WO2014139143A1 - 基于生物电阻抗的膀胱积尿实时监测方法及装置 - Google Patents
基于生物电阻抗的膀胱积尿实时监测方法及装置 Download PDFInfo
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Classifications
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/05—Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radio waves
- A61B5/053—Measuring electrical impedance or conductance of a portion of the body
- A61B5/0537—Measuring body composition by impedance, e.g. tissue hydration or fat content
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/20—Measuring for diagnostic purposes; Identification of persons for measuring urological functions restricted to the evaluation of the urinary system
- A61B5/202—Assessing bladder functions, e.g. incontinence assessment
- A61B5/204—Determining bladder volume
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7235—Details of waveform analysis
- A61B5/7239—Details of waveform analysis using differentiation including higher order derivatives
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7235—Details of waveform analysis
- A61B5/7242—Details of waveform analysis using integration
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/74—Details of notification to user or communication with user or patient ; user input means
- A61B5/746—Alarms related to a physiological condition, e.g. details of setting alarm thresholds or avoiding false alarms
Definitions
- the invention relates to the technical field of medical detection, in particular to a method and a device for real-time monitoring of bladder urine accumulation based on bioelectrical impedance. Background technique
- the methods of detecting bladder conditions mostly adopt ultrasonic, pressure, displacement and other technical means, and all of them adopt static monitoring methods, which cannot grasp the urine volume of patients in time.
- urinary insufficiency such as urinary incontinence, cystitis, spinal cord injury, etc.
- Remote monitoring is achieved by wired (or wireless) communication methods, and current methods of monitoring bladder urine volume do not meet the above requirements.
- Non-invasive non-destructive testing technology The following describes the measurement principle of bioelectrical impedance.
- the cells in the organism are covered by cell membranes, the interior is filled with cytoplasm, and the cells are extracellular fluid.
- Physiological studies have shown that when a direct or low frequency current is applied to a biological tissue, the current will bypass the cell in any possible way, mainly through the extracellular fluid: when the frequency of current applied to the biological tissue increases, the capacitive reactance of the membrane capacitance Decrease, a portion of the current will flow through the cell membrane through the intracellular fluid. This allows the biological tissue impedance to exhibit a certain dispersion characteristic to the outside world.
- the current source excitation mode is less affected by the unknown contact impedance and the amplitude of the current applied to the electrode is easily controlled without causing a safety problem, it is usually applied to the object by means of an excitation electrode placed on the body surface.
- the magnitude of the electrical impedance is the phase angle.
- the commonly used four-pole method measures electrical impedance, using two pairs of electrodes, one pair being excitation electrodes and one pair being receiving electrodes.
- the alternating current of constant amplitude is input through the excitation electrode, and then the receiving electrode is placed between the two excitation electrodes, so that the current density distribution of the middle section is relatively uniform, and the potential difference of the measured portion can be accurately measured.
- the separation of the excitation electrode and the receiving electrode of the quadrupole method if measured by a voltmeter with a high input impedance, not only the contact resistance between the measuring electrode and the measured tissue portion is negligible, but also the electrode and biological tissue electrolysis appearing in the two-electrode method.
- the effect of polarization between the liquids can also be considered.
- the Chinese invention of the patent application No. 201010213757.0 discloses a bioelectrical impedance-based bladder urine volume monitoring device, which predicts the amount of urine mainly based on the bioelectrical impedance threshold.
- the human bladder is an extremely complex adaptive system, urine conductivity is constantly changing during bladder accumulation, and it has been documented in the pre-stage of bladder accumulation (especially the first half hour of measurement). The contact resistance of the electrode rises more obviously.
- the object of the present invention is to provide a real-time monitoring method and device for bladder urine accumulation based on bioelectrical impedance, which can accurately predict the amount of urine accumulated during bladder accumulation and improve the accuracy of real-time monitoring of bladder urine accumulation.
- an aspect of the present invention provides a bioelectrical impedance-based real-time monitoring method for bladder urine, comprising:
- the calculated amount of bladder urine is compared to a urine volume threshold set in advance for different patients, and an alarm is issued when the amount of bladder urine reaches and/or exceeds the urine volume threshold.
- the amount of bladder urine accumulated by the patient is predicted based on the time domain characteristics and by the following bladder urine volume calculation function:
- Y(k), Xl (k), x 2 (k), x 3 (k), k P , k D , kl represent the current bladder volume, current electrical impedance value, current electrical impedance score , the current integral value of the absolute impedance of the electrical impedance, the current electrical impedance coefficient, the current electrical impedance differential coefficient, and c is the correction constant;
- x 2 (k) can be determined by the following function:
- x 2 (k), x 2 (kl), Xl (k), and Xl (k-1) represent the current electrical impedance differential value, the previous resistance differential value, the current electrical impedance value, and the previous moment resistance. Resistance value
- ⁇ (, ⁇ ), ⁇ ( ⁇ - 1) represent the electrical impedance values at the i-th time and the ⁇ -1 time, respectively.
- the amount of bladder urine accumulated by the patient is predicted based on the time domain characteristics and by the following bladder urine volume calculation function:
- Y(k), Xl (k), x 2 (k), x 3 (k), k P , k D , kl represent the current bladder volume, current electrical impedance value, current electrical impedance score , current resistance absolute value integral value, current electrical impedance coefficient, current electrical impedance differential coefficient, c is the correction constant, tt is the measurement time, the unit is minute; x 2 (k) can be determined by the following function:
- x 2 (k), x 2 (kl), Xl (k), and Xl (k-1) represent the current electrical impedance differential value, the resistance of the previous moment, the current electrical impedance, and the previous moment. Electrical impedance value;
- ⁇ 3 ( ⁇ ) can be determined by the following function:
- x(0, x(i - 1) represents the electrical impedance value at the first time and the first time, respectively.
- the method further comprises the steps of:
- the frequency domain characteristics including at least low frequency energy LF and high frequency energy HF, wherein the low frequency energy LF represents the electrical impedance data of the human body
- the high-low frequency energy ratio LF/HF is calculated in different periods, and the autonomic nervous regulation function of the bladder urination of the patient is evaluated by comparing the high and low frequency energy of the different period than the LF/HF.
- the method before extracting the time domain feature of the electrical impedance data of the human body, the method further includes the steps of: performing digital frequency conversion low-pass filtering processing on the collected human body electrical impedance data to remove extremely low frequency interference and breathing in the human electrical impedance data; Human physiological activity interference such as heartbeat and high-frequency interference, thereby obtaining human electrical impedance data with high signal-to-noise ratio.
- a bioelectrical impedance-based real-time monitoring device for bladder urine comprising:
- At least one lower computer includes:
- An AC signal transmitting and receiving module for transmitting a laser to a test electrode worn on a test site of a patient Excitation current and receiving voltage signal returned by the test electrode;
- a lower computer main control module connected to the AC signal transmitting and receiving module, controlling the AC signal transmitting and receiving module to emit an excitation current, and calculating a body electrical impedance data based on the returned voltage signal;
- a wireless transmitting module configured to send a signal with the electrical impedance data of the human body
- the host computer includes:
- a wireless receiving module configured to receive a signal sent by the wireless transmitting module with the electrical impedance data of the human body
- An electrical impedance time domain feature extraction module is connected to the wireless receiving module, and configured to extract a time domain characteristic of the human body electrical impedance data, where the time domain characteristic includes at least a current electrical impedance value, a current electrical impedance minimum threshold value, and Current electrical impedance absolute value integral value;
- the upper computer main control module is connected to the electrical impedance time domain feature extraction module, and calculates a bladder urine accumulation amount of the patient based on the time domain characteristic;
- a urinary alarm module connected to the upper computer main control module, for comparing the bladder urine volume calculated by the upper computer main control module with a urine volume threshold set in advance for different patients, and in the bladder An alarm is issued when the amount of urine accumulation reaches and/or exceeds the urine volume threshold.
- the host computer main control module can calculate the amount of bladder urine accumulation of the patient based on the time domain characteristics and by the following amount of bladder urine accumulation:
- Y(k), Xl (k), x 2 (k), x 3 (k), k P , k D , and kl respectively represent the current bladder volume, the current electrical impedance value, and the current electrical impedance drum Value, current resistance absolute value integral value, current electrical impedance coefficient, current electrical impedance differential value coefficient, c is a correction constant;
- x 2 (k) can be determined by the following function:
- x 2 (k), x 2 (kl), Xl (k), and Xl (k-1) represent the current electrical impedance differential value, the previous time electrical impedance component, the current electrical impedance value, and the previous moment. Electrical impedance value;
- ⁇ 3 ( 3 ⁇ 4 can be determined by the following function:
- the host computer main control module can calculate the amount of bladder urine accumulation of the patient based on the time domain characteristics and by the following amount of bladder urine accumulation:
- Y(k), Xl (k), x 2 (k), x 3 (k), k P , k D , kl represent the current bladder volume, current electrical impedance value, current electrical impedance score , current resistance absolute value integral value, current electrical impedance coefficient, current electrical impedance differential coefficient, c is the correction constant, tt is the measurement time, the unit is minute; x 2 (k) can be determined by the following function:
- x 2 (k), x 2 (kl), Xl (k), and Xl (k-1) represent the current electrical impedance differential value, the previous resistance, the current electrical impedance, and the previous moment. Electrical impedance value;
- ⁇ 3 ( ) can be determined by the following function: x ⁇ (k) - - 1)
- ⁇ (0, ⁇ ( - 1) represents the electrical impedance values of the i-th time and the -1st time, respectively.
- the upper computer further includes an electrical impedance frequency domain feature extraction module and an autonomous adjustment function evaluation module connected to the upper computer main control module;
- the electrical impedance frequency domain feature extraction module is configured to extract frequency domain features of different periods of the human electrical impedance data, where the frequency domain features include at least low frequency energy LF and high frequency energy HF, wherein the low frequency energy LF represents a human body resistance
- the power spectrum of the anti-data is in the range of 0.04 Hz to 0.15 Hz
- the high-frequency energy HF represents the energy spectrum of the human body impedance data in the interval 0.15 Hz to 0.40 Hz;
- the host computer main control module calculates a high-low frequency energy ratio LF/HF for different periods based on the time domain characteristics
- the self-regulating function evaluation module evaluates the patient's bladder urination autonomic regulation function by comparing the high and low frequency energy ratios of LF/HF at different periods.
- the upper computer further includes a filtering processing module connected between the wireless receiving module and the electrical impedance time domain feature extraction module, configured to perform digital frequency conversion low-pass filtering processing on the received human body electrical impedance data,
- the human body physiological activity interference such as extremely low frequency interference, breathing and heartbeat, and high frequency interference in the body electrical impedance data are removed, thereby obtaining a human body electrical impedance data with high signal to noise ratio.
- Using digital frequency conversion low-pass filtering algorithm it helps to filter out extremely low frequency disturbances that are less disturbing than physiological activities such as breathing and heartbeat, such as deep breathing and extremely low frequency interference caused by motion artifacts, and avoids direct use of poles.
- the low-frequency filtering algorithm has high requirements on hardware accuracy and is easy to be distorted.
- FIG. 1 is a schematic flow chart of a bioelectrical impedance-based real-time monitoring method for bladder urinary tract according to a first embodiment of the present invention
- FIG. 2 is a flow chart showing a method for real-time monitoring of bladder urinary tract based on bioelectrical impedance in a second embodiment of the present invention
- FIG. 3 is a schematic structural view of a real-time monitoring device for bladder urine accumulation based on bioelectrical impedance in an embodiment of the present invention
- Fig. 4 is a comparison diagram of the body electrical impedance time domain data of the bioelectrical impedance-based bladder urine accumulation real-time monitoring device in the embodiment of the present invention shown in Fig. 3.
