Blood pressure estimation calibration method using light volume change signal and blood pressure estimation device

Abstract

One embodiment of the present invention relates to a blood pressure estimation calibration method using a light volume change signal and a blood pressure estimation device, the method being a calibration method in which a blood pressure estimation device using a light volume change signal performs each step, the method comprising the steps of: acquiring a measurement value by controlling a sensor mounted therein in order to estimate blood pressure; judging the validity of the measured value; and calibrating a pre-stored blood pressure estimation algorithm based on the measured values, which may include simultaneously measured light volume change signals and blood pressure values.

Description

Technical Field

[0001] One embodiment of the present invention relates to a blood pressure estimation calibration method and a blood pressure estimation device using photovolume change signals, and to a calibration method and a blood pressure estimation device that can improve the estimation accuracy when estimating blood pressure using photovolume change signals. Background Technology

[0002] Recently, with the development of medical technology, an aging society, and the Westernization of lifestyles and diets, the incidence of hypertension has increased. Hypertension, as a major indicator of kidney disease and serious cardiovascular disease, is one of the most dangerous causes of death; therefore, its treatment and management are crucial.

[0003] The most important thing for preventing, detecting and treating hypertension is to continuously monitor blood pressure in daily life; however, this goal has not yet been achieved.

[0004] Typically, blood pressure monitoring primarily involves measuring blood pressure by wearing a cuff and measuring changes in pressure injected into the cuff. During measurement, the cuff method causes pressure-induced discomfort, and even portable devices, requiring the inclusion of a cuff, are practically inconvenient to carry. The cuff method is unsuitable for real-time blood pressure monitoring due to its drawbacks in both convenience and portability. Therefore, there is a need to actively research methods for unrestricted blood pressure measurement without a cuff.

[0005] In particular, research on photoplethysmography (PPG) optical sensors is increasing recently. These sensors can utilize percussion waves, tidal waves, and other characteristic values ​​to obtain vascular elasticity information based on PPG signals. Since vascular elasticity information is highly correlated with blood pressure, it can be used to estimate blood pressure.

[0006] Another reason for measuring blood pressure using photovolume change signals is the significant importance of PPG optical sensors in personal cuffless blood pressure monitoring devices. Recently, most wearable devices such as smart bracelets and watches are equipped with PPG optical sensors. This means that most wearable devices only require installation to enable blood pressure measurement without adding new sensors. Measuring blood pressure using PPG optical sensors in wearable devices can significantly improve the portability and convenience of existing blood pressure measurement methods. In terms of convenience, wearable devices such as smart bracelets and smartwatches are worn on the wrist, offering the advantage of not causing discomfort to the wearer. In conclusion, since blood pressure can be predicted using PPG signals, its application in wearable devices can solve the convenience and portability issues of existing devices.

[0007] However, when blood pressure is measured without the constraints of a cuff, calibration techniques are required because the accuracy of the measurement tends to decrease.

[0008] For example, although it is possible to estimate blood pressure by measuring pulse transit time (PTT) using PPG signals, the accuracy of the measurement decreases due to real-time factors and differences in algorithms, even when the time difference between the measured PPG volume pulse size and the P-peak (P-peak) is calculated.

[0009] Therefore, in the case of unrestricted blood pressure measurement, calibration techniques directly affect the accuracy of the measured values. Summary of the Invention

[0010] The present invention arose under the aforementioned background conditions, and the object of the present invention is to provide a calibration method for improving the accuracy of blood pressure measurements while allowing for unrestricted blood pressure measurement, and a blood pressure estimation device for performing the method.

[0011] The effects of this invention are not limited to those mentioned above. Those skilled in the art to which this invention pertains can clearly understand other effects not mentioned through the following description.

[0012] Therefore, a calibration method according to an embodiment of the present invention is performed by a blood pressure estimation device using photoplethysmography (PPG) signal. The method includes the following steps: controlling an internally mounted sensor to acquire a measurement value in order to estimate blood pressure; determining the validity of the measurement value; and calibrating a pre-stored blood pressure estimation algorithm based on the measurement value, wherein the measurement value may include the photoplethysmography signal and blood pressure value measured simultaneously.

[0013] A feature of one embodiment of the present invention is that the measured values ​​are acquired multiple times at a first waiting time interval, and calibration is performed based on the average value of the acquired measured values.

[0014] An embodiment of the present invention is characterized in that, in the step of determining the validity of the measured value, it is determined whether the signal quality of the photovolume change signal measured multiple times is above a preset benchmark, the stability of the feature of the photovolume change signal measured within a first measurement time is determined, and it is confirmed whether the difference between multiple input blood pressures is within a predetermined error range, wherein the first measurement time is within 30 seconds to 1 minute.

[0015] A feature of one embodiment of the present invention is that, in the step of obtaining the measured values, a first measured value and a second measured value are obtained by the examinee under different physiological conditions.

