Calibration method and blood pressure estimation device for blood pressure estimation using optical volume change signals

Abstract

The present invention relates to a correction method and a blood pressure estimation device for blood pressure estimation using a photoplethysmography signal, and is a calibration method in which the following steps are performed by a blood pressure measurement device using a photoplethysmography signal (PPG): the step of acquiring a measurement value by a sensor mounted inside the device for blood pressure estimation; the step of determining the validity of the measurement value; and the step of calibrating a blood pressure estimation algorithm stored in the device in advance based on the measurement value, wherein the measurement value may include a photoplethysmography signal and blood pressure values ​​measured at the same time.

Description

Technical Field

[0006]

[0001] One embodiment of the present invention relates to a calibration method and a blood pressure estimation device for blood pressure estimation using a photoplethysmogram signal, and relates to a correction method and a blood pressure estimation device capable of improving the accuracy of estimation when estimating blood pressure using a photoplethysmogram signal.

Background Art

[0002] In recent years, due to the development of medical technology, the aging of society, and the westernization of lifestyle and diet, the prevalence of hypertension has been increasing. Hypertension is a major indicator of kidney disease and serious cardiovascular disease and is one of the most dangerous factors for death, so treatment and management are essential.

[0003] For the prevention, detection, and treatment of hypertension, it is most important to continuously measure blood pressure in daily life, but this is not currently being done.

[0004] Typically, to monitor blood pressure, a method has been used in which a cuff is worn and blood pressure is measured from the change in pressure injected into the cuff. The method using a cuff has the drawback that it induces discomfort due to pressure during the measurement process and it is not easy to actually carry because a portable product must include a cuff. The method using a cuff is not suitable for real-time monitoring of blood pressure due to drawbacks in terms of both convenience and portability. As a result, research on measuring blood pressure without a cuff in an unrestrained manner has been actively conducted.

[0005] In particular, recently, research using a PPG (Photoplethysmography) optical sensor has been increasing because characteristic values such as a percussion wave and a tidal wave can be used to obtain vascular elasticity information from a PPG signal. Since vascular elasticity information has a very large correlation with blood pressure, blood pressure can be estimated using this information.

[0006] Another reason for the numerous attempts to measure blood pressure via PPG signals is the significant potential of PPG optical sensors as personal cuff-less blood pressure monitors. Most wearable devices, such as recent smartbands and smartwatches, tend to universally incorporate PPG optical sensors. This means that most wearable devices can have blood pressure measurement capabilities simply by installing a program, without the need for additional sensors. Measuring blood pressure via the PPG optical sensor in a wearable device significantly improves the portability and convenience of existing blood pressure measurement methods. In terms of convenience, wearable devices like smartbands and smartwatches are worn on the wrist, offering the advantage of minimizing inconvenience for the wearer. In summary, because blood pressure can be predicted using PPG signals, applying this to wearable devices can solve the convenience and portability problems of existing devices.

[0007] However, when measuring blood pressure without a cuff and without restraint, the accuracy of the measurement tends to decrease, so correction techniques are required.

[0008] For example, as described in Patent Document 1, a blood pressure estimation method and apparatus that estimates blood pressure based on biological signals can be used to measure PTT (Pulse Transit Time) using PPG signals and estimate blood pressure. However, in the case of a method that calculates the time difference between the P-peaks, which are the highest points in the volume pulse wave of the measured PPG, the accuracy of the measurement tends to decrease due to differences between real-time measurement factors and algorithms.

[0009] Thus, the calibration technique for estimated blood pressure directly affects the accuracy of the measurement when blood pressure is measured without restraint. [Prior art documents] [Patent Documents]

[0010] [Patent Document 1] Japanese Patent Publication No. 2017-035453 [Overview of the Initiative] [Problems that the invention aims to solve]

[0011] The present invention was made against the aforementioned background and provides a calibration method for improving the accuracy of estimated blood pressure when measuring blood pressure without restraint, and a blood pressure estimation device that performs this method.

[0012] However, the problems that this invention aims to solve are not limited thereto, and include problems that can be clearly understood by those skilled in the art from the following description. [Means for solving the problem]

[0013] The present invention relates to a method for calibrating blood pressure estimated using a photoplethysmography signal (PPG), wherein the following steps are performed by a blood pressure estimation device using a photoplethysmography signal (PPG): the steps of acquiring a measurement value by a sensor mounted inside the device for blood pressure estimation, determining the validity of the measurement value, and calibrating a blood pressure estimation algorithm pre-stored in the device based on the measurement value, wherein the measurement value may include a photoplethysmography signal and blood pressure values ​​measured at the same time.

[0014] The aforementioned measurement values ​​are acquired multiple times within the first waiting period, and calibration is performed based on the average value of these measurement values.

[0015] The step of determining the validity of the measured values ​​involves determining whether the signal quality of the optical volume change signals measured multiple times is above a predetermined standard, determining the stability of the features of the optical volume change signals measured within the first measurement time, and confirming whether the difference between multiple blood pressure readings is within a predetermined error range, wherein the first measurement time is between 30 seconds and 1 minute.

