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WO2018153060A1 - Dispositif de type patch destiné à être utilisé pour la collecte et la surveillance de paramètres multiples physiologiques - Google Patents

Dispositif de type patch destiné à être utilisé pour la collecte et la surveillance de paramètres multiples physiologiques Download PDF

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Publication number
WO2018153060A1
WO2018153060A1 PCT/CN2017/101441 CN2017101441W WO2018153060A1 WO 2018153060 A1 WO2018153060 A1 WO 2018153060A1 CN 2017101441 W CN2017101441 W CN 2017101441W WO 2018153060 A1 WO2018153060 A1 WO 2018153060A1
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WIPO (PCT)
Prior art keywords
patch
sensor
monitoring device
electrode
housing
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PCT/CN2017/101441
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English (en)
Chinese (zh)
Inventor
张跃
张烈帅
雷夏飞
冯治蒙
潘俊俊
肖志博
张拓
Original Assignee
深圳市岩尚科技有限公司
清华大学深圳研究生院
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Publication of WO2018153060A1 publication Critical patent/WO2018153060A1/fr

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    • A61B5/0205—Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition
    • A61B5/02055—Simultaneously evaluating both cardiovascular condition and temperature
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    • G01D21/00—Measuring or testing not otherwise provided for
    • G01D21/02—Measuring two or more variables by means not covered by a single other subclass

Definitions

  • the invention relates to a wearable medical intelligent device for physiological multi-parameters, human posture information, environmental multi-parameter acquisition and monitoring, in particular to a patch-type physiological multi-parameter acquisition monitoring device.
  • the main object of the present invention is to overcome the above-mentioned deficiencies of the prior art and the device, and to provide a patch-type physiological multi-parameter acquisition and monitoring device, and the present invention adopts the following technical solutions:
  • a patch type physiological multi-parameter monitoring device comprising a shell suitable for attaching to a flat flexible material on a human skin, the front side of the shell being adhered to human skin by a sticker, the front side of the shell And/or a pulse wave sensor is disposed on the back surface, and is integrally formed with the casing by liquid silicone injection molding or solid silicone molding, and the casing is sealed with a circuit module for processing the collected physiological parameter data, and the circuit is sealed.
  • a module is coupled to the pulse wave sensor.
  • the front side of the housing is the side that is in contact with the skin when worn; the other side that is not in contact with the skin when worn is the back side of the housing (including the front side and the side).
  • the patch type physiological multi-parameter monitoring device forms a shell of a flat flexible material integrated with the pulse wave sensor by liquid silicone injection molding or solid silicone molding.
  • a circuit module for processing the collected physiological parameter data is sealed in the housing.
  • the pulse wave sensor By different positions of the pulse wave sensor, it can be attached to different positions on the human skin in a targeted manner, and the pulse wave signal can be directly collected or the pulse wave signal can be collected by finger pressing, and the real-time dynamic heart rate can be obtained through the calculation algorithm.
  • Physiological multi-parameter information such as ambulatory blood pressure, dynamic oxygen saturation, dynamic respiratory rate curve, dynamic maximal oxygen uptake, dynamic calorie consumption, etc.; or a pulse wave sensor on the front and back of the housing for dual pulse wave signals Acquisition to improve the accuracy of the collection.
  • FIG. 1 is a front view of a patch type physiological multi-parameter monitoring device according to an embodiment of the present invention
  • FIG. 2 is a schematic rear view of a patch type physiological multi-parameter monitoring device according to an embodiment of the present invention
  • FIG. 3 is a patch-type physiological embodiment of the present invention.
  • FIG. 4 is a flowchart of a method for calculating blood pressure data according to an embodiment of the present invention
  • FIG. 5 is a flowchart of a method for authenticating an electrocardiogram according to an embodiment of the present invention
  • FIG. 6 is a flowchart of a method for authenticating an electrocardiogram according to an embodiment of the present invention
  • FIG. 7 is a flowchart of a method for ECG identity authentication according to an embodiment of the present invention
  • FIG. 8 is a schematic diagram of ECG identity authentication according to an embodiment of the present invention
  • FIG. 9 is a schematic diagram of an upper cover with lead wires and electrodes (implementing multi-lead) in the embodiment of the present invention
  • FIG. 10 is a schematic diagram of a connection using a 10-electrode 12 lead according to an embodiment of the present invention
  • 11 is a schematic diagram of a frank lead connection according to an embodiment of the present invention
  • FIG. 12 is a schematic diagram of an electrode placement method of an international standard lead electroencephalogram system
  • FIG. 13 is a schematic diagram of a general 10-20 electrode electroencephalogram electrode method
  • FIG. 14 is a schematic diagram of the present invention
  • Figure 15 is a schematic cross-sectional view showing the electrode of Example 7 of the present invention.
  • Fig. 16 is a first schematic view showing the package structure of the electrode of the seventh embodiment of the present invention.
  • Figure 17 is a second schematic diagram showing the package structure of the first and second electrodes of the electrode of the seventh embodiment of the present invention.
  • 18 is a schematic bottom view of a self-adhesive sheet including an electrode through hole and a sensor through hole according to Embodiment 7 of the present invention.
  • Figure 19 is a bottom plan view of a self-adhesive sheet comprising a black UV glue and a micro-sucker array according to Embodiment 7 of the present invention.
  • Figure 20 is a schematic view showing the bottom surface of a self-adhesive sheet provided with a raised-grain silicone gel according to Example 7 of the present invention.
  • Figure 21 is a schematic exploded view of a self-adhesive sheet according to Embodiment 7 of the present invention.
  • Figure 22 is an electrocardiogram using a metal electrode according to Embodiment 7 of the present invention.
  • Figure 23 is an electrocardiogram of a metal electrode and a self-adhesive sheet according to Embodiment 7 of the present invention.
  • Figure 24 is an electrocardiogram of a conventional wet electrode according to Embodiment 7 of the present invention.
  • Figure 25 is an electrocardiogram of a conductive silica gel electrode according to Embodiment 7 of the present invention.
  • Figure 26 is an electrocardiogram of a conductive silica gel electrode and a self-adhesive sheet according to Example 7 of the present invention.
  • a patch-type physiological multi-parameter monitoring device includes a housing 1 adapted to be attached to a flat flexible material on a human skin, and the front surface of the housing can be attached to human skin through a sticker. .
  • the front surface of the casing 1 is provided with first to second electrodes 2, a first temperature sensor 7 and a second temperature sensor 12, which are integrally formed with the casing by liquid silicone injection molding.
  • a circuit module for processing the collected physiological parameter data is sealed inside the circuit, and the circuit module is connected to the first to second electrodes, the first temperature sensor 7, and the second temperature sensor 12.
  • the electrode is a wet electrode sheet.
  • the wet electrode sheet is attached to the casing 1 by a wet electrode mounting structure (such as a snap) provided on the casing 1 in such a manner that the wet electrode sheets can be replaced.
  • the wet electrode mounting structure can be integrally formed with the housing 1 by liquid silicone injection molding or solid silicone molding.
  • the electrode can be conveniently applied to human skin through medical double-sided tape, so it can be reused without causing irritation to human skin.
