the heart and travels to the extremities of the body and a reflection wave that returns from the extremities. FIG. 5A shows that the waveform of the pulse wave signal (PS) acquired from the subject consists of five constituent pulses, for example, the superimposed signal of the traveling wave (fw) and the reflected waves (rw1, rw2, rw3, rw4). is exemplified.

The feature point extraction unit 410 extracts feature points by analyzing the constituent pulse waveforms (fw, rw1, fw2, rw3, rw4) of the pulse wave signal (PS). In general, the first three constituent pulses are mainly used for estimating blood pressure. Subsequent pulses typically may not be observed by humans, may be difficult to detect due to noise, and may be poorly correlated with blood pressure estimates.

As an example, the feature point extraction unit 410 extracts the time (t ₁ , t ₂ , t ₃ ) and the amplitude (P ₁ , P ₂ , P ₃ ) is extracted as a feature point. When the pulse wave signal (PS) is acquired, the feature point extraction unit 410 secondarily differentiates the acquired pulse wave signal (PS), and uses the second differentiated signal to obtain the constituent pulse waveforms (fw, rw1, rw2). ) at the maximum point (t ₁ , t ₂ , t ₃ ) and amplitude (P ₁ , P ₂ , P ₃ ). For example, search for the local minimum point in the second derivative signal, extract the time (t ₁ , t ₂ , t ₃ ) corresponding to the first to third local minimum points, and extract the pulse wave signal ( PS), the amplitudes (P ₁ , P ₂ , P ₃ ) corresponding to the times (t ₁ , t ₂ , t ₃ ) are extracted. Here, the local minimum point is a point having a downward convex shape (in other words, a concave shape) in which the signal decreases and then increases again around a specific point when observing a partial section of the second derivative signal. means

As another example, the feature point extraction unit 410 extracts the time (t _max ) and the amplitude (P _max ) at the point where the amplitude is maximum in a predetermined section of the pulse wave signal (PS) as feature points. Here, the predetermined interval indicates a point from the beginning of the pulse wave signal (PS), which means the systolic interval of blood pressure, to the point where a dicrotic notch (DN) occurs.

As still another example, the feature point extraction unit 410 extracts the passage of time (PPG _dur ), which means the total measurement time of the biosignal, or the area of the biosignal waveform (PPG _area ) as feature points. At this time, the area of the biosignal waveform means the entire area of the biosignal waveform or the area of the biosignal waveform corresponding to a predetermined ratio (eg, 70%) of the entire time course (PPG _dur ).

As yet another example, the feature point extraction unit 410 extracts an internal dividing point between two or more extracted feature points as an additional feature point. Characteristic points are extracted from erroneous positions due to the generation of unstable waveforms in the pulse wave signal due to non-ideal environments such as motion noise and sleep. In this way, blood pressure measurement is complemented by utilizing internal division points between erroneously extracted feature points.

For example, if feature points (t ₁ , P ₁ ) and (t _max , P _max ) are first extracted in the systolic period of blood pressure, the feature point extraction unit 410 extracts two feature points (t ₁ , P ₁ ) _and (t _max , P _max ) are calculated _. The feature point extraction unit 410 assigns weights to the time values t ₁ and t _max of the two feature points (t ₁ , P ₁ ) and (t _max , P _max ), and extracts the weighted values The time value (t _sys ) of the internal division point is calculated using the time value, and the amplitude (P _sys ) corresponding to the calculated time (t _sys ) of the internal division point is extracted. However, without being limited to this, through analysis of the acquired biosignal waveforms, feature points (t ₁ , P ₁ ) and (t ₂ , P ₂ ), a feature point (t ₃ , P ₃ ) associated with the third and fourth constituent pulse waveforms (rw ₂ , rw ₃ ) in the diastolic interval of blood pressure and An internal division point between (t ₄ , P ₄ ) and the like are further calculated.

The feature extractor 420 extracts cardiovascular features such as cardiac output and total vascular resistance by combining feature points extracted from various biosignals.

FIG. 5B is a graphical representation of candidate cardiac output and total vascular resistance features.