[0016] The step of acquiring the aforementioned measurement values ​​is characterized by acquiring a first measurement value and a second measurement value measured in physiologically different environments of the user.

[0017] The aforementioned distinct environments are characterized by meaning that the heart rate and peripheral resistance are above a predetermined standard.

[0018] The aforementioned distinct environments are characterized by having different user activity states, and these activity states are resting and active states.

[0019] The aforementioned activity state is characterized by being obtained by analyzing motion signals acquired from various motion sensors used to sense the user's movements.

[0020] The step of calibrating a blood pressure estimation algorithm pre-stored in the device based on the measured values ​​is characterized by calibrating the blood pressure estimation algorithm pre-stored in the device based on the difference between the blood pressure value obtained by the optical volume change signal of the first measured value and the blood pressure value obtained by the optical volume change signal of the second measured value.

[0021] According to another embodiment of the present invention, the blood pressure estimation device includes a biosignal measurement unit that measures a photoplethysmography signal (PPG), a processor that applies the photoplethysmography signal to a blood pressure estimation algorithm pre-stored in the device to estimate blood pressure, and a communication unit that acquires blood pressure values ​​from an external device, wherein the processor determines the validity of the photoplethysmography signal and the acquired blood pressure values, and calibrates the blood pressure estimation algorithm pre-stored in the device based on the photoplethysmography signal and the acquired blood pressure values.

[0022] The processor determines whether the signal quality of the photoplethysmogram signals measured multiple times is above a preset standard, determines the stability of the features of the photoplethysmogram signals measured within the first measurement time, checks whether the difference between the blood pressures input multiple times is within a predetermined error range, and the first measurement time is characterized by being within 30 seconds to 1 minute.

[0023] The blood pressure estimation device further includes a motion sensing unit including various motion sensors for sensing the motion of the user, and an output unit for displaying information for calibration or blood pressure estimation, and the processor analyzes the motion signals sensed by the motion sensors to determine whether the activity state of the user is a resting state or an active state.

[0024] The processor is characterized by controlling the output unit to provide guides for measuring blood pressure in different activity states.

[0025] The processor is characterized by calibrating a blood pressure estimation algorithm stored in the device in advance based on the difference between the blood pressure value obtained from the photoplethysmogram signal in the active state and the blood pressure value obtained from the photoplethysmogram signal in the resting state.

Advantages of the Invention

[0026] According to the present invention, the reliability of the blood pressure estimated using the photoplethysmogram signal can be improved.

[0027] The effects of the present invention are not limited to the effects mentioned above, and include effects that can be clearly understood by those skilled in the art from the following description.

Brief Description of the Drawings

[0028] [Figure 1] It is a block diagram of a blood pressure estimation device according to an embodiment of the present invention. [Figure 2] It is a block diagram of a blood pressure estimation device according to another embodiment. [Figure 3]This is a block diagram of the processor configuration according to one embodiment. [Figure 4] This is a block diagram of the processor configuration according to another embodiment. [Figure 5] This is a flowchart illustrating a method for performing single calibration according to one embodiment. [Figure 6] This is a flowchart illustrating a method for performing multiple calibration according to one embodiment. [Figure 7] This is a flowchart illustrating a blood pressure estimation method according to one embodiment of the present invention. [Best Mode for Carrying Out the Invention]

[0029] Specific details of other embodiments are included in the detailed description and drawings. The advantages and features of the described technology, and how to achieve them, will become clear when referring to the embodiments described in detail later with the drawings. Throughout the specification, the same reference numerals refer to the same components.

[0030] Terms such as "first," "second," etc., may be used to describe various components, but the components should not be limited by such terms. Such terms are used solely for the purpose of distinguishing one component from other components. A singular expression includes plural expressions unless the context clearly indicates otherwise. Also, when a part "includes" a component, this means that it may include other components, rather than excluding them, unless otherwise stated. Furthermore, terms such as "...part" and "module" as described in the specification mean a unit that processes at least one function or operation, which may be embodied in hardware or software, or in a combination of hardware and software.

[0031] The following describes in detail embodiments of the blood pressure estimation device and method with reference to the drawings.

[0032] Figure 1 is a block diagram of a blood pressure estimation device according to one embodiment of the present invention.

[0033] The blood pressure estimation device 100 may be installed on a terminal such as a smartphone, tablet PC, desktop PC, or laptop computer, or it may be configured as an independent hardware device. In this case, the independent hardware device is a wearable device that can be worn by the user, such as a wristwatch, armband, wristband, ring, glasses, or headband. However, it is not limited to these.

[0034] The blood pressure estimation device 100 includes a biosignal measurement unit 110 and a processor 120.

[0035] The biosignal measurement unit 110 includes one or more sensors and can measure various biosignals from the user via these sensors. These one or more sensors are, but are not limited to, sensors that measure photoplethysmogram (PPG), electrocardiography (ECG), seismocardiogram (SCG), electromyography (EMG), ballistocardiogram (BCG), and pulse pressure.

