WO2023019467A1 - 多项生理参数检测手套及高血压病症患病风险检测*** - Google Patents
多项生理参数检测手套及高血压病症患病风险检测*** Download PDFInfo
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Definitions
- the invention relates to the technical field of physiological signal detection technology and hypertension risk prediction, in particular to a multi-physiological parameter detection glove and a hypertension disease risk detection system.
- Cardiovascular disease is sudden and high-risk, and hypertension is an important risk factor leading to the occurrence of cardiovascular disease.
- Hypertension refers to an increase in systemic arterial blood pressure (systolic and/or diastolic) as the main feature (systolic blood pressure ⁇ 140 mm Hg, diastolic blood pressure ⁇ 90 mm Hg), which may be accompanied by heart, brain, kidney, etc. Clinical comprehensive features of organ function or organic damage. Hypertension is the most common chronic disease and the main risk factor for cardiovascular and cerebrovascular diseases.
- the method provided by the prior art is to investigate the blood pressure of the community population through a questionnaire survey, and carry out primary prevention sampling screening to the survey data, and then perform a screening Analyze the data after the survey to obtain the probability of hypertension in the community, so as to know the awareness rate, treatment rate and control rate of hypertension.
- hypertensive patients need to go to the hospital or clinic for testing in order to know the details of their hypertension, which is not convenient for patients to take corresponding treatment measures in time according to their own hypertension level; in addition, patients go to the hospital or Clinic testing will increase the cost of treatment for patients and waste a lot of time and medical resources for patients. Therefore, how to design a hypertension risk monitoring device is an urgent problem to be solved at present.
- the purpose of the present invention is to provide a multi-physiological parameter detection glove and a hypertensive disease risk detection system, so as to achieve the purpose of real-time monitoring of multiple physiological parameters of the human body, and then predict the risk of hypertension.
- the present invention provides the following scheme:
- a multi-physiological parameter detection glove comprising: a main control module, a glove body, and an electrocardiographic signal acquisition component and a comprehensive signal acquisition component arranged on the glove body;
- the electrocardiographic signal collection component includes a plurality of electrodes arranged on the palm side of the outer surface of the glove body and an electrocardiographic collection module connected to each of the electrodes, used to: collect the user's electrocardiographic signal;
- the integrated signal acquisition component is arranged at any fingertip position on the inner surface of the glove body, and is used for:
- the main control module is configured to: acquire the user's ECG signal, blood oxygen saturation and pulse wave signal, and send the acquired user's ECG signal, blood oxygen saturation and pulse wave signal to an external device to determine the user's Probability of risk of developing high blood pressure.
- a system for detecting the risk of developing hypertension including external equipment and gloves for detecting multiple physiological parameters
- the external equipment includes a mobile terminal and a cloud server; the mobile terminal is respectively connected to the multiple physiological parameter detection gloves and the cloud server through wireless communication;
- the mobile terminal is used for:
- the physiological parameter data includes ECG signal, blood oxygen saturation and pulse wave signal;
- the basic information of the user includes at least age, height and gender;
- the cloud server is used for:
- the ECG signal and the blood oxygen saturation predict the user's risk index of hypertensive disorder, and send the risk index of hypertensive disorder to the mobile terminal.
- the invention discloses the following technical effects:
- the present invention realizes real-time detection of the user's ECG information, blood oxygen saturation information, and pulse wave information through a plurality of physiological parameter detection devices in the form of gloves, and can predict the risk of high blood pressure at any time according to the information detected in real time, which is beneficial Guide users in the early prevention and treatment of cardiovascular diseases.
- Fig. 1 is the structural block diagram of a kind of multiple physiological parameter detection glove of the present invention
- Fig. 2 is the palm view of a kind of multi-physiological parameter detection glove of the present invention
- Fig. 3 is a view of the back of the hand of a multi-physiological parameter detection glove of the present invention.
- Fig. 4 is a side view of a multi-physiological parameter detection glove of the present invention.
- Fig. 5 is a structural schematic diagram of an integrated signal acquisition assembly of the present invention.
- Fig. 6 is a layout diagram of various devices on the multi-physiological parameter detection glove of the present invention.
- Fig. 7 is a structural block diagram of the hypertension disease risk detection system of the present invention.
- Fig. 8 is a diagram of the implementation process of the supervised algorithm based on BP neural network in the present invention.
- Pulse wave velocity is an important indicator for evaluating early arterial stiffness, and it is also one of the detection parameters for subclinical target organ damage.
- the methods for predicting the risk of hypertension by using pulse wave velocity (PWV) or pulse wave transit time (PTT) mainly include: the method of calculating the pulse wave velocity by using the ECG signal sign pilot and single pulse wave to obtain the blood pressure, and A method for calculating the pulse wave velocity and pulse wave transit time by using two-way pulse waves to obtain blood pressure.
- the present invention evaluates the degree of arterial sclerosis of the user and determines the risk probability of the user suffering from hypertension through an artificial intelligence algorithm. To help guide users in the early prevention and treatment of cardiovascular diseases.
- This embodiment provides a multi-physiological parameter detection glove, as shown in FIG. 1 , including a main control module, a glove body, and an ECG signal acquisition component and a comprehensive signal acquisition component arranged on the glove body.
- the electrocardiographic signal collection component includes a plurality of electrodes arranged on the palm side of the outer surface of the glove body and an electrocardiographic collection module connected to each of the electrodes, and is used for: collecting the user's electrocardiographic signal.
- the integrated signal acquisition component is arranged at any fingertip position on the inner surface of the glove body, and is used for:
- the user's blood oxygen saturation is obtained by spectrophotometry and transmission blood oxygen collection.
- the main control module is configured to: acquire the user's ECG signal, blood oxygen saturation and pulse wave signal, and send the acquired user's ECG signal, blood oxygen saturation and pulse wave signal to an external device to determine the user's Probability of risk of developing high blood pressure.
- the electrodes on the glove body are set according to the standard lead distribution position during clinical ECG monitoring. When in use, press the palm of the hand wearing the detection glove on the heart position of the left chest, and the user's ECG signal can be collected.
- the capillaries at the fingertips are densely distributed, which has a more obvious effect on the absorption of light in a specific wavelength range, that is, it is feasible to set the integrated signal acquisition component at any fingertip on the glove body; the dark environment in the glove is light.
- the detection creates a good environment, and the blood oxygen saturation information of the human body is collected at the fingertips, which can effectively avoid the interference of other light.
- the multi-physiological parameter detection glove provided in this embodiment also includes a body temperature collection module.
- the body temperature information collection module is arranged in the middle area of the palm of the inner surface of the glove body, and is used to collect the user's body temperature signal and send the body temperature signal to the main control module. In the process of use, the temperature of the palm position is collected by contacting the user's palm to obtain accurate body temperature data.
- the multi-physiological parameter detection glove provided in this embodiment also includes a Bluetooth module, a voltage regulation module, a blood pressure collection module, and an arc-shaped wristband.
- the main control module is arranged on the back of the hand on the inner surface of the glove body or embedded in the wristband; the Bluetooth module, the voltage regulation module and the blood pressure acquisition module are all embedded in the wristband, And move with the wrist strap.
- the main control module communicates with the external device through the Bluetooth module.
- the blood pressure collection module is used to collect the user's blood pressure signal and send the blood pressure signal to the main control module.
- the voltage regulation module includes a battery and a step-down chip connected to the battery; the step-down chip is used to reduce the output power of the battery to meet the power standards of various devices in the multiple physiological parameter detection gloves. Among them, the ECG signal, blood oxygen saturation, pulse wave signal, blood pressure signal and body temperature signal can be collected simultaneously.
- the appearance structure of the multiple physiological parameter detection glove provided in this embodiment is shown in Figure 2 to Figure 4, the multiple physiological parameter detection glove includes a glove body 100 and a wrist strap 200; the main control The module is arranged on the glove body 100 or the wristband 200 .
- the combination of the glove body 100 and the wristband 200 is detachable, and the glove body 100 and the wristband 200 are connected through a miniHDMI plug for data interaction.
- a switch is provided on the wristband 200 .
- the glove body 100 and the wristband 200 are made of flexible FPC to ensure the comfort of the patient.
- the wristband is designed in an arc shape, and the size can be stretched to different degrees, which meets the needs of users of different ages.
- the ECG signal acquisition assembly includes at least 10 circular electrodes, and the 10 circular electrodes are arranged on the palm side of the outer surface of the glove body according to the standard lead distribution position during clinical ECG monitoring.
- the 10 circular electrodes are all made of hardened silicone.