  • the first temperature sensor 7 and the second temperature sensor 12 jointly detect the temperature. When the two temperatures are less than a certain threshold, the temperature detection is considered to be effective, and the average value is taken as the body temperature. When the two temperatures are greater than a certain threshold, there may be a problem in the detection. If the fitting is not reliable, the first temperature sensor 7 is inaccurately detected, the device itself is faulty, etc. If the phenomenon occurs for a long time, the device alarms to alert the user. .
  • the first temperature sensor 7 is also multiplexed into a third electrode, and functions to detect the lead-off of the lead during the electrocardiogram/electroencephalogram monitoring.
  • the second temperature sensor 12 is a temperature sensor dedicated to the front surface of the casing, preferably an infrared temperature sensor.
  • the infrared temperature sensor can detect the temperature under non-contact conditions, and the measurement accuracy is higher. By comparing with the temperature measurement of the first temperature sensor 7, To some extent, it can be detected whether the patch-type physiological multi-parameter monitoring device fits the human body firmly.
  • the back surface of the housing is provided with a pulse wave sensor 11 and a touch sensor 13 adjacent to the pulse wave sensor 11, and the housing 1 is formed by liquid silicone injection molding or solid silicone molding.
  • the pulse wave sensor 11 and the touch sensor 13 are integrally formed.
  • the touch sensor 13 is configured to detect whether a finger is in contact. When a finger contact is detected, the pulse wave sensor 11 is activated to acquire a pulse wave signal, and the collected data is transmitted to a circuit module in the housing 1.
  • the acquired pulse wave signal may calculate at least one physiological parameter including blood pressure, blood oxygen saturation, heart rate, respiratory rate, and maximum oxygen uptake, and the pulse wave sensor preferably uses a photoelectric pulse wave sensor.
  • the pulse wave sensor can also be disposed at different positions of the housing, and the front surface of the monitoring device can be attached to different positions on the human skin by adhesive tape.
  • the front side of the housing is attached to the forehead or near the wrist by adhesive tape, and the pulse wave signal can be directly collected;
  • the pulse wave sensor is disposed in the housing On the back,
  • the front side of the housing is attached to the front chest by adhesive tape, and the pulse wave signal is collected by the user's finger pressing on the pulse wave sensor;
  • the housing can be attached by the adhesive tape
  • the front side is attached to the forehead or near the wrist, and the dual-acquisition pulse wave signal is collected by the direct collection on the front side and the finger press on the back side, which is more beneficial to improve the accuracy of pulse wave signal acquisition.
  • the pulse wave sensor is placed at different positions on the human skin, and real-time dynamic heart rate, real-time dynamic blood pressure, dynamic blood oxygen saturation, dynamic respiratory
  • the back cover of the housing 1 is further provided with a detachably mounted upper cover 3, which is mounted in a threaded manner in a circular recess 5 provided on the back of the housing 1, and the screwed upper cover 3 Pressing on the flexible material of the housing 1 to form a watertight seal.
  • a battery is disposed in the upper cover 3, and the battery is electrically connected to the circuit module in the housing 1 through the upper cover 3 and the corresponding metal contact 4 on the housing 1.
  • the battery is a rechargeable battery or a replacement primary battery can be taken out from the upper cover 3.
  • the surface of the casing 1 covered by the upper cover 3 is provided with a built-in memory card slot 6 for inserting a built-in memory card, and the built-in memory card slot 6 is connected to the circuit module for The physiological parameter data collected and processed is stored in the built-in memory card.
  • Figure 2 shows that the whole device is like a whole after the cover is attached to the upper cover 3.
  • the upper cover is detachable. After being disassembled, the built-in memory card for storing data can be seen. The built-in memory card can be taken out and connected to the computer. Easy to export data.
  • the battery is placed in the top cover. After the upper cover is aligned with the opening of the housing, the upper cover can be screwed into the housing.
  • the housing 1 of the monitoring device is of a circular arc shape having a wide intermediate dimension, extending from the middle to both sides and gradually narrowing, the upper cover 3 being located at an intermediate portion of the housing 1, the first to second electrodes 2 are respectively located on both sides of the upper cover 3.
  • the patch-type physiological multi-parameter monitoring device further includes a human body posture sensor disposed in the housing 1, the posture sensor for collecting various postures of the human body to determine what the user's human body is in Status, for example, lying, sitting, walking, running, riding, swimming, other sports, etc.
  • the attitude sensor can be a three-axis sensor (such as a three-axis accelerometer or gyroscope), a six-axis sensor (such as three-axis) Accelerometers and three-axis gyroscopes, or nine-axis sensors (such as three-axis accelerometers, three-axis gyroscopes, and three-axis magnetic induction sensors).
  • the patch-type physiological multi-parameter monitoring device further includes a wireless communication module disposed in the housing 1, such as a Bluetooth module, a WIFI module, a 3G/4G/5G module, and the wireless communication module
  • a wireless communication module disposed in the housing 1, such as a Bluetooth module, a WIFI module, a 3G/4G/5G module, and the wireless communication module
  • the physiological parameter data processed by the circuit module is wirelessly transmitted to the terminal.
  • the patch-type physiological multi-parameter monitoring device further includes an alarm device, and the upper cover 3 is preferably provided with an SOS button 10, and the SOS button is connected to the circuit module for manual pressing in an emergency situation. The SOS button is pressed to generate an emergency call alert.
  • the patch-type physiological multi-parameter monitoring device further includes an alarm device, such as a vibration motor, which is preferably disposed within the housing 1 or the wall of the housing 1 corresponds to the first electrode and And / or a position of the second electrode, the vibration motor is connected to the circuit module for generating a vibration alarm under a set condition, the setting condition comprising the circuit module detecting a low battery or abnormal heart rate condition.
  • the vibration motor of the preferred embodiment can set a vibration alarm when the power is low and when an abnormal heart rate occurs when the mobile phone client is used.
  • the alarm device may also employ a speaker connected to the circuit module for generating an audible alarm under set conditions, the setting condition including the circuit module detecting that the battery is low Or abnormal heart rate.
  • the alarm device may also employ a diode lamp, the diode lamp being connected to the circuit module for generating a flicker alarm under set conditions, the setting condition including the circuit module detecting the battery Low battery or abnormal heart rate.
  • the housing 1 is also preferably provided with a power on button 9, which can be operated by a long press of a button.
  • the user's pulse can be collected by the pulse wave sensor, the first and second electrodes, the temperature sensor, the near-infrared light sensor 14, the sweat sensor, the skin sensor, the attitude sensor, and the environmental parameter detector on the patch-type physiological multi-parameter monitoring device.
  • ECG, EEG, pulse wave signal body temperature, blood sugar, electrolytes, sodium, lactic acid, protein, skin characteristics, Various human body postures, as well as environmental temperature, relative humidity, atmospheric pressure, intensity of ambient light, ultraviolet intensity, number and size of respirable particles, air quality concentration and other physiological parameters and environmental parameter data, data passed by circuit modules
  • the processing module performs data processing, stores it in the storage module, and finally transmits it through the wireless communication module.
  • the blood pressure data of the user can be obtained by the data processing module using the following calculation method for obtaining blood pressure data based on the pulse wave data.