[0036] The processor 120 can control the biosignal measurement unit 110 when it receives a blood pressure estimation request from the user or when the blood pressure estimation criteria are met. The processor 120 receives a biosignal from the biosignal measurement unit 110 and can estimate blood pressure or blood pressure change information by applying a blood pressure estimation algorithm, which is pre-stored in the device 100, to the received biosignal. Here, the blood pressure change information is the degree of blood pressure change estimated by applying the blood pressure estimation algorithm multiple times. The blood pressure estimation algorithm and blood pressure change information will be described in detail later.

[0037] The processor 120 can extract features that affect blood pressure from biological signals. These features may include cardiac output (CO) and total peripheral resistance (TPR).

[0038] The processor 120 can acquire the actual pulse pressure measured from the user via the pulse pressure sensor included in the biosignal measurement unit 110 or an external pulse pressure measuring device as a feature related to pulse pressure. Furthermore, the processor 120 can extract values ​​corresponding to pulse pressure or heart rate as a feature related to pulse pressure from biosignals such as PPG and BCG through analysis of pulse wave propagation time (PTT), heart rate variability (HRV), and peak amplitude.

[0039] The processor 120 extracts features from the biological signal and can estimate blood pressure by applying a blood pressure estimation algorithm pre-stored in the device 100 to the extracted features. For example, the processor 120 can acquire one or more characteristic points from the biological signal and combine the acquired characteristic points to extract features for blood pressure estimation. At this time, the processor 120 can extract a signal of one cycle from a biological signal measured continuously for a predetermined time. Furthermore, it can acquire a representative waveform with high representativeness from the extracted one-cycle signal, or acquire a representative waveform by superimposing two or more one-cycle signals, and then acquire feature points using the acquired representative waveform. At this time, features can be extracted by combining one or more time information or amplitude information of the feature points.

[0040] A blood pressure estimation algorithm can be obtained through regression analysis of multiple feature points and blood pressure values ​​extracted at equal time intervals from the pulse wave waveform. Regression analysis can include, for example, linear regression analysis, multiple regression analysis, and nonlinear regression analysis. Assuming there is a relationship between the extracted feature points and the blood pressure values, this relationship can be obtained by performing regression analysis on multiple data points, and these relationships can be included in the algorithm.

[0041] Furthermore, blood pressure estimation algorithms can be obtained through machine learning on multiple feature points and blood pressure values ​​extracted at equal time intervals from the pulse wave waveform. Examples of machine learning algorithms include artificial neural network (ANN), k-nearest neighbor (k-NN), Bayesian network, support vector machine (SVM), and recurrent neural network (RNN). By setting at least one hidden layer between the extracted feature points and blood pressure values ​​(systolic and diastolic), a network can be formed. This structure can then be repeatedly subjected to machine learning using a large amount of data to obtain a hidden layer matrix, which can then be included in the system.

[0042] A blood pressure estimation algorithm according to one embodiment can be obtained using data collected from a randomly selected population. This data may include, for example, pulse wave signals and data related to blood pressure.

[0043] A blood pressure estimation algorithm according to another embodiment can be obtained using data collected from groups classified by certain characteristics. This data may include, for example, pulse wave signals and blood pressure-related data collected from each group classified by physical characteristics such as age, sex, weight, and height, and this data can be obtained for each group classified by physical characteristics. As another example, a blood pressure estimation algorithm can be obtained for each group classified by situation, such as during rest or during activity, using pulse wave signals and blood pressure-related data collected from each group classified by situation.

[0044] Furthermore, blood pressure estimation algorithms according to other embodiments can acquire data collected from the user. For example, this could include pulse wave and blood pressure-related data measured by the user. As another example, the blood pressure estimation algorithm can be acquired for each situation, such as when the user is at rest or active, using pulse wave signals and blood pressure-related data measured by the user.

[0045] The blood pressure estimation algorithm is obtained from an external device of the blood pressure estimation device 200 and stored in the blood pressure estimation device 200.

[0046] The blood pressure estimation algorithm according to one embodiment is applied to and calibrated using pulse wave signals and blood pressure values ​​measured by the user. For example, if the blood pressure estimation algorithm is acquired using data collected from a randomly selected population, the blood pressure estimation algorithm is calibrated using pulse wave and blood pressure-related data measured by the user.

[0047] Blood pressure-related data for acquiring and calibrating blood pressure estimation algorithms can be obtained using general blood pressure measurement methods.

[0048] Calibration can be performed using either single calibration or multiple calibration. Single calibration is based on biosignals measured without considering other factors that affect the user's blood pressure. Multiple calibration is based on biosignals measured under different conditions for at least one of the major factors affecting the user's blood pressure: heart rate and peripheral resistance.

[0049] Figure 2 is a block diagram of a blood pressure estimation device according to another embodiment.

[0050] The blood pressure estimation device 200 may include a biosignal measurement unit 110, a motion sensing unit 115, a processor 120, a communication unit 210, an output unit 220, and a storage unit 230.

