Dialysis patient household dry weight management electronic scale and method based on bioelectrical impedance

Abstract

The invention discloses a dialysis patient household dry weight management electronic scale and method based on bioelectrical impedance, and belongs to the technical field of biomedical signal detection and hemodialysis adjuvant therapy, and the method comprises the steps: collecting a multi-frequency complex impedance signal of a dialysis patient, and constructing an impedance frequency spectrum track through a Cole-Cole model based on dialysis population feature correction; self-adaptive filtering is carried out on the track in combination with a time sequence analysis technology, and high-frequency noise caused by electrode contact and electrolyte unsteady-state flow is eliminated; separating the impedance into a long-term vector drift component representing tissue mass change and a short-term modulus change component representing extracellular fluid fluctuation by using a layered decoupling algorithm; the dry tissue change and the excess moisture removal amount are calculated according to the two types of components, the dry body weight target value is calculated in combination with the current body weight, the dry body weight target value is dynamically corrected based on the substantive change of the physiological components, and therefore the risk of excessive ultrafiltration or insufficient dialysis caused by evaluation misjudgment is avoided.

Description

Technical Field

[0001] This invention relates to the field of biomedical signal detection and hemodialysis-assisted treatment, specifically to an electronic scale and method for managing dry weight at home for dialysis patients based on bioelectrical impedance. Background Technology

[0002] With the popularization of hemodialysis treatment technology, accurate assessment of the patient's dry weight is crucial for developing a reasonable dialysis dehydration plan. Dialysis patients often have electrolyte imbalances and uneven distribution of body fluids, such as ascites, pleural effusion, or tissue edema, which are third-space effusions. Currently, in clinical practice, traditional bioelectrical impedance analysis or setting dry weight based on physician experience is commonly used. However, conventional measurement models are usually based on the assumption that the human body is uniformly conductive, which makes it difficult to adapt to the pathological fluid retention characteristics of dialysis patients. They are also susceptible to high-frequency noise interference caused by unstable electrode contact and unsteady electrolyte flow in the body. More importantly, existing technologies cannot effectively distinguish between substantial changes in tissue quality and pathological fluctuations in extracellular fluid volume, making it impossible to dynamically adjust the baseline dry weight value. Such misjudgment may lead to excessive dehydration due to setting the dry weight too low, or water retention due to setting it too high. Therefore, how to eliminate non-physiological noise interference, accurately distinguish between tissue growth and fluid retention, and thus obtain accurate dry weight target values ​​has become an urgent problem to be solved in this field.

[0003] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure and therefore does not constitute information about prior art known to those skilled in the art. Summary of the Invention

[0004] The purpose of this invention is to provide a home dry weight management electronic scale and method for dialysis patients based on bioelectrical impedance analysis, in order to solve the problems mentioned in the background art. The technical solution of this invention includes: A multi-frequency excitation current covering low to high frequencies is applied to the dialysis patient through a multi-frequency signal generation module, and the multi-frequency complex impedance signal of the dialysis patient is acquired through a signal acquisition module. The multi-frequency complex impedance signal was fitted using a Cole-Cole model modified based on the characteristics of dialysis patients, and an impedance spectrum trajectory containing resistive and reactive components was constructed. By combining time series analysis techniques, the impedance spectrum trajectory is adaptively filtered to eliminate high-frequency noise interference caused by unstable electrode contact and unsteady flow of electrolyte in the body; The impedance spectrum trajectory after filtering is processed by a hierarchical decoupling algorithm, and the impedance change is separated into a long-term impedance vector drift component and a short-term impedance modulus change component. The long-term impedance vector drift component is the phase angle shift trend of the complex impedance vector on the complex plane extracted based on a preset long-period time window, which represents the substantial change in tissue quality. The short-term impedance modulus change component is the impedance modulus oscillation amplitude extracted based on a preset short-period time window, which represents the volume fluctuation of extracellular fluid. Based on the long-term impedance vector drift component, the tissue mass change is calculated using a preset tissue density conversion coefficient. The excess water removal is calculated based on the short-term impedance modulus change component. Based on the current body weight, the change in lean body weight, and the excess water removal, the target dry weight value for the dialysis patient is calculated.

[0005] Preferably, the step of fitting the multi-frequency complex impedance signal using a Cole-Cole model modified based on dialysis population characteristics specifically includes: A modified Cole-Cole model equation that incorporates a non-uniform distribution factor was constructed. The non-uniform distribution factor is a correction coefficient set for the characteristics of third-space effusion in dialysis patients, used to compensate for non-uniform distortion of the current path caused by ascites or tissue edema. The model parameters are calculated iteratively using the least squares method to obtain characteristic parameters that can characterize cell membrane capacitance and intracellular / extracellular fluid resistance, thereby establishing the impedance spectrum trajectory.