- Fig. 5 is a comparison diagram of the human electrical impedance power spectrum of the bioelectrical impedance-based real-time monitoring device for bladder urinary in the embodiment of the present invention shown in Fig. 3.
- Fig. 6 to Fig. 7 are flowcharts showing the operation of the bioelectrical impedance-based real-time monitoring device for bladder urine shown in Fig. 3. detailed description
- a real-time monitoring method for bladder urinary tract based on bioelectrical impedance includes:
- Step S11 Collecting electrical impedance data of the human body by wearing the test electrode on the test site of the patient.
- Step S12 Extract time domain characteristics of the electrical impedance data of the human body according to the collected electrical impedance data of the human body, where the time domain characteristic includes at least a current electrical impedance value, a current electrical impedance minimum score, and a current electrical impedance absolute integral value. .
- Step S13 Calculating a urine accumulation amount of the patient based on the time domain characteristic
- Step S14 Comparing the calculated amount of bladder urine accumulation with a urine volume threshold set in advance for different patients, and issuing an alarm when the amount of bladder urine reaches and/or exceeds the urine volume threshold.
- the plurality of test electrodes (at least 4, a pair of excitation electrodes, a pair of receiving electrodes) are worn on the patient test site, and the electrical impedance is measured by the quadrupole method, and the emission is started.
- the device provides a stable excitation current to act on the patient to be tested, and receives the return voltage of the object to be tested by the measuring electrode to calculate the electrical impedance amplitude data.
- the four-pole method to measure the electrical impedance in order to obtain suitable and accurate human electrical impedance data, it can be improved from the following aspects:
- test electrodes When the test electrode is worn near the bladder, the four test electrodes must be at the same level below the umbilical cord.
- a pair of receiving electrodes, a pair of excitation electrodes are outside, and the plurality of test electrodes must be symmetric about the midline.
- a pair of excitation electrodes may be fixed at a projection position of the lower end of the lower abdomen corresponding to the bladder, that is, the two excitation electrodes are respectively disposed near the tibia on both sides of the lower end of the navel, and the other pair of receiving electrodes are placed at appropriate positions between the two excitation electrodes. .
- This method of wearing the electrode conforms to the anatomical and electric field distribution principle and helps to obtain the best results.
- Multi-position measurement and comparison can be performed through multi-channel switch selection mode to obtain the best measurement position and reduce the measurement error caused by individual differences. Achieving optimal electrode placement for different subjects (especially for new test patients) minimizes the inherent disturbances caused by individual differences and improves measurement accuracy.
- the measured results are very different, and exceed the general error range value to form interference data.
- the interference data beyond the error range value must be excluded from the calculation.
- the system analyzes the measured electrical impedance value of each group of receiving electrodes, and can take the most suitable receiving electrode measurement result according to the error range value, for example, according to experimental data, when the measuring frequency is 50 kHz,
- the impedance value is preferably set within 100 ohms. Exceeding this error range means that the measurement is inaccurate; or according to the principle of minimum resistance value, the body resistance value of the smallest group of electrodes is selected; or the body resistance measured by all electrode pairs is used. The value of the resistance is averaged to obtain the final human electrical impedance value.
- the collected original signal can be filtered, denoised, and amplified to preprocess the signal to calculate an accurate measurement calculation result.
- step S12 before the dynamic input of the time domain feature is required to calculate the original electrical impedance data signal of the human body, the signal is first filtered.
- the collected human body electrical impedance data is subjected to digital frequency conversion low-pass filtering processing to remove extremely low frequency interference, respiratory and heartbeat and other human physiological activity interferences and high frequency interferences in the human electrical impedance data, thereby Obtain high electrical signal to noise ratio data for human body impedance.
- the average filtering method of the rolling window is used to remove periodic and binary noise, wherein N is a natural number greater than 5, and the time corresponding to the sampling point is shortened to the original N times, which is equivalent to increasing the noise frequency by N times; then using low-pass filtering with a cutoff frequency of 0.01 Hz, which can remove physiological activities such as breathing and heartbeat and high-frequency interference, and help to remove deep breathing and low-frequency motion.
- the extremely low frequency interference caused by the same, and also avoids the precision requirements of the hardware of the extremely low frequency filter.
- the human body impedance data signal subjected to the relevant signal processing is dynamically extracted from the time domain feature, and the current electrical impedance value Xl (k) of the human electrical impedance data signal, the current electrical impedance differential value x 2 (k) and The current electrical impedance absolute value integral value x 3 (k) and other time domain characteristics, where: (1) The current electrical impedance value Xl (k) is determined by equation (1):
- x(k) is the currently detected electrical impedance value of the human body
- x 2 (k), x 2 (kl), Xl (k), and Xl (k-1) represent the current electrical impedance differential value, the front electrical impedance differential value, the current electrical impedance value, and the previous electrical impedance value.
- step S13 based on the current electrical impedance value Xl (k) of the human body electrical impedance data signal that has been extracted, the current electrical impedance differential value x 2 (k), and the current electrical impedance absolute integral value x 3 (k), etc. Domain characteristics, the bladder accumulation value of the patient can be predicted by the following bladder urine volume calculation function (Equation 4):
- Y(k), Xl (k), x 2 (k), x 3 (k), k P , k D , kl represent the current bladder volume, current electrical impedance value, current electrical impedance index
- the current electrical impedance absolute value integral value, the current electrical impedance coefficient, the current electrical impedance differential value coefficient, the current electrical impedance absolute integral value coefficient, and c is the correction constant.
- the weight coefficient (including the current electrical impedance coefficient k P , the current electrical impedance differential coefficient k D , the current electrical impedance absolute integral coefficient kl ) according to different tests
- the patient settings are different, and adaptive adjustment can be achieved according to test experience or using neural network training to obtain the best weight coefficient to maximize the accuracy of urine volume prediction.
- the constant c is the correction factor, which is used to compensate for the decrease in the integral integral of the absolute value caused by the change in the contact resistance of the electrode before the measurement (mainly in the first half of the measurement), and its value is generally greater than 0.05.
- the present invention can add a time modification parameter to improve the accuracy of the real-time urine volume, that is, in step S13, based on the extracted electrical impedance data of the human body.
- Y(k), Xl (k), x 2 (k), x 3 (k), k P , k D , kl represent the current bladder volume, current electrical impedance value, current electrical impedance score
- the current resistance absolute value integral value, the current electrical impedance coefficient, the current electrical impedance differential value coefficient, the current electrical impedance absolute value integral value coefficient, c is the correction constant
- tt is the measurement time in minutes.
- step S14 the amount of bladder urine (predicted urine volume value) calculated in step S13 is compared with a preset urine amount threshold, and it is determined whether the calculated amount of bladder urine is reached and/or greater than a predetermined value.
- the urine volume threshold when the calculated bladder urine volume reaches and/or is greater than the preset urine volume threshold, an alarm is issued (a variety of alarm methods, such as sound and light alarms), to prompt the patient to urinate in time, or to notify Medical staff.
- a bioelectrical impedance-based real-time monitoring method for bladder urinary tract includes:
- Step S21 Collecting electrical impedance data of the human body by using a test electrode worn on the test site of the patient.
- Step S22 Extract time domain characteristics and frequency domain characteristics of the electrical impedance data of the human body according to the collected electrical impedance data of the human body, where the time domain characteristic includes at least a current electrical impedance value, a current electrical impedance minimum differential value, and a current electrical impedance An absolute value integral value; the frequency domain characteristic includes at least low frequency energy LF and high frequency energy HF;
- Step S23 calculating the amount of bladder urine accumulated by the patient based on the time domain feature, comparing the calculated amount of bladder urine accumulation with a urine volume threshold set in advance for different patients, and achieving the urine volume in the bladder and/or Alerting when the urine volume threshold is exceeded or exceeded;
- Step S24 Calculating the high-low frequency energy ratio LF/HF in different periods based on the frequency domain characteristics, and evaluating the autonomic nervous regulation function of the patient's bladder urination by comparing the high-low frequency energy ratio LF HF in different periods.
- the steps of the first embodiment are the same as the step of collecting the electrical impedance data of the human body, extracting the time domain characteristic of the electrical impedance data of the human body, calculating the amount of bladder urine accumulation of the patient according to the time domain characteristic, and calculating the bladder product in the bladder product.
- the process of issuing an alarm when the amount of urine reaches and/or exceeds the threshold for urine output is the same, unlike the steps of the first embodiment:
- step S22 extracting the time domain characteristics of the electrical impedance data of the human body according to the collected electrical impedance data of the human body (the time domain features need to be filtered first), and extracting the electrical impedance data of the human body.
- the frequency domain characteristic, the frequency domain characteristic includes at least low frequency energy LF and high frequency energy HF, wherein the low frequency energy LF represents the power of the human body electrical impedance data ridge in the interval 0.04 Hz ⁇ 0.15 Hz energy and high frequency energy HF
- the power spectrum representing the electrical impedance data of the human body is in the energy range of 0.15 Hz to 0.40 Hz.
- step S24 in which the high-frequency energy ratio LF/HF of different periods is calculated based on the frequency domain characteristics, and by comparing different periods (for example, pre- and post-stage of bladder accumulation)
- the high- and low-frequency energy of the late stage is used to assess the autonomic neuromodulation function of the patient's bladder urination than the change in LF/HF. If the change of high and low frequency ratio is more obvious, the more obvious the autonomic regulation effect is, the bladder urination function recovers better.
- Rehabilitation training is assisted in patients with urinary insufficiency by effectively assessing the autonomic nervous function of the patient's bladder urination.
- a third embodiment of the present invention provides a real-time monitoring device for bladder urine accumulation based on bioelectrical impedance, comprising: at least one lower computer 10 and one upper computer 20, wherein the lower computer 10 and the upper computer 20 pass Zigbee
- the lower computer 10 can be placed on the test patient to be responsible for the body electrical resistance data collection
- the upper computer 20 can be placed near the patient (in order to prompt the patient to promptly urinate) or away from the patient's monitoring room (in order to facilitate the notification of medical services) Personnel), responsible for human body electrical impedance data processing.
- the lower computer 10 mainly includes an AC signal transmitting and receiving module 11, a lower computer main control module 12, and a Zigbee wireless transmitting module 13, wherein:
- An AC signal transmitting and receiving module configured to emit an excitation current to the test electrode 100 worn on the patient test site and receive a voltage signal returned by the test electrode;
- the lower computer main control module 12 is connected to the AC signal transmitting and receiving module, controls the AC signal transmitting and receiving module to emit an excitation current, and calculates the body electrical impedance data based on the returned voltage signal;
- the Zigbee wireless transmitting module 13 is configured to send a signal with the body electrical impedance data.
- the AC signal transmitting and receiving module includes an AC signal transmitting module (the AC signal transmitting module is composed of an intermediate frequency sine wave generating unit 111 and a voltage controlled constant current source unit 112) and an AC signal receiving module 113, and the intermediate frequency is
- the sine wave generating unit 111 generates a sine wave excitation current and is stabilized by the voltage-controlled constant current source unit 112 to form a stable excitation current for input to a pair of excitation electrodes worn on the patient test site (using the quadrupole method) Test, the test electrode 100 includes a pair of excitation electrodes and a pair of receiving electrodes), and then the AC signal receiving module 113 receives a return voltage signal from the receiving electrode and transmits it to the lower computer main control module 12 for calculation, and calculates the calculated human electrical impedance Data is transmitted through the wireless transmitting module 13.
- the lower computer 10 further includes a multi-channel switch module 14, and the multi-channel switch module 14 is disposed between the AC signal transmitting and receiving module and the test electrode 100.
- the multi-channel switch module 14 is configured to connect a plurality of test electrodes through a plurality of groups of wires to simultaneously measure body electrical impedance at a plurality of positions of the patient, and find an optimal measurement position by comparison, thereby reducing measurement errors caused by individual differences. Achieving optimal electrode placement for different subjects (especially for new test patients) minimizes the inherent disturbances caused by individual differences and improves measurement accuracy.