For example, referring to FIG. 5B, the feature extractor 420 extracts (1) heart rate per minute (HR), (2) biosignal area (PPG _area ), (3) third constituent pulse waveform (rw ₂ ). (P3/Pmax) obtained by dividing the amplitude (P ₃ ) from which the was extracted by the maximum amplitude (P _{max ) of the systolic interval (P max} ); (4) the amplitude from which the third constituent pulse waveform (rw ₂ ) was extracted ( P ₃ ) divided by the amplitude of the internal dividing point (P _sys ) (P3/Psys), (5) the maximum amplitude (P _max ) of the blood pressure systolic interval divided by the area of the biosignal waveform (PPG _area ) The value (Pmax/PPGarea), (6) the reciprocal of the biosignal over time (PPG _dur ), etc. are extracted as cardiac output (CO) features.

As another example, the feature extracting unit 420 may (1) extract the first constituent pulse waveform (fw) from the time (T ₃ ) at which the third constituent pulse waveform (rw ₂ ) is extracted (T ₁ ), (2) the reciprocal of the time (T ₃ ) at which the third constituent pulse waveform (rw ₂ ) was extracted minus the time (T _sys ) of the internal division point, (3) (4) the reciprocal of the value obtained by subtracting the time (T _max ) corresponding to the maximum amplitude (P _max ) of the systolic interval from the time (T ₃ ) at which the third constituent pulse waveform (rw ₂ ) was extracted; the reciprocal of the time (T ₂ ) at which the second constituent pulse waveform (rw ₁ ) was extracted minus the time (T ₁ ) at which the first constituent pulse waveform (fw) was extracted; (5) the second constituent; The amplitude (P ₂ ) at which the pulse waveform (rw ₁ ) was extracted divided by the amplitude (P ₁ ) at which the first constituent pulse waveform (fw) was extracted, and the third constituent pulse waveform (rw ₂ ) was extracted. ( ₆ ) _{the amplitude at which the third constituent pulse waveform (rw 2 )} was extracted (P ₃ ) divided by the maximum amplitude ( _Pmax ), and (7) the amplitude ( _P3 ) at which the third constituent pulse waveform ( _rw2 ) was extracted is the amplitude at which the first constituent pulse waveform (fw) was extracted. A value divided by (P ₁ ), (8) biosignal area (PPG _area ), etc. are extracted as total vascular resistance (TPR) features. However, it is not limited to such examples, and various other combinations of feature points can be used to extract cardiac output features and total vascular resistance features.

The scaling unit 430 scales the cardiovascular features extracted by the feature extraction unit 420 into a first cardiovascular feature and a second cardiovascular feature based on the change tendency of the cardiovascular features. The first cardiovascular feature is a cardiovascular feature for estimating systolic blood pressure and the second cardiovascular feature is a cardiovascular feature for estimating diastolic blood pressure. The scaling unit 430 scales the first cardiovascular feature and the second cardiovascular feature according to the extracted cardiovascular feature type (eg, cardiac output or total vascular resistance) and change tendency (eg, increase or decrease). Determines the degree of scaling. Here, the degree of scaling is predefined through a preprocessing process for each user. For example, the degree of scaling is optimized for each user based on the user's characteristics or previous experiments.

FIG. 5C illustrates scaling of a first cardiovascular feature for estimating systolic pressure and a second cardiovascular feature for estimating diastolic pressure by cardiac output. As an example, as described above, when cardiac output increases, cardiac output features have a greater effect on systolic blood pressure than on diastolic blood pressure. Thus, referring to FIG. 5C, scaling of the first cardiovascular feature (eg, first cardiac output) for estimating systolic blood pressure for intervals of increasing cardiac output (x>1) If the degree is defined by the formula y=x, then the degree of scaling of the second cardiac output for estimating diastolic pressure is greater than the rate of increase of the first cardiac output for estimating systolic pressure. Below, the scaling degrees for secondary cardiovascular features (eg, secondary cardiac output) are y=x, y=2*sqrt(x)−1, y=ln(x), and y = any one of sqrt(x).

In addition, the degree of scaling of the first cardiac output and the second cardiac output for the interval (0≤x≤1) in which the cardiac output decreases is given by the same formula (eg, y=x) Defined. However, the illustrated function expression is only an example and is not particularly limited. Note that in FIG. 5C, the x-axis indicates the cardiac output before scaling, and the y-axis indicates the cardiac output after being scaled by the scaling formula.