[0016] A feature of one embodiment of the present invention is that the different environments are environments in which the difference in heart rate and peripheral resistance is above a preset benchmark.

[0017] A feature of one embodiment of the present invention is that the different environments are environments in which the user's activity state is different, and the activity state is a rest state and an active state.

[0018] A feature of one embodiment of the present invention is that the activity state is obtained by analyzing motion signals acquired from multiple motion sensors, which are used to detect the movement of the person being examined.

[0019] A feature of one embodiment of the present invention is that, in the step of calibrating the pre-stored blood pressure estimation algorithm based on the measured value, the pre-stored blood pressure estimation algorithm is calibrated based on the difference between the blood pressure value of the first measured value and the blood pressure value of the second measured value, which are measured according to the photovolume change signal of the first measured value and the photovolume change signal of the second measured value.

[0020] According to another embodiment of the present invention, a blood pressure estimation device includes: a biosignal measuring unit for measuring a photoplethysmography (PPG) signal; a processor for applying the PPG signal to a pre-stored blood pressure estimation algorithm to estimate blood pressure; and a communication unit for acquiring blood pressure values ​​from an external device. The processor determines the validity of the PPG signal and the acquired blood pressure values, and calibrates the pre-stored blood pressure estimation algorithm based on the PPG signal and the acquired blood pressure values.

[0021] In another embodiment of the present invention, the processor determines whether the signal quality of the photovolume change signal measured multiple times is above a preset benchmark, determines the stability of the characteristics of the photovolume change signal measured within a first measurement time, and confirms whether the difference between the multiple input blood pressures is within a predetermined error range, wherein the first measurement time is within 30 seconds to 1 minute.

[0022] A further embodiment of the present invention is characterized in that the blood pressure estimation device further includes: a motion detection unit including a variety of motion sensors for detecting the motion of the subject; and an output unit for displaying information for calibrating or estimating blood pressure, wherein the processor determines whether the user's activity state is resting or active by analyzing the motion signals detected by the motion sensors.

[0023] A further embodiment of the present invention is characterized in that the processor provides guidance for measuring blood pressure under different activity states by controlling the output unit.

[0024] A further embodiment of the present invention is characterized in that the processor calibrates a pre-stored blood pressure estimation algorithm based on the difference between the blood pressure value in the active state and the blood pressure value in the resting state, which is measured according to the photovolume change signal in the active state and the photovolume change signal in the resting state.

[0025] According to the present invention, the reliability of estimating blood pressure using photovolume change signals can be improved.

[0026] The effects of this invention are not limited to those mentioned above. Those skilled in the art to which this invention pertains can clearly understand other effects not mentioned through the following description. Attached Figure Description

[0027] Figure 1 This is a block diagram of a blood pressure estimation device according to an embodiment of the present invention.

[0028] Figure 2 This is a block diagram of a blood pressure estimation device according to another embodiment.

[0029] Figure 3 This is a block diagram of the structure of a processor according to one embodiment.

[0030] Figure 4 This is a block diagram of the processor structure in another embodiment.

[0031] Figure 5 This is a flowchart illustrating a single-point calibration execution method according to an embodiment.

[0032] Figure 6 This is a flowchart illustrating a multi-point calibration execution method according to an embodiment.

[0033] Figure 7 This is a flowchart illustrating a blood pressure estimation method according to an embodiment of the present invention.

[0034] Explanation of reference numerals in the attached figures

[0035] 100, 200: Blood pressure estimation device;

[0036] 110: Biosignal Measurement Unit;

[0037] 115: Sports Testing Department;

[0038] 120: Processor;

[0039] 211: Signal Analysis Department;

[0040] 222: Signal Processing Unit;

[0041] 223: Feature Extraction Unit;

[0042] 224: Calibration Department;

[0043] 225: Blood pressure estimation section;

[0044] 231: Motion Signal Analysis Department. Detailed Implementation

[0045] Other specific details of the embodiments are included in the detailed description and accompanying drawings. The advantages, features, and implementation methods of the described technology can be found by referring to the accompanying drawings. Figure 1 The detailed embodiments become clear. Throughout the specification, the same reference numerals denote the same structural elements.

[0046] Terms such as "first" and "second" can be used to describe multiple structural elements, and structural elements are not limited to these terms. Terms are only used to distinguish one structural element from others. Unless explicitly stated otherwise in the context, singular expressions include plural expressions. Furthermore, when indicating that a part "includes" a structural element, unless specifically contradicted, it implies that other structural elements may also be included, not excluded. Also, in the specification, terms such as "part" and "module" refer to units used to perform at least one function or action, which may be implemented by hardware or software, or a combination of hardware and software.

[0047] Hereinafter, several embodiments of the blood pressure estimation device and method are described in detail with reference to the accompanying drawings.

[0048] Figure 1 This is a block diagram of a blood pressure estimation device according to an embodiment of the present invention.