- the signal acquisition mode of the ECG acquisition module is full-lead ECG acquisition; the ECG acquisition module uses a digital analog physiological signal processing chip ADS1298 based on a high-precision signal sampling method produced by TI Company.
- the digital analog physiological signal processing chip ADS1298 is 3.3V unipolar power supply, highly integrated high-speed data conversion channel composed of 8-way EMI filter, programmable gain amplifier (PGA), 24-bit analog-to-digital converter, and also integrated Commonly used functional circuits for 12-lead ECG detection such as right leg drive circuit (RLD), Wilson center detection circuit (WCT), and lead off detection circuit (Leadoff Detection), together with typical peripheral circuits provided in the manual, can be used in a more streamlined manner. Realize the collection of ECG signal.
- RLD right leg drive circuit
- WCT Wilson center detection circuit
- Leadoff Detection lead off detection circuit
- the working principle of blood oxygen saturation collection is spectrophotometry, using red light with a wavelength of 660nm and infrared light with a wavelength of 940nm;
- the infrared light absorption of 940nm is more, and the hemoglobin is the opposite; therefore, the ratio of infrared light absorption to red light absorption can be determined by spectrophotometry, and the oxygenation degree of hemoglobin can be determined.
- the integrated signal acquisition component provided in this embodiment is arranged at the tip of the middle finger on the inner surface of the glove body.
- the integrated signal acquisition component described in this embodiment includes: a light emitting device 300 , a photoelectric detection device 400 and a calculation module.
- the light emitting device 300 is arranged on the belly side of the fingertip of the middle finger; the light emitting device 300 includes a first light emitting diode and a second light emitting diode, the first light emitting diode is used for emitting a red light signal, and the second light emitting diode is used for Infrared light signals are sent out, and the first light emitting diode and the second light emitting diode can work alternately.
- the light emitting diodes are LED devices.
- the photoelectric detection device 400 is arranged on the back side of the fingertip of the middle finger, and is used for receiving the calibration red light signal and the calibration infrared light signal, and converting the calibration red light signal into a first electrical signal, and converting the calibration red light signal into a first electrical signal.
- the calibration infrared light signal is converted into a second electrical signal; the calibration red light signal is a red light signal passing through the fingertip of the middle finger, and the calibration infrared light signal is a red light signal passing through the fingertip of the middle finger.
- the photodetection device 400 is a photodiode.
- the calculation module is arranged on the dorsal side of the fingertip of the middle finger, and is used for:
- the first light-emitting diode and the second light-emitting diode are turned on and off alternately, so that the photoelectric detection device 400 can distinguish light of different wavelengths, and the photoelectric detection device 400 will detect the red light and infrared light that pass through the finger arteries. converted into an electrical signal. Since the absorption coefficients of the skin, muscle, fat, venous blood, pigment and bone to these two kinds of red light and infrared light are constant, only the concentration of oxygenated hemoglobin Hb02 and hemoglobin Hb in the arterial blood flow increases with the blood The periodic changes of the arteries cause the signal intensity output by the photoelectric detection device 400 to change periodically accordingly, and the corresponding blood oxygen saturation can be measured by processing these periodically changed signals.
- the first light-emitting diode is turned on and the second light-emitting diode is turned off, so that the photoelectric detection device 400 detects red light, and the photoelectric detection device 400 converts the detected red light through the finger artery into an electrical signal, and then The calculation module determines the pulse wave signal according to the electrical signal.
- the body temperature information acquisition module is composed of a temperature sensor LMT70 (hereinafter referred to as LMT70) and its peripheral circuits.
- LMT70 is an ultra-small, high-precision, low-power CMOS analog temperature sensor with an output enable pin, suitable for almost all high-precision, low-power cost-effective temperature sensing applications, Examples include medical thermometers, precision instrumentation, and battery-operated equipment.
- the sensor dissipates less than 36 ⁇ W of heat, an ultra-low self-heating feature that supports high accuracy over a wide temperature range.
- LMT70 has excellent temperature matching performance. Two adjacent LMT70s are taken out from the same reel, and the temperature difference between them is at most 0.1°C.
- the LMT70 also has a linear low-impedance output that supports seamless interfacing with off-the-shelf microcontrollers (MCUs)/ADCs. Therefore, this temperature sensor has a fairly good performance in terms of glove fit.
- MCUs off-the-shelf micro
- the main control module adopts the STM32F407 series chips with ARM as the core, specifically, STM32F407ZGT6 and other chips with moderate performance can be selected; the main control module uses the smallest system composed of STM32F4 series single-chip microcomputers as the control circuit.
- the STM32F407 series chip adopts 90nm NVM process and ART (adaptive real-time memory accelerator), integrates new DSP and FPU instructions,
- the high-speed performance of 168MHz improves the execution speed and code efficiency of the control algorithm.
- it has 1MB of FLASH, high-speed USART up to 10.5Mbits/s, and high-speed SPI up to 37.5Mbits/s.
- the Bluetooth module uses the CC2541 chip, which supports data transmission rates of 250Kbps, 500Kbps, 1Mbps and 2Mbps, has excellent receiving sensitivity, powerful five-channel DMA, accurate digital RSSI, eight-channel 12-bit ADC, and has It has configurable resolution, two powerful USART interfaces, 3.3V power supply, supports multiple serial protocols, and I2C interface supports fast data exchange with the main control module. Accordingly, by pairing the Bluetooth module connected to the main control module and the Bluetooth on the external device, wireless data transmission between the two can be realized, and the uncomfortable situation that the user is surrounded by connecting wires during detection can be avoided.
- the blood pressure collection module is used with the glove body and is equipped with an air pump + solenoid valve.
- the voltage regulation circuit includes a 3.7V lithium battery and a TLV70033DDCR step-down chip, and the voltage regulation circuit is used to output a stable voltage of 3.3V.
- the operating temperature range of the TLV70033DCKR step-down chip is -40°C to 150°C. It has excellent line and load transient performance, low output noise, very high power supply rejection ratio (PSRR) and low dropout (LDO) voltage.
- PSRR power supply rejection ratio
- LDO low dropout
- the chip is well suited for most battery-operated handheld devices. The handheld features thermal shutdown and current limit for safety. And it can be adjusted to the specified accuracy without output load to meet the power supply requirements of all modules. At the same time, it is equipped with a charging socket to charge the lithium battery, which makes the power supply part of the detection glove more coordinated.
- the ECG signal acquisition component, comprehensive signal acquisition component and body temperature information acquisition module are all connected to the main control module on the back of the hand through wires. Except for the 10 circular electrodes exposed outside, other components or modules are embedded in the glove Inside, you can't see it from the outside.
- the 10 circular electrodes include ECG RL lead electrode 1, ECG V3 lead electrode 2, ECG V4 lead electrode 3, ECG LL lead electrode 4, ECG V6 lead electrode 5, ECG V5 lead electrode 6, ECG LA lead electrode 8, ECG RA lead electrode 9, ECG V2 lead electrode 10, and ECG V1 lead electrode 11.
- number 7 is an integrated signal collection component
- number 12 is a body temperature information collection module
- number 13 is a wrist blood pressure cuff.
- the electrocardiogram is a means of checking the electrical activity of the heart.
- arrhythmia, premature beats, and acute myocardial infarction can all be diagnosed through the electrocardiogram.
- These diseases are accompanied by changes in the electrical activity of the heart.
- the symptoms of palpitation, the ECG changes during the attack period, and the ECG can completely return to normal during the remission period, so when there is heart discomfort, such as chest tightness, palpitation, and chest pain.
- the first priority is not to go to a big hospital to find an expert, but to capture an electrocardiogram at the time of the onset of the disease, capture the electrocardiogram at the time of the onset, and then find an expert for diagnosis.
- an electrocardiographic detection device to collect electrocardiogram is the main way to check various heart diseases.
- the traditional ECG acquisition method is to use disposable electrode sheets and ECG monitors to collect ECG from patients under the operation of professional medical staff.
- this traditional ECG acquisition method has many lead lines and cumbersome operations.
- the electrocardiographic monitor is heavy and inconvenient to move, which delays valuable rescue time and other defects in emergency situations.
- the multi-physiological parameter detection glove provided by the present invention can solve the above problems.
- the electrocardiogram signal can be collected by wearing the glove and sticking to the left chest, and can be collected anytime and anywhere.
- blood oxygen saturation is the percentage of the capacity of oxygenated hemoglobin (HbO 2 ) in the blood to the total capacity of hemoglobin (Hb) that can be combined, that is, the concentration of blood oxygen in the blood; blood oxygen Saturation is an important physiological parameter of the respiratory cycle, and functional oxygen saturation is the ratio of concentration (that is, the sum of the concentration of oxyhemoglobin and the concentration of hemoglobin), which is different from blood oxygen saturation. Therefore, monitoring the oxygen saturation of arterial blood can estimate the oxygen-carrying capacity of lung hemoglobin.