The formula for determining the degree of scaling of the first cardiovascular feature and the formula for determining the degree of scaling of the second cardiovascular feature are preset in the blood pressure estimator and are modified or adjusted in the future if necessary.

FIG. 5D illustrates an example of scaling a first cardiovascular feature for estimating systolic pressure and a second cardiovascular feature for estimating diastolic pressure by total vascular resistance. As mentioned above, when total vascular resistance decreases, total vascular resistance has a greater effect on diastolic blood pressure than on systolic blood pressure. Thus, referring to FIG. 5D, scaling of a first cardiovascular feature (eg, first total vascular resistance) to estimate systolic blood pressure for intervals of decreasing total vascular resistance (0≦x≦1). The scaling degree of the second total vascular resistance for estimating diastolic pressure is above the first total vascular resistance reduction rate for estimating systolic blood pressure, if the degree is defined by the formula y=x. The scaling degrees of the secondary cardiovascular feature (eg, secondary total vascular resistance) for estimating diastolic pressure are y=x, y=2*x−1, y=−1/x+2, and It is predefined as one of y=ln(x)+1. Also, the scaling degrees of the first total vascular resistance and the second total vascular resistance are defined by the same formula (eg, y=x) for the interval (x>1) where the total vascular resistance increases. However, the illustrated function expression is only an example and is not particularly limited. Note that in FIG. 5D, the x-axis indicates the total vascular resistance before scaling, and the y-axis indicates the total vascular resistance after being scaled by the scaling formula.

When the cardiovascular features are scaled to the first cardiovascular features and the second cardiovascular features by the scaling section 430, the blood pressure estimating section 440 calculates the systolic blood pressure from the first cardiovascular features and the second cardiovascular features, respectively. Input the estimation model and the diastolic pressure estimation model to independently estimate the systolic and diastolic pressures. For example, the systolic blood pressure estimation model and the diastolic blood pressure estimation model are in the form of functional expressions such as Equations 2 to 4 below, but are not limited thereto.

Formula 2 shows a function formula as an example of the blood pressure estimation model. Here, SBP _est means the systolic blood pressure estimate. F _{CO_SBP} means primary cardiac output scaled, F _{TPR_SBP} means primary total vascular resistance scaled, A _{CO_SBP} is primary cardiac output weighted value for systolic pressure, A _{TPR_SBP} is systolic means the first total vascular resistance weighted value for blood pressure. B _SBP means Systolic Blood Pressure Offset. Also, DBP _est means the diastolic blood pressure estimate. F _{CO_DBP} is scaled secondary cardiac output, F _{TRP_DBP} is scaled secondary total vascular resistance, A _{CO_DBP} is secondary cardiac output weighted value for diastolic pressure, _{ATPR_DBP} is diastolic means the second total vascular resistance weighted value for blood pressure. B _DBP means diastolic blood pressure offset.

The blood pressure estimator 440 applies the first cardiac output weighting value and the second cardiac output weighting value to the scaled first cardiac output and the first total vascular resistance, respectively, as illustrated in Equation 2. After application, the systolic blood pressure is estimated. Similarly, the blood pressure estimator 440 assigns the first and second cardiac output weights to the scaled first cardiac output and first total vascular resistance, respectively, and then systolic Estimate period blood pressure.

Formula 3 shows a function formula as another example of the blood pressure estimation model. ρ _SBP denotes a systolic blood pressure scaling factor for adjusting global scaling of cardiovascular features, and ρ _DBP denotes a diastolic blood pressure scaling factor for adjusting global scaling of cardiovascular features.

Equation 4 shows a functional expression of still another example of the blood pressure estimation model. Here, A _SBP means the coefficient for systolic blood pressure, and _ADBP means the coefficient for diastolic blood pressure.

FIG. 6 is a flowchart of a blood pressure estimation method according to one embodiment.

FIG. 6 is one embodiment of the blood pressure estimation method according to the embodiment of FIG. 1 or 2, and the above contents will be briefly described below to avoid duplication of description.

First, the blood pressure estimation device receives a blood pressure estimation request (step 610). A blood pressure estimation device provides an interface to a user and receives a blood pressure estimation request input by the user through the interface. Alternatively, the blood pressure estimation device communicates with an external device and receives a blood pressure estimation request from the external device. The external device is a smartphone, tablet PC, or the like carried by the user, and the user can control the operation of the blood pressure estimating apparatus through a device with superior interface performance and computing performance.