[0049] Figure 1 This is a block diagram of a blood pressure estimation device according to one embodiment. The blood pressure estimation device 100 can be a terminal such as a smartphone, tablet, desktop computer, or laptop computer, or a standalone hardware device. In this case, a standalone hardware device refers to a wearable device that can be worn on the subject, such as a watch, bracelet, wristband, ring, glasses, or headband. However, it is not limited to this.

[0050] Reference Figure 1 The blood pressure estimation device 100 includes a biosignal measuring unit 110 and a processor 120.

[0051] The biosignal measurement unit 110 includes one or more sensors, which can measure various biosignals of the subject. In this case, "one or more sensors" refers to sensors capable of measuring photoplethysmogram (PPG), electrocardiogram (ECG), seismocardiogram (SCG), electromyography (EMG), ballistocardiogram (BCG), and pulse pressure, etc. However, it is not limited to these.

[0052] When a user requests a blood pressure estimation, or when a blood pressure estimation benchmark is met, the processor 120 can control the biosignal measurement unit 110. The processor 120 receives a biosignal from the biosignal measurement unit 110 and can apply the received biosignal to a pre-stored blood pressure estimation algorithm to estimate blood pressure or blood pressure change information. Here, blood pressure change information refers to the degree of blood pressure change estimated by executing the blood pressure estimation algorithm multiple times. The blood pressure estimation algorithm and blood pressure change information will be described in detail later.

[0053] The processor 120 can extract features that affect blood pressure from biological signals. In this case, the features may include cardiac output (CO) and total peripheral resistance (TPR).

[0054] In this case, the processor 120 can acquire the actual pulse pressure measured by the user as a pulse pressure-related feature through the pulse pressure sensor included in the biosignal measurement unit 110 or an external pulse pressure measurement device. Furthermore, the processor 120 can extract values ​​corresponding to pulse pressure or heart rate as pulse pressure-related features from biosignals such as PPG and BCG through pulse transit time (PTT), heart rate variability (HRV), and peak amplitude analysis.

[0055] If the processor 120 extracts features from the biological signal, the extracted features can be applied to a pre-stored blood pressure estimation algorithm to estimate blood pressure. For example, if the processor 120 obtains one or more characteristic points from the biological signal, it can extract features for estimating blood pressure by combining the obtained characteristic points. In this case, the processor 120 can extract a single-cycle signal from the biological signal continuously measured within a predetermined time period. Furthermore, a highly representative waveform can be obtained from the extracted single-cycle signal, or a representative waveform can be obtained by overlapping two or more single-cycle signals, and the obtained representative waveform can be used to obtain characteristic points. At this time, features can be extracted by combining the time information or amplitude information of one or more characteristic points.

[0056] One embodiment of the present invention provides a blood pressure estimation algorithm that can be obtained by performing regression analysis on multiple feature points extracted from pulse signal waveforms at equal time intervals and blood pressure values. For example, the blood pressure estimation algorithm can be obtained by performing linear regression analysis, multivariate regression analysis, or nonlinear regression analysis on multiple feature points extracted at equal time intervals and blood pressure values. Assuming that there is a relationship between the extracted feature points and blood pressure values, various data acquisition relationships can be analyzed, and the blood pressure estimation algorithm can include such relationships.

[0057] In another embodiment of the present invention, the blood pressure estimation method can be obtained by performing machine learning on multiple feature points extracted from the pulse signal waveform at equal time intervals and blood pressure values. For example, the blood pressure estimation algorithm can utilize artificial neural network (ANN) algorithms, k-nearest neighbor (k-NN) algorithms, Bayesian network algorithms, support vector machine (SVM) algorithms, recurrent neural network (RNN) algorithms, etc. At least one layer is set up to hide between the extracted feature points and blood pressure values ​​(maximum and minimum blood pressure) to form a network. Machine learning can be repeatedly performed on this structure using various data to obtain the hidden layer matrix. The blood pressure estimation algorithm may include such a hidden layer matrix.

[0058] One embodiment of the blood pressure estimation algorithm can be obtained using data collected from a randomly selected set. That is, the blood pressure estimation algorithm can be obtained using pulse signals and blood pressure-related data collected from a randomly selected set.

[0059] Another embodiment of the blood pressure estimation algorithm can be obtained using data collected from sets categorized according to prescribed characteristics. For example, using pulse signals and blood pressure-related data collected from various sets categorized according to physical characteristics such as age, gender, weight, and height, a blood pressure estimation algorithm can be obtained based on each set categorized according to physical characteristics. As yet another example, using pulse signals and blood pressure-related data collected from various sets categorized according to conditions such as rest and activity, a blood pressure estimation algorithm can be obtained based on each set categorized according to condition.

[0060] Another embodiment of the blood pressure estimation algorithm can be obtained using data collected from the user. For example, a blood pressure estimation algorithm can be obtained by measuring the user's pulse and blood pressure-related data. As yet another example, by measuring the user's pulse signal and blood pressure-related data according to states such as resting state and activity state, a blood pressure estimation algorithm can be obtained based on each state.