  • the blood pressure data calculation method is as shown in FIG. 4, and includes the steps of establishing a plurality of regression equations and calculating a blood pressure value, wherein the steps of establishing a plurality of regression equations include: A11, acquiring a pulse wave and a corresponding blood pressure value; A12, The pulse wave obtained by the preprocessing; A13, extracting the pulse wave feature point from the preprocessed pulse wave, and acquiring the pulse wave global feature parameter value; A14, using the acquired pulse wave global feature parameter value and the corresponding blood pressure value to randomly select The method establishes a plurality of sets of regression test sets, wherein the regression test set is composed of a test set and a training set; A15, obtaining a global optimal regression equation for each set of regression test sets; A16, evaluating and screening out a regression equation with high accuracy; A17: assigning a corresponding weight to the high accuracy regression equation; the steps of calculating the blood pressure value include: A21, acquiring a pulse wave; A22, preprocessing the collected pulse
  • the globally optimal regression equation is obtained in step A15 using stepwise regression analysis; the stepwise regression analysis analyzes the training set in a global traversal manner.
  • the pulse wave feature points described in step A13 or step A23 include an aortic valve open point, a systolic maximum pressure point, a heavy beat wave start point, and a heavy beat wave highest pressure point;
  • the wave feature points include smoothing processing of the pulse wave, and the smoothing processing adopts a three-point line smoothing process.
  • the step of extracting the aortic valve opening point and the systolic maximum pressure point comprises: a1. acquiring all generalized extreme points of the pulse wave and obtaining an extreme point set; b1. determining an aortic valve opening point, The threshold value of the highest pressure point difference in the systolic period; c1. The pulse opening point and the highest pressure point in the systolic period are determined and extracted according to the threshold value.
  • the extreme point judgment condition in step a1 is: (Pc[i] - Pc[i-1]) * (Pc[i+1] - Pc[i]) ⁇ 0, Pc represents the beat wave data point.
  • the discriminant formula for determining and extracting the pulse opening point and the systolic maximum pressure point according to the threshold is ext[i+1]-ext[i]>thd, 1 ⁇ i ⁇ len-1, Ext[i] represents the ith extreme point, ext[i+1] represents the i+1th extreme point, thd represents the threshold of the aortic valve opening point, the highest pressure point difference during systole, and len represents the extreme value.
  • the steps of extracting the starting point of the beat wave and the highest pressure point of the beat wave wave include: a2, a period interval of dividing the pulse wave; b2, determining a starting point of the beat wave, and a highest pressure point of the beat wave. a pulse wave period interval; c2, extracting the starting point of the beat wave by calculating a set of average slope angle change index values of each point in the pulse wave period interval where the peak of the beat wave and the highest pressure point of the beat wave are located, The highest pressure point of the beat wave.
  • acquiring the pulse wave global feature parameter value in step A13 or step A23 includes removing the outlier value and averaging each feature parameter set for removing the outlier value;
  • the pulse wave global feature parameter value includes global Systolic time ratio, global main wave height, global relative gorge relative height, global tremor wave relative height, global systolic area ratio, global main wave rising slope and global K value; The Weiler method is carried out.
  • the expression of the regression equation is where param is the set of final selection parameters, coef is the set of corresponding parameters of each parameter, cont is a constant term, lenParam is the number of selected parameters, and BPest is the estimated blood pressure value.
  • the user can perform ECG identification and authentication through the data processing module by using any of the following ECG identification methods or three ECG identity authentication methods.
  • the data processing module is used to perform ECG identification:
  • the method for ECG identification is as shown in FIG. 5, and includes a model training phase and a real-time testing phase.
  • the model training phase includes the following steps: B1, preprocessing, and segment extraction: performing an ECG signal for training. Position acquisition, pre-processing the collected ECG signals, segmenting the pre-processed ECG signals to obtain multiple feature segments; B2, fully automatic feature extraction layer training: based on fully automatic extraction feature network pairs The feature segments are trained by the fully automatic feature extraction layer and the fully connected layer of the fully automatic extraction training module, and the fully automatic feature extraction layer after training is extracted as a feature extractor;
  • the real-time test phase includes the following steps: B3, pre- Processing and fragment extraction: the ECG signal to be identified is collected at any position, and the collected ECG signals are preprocessed, and the pre-processed ECG signals are extracted to obtain a plurality of feature segments; B4, features Identification: parallel feature extraction of the ECG signals to be identified by multiple feature extractors trained in the training phase of the model, Characterized taken parallel to
  • the preprocessing and fragment processing comprises the following steps: B5: filtering the ECG signal; B6: determining the length of the window including at least one ECG information on the intercepted ECG signal; B7: determining according to a good window length, a sliding window, and an ECG window of a corresponding length is cut from an arbitrary position of the ECG signal; B8: dividing the ECG window into a plurality of fixed length segments to obtain a plurality of the feature segments.
  • the segment length of the feature segment is less than or equal to the length of the electrocardiogram window.
  • the fully automatic feature extraction layer includes a plurality of convolution layers and a maximum pool layer
  • the method includes the following steps: B21: pairing the feature segments by using the plurality of convolution layers Performing parallel convolution to obtain a plurality of vector values; B22: a plurality of the vector values generate depth fusion features through the maximum pool layer; B23: the depth fusion features are trained and classified by the fully connected layer, and output classification is determined
  • the fully automatic feature extraction layer is extracted as the feature extractor based on the classification judgment result.
  • step B23 when the training recognition rate is greater than the threshold, the training is stopped, the depth fusion feature is extracted, and the fully automatic feature extraction layer composed of the depth fusion feature is extracted as the feature extractor, otherwise continue training.
  • step B2 according to the collection condition of the ECG signal in the step B1, if the ECG signal can be continuously collected, the fully automatic feature extraction layer is further trained, and when the threshold is reached, the original feature extractor is replaced. .
  • step B4 the method includes the following steps: B41, feature extraction, performing parallel feature extraction on the ECG signals to be recognized according to the plurality of feature extractors, and obtaining a depth fusion feature of the ECG signal to be identified.
  • B42. Feature classification, the depth fusion feature of the ECG signal to be identified is classified in parallel by a plurality of classifiers according to the category number of the ECG signal to be identified, and the identity recognition is completed.
  • the classifier in the model training phase, is a nonlinear classifier, and further includes training the nonlinear classifier, including: the depth fusion feature extracted in step B41.
  • the category number above trains the predetermined nonlinear classifier using a nonlinear classifier training module.
  • step B4 the identification is performed by a plurality of non-linear classifiers for preliminary identification, and the step of performing a final identification process by feature voting: voting with the highest entropy, statistical preliminary identity
  • the entropy value of each category in the identification is determined, and the category number corresponding to the maximum entropy value is used as the final recognition result according to the calculated entropy value.
  • the feature extraction step as described above is also applicable to the feature extraction step in ECG identity authentication.
  • the first method used by the data processing module to perform ECG authentication is the first method used by the data processing module to perform ECG authentication:
  • the method for authenticating the ECG is as shown in FIG. 6 , and includes a pre-processing step, a feature extraction step, and an authentication step, where the pre-processing step includes filtering the ECG signal collected by the electrode to eliminate interference.