Next, the blood pressure estimating apparatus controls an internal sensor to obtain a biomedical signal from the subject or receives a biomedical signal from an external sensor for blood pressure estimation (step 620). Sensors installed in the blood pressure estimation device and external sensors acquire various biological signals such as PPG signals, ECG signals, EMG signals, and BCG signals from various parts of the subject (e.g., wrist, chest, fingers, etc.) do.

Next, the acquired biosignals are analyzed to extract feature points (step 630). The feature points include heartbeat information, the shape of the biosignal waveform, the time and amplitude of the maximum point of the biosignal, the time and amplitude of the minimum point of the biosignal, the area of the biosignal waveform, the passage of time of the biosignal, and the biosignal. including amplitude and time information of the constituent pulse waveforms, internal division points between two or more feature points, etc.

Next, cardiovascular features are extracted based on the extracted feature points (step 640). Cardiovascular characteristics include cardiac output characteristics and total vascular resistance characteristics. For example, the blood pressure estimating apparatus uses the extracted feature points as they are, or combines two or more to extract cardiovascular features, as shown in FIG. 5B.

The blood pressure estimator then scales the extracted cardiovascular features to a first cardiovascular feature for estimating systolic blood pressure (step 650). Considering the correlation between the tendency of change in the cardiac output feature and the total vascular resistance feature on the systolic blood pressure, the first cardiac output feature and the first total vascular resistance feature are calculated based on a predetermined scaling degree. scale.

The blood pressure estimator also scales the extracted cardiovascular features to a second cardiovascular feature for estimating diastolic blood pressure (step 660). The second cardiac output feature and the second total vascular resistance feature are calculated based on a predetermined degree of scaling, taking into account the correlation of the variation trends of the cardiac output feature and the total vascular resistance feature on the diastolic blood pressure. scale.

For example, when the cardiac output increases, the cardiac output features have a greater effect on the systolic blood pressure than on the diastolic blood pressure. The second cardiac output feature is scaled so that its rate of increase is the same or smaller than the first cardiac output feature. In addition, when the total vascular resistance decreases, the total vascular resistance characteristic has a greater effect on the diastolic blood pressure than the systolic blood pressure. has a greater rate of decrease or scales similarly to the first total vascular resistance feature.

The blood pressure estimator then uses the first cardiovascular feature to estimate systolic blood pressure (step 670) and the second cardiovascular feature to estimate diastolic blood pressure (step 680). For example, the first cardiovascular feature and the second cardiovascular feature are input to the systolic blood pressure estimation model and the diastolic blood pressure estimation model such as Equations 2 to 4 above, and the systolic blood pressure and the diastolic blood pressure are independently calculated. to estimate.

Next, the blood pressure estimating device outputs additional information such as the obtained biosignal, the blood pressure estimation result, and a warning based on the blood pressure estimation result, and provides them to the user (step 690). For example, the blood pressure estimation result is visually output through the display module, and if the estimated blood pressure is out of the normal range when the blood pressure estimation result is displayed, it is highlighted in red to provide warning information to the user. provide together. Alternatively, the speaker module outputs the blood pressure estimation result by voice, and when the estimated blood pressure is out of the normal range, the haptic module notifies the user of the health condition abnormality by vibration or tactile sensation.

7A and 7B are diagrams illustrating a wearable device according to one embodiment. Various embodiments of the blood pressure estimating device described above are mounted on a smart watch worn on the wrist or a smart band type wearable device, as illustrated. However, this is only one example for convenience of explanation, and the form of the wearable device is not particularly limited to this.

7A and 7B, wearable device 700 includes device body 710 and strap 720 .

The strap 720 is configured to be flexible and can be bent to encircle or separate from the user's wrist. Alternatively, strap 720 is configured in the form of a non-separable band. The strap 720 is inflated or includes an air bladder so as to be elastic according to changes in pressure applied to the wrist, and transmits changes in wrist pressure to the main body 710 .

A battery that supplies power to the wearable device is built into the body 710 or the strap 720 .