- the lower-level machine 10 further includes a signal pre-processing module 15, and the lower-level machine main control module 12 first sends a signal to the signal pre-processed after receiving the return voltage signal from the AC signal receiving module 113.
- the module 15 performs filtering, noise reduction, amplification, and the like to preprocess the signal to amplify the signal and remove the interference signal; and then sends the preprocessed signal back to the lower computer main control module 12 for calculation to obtain the human body resistance. Resistance to data.
- the lower computer 10 further includes a power module 16 and a USB data storage module 17, and the power module 16 implements long-term measurement by energy management.
- the USB data storage module 17 is used to back up the electrical impedance data of the human body.
- the upper computer 20 includes a Zigbee wireless receiving module 21, a filtering processing module 22, an electrical impedance time domain feature extraction module 23, an electrical impedance frequency domain feature extraction module 24, a host computer main control module 25, and a urination alarm module. 26 and an autonomous adjustment function evaluation module 27, wherein:
- the Zigbee wireless receiving module 21 is configured to receive a signal sent by the Zigbee wireless transmitting module 13 with the electrical impedance data of the human body;
- the filter processing module 22 is configured to perform digital frequency conversion low-pass filtering processing on the human body electrical impedance data received by the wireless receiving module, so as to remove extremely low frequency interference, respiratory and heartbeat and other human physiological activity interferences and high frequency interferences in the human electrical impedance data. , thereby obtaining human electrical impedance data with high signal to noise ratio;
- the electrical impedance time domain feature extraction module 23 is connected to the filter processing module 22, and is configured to extract a time domain characteristic of the filtered electrical impedance data, wherein the time domain characteristic includes at least a current resistance of the human body electrical impedance data signal.
- the resistance value Xl (k), the current electrical impedance score x 2 (k), and the current electrical impedance absolute value integral value x 3 (k), and the current electrical impedance value Xl (k), the current electrical impedance differential value x 2 (k) and the current electrical impedance absolute value integral value x 3 (k) can be determined by the formula described above, and the description will not be repeated here.
- the electrical impedance frequency domain feature extraction module 24 is connected to the Zigbee wireless receiving module 21, and is configured to extract frequency domain features of different periods of the human body electrical impedance data, where the frequency domain features include at least low frequency energy LF and high frequency energy.
- HF low-frequency energy
- the low-frequency energy LF represents the power of the human body electrical impedance data ⁇ in the interval of 0.04 Hz ⁇ 0.15 Hz energy
- the high-frequency energy HF represents the power spectrum of the human electrical impedance data in the interval of 0.15 Hz ⁇ 0.40 Hz energy
- the host computer main control module 25 is connected to the electrical impedance time domain feature extraction module and the electrical impedance frequency domain feature extraction module 24, and calculates a patient's bladder urine volume based on the time domain feature, and based on the time domain feature Calculate the high and low frequency energy ratio LF/HF for different periods;
- the urinary alarm module 26 is connected to the upper computer main control module 25, and is used for comparing the amount of bladder urine calculated by the upper computer main control module with a urine urine threshold set in advance for different patients, and An alarm is issued when the amount of bladder urine reaches and/or exceeds the urine volume threshold, for example, by an audible and visual alarm;
- the autonomous adjustment function evaluation module 27 is connected with the upper computer main control module 25 to evaluate the patient's bladder urination autonomic adjustment function by comparing the high and low frequency energy ratio LF/HF in different periods.
- the filtering processing module 22 removes periodic and binary noises by using a real-time digital frequency conversion low-pass filtering algorithm, first adopting a mean filtering method with a rolling window of N, wherein N is a natural number greater than 5, and the time corresponding to the sampling points Shortening to N times, which is equivalent to increasing the noise frequency by N times; then using low-pass filtering with a cutoff frequency of 0.01 Hz, which can remove physiological activities such as breathing and heartbeat and high-frequency interference, and help to remove deep breathing. Very low frequency interference caused by low frequency motion, etc., and also avoids the precision requirements of the hardware of the extremely low frequency filter.
- the host computer main control module 25 is based on the current electrical impedance value Xl (k) of the human body electrical impedance data signal that has been extracted, the current electrical impedance differential value x 2 (k), and the current electrical impedance absolute value integral value x. 3 (k) Equal time domain characteristics, the patient's bladder urine volume can be predicted by the following bladder urine volume calculation function (Equation 6):
- Y( k), Xl (k), x 2 (k), x 3 (k), k P , k D , kl represent the current bladder volume, current electrical impedance, current electrical impedance, current electrical impedance Absolute value integral value, current electrical impedance coefficient, current electrical impedance differential value coefficient, current electrical impedance absolute value integral value coefficient, c is the correction constant.
- the weight coefficient (including the current electrical impedance coefficient k P , the current electrical impedance differential coefficient k D , the current electrical impedance absolute integral coefficient kl ) according to different tests
- the patient settings are different, and adaptive adjustment can be achieved according to test experience or using neural network training to obtain the best weight coefficient to maximize the accuracy of urine volume prediction.
- the constant c is the correction factor, which is used to compensate for the decrease in the integral integral of the absolute value caused by the change in the contact resistance of the electrode before the measurement (mainly in the first half of the measurement), and its value is generally greater than 0.05.
- the present invention can add a time modification parameter to improve the accuracy of real-time urine volume, that is, the upper computer main control module 25 is based on the extracted electrical impedance of the human body.
- the time domain characteristics of the current electrical impedance value Xl (k) of the data signal, the current electrical impedance differential value x 2 (k), and the current electrical impedance absolute integral value x 3 (k) can also be calculated by the following bladder accumulation
- the function (Equation 7) predicts the amount of bladder urine in a patient:
- Y(k), Xl (k), x 2 (k), x 3 (k), k P , k D , kl represent the current bladder volume
- the current resistance absolute value integral value, the current electrical impedance coefficient, the current electrical impedance differential value coefficient, the current electrical impedance absolute integral value coefficient, c is the correction constant
- tt is the measurement time in minutes.
- Figure 4 illustrates a comparison of electrical impedance time domain data in an embodiment of the invention.
- the upper picture shows the bladder body electrical impedance raw data collected by the lower computer 10; the middle figure shows the data after the digital frequency conversion low-pass filtering process by the filtering processing module 22 of the upper computer 20; and the lower figure is the upper computer main control module 25 calculated real-time bladder urine volume prediction data. It can be seen from Fig. 4 that during the urinary bladder accumulation, the bladder urine volume rises significantly in the anterior stage of bladder accumulation (especially 30 minutes before the measurement), and after 30 minutes, the bladder urine volume rises slowly.
- the host computer main control module 25 calculates the level of different periods based on the frequency domain characteristics.
- the frequency-to-energy ratio LF/HF, and the calculated high-low frequency energy ratio LF/HF of different periods is sent to the autonomous adjustment function evaluation module 27, which compares different periods (for example, pre-urinary urinary tract -
- the mid-to-late phase of the high- and low-frequency energy is compared to the change in LF/HF to assess autonomic neuromodulation in patients with bladder urination. If the change of high and low frequency ratio is more obvious, the more obvious the autonomic regulation effect is, the bladder urination function recovers better.
- Rehabilitation training is assisted in patients with urinary insufficiency by effectively assessing the autonomic nervous function of the patient's bladder urination.
- Figure 5 shows a comparison of the electrical impedance power spectrum of the human body in the embodiment of the present invention.
- the above figure is the power ⁇ curve of the bioelectrical impedance anti-original data of the bladder pre-urinary system.
- the middle figure shows the power spectrum curve of the bio-resistance data of the bladder urine accumulation, and the power spectrum curve of the bio-resistance data of the bladder accumulation urine.
- Figure 5 is the impedance data power spectrum of bladder urine in different periods of a normal person.
- the urination alarm module 26 and the autonomous adjustment function evaluation module 27 can also be connected to the display screen to send relevant data to the display screen for display.
- the urination alarm module 26 adopts an abnormal alarm protection measure, that is, if the test time reaches a certain time. (eg 2.5 hours), if there is still no alarm, perform a urination forced alarm and notify the test device and the electrode.
- the parameter setting may be based on some experimental data in the previous period, and the neural network training is adopted. Or empirical methods to adjust. Therefore, in the embodiment, the upper computer 20 further includes an adaptive weight corrector connected to the upper computer main control module 25, and is used for predicting the bladder urine volume calculated by the upper computer main control module 25. The patient's actual urine output was compared and the weight kp' Hc was adjusted according to the comparison.
- Step S101 Initializing
- the patient Before the test begins, the patient must first drain the urine before starting the measurement. Then, before wearing the electrode, it is best to remove the unclean material on the surface of the skin, and then wear a plurality of test electrodes (including the excitation electrode and the receiving electrode) near the bladder for detection.
- Step S102 Measuring position setting
- the multi-channel switch module 14 After inputting new patient personal information (S 102a ), the multi-channel switch module 14 is activated to perform a test (S102b), thereby obtaining an optimum measurement position parameter (S102c).
- Step S103 Starting the body impedance data collection
- the intermediate frequency sine wave generating unit 111 is controlled by the lower computer main control module 12 of the lower computer 10 to generate a sine wave excitation current and is stabilized by the voltage controlled constant current source unit 112 to form a stable excitation current for input to be worn.
- a pair of excitation electrodes on the patient test site and then the AC signal receiving module 113 receives the return voltage signal from the receiving electrode and transmits it to the lower computer main control module 12 for calculation, and passes the calculated human body electrical impedance data through the Zigbee wireless
- the transmitting module 13 starts to send to the upper computer 20, and proceeds to step S104 and/or step S108.
- Step S104 Frequency conversion low-pass filtering processing
- the Zigbee wireless receiving module 21 of the host computer receives the signal with the body electrical impedance data, and first performs a digital frequency conversion low-pass filtering process by the filtering processing module 22.
- Step S105 Time domain feature extraction
- the time domain characteristic of the filtered electrical impedance data is extracted by the electrical impedance time domain feature extraction module 23.
- the time domain characteristic includes at least the current electrical impedance value of the human electrical impedance data signal Xl (k ), the current electrical impedance differential value x 2 (k) and the current electrical impedance absolute integral value x 3 (k), and the current electrical impedance value Xl (k), the current electrical impedance differential value x 2 (k) and the current
- the electrical impedance absolute value integral value x 3 (k) can be determined by the formula described above, and the description will not be repeated here.
- Step S106 predicting the amount of bladder accumulation in the patient
- the upper computer main control module 25 and the electrical impedance time domain feature calculate the patient's bladder urine accumulation based on the extracted time domain feature, wherein the upper computer main control module 25 can pass the above formula 7 or formula 8. The calculation predicts the amount of bladder urine in the patient.
- Step S107 The urination alarm module 26 compares the amount of bladder urine calculated by the host computer main control module with a urine volume threshold set in advance for different patients, and determines that the bladder urine volume reaches and/or Or exceed the urine volume threshold, if yes, an alarm is issued; otherwise, an abnormality judgment is made, for example, when the test time reaches a certain time (for example, 2.5 hours), and there is still no alarm, a urination forced alarm is performed.
- a urine volume threshold set in advance for different patients
- Step S108 Electrical impedance frequency domain feature extraction
- the electrical impedance frequency domain feature extraction module 24 extracts frequency domain features of different periods of the electrical impedance data of the human body, and the frequency domain
- the characteristic includes at least low-frequency energy LF and high-frequency energy HF; wherein, the low-frequency energy LF represents the power spectrum of the human body electrical impedance data in the interval 0.04 Hz to 0.15 Hz, and the high-frequency energy HF represents the power of the human electrical impedance data ⁇ in the interval Energy sum of 0.15 Hz to 0.40 Hz.