  • the feature extraction step includes detecting each reference point in the electrocardiographic signal to extract a quasi-periodic heart beat signal as the original electrocardiographic feature, and performing segmentation waveform correction on the heart beat, and then using the PCA dimension reduction and extracting the coefficient feature as The final ECG feature, the authentication step includes using a template matching based method to determine if the test sample is successfully authenticated.
  • the respective reference points include a P wave start point (Ps), a P wave end point (Pe), an R wave peak (R), a J wave start point (J), a T wave peak (Tp), and a T of the heart beat.
  • Wave end point (Te) in the feature extraction step, the reference point detection and the waveform segmentation are performed by:
  • the ECG signal determines the position of the R wave of the heart beat by wavelet transform, or determines the rough position of the R wave of the heart beat by the minimum value of the second-order differential signal of the ECG signal, and then determines the rough position of the R wave.
  • the point at which the first-order differential signal is closest to zero, and the position of the R-peak (R) is located accordingly;
  • One point in the range of 160-180 milliseconds from the left side of each R peak (R) is the P wave starting point (Ps); one point in the range of 80-100 milliseconds from the left side of each R peak (R) is the P wave end point. (Pe); one point in the range of 80-100 milliseconds from the right side of each R peak is the J wave starting point (J); the maximum value in the right side of each R peak (R) is the T peak (Tp)
  • the segment region is cut off from the J wave start point (J) to 2/3 current RR intervals; the first-order differential signal on the right side of the T wave peak (Tp) is the T wave end point for the first time from negative to positive (Te ).
  • segmentation waveform correction is performed by segmentation resampling the heartbeat signal, wherein each P-band is upsampled, and the P-band duration is extended after upsampling.
  • the duration of each P-band is unified to 460-500 milliseconds; the duration of each QRS band remains unchanged; for each T-band, the J-Tp segment and the Tp-Tp segment are down-sampled respectively, so that each T-band is resampled
  • the length of each of the two segments is unified to 10-20 milliseconds.
  • each axis coefficient whose retention contribution rate is equal to or higher than a set threshold value is extracted as a coefficient feature, and the set threshold value is preferably 99%.
  • the feature extraction step as described above is also applicable to the feature extraction step in ECG identification.
  • the second method used by the data processing module to perform ECG authentication is the second method used by the data processing module to perform ECG authentication:
  • the method for authenticating the ECG is as shown in FIG. 7.
  • the steps include: including electrocardiographic extraction and ECG authentication, the ECG extraction includes: C11, preprocessing the ECG signal collected by the electrode, and detecting the R wave Position, intercept QT band; C12, the intercepted QT band adopts autocorrelation transform algorithm for feature extraction, and obtain ECG autocorrelation sequence; C13, the acquired ECG autocorrelation sequence is reduced by orthogonal polynomial fitting regression, Generating a feature template; C14, selecting and evaluating an optimal ECG feature template from the generated feature template; C15, obtaining an optimal threshold from the ECG optimal feature template.
  • the ECG certification includes: C21, preprocessing the electrocardiographic signal collected by the electrode, detecting the R wave position, and intercepting the QT band; C22, extracting the extracted QT band using an autocorrelation transform algorithm to obtain the ECG self-correlation Correlation sequence; C23, the obtained ECG autocorrelation sequence is subjected to dimensionality reduction by orthogonal polynomial fitting regression to generate a feature template; C24, the generated feature template is compared with the ECG optimal feature template, according to the best The threshold completes the authentication.
  • the dimension reduction generation feature template described by the orthogonal polynomial fitting regression described in step C14 or step C24 is obtained by approximating the ECG autocorrelation sequence by a polynomial, and the representation by the feature template is obtained.
  • the ECG autocorrelation sequence is described.
  • the calculation formula for the feature template is:
  • ⁇ , ⁇ ⁇ (0, 1), ⁇ and ⁇ are regularization coefficients
  • p ⁇ i 0, 1, 2, ..., M-1
  • J (A, F) is the solved vector
  • F (F 1 , F 2 , ..., F M-1 )
  • n templates are generated to generate n feature templates A 1 , A 2 , . . . , A n , 10 ⁇ n ⁇ 20.
  • the ECG optimal feature template described in step C15 is obtained by using the leave-one method, and the discriminant is Where D(A i , A j ) represents a distance metric between the feature vector A i and the feature vector A j ; Indicates that 1 is taken when the distance between the feature A i and the feature A j is less than the preset threshold THD, otherwise 0 is taken; the value of THD is the average value of the distance between the n feature vectors, and i, j is 1 to n. I ⁇ j.
  • the feature extraction step as described above is also applicable to the feature extraction step in ECG identification.
  • the third method used by the data processing module to perform ECG authentication is the third method used by the data processing module to perform ECG authentication:
  • the method for the ECG identity authentication is as shown in FIG. 8.
  • the steps include: D1: pre-processing the collected ECG signals in the pre-processing and intercepting QT wave module, detecting the R wave position, and intercepting the QT wave;
  • the intercepted QT wave generates a sparse feature by using a differentiated dictionary learning algorithm for sparse representation in multiple ECG feature extraction and data processing modules;
  • D3 the generated sparse feature is fuzzy matched in the template matching module based on the optimal threshold, and is completed.
  • Initial certification after which the certification is completed based on the highest entropy vote.
  • the sparse feature described in step D2 is compressed in the plurality of ECG feature extraction and data processing modules and transmitted to a third party authentication prior to the preliminary authentication in step D3. The device is then decompressed into the sparse features described in step D2.
  • the distinguishing dictionary learning algorithm for sparse representation described in step D2 comprises:
  • J (D, C) is the solved dictionary D and the sparse feature C
  • Verif (X i , X j , D, C i , C j ) is the feature distinguishing attribute
  • ⁇ is the sparsity degree coefficient
  • ⁇ is the regularization.
  • the coefficients, ⁇ and ⁇ have values ranging from 0 to 1;
  • X i and X j represent the i-th and j-th QT waves, respectively, and C i and C j represent the sparse features corresponding to X i and X j , respectively.
  • dm is a minimum distance between the different types of settings, label (X i) X i represents the class number;
  • X (X 1 , X 2 , . . . , X n ) represents n QT waves;
  • D (d 1 , d 2 , . . . , d l ) represents the dimension of the dictionary, and l is greater than 1 Any value;
  • T represents transpose of matrices.
  • the search process for the optimal threshold described in step D3 is to search using Euclidean distance, which is based on frequency.
  • the feature extraction step as described above is also applicable to the feature extraction step in ECG identification.
  • the patch-type physiological multi-parameter monitoring device further includes other electrodes for collecting an electrocardiogram or an electroencephalogram signal, the other electrodes being led out from the upper cover through the lead wires, and corresponding to the upper cover and the housing Providing a metal contact electrically connected to the circuit module in the housing, and for electrocardiographic monitoring, the other electrode and the first electrode and the second electrode may be multiplexed into a third electrode
  • the first temperature sensor 7 together form a variety of lead forms, including typical forms in a conventional 12-lead system, such as a three-electrode three-lead, a five-electrode seven-lead, and a complete ten-electrode twelve-lead, which may also be constructed.