One or more sensors that measure various biological signals are mounted inside the main body 710 . For example, the pulse wave sensor 711 is mounted on the rear surface of the main body 710 with which the subject (OBJ), for example, the wrist, is exposed to the subject (OBJ). The pulse wave sensor 711 includes a light source 711a that irradiates the subject (OBJ) with light, and a detector 711b that detects scattered or reflected light from the subject (OBJ) and measures a pulse wave signal. The light source 711a includes at least one of a light emitting diode (LED), a laser diode, and a phosphor, and is formed of one or more arrays. Light sources formed of two or more arrays are configured to emit light of different wavelengths. Also, the detector includes a photodiode, an image sensor, etc., and is formed of one or more arrays.

A main body 710 of the wearable device 700 is equipped with a processor 712 that estimates blood pressure based on biological signals received from a pulse wave sensor 711 and/or an external sensor. The processor 712 generates a control signal to control the pulse wave sensor 711 according to a user's blood pressure estimation request, and if necessary, controls the communication unit 713 to receive a biosignal from an external sensor.

A communication unit 713 is mounted inside the main body 710 and communicates with an external device under the control of the processor 712 to transmit and receive necessary information. For example, the communication unit 713 receives biosignals from an external sensor that measures biosignals, such as an ECG sensor, an EMG sensor, a BCG sensor, or the like. It also receives a blood pressure estimation request from the user's portable terminal. Also, the extracted feature points and feature information are transmitted to an external device to estimate the blood pressure. In addition, the blood pressure estimation result is transmitted to an external device and displayed to the user, or used for various purposes such as blood pressure history management and disease research. It also receives reference information, such as a blood pressure estimation model and scaling equations for cardiovascular characteristics, from an external device.

Processor 712, when a biomedical signal is received from pulse wave sensor 711 and/or an external sensor, extracts feature points from the received biomedical signal. For example, when a pulse wave signal is received, as described above, the component pulse waveforms that make up the pulse wave signal are analyzed to determine the time and amplitude of the maximum point of each component pulse waveform, and the amplitude at the systolic interval of blood pressure. The time and amplitude of the maximum point, the passage of time of the pulse wave signal, the area of the pulse wave signal waveform, and the like are extracted as feature points.

Once the feature points are extracted, processor 712 combines the feature points to extract cardiovascular features, eg, cardiac output and total vascular resistance features, as illustrated in FIG. 5B.

Processor 712 scales the cardiovascular features into systolic and diastolic cardiovascular features, and uses the systolic and diastolic cardiovascular features to calculate systolic and diastolic pressure, respectively. Estimate blood pressure and independently. The degree of scaling of cardiovascular features for systolic blood pressure and the degree of scaling of cardiovascular features for diastolic blood pressure are the effects of changes in cardiovascular features, e.g., cardiac output features and total vascular resistance features, on systolic and diastolic blood pressure. It is predefined considering the correlation.

The processor 712 stores blood pressure estimation results, blood pressure history information, biosignals used to measure blood pressure, extracted feature points, cardiovascular features before and after scaling, etc. in a storage device.

Wearable device 700 further includes an operation unit 715 and a display unit 714 attached to main body 710 .

The operation unit 715 includes a power button for receiving a user's control command, transmitting it to the processor 712 , and inputting a command to power on/off the wearable device 700 .

The display 714 provides the user with various information related to the detected blood pressure under the control of the processor 712 . For example, display 714 may provide additional information to the user, such as detected blood pressure, alarms, warnings, etc., in a variety of visual and non-visual manners.

Meanwhile, the present embodiment is embodied as computer-readable program code on a computer-readable recording medium. A computer-readable recording medium includes all kinds of recording devices on which data readable by a computer system are stored.

Examples of computer readable storage media include ROM, RAM, CD-ROM, magnetic tapes, floppy disks, optical data storage devices, and carrier waves (eg, transmission over the Internet). Including those embodied in forms. The computer readable recording medium can also be distributed over network coupled computer systems so that the computer readable program code is stored and executed in a distributed fashion. Functional program codes and code segments for implementing the present embodiment can be easily inferred by programmers skilled in the art.

Although the embodiments of the present invention have been described in detail with reference to the drawings, the present invention is not limited to the above-described embodiments, and can be modified in various ways without departing from the technical scope of the present invention. It is possible to implement.

