Saliva acquisition electric signal monitoring system and intelligent acquisition method

Abstract

The invention belongs to the technical field of saliva detection, and particularly relates to a saliva acquisition electric signal monitoring system and an intelligent acquisition method.The saliva acquisition electric signal monitoring system comprises a saliva quantitative acquisition unit and a central processing unit which are electrically connected with each other; the saliva quantitative collection unit comprises a flow control module, an impedance detection module and a volume detection module; the central processing unit generates a starting instruction according to an initial signal of the impedance detection module and / or the volume detection module and sends the starting instruction to the flow control module to start collection; in the acquisition process, receiving an impedance signal and a volume signal in real time; and calculating a quality parameter for evaluating the quality of the saliva based on the impedance signal, and dynamically generating a flow regulation instruction to the flow control module based on the volume signal and the quality parameter, so that the saliva collection process meets the preset volume and quality requirements. Accurate control of the collection amount, effective improvement of the electric signal collection quality and intelligence and self-adaption of the collection process can be achieved, and the requirement for high-precision medical detection is met.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of saliva detection, and particularly relates to a saliva collection electric signal monitoring system and an intelligent collection method. BACKGROUND

[0002] In the fields of medical diagnosis, health monitoring, etc., saliva as a non-invasive and easily accessible biological sample is increasingly valued. Consequently, there is an urgent need for precise and intelligent saliva collection and real-time analysis technology. Some automatic saliva collection schemes have appeared in the prior art.

[0003] The prior art (CN109219396A) discloses a saliva detection device and system for animals. The technology sets a detection device on a water supply facility, and when an animal contacts the water inlet, the saliva is sucked in by a peristaltic pump, and after the collection reaches a set amount, subsequent disease detection is performed. Although this scheme realizes the automation of saliva collection, its core is a passive collection based on a fixed threshold. Specifically, the device only starts the pump when the animal is detected to contact, and stops when the collection amount reaches a preset fixed volume. This mode has the following inherent defects: 1. Unable to adapt to individual differences in saliva properties: The saliva viscosity and secretion rate of different individuals or the same individual in different physiological states differ greatly. For high-viscosity saliva, fixed-volume collection may result in insufficient effective sample amount due to flow channel residues; while for low-viscosity saliva, air bubbles may be introduced due to the fast flow rate, affecting detection accuracy. The prior art lacks real-time sensing and adaptive adjustment capabilities for saliva physical properties.

[0004] 2. No guarantee of electric signal collection quality: The prior art focuses on biochemical detection (such as disease detection) after collection, and does not involve real-time electric signal (such as impedance) monitoring of saliva itself during collection. As an electrolyte, saliva is susceptible to noise interference, and the electrode contact impedance fluctuates. The existing scheme lacks high-fidelity signal collection circuits and dynamic signal processing algorithms, and cannot provide reliable data basis for real-time quality evaluation.

[0005] 3. System response lag and low intelligence level: The entire collection process is open-loop, and the system can only passively execute "start or stop" instructions, and cannot make predictions and actively adjust based on real-time data (such as flow rate changes, signal quality) during collection. This easily leads to over-collection or insufficient collection, and cannot complete real-time quality evaluation at the electric signal level at the same time of collection, resulting in low efficiency.

[0006] Therefore, there is an urgent need for a new technical scheme to solve the above problems. SUMMARY

[0007] One of the purposes of the present application is to provide a saliva collection electric signal monitoring system to solve the problem that the prior art cannot guarantee the quality of saliva samples and the reliability of electric signals in real time during the collection process.

[0008] In order to achieve the above-mentioned purpose, the present application adopts the following technical solutions: A saliva collection electric signal monitoring system, comprising a saliva quantitative collection unit and a central processing unit electrically connected with the saliva quantitative collection unit. The saliva quantitative collection unit comprises: A flow control module for controlling the flow and flow rate of saliva in the saliva pipeline. An impedance detection module arranged on the saliva pipeline for real-time collection of impedance signals of saliva. A volume detection module arranged on the saliva pipeline for real-time collection of volume signals of the collected saliva. The central processing unit is configured to: According to the initial signals of the impedance detection module and / or the volume detection module, it is determined that saliva starts to flow in, and a start instruction is generated to the flow control module to start collection. During the collection process, the impedance signals of the impedance detection module and the volume signals of the volume detection module are received in real time. Based on the impedance signals, a quality parameter for evaluating the quality of saliva is calculated, and based on the volume signals and the quality parameter, a flow adjustment instruction is dynamically generated to the flow control module to make the saliva collection process meet the preset capacity and quality requirements.