[0061] The blood pressure estimation algorithm is obtained from an external device of the blood pressure estimation device 200 and can be stored in the blood pressure measuring device 200.

[0062] One embodiment of the blood pressure estimation algorithm can be calibrated using the pulse signal and blood pressure value of the subject being measured. For example, when the blood pressure estimation algorithm is obtained using data collected from a randomly extracted set, the blood pressure estimation algorithm can be calibrated using the pulse and blood pressure data of the user being measured.

[0063] Blood pressure-related data used to acquire or calibrate blood pressure estimation algorithms can be obtained using conventional blood pressure measurement methods.

[0064] One embodiment of calibration can be either single calibration or multiple calibration. Single calibration can be performed based on a biosignal measured under other conditions that do not affect the subject's blood pressure. Multiple calibration can be performed based on a biosignal measured under other conditions that is a major factor affecting the subject's blood pressure, namely heart rate and peripheral resistance.

[0065] Figure 2 This is a block diagram of a blood pressure estimation device according to another embodiment.

[0066] Reference Figure 2 One embodiment of the blood pressure estimation device 200 may include a biosignal measuring unit 110, a motion detection unit 115, a processor 120, a communication unit 210, an output unit 220, and a storage unit 230.

[0067] The biosignal measurement unit 110 can be electrically connected to the processor 120 and can measure various biosignals of the user under the control of the processor 120.

[0068] The motion detection unit 115 includes one or more sensors that can detect the motion of the subject. In this case, the one or more sensors may include at least one of a motion sensor, an acceleration sensor, a gyroscope sensor, and an angular velocity sensor.

[0069] The motion detection unit 115 can be electrically connected to the processor 120 and can measure the motion of the subject under the control of the processor 120. For example, the motion detection unit 115 can detect whether the user is in an active state or a resting state.

[0070] The processor 120 receives biological signals from the biosignal measurement unit 110 and can use the received biological signals to extract features for estimating blood pressure, such as cardiac output features and total peripheral resistance features.

[0071] Furthermore, the processor 120 receives motion signals from the motion detection unit 115 and can use the received motion signals to infer the subject's posture and lifestyle. For example, the processor 120 analyzes the motion signals and can distinguish and record the subject's "wake-up and bedtime" and "activity and rest time".

[0072] 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 required for performing blood pressure estimation to an external terminal or external device. Under the control of the processor 120, the communication unit 210 can connect and communicate with the external terminal or external device, receiving information request messages from the external terminal or external device requesting the transmission of information, or receiving blood pressure values ​​for calibration based on the received information request messages or according to a preset time and period.

[0073] The communication unit 210 can access a communication network using wireless communication technology under 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 Bluetooth communication, Bluetooth Low Energy (BLE) communication, Near Field Communication (NFC), WLAN communication, Zigbee communication, Infrared Data Association (IrDA) communication, Wi-Fi Direct (WFD) communication, Ultra-Wideband (UWB) communication, Ant+ communication, Wi-Fi communication, Radio Frequency Identification (RFID) communication, 3G communication, 4G communication, and 5G communication, etc. However, it is not limited to these.

[0074] On the other hand, the processor 120 can guide the user to measure blood pressure for calibration via the output unit 220. The output unit 220 can output information for guiding standard blood pressure measurement and / or pulse pressure measurement via a visual output module such as a display, a voice output module such as a speaker, or a tactile module such as vibration or touch, according to the control of the processor 120. The processor 120 can output a user interface via the output unit 220. The user can use the user interface to input blood pressure and / or pulse pressure measured by the internal input device or external input device of the blood pressure estimation device 100. The external device can be, but is not limited to, a smartphone, tablet, desktop computer, or laptop connected via a communication unit.

[0075] The processor 120 can store reference information for estimating blood pressure, such as biosignals measured by the biosignal measuring unit 110 at a calibration time point, features extracted from the biosignals, and blood pressure values ​​obtained at the calibration time point.

[0076] Furthermore, the storage unit 230 can store various types of information processed by the blood pressure estimation device 200.

[0077] For example, the storage unit 230 can store various reference information, excluding algorithms or programs executed 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, and deviation estimation models. The storage unit 230 can store data related to pulse signals, body temperature, and movement status measured by the blood pressure estimation device 200. The memory 230 can store at least one blood pressure estimation algorithm.

[0078] 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, memory card (e.g., SD or XD memory), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic storage, magnetic disk, optical disk, etc.

[0079] The processor 120 extracts features from the biosignal measured by the biosignal measuring unit 110 and can estimate blood pressure by referring to the reference information at calibration time points stored in the storage unit 230. In this case, if heart rate and peripheral resistance are measured by the biosignal measuring unit 110, the processor 120 can obtain the deviation based on the received user blood pressure through the interface of the communication unit 210 or the output unit 220. The processor 120 can use the obtained deviation to calibrate the error between the cardiac output features and total peripheral resistance features extracted from the biosignal and the actual cardiac output and total peripheral resistance.