  • Frank lead system For EEG monitoring, different EEG signal acquisition electrodes can also be configured as needed.
  • the other electrodes may also be drawn directly from the housing through the lead wires.
  • the patch-type physiological multi-parameter monitoring device includes fourth to fifth electrodes 8 for collecting electrocardiographic signals, and the fourth to fifth electrodes 8 pass through two guides.
  • the connecting wire is taken out from the upper cover 3 and electrically connected to the circuit module in the housing 1 through the upper cover 3 and the corresponding metal contact 4 on the housing 1, the fourth to The fifth electrode 8 and the first to second electrodes 2 and the temperature sensor 7 multiplexed electrodes together constitute a five-electrode seven-lead.
  • the blood pressure data of the user can be obtained by using the data processing module to calculate the blood pressure data based on the pulse wave data in the first embodiment.
  • the user can perform ECG identification and authentication by using the four ECG identification and authentication methods in Embodiment 1 through the data processing module.
  • This embodiment provides an electrocardiographic multi-lead mode. Since many heart diseases can only see arrhythmia events under certain specific leads, more ECG lead data can provide a more comprehensive and accurate judgment basis for doctors.
  • an upper cover design with additional lead wires is used, as shown in Figure 9.
  • the multi-lead can be various typical forms in a classic 12-lead system, such as a ten-electrode twelve-lead, Five-electrode seven-lead, three-electrode three-lead, and the like.
  • the main body of the patch-type physiological multi-parameter monitoring device is normally pasted, and the two electrodes on the front side are simulated RA (upper right) and LA (upper left) data, temperature.
  • the sensor 7 multiplexed electrode is RL (bottom right), and another 7 lead wires are drawn, one of which is attached to the lower left (LL), that is, the left lower abdomen, and the remaining six leads are the conventional six chest leads, which are respectively attached to
  • the simulated twelve-lead data thus measured are:
  • RA, LA, LL, and RL are all consistent with the above-mentioned ten-electrode twelve-lead, except that in the chest lead, one of V1 to V6 is selected as needed.
  • the multi-lead system can also be a frank lead system, where the seven electrode positions are the front median line E; the back median line is M; the right medial line is I; the left median line is A; the left front chest is E and A The midpoint is C.
  • the other two electrodes are placed in the left lower limb for F and the neck for H.
  • the two electrodes of the main body of the ECG are attached to the front of the body, which are the E and C electrodes, and the remaining upper cover electrodes are attached to the corresponding positions.
  • the electrodes A and C are combined, and the I is coupled with a resistor to form a X-lead, and the direction of the X-axis is from right to left.
  • the blood pressure data of the user can be obtained by using the data processing module to calculate the blood pressure data based on the pulse wave data in the first embodiment.
  • the user can perform ECG identification and authentication by using the four ECG identification and authentication methods in Embodiment 1 through the data processing module.
  • the EEG signal is the potential difference formed between the brain cortical cell populations when the brain is active, thereby generating electrical current outside the cerebral cortex.
  • the electroencephalogram is placed on a specific position of the head, and each electrode corresponds to a lead.
  • the electrode placement method of the international standard lead system is shown in FIG.
  • the 10-20 system electrode method has been widely used internationally, as shown in Fig. 13, 19 recording electrodes and 2 reference electrodes (A1, A2 are located in the ear).
  • the measurement of the anterior-posterior direction is based on the midline of the nasal root to the occipital trochanter.
  • the left and right forehead points (FP1, FP2), the forehead (F3, F4), and the center are determined at the corresponding parts of the line.
  • the position of the forehead point is equivalent to 10% of the nasal root to the occipital trochanter on the nasal root, and the forehead is equivalent after the forehead point
  • the distance from the nasal root to the forehead point is 20% of the distance from the midline of the nose, and the interval between the posterior center, the top, and the pillow is 20%.
  • the unipolar lead method and the bipolar lead method can be selected.
  • the movable electrode 2 is placed on the scalp, and an unrelated electrode is drawn from the upper cover to the earlobe.
  • the negative potential change thus generated at the movable electrode is recorded as a negative waveform of the waveform upward.
  • the bipolar lead method does not use an extraneous electrode, only the two movable electrodes on the scalp. This record is the difference in EEG changes between the two electrode parts, so the interference can be greatly reduced, and the error caused by the unrelated electrodes can be eliminated. But, like If the distance between the two movable electrodes of the bipolar lead is within 3 cm, the brain potential from a large range (distance greater than 3 cm) is simultaneously recorded by the two movable electrodes, and the potential difference cancels each other, and the recorded amplitude is low. It is also not constant, so the distance between the two electrodes should be above 3-6cm.
  • the direct Fp1-Fp2 lead signal can be acquired when the device is attached to the forehead.
  • the upper cover is required to lead out additional lead wires and placed in the corresponding collection position.
  • the patch-type physiological multi-parameter acquisition device of this embodiment further includes a near-infrared light sensor 14 disposed on the front side and/or the back surface of the front surface, and the sensor is used for performing non-invasive detection of blood glucose in the human body.
  • the main purpose is to pass near-infrared light through a part of the blood vessel region of the human body, and extract corresponding blood glucose concentration information from the obtained spectral information.
  • the measurement light is generally reflected by three parts, including skin indicating reflection, epidermal reflection, and dermal layer reflection, and one or more reference lights are set to obtain blood glucose data by spectral analysis.
  • the front side of the collecting device can be attached to different positions on the human skin by adhesive tape.
  • the front side of the housing is attached to the forehead or near the wrist by adhesive tape, and the blood glucose detection can be directly performed;
  • the near-infrared light sensor is disposed on the back of the housing
  • the front side of the housing is attached to the front chest by adhesive tape, and the blood glucose detection is performed by pressing the user's finger on the near-infrared light sensor;
  • the shell can be attached by adhesive tape
  • the front of the body is attached to the forehead or near the wrist, and the blood glucose detection is facilitated by the direct collection of the front side and the finger pressing of the back side, which is more conducive to improving the accuracy of blood glucose detection.
  • a touch sensor 13 in close proximity to the near-infrared light sensor disposed on the back of the housing, the touch sensor for detecting whether the finger is in contact, the basic configuration and function of the touch sensor used at the pulse wave sensor the same.
  • the blood pressure data of the user can be obtained by using the data processing module to calculate the blood pressure data based on the pulse wave data in the first embodiment.
  • the user can perform ECG identification and authentication by using the four ECG identification and authentication methods in Embodiment 1 through the data processing module.
  • FIG. 14 is a schematic diagram of the structure of a circuit module of the patch type physiological multi-parameter monitoring device.
  • the power supply is used to supply power to the patch-type physiological multi-parameter monitoring device;
  • the acquisition chip is used to collect ECG/EEG data;
  • the pulse wave sensor is used to collect pulse wave data;
  • the attitude sensor is used to collect various postures of the human body to determine the user.
  • the state of the human body for example, lying, sitting, walking, running, riding, swimming, other sports, etc.; temperature sensor is used to collect the body temperature of the human body, or reused as the third electrode, in the ECG For monitoring or EEG monitoring, it is used to detect whether the lead is off; the near-infrared light sensor user measures the user's blood sugar; the skin sensor is used to collect the user's skin information, such as dry skin, oily skin, neutral skin, skin aging, etc.