[0009] Preferably, the central processing unit is configured to perform the following adaptive control cycle: During the collection process, a dynamic quality parameter is calculated in real time, and the value of the parameter is determined by the real-time impedance signals and the real-time volume signals. Based on the numerical value of the dynamic quality parameter and its change trend, a target flow rate change rate instruction is generated in real time. The target flow rate change rate instruction is sent to the flow control module to control the adjustment of the flow rate of saliva. Wherein, the central processing unit is configured to: when the dynamic quality parameter indicates that the quality of saliva is decreasing, the target flow rate change rate instruction is negative to reduce the flow rate or start the stop program; when the dynamic quality parameter indicates that the quality of saliva is excellent and the collection amount has not reached the target, the target flow rate change rate instruction is positive to maintain or improve the collection flow rate.

[0010] Preferably, the algorithm formula of the quality parameter Q is: ; wherein Q is a dynamic mass parameter; j t is the instantaneous flow rate change acceleration at the current time , is a volume function varying with time; is a preset acceleration threshold value; is the phase difference between the impedance signal and the flow rate signal at the current time; is a preset reference phase difference; is a preset phase difference tolerance threshold value; is a real-time dynamic pattern confidence, wherein e is the base of the natural logarithm, is a scaling coefficient, is the shape distance between the trajectory curve of the current collected flow rate and impedance and the pre-stored standard secretion dynamic model curve, calculated by a dynamic time warping algorithm.

[0011] Preferably, the target flow rate change rate is calculated by the following algorithm formula: ; wherein, is the target flow rate change rate; is a preset system inertia coefficient; is the mass parameter at the current time; is a confidence gain coefficient; is a mass trend confidence value, which is calculated based on the historical data of the mass parameter Q, and the calculation formula is wherein, is a Sigmoid function, is an amplification coefficient, and are short-term and long-term moving average operators, respectively.

[0012] Preferably, the impedance detection module comprises a high-fidelity electrical signal acquisition circuit, which comprises, in sequence and electrically connected: a differential amplification unit, configured to receive and amplify the original differential analog signal of the impedance probe and suppress common-mode noise; an analog-to-digital converter unit, having an input end electrically connected to an output end of the differential amplification unit, and configured to convert the amplified analog signal into a digital signal; a reference voltage source unit, having an output end electrically connected to a reference voltage input end of the analog-to-digital converter unit, and configured to provide a reference voltage for the analog-to-digital conversion; wherein the voltage drift coefficient of the reference voltage source unit is not greater than 5ppm / ℃.

[0013] Preferably, an environmental parameter compensation module is further included for collecting environmental temperature, humidity and air pressure data and transmitting the data to the central processing unit, the environmental parameter compensation module being electrically connected to the central processing unit, and the central processing unit being further configured to dynamically correct the detection results of the impedance signal and the volume signal by using the environmental parameter data.

[0014] Preferably, a wireless data transmission module and a cloud analysis platform are further included, the wireless data transmission module being electrically connected to the central processing unit and the cloud analysis platform, respectively.

[0015] Preferably, an optical turbidity sensor and a pH value sensor are further included, the optical turbidity sensor and the pH value sensor both being electrically connected to the central processing unit, and the central processing unit being further configured to comprehensively use the optical turbidity signal and the pH value signal to assist in evaluating the saliva quality.

[0016] Preferably, an infrared heart rate sensor and a galvanic skin response sensor are further included, the infrared heart rate sensor and the galvanic skin response sensor both being electrically connected to the central processing unit, and the central processing unit being further configured to use the heart rate signal and the galvanic skin response signal to monitor the state of the saliva collection process.