INDUSTRIAL APPLICABILITY The present invention is applicable to technical fields related to blood pressure estimation devices and methods.

100, 200 blood pressure estimation device 110 acquisition unit 120, 400, 712 processor 210, 713 communication unit 220 output unit 230 storage unit 250 external device 410 feature point extraction unit 420 feature extraction unit 430 scaling unit 440 blood pressure estimation unit 700 wearable device 710 device Body 711 Pulse wave sensor 711a Light source 711b Detector 714 Display unit 715 Operation unit 720 Strap

Claims (23)

a sensor that acquires a biological signal of a subject;
a processor for estimating blood pressure;
The processor
extracting cardiovascular features, including cardiac output and total vascular resistance, based on the biosignal;
Cardiac output extracted as the cardiovascular feature tends to increase or decrease compared to the cardiac output extracted in the stable period (hereinafter referred to as "cardiac output change trend"), and stability The tendency of increase or decrease of the total vascular resistance extracted as the cardiovascular feature (hereinafter referred to as "total vascular resistance change tendency") compared to the total vascular resistance extracted in the period is independently grasped. ,
Based on the change trend of the cardiac output, the cardiac output extracted as the cardiovascular feature is a first scaling the cardiac output of the first cardiovascular feature, and independently based on the trend of change in the cardiac output, the cardiac output extracted as the cardiovascular feature; scaling to said second cardiac output of a second cardiovascular feature comprising a second cardiac output and a second total vascular resistance for estimating diastolic blood pressure;
Based on the change trend of the total vascular resistance, the total vascular resistance extracted as the cardiovascular feature is used as the first cardiac output and the first total vascular resistance for estimating systolic blood pressure. scaling the first total vascular resistance of one cardiovascular feature, and independently scaling the total vascular resistance extracted as the cardiovascular feature to diastole based on the trend of change in the total vascular resistance; scaling the second total vascular resistance of the second cardiovascular features comprising the second cardiac output and the second total vascular resistance to estimate blood pressure;
estimating systolic blood pressure by linearly combining the first cardiac output and the first total vascular resistance obtained by scaling, and the second cardiac output and the second total vascular resistance obtained by scaling; A blood pressure estimating device characterized by linearly combining to estimate a diastolic blood pressure. 2. The method of claim 1, wherein the biosignal comprises one or more of a photoplethysmogram (PPG), an electrocardiogram (ECG), an electromyogram (EMG), and a ballistocardiogram (BCG). A blood pressure estimator as described. 3. The blood pressure estimating device of claim 2, wherein the sensors include a plurality of sensors for acquiring the biosignals. 2. The blood pressure estimation device according to claim 1, further comprising a communication unit that receives a biological signal from an external device. The processor extracts heartbeat information from the biosignal, the waveform form of the biosignal, the time and amplitude of the maximum point of the biosignal, the time and amplitude of the minimum point of the biosignal, the area of the waveform of the biosignal, and 2. The blood pressure estimation device according to claim 1, wherein the cardiovascular features are extracted based on feature points including one or more of amplitude and time information of constituent pulse waveforms forming the biosignal. The processor
linearly combining the first cardiac output and the first total vascular resistance after giving a first cardiac output weighted value and a first total vascular resistance weighted value, respectively;
2. The method of claim 1, wherein the second cardiac output and the second total vascular resistance are linearly combined after being given a second cardiac output weight and a second total vascular resistance weight, respectively. Blood pressure estimator. The processor
estimating systolic blood pressure by applying a predetermined first scaling factor to the result of linearly combining the first cardiac output and the first total vascular resistance;
2. The blood pressure estimating apparatus of claim 1, wherein the diastolic blood pressure is estimated by applying a predetermined second scaling factor to the result of linearly combining the second cardiac output and the second total vascular resistance. . The processor adjusts the rate of increase of the second cardiac output with respect to the rate of increase of the first cardiac output according to the increase in the cardiac output when the change tendency of the cardiac output increases. 2. The blood pressure estimating device according to claim 1, wherein the scaling is the same or decreasing. The processor makes the rate of decrease of the second total vascular resistance equal to the rate of decrease of the first total vascular resistance by the decrease of the total vascular resistance when the trend of change in the total vascular resistance decreases. 2. The blood pressure estimating apparatus according to claim 1, wherein scaling is performed so as to increase or increase. 2. The blood pressure estimating apparatus according to claim 1, further comprising an output unit for outputting the blood pressure estimation result of said processor. acquiring a biological signal of a subject with a sensor;