[0051] The biosignal measurement unit 110 is electrically connected to the processor 120 and can measure various biosignals of the user based on the control of the processor 120.

[0052] The motion sensing unit 115 includes one or more sensors that can sense the user's movements via the sensors. The sensors may include at least one of the following: a motion sensor, an acceleration sensor, a gyroscope, and an angular velocity sensor.

[0053] The motion sensing unit 115 is electrically connected to the processor 120 and can measure the user's movements based on the control of the processor 120. For example, the motion sensing unit 115 can detect whether the user is active or at rest.

[0054] The processor 120 receives biological signals from the biological signal measurement unit 110 and can use the received biological signals to extract features for blood pressure estimation, such as cardiac output features and total vascular resistance features.

[0055] Furthermore, the processor 120 can receive motion signals from the motion sensing unit 115 and use the received motion signals to estimate the user's posture and lifestyle patterns. For example, the processor 120 can analyze the motion signals to categorize and record the user's 'wake-up and bedtime' and 'activity and rest time'.

[0056] According to this embodiment, the processor 120 can control the communication unit 210 to transmit at least one of the initial information, intermediate information, and final information for blood pressure estimation to an external terminal or device. Based on the control of the processor 120, the communication unit 210 can communicate with an external terminal or device and receive an information request message from the external terminal or device requesting the transmission of information, or receive blood pressure values ​​for calibration at a preset time and period upon receiving the information request message.

[0057] The communication unit 210 can connect to a communication network using wired or wireless communication technology based on the control of the processor 120. The communication unit 210 can transmit the processing results of the processor 120 to an external device 250. In this case, the communication technology may include, but is not limited to, Bluetooth® communication, BLE® (Bluetooth Low Energy) communication, Near Field Communication (NFC®), WLAN® communication, Zigbee® communication, 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, and 5G communication.

[0058] On the other hand, the processor 120 can guide the user to measure blood pressure for calibration via the output unit 220. Based on the control of the processor 120, the output unit 220 can output information guiding the measurement of reference blood pressure and / or pulse pressure using a visual output module such as a display, an audio output module such as a speaker, or a haptic module such as vibration or touch. The processor 120 can output via the output unit 220 to a user interface connected to the blood pressure estimation device 200. The user can input the measured blood pressure and / or pulse pressure via an internal or external input device of the blood pressure estimation device 100. Here, the external input device may be, but is not limited to, a smartphone, tablet PC, desktop PC, or notebook computer connected via the communication unit 210.

[0059] The processor 120 can store in the storage unit 230, as reference information for blood pressure estimation, the biosignal measured via the biosignal measurement unit 110 at the time of calibration, the features extracted via that biosignal, and the blood pressure values ​​acquired at the time of calibration.

[0060] Furthermore, the storage unit 230 can store various information processed by the blood pressure estimation device 200.

[0061] For example, the storage unit 230 can store various reference information such as algorithms and programs performed within the blood pressure estimation device 200, input / output biosignal data, user characteristic information related to the subject's physical information, blood pressure estimation models, offset estimation models, and other reference information. The storage unit 230 can store data related to pulse wave signals, body temperature, exercise status, etc., measured by the blood pressure estimation device 200. The storage unit 230 can store at least one blood pressure estimation algorithm.

[0062] In this case, the storage unit 230 includes, but is not limited to, storage media such as flash memory type, hard disk type, multimedia card micro type, card type memory (e.g., 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 Read-Only Memory), magnetic memory, magnetic disk, and optical disk.

[0063] The processor 120 can extract features from the biosignals measured by the biosignal measurement unit 110 and estimate blood pressure by referring to the reference information at the time of calibration stored in the storage unit 230. At this time, when the heart rate and peripheral resistance are measured by the biosignal measurement unit 110, the processor 120 can receive this information via the interface of the communication unit 210 or the output unit 220 and obtain an offset based on the received user's blood pressure. The processor 120 can use the offset thus obtained to correct the error between the cardiac output features and total vascular resistance features extracted via the biosignals and the actual cardiac output and total vascular resistance.

[0064] The output unit 220 can output biosignals, blood pressure estimation results, and additional information based on the blood pressure estimation results once the blood pressure estimation is completed by the processor 120. For example, the output unit 220 can provide various information to the user visually via a display module. For instance, when displaying the blood pressure estimation results, warning information can be displayed to the user by highlighting the estimated blood pressure outside the normal range, for example, by displaying it in red. As another example, various information can be provided to the user in non-visual ways, such as sound, vibration, or touch, via a speaker module or haptic module. For example, systolic and diastolic blood pressure can be announced by voice. In this case, if the estimated blood pressure is outside the normal range, the user can be notified of an abnormality in their health condition through vibration or touch.

[0065] The storage unit 230 can store biological signals and / or blood pressure estimation results once the blood pressure estimation by the processor 120 is complete.

[0066] Figure 3 is a block diagram showing the configuration of a processor according to one embodiment.