[0017] The second object of the present application is to provide a saliva electrical signal intelligent collection method, which is executed by using the saliva collection electrical signal monitoring system as described above, and the method comprises the following steps: S1, collecting saliva key data in at least 15 collection cycles by using the impedance detection module and the volume detection module, the key data at least including a saliva flow rate signal, an impedance amplitude signal and an impedance phase signal, and constructing a multi-dimensional feature matrix based on the key data; S2, inputting the multi-dimensional feature matrix into a GRU neural network model pre-trained in the central processing unit, pre-judging whether the saliva collection amount reaches a preset target threshold by using the GRU neural network model in advance by 30 ms, and synchronously outputting a viscosity adaptation level; S3, monitoring the electrical signal noise intensity in the collection process in real time by using the impedance detection module; dynamically adjusting the filter scale of the wavelet filtering algorithm based on the noise intensity, wherein the filter scale and the noise intensity are in a positive correlation; when the noise intensity is lower than a first preset threshold, a first filter scale matched therewith is used to retain signal details; when the noise intensity is higher than a second preset threshold, a second filter scale matched therewith is used to enhance the filtering strength, so as to improve the signal-to-noise ratio; the second filter scale is greater than the first filter scale; and the second preset threshold is greater than the first preset threshold. S4, the central processing unit sends a pre-collection instruction to the flow control module based on the prediction result of the GRU neural network model; during the collection process, the volume data and the electrical signal data fed back by the volume detection module and the impedance detection module are received in real time; if the volume data indicates that the collection amount reaches 90-95% of the target threshold, an instruction is sent to the flow control module to reduce the rotating speed of the collection pump; if the electrical signal data indicates that the noise intensity exceeds the third preset threshold in a collection cycle, the filtering parameter in S3 is dynamically adjusted, and the reference voltage source unit is triggered to calibrate the reference voltage; S5, when the volume detection module returns a collection completion signal, the effectiveness of the collected electrical signal is automatically analyzed; if the analysis result is invalid, the flow control module is triggered to perform secondary collection.

[0018] The beneficial effects of the present application are that the present application comprises a saliva quantitative collection unit and a central processing unit electrically connected with the saliva quantitative collection unit; the saliva quantitative collection unit comprises: a flow control module for controlling the flow and flow rate of saliva in a saliva pipeline; an impedance detection module arranged on the saliva pipeline for real-time collection of impedance signals of saliva; a volume detection module arranged on the saliva pipeline for real-time collection of volume signals of the collected saliva; the central processing unit is configured to: according to the initial signals of the impedance detection module and / or the volume detection module, determine that saliva starts to flow in, and generate a start instruction to the flow control module to start collection; during the collection process, the impedance signals of the impedance detection module and the volume signals of the volume detection module are received in real time; based on the impedance signals, a quality parameter for evaluating the quality of saliva is calculated, and based on the volume signals and the quality parameter, a flow regulation instruction is dynamically generated to the flow control module, so that the saliva collection process meets the preset capacity and quality requirements. The present application can realize precise control of the collection amount, effective improvement of the electrical signal collection quality, and intelligentization and self-adaptation of the collection process, so as to meet the needs of high-precision medical detection. BRIEF DESCRIPTION OF DRAWINGS

[0019] The features, advantages, and technical effects of the exemplary embodiments of the present application will be described below with reference to the accompanying drawings. Figures 1-3

[0020] Figure 1 A modular schematic diagram of a saliva collection electrical signal monitoring system according to an embodiment of the present application.

[0021] Figure 2 A modular schematic diagram of an impedance detection module according to an embodiment of the present application.

[0022] Figure 3 A flowchart of a saliva electrical signal intelligent collection method according to an embodiment of the present application.

[0023] ​Wherein: 1, saliva quantitative collection unit; 11, flow control module; 12, impedance detection module; 121, differential amplification unit; 122, analog-to-digital converter unit; 123, reference voltage source unit; 13, volume detection module; 2, central processing unit; 3, environmental parameter compensation module; 4, wireless data transmission module; 5, cloud analysis platform; 6, optical turbidity sensor; 7, pH sensor; 8, infrared heart rate sensor; 9, galvanic skin response sensor. DETAILED DESCRIPTION

[0024] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the present application; the present specification and claims can be referred to in connection with the above description of drawings and the detailed description of the application, the terms "comprising", "having" and "including" and any variations thereof are intended to cover a non-exclusive inclusion.

[0025] Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are they necessarily mutually exclusive of one another. Those skilled in the art will recognize that the embodiments described herein can be combined with one another.

[0026] In the description of the embodiments of the application, the term "and / or" only describes an association relationship for associated objects, which means that there can be three relationships, for example, A and / or B, which means that A exists alone, A and B exist together, and multiple cases exist alone. In addition, the character " / " in this paper generally represents an "or" relationship between the front and rear associated objects.

[0027] In the description of the embodiments of the application, unless otherwise explicitly specified and limited, the technical terms "mounting", "connection", "connecting", "fixing" and the like should be understood in a broad sense, for example, it can be fixedly connected, or it can be detachably connected, or it can be integrated; it can be mechanical connection, or it can be electrical connection; it can be directly connected, or it can be indirectly connected through an intermediate medium; it can be the internal communication of two elements or the interaction relationship between two elements. For those skilled in the art, the specific meaning of the above terms in the embodiments of the application can be understood according to the specific circumstances.

[0028] The application will be further described in detail below in combination with the accompanying drawings and specific embodiments, but not as a limitation to the application. Figures 1-3 The application will be further described in detail below in combination with the accompanying drawings and specific embodiments, but not as a limitation to the application.