[0006] Preferably, the step of adaptively filtering the impedance spectrum trajectory using time series analysis techniques includes: Real-time monitoring of the time series data of the multi-frequency complex impedance signal to identify nonlinear drift signals; The nonlinear drift signal is analyzed using wavelet transform or moving average algorithms to separate high-frequency fluctuation features with frequencies higher than a preset physiological fluctuation threshold. The high-frequency fluctuation characteristics are identified as contact impedance noise or electrolyte transient fluctuation noise, and are filtered out from the original signal.

[0007] Preferably, the step of separating the impedance change into a long-term impedance vector drift component and a short-term impedance modulus change component based on the hierarchical decoupling algorithm includes: A time threshold is set to distinguish between physiological tissue growth and fluid retention, and the time threshold is set to be between 24 hours and 72 hours; The trajectory movement trend of impedance vectors with a time span greater than the time threshold on the complex impedance plane is extracted as the long-term impedance vector drift component and associated with the muscle or fat growth model. The impedance modulus changes with a time span less than the time threshold and exhibiting periodic recovery characteristics are extracted as the short-term impedance modulus change component and correlated with the extracellular fluid retention model.

[0008] Preferably, after the step of calculating the target dry weight value of the dialysis patient, the method further includes: Generate a dialysis dehydration plan for the next dialysis treatment; The total ultrafiltration volume of the dialysis dehydration program is set as: current body weight minus the target dry body weight; In calculating the total ultrafiltration volume, the dry weight baseline value is positively or negatively corrected using the tissue mass increment corresponding to the long-term impedance vector drift component, so as to eliminate misjudgment of dry weight assessment caused by the patient's actual weight gain or loss.

[0009] A bioelectrical impedance-based home dry weight management electronic scale device for dialysis patients, comprising: A multi-frequency measurement base, including excitation and detection electrodes, is used to attach to the body surface of a dialysis patient to establish a current loop and voltage detection point; The signal processing unit, electrically connected to the multi-frequency measurement base, includes a memory and a processor; The memory stores a computer program, which, when executed by the processor, implements the method steps.

[0010] Preferably, the signal processing unit further includes: The dynamic tracking module is configured to establish a personal historical impedance database for each patient and record the historical trajectory of the long-term impedance vector drift components in real time. The interactive interface is connected to the dynamic tracking module and configured to graphically display the long-term trend curve representing changes in tissue quality and the short-term oscillation curve representing fluid fluctuations, and intuitively prompt the recommended dry weight adjustment value.

[0011] This invention provides a home dry weight management electronic scale and method for dialysis patients based on bioelectrical impedance, which has the following improvements and advantages compared with the prior art: 1. This invention effectively overcomes the shortcomings of traditional bioimpedance technology, which cannot distinguish between the growth of solid tissue and pathological fluid retention in dialysis patients, by employing a time-series-based hierarchical decoupling algorithm. The system accurately separates impedance changes into long-term impedance vector drift components that characterize tissue quality and short-term impedance modulus change components that characterize extracellular fluid volume. This decoupling process enables the electronic scale to eliminate interference caused by the patient's actual weight gain or loss in dry weight assessment. It no longer relies solely on changes in total weight, but dynamically corrects the target dry weight value based on substantial changes in physiological components, thereby avoiding the risk of over-filtration or inadequate dialysis due to misjudgment. 2. This invention constructs a modified Cole-Cole model that incorporates a non-uniform distribution factor, specifically optimized for the unique physiological and pathological characteristics of dialysis patients. Traditional general models often assume uniform conductivity in the human body, while dialysis patients frequently experience ascites or severe tissue edema, leading to non-uniform distortion of the current path. This application compensates for this by using a non-uniform distribution factor and iteratively calculates feature parameters using the least squares method, which can more realistically restore the characteristics of cell membrane capacitance and intracellular / extracellular fluid resistance. This significantly improves the model's robustness in the face of dialysis complications, ensuring that accurate impedance spectrum trajectories can still be obtained under abnormal fluid distribution conditions. 3. This invention combines multi-frequency excitation with time-series adaptive filtering technology, significantly improving the signal-to-noise ratio of data in home self-testing scenarios. Addressing potential electrode contact instability and transient electrolyte fluctuations in the home environment, the system monitors the time series of multi-frequency complex impedance signals in real time, using wavelet transform or moving average algorithms to accurately identify and eliminate high-frequency fluctuation features. This processing method effectively filters out nonlinear drift noise caused by improper operation or non-physiological factors, solving the problem of large data fluctuations and low reliability in home electronic scales under non-professional operation, and providing a high-quality, highly stable raw data foundation for subsequent dry weight calculation. 4. This invention goes beyond just weight measurement; it further enables the generation of dialysis dehydration plans for the next treatment. By combining the calculated excess water removal amount with a dry weight baseline value corrected for tissue quality, the system can automatically provide recommendations including the specific total ultrafiltration volume. With the dynamic tracking module and graphical display interface, patients can intuitively see the trend curve representing long-term tissue changes and the oscillation curve representing short-term fluid fluctuations. This visual feedback mechanism helps patients understand their own fluid status, avoids blindly controlling fluid levels or excessive anxiety, effectively reduces the probability of dialysis-related hypotension and cardiovascular complications, and achieves scientific closed-loop management of dry weight at home. Attached Figure Description