- Step S109 Calculating the ratio of high to low frequency energy LF/HF
- the host computer main control module 25 calculates a high-low frequency energy ratio LF/HF for different periods based on the time domain characteristics.
- Step S110 Assessing the patient's bladder urination autonomic regulation function
- the autonomic adjustment function evaluation module 27 evaluates the patient's bladder urination autonomic adjustment function by comparing the high and low frequency energy ratios LF/HF for different periods.
- the device has the characteristics of low load, portable, miniaturization, etc., suitable for clinical, family and personal, etc. Different applications.
- bioelectrical impedance-based real-time monitoring method and device for bladder urinary tract at least includes the following beneficial effects:
- the above is a preferred embodiment of the present invention, and it should be noted that those skilled in the art can make various improvements and changes without departing from the principles of the present invention.
- the scope of protection of the present invention It should be noted that the method and the measuring device of the present invention can also measure the thickness of the fat, the condition of the ascites, the food retained in the stomach, and the respiratory condition, etc., which are all within the scope of protection of the present invention.
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Abstract
一种基于生物电阻抗的膀胱积尿实时监测方法及相应的装置，所述方法包括步骤：通过佩戴在患者测试部位上的测试电极，采集人体电阻抗数据；根据采集到的人体电阻抗数据，提取所述人体电阻抗数据的时域特征，所述时域特征至少包括当前电阻抗值、当前电阻抗最小微分值和当前电阻抗绝对值积分值；基于所述时域特征计算患者的膀胱积尿量；将计算出的膀胱积尿量与预先针对不同患者设定的尿量阈值进行比较，并在所述膀胱积尿量达到和/或超过所述尿量阈值时发出警报。该膀胱积尿实时监测方法及装置，能够准确地预测膀胱积尿过程中的积尿量，提高膀胱积尿实时监测的精度。
Description
基于生物电阻抗的膀胱积尿实时监测方法及装置 技术领域
本发明涉及医疗检测技术领域， 尤其涉及一种基于生物电阻抗的膀胱积尿 实时监测方法及装置。 背景技术
目前膀胱状况检测方法多采用超声、 压力、 位移等技术手段， 采用的都是 静态监测方法， 无法及时掌握患者尿量。 对于像尿失禁、 膀胱炎、 脊髓损伤等 尿意缺失患者， 需要实时监测膀胱尿量， 并及时提醒患者排尿， 才能预防积尿 过多、 排尿过频或排尿不尽等并发症； 同时， 也可以通过有线 （或无线） 通信 方法实现远程监控， 目前的膀胱尿量监测方法无法满足上述要求。 的无创无损检测技术。 下面介绍一下生物电阻抗的测量原理， 生物体中细胞由 细胞膜包裹， 内部充满细胞质， 细胞之间是细胞外液。 生理学研究表明， 当直 流或低频电流施加于生物组织时， 电流将以任意一种可能的方式绕过细胞， 主 要流经细胞外液： 当施加于生物组织电流的频率增加， 细胞膜电容的容抗减小， 一部分电流将穿过细胞膜流经细胞内液。 这使得生物组织阻抗对外界呈现一定 的频散特性。
在生物电阻抗测量中， 由于电流源激励模式受未知接触阻抗的影响小且加 到电极的电流的幅值容易控制不致引起安全问题， 通常是借助置于体表的激励 电极向被测对象施加微小的交变电流信号 I((t) , 其值为 I。Sinwt， 通过置于人体 不同部位的测量电极， 检测出组织表面的微弱电压信号为：
Vc (0 = I {t)Z = \z
* sin( ωί + φ) 将该微弱信号进行放大等一系列的预处理， 选用合适的解调方法， 计算出 相应的测量电极间生物组织的电阻抗： Z=V/I, |z |为电阻抗的幅值， 为相角。
比如常用的四极法测量电阻抗， 是釆用两对电极， 一对是激励电极， 一对 是接收电极。 通过激励电极输入恒定幅值的交变电流， 然后在介于两激励电极 之间， 贴入接收电极， 这样中间段的电流密度分布比较均匀， 可以准确测量出 被测部位的电位差。 四极法的激励电极和接收电极的分离， 如果采用高输入阻 抗的电压表测量时， 不仅测量电极与被测组织部位间的接触电阻都可以忽略不 计， 双电极法出现的电极与生物组织电解液间的极化的影响也可以不子考虑。
但是， 现有的基于生物电阻抗的测量方法和装置都无法清晰准确根据测量
数据， 以寻找到合适的报警点， 及时提示患者排尿。 例如， 在现有技术中， 专 利申请号为 201010213757.0的中国发明公开了一种基于生物电阻抗的膀胱尿量 监测装置， 主要根据生物电阻抗阀值来预测尿量多少。 但是， 考虑到人体膀胱 器官是一种极其复杂的自适应系统， 在膀胱积尿过程中， 尿液电导率是在不断 变化， 且已有文献证实在膀胱积尿前阶段（尤其测量前半小时）， 电极的接触阻 抗上升比较明显。 因此可见， 基于阻抗下降多少 （阀值） 来预测尿量必然存在 精度不高的问题， 且千扰较大； 同时， 已有技术研究也没有考虑到尿意缺失患 者的自主神经调节功能的评估及其对测量的影响。 发明内容
本发明的目的提供一种基于生物电阻抗的膀胱积尿实时监测方法及装置， 能够准确地预测膀胱积尿过程中的积尿量， 提高膀胱积尿实时监测的精度。
为实现上述目的， 本发明的一个方面提供了一种基于生物电阻抗的膀胱积 尿实时监测方法， 包括：
通过佩戴在患者测试部位上的测试电极， 采集人体电阻抗数据；
根据采集到的人体电阻抗数据， 提取所述人体电阻抗数据的时域特征， 所 述时域特征至少包括当前电阻抗值、 当前电阻抗最小啟分值和当前电阻抗绝对 值积分值；
基于所述时域特征计算患者的膀胱积尿量；
将计算出的膀胱积尿量与预先针对不同患者设定的尿量阈值进行比较， 并 在所述膀胱积尿量达到和 /或超过所述尿量阈值时发出警报。
优选地， 基于所述时域特征并通过以下的膀胱积尿量计算函数预测患者的 膀胱积尿量：
Y(k)=kP*x1(k)+kD*x2(k)+kl*x3(k)+c k=l,2,3,...
其中， Y(k)、 Xl(k), x2(k)、 x3(k)、 kP、 kD、 kl分别表示当前膀胱积尿量、 当 前电阻抗值、 当前电阻抗敫分值、 当前电阻抗绝对值积分值、 当前电阻抗值系 数、 当前电阻抗微分值系数， c为修正常数；
x2(k)可由以下函数确定：
x2(k)=0 k=l ,
x2(k)= xi(k-l)- Xl(k) x2(k)≤x2(k-l)且 k=2,3, · .. ,
x2(k)= x2(k-l) x2(k) > x2(k-l)J_ k=2,3,...
其中， x2(k)、 x2(k-l)、 Xl(k)、 Xl(k-1)分别表示当前电阻抗微分值， 前一时刻 电阻抗微分值， 当前电阻抗值和前一时刻电阻抗值；
而 x3(k)可由以下函数确定：
k
x3 (k)二 1 (ζ·) - - 1)1 k二 1 , 2 , 3 ,- .
其中， χ(,·)，χ(· - 1)分别表示第 i时刻和第 ί·-1时刻的电阻抗值。 优选地， 基于所述时域特征并通过以下的膀胱积尿量计算函数预测患者的 膀胱积尿量：
Y(k)=kP*x1(k)*ecc+kD*x2(k)+kl*x3(k)+c*ecc k=l,2,3,...
(30-ίί)/6 ίί < 30
cc = { }
0 tt≥ 30
其中， Y(k)、 Xl(k)、 x2(k)、 x3(k)、 kP、 kD、 kl分别表示当前膀胱积尿量、 当 前电阻抗值、 当前电阻抗敖分值、 当前电阻抗绝对值积分值、 当前电阻抗值系 数、 当前电阻抗微分值系数， c为修正常数, tt表示测量时间， 单位为分钟； x2(k)可由以下函数确定：
x2(k)=0 k=l ,
x2(k)= xj(k-l)- Xl(k) x2(k)≤x2(k-l)且 k=2,3,...，
x2(k)= x2(k-l) x2(k) > x2(k-l)且 k=2,3，. · .
其中， x2(k)、 x2(k-l)、 Xl(k)、 Xl(k-1)分别表示当前电阻抗微分值， 前一时刻 电阻抗啟分值， 当前电阻抗值和前一时刻电阻抗值；
而 χ3(Χ)可由以下函数确定：
k
x3 (^) = -jc1(i -l)| k = 1,2,3,··.
优选地， 还包括步骤：
根据采集到的人体电阻抗数据， 提取所述人体电阻抗数据的不同时期的频 域特征， 所述频域特征至少包括低频能量 LF和高频能量 HF, 其中， 低频能量 LF表示人体电阻抗数据的功率谱在区间 0.04Hz〜0.15Hz的能量和，高频能量 HF 表示人体电阻抗数据的功率谱在区间 0.15Hz~0.40Hz的能量和；
基于所述频域特征计算出不同时期的高低频能量比 LF/HF, 并通过比较不 同时期的高低频能量比 LF/HF而评估患者膀胱排尿的自主神经调节功能。
优选地， 在提取所述人体电阻抗数据的时域特征前， 还包括步骤： 将采集 到的人体电阻抗数据进行数字变频低通滤波处理， 以去除人体电阻抗数据中的 极低频干扰、 呼吸和心跳等人体生理活动干扰以及高频干扰， 从而获得高信噪 比的人体电阻抗数据。
为实现上述目的， 本发明的另一个方面提供了一种基于生物电阻抗的膀胱 积尿实时监测装置， 包括：
至少一个下位机， 所述下位机包括：
交流信号发射接收模块， 用于向佩戴在患者测试部位上的测试电极发射激
励电流以及接收测试电极返回的电压信号；
下位机主控模块， 与所述交流信号发射接收模块连接， 控制所述交流信号 发射接收模块发射激励电流， 并基于所述返回的电压信号计算出人体电阻抗数 据；
无线发射模块， 用于将带所述人体电阻抗数据的信号发送；
以及一个上位机， 所述上位机包括：
无线接收模块， 用于接收所述无线发射模块发送的带所述人体电阻抗数据 的信号；
电阻抗时域特征提取模块， 与所述无线接收模块连接， 用于提取所述人体 电阻抗数据的时域特征， 所述时域特征至少包括当前电阻抗值、 当前电阻抗最 小敫分值和当前电阻抗绝对值积分值；
上位机主控模块， 与所述电阻抗时域特征提取模块连接， 基于所述时域特 征计算患者的膀胱积尿量；
排尿报警模块， 与所述上位机主控模块连接， 用于将所述上位机主控模块 计算出的膀胱积尿量与预先针对不同患者设定的尿量阈值进行比较， 并在所述 膀胱积尿量达到和 /或超过所述尿量阈值时发出警报。
优选地， 所述上位机主控模块可基于所述时域特征并通过以下的膀胱积尿 量计算患者的膀胱积尿量：
Y(k)=kP*x1(k)+kD*x2(k)+kl*x3(k)+c k=l,2,3,...
其中， Y(k)、 Xl(k)、 x2(k)、 x3(k)、 kP、 kD、、 kl 分别表示当前膀胱积尿量、 当前电阻抗值、 当前电阻抗鼓分值、 当前电阻抗绝对值积分值、 当前电阻抗值 系数、 当前电阻抗微分值系数， c为修正常数；
x2(k)可由以下函数确定：
x2(k)=0 k=l ,
x2(k)= xj(k-l)- Xl(k) x2(k)≤x2(k-l)且 k=2,3" ..，
x2(k)= x2(k-l) x2(k) > x2(k-l)且 k=2,3" · .