- the oxygen saturation of normal human arterial blood is 98%, and the oxygen saturation of venous blood is 75%.
- the metabolic process of the human body is a biological oxidation process, and the oxygen needed in the metabolic process enters the human blood through the respiratory system, combines with hemoglobin (Hb) in red blood cells to form oxyhemoglobin (HbO 2 ), and then transports it to the human body Each part of the tissue cells is removed, so the ability of blood to carry and transport oxygen is measured by blood oxygen saturation.
- the traditional blood oxygen saturation measurement method is to first collect blood from the human body, then use a blood gas analyzer to perform electrochemical analysis, measure the blood oxygen partial pressure PO 2 , and finally calculate the blood oxygen saturation based on the blood oxygen partial pressure PO 2 .
- the traditional blood oxygen saturation measurement method is cumbersome and cannot be continuously monitored.
- the multi-physiological parameter detection glove provided by the present invention is equipped with a finger cot photoelectric sensor.
- a finger cot photoelectric sensor When measuring, you only need to put the detection glove on your finger, use the finger as a transparent container for containing hemoglobin, and use red light with a wavelength of 660nm and 940nm
- the near-infrared light is used as the incident light source to measure the light transmission intensity through the tissue bed to calculate the hemoglobin concentration and blood oxygen saturation.
- the instrument connected with a number of physiological parameter detection gloves can display the blood oxygen saturation of the human body, providing a clinical A continuous and non-invasive blood oxygen measuring instrument.
- the finger cot photoelectric sensor is embedded in the fingertip of the detection glove. When measuring, the dark environment inside the detection glove creates a good environment for light detection, which can effectively avoid the interference of other light.
- body temperature refers to the temperature inside the human body. Because the temperature inside the body is not easy to measure, clinically, the temperature of the mouth, armpit and rectum is often used to represent the body temperature.
- the oral temperature of normal people is 36.7-37.7°C (average 37.2°C)
- the armpit temperature is 36.0-37.4°C (average 36.8°C)
- the rectal temperature is 36.9-37.9°C (average 37.5°C).
- the rectal temperature is closest to the internal temperature of the human body, but it is inconvenient to measure, so armpit and oral cavity are mostly used to measure body temperature.
- Accurate body temperature has certain reference significance for diagnosing human diseases. At present, the most common thermometer is a glass thermometer.
- the mercury column in the glass thermometer can change with the body temperature, which is convenient for users to observe at any time. Because the structure of glass is relatively dense and the performance of mercury is very stable, glass thermometers have the advantages of accurate indication, high stability, low price, and no need for external power supply. They are deeply trusted by people, especially medical workers. However, the defects of glass thermometers are also more obvious, and they are easy to break, and there is a possibility of mercury pollution. And the measurement time is relatively long, it is inconvenient to use for patients with acute and serious diseases, the elderly, infants, etc., and the reading is more troublesome.
- thermometers which use the physical parameters of certain substances (such as resistance, voltage, current, etc.) Relationship, the body temperature is displayed in the form of numbers, the reading is clear, and it is easy to carry.
- the disadvantage is that the accuracy of the indication is affected by factors such as electronic components and battery power supply conditions, which is not as good as that of glass thermometers.
- the temperature information collection module can directly collect the body temperature of the human body accurately and efficiently, so as to overcome the above-mentioned defects.
- This embodiment provides a detection system for the risk of hypertension, specifically a combination of gloves + wristbands, and used with a mobile terminal; this detection technology can simultaneously collect the user's ECG, blood oxygen saturation, pulse wave Three physiological parameters, and the collected physiological parameters are transmitted to the mobile terminal in real time for users to view.
- the mobile terminal sends the physiological parameters and the user's age, height, gender and other information to the cloud server through the Internet.
- the cloud server has built-in artificial
- the intelligent algorithm performs big data processing on the above data, evaluates the degree of arteriosclerosis of the user and obtains the risk probability of hypertension, and feeds back the mobile terminal for the user to view, so as to guide the early prevention of cardiovascular diseases in hypertensive patients and treatment.
- a system for detecting the risk of hypertension includes external equipment and a multi-physiological parameter detection glove described in Embodiment 1.
- the external equipment includes a mobile terminal and a cloud server; the mobile terminal is respectively connected to the multiple physiological parameter detection gloves and the cloud server through wireless communication;
- the mobile terminal is used for:
- the physiological parameter data includes ECG signal, blood oxygen saturation and pulse wave signal;
- the basic information of the user includes at least age, height and gender;
- the cloud server is used for:
- the ECG signal and the blood oxygen saturation predict the user's risk index of hypertensive disorder, and send the risk index of hypertensive disorder to the mobile terminal.
- the cloud server is introduced in more detail below.
- the cloud server has a built-in pulse wave propagation distance prediction neural network model.
- the pulse wave propagation distance prediction neural network model is composed of a BP neural network and is used to predict the distance between the brachial artery and the heart of different groups, that is, the pulse wave propagation distance. Therefore, the function of the pulse wave propagation distance prediction neural network model is to predict the pulse wave propagation distance corresponding to the current user based on the basic information input by the current user.
- the cloud server is configured to: determine the pulse wave transmission distance of the user based on the basic information of the user and the pulse wave transmission distance prediction neural network model Wave conduction distance; the pulse wave conduction distance prediction neural network model is determined according to the body feature sample and BP neural network; the body feature sample includes multiple sets of calibrated user basic information and labels corresponding to each set of the calibrated user basic information information; the label information is to calibrate the pulse wave transmission distance of the user.
- the pulse wave transit time PTT is calculated based on the pulse wave information, and then the pulse wave transit velocity PWV is calculated according to formula (1).
- D represents the pulse wave propagation distance.
- the cloud server is used to determine the pulse wave transit time based on the pulse wave information;
- the ratio of the wave propagation distance to the pulse wave propagation time is determined as the pulse wave propagation velocity.
- the cloud server also has built-in calibration physiological parameter data-arteriosclerosis degree-hypertension risk relationship curve, this curve is not limited to one, nor for a specific age group, height group, gender group, but for different Multiple mapping relationships and related empirical formulas established for age groups, users of different heights, and gender groups.
- the cloud server is used for:
- the ECG signal Based on the pulse wave velocity, the ECG signal, the blood oxygen saturation and the calibration physiological parameter data-arteriosclerosis degree-hypertension risk relationship curve, calculate the percentage of arteriosclerosis and the prevalence of hypertension risk index.
- the BP neural network performs supervised learning on the learning results of sparse self-encoding to realize fine-tuning of network parameters.
- BP neural network includes input layer, Mini-batch layer, fully connected layer, hidden layer, encoding layer, output layer and iterative layer.
- the height, age, gender information of 8,000 individuals and the length of each individual's brachial artery are collected as physical feature samples, which are used for BP neural network training and prediction respectively.
- the input layer takes 80% of the body feature samples as the training data set, with a dimension of N ⁇ Len; 80% of the brachial artery length data set as label data, with a dimension of N ⁇ Len. 20% of the body feature samples are used as verification data, and the dimension is 0.25N ⁇ Len, and 20% of the brachial artery length data group is used as control data, and the dimension is 0.25N ⁇ Len.
- the Mini-batch layer is divided into batches according to the size of p′, one batch contains p′ groups of body feature information, and there are 0.4N/p′ batches in total. The body feature information in each batch enters the fully connected layer one by one, that is, the dimension of entering the hidden layer is 1 ⁇ Len.
- the hidden layer has the same number as the encoding layer, and the number of neurons in each hidden layer is also the same, and the weight result of sparse autoencoder learning is used as the initialization parameter of the BP neural network.
- the dimension of the body feature matrix of the output layer is 1 ⁇ Len, and the body feature matrix of p′ group is output after all the body features pass through the fully connected layer The dimension is p' ⁇ Len.
- the iterative layer is to build a loss function, and update the weights through the backpropagation theorem, so that the output after body feature conversion has a high similarity with the corresponding brachial artery length. After traversing a batch of body features, calculate the loss function once and update the network weight once. After all batches of body feature data have been traversed, one iteration is completed, and the loss function J' is:
- y h is the control data of the brachial artery length data of the hth group, that is, the predicted brachial artery length
- p′ is the number of a batch of physical feature data.
- the mobile terminal mainly receives the data of the Bluetooth module, and transmits the received data to the cloud server for analysis, and displays it completely and in real time.