[0067] The processor described in Figure 3 is the processor 210 shown in Figures 1 and 2. The processor 210 may include a signal analysis unit 211, a signal processing unit 222, a feature extraction unit 223, a calibration unit 224, and a blood pressure estimation unit 225.

[0068] The signal analysis unit 211 can determine whether the measured biological signal is a valid signal. In order to determine whether the measured biological signal is a valid signal, the signal analysis unit 211 can check whether the signal quality of the measured biological signal is above a standard value and whether the features of the biological signal are stable for a predetermined time.

[0069] For example, the signal analysis unit 211 can determine a biological signal as a valid signal if the power spectrum value of the biological signal within a preset frequency range in the frequency domain is greater than or equal to a preset value, and the waveform of the biological signal is free from interruptions or noise within a preset first time period. This utilizes the fact that the components of the biological signal are mainly contained in the low-frequency band in the frequency domain, and the noise components are mainly contained in the high-frequency band in the frequency domain. The preset frequency range may be, for example, 0.67 Hz to 3 Hz. As another example, the signal analysis unit 211 can determine a biological signal as a valid signal if the difference between the highest and lowest values ​​of the biological signal in the time domain is greater than or equal to a preset value.

[0070] If the pulse wave signal is determined to be a valid signal, the blood pressure estimation device 200 can estimate the blood pressure value using the biosignal. If the biosignal is determined to be not a valid signal, the blood pressure estimation device 200 can measure the biosignal again by having the processor 120 control the biosignal measurement unit 110.

[0071] The signal analysis unit 211 may further include a noise filtering unit (not shown) for removing noise components contained in the pulse wave signal. The signal analysis unit 211 may also further include an amplification unit (not shown) for amplifying the received biological signal to make it suitable for signal processing.

[0072] Furthermore, the signal analysis unit 211 can analyze the baseline for biological signals and update the baseline.

[0073] Here, the baseline can be represented by a line connecting, for example, the midpoint between the highest and lowest points of the waveform of each period of the biological signal. The baseline can be in the form of a quadratic function, a cubic function, or a function with multiple inflection points. After confirming the baseline of the biological signal, the signal analysis unit 211 can update the baseline for the biological signal.

[0074] Furthermore, the signal analysis unit 211 can determine the validity of the blood pressure values ​​acquired for calibration. The signal analysis unit 211 can determine that the blood pressure values ​​are valid if the difference between the blood pressure values ​​(SBP / DBP, respectively) acquired within a predetermined first hour is within a predetermined error range.

[0075] The feature extraction unit 223 extracts feature points at predetermined time intervals from the waveform of one period of the biological signal from which noise components have been removed, and by combining the extracted feature points, it can extract cardiac output features and total vascular resistance features by combining various feature points obtained from the biological signal. For example, the feature extraction unit 223 can identify the highest and lowest points of the signal from which noise components have been removed and extract a waveform of one period. Next, the feature extraction unit 223 can extract feature points within a first time period from the waveform of one period. By applying the blood pressure estimation algorithm stored in the storage unit 230 in advance to the multiple feature points extracted for each period of the biological signal, the highest blood pressure (systolic blood pressure) value and the lowest blood pressure (diastolic blood pressure) value can be obtained.

[0076] On the other hand, cardiac output characteristics and total vascular resistance characteristics can be extracted by combining feature points in a way that differs from each other, according to the user's characteristics. Furthermore, cardiac output characteristics and total vascular resistance characteristics can be extracted separately for each blood pressure type, for example, mean arterial pressure, diastolic blood pressure, and systolic blood pressure, by combining feature points in a way that differs from each other.

[0077] The feature extraction unit 223 can extract features related to pulse pressure from the measured biological signals. For example, by analyzing the measured biological signals such as PTT (Pulse Transit Time) and HRV (Heart rate variation), heart rate (HR) and other features related to pulse pressure can be extracted.

[0078] The calibration unit 224 can calibrate the blood pressure estimation algorithm, which is pre-stored in the storage unit 230, to the subject's biological signals at a preset calibration time using a single calibration technique.

[0079] Single calibration is a technique that does not take into account the user's activity level, but instead acquires blood pressure values ​​measured at the same time as the measured biosignal, and performs calibration based on the biosignal and blood pressure values ​​measured at the same time.

[0080] For example, the calibration unit 224 performs a single calibration according to a preset first condition. The first condition is that the calibration is performed multiple times within a preset time interval. The preset time interval may be, for example, 30 minutes or less. Such multiple calibrations can be repeated in each cycle, for example, every month. Previous data can be updated based on the result values ​​of the newly performed calibration.

[0081] If, under these conditions, the user's systolic blood pressure measured in the first calibration is, for example, 120 mmHg in the first calibration and 124 mmHg in the second calibration, and the user's systolic blood pressure measured in the second calibration performed in the next cycle is, for example, 140 mmHg in the first calibration and 138 mmHg in the second calibration, then the average value from the first calibration will be 122 mmHg, and the average value from the second calibration will be 139 mmHg. In such a case, the average value is updated with the new value of 139 mmHg obtained from the second calibration.