[0080] If the blood pressure estimation is completed by the processor 120, the output unit 220 can output the biosignal, the blood pressure estimation result, and additional information related to the blood pressure estimation result. For example, the output unit 220 can visually provide various information to the user through a display module. For instance, when displaying the blood pressure estimation result, if the estimated blood pressure deviates from the normal range, a warning message can be displayed to the user using a red emphasis display. As another example, various information can be provided to the user through a speaker module or haptic module using non-visual methods such as voice, vibration, and touch. For example, systolic and diastolic blood pressure can be prompted by voice. In this case, if the estimated blood pressure deviates from the normal range, the user can be alerted to an abnormal health status through vibration or touch.

[0081] If the blood pressure estimation by the processor 120 is completed, the storage unit 230 can store biological signals and / or blood pressure estimation results, etc.

[0082] Figure 3 This is a block diagram of the structure of a processor according to one embodiment.

[0083] Figure 3 The processor shown can be Figure 1 and Figure 2 The processor 120 used 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.

[0084] 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 confirm whether the signal quality of the measured biological signal is above the reference value and whether the feature of the biological signal is stable within a predetermined time.

[0085] For example, if the power spectrum value of the biological signal within a preset frequency range in the frequency domain is above a preset value and the waveform of the biological signal is not interrupted or disturbed within a preset first time period, then the signal analysis unit 211 can determine that the biological signal is a valid signal. This is because the components of the biological signal are mainly contained in the low-frequency band of the frequency domain, while the noise components are mainly contained in the high-frequency band of the frequency domain. For example, the preset frequency range can be from 0.67Hz to 3Hz. As another example, if the difference between the highest and lowest values ​​of the biological signal in the time domain is above a preset value, then the signal analysis unit 211 can determine that the biological signal is a valid signal.

[0086] If the pulse signal is determined to be valid, the blood pressure estimation device 200 can estimate the blood pressure value using the biosignal. If the biosignal is determined to be invalid, the blood pressure estimation device 200 can control the biosignal measuring unit 110 to measure the detailed signal again via the processor 120.

[0087] The signal analysis unit 211 may also include a noise filtering unit (not shown) for removing noise components contained in the pulse signal. Furthermore, the signal analysis unit 211 may also include an amplification unit (not shown) for amplifying the received biological signal to a level suitable for signal processing.

[0088] Furthermore, the signal analysis unit 211 can analyze the baseline of biological signals and update the baseline.

[0089] The baseline refers to the line connecting the highest and lowest points of the various periodic waveforms of the biological signal; for example, it can represent a line connecting intermediate points. The baseline can be 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 of the biological signal.

[0090] Furthermore, the signal analysis unit 211 can determine the validity of the blood pressure values ​​acquired for calibration. If the difference between the blood pressure values ​​acquired within a predetermined first time period (which are systolic blood pressure (SBP) / diastolic blood pressure (DBP) respectively) is within a predetermined error range, the signal analysis unit 211 can determine that the corresponding blood pressure values ​​are valid.

[0091] The feature extraction unit 223 extracts feature points from a single-cycle waveform of a noise-removed biological signal at predetermined time intervals, and combines the extracted feature points. By combining multiple feature points obtained from the biological signal, cardiac output features and total peripheral resistance features can be extracted. For example, the feature extraction unit 223 can extract a single-cycle waveform by identifying the highest and lowest points of the noise-removed signal. Then, the feature extraction unit 223 can extract feature points from the single-cycle waveform according to a first time interval. By applying multiple feature points extracted according to the single cycle of the biological signal to a pre-stored blood pressure estimation algorithm, the highest blood pressure (systolic blood pressure) value and the lowest blood pressure (diastolic blood pressure) value can be obtained.

[0092] On the other hand, different feature points can be combined based on user characteristics to extract cardiac output features and total peripheral resistance features. Furthermore, based on the blood pressure to be extracted, for example, different feature points can be combined based on mean blood pressure, diastolic blood pressure, and systolic blood pressure to extract cardiac output features and total peripheral resistance features according to blood pressure type.

[0093] The feature extraction unit 223 can extract pulse pressure-related features from the measured biological signal. For example, heart rate (HR) and other pulse pressure-related features can be extracted through analysis of pulse transit time (PTT) and heart rate variation (HRV) of the measured biological signal.

[0094] The calibration unit 224 can perform calibration using single-point calibration technology, and calibrate the pre-stored blood pressure estimation algorithm using the biological signals of the test subject at a preset calibration time point.

[0095] Single-point calibration refers to a technique that acquires the measured biosignals and blood pressure values ​​measured at the same time point without considering the user's activity status, and performs calibration based on the biosignals and blood pressure values ​​measured at the same time point.

[0096] For example, calibration unit 224 can perform single-point calibration according to a preset first condition. The first condition refers to performing multiple calibrations at preset time intervals. For example, the preset time interval can be within 30 minutes. Such multiple calibrations can be repeated according to a first cycle, for example, monthly. Historical data can be updated based on the results of previously performed calibrations.