其中， x2(k)、 x2(k-l)、 Xl(k)、 Xl(k-1)分别表示当前电阻抗微分值， 前一时刻 电阻抗敫分值， 当前电阻抗值和前一时刻电阻抗值；
而 χ3(¾可由以下函数确定：
k
x} (k) - ^ |¾ ( - x{ (i - 1)| k - 1,2,3,...
(=1 其中， χ(0, χ( - 1)分別表示第 i时刻和第 -1时刻的电阻抗值。
优选地， 所述上位机主控模块可基于所述时域特征并通过以下的膀胱积尿 量计算患者的膀胱积尿量：
Y(k)=kp*Xl(k)*ecc+kD*x2(k)+kl*X3(k)+c*ecc k=l,2,3,...
(30— ίί)/6 « < 30
cc = { }
0 tt≥ 30
其中， Y(k)、 Xl(k)、 x2(k)、 x3(k)、 kP、 kD、 kl分别表示当前膀胱积尿量、 当 前电阻抗值、 当前电阻抗敖分值、 当前电阻抗绝对值积分值、 当前电阻抗值系 数、 当前电阻抗微分值系数， c为修正常数， tt表示测量时间， 单位为分钟； x2(k)可由以下函数确定：
x2(k)=0 k=l ,
x2(k)= xj(k-l)- Xl(k) x2(k)≤x2(k-l)且 k=2,3, ...，
x2(k)= x2(k-l) x2(k) > x2(k-l)且 k=2,3" · .
其中， x2(k)、 x2(k-l)、 Xl(k)、 Xl(k-1)分别表示当前电阻抗微分值， 前一时刻 电阻抗 £分值， 当前电阻抗值和前一时刻电阻抗值；
而 χ3( )可由以下函数确定： x} (k) - - 1)| k - 1,2,3,...
优选地， 所述上位机还包括与所述上位机主控模块连接的电阻抗频域特征 提取模块、 自主调节功能评估模块；
所述电阻抗频域特征提取模块用于提取所述人体电阻抗数据的不同时期的 频域特征， 所述频域特征至少包括低频能量 LF和高频能量 HF, 其中， 低频能 量 LF表示人体电阻抗数据的功率谱在区间 0.04Hz~0.15Hz的能量和， 高频能量 HF表示人体电阻抗数据的功率谱在区间 0.15Hz~0.40Hz的能量和；
所述上位机主控模块基于所述时域特征而计算出不同时期的高低频能量比 LF/HF;
所述自主调节功能评估模块通过比较不同时期的高低频能量比 LF/HF而评 估患者的膀胱排尿自主调节功能。
优选地， 所述上位机还包括连接于所述无线接收模块和电阻抗时域特征提 取模块之间的滤波处理模块， 用于将接收到的人体电阻抗数据进行数字变频低 通滤波处理， 以去除人体电阻抗数据中的极低频干扰、 呼吸和心跳等人体生理 活动干扰以及高频干扰， 从而获得高信噪比的人体电阻抗数据。
本发明提供的基于生物电阻抗的膀胱积尿实时监测方法及装置至少包括如 下有益效果：
1. 首次提出结合生物阻抗当前值、 最小微分值、 绝对值积分、 电极接触电 阻补偿等特征， 可以更加准确地预测膀胱积尿过程中尿量多少， 提高膀胱积尿 实时监测的精度， 更合理进行排尿提醒与报警。
2. 首次提出了阻抗频谱变化规律， 通过对比膀胱积尿前期与后期高低频比
变化， 可以有效地评估膀胱自主神经排尿调节功能， 帮助尿意缺失患者进行康 复训练。
3. 釆用数字变频低通滤波算法， 有助于滤掉比呼吸、 心跳等生理活动千扰 更低的极低频扰动， 如深呼吸、 运动伪差引起的极低频干扰， 也避免了直接采 用极低频滤波算法对硬件精度要求过高和容易失真等问题。 附图说明
图 1 是本发明第一实施例中一种基于生物电阻抗的膀胱积尿实时监测方法 的流程示意图；
图 2 是本发明第二实施例中一种基于生物电阻抗的膀胱积尿实时监测方法 的流程示意图；
图 3 是本发明实施例中一种基于生物电阻抗的膀胱积尿实时监测装置的结 构示意图；
图 4是图 3所示的本发明实施例中基于生物电阻抗的膀胱积尿实时监测装 置的人体电阻抗时域数据比较图。
图 5是图 3所示的本发明实施例中基于生物电阻抗的膀胱积尿实时监测装 置的人体电阻抗功率谱比较图。
图 6〜图 7展示了图 3所示的基于生物电阻抗的膀胱积尿实时监测装置的工 作流程图。 具体实施方式
下面将结合本发明实施例中的附图， 对本发明实施例中的技术方案进行清 楚、 完整地描述， 显然， 所描述的实施例仅仅是本发明一部分实施例， 而不是 全部的实施例。 基于本发明中的实施例， 本领域普通技术人员在没有作出创造 性劳动前提下所获得的所有其他实施例， 都属于本发明保护的范围。
参见图 1 ,本发明第一实施例提供的一种基于生物电阻抗的膀胱积尿实时监 测方法， 包括：
步骤 S 11、 通过佩戴在患者测试部位上的测试电极， 采集人体电阻抗数据。 步骤 S12、根据采集到的人体电阻抗数据， 提取所述人体电阻抗数据的时域 特征， 所述时域特征至少包括当前电阻抗值、 当前电阻抗最小 分值和当前电 阻抗绝对值积分值。
步骤 S13、 基于所述时域特征计算患者的膀胱积尿量；
步骤 S14、将计算出的膀胱积尿量与预先针对不同患者设定的尿量阈值进行 比较， 并在所述膀胱积尿量达到和 /或超过所述尿量阈值时发出警报。
具体的， 在步骤 S 11中， 通过将多个测试电极（至少 4个， 一对激励电极， 一对接收电极） 佩戴在在患者测试部位上并釆用四极法测量电阻抗， 启动发射
器提供稳定的激励电流作用于待测患者， 由测量电极接收得到待测对象的返回 电压而计算出电阻抗幅值数据。 采用四极法测量电阻抗， 为了获得合适准确的 人体电阻抗数据， 可从以下几个方面进行改进：
( 1 )测试电极佩戴在膀胱附近时， 四个测试电极必须处于脐肚下同一水平 面， 一对接收电极在内， 一对激励电极在外， 其中多个测试电极必须关于中线 对称。 具体的， 可将一对激励电极固定在小腹下端对应膀胧的投影位置， 即两 激励电极分别设在肚脐下端两侧胯骨的附近， 另一对接收电极置于两激励电极 之间的适当位置。 此种电极佩戴方法， 符合解剖学与电场分布原理， 有助于获 得最佳的效果。
( 2 )可以通过多通道开关选择模式进行多位置测量与比较， 获得最佳的测 量位置， 减小个体差异造成的测量误差。 针对不同受试者获得其最佳的电极安 放位置（尤其新的测试患者）， 可最大化减小个体差异带来的人体固有干扰， 提 高测量精度。
( 3 )对测出的结果差别很大， 超出了一般误差范围值， 形成干扰数据。 为 了尽可能得出准确的测量计算结果， 计算时就必须排除超出误差范围值的千扰 数据。 系统分析每组接收电极对测量的人体电阻抗值， 可以根据误差范围值， 取最合适的接收电极的测量结果， 所述误差范围， 比如根据实验数据证明， 在 测量频率为 50千赫兹时， 阻抗值最好设在 100欧姆以内， 超过这个误差范围表 示测量不准确；或者根据最小电阻值原理， 选择最小的几组电极对的人体电阻抗 值进行计算； 或者采用所有电极对测量的人体电阻抗值取平均值方式，得到最终 的人体电阻抗值。
( 4 )在接收返回的电压信号计算电阻抗幅值数据前， 可以对采集的原始信 号进行滤波、 降噪， 放大信号预处理， 以计算出准确的测量计算结果。
在步骤 S12 中， 当计算出人体原始电阻抗数据信号而需要进行时域特征的 动态提取前， 先对信号进行滤波处理。
其中， 在进行滤波处理时， 将采集到的人体电阻抗数据进行数字变频低通 滤波处理， 以去除人体电阻抗数据中的极低频干扰、 呼吸和心跳等人体生理活 动干扰以及高频干扰， 从而获得高信噪比的人体电阻抗数据。 具体的， 通过实 时数字变频低通滤波算法， 首先采用滚动窗口为 N的均值滤波方法去除周期性 和二值等噪声， 其中 N为大于 5的自然数， 并将采样点对应的时间缩短为原来 的 N倍， 即相当于将噪声频率提高了 N倍； 然后采用截止频率为 0.01Hz的低通 滤波， 即可以去除呼吸、 心跳等生理活动和高频干扰， 又有助于去除深呼吸、 低频动动等造成的极低频干扰， 而且也避免了极低频滤波器对硬件的精度要求。
然后， 将进行了相关信号处理的人体电阻抗数据信号进行时域特征的动态 提取,提取出人体电阻抗数据信号的当前电阻抗值 Xl(k)、当前电阻抗微分值 x2(k) 和当前电阻抗绝对值积分值 x3(k)等时域特征， 其中：
(一）当前电阻抗值 Xl(k)由公式 （1 ) 确定：
xi(k)=x(k) k=l,2,3,... 公式 （ 1 )
x(k)为当前检测到的人体电阻抗值；
(二） 当前电阻抗微分值 x2(k)可由公式 （ 2 ) 确定： 公式 （ 2 )
其中， x2(k)、 x2(k-l)、 Xl(k)、 Xl(k-1)分别表示当前电阻抗微分值， 前 电阻抗微分值， 当前电阻抗值和前一时刻电阻抗值；
(三） 当前电阻抗绝对值积分值 x3(k)可由公式 （3 ) 确定： x#) = ih(0— ·¾(!·— 1)1 k = 1,2,3,.. 公式 （ 3 ) 其中， x(0, x( - l)分别表示第 ί时刻和第 -1时刻的电阻抗值。
在步骤 S13 中， 基于已经提取出的人体电阻抗数据信号的当前电阻抗值 Xl(k)、 当前电阻抗微分值 x2(k)和当前电阻抗绝对值积分值 x3(k)等时域特征， 可 通过以下的膀胱积尿量计算函数（公式 4 )预测患者的膀胱积尿量：
Y(k)=kP*x1(k)+kD*x2(k)+kl*x3(k)+c k=l,2,3,... 公式 （4 )
其中， Y(k)、 Xl(k)、 x2(k)、 x3(k)、 kP、 kD、 kl分别表示当前膀胱积尿量、 当 前电阻抗值、 当前电阻抗孩分值、 当前电阻抗绝对值积分值、 当前电阻抗值系 数、 当前电阻抗微分值系数、 当前电阻抗绝对值积分值系数， c为修正常数。
其中， 针对不同尿意缺失疾病和患者的个体差异性， 权值系数（包括当前 电阻抗值系数 kP、 当前电阻抗微分值系数 kD、 当前电阻抗绝对值积分值系数 kl ) 根据不同的测试患者设定不同， 并且可根据测试经验或者采用神经网络训练， 实现自适应调节， 获得最佳的权值系数， 以最大化提尿量预测精度。
常数 c为修正系数， 用来弥补测量前阶段（主要体现于测量前半小时）电极 接触电阻变化导致的绝对值积分积累量減小， 其值一般大于 0.05。
作为本实施例的优化设计， 考虑到阻时域特征与尿量的相关性存在一定的 时间积累效应， 尤其是当前阻抗值和电极接触电阻修正系数与尿量存在较大相 关性， 但它们主要反应了膀胱积尿前期的积尿量， 为此， 本发现可以增加一个 时间修改参数， 以提高实时的尿量精度， 也即， 在步骤 S13 中， 基于已经提取 出的人体电阻抗数据信号的当前电阻抗值 Xl(k)、 当前电阻抗微分值 x2(k)和当前 电阻抗绝对值积分值 x3(k)等时域特征， 可通过以下的膀胱积尿量计算函数（公 式 5 )预测患者的膀胱积尿量：