  • the sweat sensor is used to collect physiological parameters such as glucose, electrolyte, sodium, lactic acid and protein in the user's sweat; the environmental detector detects the ambient temperature and humidity, the amount of inhalable particles, the air mass concentration, the atmospheric pressure, and the ultraviolet intensity.
  • the data processing chip is used for processing the collected data according to requirements, such as filtering preprocessing of ECG/EEG data, and writing corresponding algorithms according to need to perform data analysis, such as ECG identification authentication, pulse wave analysis, etc.;
  • the storage chip is used for data storage operation;
  • the wireless communication module is used for wirelessly transmitting the processed data to the mobile terminal or the server end, and can perform more comprehensive analysis of physiological parameters to more comprehensively and accurately determine the physical condition of the user, and provide a large data.
  • the user firstly applies the medical double-sided adhesive. Attached to the two electrode sheets, press the switch button to turn on the skin, and paste the physiological multi-parameter monitoring device on the skin in front of the heart to receive accurate ECG signals.
  • the two electrode sheets form a loop with the human body and the circuit body, and the acquisition chip in the circuit module can collect the corresponding ECG signal (electrocardiogram data); when the finger is pressed on the pulse wave sensor, the touch sensor senses and sends a signal, the pulse The wave sensor starts to work to collect the pulse wave signal, the attitude sensor collects the human body posture, the temperature sensor collects the body temperature of the human body, the near-infrared light sensor is used to measure the user's blood sugar, the skin sensor collects the user's skin information, and the sweat sensor collects the glucose and electrolyte in the user's sweat.
  • ECG signal electrocardiogram data
  • the touch sensor senses and sends a signal
  • the pulse The wave sensor starts to work to collect the pulse wave signal
  • the attitude sensor collects the human body posture
  • the temperature sensor collects the body temperature of the human body
  • the near-infrared light sensor is used to measure the user's blood sugar
  • the skin sensor collects the user's skin information
  • Physiological parameter data such as sodium, lactic acid and protein, and environmental detectors to detect ambient temperature and humidity, the amount of inhalable particles, air mass concentration, ultraviolet intensity and atmospheric pressure.
  • the data processing chip performs large data fusion processing on the collected physiological majority data, the human body posture information and the environment multi-parameter data; finally, the processed physiological parameter data, the human body posture information and the environmental data are stored in the storage chip.
  • the user can select two modes, one is to transmit the data in the memory chip to the terminal in real time through the wireless communication module for big data fusion processing analysis, or send it to the cloud for analysis and return the analysis result, or wait for the whole collection process to be completed. Copy the complete data from the built-in memory card for comprehensive analysis of the data.
  • the user When the patch-type physiological multi-parameter monitoring device of another embodiment is attached to the forehead, the user first pastes the medical double-sided tape around the two electrode sheets, and presses the switch button to turn on the physiological multi-parameter monitoring.
  • the device is attached to the skin on the forehead position to receive accurate EEG signals.
  • the two electrode sheets form a circuit with the human body and the circuit body, and the acquisition chip in the circuit module can collect the corresponding EEG signals (EEG data); the pulse wave sensor collects the pulse wave signal, the attitude sensor collects the human body posture, and the temperature sensor collects the human body.
  • the body temperature, the near-infrared light sensor is used to measure the user's blood sugar, the skin sensor collects the user's skin information, the sweat sensor collects the physiological parameter data such as glucose, electrolyte, sodium, lactic acid and protein in the user's sweat, and the environmental detector detects the ambient temperature and humidity, The amount of inhalable particles, air mass concentration, ultraviolet intensity, and atmospheric pressure.
  • the data processing chip performs large data fusion processing on the collected physiological multi-parameter data, human posture information and environmental multi-parameter data; finally, the processed physiological parameter data, human posture information and environmental data are stored in the storage chip.
  • the user can select two modes, one is to transmit the data in the memory chip to the terminal for analysis by the wireless communication module, or send it to the cloud for real-time analysis and return the analysis result, or wait for the entire collection process from the built-in storage.
  • the card is copied into the complete data for comprehensive analysis of the data.
  • the pulse wave signal can be directly collected by the pulse wave sensor disposed on the front of the device
  • the EEG signal can be collected through the electrode
  • the body temperature can be collected by the temperature sensor
  • the collected raw data can be analyzed.
  • the user's health status, body temperature, pulse, blood pressure, blood oxygen and other physiological parameter information, human posture information and environmental multi-parameter information can be obtained, thereby making a more comprehensive judgment on the human health state.
  • the patch-type physiological multi-parameter acquisition of the present embodiment can also be used to quickly judge the vital signs of the human body, such as the monitoring of individual life state during a war or the rescue after a disaster.
  • the monitoring device is placed on the user's forehead
  • the body temperature data is collected by the body temperature sensor disposed on the front side
  • the brain electricity data is collected by the electrodes disposed on the front side
  • the pulse wave data is collected by the pulse wave sensor disposed on the front side
  • the pulse wave data can be collected.
  • physiological multi-parameter information such as heart rate, respiration, body temperature, pulse, blood pressure and blood oxygen.
  • the information collected by the device is transmitted to the supporting mobile phone client through the wireless transmission module, and the physiological parameters of the human body can be visually seen on the client side, and the electrocardiogram and the pulse wave map can also be drawn. After a short period of time, the collection and analysis can be completed. According to the obtained physiological parameter information, the user's vital signs and injury degree can be quickly judged, and the emergency level of the rescue can be determined to ensure the maximum treatment of more wounded.
  • the mobile phone client can be provided with an information input part, which can input current individual information or rescued personnel information, such as name (known) / rescue code (name unknown) / individual soldier number, gender, age and other personal information. At this time, it can be set to not enable the touch sensor on the device, but the device starts to collect after the client clicks to start, so as to distinguish different individual/rescue personnel.
  • information input part which can input current individual information or rescued personnel information, such as name (known) / rescue code (name unknown) / individual soldier number, gender, age and other personal information.
  • it can be set to not enable the touch sensor on the device, but the device starts to collect after the client clicks to start, so as to distinguish different individual/rescue personnel.
  • the equipment is used for individual soldier detection, it can not only be used as a rapid vital sign detection for the wounded, but also has significant analytical significance for the physiological information collection of soldiers under different circumstances and different combat states under normal circumstances.
  • the ECG identity authentication function can also be directly used, that is, the individual ECG information is collected and registered in advance, and the identity is directly obtained through the collected ECG signals during normal use, and the individual soldier identity is determined according to the authentication result.
  • This kind of living certification method It is all convenient and cannot be forged, to prevent the enemy from generating misleading information after obtaining the corresponding monitoring equipment.
  • the identification algorithm can be placed on the monitoring device or the terminal device as needed.
  • the blood pressure data of the user can be obtained by using the data processing module to calculate the blood pressure data based on the pulse wave data in the first embodiment.
  • the collected ECG information of the user the user can perform ECG identification and authentication by using the four ECG identification and authentication methods in Embodiment 1 through the data processing module.