[0097] Based on this condition, for example, if the examinee's systolic blood pressure was 120 mmHg in the first calibration and 124 mmHg in the second, and then 140 mmHg and 138 mmHg in the second calibration performed in the next cycle, then the baseline for the first calibration is 122 mmHg, and the baseline for the second calibration is 139 mmHg. In this case, the new value of 139 mmHg obtained from the second calibration can be used to update the baseline.

[0098] The blood pressure estimation unit 225 uses a pre-stored blood pressure estimation algorithm to apply multiple feature points to the pre-stored blood pressure estimation algorithm to obtain blood pressure values ​​or blood pressure change information.

[0099] Figure 4 This is a block diagram of the processor structure in another embodiment.

[0100] Reference Figure 4 The processor described is only available in Figure 3The motion signal analysis unit 231 and calibration unit 234 are different, therefore, the different structural elements will be explained in detail.

[0101] The motion signal analysis unit 231 analyzes the data from the motion detection unit ( Figure 2 The motion detection unit (115) receives motion signals to infer the activity state of the person being examined. The activity state can include both a resting state and an active state. For example, if the motion detection unit (115) receives motion signals... Figure 2 If 115) includes an accelerometer, the accelerometer can receive measured acceleration values ​​over time and determine the amount of movement based on the acceleration values. The motion signal analysis unit 231 can differentiate the user's wake-up / bedtime and activity / rest time based on the acceleration values ​​measured by the accelerometer within a preset time to infer their lifestyle.

[0102] The calibration unit 234 can calibrate a pre-stored blood pressure estimation algorithm based on the biological signals of the test subject using a multi-point calibration technique according to a preset technology.

[0103] Multi-point calibration refers to a technique that acquires measurement values ​​under different physiological conditions and performs calibration based on these values. Different physiological conditions refer to at least one different environment, such as heart rate and peripheral resistance, which are major factors affecting the blood pressure of the subject. Heart rate and peripheral resistance can vary depending on the user's activity level. For example, calibration unit 234 considers the user's activity level and performs calibration based on measured biosignals and blood pressure values ​​measured at the same time points. For ease of explanation, biosignals and blood pressure values ​​measured at the same time points are referred to as measurement values. For example, activity levels can include resting and active states. Therefore, a pre-stored blood pressure estimation algorithm can be calibrated based on measurement values ​​from both the resting and active states.

[0104] For example, the inherent blood pressure variation characteristics under the second and third conditions can be extracted from the systolic blood pressure measured under the second condition, such as 120 mmHg the first time and 124 mmHg the second time, and the systolic blood pressure measured under the third condition, which is physiologically different from the second condition, such as 140 mmHg the first time and 138 mmHg the second time.

[0105] When the calibration unit 234 performs calibration, it can provide guidance to the user for performing the calibration. For example, it can guide the user to bring the sample used to measure the biosignal into contact with the biosignal measuring unit 110. Furthermore, it can prompt the user for the measurement time point or measurement method that meets preset conditions, as well as information on external devices and communication units. Figure 2Communication connections between (210) and others. Among them, the measurement time points that meet the preset conditions can be the activity period (e.g., after waking up) and rest period (e.g., before going to bed) estimated by the motion signal analysis unit, but are not limited to these.

[0106] If the calibration unit 234 fails to perform calibration according to the guidance, the controllable output unit 220 can provide a notification for guiding the calibration.

[0107] By applying the calibration result value performed by the calibration unit 234 to the blood pressure estimation algorithm, the blood pressure estimation algorithm can be calibrated.

[0108] Figure 5 This is a flowchart illustrating a single-point calibration execution method according to an embodiment.

[0109] In step S510, the blood pressure estimation device 200 controls the sensor installed inside to acquire the measured value in order to estimate the blood pressure.

[0110] A first measurement value is obtained from the user at a first calibration time point. The measurement is performed within a preset first measurement time. The preset first measurement time can be from 30 seconds to 1 minute, preferably 40 seconds, but is not limited to this. As mentioned above, the measurement value includes simultaneously measured biosignals and blood pressure values. In one embodiment, the first measurement value can be the average of multiple acquired measurement values. For example, the first measurement value can be the average of the measurement value measured at a first time point and the measurement value measured at a second time point. In this example, although the average of two measurement values ​​is calculated, the number of measurements is not limited to this, and multiple measurements can be performed. However, the time between the first time point and the second time point is within a predetermined first waiting time. The first waiting time can be from approximately 30 minutes to 1 hour, but is not limited to this.

[0111] In step S520, the validity of the measured value is determined.

[0112] For example, regarding the validity of biological signals, if the signal quality of the photovolume change signal measured multiple times is above a preset benchmark and the biological signal feature measured within the first measurement time is stable, the biological signal can be judged to be valid.