Y(k)=kP*x1(k)*ecc+kD*x2(k)+kl*x3(k)+c*el
(30 - tt) / 6 tt < 30 -公式 （ 5 ) cc = { }
0 tt≥ 30
其中， Y(k)、 Xl(k)、 x2(k)、 x3(k)、 kP、 kD、 kl分别表示当前膀胱积尿量、 当 前电阻抗值、 当前电阻抗敫分值、 当前电阻抗绝对值积分值、 当前电阻抗值系 数、 当前电阻抗微分值系数、 当前电阻抗绝对值积分值系数， c 为修正常数， tt 表示测量时间， 单位为分钟。
在步骤 S14中， 将步骤 S13 中计算出的膀胱积尿量 （预测尿量值） 与预先 设定的尿量阈值进行比较， 判断计算出的膀胱积尿量是否达到和 /或大于预先设 定的尿量阈值； 当计算出的膀胱积尿量达到和 /或大于预先设定的尿量阈值时发 出报警（可通过多种报警方式， 例如声光报警）， 以提示患者及时排尿， 或通知 医务人员。
参见图 2，本发明第二实施例提供的一种基于生物电阻抗的膀胱积尿实时监 测方法， 包括：
步骤 S21、 通过佩戴在患者测试部位上的测试电极， 采集人体电阻抗数据。 步骤 S22、根据采集到的人体电阻抗数据， 提取所述人体电阻抗数据的时域 特征和频域特征， 所述时域特征至少包括当前电阻抗值、 当前电阻抗最小微分 值和当前电阻抗绝对值积分值； 所述频域特征至少包括低频能量 LF和高频能量 HF;
步骤 S23、基于所述时域特征计算患者的膀胱积尿量， 将计算出的膀胱积尿 量与预先针对不同患者设定的尿量阈值进行比较， 并在所述膀胱积尿量达到和 / 或超过所述尿量阈值时发出警报；
步骤 S24、基于所述频域特征计算出不同时期的高低频能量比 LF/HF, 并通 过比较不同时期的高低频能量比 LF HF而评估患者膀胱排尿的自主神经调节功 h
与第一实施例的步骤相同的是， 本实施例的采集人体电阻抗数据、 提取所 述人体电阻抗数据的时域特征、 根据时域特征计算患者的膀胱积尿量并在所述 膀胱积尿量达到和 /或超过所述尿量阈值时发出警报的过程是相同的， 与第一实 施例的步骤不同的是：
(一）在步骤 S22 中， 根据釆集到的人体电阻抗数据， 提取所述人体电阻 抗数据的时域特征 （提取时域特征需先经过滤波处理） 的同时也提取所述人体 电阻抗数据的频域特征， 所述频域特征至少包括低频能量 LF和高频能量 HF , 其中， 低频能量 LF表示人体电阻抗数据的功率 ^脊在区间 0.04Hz〜0.15Hz的能量 和，高频能量 HF表示人体电阻抗数据的功率谱在区间 0.15Hz~0.40Hz的能量和。
(二）相应的， 增加步骤 S24, 在该步骤中， 基于所述频域特征计算出不同 时期的高低频能量比 LF/HF, 并通过比较不同时期 （例如膀胱积尿前期-中期-
后期）的高低频能量比 LF/HF的变化而评估患者膀胱排尿的自主神经调节功能。 如果高低频比变化越显著， 说明自主神经调节作用越明显， 膀胱排尿功能恢复 较好。 通过有效地评估患者膀胱排尿的自主神经调节功能， 帮助尿意缺失患者 进行康复训练。
参考图 3 ,本发明第三实施例提供了一种基于生物电阻抗的膀胱积尿实时监 测装置， 包括： 至少一个下位机 10和一个上位机 20, 其中， 下位机 10和上位 机 20通过 Zigbee方式通信， 下位机 10可置于测试患者上以负责人体电阻抗数 据釆集， 而上位机 20可置于患者附近（为了方便提醒患者及时排尿 )或者远离 患者的监控室里 （为了方便通知医务人员）， 负责人体电阻抗数据处理。
其中， 所述下位机 10主要包括交流信号发射接收模块 11、 下位机主控模块 12、 Zigbee无线发射模块 13 , 其中：
交流信号发射接收模块， 用于向佩戴在患者测试部位上的测试电极 100发 射激励电流以及接收测试电极返回的电压信号；
下位机主控模块 12， 与所述交流信号发射接收模块连接， 控制所述交流信 号发射接收模块发射激励电流， 并基于所述返回的电压信号计算出人体电阻抗 数据；
Zigbee无线发射模块 13 , 用于将带所述人体电阻抗数据的信号发送。
具体的， 所述交流信号发射接收模块包括交流信号发射模块（所述交流信 号发射模块由中频正弦波发生单元 111和压控恒流源单元 112组成）和交流信号 接收模块 113 , 由所述中频正弦波发生单元 111产生正弦波激励电流并通过所述 压控恒流源单元 112 进行稳压后形成稳定的激励电流以输入到佩戴在患者测试 部位上的一对激励电极上 （采用四极法测试， 测试电极 100 包括一对激励电极 和一对接收电极 ), 然后交流信号接收模块 113从接收电极接收返回电压信号并 传送到下位机主控模块 12进行计算， 并将计算出的人体电阻抗数据通过所述无 线发射模块 13射出。
优选的， 在本实施例中， 所述下位机 10还包括多通道开关模块 14, 该多通 道开关模块 14置于交流信号发射接收模块和测试电极 100之间。 该多通道开关 模块 14用于通过多组联线连接多个测试电极以同时测量患者的多个位置的人体 电阻抗， 并通过对比找到最佳的测量位置， 减小个体差异造成的测量误差。 针 对不同受试者获得其最佳的电极安放位置 （尤其新的测试患者）， 可最大化减小 个体差异带来的人体固有干扰， 提高测量精度。
优选的， 在本实施例中， 所述下位机 10还包括信号预处理模块 15, 所述下 位机主控模块 12从交流信号接收模块 113接收到的返回电压信号后， 首先发送 给信号预处理模块 15进行滤波、 降噪， 放大等一系列信号预处理后， 以放大信 号并去除千扰信号；再将经过预处理的信号发回给下位机主控模块 12进行计算， 以得出人体电阻抗数据。
优选的， 在本实施例中， 所述下位机 10还包括电源模块 16和 USB数据存 储模块 17， 所述电源模块 16通过能耗管理实现长时间测量。 所述 USB数据存 储模块 17用于备份人体电阻抗数据。
继续参考图 3 , 所述上位机 20包括 Zigbee无线接收模块 21、 滤波处理模块 22、 电阻抗时域特征提取模块 23、 电阻抗频域特征提取模块 24、 上位机主控模 块 25、 排尿报警模块 26以及自主调节功能评估模块 27, 其中：
Zigbee无线接收模块 21 , 用于接收所述 Zigbee无线发射模块 13发送的带 所述人体电阻抗数据的信号；
滤波处理模块 22， 用于将无线接收模块接收到的人体电阻抗数据进行数字 变频低通滤波处理， 以去除人体电阻抗数据中的极低频干扰、 呼吸和心跳等人 体生理活动干扰以及高频干扰， 从而获得高信噪比的人体电阻抗数据；
电阻抗时域特征提取模块 23 , 与所述滤波处理模块 22连接， 用于提取经过 滤波处理后的人体电阻抗数据的时域特征， 所述时域特征至少包括人体电阻抗 数据信号的当前电阻抗值 Xl(k)、 当前电阻抗 分值 x2(k)和当前电阻抗绝对值积 分值 x3(k)， 而所述当前电阻抗值 Xl(k)、 当前电阻抗微分值 x2(k)和当前电阻抗 绝对值积分值 x3(k)可通过上面描述的公式确定， 在此不再重复描述。
电阻抗频域特征提取模块 24, 与所述 Zigbee无线接收模块 21连接， 用于 提取所述人体电阻抗数据的不同时期的频域特征， 所述频域特征至少包括低频 能量 LF和高频能量 HF; 其中，低频能量 LF表示人体电阻抗数据的功率谙在区 间 0.04Hz~0.15Hz的能量和， 高频能量 HF表示人体电阻抗数据的功率谱在区间 0.15Hz~0.40Hz的能量和；
上位机主控模块 25 , 与所述电阻抗时域特征提取模块、 电阻抗频域特征提 取模块 24连接， 基于所述时域特征计算患者的膀胱积尿量， 并基于所述时域特 征而计算出不同时期的高低频能量比 LF/HF;
排尿报警模块 26， 与所述上位机主控模块 25连接， 用于将所述上位机主控 模块计算出的膀胱积尿量与预先针对不同患者设定的尿量阈值进行比较， 并在 所述膀胱积尿量达到和 /或超过所述尿量阈值时发出警报， 例如可以通过声光报 警； 以及
自主调节功能评估模块 27, 与所述上位机主控模块 25连接， 通过比较不同 时期的高低频能量比 LF/HF而评估患者的膀胱排尿自主调节功能。
具体的， 滤波处理模块 22通过实时数字变频低通滤波算法， 首先采用滚动 窗口为 N的均值滤波方法去除周期性和二值等噪声，其中 N为大于 5的自然数， 并将采样点对应的时间缩短为原来的 N倍， 即相当于将噪声频率提高了 N倍； 然后采用截止频率为 0.01Hz的低通滤波， 即可以去除呼吸、 心跳等生理活动和 高频干扰， 又有助于去除深呼吸、 低频动动等造成的极低频干扰， 而且也避免 了极低频滤波器对硬件的精度要求。
具体的， 所述上位机主控模块 25基于已经提取出的人体电阻抗数据信号的 当前电阻抗值 Xl(k)、 当前电阻抗微分值 x2(k)和当前电阻抗绝对值积分值 x3(k) 等时域特征， 可通过以下的膀胱积尿量计算函数（公式 6 )预测患者的膀胱积尿 量：
Y(k)=kP*x1(k)+kD*x2(k)+kl*x3(k)+c k=l,2,3,... (公式 6 ) 其中， Y(k)、 Xl(k)、 x2(k)、 x3(k)、 kP、 kD、 kl分别表示当前膀胱积尿量、 当 前电阻抗值、 当前电阻抗敖分值、 当前电阻抗绝对值积分值、 当前电阻抗值系 数、 当前电阻抗微分值系数、 当前电阻抗绝对值积分值系数， c为修正常数。
其中， 针对不同尿意缺失疾病和患者的个体差异性， 权值系数（包括当前 电阻抗值系数 kP、 当前电阻抗微分值系数 kD、 当前电阻抗绝对值积分值系数 kl ) 根据不同的测试患者设定不同， 并且可根据测试经验或者采用神经网络训练， 实现自适应调节， 获得最佳的权值系数， 以最大化提尿量预测精度。
常数 c为修正系数， 用来弥补测量前阶段（主要体现于测量前半小时）电极 接触电阻变化导致的绝对值积分积累量减小， 其值一般大于 0.05。
作为本实施例的优化设计， 考虑到阻时域特征与尿量的相关性存在一定的 时间积累效应， 尤其是当前阻抗值和电极接触电阻修正系数与尿量存在较大相 关性， 但它们主要反应了膀胱积尿前期的积尿量， 为此， 本发现可以增加一个 时间修改参数， 以提高实时的尿量精度， 也即， 所述上位机主控模块 25基于已 经提取出的人体电阻抗数据信号的当前电阻抗值 Xl(k)、 当前电阻抗微分值 x2(k) 和当前电阻抗绝对值积分值 x3(k)等时域特征， 也可通过以下的膀胱积尿量计算 函数（公式 7 )预测患者的膀胱积尿量：