  • This embodiment provides an idea of big data fusion analysis. such as:
  • the environmental sensor data on the device reflects the surrounding environment and weather conditions, and excludes unsuitable sports (eg, outdoor sports are not recommended when the air quality is poor).
  • the human body's ECG, pulse, body temperature, and sweat data can be used to calculate the human heart, blood pressure, Heart rate, blood sugar and other health conditions, combined with the analysis of big data, can make human health trends within a certain time range. For example, users with poor physiological parameters within a certain period of time will not recommend strenuous exercise, reminding them to take more rest. For example, whether the current state is suitable for aerobic exercise or a comprehensive analysis of big data collected by the device can be obtained.
  • the attitude information is collected by the attitude sensor to determine the current state of the user, and the ECG signal, the EEG signal, the pulse wave signal, and the like in different states may have different degrees of variability.
  • the weighted judgment is made when the signal automatic analysis such as arrhythmia analysis, pulse wave analysis and blood pressure are obtained, which can improve the accuracy of automatic analysis.
  • the skin sensor can also perform statistical analysis of specific environment/region according to the collected information, and obtain statistical rules of skin characteristics and sweat characteristics in different environments/regions, which have significant medical statistics and clinical significance.
  • the first and second electrodes each include an exposed portion 11 and an inner fitting portion 12.
  • the exposed portion 11 is exposed on the outer side of the front surface of the casing, and the inner fitting portion 12 passes through the hard portion.
  • a plastic closure 24 is mounted within the housing.
  • the hard plastic is also called hard plastic, and the texture is hard.
  • engineering plastics such as ABS plastic, POM plastic, polycarbonate (PC), polyethylene terephthalate (PET). , polybutylene terephthalate (PBT), polyphenylene ether (PPO).
  • POM plastic polycarbonate
  • PET polyethylene terephthalate
  • PBT polybutylene terephthalate
  • PPO polyphenylene ether
  • Some of the harder textures can be in medical grades of general purpose plastics such as polystyrene (PS).
  • the adhesive tape 4 is provided with a through hole, the bottom surface thereof is for adhering to the signal acquisition target, and the back surface of the adhesive tape is used for adhesion to the monitoring device;
  • the through hole includes an electrode opposite to the monitoring device a mating first type of through hole; and/or the through hole includes a second type of through hole that cooperates with a sensor of the monitoring device.
  • the size and position of the through hole are adapted to the position of the sensor or the electrode;
  • the adhesive tape of the embodiment is a double-sided adhesive, which is composed of a medical double-sided adhesive.
  • the sticker when it is applied When it is a double-sided tape, the sticker may also be a double-sided adhesive of a non-woven fabric, and the adhesive may also be a flexible insulating material composed of an endless silica gel.
  • the first electrode or the second electrode comprises an integrally formed contact body 14 and a limiting flange 15, the limiting flange 15 being convex Out of the outer side wall of the contact body 14, at the same time, the contact body 14 and the limiting flange 15 combined with the hard plastic 24 will be more waterproof.
  • the front surface 142 of the contact body 14 is exposed, so the front surface 142 of the contact body 14 is an exposed portion; the remaining structural portions are buried in the casing 23, so the embedded portion includes the a limiting flange 15 and a portion of the contact body 14 other than the front surface; the inner fitting portion needs to be fitted with the rigid plastic 24, and at this time, the limiting flange 15 and the contact body 14 A portion other than the front surface 142 is embedded in the rigid plastic 24, and a waterproof fitting surface is formed in contact with the hard plastic 24, because the limiting fitting surface is not a surface but a plurality of planes or Song
  • the faces are combined, which are the outer wall surface 141 of the contact body 14, the front surface 151 of the limiting flange 15, the outer wall surface 152 of the limiting flange 15, and the back surface 153 of the limiting flange 15, so that the multi-section surface is composed of The boundary is tortuous, and the liquid will not enter easily from this tortuous boundary.
  • the inner fitting portion includes the limiting flange 15; the limiting flange 15 is embedded in the rigid plastic 24, and a waterproof fitting surface is formed at the contact with the hard plastic 24,
  • the bit fitting surface is also not a face, but is composed of a plurality of planes or curved surfaces, which are the front surface 151 of the limiting flange 15, the outer wall surface 152 of the limiting flange 15, and the back surface 153 of the limiting flange 15, respectively. , so the boundary is also tortuous, can play a better waterproof effect.
  • the joint surface of the rigid plastic 24 and the inner fitting portion is meandered, and the overall shape of the corresponding rigid plastic 24 matches the overall shape of the inner fitting portion, so that the flexible silicone and the rigid plastic 24 can be used.
  • the contact surface is also set to be meandering, so that the contact area of the flexible silicone rubber with the hard plastic 24 is increased to improve the waterproof effect.
  • the rigid plastic can be disposed in the flexible housing and the first and second electrodes.
  • the patch type physiological multi-parameter monitoring device can achieve the waterproof level of IPX7, which is higher than that of the general dry electrode.
  • the physiological multi-parameter monitoring device is attached to the front side of the shell, and can also be called a self-adhesive sheet.
  • a self-adhesive sheet In order to adhere to the human skin, especially under the sports conditions, sweating is more, which can solve the problem that the existing wearing is not reliable. problem.
  • the self-adhesive sheet is as shown in FIG. 18, and has the same size and size as the physiological multi-parameter monitoring device, the double-sided adhesive tape 411, the two electrode through holes 412, and the silicone gel adhered to the through hole 412 (in the figure) Not shown).
  • the shape of the silicone gel matches the through hole and is aligned with the through hole.
  • a sensor via 413 that can be coupled to a pulse wave sensor, temperature sensor, or the like.
  • gripping ears 414 on both sides of the self-adhesive sheet, and the gripping ears are non-adhesive, and are integrated with the non-woven double-sided adhesive 411 for facilitating the extraction of the non-adhesive film or the release paper, or for taking the self-adhesive sheet from Tear off the signal acquisition target and/or signal acquisition device.
  • the shape of the self-adhesive sheet is not limited to the shape shown in FIG. 18, and may be a circle, a square, a rectangle, a diamond, etc., and the shape and size thereof may be matched with the shape and size of the signal acquisition device, and the area may be larger than the signal. Acquisition equipment to cover signal acquisition equipment.
  • medical double-sided tape or electrodeless silica gel can also be used.
  • the medical double-sided adhesive has low production cost and high safety, and is favorable for adhesion; when the silicone gel is peeled off from the skin, it does not cause damage to the fur and reduces pain.
  • the position of the gripper can be set anywhere around the self-adhesive sheet, and is not limited to the sides of the self-adhesive sheet as shown.
  • the physiological multi-parameter monitoring device forms a shell of a flat flexible material integrated with a pulse wave sensor by liquid silicone injection molding or solid silicone molding, and the shell is sealed with data for processing the collected physiological parameters. Circuit module.
  • the above-mentioned integrated design can not only conveniently attach the shell to the human body through the medical double-sided tape, thereby increasing the comfort and simplicity of the human body wearing, and also making the patch-type physiological multi-parameter monitoring device easy to wear, easy to use, and at the same time It also greatly improves the waterproof sealing performance and completely solves the problem that the existing physiological multi-parameter monitoring equipment can not be used when showering.