[0029] As Figures 1-2As shown in the embodiment of the present application, a saliva collection electric signal monitoring system comprises a saliva quantitative collection unit 1 and a central processing unit 2 electrically connected with the saliva quantitative collection unit 1. The saliva quantitative collection unit 1 comprises: a flow control module 11 electrically connected with the central processing unit 2, for controlling the flow and flow rate of saliva in the saliva pipeline; an impedance detection module 12 electrically connected with the central processing unit 2, arranged on the saliva pipeline, for collecting the impedance signal of saliva in real time; a volume detection module 13 electrically connected with the central processing unit 2, arranged on the saliva pipeline, for collecting the volume signal of collected saliva in real time; The central processing unit 2 is configured to: judge that saliva starts to flow in according to the initial signal of the impedance detection module 12 and / or the volume detection module 13, and generate a start instruction to the flow control module 11 to start collection; in the collection process, the impedance signal of the impedance detection module 12 and the volume signal of the volume detection module 13 are collected in real time; a quality parameter for evaluating the quality of saliva is calculated based on the impedance signal, and a flow adjustment instruction is dynamically generated to the flow control module 11 based on the volume signal and the quality parameter, so that the saliva collection process meets the preset capacity and quality requirements.

[0030] In some embodiments, the central processing unit 2 comprises a micro processing unit (MCU) for storing and executing algorithms for driving various components. Preferably, the micro processing unit can be an STM32L4 chip, which has low power consumption in acquisition and standby states, and is suitable for portable detection scenarios.

[0031] In some embodiments, the flow control module 11 can be a micro peristaltic pump, the impedance detection module 12 can be an impedance probe, and the volume detection module 13 can be a volume sensor.

[0032] In some embodiments, the central processing unit 2 is configured to perform the following adaptive control cycle: in the collection process, a dynamic quality parameter is calculated in real time, and the value of the parameter is determined by the real-time impedance signal and the real-time volume signal; based on the value of the dynamic quality parameter and its change trend, a target flow rate change rate instruction is generated in real time; the target flow rate change rate instruction is sent to the flow control module 11 to control the adjustment of the saliva flow rate; Wherein, the central processing unit 2 is configured to: when the dynamic quality parameter indicates that the quality of the saliva is reduced, the target flow rate change rate instruction is negative, so as to reduce the flow rate or start the stop program; when the dynamic quality parameter indicates that the quality of the saliva is excellent and the collection amount does not reach the target, the target flow rate change rate instruction is positive, so as to maintain or improve the collection flow rate.

[0033] Wherein, in some embodiments, the algorithm formula of the quality parameter Q is: ; Wherein, Q is the dynamic quality parameter, unitless, and the value range is 0-1; j t is the instantaneous flow rate change acceleration at the current moment, unit: μL / s 3 ; , is the volume function changing over time, unit: μL; is the instantaneous saliva flow rate, unit: μL / s; is the preset acceleration threshold, unit: μL / s 3 ; is the phase difference between the impedance signal and the flow rate signal at the current moment, unit: rad; is the preset reference phase difference, unit: rad; is the preset phase difference tolerance threshold, unit: rad; is the real-time dynamic mode confidence, unitless, and the value range is 0-1, Wherein, e is the base number of natural logarithm; is the scaling coefficient, unitless, and is an adjustable parameter greater than zero, which is used to adjust the dynamic time warping distance of the dynamic mode confidence ; is the shape distance between the current collected flow rate and impedance trajectory curve and the pre-stored standard secretion dynamic model curve, calculated by the dynamic time warping algorithm, unitless, and the DTW algorithm calculates a distance, and the numerical value itself is a scalar without direct physical unit.

[0034] Specifically, in some embodiments, the algorithm formula of the target flow rate change rate is: ; Wherein, is the target flow rate change rate, unit: μL / s 2 ; The preset system inertia coefficient, in μL / s 2 ; This is the mass parameter at the current moment, without units; This is the confidence gain coefficient, in seconds. The confidence level for the quality trend is calculated based on historical data of the quality parameter Q, using the following formula: No unit, among which, This is the Sigmoid function, which is unitless and takes values ​​from 0 to 1. This is the magnification factor, which has no unit and ranges from 0 to 1. Quality parameters The short-term moving average, Quality parameters The long-term moving average is calculated using a window length of 3 to 10 data collection periods. The long-term moving average is calculated using a window length that is 2 to 5 times the length of the short-term window. For example, if the short-term moving average window is 5 collection periods, which corresponds to approximately 2.5 seconds, then the long-term moving average window is 15 collection periods, which corresponds to approximately 7.5 seconds.