其中， Y(k)、 Xl(k)、 x2(k)、 x3(k)、 kP、 kD、 kl分别表示当前膀胱积尿量、 当 前电阻抗值、 当前电阻抗微分值、 当前电阻抗绝对值积分值、 当前电阻抗值系 数、 当前电阻抗微分值系数、 当前电阻抗绝对值积分值系数， c 为修正常数， tt 表示测量时间， 单位为分钟。
图 4展示了本发明实施例中电阻抗时域数据的比较。 其中， 上图为下位机 10采集的膀胱人体电阻抗原始数据； 中图为经过上位机 20的滤波处理模块 22 进行数字变频低通滤波处理后的数据； 而下图为经过上位机主控模块 25计算的 实时膀胱积尿量预测数据。 由图 4 可知， 在膀胱积尿过程中， 在膀胱积尿前阶 段（尤其测量前 30分钟）， 膀胱积尿量上升比较明显， 而过了 30分钟后， 膀胱 积尿量上升比较緩慢。
具体的， 所述上位机主控模块 25基于所述频域特征计算出不同时期的高低
频能量比 LF/HF, 并将计算到的不同时期的高低频能量比 LF/HF发送给自主调 节功能评估模块 27， 所述自主调节功能评估模块 27通过比较不同时期（例如膀 胱积尿前期 -中期 -后期）的高低频能量比 LF/HF的变化而评估患者膀胱排尿的自 主神经调节功能。 如果高低频比变化越显著， 说明自主神经调节作用越明显， 膀胱排尿功能恢复较好。 通过有效地评估患者膀胱排尿的自主神经调节功能， 帮助尿意缺失患者进行康复训练。
图 5 展示了本发明实施例中的人体电阻抗功率谱的比较。 其中， 上图为膀 胱积尿前期生物电阻抗原始数据的功率谙曲线， 中图为膀胱积尿中期生物电阻 抗原始数据的功率谱曲线， 膀胱积尿后期生物电阻抗原始数据的功率谱曲线。 图 5 为一位正常人的不同时期的膀胱积尿的阻抗数据功率谱， 由此图可知： 正 常人的膀胱积尿的不同阶段， 其自主神经张力是不同的， 即体现其对膀胱积尿 的调节， 如果高低频比变化越显著， 说明自主神经调节作用越明显， 膀胱排尿 功能恢复较好。 而对尿意完全缺失的患者， 即不存在神经调节， 通常是不体现 这种差异性。
优选的， 在本实施例中， 所述排尿报警模块 26、 自主调节功能评估模块 27 还可以连接显示屏以将相关数据发送到显示屏显示出来。
优选的， 在本实施例中， 为了避免测量故障 （如电极接触不良， 电源电量 不足等）造成排尿漏报危险，所述排尿报警模块 26采取一个异常报警保护措施， 即如果测试时间达一定时间 （如 2.5小时）， 仍然没有报警， 则进行排尿强制报 警， 并通知排查测试装置与电极。
优选的， 由于尿量预测方程中的调节权值系数（ ， ， c ) , 因不同类型 的人群、 不同类型的尿意缺失疾病等不同， 其参数设置可基于前期的部分实验 数据， 采用神经网络训练或者经验方法进行调节。 因此， 在本实施例中， 所述 上位机 20还包括与上位机主控模块 25连接的自适应权值修正器， 用于将上位 机主控模块 25计算出来的膀胱积尿量预测值与患者的真实排尿尿量进行对比， 并根据对比结果调节权值 kp' Hc )。
下面， 结合图 3、 图 6〜图 7， 具体描述本发明实施例的基于生物电阻抗的膀 胱积尿实时监测装置的工作过程， 包括：
步骤 S101:初始化
在检测开始前， 必须让患者先排尽尿液才开始测量。 然后在佩戴电极前， 最好要剔除皮肤表面的不洁净物质， 便将多个测试电极（包括激励电极和接收 电极）佩戴在膀胱附近进行检测。
步骤 S102:测量位置设定
输入新的患者个人信息 （S 102a ) 后， 启动多通道开关模块 14 进行测试 ( S102b ), 从而获得最佳测量位置参数（S102c )。
步骤 S103: 开始人体阻抗数据釆集
通过下位机 10的下位机主控模块 12控制所述中频正弦波发生单元 111产生 正弦波激励电流并通过所述压控恒流源单元 112 进行稳压后形成稳定的激励电 流以输入到佩戴在患者测试部位上的一对激励电极上， 然后交流信号接收模块 113从接收电极接收返回电压信号并传送到下位机主控模块 12进行计算， 并将 计算出的人体电阻抗数据通过所述 Zigbee无线发射模块 13射出发送给上位机 20, 并进入步骤 S104和 /或步骤 S108。
步骤 S104: 变频低通滤波处理
上位机的 Zigbee无线接收模块 21接收所述带人体电阻抗数据的信号，首先 经过滤波处理模块 22进行数字变频低通滤波处理。
步驟 S 105: 时域特征提取
利用电阻抗时域特征提取模块 23提取经过滤波处理后的人体电阻抗数据的 时域特征， 如图 7 所示， 所述时域特征至少包括人体电阻抗数据信号的当前电 阻抗值 Xl(k)、 当前电阻抗微分值 x2(k)和当前电阻抗绝对值积分值 x3(k) , 而所 述当前电阻抗值 Xl(k)、 当前电阻抗微分值 x2(k)和当前电阻抗绝对值积分值 x3(k) 可通过上面描述的公式确定， 在此不再重复描述。
步骤 S106: 预测患者的膀胱积尿量
该上位机主控模块 25 , 与所述电阻抗时域特征提基于提取的所述时域特征 计算患者的膀胱积尿量， 其中， 上位机主控模块 25可通过上述的公式 7或公式 8计算预测患者的膀胱积尿量。
步骤 S107: 所述排尿报警模块 26通过将所述上位机主控模块计算出的膀胱 积尿量与预先针对不同患者设定的尿量阈值进行比较， 并判断所述膀胱积尿量 达到和 /或超过所述尿量阈值， 若是， 则发出警报； 否则， 进行异常判断， 例如 当测试时间达一定时间 （如 2.5小时）， 仍然没有报警， 则进行排尿强制报警。
步骤 S 108: 电阻抗频域特征提取
上位机的 Zigbee无线接收模块 21接收所述带人体电阻抗数据的信号后，所 述电阻抗频域特征提取模块 24, 提取所述人体电阻抗数据的不同时期的频域特 征， 所述频域特征至少包括低频能量 LF和高频能量 HF; 其中， 低频能量 LF表 示人体电阻抗数据的功率谱在区间 0.04Hz〜0.15Hz的能量和， 高频能量 HF表示 人体电阻抗数据的功率旙在区间 0.15Hz〜0.40Hz的能量和。
步骤 S109: 计算高低频能量比 LF/HF
所述上位机主控模块 25基于所述时域特征而计算出不同时期的高低频能量 比 LF/HF。
步骤 S110: 评估患者的膀胱排尿自主调节功能
所述自主调节功能评估模块 27通过比较不同时期的高低频能量比 LF/HF而 评估患者的膀胱排尿自主调节功能。
本装置具有低负荷、 便携式、 小型化等特点， 适合于临床、 家庭和个人等
不同应用场合。
综上所述， 本发明提供的基于生物电阻抗的膀胱积尿实时监测方法及装置 至少包括如下有益效果：
1. 首次提出结合生物阻抗当前值、 最小微分值、 绝对值积分、 电极接触电 阻补偿等特征， 可以更加准确地预测膀胱积尿过程中尿量多少， 提高膀胱积尿 实时监测的精度， 更合理进行排尿提醒与报警。
2. 首次提出了阻抗频谱变化规律， 通过对比膀胱积尿前期与后期高低频比 变化， 可以有效地评估膀胱自主神经排尿调节功能， 帮助尿意缺失患者进行康 复训练。
3. 采用数字变频低通滤波算法， 有助于滤掉比呼吸、 心跳等生理活动千扰 更低的极低频扰动， 如深呼吸、 运动伪差引起的极低频干扰， 也避免了直接采 用极低频滤波算法对硬件精度要求过高和容易失真等问题。
以上所述是本发明的优选实施方式， 应当指出， 对于本技术领域的普通技 术人员来说， 在不脱离本发明原理的前提下还可以做出若干改进和变动， 这些 改进和变动也视为本发明的保护范围。 需要说明的是， 采用本发明的方法和测 量装置还可以测量脂肪厚度， 腹腔积水病情， 胃部滞留的食物， 以及呼吸情况 等等， 这些都属于本发明保护的范围。
Claims
1、 一种基于生物电阻抗的膀胱积尿实时监测方法， 其特征在于， 包括： 通过佩戴在患者测试部位上的测试电极， 采集人体电阻抗数据；
根据采集到的人体电阻抗数据， 提取所述人体电阻抗数据的时域特征， 所述时 域特征至少包括当前电阻抗值、 当前电阻抗最小 ί分值和当前电阻抗绝对值积分值； 基于所述时域特征计算患者的膀胱积尿量；
将计算出的膀胱积尿量与预先针对不同患者设定的尿量阈值进行比较， 并在所 述膀胱积尿量达到和 /或超过所述尿量阈值时发出警报。
2、如权利要求 1所述的基于生物电阻抗的膀胱积尿实时监测方法，其特征在于， 基于所述时域特征并通过以下的膀胱积尿量计算函数预测患者的膀胱积尿量：
Y(k)=kP*x1(k)+kD*x2(k)+kl*x3(k)+c k=l,2,3,...
其中， Y(k)、 Xl(k)、 x2(k)、 x3(k)、 kP、 kD、 kl分别表示当前膀胱积尿量、 当前电 阻抗值、 当前电阻抗微分值、 当前电阻抗绝对值积分值、 当前电阻抗值系数、 当前 电阻抗微分值系数、 当前电阻抗绝对值积分值系数， c为修正常数；
x2(k)可由以下函数确定：
x2(k)=0 k=l ,
x2(k)= xj(k-l)- Xl(k) x2(k)≤x2(k-l)且 k=2,3,... ,
x2(k)= x2(k-l) x2(k) > x2(k-l)且 k=2,3" · .
其中， x2(k)、 x2(k-l)、 Xl(k)、 Xl(k-1)分别表示当前电阻抗微分值， 前一时刻电阻 抗敎分值， 当前电阻抗值和前一时刻电阻抗值；
而 x3( )可由以下函数确定：
k
x3 (k) = 2¾ (0 - ·¾ 0' - 1)| k = l,2,3,..
i=\ 其中， χ(Ο,^-Ι)分别表示第 i时刻和第 时刻的电阻抗值。
3、如权利要求 1所述的基于生物电阻抗的膀胱积尿实时监测方法，其特征在于， 基于所述时域特征并通过以下的膀胱积尿量计算函数预测患者的膀胱积尿量：
Y(k)=kP*x1(k)*ecc+kD*x2(k)+kl*x3(k)+c*ecc k=l,2,3" · .
(30 - tt) / 6 tt < 30
cc = { }
0 tt > 30 其中， Y(k)、 Xl(k)、 x2(k)、 x3(k)、 kP、 kD、 kl分别表示当前膀胱积尿量、 当前电 阻抗值、 当前电阻抗敖分值、 当前电阻抗绝对值积分值、 当前电阻抗值系数、 当前
电阻抗微分值系数、 当前电阻抗绝对值积分值系数， C为修正常数， tt表示测量时间， 单位为分钟；
x2(k)可由以下函数确定：
x2(k)=0 k=l ,
x2(k)= xi(k- l)- xi(k) x2(k)≤x2(k-l)且 k=2,3, · .. ,
x2(k)= x2(k- l) x2(k) > x2(k- l)且 k=2,3，. · .