  • a black UV glue 415 and a micro suction cup array 416 are disposed around the sensor through hole 413.
  • the black UV glue can shield the periphery of the sensor LED, effectively preventing crosstalk between different LEDs. Interference, Improve the acquisition quality of signals such as pulse waves.
  • the micro-sucker array 416 can enhance the fit around the through-hole with the skin, especially for a thinner user, which is affected by the sag protruding skin depression, the common suction cup cannot be adsorbed on the skin surface, and the micro-suck array is small due to its suction cup area. A large number can effectively overcome this problem.
  • the black UV glue around the sensor through hole it can also be set as other glue or other device that can block light.
  • the raised lines increase the friction between the solid gel or the conductive silicone and the skin, and act as a perspiration. Especially when the human body is exercising, the sweat will fill the groove between the raised lines, which not only makes the solid gel or the conductive silicone and the skin more closely and firmly adhered, but also the sweat acts as a dielectric, which increases the conductivity. The more sweat, the more conductive it is, and it is more suitable for use during exercise.
  • the 21 is a schematic exploded view of the self-adhesive sheet, which is a first type of release layer 421, a nonwoven fabric double-sided tape 411 including a through hole, and a second release layer 431, from top to bottom.
  • the first type of release layer 421 is attached to the back side of the self-adhesive sheet, that is, the side from which the self-adhesive sheet needs to be adhered to the collecting device;
  • the second type of release layer 431 is attached to the bottom surface of the self-adhesive sheet, that is, the self-adhesive sheet is required. The side that sticks to the human skin.
  • the first type of release layer 421 is a segmented structure, which is segmented between the through hole and the through hole of the adhesive sheet, 422 is a tear line, and 423 is a segmented void line.
  • the electrode through hole on the left side is opposite to the electrode of the physiological multi-parameter collecting device, and after the alignment is accurate, the segmented release layer on the left side of the easy-to-peel line is pulled away;
  • the sensor through hole is opposite to the sensor of the physiological multi-parameter acquisition device.
  • the intermediate segmented release layer is pulled away; then the right electrode through hole is opposite to the electrode of the physiological multi-parameter acquisition device.
  • the segmented release layer on the right side of the void line is pulled away.
  • the self-adhesive sheet can be affixed to the collection device at one time, and the through holes are aligned with the electrodes or the sensors to avoid the problem of repeated alignment.
  • the second type of release layer 431 is pulled away and attached to human skin.
  • the easily tearable line or the void line on the segmented release layer may be one or more, which is disposed between the corresponding through hole and the through hole.
  • the easy-to-break line or the void line can be segmented correspondingly between all the through holes and the through holes, so that each through hole can be accurately aligned; or the partial through hole and the through hole can be segmented. Settings.
  • Figure 22 shows the ECG collection using the electrode as the metal electrode.
  • the ECG signal obtained by the device for ECG acquisition does not adhere to the disposable self-adhesive sheet as described above on the metal electrode. Due to slight stretching, the skin may stretch or contract, but the flexibility and self-adhesiveness of the metal are poor, the stretchability is limited, the electrode and the skin contact surface move relatively, and the skin is not tightly attached, which may cause severely distorted ECG signals. .
  • FIG. 23 is an electrocardiogram signal obtained by electrocardiographic collection of an electrocardiogram collecting device using an electrode as a metal electrode, and a disposable self-adhesive sheet is attached to the metal electrode, and the bottom surface of the electrode through hole is hollowed out, and the conductive medium is not pasted. . It can be seen from the figure that the adhesion of the electrode to the skin by the adhesion of the self-adhesive sheet, the quality of the signal acquisition is improved, and the signal waveform is not distorted except for the baseline drift and slight electromyography noise. Fig.
  • FIG. 24 is an electrocardiographic signal obtained by electrocardiographic collection of an electrocardiogram collecting device using an electrode as a common wet electrode, and a disposable self-adhesive sheet as described above is not adhered to the common wet electrode.
  • the ECG signal contains significant myoelectric noise (burr). Since the ordinary wet electrode is two electrodes which are additionally taken out from the electrocardiographic collecting device, it is directly attached to the human skin, and it cannot be pasted with the patch, so there is no comparative test.
  • FIG. 25 is an electrocardiogram signal obtained by electrocardiographic collection of an electrocardiogram collecting device using an electrode as a conductive silica gel electrode, and the disposable conductive self-adhesive sheet as described above is not adhered to the common conductive silica gel electrode.
  • FIG. 26 is an electrocardiogram signal obtained by electrocardiographic collection of an electrocardiogram collecting device using an electrode as a conductive silica gel electrode, and the common conductive silica electrode is adhered thereto.
  • a self-adhesive sheet, the conductive medium is adhered to the bottom surface of the electrode through hole of the self-adhesive sheet, and the conductive medium is a solid gel or a conductive silica gel.
  • the signal has almost no waveform of myoelectric noise and morphological abnormality, and the signal quality is significantly improved.
  • a self-adhesive sheet as shown in FIG. 20 is pasted on the electrocardiographic collecting device, and a silicon gel with a grain is adhered to the electrode through-hole of the patch, and the ECG acquisition effect at this time is as shown in FIG. similar.
  • the self-adhesive sheet is pasted on the electrocardiographic collecting device, and the conductive through-silicone is adhered to the electrode through-hole of the patch, and the liquid conductive medium is filled in the grain before the ECG signal is collected. If the conductive silica gel is applied with mineral water, the electrocardiographic acquisition effect at this time is similar to that of FIG.
  • the self-adhesive sheet is pasted on the electrocardiographic collecting device, and two conductive media are adhered to the electrode through-hole of the patch, and one layer is conductive silica gel and pasted on the double-sided tape; It is a silicone gel with one side bonded to the conductive silicone layer and the other side in contact with human skin. The ECG acquisition effect at this time is similar to that of FIG.

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Abstract

L'invention concerne un dispositif de type patch destiné à être utilisé pour la collecte et la surveillance de paramètres multiples physiologiques, comprenant : un boîtier plat (1) ayant un matériau souple, qui est approprié pour être fixé à la peau d'un corps humain, un côté avant du boîtier (1) pouvant adhérer sur la peau du corps humain au moyen d'un adhésif ; le côté avant et/ou un côté arrière est pourvu d'un capteur d'onde d'impulsion (11), qui est intégré au boîtier (1) au moyen d'un moulage par injection de silicone liquide ou d'un moulage par compression de silicone solide ; un module de circuit, qui est utilisé pour traiter des données de paramètres physiologiques collectés, est installé de manière étanche à l'intérieur du boîtier (1), et le module de circuit est connecté au capteur d'onde d'impulsion (11). Le dispositif est facile à utiliser, l'électrode peut être recyclée pour économiser des coûts, et porter le dispositif pendant une longue durée ne provoquera pas d'inconfort pour la peau, tandis que le dispositif présente de bonnes performances de scellement et d'étanchéité à l'eau, le temps d'utilisation continu est long, et la collecte de données est plus complète.
PCT/CN2017/101441 2017-02-22 2017-09-12 Dispositif de type patch destiné à être utilisé pour la collecte et la surveillance de paramètres multiples physiologiques WO2018153060A1 (fr)

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