[0035] In some embodiments, the impedance detection module 12 includes a high-fidelity electrical signal acquisition circuit, which comprises the following components connected in sequence: The differential amplifier unit 121 is used to receive and amplify the original differential analog signal from the impedance probe and suppress common-mode noise; The analog-to-digital converter unit 122 has its input terminal electrically connected to the output terminal of the differential amplifier unit 121, and is used to convert the amplified analog signal into a digital signal. The reference voltage source unit 123 is electrically connected to the reference voltage input terminal of the analog-to-digital converter unit 122 to provide a reference voltage for analog-to-digital conversion. The voltage drift coefficient of the reference voltage source unit 123 is no greater than 5ppm / ℃. This setting can ensure that the electrical signal acquisition error is small. The analog-to-digital converter unit 122 can adopt a 16-bit ADC sampling module.

[0036] In some embodiments, the environmental parameter compensation module 3 is electrically connected to the central processing unit 2, and is configured to collect environmental temperature, humidity and air pressure data, and transmit the environmental parameter data to the central processing unit 2. The central processing unit 2 is further configured to dynamically correct the detection results of the impedance signal and the volume signal by using the environmental parameter data. The environmental parameter compensation module 3 includes a temperature sensor, a humidity sensor and an air pressure sensor. The temperature sensor is configured to detect the temperature of the test environment, generate an environmental temperature signal, and transmit the environmental temperature signal to the central processing unit 2. The humidity sensor is configured to detect the humidity of the test environment, generate an environmental humidity signal, and transmit the environmental humidity signal to the central processing unit 2. The air pressure sensor is configured to detect the air pressure of the test environment, generate an environmental air pressure signal, and transmit the environmental air pressure signal to the central processing unit 2. The temperature sensor, the humidity sensor and the air pressure sensor are all electrically connected to the central processing unit 2.

[0037] In some embodiments, the saliva collection device further comprises a wireless data transmission module 4 and a cloud analysis platform 5. The wireless data transmission module 4 is electrically connected to the central processing unit 2 and the cloud analysis platform 5, respectively. The central processing unit 2 can transmit the analysis data to the cloud analysis platform 5 for further analysis, so as to obtain more accurate analysis results. Specifically, the wireless data transmission module 4 can be a 4G communication module or a 5G communication module.

[0038] In some embodiments, the saliva collection device further comprises an optical turbidity sensor 6 and a pH value sensor 7. The optical turbidity sensor 6 and the pH value sensor 7 are both electrically connected to the central processing unit 2. The optical turbidity sensor 6 is configured to detect the turbidity of the saliva, generate a turbidity signal, and transmit the optical turbidity signal to the central processing unit 2. The pH value sensor 7 is configured to detect the pH value of the saliva, generate a pH value signal, and transmit the pH value signal to the central processing unit 2. The central processing unit 2 is further configured to comprehensively analyze the optical turbidity signal and the pH value signal to assist in evaluating the quality of the saliva.

[0039] In some embodiments, the saliva collection device further comprises an infrared heart rate sensor 8 and a galvanic skin response sensor 9. The infrared heart rate sensor 8 and the galvanic skin response sensor 9 are both electrically connected to the central processing unit 2. The infrared heart rate sensor 8 is configured to detect the heart rate of the testee, generate a heart rate signal, and transmit the heart rate signal to the central processing unit 2. The galvanic skin response sensor 9 is configured to detect the skin of the testee, generate a galvanic skin response signal, and transmit the galvanic skin response signal to the central processing unit 2. The central processing unit 2 is further configured to use the heart rate signal and the galvanic skin response signal to monitor the state of the saliva collection process.