extracting, by a processor, cardiovascular features, including cardiac output and total vascular resistance, based on the biosignal;
The processor causes the cardiac output extracted as the cardiovascular feature to increase or decrease compared to the cardiac output extracted in the stable period (hereinafter referred to as "cardiac output change trend") ), and the tendency to increase or decrease in the total vascular resistance extracted as the cardiovascular feature compared to the total vascular resistance extracted in the stable period (hereinafter referred to as "total vascular resistance change tendency"), independently grasping; and
A first cardiac output and a first total vascular resistance for estimating the systolic blood pressure, which are extracted as the cardiovascular features, by the processor, based on the trend of change in the cardiac output. and independently extracted as the cardiovascular feature based on the trend of change in the cardiac output. scaling cardiac output to said second of a second cardiovascular characteristic including a second cardiac output and a second total vascular resistance for estimating diastolic blood pressure;
the first cardiac output and the first total vascular resistance for estimating systolic blood pressure; and independently extracted as the cardiovascular feature based on the trend of change in the total vascular resistance. to the second one of the second cardiovascular features including the second cardiac output and the second total vascular resistance for estimating diastolic blood pressure;
The processor estimates systolic blood pressure by linearly combining the first cardiac output obtained by scaling and the first total vascular resistance, and estimates the second cardiac output obtained by scaling and the first total vascular resistance. 2. linearly combining total vascular resistances to estimate diastolic blood pressure. 12. The method of claim 11, wherein the biosignal comprises one or more of a photoplethysmogram (PPG), an electrocardiogram (ECG), an electromyogram (EMG), and a ballistocardiogram (BCG). The blood pressure estimation method described. The step of extracting the cardiovascular features comprises heartbeat information from the biosignal, waveform form of the biosignal, time and amplitude of the maximum point of the biosignal, time and amplitude of the minimum point of the biosignal, and the biosignal. one or more of the area of the waveform, the time course of the biosignal, the amplitude and time information of the constituent pulse waveforms that make up the biosignal, and the internal division points between the two or more extracted feature points 12. The blood pressure estimation method according to claim 11, wherein the cardiovascular features are extracted based on the included feature points. The cardiac output extracted as the cardiovascular feature is the first cardiac output among first cardiovascular features including a first cardiac output and a first total vascular resistance for estimating systolic blood pressure. and, independently, the cardiac output extracted as the cardiovascular feature is scaled to a second volume including a second cardiac output and a second total vascular resistance for estimating diastolic blood pressure. The step of scaling to the second cardiac output of cardiovascular features increases the first cardiac output by increasing the cardiac output when the cardiac output trend increases. 12. The blood pressure estimation method according to claim 11, wherein the rate of increase of the second cardiac output is scaled to be the same or to be decreased. The total vascular resistance extracted as the cardiovascular feature is the first total of the first cardiovascular features including the first cardiac output and the first total vascular resistance for estimating systolic blood pressure. scaling to vascular resistance and independently including the second cardiac output and the second total vascular resistance for estimating diastolic blood pressure, the extracted total vascular resistance as the cardiovascular feature; The step of scaling the second total vascular resistance among the second cardiovascular characteristics includes decreasing the rate of decrease of the first total vascular resistance by decreasing the total vascular resistance when the change trend of the total vascular resistance decreases. 12. The blood pressure estimation method according to claim 11, wherein scaling is performed so that the rate of decrease of the second total vascular resistance is the same or increased with respect to . 12. The blood pressure estimation method of claim 11, further comprising outputting the estimated blood pressure result by the processor. a main body worn by a subject;
straps connected to both ends of the main body for fixing the main body to the subject while surrounding the subject;
a sensor mounted on the main body and acquiring a biological signal from a subject;
a processor for estimating blood pressure;
The processor
extracting cardiovascular features, including cardiac output and total vascular resistance, based on the biosignal;
Cardiac output extracted as the cardiovascular feature tends to increase or decrease compared to the cardiac output extracted in the stable period (hereinafter referred to as "cardiac output change trend"), and stability The tendency of increase or decrease of the total vascular resistance extracted as the cardiovascular feature (hereinafter referred to as "total vascular resistance change tendency") compared to the total vascular resistance extracted in the period is independently grasped. ,