[0082] The blood pressure estimation unit 225 applies multiple feature points to a blood pressure estimation algorithm pre-stored in the storage unit 230 to obtain blood pressure values ​​or blood pressure change information.

[0083] Figure 4 is a block diagram showing the configuration of a processor according to another embodiment.

[0084] The processor in Figure 4 differs from the one in Figure 3 only in the motion signal analysis unit 231 and the calibration unit 234, so we will focus on explaining the different components.

[0085] The motion signal analysis unit 231 analyzes the motion signal received from the motion sensing unit (115 in Figure 2) to estimate the user's activity state. Here, the activity state can include resting and active states. For example, if the motion sensing unit (115 in Figure 2) includes an acceleration sensor, it can receive acceleration measurements taken by the acceleration sensor over time and determine the amount of movement based on the acceleration measurements. Based on the acceleration measurements taken by the acceleration sensor over a preset period, the motion signal analysis unit 231 can estimate the user's lifestyle pattern by classifying their wake / sleep times and activity / rest times.

[0086] The calibration unit 234 can calibrate the blood pressure estimation algorithm, which is stored in the storage unit 230 in advance, to the subject's biological signals using a multiple calibration technique based on a pre-set technique.

[0087] Multiple calibration is a technique that acquires measurements under physiologically different environments and performs calibration based on the acquired measurements. Here, physiologically different environments mean environments in which at least one of the major factors affecting the user's blood pressure, namely heart rate and peripheral resistance, is different. Heart rate and peripheral resistance may vary depending on the user's activity level. For example, the calibration unit 234 performs calibration based on a biosignal measured considering the user's activity level and a blood pressure value measured at the same time. Here, for the sake of explanation, the biosignal and blood pressure values ​​measured at the same time are referred to as measured values. The activity level can include, for example, a resting state and an active state. Therefore, the measured values ​​in the resting state and the measured values ​​in the active state are calibrated by a blood pressure estimation algorithm pre-stored in the storage unit 230.

[0088] For example, if the user's systolic blood pressure measured under condition 2 is, for example, 120 mmHg first and 124 mmHg second, and the user's systolic blood pressure measured under condition 3, which is physiologically different from condition 2, is, for example, 140 mmHg first and 138 mmHg second, then the unique blood pressure change characteristics for condition 2 and condition 3 can be extracted.

[0089] The calibration unit 234 can guide the user during calibration. For example, it can guide the user to touch the biosignal measurement unit 110 in order to measure biosignals. It can also guide the user regarding measurement timing and measurement methods that satisfy pre-set conditions, and communication connections between external devices and the communication unit (210 in Figure 2). Here, the measurement timing that satisfies the pre-set conditions may be, but is not limited to, the active period (e.g., immediately after waking up) and the resting period (e.g., immediately before going to sleep) estimated by the motion signal analysis unit.

[0090] The calibration unit 234 can control the output unit 220 to provide an alarm to guide calibration if calibration is not performed according to the guide.

[0091] The blood pressure estimation algorithm can be calibrated based on the results of the calibration performed by the calibration unit 234.

[0092] Figure 5 is a flowchart illustrating a method for performing single calibration according to one embodiment.

[0093] In step S510, the blood pressure estimation device 200 acquires measurement values ​​by sensors mounted inside the device 200 for the purpose of blood pressure estimation.

[0094] The first measurement value at the first correction point is obtained from the user. The measurement is performed within a predetermined first measurement time. Here, the predetermined first measurement time may be 30 seconds to 1 minute, but is preferably 40 seconds. However, it is not limited to this. Here, the measurement value includes biological signals and blood pressure values ​​measured at the same time, as described above. In one embodiment, the first measurement value is the average value of multiple measurement values ​​obtained. For example, the first measurement value may be the average value of the measurement value measured at the first time and the measurement value measured at the second time. In this embodiment, only the calculation of the average value of two measurement values ​​has been described, but the number of measurements is not limited to this and may be performed multiple times. However, the time between the first time and the second time must be within a predetermined first waiting time. The first waiting time may be approximately 30 minutes to 1 hour, but is not limited to this.

[0095] In step S520, the validity of the measurement is determined.

[0096] For example, the effectiveness of a biosignal is determined if the signal quality of the biosignal measured multiple times is above a predetermined standard, and the features of the biosignal measured within the first measurement time are stable.

[0097] Furthermore, if the difference between multiple entered blood pressure values ​​falls within a predetermined error range, the blood pressure value is considered valid.

[0098] In step S530, the blood pressure estimation algorithm, which has been pre-stored in the storage unit 230, is calibrated based on the acquired first measurement value.

[0099] Subsequently, steps S510 through S530 can be repeated to continue calibrating the blood pressure estimation algorithm.