[0040] As Figure 3As shown, in another embodiment of the present invention, a method for intelligent acquisition of saliva electrical signals is implemented using the saliva acquisition electrical signal monitoring system described above, and the method includes the following steps: S1. Through the impedance detection module 12 and the volume detection module 13, at least 15 collection cycles of key saliva data are continuously collected. The key data includes at least the saliva flow rate signal, impedance amplitude signal, and impedance phase signal. The central processing unit 2 processes the volume signal detected by the volume detection module 13. Input to Algorithm In this process, the instantaneous flow velocity change acceleration at the current moment can be obtained; the core of the impedance detection module 12 is a miniature impedance analysis circuit, which, when working, first generates a frequency f by a signal generator. z A sinusoidal AC excitation voltage is applied to the excitation electrode. The response current flowing through the saliva is converted into a voltage signal by the detection circuit, and then processed by an orthogonal demodulator. The output signal consists of the real part A and the imaginary part B. Based on the real part A and the imaginary part B, the central processing unit 2 calculates the impedance amplitude signal using the following formula: Where the impedance amplitude |Z| is in ohms, k is the system gain constant in volt-ohms, and A and B are in volts; impedance phase signal: A multi-dimensional feature matrix is ​​constructed based on key data. The impedance phase φ is unitless, and its value is expressed in radians. S2. Input the multi-dimensional feature matrix into the pre-trained GRU neural network model in the central processing unit 2. The GRU neural network model predicts whether the saliva collection volume has reached the preset target threshold 30ms in advance and outputs the viscosity adaptation level simultaneously. S3. The impedance detection module 12 monitors the electrical signal noise intensity in real time during the acquisition process; the filtering scale of the wavelet filtering algorithm is dynamically adjusted based on the noise intensity, wherein the filtering scale is positively correlated with the noise intensity; when the noise intensity is lower than the first preset threshold, a first filtering scale matching it is used to preserve signal details; when the noise intensity is higher than the second preset threshold, a second filtering scale matching it is used to enhance the filtering intensity, thereby improving the signal-to-noise ratio; the second filtering scale is greater than the first filtering scale; the second preset threshold is greater than the first preset threshold. S4, the central processing unit 2 sends a pre-collection instruction to the flow control module 11 based on the prediction result of the GRU neural network model, and the flow control module 11 starts to control the saliva flow; during the collection process, the volume data and electrical signal data fed back by the volume detection module 13 and the impedance detection module 12 are received in real time; if the volume data indicates that the collection amount reaches 90-95% of the target threshold, an instruction is sent to the flow control module 11 to reduce the rotating speed of the collection pump; if the electrical signal data indicates that the noise intensity exceeds the third preset threshold within one collection cycle, the filtering parameters in S3 are dynamically adjusted, and the reference voltage source unit 123 is triggered to calibrate the reference voltage; S5, when the volume detection module 13 returns a collection completion signal, the effectiveness of the collected electrical signal is automatically analyzed; if the analysis result is invalid, the flow control module 11 is triggered to perform secondary collection.

[0041] In step S3, the central processing unit 2 performs real-time frequency domain analysis on the collected raw impedance signal . Specifically, through fast Fourier transform (FFT) or a set of digital bandpass filters, the signal is separated into two main frequency bands: effective signal frequency band : This frequency band is concentrated in the main energy distribution interval of the impedance signal, and contains the effective information of the saliva impedance; noise characteristic frequency band : This frequency band is located outside the effective signal frequency band, and is usually selected as the power frequency interference frequency point (such as 50Hz or 60Hz), its harmonic frequency point or the high frequency band with weak signal energy (such as the frequency band greater than 20kHz), which mainly contains noise components; In a continuous short time window (for example, each window length is 10-50 sampling points), the signal energy of the noise characteristic frequency band is calculated as the measurement of noise intensity. The calculation formula is as follows: ; Wherein, the real-time noise intensity is the signal average power of the noise characteristic frequency band in the current time window, and the unit is volt squared; is the frequency domain representation of the signal in the current time window, and the unit is volt; is the frequency point number in the noise characteristic frequency band , without unit.

[0042] In step S5, the effectiveness analysis process is as follows: check whether the final dynamic quality parameter is greater than 0.6, if not, it is determined to be invalid; Check signal coefficient of variation If not, determine invalid; Check average signal-to-noise ratio If not, determine invalid; If the above three checks are passed, determine that the sample electrical signal is valid, and prompt the collection success. Otherwise, determine invalid and prompt "sample quality is poor, please re-collect" through the user interface, and automatically trigger the secondary collection process.

[0043] Calculate the standard deviation σz of the impedance value throughout the collection process and its coefficient of variation , Wherein, is the mean value of the whole impedance, the coefficient of variation Unitless, the standard deviation σz of the whole impedance value and the mean value of the whole impedance The unit of is ohm, z is impedance symbol, if the coefficient of variation exceeds the preset stability threshold (default setting is 0.15), it indicates that the signal fluctuation is too large and the stability is unqualified.

[0044] Based on the noise intensity monitored in real time during the collection process, the average signal-to-noise ratio throughout the whole process is calculated, and the calculation formula is as follows: ; Wherein, is the average signal-to-noise ratio, if the average signal-to-noise ratio is lower than the preset minimum requirement threshold (default setting is 15 dB), it is determined that the signal quality is unacceptable; Average signal-to-noise ratio The unit of is dB; is the average power of the effective signal band, the unit is watt; is the average power of the noise characteristic band, the unit is watt.