- the mobile terminal will be introduced in more detail below.
- the mobile terminal has built-in APP software; the APP software includes an acquisition module, an input module, an interface display module and an output module.
- the acquisition module is configured to: acquire the user's physiological parameter data sent by the multiple physiological parameter detection gloves through wireless communication, specifically: the APP software establishes a connection with the multiple physiological parameter detection gloves by turning on the Bluetooth function of the mobile terminal , with the help of the Bluetooth function to receive the physiological parameter data collected by the multiple physiological parameter detection gloves.
- the acquisition module is further configured to: acquire the arteriosclerosis program percentage and hypertension risk index sent by the cloud server through wireless communication.
- the input module is used to obtain the basic information of the user, specifically, the built-in algorithm program of the APP software.
- This algorithm program is used for the first step of opening the APP software every time the user needs to input the information of the person's height, age, gender or to store information. Enter the height, age, gender information to confirm.
- the output module is configured to send the user's physiological parameter data and the basic information to the cloud server through wireless communication.
- the interface display module is used to display the user's twelve-lead electrocardiogram, heart rate, pulse wave, blood oxygen saturation, arteriosclerosis program percentage and hypertension risk index; the twelve-lead electrocardiogram and heart rate are based on the determined by the ECG signal.
- the mobile terminal needs to stay connected to the Internet, turn on the Bluetooth, open the APP software and wait for pairing with the Bluetooth of the multi-physiological parameter detection glove; after the pairing is successful, the user will be prompted to enter height, age, and gender information.
- the user After putting on the glove body and the wristband with the right hand, the user presses the switch in the middle of the back of the hand, and waits for the successful connection between the Bluetooth and the mobile terminal.
- the ECG acquisition module collects the original ECG signal, and the comprehensive signal acquisition component collects blood oxygen saturation and pulse wave information.
- the above three original physiological parameters are amplified and filtered by the main control module and converted into data packets with communication protocols , and finally transmit the valid data to the mobile terminal via bluetooth.
- the mobile terminal uploads the data to the cloud server in real time, and the cloud server processes the data and feeds it back to the mobile terminal for display.
- the mobile terminal displays and saves the received ECG data in real time in the form of a GUI interface, which not only facilitates patients to check their own conditions, but also allows doctors to diagnose and analyze the real-time ECG and historical ECG of the monitored person;
- the mobile terminal displays the feedback result of the cloud server in real time.
- the collected ECG signal is accurate, and 10 specific parts of the palm are selected as the lead collection position.
- the signal around the heart is strong and can reflect the conditions of each chamber of the heart.
- Point 2 The efficiency of collecting ECG is high. It is more convenient and efficient to use gloves as the carrier of ECG collection than to use large-scale equipment and electrode sheets in clinical diagnosis.
- the collection of blood oxygen saturation is accurate.
- the dark environment in the glove creates a good environment for light detection.
- the collection of human blood oxygen information at the middle finger position can effectively avoid the interference of other light fronts.
- Body temperature detection is more convenient and fast.
- mercury thermometers, electronic thermometers, forehead thermometers, etc. are generally used to detect the core temperature of the human body, which is easily affected by the external environment. The measurement time is long and the subject needs to keep sitting still during the temperature measurement Or lying down, it is more convenient and quick to collect body temperature information in the form of gloves.
- Advantage 5 Easy to use and operate. When operating, just turn on the switch on the back of the hand, put on gloves and stick to the left chest, and the user can see his various physiological parameters on the mobile terminal.
- Advantage 6 Improve the comfort of the testee. Compared with the traditional method of collecting ECG with electrodes, the gloves are characterized by being thin and skin-friendly. Make the subject feel more comfortable.
- the whole machine is small and light.
- doctors generally use the multi-physiological parameter monitor equipped in the hospital to obtain various physiological parameters of postoperative patients or emergency patients.
- Such equipment is bulky and inconvenient to move, and must be in the hospital.
- the operation is performed by medical staff such as doctors and nurses, and the circuit is complicated and the operation is cumbersome.
- the whole machine of the present invention is small and light, and the operation is simple. It is not only suitable for emergency situations, but also suitable for monitoring physiological parameters of patients after surgery or at home.