其中， x2(k)、 x2(k-l)、 Xl(k)、 Xl(k-1)分别表示当前电阻抗微分值， 前一时刻电阻 抗微分值， 当前电阻抗值和前一时刻电阻抗值；
而 x3(k)可由以下函数确定：
k
¾3 (^) = ^|^( ) -¾( -1)| k = 1,2,3,··.
i=\ 其中， χ(0,χ( - 1)分别表示第 i时刻和第 -1时刻的电阻抗值。
4、如权利要求 1所述的基于生物电阻抗的膀胱积尿实时监测方法，其特征在于， 还包括步骤：
根据采集到的人体电阻抗数据， 提取所述人体电阻抗数据的不同时期的频域特 征， 所述频域特征至少包括低频能量 LF和高频能量 HF, 其中， 低频能量 LF表示人 体电阻抗数据的功率 i普在区间 0.04Ηζ~0.15Hz的能量和， 高频能量 HF表示人体电阻 抗数据的功率谱在区间 0.15Ηζ~0.40Ηζ的能量和；
基于所述频域特征计算出不同时期的高低频能量比 LF/HF, 并通过比较不同时 期的高低频能量比 LF/HF而评估患者膀胱排尿的自主神经调节功能。
5、如权利要求 1所述的基于生物电阻抗的膀胱积尿实时监测方法，其特征在于， 在提取所述人体电阻抗数据的时域特征前， 还包括步骤：
将采集到的人体电阻抗数据进行数字变频低通滤波处理， 以去除人体电阻抗数 据中的极低频千扰、 呼吸和心跳等人体生理活动干扰以及高频干扰， 从而获得高信 噪比的人体电阻抗数据。
6、 一种基于生物电阻抗的膀胱积尿实时监测装置， 其特征在于， 包括： 至少一个下位机， 所述下位机包括：
交流信号发射接收模块， 用于向佩戴在患者测试部位上的测试电极发射激励电 流以及接收测试电极返回的电压信号；
下位机主控模块， 与所述交流信号发射接收模块连接， 控制所述交流信号发射 接收模块发射激励电流， 并基于所述返回的电压信号计算出人体电阻抗数据；
无线发射模块， 用于将带所述人体电阻抗数据的信号发送；
以及一个上位机， 所述上位机包括：
无线接收模块， 用于接收所述无线发射模块发送的带所述人体电阻抗数据的信 号；
电阻抗时域特征提取模块， 与所述无线接收模块连接， 用于提取所述人体电阻 抗数据的时域特征， 所述时域特征至少包括当前电阻抗值、 当前电阻抗最小微分值 和当前电阻抗绝对值积分值；
上位机主控模块， 与所述电阻抗时域特征提取模块连接， 基于所述时域特征计 算患者的膀胱积尿量；
排尿报警模块， 与所述上位机主控模块连接， 用于将所述上位机主控模块计算 出的膀胱积尿量与预先针对不同患者设定的尿量阈值进行比较， 并在所述膀胱积尿 量达到和 /或超过所述尿量阈值时发出警报。
7、如权利要求 6所述的基于生物电阻抗的膀胱积尿实时监测装置，其特征在于， 所述上位机主控模块可基于所述时域特征并通过以下的膀胱积尿量计算患者的膀胱 积尿量：
Y(k)=kP*x1(k)+kD*x2(k)+kl*x3(k)+c k=l,2,3,...
其中， Y(k)、 Xl(k), x2(k)、 x3(k)、 kP、 kD、 kl分别表示当前膀胱积尿量、 当前电 阻抗值、 当前电阻抗孩分值、 当前电阻抗绝对值积分值、 当前电阻抗值系数、 当前 电阻抗微分值系数、 当前电阻抗绝对值积分值系数， c为修正常数；
x2(k)可由以下函数确定：
x2(k)=0 k=l ,
x2(k)= xj(k-l)- Xl(k) x2(k)≤x2(k-l)且 k=2,3" ..，
x2(k)= x2(k-l) x2(k) > x2(k-l)且 k=2，3，. · .
其中， x2(k)、 x2(k-l)、 Xl(k)、 Xl(k-1)分别表示当前电阻抗微分值， 前一时刻电阻 抗微分值， 当前电阻抗值和前一时刻电阻抗值；
而 x3(k)可由以下函数确定： x3 (k) - (ί) - x{ (i - 1)| k = 1,2,3,···
8、如权利要求 6所述的基于生物电阻抗的膀胱积尿实时监测装置，其特征在于， 所述上位机主控模块可基于所述时域特征并通过以下的膀胱积尿量计算患者的膀胱 积尿量：
Y(k)=kP*x1(k)*ecc+kD*x2(k)+kl*x3(k)+c*ecc k=l,2,3,...
(30— ίί)/6 « < 30
cc = { }
0 tt≥ 30 其中， Y(k)、 Xl(k)、 x2(k)、 x3(k)、 kP、 kD、 kl分别表示当前膀胱积尿量、 当前电 阻抗值、 当前电阻抗敖分值、 当前电阻抗绝对值积分值、 当前电阻抗值系数、 当前 电阻抗微分值系数、 当前电阻抗绝对值积分值系数， c为修正常数， tt表示测量时间， 单位为分钟；
x2(k)可由以下函数确定：
x2(k)=0 k=l ,
x2(k)= xi(k-l)- xi(k) x2(k)≤x2(k-l)且 k=2,3, · .. ,
x2(k)= x2(k-l) x2(k) > x2(k-l)且 k=2,3，. · .
其中， x2(k)、 x2(k-l)、 Xl(k). Xl(k-1)分别表示当前电阻抗微分值， 前一时刻电阻 抗1分值， 当前电阻抗值和前一时刻电阻抗值；
而 x3(k)可由以下函数确定：
k
x3 (^) = H (0 - Λ: ' - 1)| k = 1,2,3,···
i=l 其中， x(0, x( - l)分别表示第 i时刻和第 -1时刻的电阻抗值。
9、 如权利要求 6或 7所述的基于生物电阻抗的膀胱积尿实时监测装置， 其特征 在于， 所述上位机还包括分别与所述上位机主控模块连接的电阻抗频域特征提取模 块、 自主调节功能评估模块；
所述电阻抗频域特征提取模块用于提取所述人体电阻抗数据的不同时期的频域 特征， 所述频域特征至少包括低频能量 LF和高频能量 HF, 其中， 低频能量 LF表示 人体电阻抗数据的功率 i香在区间 0.04Hz〜0.15Hz的能量和， 高频能量 HF表示人体电 阻抗数据的功率谱在区间 0.15Hz~0.40Hz的能量和；
所述上位机主控模块基于所述时域特征而计算出不同时期的高低频能量比 LF/HF;
所述自主调节功能评估模块通过比较不同时期的高低频能量比 LF/HF而评估患 者的膀胱排尿自主调节功能。
10、 如权利要求 6 所述的基于生物电阻抗的膀胱积尿实时监测装置， 其特征在 于， 所述上位机还包括连接于所述无线接收模块和电阻抗时域特征提取模块之间的 滤波处理模块， 用于将无线接收模块接收到的人体电阻抗数据进行数字变频低通滤 波处理， 以去除人体电阻抗数据中的极低频干扰、 呼吸和心跳等人体生理活动干扰 以及高频千扰， 从而获得高信噪比的人体电阻抗数据。
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Families Citing this family (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103340639A (zh) * | 2013-07-26 | 2013-10-09 | 中山大学 | 一种基于生物电阻抗的急迫性尿失禁识别方法 |
| CN103445770B (zh) * | 2013-09-02 | 2015-04-22 | 中山大学 | 排尿感知检测方法及装置 |
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| CN103622693A (zh) * | 2013-11-28 | 2014-03-12 | 中山大学 | 一种便携式生物电阻抗测量装置 |
| CN103598886B (zh) * | 2013-11-28 | 2015-10-28 | 中山大学 | 基于模型补偿法监测积尿过程中动态尿量的方法 |
| US20160120454A1 (en) | 2014-11-04 | 2016-05-05 | Stichting Imec Nederland | System for monitoring incontinence |
| US20160120455A1 (en) | 2014-11-04 | 2016-05-05 | Stichting Imec Nederland | Method for monitoring incontinence |
| NL2013734B1 (en) * | 2014-11-04 | 2016-09-07 | Stichting Imec Nederland | A system for monitoring incontinence |
| NL2013738B1 (en) * | 2014-11-04 | 2016-09-07 | Stichting Imec Nederland | A method for monitoring incontinence. |
| NL2013740B1 (en) * | 2014-11-04 | 2016-09-07 | Stichting Imec Nederland | A system for monitoring incontinence. |
| CN104605850B (zh) * | 2015-01-19 | 2017-08-01 | 中山大学 | 一种基于电阻抗断层成像技术的膀胱尿量实时监测装置和方法 |
| JP7000342B2 (ja) * | 2016-04-12 | 2022-01-19 | ゴーゴー バンド，インコーポレイテッド | 排尿の予測およびモニタリング |
| US11083392B2 (en) * | 2017-07-13 | 2021-08-10 | Samsung Electronics Co., Ltd. | Bio-processor, bio-signal detecting system, and operation method of bio-processor |
| CN110151204A (zh) * | 2019-06-23 | 2019-08-23 | 复旦大学 | 可穿戴非侵入式膀胱容量监测报警装置 |
| CN110840457B (zh) * | 2019-12-12 | 2020-11-13 | 北京航空航天大学 | 一种基于边缘场检测的二维eit电极阵列结构优化方法 |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH09206292A (ja) * | 1996-01-31 | 1997-08-12 | Soatec:Kk | 尿失禁検知センサおよびこれを用いた尿失禁報知装置 |
| US20080300650A1 (en) * | 2007-05-30 | 2008-12-04 | Medtronic, Inc. | Implantable medical lead including voiding event sensor |
| JP2011078791A (ja) * | 2010-10-27 | 2011-04-21 | Kao Corp | 排泄検知装置 |
| CN102551712A (zh) * | 2011-06-13 | 2012-07-11 | 广州安德生物科技有限公司 | 基于生物电阻抗的非侵入式排尿报警装置及监测方法 |
| CN101940469B (zh) * | 2010-06-29 | 2012-09-05 | 广州安德生物科技有限公司 | 一种便携式膀胱尿量检测装置 |
-
2013
- 2013-03-13 CN CN201310080354.7A patent/CN103126672B/zh not_active Expired - Fee Related
- 2013-03-15 WO PCT/CN2013/072682 patent/WO2014139143A1/zh active Application Filing
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH09206292A (ja) * | 1996-01-31 | 1997-08-12 | Soatec:Kk | 尿失禁検知センサおよびこれを用いた尿失禁報知装置 |
| US20080300650A1 (en) * | 2007-05-30 | 2008-12-04 | Medtronic, Inc. | Implantable medical lead including voiding event sensor |
| CN101940469B (zh) * | 2010-06-29 | 2012-09-05 | 广州安德生物科技有限公司 | 一种便携式膀胱尿量检测装置 |
| JP2011078791A (ja) * | 2010-10-27 | 2011-04-21 | Kao Corp | 排泄検知装置 |
| CN102551712A (zh) * | 2011-06-13 | 2012-07-11 | 广州安德生物科技有限公司 | 基于生物电阻抗的非侵入式排尿报警装置及监测方法 |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107320102A (zh) * | 2017-08-01 | 2017-11-07 | 广州安德生物科技有限公司 | 一种基于预测模型的膀胱排尿时间提醒方法及评估装置 |
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