[0113] Furthermore, if the difference between multiple input blood pressure values ​​is within the predetermined error range, the blood pressure value can be determined to be valid.

[0114] In step S530, the pre-stored blood pressure estimation algorithm can be calibrated based on the acquired first measurement value.

[0115] Next, step S530 can be repeatedly executed in step S510 to continuously calibrate the blood pressure estimation algorithm.

[0116] For example, the blood pressure estimation device 200 controls an internally mounted sensor to acquire a second measurement value from a user at a second calibration time point in order to estimate blood pressure. In one embodiment, the second measurement value may be the average of multiple acquired measurement values. Since the step of acquiring the average of the measurement values ​​has already been described in step S510, the relevant description will be omitted.

[0117] The first and second calibration time points can be preset, for example, they can be 30-minute intervals, 1-hour intervals, 3-hour intervals, etc., but are not limited to these.

[0118] The blood pressure estimation algorithm calibrated in step 520 is recalibrated based on the second measurement obtained.

[0119] In another embodiment, the single-point calibration may be performed periodically, for example, every month.

[0120] As mentioned above, single-point calibration can be performed each time a measurement is acquired.

[0121] Figure 6 This is a flowchart illustrating a multi-point calibration execution method according to an embodiment.

[0122] In step S610, the blood pressure estimation device 200 determines whether the user's activity state is in the first state based on the action signal.

[0123] If the user's activity state is determined to be in a first state, then to estimate blood pressure, step S620 is executed to control the internally mounted sensor to acquire a first measurement value from the user. In one embodiment, the first measurement value can be the average of multiple acquired measurement values. For example, the first measurement value can be the average of the measurement value measured at a first time point and the measurement value measured at a second time point. In this example, although the average of two measurement values ​​is calculated, the number of measurements is not limited to this, and multiple measurements can be performed. However, the time between the first time point and the second time point is within a predetermined first waiting time. The first waiting time can be approximately 30 minutes to 1 hour, but is not limited to this. As mentioned above, the measurement value includes simultaneously measured biosignals and blood pressure values.

[0124] Subsequently, in step S630, the blood pressure estimation device 200 determines whether the user's activity state is a second state based on the action signal. The first state and the second state can be different activity states. For example, if the first state is a resting state, then the second state is an active state; if the first state is an active state, then the second state can be a resting state.

[0125] If the user's activity state is determined to be the second state, then in order to estimate blood pressure, step S640 is executed to control the internally mounted sensor to acquire a second measurement value from the user. In one embodiment, the second measurement value can be the average of multiple acquired measurement values. Since the step of acquiring the average of the measurement values ​​has already been described in step S620, the relevant description will be omitted.

[0126] The first and second calibration time points can be preset, for example, they can be 1-hour intervals, 3-hour intervals, 12-hour intervals, etc., but are not limited to these.

[0127] Then, in step S650, the pre-stored blood pressure estimation algorithm is calibrated based on the first and second measured values.

[0128] Figure 7 This is a flowchart illustrating a blood pressure estimation method according to an embodiment of the present invention.

[0129] In step S710, the biosignal measuring unit measures the biosignal of the subject.

[0130] Biological signals can be one or more of the following: photoplethysmogram (PPG), electrocardiogram (ECG), seismocardiogram (SCG), electromyography (EMG), ballistocardiogram (BCG), and pulse pressure. However, they are not limited to these.

[0131] In step S720, the processor can analyze the measured biosignal to determine whether it is a valid signal.

[0132] In step S730, the processor acquires feature points of the measured biological signal. For example, multiple feature points extracted from the waveform of the biological signal at equal time intervals can be acquired.

[0133] In step S740, the processor can apply multiple feature points to a pre-stored blood pressure estimation algorithm to estimate blood pressure values.

[0134] The device in this embodiment may include a processor, a memory for storing and executing program data, a communication port for communicating with external devices, a touchpad, a key, buttons, and other user interface devices.

[0135] In this context, it should be understood that the individual blocks and combinations of flowcharts can be executed by computer program instructions. These computer program instructions can be loaded onto the processor of a general-purpose computer, a special-purpose computer, or other programmable data processing equipment. Therefore, the instructions executed by the processor of the computer or other programmable data processing equipment can generate a manner for performing the functions described by the flowchart blocks(s). These computer program instructions can also be stored in computer-usable or computer-readable storage that can point to the computer or other programmable data processing equipment to perform functions in a specific manner. Articles of manufacture can also be produced that include the instruction means required to perform the functions described by the instructions stored in the computer-usable or computer-readable storage. The computer program instructions can also be loaded onto a computer or other programmable data processing equipment. Therefore, a series of operational steps executed on the computer or other programmable data processing equipment can generate a computer-executed process. Thus, the instructions executed by the computer or other programmable data processing equipment can also provide steps for performing the functions described by the flowchart blocks(s).