- the detection system for predicting early hypertension provided by the present invention can quickly, real-time, accurately, and portable detect human twelve-lead electrocardiogram information, blood oxygen information, body temperature data, and obtain the PWV value based on the above information And predict the user's risk of suffering from high blood pressure.
- it not only saves the time of investigating the user's physical signs and basic information, but also saves labor costs, and can predict the risk level of high blood pressure before the user's physical signs are out of the healthy state, which is convenient for the user's physical The status is monitored in real time.
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Abstract
一种多项生理参数检测手套和高血压病症患病风险检测***，手套包括：主控模块、心电信号采集组件和综合信号采集组件；心电信号采集组件，包括布置在手套本体（100）外表面的手心侧的多个电极以及与每个电极均连接的心电采集模块，用于采集心电信号；综合信号采集组件，设置于手套本体（100）内表面的任一指尖位置，用于采用透射式血氧采集方式获取脉搏波信号；采用分光光度测定法和透射式血氧采集方式获取血氧饱和度；主控模块用于：将获取的心电信号、血氧饱和度和脉搏波信号发送至外界设备以确定用户患高血压的风险概率。多项生理参数检测手套和高血压病症患病风险检测***能够达到实时监测人体多项生理参数，进而预测高血压风险的目的。
Description
本发明涉及生理信号检测技术以及高血压患病风险预测技术领域，特别是涉及一种多项生理参数检测手套和高血压病症患病风险检测***。
心血管疾病具有突发性和高危险性，高血压是导致心血管疾病发生的重要危险因素。高血压(hypertension)是指以体循环动脉血压(收缩压和/或舒张压)增高为主要特征(收缩压≥140毫米汞柱，舒张压≥90毫米汞柱)，可伴有心、脑、肾等器官的功能或器质性损害的临床综合特征。高血压是最常见的慢性病，也是心脑血管病最主要的危险因素。
为了提高高血压的知晓率、治疗率及控制率，现有技术所提供的方法是通过问卷调查方式对社区人群的血压进行调查，并对调查数据进行一级预防抽样筛查，然后再对筛查后的数据进行分析，以得到该社区人群患高血压的机率，从而了解到高血压的知晓率、治疗率及控制率。
然而，采用问卷调查方式获取社区人群的血压，存在花费时间较长，耗费人工成本较高，采集数据不客观等缺陷，而且不能持续监测社区人群患高血压的风险。
目前，高血压患者为了获知自己的高血压详细情况，需要到医院或诊所进行检测，不便于患者根据自己的高血压级别及时采取相应的治疗措施；此外，患者在一定时间内多次去医院或诊所检测，将会增大患者的治疗成本，以及浪费患者大量时间和就医资源。因此，如何设计一种高血压风险监测装置是目前迫切需要解决的问题。
发明内容
本发明的目的是提供一种多项生理参数检测手套和高血压病症患病风险检测***，以达到实时监测人体多项生理参数，进而预测高血压风险的目的。
为实现上述目的，本发明提供了如下方案：
一种多项生理参数检测手套，包括：主控模块、手套本体以及设置在所述 手套本体上的心电信号采集组件和综合信号采集组件；
所述心电信号采集组件，包括布置在手套本体外表面的手心侧的多个电极以及与每个所述电极均连接的心电采集模块，用于：采集用户的心电信号；
所述综合信号采集组件，设置于手套本体内表面的任一指尖位置，用于：
采用透射式血氧采集方式获取用户的脉搏波信号；
采用分光光度测定法和透射式血氧采集方式获取用户的血氧饱和度；
所述主控模块，用于：获取用户的心电信号、血氧饱和度和脉搏波信号，并将获取的用户的心电信号、血氧饱和度和脉搏波信号发送至外界设备以确定用户患高血压的风险概率。
一种高血压病症患病风险检测***，包括外界设备和多项生理参数检测手套；
所述外界设备包括移动终端和云端服务器；所述移动终端通过无线通信方式分别与所述多项生理参数检测手套、所述云端服务器连接；
所述移动终端，用于：
接收所述多项生理参数检测手套发送的用户的生理参数数据；所述生理参数数据包括心电信号、血氧饱和度和脉搏波信号；
获取用户的基本信息；所述基本信息至少包括年龄、身高以及性别；
将所述用户的生理参数数据和所述基本信息发送至所述云端服务器；
所述云端服务器，用于：
基于所述用户的基本信息，确定脉搏波传导距离；
基于所述脉搏波信号和所述脉搏波传导距离，计算脉搏波传导速度；
基于所述脉搏波传导速度、所述心电信号和所述血氧饱和度，预测用户的高血压病症患病风险指数，并将所述高血压病症患病风险指数发送至所述移动终端。
根据本发明提供的具体实施例，本发明公开了以下技术效果：
本发明通过手套形式的多项生理参数检测装置，实现实时对用户的心电信息、血氧饱和度信息以及脉搏波信息的检测，进而能够根据实时检测到的信息随时预测高血压风险，有利于指导用户心血管疾病早期预防及治疗。
为了更清楚地说明本发明实施例或现有技术中的技术方案，下面将对实施例中所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图仅仅是本发明的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动性的前提下，还可以根据这些附图获得其他的附图。
图1为本发明一种多项生理参数检测手套的结构框图；
图2为本发明一种多项生理参数检测手套的手心视图；
图3为本发明一种多项生理参数检测手套的手背视图；
图4为本发明一种多项生理参数检测手套的侧面视图；
图5为本发明综合信号采集组件的结构示意图；
图6为本发明多项生理参数检测手套上的各个器件布置图；
图7为本发明高血压病症患病风险检测***的结构框图；
图8为本发明基于BP神经网络的有监督算法的实现过程图。
下面将结合本发明实施例中的附图，对本发明实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例仅仅是本发明一部分实施例，而不是全部的实施例。基于本发明中的实施例，本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例，都属于本发明保护的范围。
高血压所致的心、脑、肾等靶器官损害的主要机制是血管发生了动脉僵硬、动脉粥样硬化、狭窄和闭塞。脉搏波传导速度(PWV)是评估早期动脉硬度的重要指标，也是亚临床靶器官损害的检测参数之一。
利用脉搏波传导速度(PWV)或脉搏波传导时间(PTT)来预测高血压患病风险的方法主要有：利用心电信号标试点和单路脉搏波计算脉搏波速度以得到血压的方法，和利用双路脉搏波计算脉搏波速度和脉搏波传导时间以得到血压的方法。
鉴于此，本发明基于对用户采集到的心电信息、血氧饱和度信息以及脉搏波信息，通过人工智能算法，对用户的动脉血管硬化程度进行评估以及确定用户患高血压病症的风险概率，以利于指导用户心血管疾病早期预防及治疗。
为使本发明的上述目的、特征和优点能够更加明显易懂，下面结合附图和具体实施方式对本发明作进一步详细的说明。
实施例一
本实施例提供了一种多项生理参数检测手套，如图1所示，包括主控模块、手套本体以及设置在所述手套本体上的心电信号采集组件和综合信号采集组件。
所述心电信号采集组件，包括布置在手套本体外表面的手心侧的多个电极以及与每个所述电极均连接的心电采集模块，用于：采集用户的心电信号。
所述综合信号采集组件，设置于手套本体内表面的任一指尖位置，用于：
采用透射式血氧采集方式获取用户的脉搏波信号；
采用分光光度测定法和透射式血氧采集方式获取用户的血氧饱和度。
所述主控模块，用于：获取用户的心电信号、血氧饱和度和脉搏波信号，并将获取的用户的心电信号、血氧饱和度和脉搏波信号发送至外界设备以确定用户患高血压的风险概率。
由于成年人的手在攥拳时与自身的心脏大小相仿，用自己的手掌测量自己的心脏周边信号，具有可行性。手套本体上的电极是按照临床心电监测时的标准导联分布位置进行设置。使用时，将穿戴检测手套的手掌按压在左胸口心脏位置，即可以采集到该用户的心电信号。
指端处的毛细血管分布密集，对光在特定波长范围内的吸收有较为明显的效果，即综合信号采集组件设置于手套本体任一指尖位置具有可行性；手套内黑暗的环境为光的检测营造了良好的环境，且在指尖位置采集人体血氧饱和度信息，可以有效避免其他光线的干扰。
作为一种优选的具体实施方式，本实施例提供的多项生理参数检测手套还包括体温采集模块。所述体温信息采集模块，设置在手套本体内表面的手心中间区域，用于采集用户的体温信号，并将所述体温信号发送至所述主控模块。在使用过程中，采用与用户手心接触方式来采集手心位置温度，得出准确的体温数据。
进一步，本实施例提供的多项生理参数检测手套，还包括蓝牙模块、电压调节模块、血压采集模块以及圆弧式的腕带。
所述主控模块设置在所述手套本体内表面的手背侧或者嵌入在所述腕带内；所述蓝牙模块、所述电压调节模块和所述血压采集模块均嵌入在所述腕带 内，并随着腕带移动。所述主控模块通过所述蓝牙模块与所述外界设备通信。所述血压采集模块，用于采集用户的血压信号，并将所述血压信号发送至所述主控模块。所述电压调节模块包括电池以及与所述电池连接的降压芯片；所述降压芯片用于降低所述电池输出的电源，以符合所述多项生理参数检测手套中各个器件的电源标准。其中，心电信号、血氧饱和度、脉搏波信号、血压信号和体温信号可以同时采集。
以右手佩戴为例，本实施例提供的多项生理参数检测手套的外形结构如图2至图4所示，所述多项生理参数检测手套包括手套本体100和腕带200；所述主控模块设置在所述手套本体100上或者所述腕带200上。
手套本体100和腕带200的组合为可分离式，手套本体100和腕带200是通过通过miniHDMI插头相连接，进行数据的交互。腕带200上设置有开关。手套本体100和腕带200的材料为柔性FPC上，保证患者佩戴舒适性。腕带为优圆弧式设计，大小可进行不同程度伸缩，满足了不同年龄段的使用者佩戴。
具体地，所述心电信号采集组件至少包括10个圆形电极，且10个所述圆形电极是按照临床心电监测时的标准导联分布位置设置在手套本体外表面的手心侧上。10个圆形电极皆为硬化后的硅胶材质。
所述心电采集模块的信号采集方式为全导联采集心电方式；心电采集模块使用的是TI公司生产的一款基于高精度信号采样方法的数字化模拟生理信号处理芯片ADS1298。该数字化模拟生理信号处理芯片ADS1298为3.3V单极性供电，高度集成了8路由EMI滤波器、可编程增益放大器(PGA)、24位模数转换器组成的高速数据转换通道，同时还集成了右腿驱动电路(RLD)、威尔逊中心检测电路(WCT)、导联脱落检测电路(LeadoffDetection)等12导联心电图检测的常用功能电路，搭配手册中提供的典型***电路，能以较为精简的方式实现心电信号的采集。
具体地，血氧饱和度采集的工作原理为分光光度测定法，采用波长为660nm的红光和波长为940nm的红外光；氧合血红蛋白对波长为660nm的红光吸收量较少，而对波长为940nm的红外光吸收量较多，而血红蛋白则反之；故用分光光度法测定红外光吸收量与红光吸收量的比值，就能确定血红蛋白的氧合程度。
本实施例提供的综合信号采集组件，设置于手套本体内表面的中指指尖位置。请参见图5，本实施例所述的综合信号采集组件包括：发光设备300、光电检测设备400以及计算模块。
所述发光设备300设置在所述中指指尖的指腹侧；所述发光设备300包括第一发光二极管和第二发光二极管，第一发光二极管用于发出红光信号，第二发光二极管用于发出红外光信号，且第一发光二极管和第二发光二极管可以交替工作。优选地，所述发光二极管为LED设备。
所述光电检测设备400，设置在所述中指指尖的指背侧，用于接收标定红光信号和标定红外光信号，并将所述标定红光信号转换成第一电信号，将所述标定红外光信号转换成第二电信号；所述标定红光信号为穿过所述中指指尖后的红光信号，所述标定红外光信号为穿过所述中指指尖后的红光外信号。优选地，所述光电检测设备400为光电二极管。
所述计算模块，设置在所述中指指尖的指背侧，用于：
根据所述第一电信号，确定脉搏波信号；
根据所述第一电信号和所述第二电信号，确定血氧饱和度.