[0100] For example, the blood pressure estimation device 200 acquires a second measurement value from the user at the second correction point using a sensor mounted inside the device 200 for the purpose of blood pressure estimation. In one embodiment, the second measurement value is the average value of multiple measurements. The acquisition of the average value of the measurements was explained in step S510, so it will be omitted here.

[0101] The first and second correction points can be set in advance, and may be, for example, at 30-minute intervals, 1-hour intervals, or 3-hour intervals, but are not limited to these.

[0102] Based on the acquired second measurement, the blood pressure estimation algorithm calibrated in step S520 is calibrated again.

[0103] In other embodiments, such single calibrations can be performed periodically, for example, at one-month intervals.

[0104] Thus, with single calibration, calibration is performed each time a measurement is taken.

[0105] Figure 6 is a flowchart illustrating a method for performing multiple calibration according to one embodiment.

[0106] In step S610, the blood pressure estimation device 200 determines, based on the motion signal, whether the user's activity state is the first state.

[0107] If the user's activity state is determined to be the first state, the process proceeds to step S620, where a first measurement value is obtained from the user by a sensor installed inside the device 200 for blood pressure estimation. In one embodiment, the first measurement value is the average of multiple measurements. For example, the first measurement value may be the average of a measurement taken at the first time and a measurement taken at the second time. In this embodiment, only the calculation of the average of two measurement values ​​has been described, but the number of measurements is not limited to this and may be performed multiple times. However, the time between the first time and the second time must be within a predetermined first waiting time. The first waiting time may be approximately 30 minutes to 1 hour, but is not limited to this. Here, the measurement value includes the biosignal and blood pressure value measured at the same time, as described above.

[0108] In the next step S630, the blood pressure estimation device 200 determines, based on the motion signal, whether the user's activity state is the second state. The first state and the second state are different activity states. For example, if the first state is a resting state, the second state is an active state, and if the first state is an active state, the second state is a resting state.

[0109] If the user's activity state is determined to be the second state, the process proceeds to step S640, where a second measurement value is obtained from the user by a sensor installed inside the device 200 for blood pressure estimation. In one embodiment, the second measurement value is the average value of multiple measurements. The process of obtaining the average value of the measurements was explained in step S620, so it will be omitted here.

[0110] The first and second correction points can be set in advance, and may be, for example, at 1-hour intervals, 8-hour intervals, or 12-hour intervals, but are not limited to these.

[0111] In the next step, S650, the blood pressure estimation algorithm, which has been pre-stored in the storage unit 230, is calibrated based on the first and second measurement values.

[0112] Figure 7 is a flowchart illustrating a blood pressure estimation method according to one embodiment of the present invention.

[0113] In step S710, the biosignal measurement unit measures the user's biosignals.

[0114] Biosignals include one or more of the following: photoplethysmogram (PPG), electrocardiography (ECG), seismocardiogram (SCG), electromyography (EMG), ballistocardiogram (BCG), and pulse pressure. However, they are not limited to these.

[0115] In step S720, the processor can analyze the measured biological signal to determine whether or not it is a valid signal.

[0116] In step S730, the processor acquires feature points of the measured biological signal. For example, multiple feature points extracted at equal time intervals from the waveform of the biological signal can be acquired.

[0117] In step S740, the processor applies a blood pressure estimation algorithm, which has been pre-stored in the storage unit 230, to multiple feature points to correct the blood pressure values.

[0118] The apparatus according to this embodiment may include a processor, memory for storing and executing program data, a communication port for communicating with external devices, and user interface devices such as a touch panel, keys, and buttons.

[0119] Each block and each drawing in this flowchart is performed by a computer program instruction. These computer program instructions can be implemented on the processor of a general-purpose computer, a special-purpose computer, or other programmable data processing equipment, so that the instructions, performed through the processor of the computer or other programmable data processing equipment, generate means for performing the functions described in the blocks of the flowchart. These computer program instructions can also be stored in computer-available or computer-readable memory that can be directed to the computer or other programmable data processing equipment to embody the functions in a particular manner, so that the instructions stored in such computer-available or computer-readable memory can produce a manufactured item containing instruction means for performing the functions described in the blocks of the flowchart. Since computer program instructions can also be implemented on the computer or other programmable data processing equipment, the instructions on the computer or other programmable data processing equipment can provide steps for performing the functions described in the blocks of the flowchart by performing a series of operational steps on the computer or other programmable data processing equipment and generating a process executed on the computer.

[0120] Furthermore, each block can represent a module, segment, or portion of code containing one or more executable instructions for performing a specific logical function. Also, in some alternative execution examples, the functions mentioned in a block may occur out of order. For example, two blocks illustrated consecutively may effectively occur simultaneously, or they may sometimes occur in reverse order depending on the function in question.

[0121] In this embodiment, the term "~part" refers to software or hardware components such as FPGAs and ASICs, and "~part" performs a certain role. However, "~part" is not limited to software or hardware. "~part" may be configured to reside in an addressable storage medium and may be configured to regenerate one or more processors. Thus, as an example, "~part" includes components such as software components, object-oriented software components, class components, and task components, as well as processes, functions, attributes, procedures, subroutines, segments of program code, drivers, firmware, microcode, circuits, data, databases, data structures, tables, arrays, and variables. Components and the functions provided within "~part" may be combined into a smaller number of components and "~parts" or further separated into additional components and "~parts". Not only that, components and "~part" may be embodied to regenerate one or more CPUs within a device or security multimedia card.