[0136] Furthermore, each block may represent a module, segment, or portion of code comprising one or more executable instructions for performing a specific logical function. It should also be noted that in several alternative instances, the functions referred to by the blocks may occur in different orders. For example, two blocks shown consecutively may be executed simultaneously, or the blocks may be executed in reverse order according to their respective functions.

[0137] In this context, the term "~part" as used in this embodiment refers to a software structural element or a hardware structural element such as a Field-Programmable Gate Array (FPGA) or Application-Specific Integrated Circuit (ASIC), where the "~part" performs a certain function. However, the meaning of "~part" is not limited to software or hardware. A "~part" can be configured on an accessible storage medium and can also reproduce one or more processors. Therefore, as an example, a "~part" may include structural elements such as software structural elements, object-oriented software structural elements, class structural elements, and task structural elements, as well as processes, functions, attributes, procedures, subroutines, program code segments, drivers, firmware, microcode, circuits, data, databases, data structures, tables, arrays, and variables. The functionality provided within a structural element and a "~part" can be formed by combining a smaller number of structural elements and "~parts," or it can be further divided into additional structural elements and "~parts." Furthermore, structural elements and "~parts" can also reproduce one or more central processing units within a device or secure multimedia card.

[0138] It should be understood that those skilled in the art can implement this invention through other specific embodiments without changing the technical concept or essential features of the invention. Therefore, the embodiments described above are merely examples at all levels and should not be construed as limiting. The scope of this invention should be defined based on the appended claims, rather than the detailed description provided. All modifications or variations derived from the meaning, scope, and equivalents of the claims are within the scope of this invention.

[0139] On the other hand, although preferred embodiments of the invention are disclosed in this specification and accompanying drawings, and specific terminology is used, these are only used in a general sense to easily illustrate the technical content of the invention and to aid in understanding the invention, and do not limit the scope of the invention. It is obvious that, in addition to the embodiments disclosed herein, all modifications based on the technical concept of the invention can be implemented by those skilled in the art.

Claims

1. A calibration method, wherein each step is performed by a blood pressure estimation device utilizing photovolume change signals, characterized in that, Includes the following steps: The sensors installed inside the device are controlled to obtain measurement values ​​in order to estimate blood pressure; Determine the validity of the measured values; and The pre-stored blood pressure estimation algorithm is calibrated based on the measured values. The measured values ​​include the photovolume change signal and blood pressure value measured simultaneously.

2. The calibration method according to claim 1, characterized in that, The measured values ​​are acquired multiple times at first waiting time intervals, and calibration is performed based on the average value of the acquired measured values.

3. The calibration method according to claim 2, characterized in that, In the step of determining the validity of the measured value Determine whether the signal quality of the photovolume change signal measured multiple times is above a preset benchmark. To determine the stability of the characteristics of the light volume change signal measured within the first measurement time, Confirm that the difference between multiple blood pressure readings is within the predetermined error range. The first measurement time is within 30 seconds to 1 minute.

4. The calibration method according to claim 1, characterized in that, In the step of obtaining the measured values, the first and second measured values ​​of the examinee under different physiological conditions are obtained.

5. The calibration method according to claim 4, characterized in that, The different environments refer to environments where the difference in heart rate and peripheral resistance is above a preset benchmark.

6. The calibration method according to claim 4, characterized in that, The different environments refer to environments where the user's activity state is different. The activity states are rest state and activity state.

7. The calibration method according to claim 6, characterized in that, The activity status is obtained by analyzing motion signals acquired from multiple motion sensors used to detect the movement of the person being examined.

8. The calibration method according to claim 4, characterized in that, In the step of calibrating the pre-stored blood pressure estimation algorithm based on the measured values, the pre-stored blood pressure estimation algorithm is calibrated based on the difference between the blood pressure value of the first measured value and the blood pressure value of the second measured value, which are measured according to the photovolume change signal of the first measured value and the photovolume change signal of the second measured value.

9. A blood pressure estimation device, characterized in that, include: Biosignal measurement unit, used to measure photovolume change signals; and The processor applies the photovolume change signal to a pre-stored blood pressure estimation algorithm to estimate blood pressure. It also includes a communications unit that obtains blood pressure values ​​from external devices. The processor determines the validity of the photovolume change signal and the acquired blood pressure value, and calibrates the pre-stored blood pressure estimation algorithm based on the photovolume change signal and the acquired blood pressure value.

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

11. The blood pressure estimation device according to claim 9, characterized in that, Also includes: The motion detection unit includes multiple motion sensors for detecting the movement of the person being tested; and The output section displays information used for calibration or estimated blood pressure. The processor determines whether the user's activity state is resting or active by analyzing the motion signals detected by the motion sensor.

12. The blood pressure estimation device according to claim 11, characterized in that, The processor provides guidance for measuring blood pressure under different activity states by controlling the output unit.

13. The blood pressure estimation device according to claim 11, characterized in that, The processor calibrates a pre-stored blood pressure estimation algorithm based on the difference between the blood pressure values ​​in the active and resting states, measured according to the photovolume change signals in the active and resting states.

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