工作时，第一发光二极管和第二发光二极管交替打开和关闭，从而使得光电检测设备400分辨出不同波长的光线，光电检测设备400将检测到的且透过手指动脉血管的红光和红外光转换成电信号。由于皮肤、肌肉、脂肪、静脉血、色素和骨头等对这两种红光和红外光的吸收系数是恒定的，只有动脉血流中的氧合血红蛋白Hb0
2和血红蛋白Hb浓度是随着血液的动脉周期性的变化，从而引起光电检测设备400输出的信号强度随之周期性变化，将这些周期性变化的信号进行处理，就可测出对应的血氧饱和度。
工作时，将第一发光二极管打开和第二发光二极管关闭，从而使得光电检测设备400检测红光光线，光电检测设备400将检测到的且透过手指动脉血管的红光转换成电信号，然后计算模块根据此电信号，确定脉搏波信号。
具体地，体温信息采集模块由温度传感器LMT70(以下简称LMT70)搭配其***电路组合而成。LMT70是一款带有输出使能引脚的超小型、高精度、低功耗的互补金属氧化物半导体模拟温度传感器，几乎适用于所有高精度、低功耗的经济高效型温度感测应用，例如医疗温度计、高精度仪器仪表和电池供 电设备。此款传感器的热耗散低于36μW，这种超低自发热特性支持其在宽温度范围内保持高精度。LMT70具有出色的温度匹配性能，同一卷带中取出相邻两个LMT70，两者的温度最多相差0.1℃。LMT70还具有一个线性低阻抗的输出，支持与现成的微控制器(MCU)/ADC无缝连接。因此，此温度传感器在手套适配方面都具有相当好的表现。
具体地，主控模块采用以ARM为内核的STM32F407系列芯片，具体可选择STM32F407ZGT6等性能适中的芯片；主控模块使用STM32F4系列单片机组成的最小***作为控制电路。该STM32F407系列芯片采用90纳米的NVM工艺和ART(自适应实时存储器加速器)，集成了新的DSP和FPU指令，
168MHz的高速性能提升了控制算法的执行速度和代码效率。同时其具有1MB的FLASH，高速USART可达10.5Mbits/s，高速SPI可达37.5Mbits/s，这些参数使得该STM32F407系列芯片在处理多项生理参数，特别是心电信号时表现出了良好的精确性和快速性。
具体地，蓝牙模块采用CC2541芯片，该CC2541芯片支持250Kbps、500Kbps、1Mbps和2Mbps的数据传输速率，具有卓越的接收灵敏度，强大的五通道DMA，精确的数字RSSI，八通道12位ADC，并且带有和可配置的分辨率，两个强大的USART接口，3.3V供电，支持多个串行协议，I2C接口支持与主控模块实现数据的快速交换。据此，通过配对与所述主控模块连接的蓝牙模块和外界设备上的蓝牙，实现两者之间的无线数据传输，摆脱用户检测时周围是连接线的不适境地。
具体地，血压采集模块为配合手套本体使用的、并且装有气压泵+电磁阀。
具体地，电压调节电路包括3.7V锂电池与TLV70033DDCR降压芯片组成，所述电压调节电路用于输出3.3V稳定电压。TLV70033DCKR降压芯片的工作温度范围为-40℃～150℃，具有出色的线路和负载瞬态性能，低输出噪声，非常高的电源抑制比(PSRR)和低压降(LDO)电压使该降压芯片非常适合大多数电池供电的手持式设备。该手持式设备具有热关断功能和电流限制功能，以确保安全。并且可在无输出负载的情况下调节至指定的精度，满足所有模块供电需求，同时设置有充电插口可对锂电池进行充电，使的检测手套供电部分更具协调性。
请参见图6，心电信号采集组件、综合信号采集组件和体温信息采集模块均通过导线连接至手背处的主控模块，除了10个圆形电极暴露在外面，其他组件或者模块均嵌入在手套内部，外观上是看不到。
其中，10个圆形电极包括心电RL导联电极1、心电V3导联电极2、心电V4导联电极3、心电LL导联电极4、心电V6导联电极5、心电V5导联电极6、心电LA导联电极8、心电RA导联电极9、心电V2导联电极10、心电V1导联电极11。其中，标号7为综合信号采集组件，标号12为体温信息采集模块，标号13为腕式血压袖带。
与现有技术相比，本发明实施例具有下优势：
第一，心电图是一种检查心脏电活动的手段，比如心律不齐、早搏以及急性心肌梗死都可以通过心电图来诊断。这些疾病都伴随着心脏电活动的改变，比如心慌这个症状，在发作期心电有改变，而缓解期心电完全可以恢复正常，所以有心脏不舒服，比如感到胸闷、心慌、胸痛的时候，第一要务不是去大医院找专家，而是就近立刻在发病时捕捉一份心电图，捕捉好发病时的心电图再找专家诊断。采用心电检测装置进行心电采集是检查各种心脏疾病的主要途径。传统心电采集方式为在专业医护人员的操作下，使用一次性电极片以及心电监护仪对病人进行心电采集，但是这种传统心电采集方式存在导联线线路较多，操作繁琐，且心电监护仪笨重不方便移动，在急救场合下延误了宝贵的抢救时间等缺陷。本发明提供的多项生理参数检测手套可以解决上述问题，使用时，带上手套紧贴左胸口就可以采集到心电信号，随时随地都可以进行采集。
第二，血氧饱和度(SpO
2)是血液中被氧结合的氧合血红蛋白(HbO
2)的容量占全部可结合的血红蛋白(Hb)容量的百分比，即血液中血氧的浓度；血氧饱和度是呼吸循环的重要生理参数，而功能性氧饱和度为浓度(即氧合血红蛋白的浓度和血红蛋白的浓度的和)之比，有别于血氧饱和度。因此，监测动脉血氧饱和度可以对肺的血红蛋白携氧能力进行估计，正常人体动脉血的血氧饱和度为98％，静脉血的血氧饱和度为75％。人体的新陈代谢过程是生物氧化过程，而新陈代谢过程中所需要的氧，是通过呼吸***进入人体血液，与血液红细胞中的血红蛋白(Hb)结合，形成氧合血红蛋白(HbO
2)，再输送到人体各部分组织细胞中去，故血液携带输送氧气的能力即用血氧饱和度来衡量。传统血氧饱和 度测量方法是先进行人体采血，再利用血气分析仪进行电化学分析，测出血氧分压PO
2，最后基于血氧分压PO
2计算出血氧饱和度，然而这种传统血氧饱和度测量方法比较麻烦，且不能进行连续的监测。本发明提供的多项生理参数检测手套中设置有指套式光电传感器，测量时，只需将该检测手套套在手指上，利用手指作为盛装血红蛋白的透明容器，使用波长660nm的红光和940nm的近红外光作为入射光源，测定通过组织床的光传导强度，来计算血红蛋白浓度和血氧饱和度，与多项生理参数检测手套连接的仪器即可显示人体血氧饱和度，为临床提供了一种连续无损伤的血氧测量仪器。此外，指套式光电传感器嵌入在该检测手套的指尖位置，测量时，该检测手套内黑暗的环境为光的检测营造了良好的环境，可以有效避免其他光线的干扰。
第三，体温是指人身体内部的温度。由于身体内部的温度不容易被测量，所以临床上常以口腔、腋窝和直肠的温度来代表体温。正常人的口腔温度为36.7～37.7℃(平均为37.2℃)，腋窝温度为36.0～37.4℃(平均为36.8℃)，直肠温度为36.9～37.9℃(平均为37.5℃)。其中，直肠温度最接近人体内部的温度，但测量不方便，因此大多采用腋下和口腔来测量体温。准确的体温对诊断人体疾病有一定的参考意义。目前，最常见的体温计是玻璃体温计，该玻璃体温计中的水银柱可随体温变化而变化，便于使用者随时观测。由于玻璃的结构比较致密，水银的性能非常稳定，所以玻璃体温计具有示值准确、稳定性高、价格低廉、不用外接电源的优点，深受人们，特别是医务工作者的信赖。但玻璃体温计的缺陷也比较明显，易破碎，存在水银污染的可能。且测量时间比较长，对急重病患者、老人、婴幼儿等使用不方便，读数比较费事等。随着科学技术的发展，目前已经出现很多类型的新式体温计，例如电子式体温计，该电子式体温计是利用某些物质的物理参数(如电阻、电压、电流等)与环境温度之间存在的确定关系，将体温以数字的形式显示出来，读数清晰，携带方便，其不足之处在于示值准确度受电子元件及电池供电状况等因素影响，不如玻璃体温计。本发明实施例通过体温信息采集模块能够准确高效直接采集人体体温，克服上述缺陷。
实施例二
本实施例提供了一种高血压病症患病风险检测***，具体为手套+腕带的 组合方式，并搭配移动终端使用；该检测技术可以同时采集用户的心电、血氧饱和度、脉搏波三项生理参数，并将采集到的生理参数实时传输至移动终端以供用户查看，同时移动终端通过互联网将生理参数以及用户的年龄、身高、性别等信息发送至云端服务器，云端服务器内置有人工智能算法，对上述数据进行大数据处理，对用户的动脉血管硬化程度进行评估以及得出患高血压的风险概率，并反馈移动终端以供用户查看，以利于指导高血压患者心血管疾病早期预防及治疗。