[0045] Obviously, the present application comprises a saliva quantitative collection unit and a central processing unit electrically connected with the saliva quantitative collection unit; the saliva quantitative collection unit comprises: a flow control module for controlling the flow and flow rate of saliva in a saliva pipeline; an impedance detection module arranged on the saliva pipeline for collecting impedance signals of saliva in real time; a volume detection module arranged on the saliva pipeline for collecting volume signals of collected saliva in real time; the central processing unit is configured to: according to initial signals of the impedance detection module and / or the volume detection module, judge that saliva starts to flow in, and generate a start instruction to the flow control module to start collection; in the collection process, impedance signals of the impedance detection module and volume signals of the volume detection module are received in real time; a quality parameter for evaluating saliva quality is calculated based on the impedance signals, and based on the volume signals and the quality parameter, a flow adjustment instruction is dynamically generated to the flow control module, so that the saliva collection process meets the preset capacity and quality requirements. The present application can realize accurate control of the collection amount, effective improvement of the quality of electric signal collection, and intelligentization and self-adaptation of the collection process, to meet the needs of high-precision medical detection; in different saliva viscosity and electrolyte-containing scenarios, the collection amount error is small, the electric signal signal-to-noise ratio is high, and the response delay is short; the secondary collection rate is low, and the sample utilization rate is high; in the portable nucleic acid detection scene, the collection, electric signal analysis and result preliminary determination can be integrated, the total time consumption is short, and the saliva characteristics of most users are adapted, breaking through the limitations of traditional fixed parameter post-detection, and taking into account the accuracy, anti-interference and portability.

[0046] Based on the disclosure and teachings of the above specification, those skilled in the art can also make changes and modifications to the above embodiments. Therefore, the invention is not limited to the above specific embodiments, and any obvious improvements, replacements or modifications made by those skilled in the art based on the invention shall fall within the protection scope of the invention. In addition, although some specific terms are used in the specification, these terms are only for convenience of description and do not constitute any limitation on the invention.

Claims

1. A saliva collection electrical signal monitoring system, characterized in that, It includes a saliva quantitative collection unit (1) and a central processing unit (2) electrically connected to the saliva quantitative collection unit (1). The saliva quantitative collection unit (1) includes: The flow control module (11) is used to control the flow and velocity of saliva in the saliva tubing; Impedance detection module (12) is installed on the saliva tube to collect the impedance signal of saliva in real time; A volume detection module (13) is installed on the saliva tube to collect the volume signal of the collected saliva in real time. The central processing unit (2) is configured as follows: Based on the initial signals from the impedance detection module (12) and / or the volume detection module (13), it is determined that saliva has started to flow in, and a start command is generated to the flow control module (11) to start the collection. During the acquisition process, the impedance signal of the impedance detection module (12) and the volume signal of the volume detection module (13) are received in real time. Based on the impedance signal, a quality parameter for evaluating saliva quality is calculated, and based on the volume signal and the quality parameter, a flow rate adjustment command is dynamically generated and sent to the flow control module (11) so that the saliva collection process meets the preset capacity and quality requirements.

2. The saliva collection electrical signal monitoring system as described in claim 1, characterized in that, The central processing unit (2) is configured to execute the following adaptive control loop: During the acquisition process, a dynamic quality parameter is calculated in real time. The value of this parameter is determined by the real-time impedance signal and the real-time volume signal. Based on the values ​​and trends of the dynamic quality parameters, a target flow rate change command is generated in real time. The target flow rate change command is sent to the flow control module (11) to control it to adjust the saliva flow rate; The central processing unit (2) is configured to: when the dynamic quality parameter indicates a decrease in saliva quality, the target flow rate change command is negative to reduce the flow rate or start a stop procedure; when the dynamic quality parameter indicates excellent saliva quality and the collection volume does not reach the target, the target flow rate change command is positive to maintain or increase the collection flow rate.

3. The saliva collection electrical signal monitoring system as described in claim 2, characterized in that, The algorithm formula for the mass parameter Q is: ； Where Q is the dynamic quality parameter; j t The instantaneous velocity change acceleration at the current moment , It is a volume function that varies with time; The preset acceleration threshold; This represents the phase difference between the impedance signal and the flow velocity signal at the current moment. The preset reference phase difference; This is the preset phase difference tolerance threshold; For real-time dynamic mode confidence, Where e is the base of the natural logarithm, This is the scaling factor. The shape distance between the trajectory curve of the currently collected flow velocity and impedance and the pre-stored standard secretion dynamic model curve is calculated using a dynamic time warping algorithm.