[0122] A person with ordinary skill in the art to which this specification belongs can implement this specification in other specific forms without altering its technical idea or essential features. Therefore, the embodiments described above are illustrative in all respects and not limiting. The scope of this specification is defined more by the claims set forth below than by the detailed description above, and all variations or alterations derived from the meaning and scope of the claims and the concept of equivalents thereof are included within the scope of this specification.

[0123] On the other hand, while this specification and its drawings disclose preferred embodiments thereof, and specific terms are used, these are merely general terms intended to facilitate the explanation of the technical content of this specification and to aid in the understanding of the invention, and are not intended to limit the scope of this specification. It will be obvious to a person ordinary skill in the art to which this specification belongs that other modifications based on the technical idea of ​​this specification are also possible, in addition to the embodiments disclosed herein. [Explanation of Symbols]

[0124] 100,200 Blood pressure estimation device 110 Biosignal Measurement Unit 115 Motion sensing unit 120 processors 211 Signal analysis section 222 Signal Processing Unit 223 Feature Extraction Unit 224 Calibration section 225 Blood pressure estimation unit 231 Motion Signal Analysis Unit

Claims

1. A calibration method in which the following steps are performed by a blood pressure estimation device using an optical volume change signal, The steps include acquiring measurement values ​​by a sensor mounted inside the device for blood pressure estimation, A step of determining the validity of the measured value, The step includes calibrating a blood pressure estimation algorithm pre-stored in the device based on the measured values, The aforementioned measurement is a calibration method that includes a light volume change signal and blood pressure values ​​measured at the same time.

2. The aforementioned measurement values ​​are acquired multiple times within the first waiting period, and calibration is performed based on the average value of these measurement values. The calibration method according to claim 1, characterized in that

3. The step of determining the validity of the measured value is: Determine whether the signal quality of the optical volume change signals measured multiple times is above a predetermined standard. The stability of the features of the optical volume change signal measured within the first measurement time is determined. Check if the difference between multiple blood pressure readings is within a predetermined error range. The first measurement time is between 30 seconds and 1 minute. The calibration method according to claim 1, characterized in that

4. The step of obtaining the aforementioned measurement value is: Obtain first and second measurement values ​​taken in physiologically different environments of the user. The calibration method according to claim 1, characterized in that

5. The aforementioned differing environments mean that the heart rate and peripheral resistance are above a predetermined standard. The calibration method according to claim 4, characterized in that

6. The aforementioned different environments are environments in which the user's activity state is different from that of the other. The aforementioned activity states are resting and active states. The calibration method according to claim 4, characterized in that

7. The aforementioned activity state is obtained by analyzing motion signals acquired from various motion sensors used to detect the user's movements. The calibration method according to claim 6, characterized in that

8. The step of calibrating the blood pressure estimation algorithm pre-stored in the device based on the measured values ​​is: Based on the difference between the blood pressure value obtained from the optical volume change signal of the first measurement and the blood pressure value obtained from the optical volume change signal of the second measurement, the blood pressure estimation algorithm stored in the device beforehand is calibrated. The calibration method according to claim 1, characterized in that

9. A biosignal measurement unit that measures optical volume change signals, The device includes a processor that applies the optical volume change signal to a blood pressure estimation algorithm pre-stored in the device to estimate blood pressure, It further includes a communication unit that acquires blood pressure values ​​from an external device, The processor determines the validity of the optical volume change signal and the acquired blood pressure value, and calibrates the blood pressure estimation algorithm pre-stored in the device based on the optical volume change signal and the acquired blood pressure value. A blood pressure estimation device characterized by the following features.

10. The processor determines whether the signal quality of the optical volume change signals measured multiple times is above a preset standard, determines the stability of the features of the optical volume change signals measured within the first measurement time, and confirms whether the difference between the multiple input blood pressure readings is within a predetermined error range. The first measurement time is between 30 seconds and 1 minute. The blood pressure estimation device according to claim 9, characterized in that...

11. A motion sensing unit including various motion sensors for sensing user movements, It further includes an output unit that displays information for calibration or blood pressure estimation, The processor analyzes the motion signal detected by the motion sensor to determine whether the user is in a resting state or an active state. The blood pressure estimation device according to claim 9, characterized in that...

12. The processor controls the output unit to provide guidance for measuring blood pressure under different activity conditions. The blood pressure estimation device according to claim 11, characterized in that

13. The processor calibrates a blood pressure estimation algorithm pre-stored in the device based on the difference between the blood pressure value obtained from the optical volume change signal during activity and the blood pressure value obtained from the optical volume change signal during rest. The blood pressure estimation device according to claim 11, characterized in that

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