请参见图7，本实施例提供的一种高血压病症患病风险检测***，包括外界设备以及实施例一所述的一种多项生理参数检测手套。
所述外界设备包括移动终端和云端服务器；所述移动终端通过无线通信方式分别与所述多项生理参数检测手套、所述云端服务器连接；
所述移动终端，用于：
接收所述多项生理参数检测手套发送的用户的生理参数数据；所述生理参数数据包括心电信号、血氧饱和度和脉搏波信号；
获取用户的基本信息；所述基本信息至少包括年龄、身高以及性别；
将所述用户的生理参数数据和所述基本信息发送至所述云端服务器。
所述云端服务器，用于：
基于所述用户的基本信息，确定脉搏波传导距离；
基于所述脉搏波信号和所述脉搏波传导距离，计算脉搏波传导速度；
基于所述脉搏波传导速度、所述心电信号和所述血氧饱和度，预测用户的高血压病症患病风险指数，并将所述高血压病症患病风险指数发送至所述移动终端。
下面对云端服务器进行更为详细的介绍。
云端服务器内置有脉搏波传导距离预测神经网络模型，该脉搏波传导距离预测神经网络模型由BP神经网络构成，用于预测不同群体的肱动脉距离心脏的距离，即脉搏波传导距离。故脉搏波传导距离预测神经网络模型的作用是根据当前用户输入的基本信息，预测当前用户对应的脉搏波传导距离进行预测。
因此，在所述基于所述用户的基本信息，确定脉搏波传导距离的方面，所述云端服务器，用于：基于所述用户的基本信息和脉搏波传导距离预测神经网 络模型，确定用户的脉搏波传导距离；所述脉搏波传导距离预测神经网络模型是根据身体特征样本和BP神经网络确定的；所述身体特征样本包括多组标定用户基本信息以及每组所述标定用户基本信息对应的标签信息；所述标签信息为标定用户的脉搏波传导距离。
基于脉搏波信息计算脉搏波传导时间PTT，然后根据公式(1)计算脉搏波传导速度PWV。
其中，D表示脉搏波传导距离。
故在所述基于所述脉搏波信号和所述脉搏波传导距离，计算脉搏波传导速度的方面，所述云端服务器，用于基于所述脉搏波信息，确定脉搏波传导时间；将所述脉搏波传导距离与所述脉搏波传导时间的比值，确定为脉搏波传导速度。
该云端服务器还内置有标定生理参数数据-动脉血管硬化程度-高血压患病风险关系曲线，此曲线不限于一个，也不针对某个特定的年龄段、身高群体、性别群体，而是针对不同年龄段、不同身高用户、不同性别群体而建立的多个映射关系及相关经验公式。
故在所述基于所述脉搏波传导速度、所述心电信号和所述血氧饱和度，预测用户的高血压病症患病风险指数，并将所述高血压病症患病风险指数发送至所述移动终端的方面，所述云端服务器，用于：
基于所述脉搏波传导速度、所述心电信号、所述血氧饱和度以及标定生理参数数据-动脉血管硬化程度-高血压患病风险关系曲线，计算动脉血管硬化程序百分比和高血压患病风险指数。
具体地，BP神经网络是在稀疏自编码的学习结果上进行监督学习，实现网络参数的微调。BP神经网络包含输入层、Mini-batch层、全连接层、隐藏层、编码层、输出层和迭代层。本实施例收集8000个人的身高、年龄、性别信息以及每个人肱动脉长度(即脉搏波传导距离)作为身体特征样本，分别用于BP神经网络训练以及预测。
输入层将80％的身体特征样本作为训练数据集，维度为N×Len；80％的 肱动脉长度数据组作为标签数据，维度为N×Len。将20％的身体特征样本作为验证数据，维度为0.25N×Len，20％的肱动脉长度数据组作为对照数据，维度为0.25N×Len。Mini-batch层按照p′的大小分批，一批含有p′组身体特征信息，共有0.4N/p′批。每批中的身体特征信息逐一进入全连接层，即进入隐藏层的维度为1×Len。隐藏层与编码层有相同的个数，且每个隐藏层中的神经元个数也相同，将稀疏自编码学习的权重结果作为BP神经网络的初始化参数。输出层的身体特征矩阵维度为1×Len，p′组身体特征全部经过全连接层后输出身体特征矩阵
维度为p′×Len。迭代层是构建损失函数，通过反向传播定理更新权重，使得身体特征转换后的输出与对应的肱动脉长度有较高的相似度。一批的身体特征遍历结束后，计算一次损失函数，更新一次网络权重。当所有批的身体特征数据都遍历结束后，完成一次迭代，所述损失函数J′为：
式中，
为第h组身体特征转换后的输出结果，y
h为第h组肱动脉长度数据的对照数据，即预测到的肱动脉长度，p′为一批的身体特征数据数量。本节算法实现的流程如图8所示。
移动终端主要是接收蓝牙模块的数据，并将接收到的数据传输至云端服务器进行解析后，完整、实时地进行展示。下面对移动终端进行更为详细的介绍。
移动终端内置有APP软件；所述APP软件包括获取模块、输入模块、界面显示模块以及输出模块。
所述获取模块，用于：通过无线通信方式获取所述多项生理参数检测手套发送的用户的生理参数数据，具体为：APP软件通过打开移动终端的蓝牙功能与多项生理参数检测手套建立连接，借助蓝牙功能接收多项生理参数检测手套采集到的生理参数数据。
所述获取模块，进一步用于：通过无线通信方式获取所述云端服务器发送的动脉血管硬化程序百分比和高血压患病风险指数。
所述输入模块，用于获取用户的基本信息，具体为APP软件内置算法程序，此算法程序用于用户每次打开APP软件的第一步需要输入本人的身高、 年龄、性别信息或者对以存入的身高、年龄、性别信息进行确认。
所述输出模块，用于通过无线通信方式将所述用户的生理参数数据和所述基本信息发送至所述云端服务器。
所述界面显示模块，用于显示用户的十二导心电图、心率、脉搏波、血氧饱和度、动脉血管硬化程序百分比和高血压患病风险指数；所述十二导心电图和心率是根据所述心电信号确定的。
上述高血压病症患病风险检测***具体使用步骤如下：
(1)移动终端需要保持联网状态，开启蓝牙，打开APP软件等待与多项生理参数检测手套的蓝牙进行配对；配对成功后会提示用户输入身高、年龄、性别信息。(2)使用者右手戴上手套本体以及腕带后，按一下手背中间位置的开关，等待蓝牙与移动终端连接成功。(3)手掌五指伸开并按在自己左胸口位置，与皮肤直接接触，不隔衣物，尽量保证手掌中心位于胸骨中间，高度与心脏齐平；大拇指朝右上方伸展，食指朝左上方伸展，调整右手的角度，保证大拇指指尖与食指指尖在同一高度；中指、无名指自然伸展，保证大拇指指根到无名指指尖形成自然弧线，且无名指指尖尽量靠近左腋前线；小拇指朝左下方伸展，尽量保证小拇指指尖位置在在大拇指与食指指尖连线的中线上。(4)心电采集模块对原始心电信号进行采集，综合信号采集组件采集血氧饱和度和脉搏波信息，以上三项原始生理参数经主控模块放大、滤波转换为有通信协议的数据包，最后通过蓝牙将有效数据传送给移动终端。(5)移动终端实时将数据上传至云端服务器，云端服务器对数据进行处理后反馈给移动终端显示。(6)移动终端以GUI界面的形式对接收到的心电数据进行实时显示并及时保存，既方便患者查看自身情况，也可供医生对被监测者的实时心电图和历史心电进行诊断分析；移动终端将云端服务器的反馈结果进行实时显示。
本发明的有益效果是：
优点1：采集的心电信号准确，选取掌心10个特定部位作为导联采集位置，心脏周边信号强且能反映出心脏的各个腔室状况。
有点2：采集心电效率高，采用手套作为心电采集的载体比起临床诊断中使用大型设备以及电极片更为方便，采集效率更高。
优点3：采集血氧饱和度准确，在手套内黑暗的环境为光的检测营造了良 好的环境，在中指位置采集人体血氧信息，可以有效避免其他光先线的干扰。
优点4：体温检测更方便快捷，临床诊断上一般采用水银温度计、电子体温计、额温枪等方法检测人体核心温度，易受外界环境影响，测量时间长且在测温时被测者需要保持静坐或平躺，以手套的形式采集体温信息更为方便快捷。
优点5：使用操作简单，操作时只需打开手背上的开关，戴上手套紧贴左胸口，用户即可在移动终端上看到自己的各项生理参数。
优点6：提高被测者舒适程度，相较于传统的电极片采集心电的方法，手套的特点为轻薄贴肤，在使用时手套的温度可以与胸口相差更小，比冰冷的电极片会使被测者感到更舒适。