4. The saliva collection electrical signal monitoring system as described in claim 3, characterized in that, The target flow rate change The algorithm formula is: ； in, The target flow rate change rate; The preset system inertia coefficient; The quality parameters at the current moment; This is the confidence gain coefficient; The confidence level for the quality trend is calculated based on historical data of the quality parameter Q, using the following formula: ,in, For the Sigmoid function, This is the magnification factor. and These are the short-term and long-term moving average operators, respectively.

5. The saliva collection electrical signal monitoring system as described in claim 1, characterized in that, The impedance detection module (12) includes a high-fidelity electrical signal acquisition circuit, which comprises the following components connected in sequence: The differential amplifier unit (121) is used to receive and amplify the original differential analog signal from the impedance probe and suppress common-mode noise; The analog-to-digital converter unit (122) has its input terminal electrically connected to the output terminal of the differential amplifier unit (121) and is used to convert the amplified analog signal into a digital signal. The reference voltage source unit (123) is electrically connected to the reference voltage input terminal of the analog-to-digital converter unit (122) to provide a reference voltage for analog-to-digital conversion; wherein the voltage drift coefficient of the reference voltage source unit (123) is not greater than 5ppm / ℃.

6. The saliva collection electrical signal monitoring system as described in claim 1, characterized in that, It also includes an environmental parameter compensation module (3) for collecting ambient temperature, humidity and air pressure data and transmitting them to the central processing unit (2). The environmental parameter compensation module (3) is electrically connected to the central processing unit (2). The central processing unit (2) is also configured to dynamically correct the detection results of the impedance signal and volume signal using the environmental parameter data.

7. The saliva collection electrical signal monitoring system as described in claim 1, characterized in that, It also includes a wireless data transmission module (4) and a cloud analysis platform (5), wherein the wireless data transmission module (4) is electrically connected to the central processing unit (2) and the cloud analysis platform (5), respectively.

8. The saliva collection electrical signal monitoring system as described in claim 1, characterized in that, It also includes an optical turbidity sensor (6) and a pH sensor (7), both of which are electrically connected to the central processing unit (2), which is further configured to integrate the optical turbidity signal and the pH signal to assist in assessing saliva quality.

9. The saliva collection electrical signal monitoring system as described in claim 1, characterized in that, It also includes an infrared heart rate sensor (8) and a skin conductance sensor (9), both of which are electrically connected to the central processing unit (2). The central processing unit (2) is also configured to monitor the status of the saliva collection process using the heart rate signal and the skin conductance signal.

10. A method for intelligent acquisition of salivary electrical signals, characterized in that, The method is performed using the saliva collection electrical signal monitoring system as described in any one of claims 1 to 9, and includes the following steps: S1. Through the impedance detection module (12) and the volume detection module (13), at least 15 collection cycles of key saliva data are continuously collected. The key data includes at least saliva flow rate signal, impedance amplitude signal and impedance phase signal. A multi-dimensional feature matrix is ​​constructed based on the key data. S2. Input the multi-dimensional feature matrix into the pre-trained GRU neural network model in the central processing unit (2). The GRU neural network model predicts whether the saliva collection volume has reached the preset target threshold 30ms in advance and outputs the viscosity adaptation level simultaneously. S3. The impedance detection module (12) monitors the electrical signal noise intensity during the acquisition process in real time; the filtering scale of the wavelet filtering algorithm is dynamically adjusted based on the noise intensity, wherein the filtering scale is positively correlated with the noise intensity; when the noise intensity is lower than a first preset threshold, a first filtering scale matching it is used to preserve signal details; when the noise intensity is higher than a second preset threshold, a second filtering scale matching it is used to enhance the filtering intensity, thereby improving the signal-to-noise ratio; the second filtering scale is greater than the first filtering scale; the second preset threshold is greater than the first preset threshold; S4. The central processing unit (2) sends a pre-collection command to the flow control module (11) based on the prediction result of the GRU neural network model. During the collection process, it receives volume data and electrical signal data fed back by the volume detection module (13) and the impedance detection module (12) in real time. If the volume data indicates that the collection amount reaches 90~95% of the target threshold, it sends a command to the flow control module (11) to reduce the speed of the collection pump. If the electrical signal data indicates that the noise intensity exceeds the third preset threshold within a collection cycle, it dynamically adjusts the filtering parameters in S3 and triggers the reference voltage source unit (123) to calibrate the reference voltage. S5. When the volume detection module (13) returns the acquisition completion signal, it automatically performs validity analysis on the acquired electrical signal; if the analysis result is invalid, it triggers the flow control module (11) to perform secondary acquisition.

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