Based on the change trend of the cardiac output, the cardiac output extracted as the cardiovascular feature is a first scaling the cardiac output of the first cardiovascular feature, and independently based on the trend of change in the cardiac output, the cardiac output extracted as the cardiovascular feature; scaling to said second cardiac output of a second cardiovascular feature comprising a second cardiac output and a second total vascular resistance for estimating diastolic blood pressure;
Based on the change trend of the total vascular resistance, the total vascular resistance extracted as the cardiovascular feature is used as the first cardiac output and the first total vascular resistance for estimating systolic blood pressure. scaling the first total vascular resistance of one cardiovascular feature, and independently scaling the total vascular resistance extracted as the cardiovascular feature to diastole based on the trend of change in the total vascular resistance; scaling the second total vascular resistance of the second cardiovascular features comprising the second cardiac output and the second total vascular resistance to estimate blood pressure;
estimating systolic blood pressure by linearly combining the first cardiac output and the first total vascular resistance obtained by scaling, and the second cardiac output and the second total vascular resistance obtained by scaling; A wearable device characterized by estimating a diastolic blood pressure by linearly combining the . 4. The claim wherein the sensor comprises one or more of a photoplethysmography (PPG) sensor, an electrocardiogram (ECG) sensor, an electromyography (EMG) sensor, and a ballistocardiogram (BCG) sensor. Item 18. The wearable device according to Item 17. 18. The wearable device according to claim 17, further comprising a display for outputting the blood pressure estimation result of said processor. 18. The wearable device of claim 17, further comprising a communication unit that transmits the blood pressure estimation result of the processor to an external device. A wearable device that is worn by a subject and estimates blood pressure from the subject,
a sensor that acquires a biological signal from a subject;
a processor for estimating blood pressure;
The processor
extracting cardiovascular features, including cardiac output and total vascular resistance, based on the biosignals;
Cardiac output extracted as the cardiovascular feature tends to increase or decrease compared to the cardiac output extracted in the stable period (hereinafter referred to as "cardiac output change trend"), and stability The tendency of increase or decrease of the total vascular resistance extracted as the cardiovascular feature (hereinafter referred to as "total vascular resistance change tendency") compared to the total vascular resistance extracted in the period is independently grasped. ,
Based on the change trend of the cardiac output, the cardiac output extracted as the cardiovascular feature is a first scaling the cardiac output of the first cardiovascular feature, and independently based on the trend of change in the cardiac output, the cardiac output extracted as the cardiovascular feature; scaling to said second cardiac output of a second cardiovascular feature comprising a second cardiac output and a second total vascular resistance for estimating diastolic blood pressure;
Based on the change trend of the total vascular resistance, the total vascular resistance extracted as the cardiovascular feature is used as the first cardiac output and the first total vascular resistance for estimating systolic blood pressure. scaling the first total vascular resistance of one cardiovascular feature, and independently scaling the total vascular resistance extracted as the cardiovascular feature to diastole based on the trend of change in the total vascular resistance; scaling the second total vascular resistance of the second cardiovascular features comprising the second cardiac output and the second total vascular resistance to estimate blood pressure;
estimating systolic blood pressure by linearly combining the first cardiac output and the first total vascular resistance obtained by scaling, and the second cardiac output and the second total vascular resistance obtained by scaling; A wearable device characterized by estimating a diastolic blood pressure by linearly combining the . The processor determines the second cardiac output for estimating the diastolic blood pressure based on the first cardiac output having an increasing tendency compared to the cardiac output extracted in the stable period. 22. The wearable device according to claim 21, wherein the second scaling degree is determined to have a smaller rate of increase than the first scaling degree of the first cardiac output for estimating the systolic blood pressure. . The processor performs a second scaling of the second total vascular resistance to estimate the diastolic blood pressure based on the second total vascular resistance having a decreasing trend compared to the steady phase sampled total vascular resistance. 22. The wearable device of claim 21, wherein a degree is determined such that a rate of decrease is greater than a first scaling degree of the first total vascular resistance to estimate the systolic blood pressure.

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