优点7：整机小巧轻便，在临床监测环境下，医生一般采用医院配备的多生理参数监护仪获取术后病人或急诊患者的各项生理参数，此类设备笨重不方便移动，且必须在医院场合下由医生护士等医护人员进行操作，线路复杂，操作繁琐，本发明整机小巧轻便，操作简单，不仅适用于急救场合，也适用于对术后病人或居家场合的生理参数监测。
优点8：采用本发明提供的预测早期高血压病症的检测***，能够快速、实时、准确、便携的检测人体十二导心电图信息、血氧信息、体温数据，并可根据上述信息得出PWV值以及预判出使用者存在患高血压的风险程度。在预测高血压风险方面，不仅节省了调查用户体征信息和基本信息的时间，节约了人工成本，而且可以在用户的体征状态脱离健康状态前预测其患高血压的风险等级，方便对用户的身体状况进行实时监控。
本说明书中各个实施例采用递进的方式描述，每个实施例重点说明的都是与其他实施例的不同之处，各个实施例之间相同相似部分互相参见即可。本文中应用了具体个例对本发明的原理及实施方式进行了阐述，以上实施例的说明只是用于帮助理解本发明的方法及其核心思想；同时，对于本领域的一般技术人员，依据本发明的思想，在具体实施方式及应用范围上均会有改变之处。综上所述，本说明书内容不应理解为对本发明的限制。
Claims (10)
- 一种多项生理参数检测手套，其特征在于，包括：主控模块、手套本体以及设置在所述手套本体上的心电信号采集组件和综合信号采集组件；所述心电信号采集组件，包括布置在手套本体外表面的手心侧的多个电极以及与每个所述电极均连接的心电采集模块，用于：采集用户的心电信号；所述综合信号采集组件，设置于手套本体内表面的任一指尖位置，用于：采用透射式血氧采集方式获取用户的脉搏波信号；采用分光光度测定法和透射式血氧采集方式获取用户的血氧饱和度；所述主控模块，用于：获取用户的心电信号、血氧饱和度和脉搏波信号，并将获取的用户的心电信号、血氧饱和度和脉搏波信号发送至外界设备以确定用户患高血压的风险概率。
- 根据权利要求1所述的一种多项生理参数检测手套，其特征在于，所述心电信号采集组件至少包括10个圆形电极，且10个所述圆形电极是按照临床心电监测时的标准导联分布位置设置在手套本体外表面的手心侧上；所述心电采集模块的信号采集方式为全导联采集心电方式。
- 根据权利要求1所述的一种多项生理参数检测手套，其特征在于，所述综合信号采集组件，设置于手套本体内表面的中指指尖位置；所述综合信号采集组件，包括第一发光二极管、第二发光二极管、光电检测设备以及计算模块；所述第一发光二极管，设置在所述中指指尖的指腹侧，用于发射红光信号；所述第二发光二极管，设置在所述中指指尖的指腹侧，用于发射红外光信号；所述光电检测设备，设置在所述中指指尖的指背侧，用于接收标定红光信号和标定红外光信号，并将所述标定红光信号转换成第一电信号，将所述标定红外光信号转换成第二电信号；所述标定红光信号为穿过所述中指指尖后的红光信号，所述标定红外光信号为穿过所述中指指尖后的红光外信号；所述计算模块，设置在所述中指指尖的指背侧，用于：根据所述第一电信号，确定脉搏波信号；根据所述第一电信号和所述第二电信号，确定血氧饱和度。
- 根据权利要求1所述的一种多项生理参数检测手套，其特征在于，还包括蓝牙模块、电压调节模块、血压采集模块以及圆弧式的腕带；所述主控模块设置在所述手套本体内表面的手背侧或者嵌入在所述腕带内；所述蓝牙模块、所述电压调节模块和所述血压采集模块均嵌入在所述腕带内；所述主控模块通过所述蓝牙模块与所述外界设备通信；所述血压采集模块，用于采集用户的血压信号，并将所述血压信号发送至所述主控模块；所述电压调节模块包括电池以及与所述电池连接的降压芯片；所述降压芯片用于降低所述电池输出的电源，以符合所述多项生理参数检测手套中各个器件的电源标准。
- 根据权利要求1所述的一种多项生理参数检测手套，其特征在于，还包括体温采集模块；所述体温信息采集模块，设置在手套本体内表面的手心中间区域，用于采集用户的体温信号，并将所述体温信号发送至所述主控模块。
- 一种高血压病症患病风险检测***，包括外界设备以及权利要求1-5任意一项所述的一种多项生理参数检测手套；所述外界设备包括移动终端和云端服务器；所述移动终端通过无线通信方式分别与所述多项生理参数检测手套、所述云端服务器连接；所述移动终端，用于：接收所述多项生理参数检测手套发送的用户的生理参数数据；所述生理参数数据包括心电信号、血氧饱和度和脉搏波信号；获取用户的基本信息；所述基本信息至少包括年龄、身高以及性别；将所述用户的生理参数数据和所述基本信息发送至所述云端服务器；所述云端服务器，用于：基于所述用户的基本信息，确定脉搏波传导距离；基于所述脉搏波信号和所述脉搏波传导距离，计算脉搏波传导速度；基于所述脉搏波传导速度、所述心电信号和所述血氧饱和度，预测用户的 高血压病症患病风险指数，并将所述高血压病症患病风险指数发送至所述移动终端。
- 根据权利要求6所述的一种高血压病症患病风险检测***，其特征在于，在所述基于所述用户的基本信息，确定脉搏波传导距离的方面，所述云端服务器，用于：基于所述用户的基本信息和脉搏波传导距离预测神经网络模型，确定用户的脉搏波传导距离；所述脉搏波传导距离预测神经网络模型是根据身体特征样本和BP神经网络确定的；所述身体特征样本包括多组标定用户基本信息以及每组所述标定用户基本信息对应的标签信息；所述标签信息为标定用户的脉搏波传导距离。
- 根据权利要求6所述的一种高血压病症患病风险检测***，其特征在于，在所述基于所述脉搏波信号和所述脉搏波传导距离，计算脉搏波传导速度的方面，所述云端服务器，用于基于所述脉搏波信息，确定脉搏波传导时间；将所述脉搏波传导距离与所述脉搏波传导时间的比值，确定为脉搏波传导速度。
- 根据权利要求6所述的一种高血压病症患病风险检测***，其特征在于，在所述基于所述脉搏波传导速度、所述心电信号和所述血氧饱和度，预测用户的高血压病症患病风险指数，并将所述高血压病症患病风险指数发送至所述移动终端的方面，所述云端服务器，用于：基于所述脉搏波传导速度、所述心电信号、所述血氧饱和度以及标定生理参数数据-动脉血管硬化程度-高血压患病风险关系曲线，计算动脉血管硬化程序百分比和高血压患病风险指数。
- 根据权利要求9所述的一种高血压病症患病风险检测***，其特征在于，所述移动终端上设置有APP软件；所述APP软件包括获取模块、输入模块、界面显示模块以及输出模块；所述获取模块，用于：通过无线通信方式获取所述多项生理参数检测手套发送的用户的生理参数数据；通过无线通信方式获取所述云端服务器发送的动脉血管硬化程序百分比和高血压患病风险指数；所述输入模块，用于获取用户的基本信息；所述输出模块，用于通过无线通信方式将所述用户的生理参数数据和所述基本信息发送至所述云端服务器；所述界面显示模块，用于显示用户的十二导心电图、心率、脉搏波、血氧饱和度、动脉血管硬化程序百分比和高血压患病风险指数；所述十二导心电图和心率是根据所述心电信号确定的。
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| CN202180002473.3A CN114072047A (zh) | 2021-08-18 | 2021-08-18 | 多项生理参数检测手套及高血压病症患病风险检测*** |
| PCT/CN2021/113223 WO2023019467A1 (zh) | 2021-08-18 | 2021-08-18 | 多项生理参数检测手套及高血压病症患病风险检测*** |
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- 2021-08-18 WO PCT/CN2021/113223 patent/WO2023019467A1/zh unknown
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| GB202403014D0 (en) | 2024-04-17 |
| US20230142080A1 (en) | 2023-05-11 |
| GB2624807A (en) | 2024-05-29 |
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