[0012] The present invention will be further explained below with reference to the accompanying drawings and embodiments: Figure 1 This is a schematic diagram of the overall structure of the device; Figure 2 This is a schematic diagram of the structure of the multi-frequency measurement base; Figure 3 This is a schematic diagram of the signal processing unit. Figure 4 This is a schematic diagram of the process flow of the method of the present invention.

[0013] In the diagram: 100, multi-frequency measuring base; 110, excitation electrode; 120, detection electrode; 200, signal processing unit; 210, memory; 220, processor; 230, dynamic tracking module; 240, display interface. Detailed Implementation

[0014] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0015] Example 1:

[0016] Please see Figure 1-4 A home-based dry weight management method for dialysis patients based on bioelectrical impedance includes: applying a multi-frequency excitation current covering low to high frequencies to the dialysis patient through a multi-frequency signal generation module, and acquiring the multi-frequency complex impedance signal of the dialysis patient through a signal acquisition module; The multi-frequency complex impedance signal was fitted using a Cole-Cole model modified based on the characteristics of dialysis patients, and an impedance spectrum trajectory containing resistive and reactive components was constructed. By combining time series analysis techniques, the impedance spectrum trajectory is adaptively filtered to eliminate high-frequency noise interference caused by unstable electrode contact and unsteady flow of electrolyte in the body; The impedance spectrum trajectory after filtering is processed by a hierarchical decoupling algorithm, and the impedance change is separated into a long-term impedance vector drift component and a short-term impedance modulus change component. The long-term impedance vector drift component is the phase angle shift trend of the complex impedance vector on the complex plane extracted based on a preset long-period time window, which represents the substantial change in tissue quality. The short-term impedance modulus change component is the impedance modulus oscillation amplitude extracted based on a preset short-period time window, which represents the volume fluctuation of extracellular fluid. Based on the long-term impedance vector drift component, the tissue mass change is calculated using a preset tissue density conversion coefficient. The excess water clearance is calculated based on the short-term impedance modulus change component. Based on the current body weight, the change in lean body weight, and the excess water clearance, the target dry weight value for dialysis patients is calculated.

[0017] In this embodiment, to address the technical problem of electrolyte imbalance and non-uniform distribution of body fluids in dialysis patients, such as the failure of conventional BIA measurements due to fluid accumulation in the third interstitial space, this method adopts a strategy of multi-frequency complex impedance vector analysis combined with hierarchical decoupling. In the specific execution process, the excitation current frequency range applied by the multi-frequency signal generation module is set to 1kHz to 1000kHz, containing at least 50 frequency points. These 50 frequency points are evenly distributed on the logarithmic coordinate axis to ensure sufficient sampling density in both low-frequency and high-frequency bands, so as to ensure that the cell membrane can be penetrated and the impedance characteristics of extracellular and intracellular fluid can be fully covered. The acquired multi-frequency complex impedance signal is not a single value, but contains a complex sequence of real part, i.e., resistance R and imaginary part, i.e. reactance Xc. In the fitting process, considering the pathological fluid retention in dialysis patients, the conventional Cole-Cole model cannot accurately describe its electrical characteristics. Therefore, a modified model is introduced to construct an impedance spectrum trajectory, which reflects the semi-circular arc characteristic of impedance change with frequency. Adaptive filtering using time series analysis techniques is a key step because changes in skin dryness, slight body movements, or drastic electrolyte shifts after dialysis can introduce non-physiological high-frequency noise into the signal in the home measurement environment of dialysis patients. This step ensures the signal-to-noise ratio of the data in subsequent analysis. The core layered decoupling algorithm solves the pain point of distinguishing between muscle gain and water retention. From a pathophysiological perspective, the growth and consumption of muscle or fat tissue is a slow metabolic process, which is represented by an impedance vector on the complex impedance plane, i.e., the long-term directional drift of the combined vector of phase angle and modulus. In contrast, the fluid increase during interdialysis is rapid and periodic, mainly manifested as short-period oscillations in the impedance modulus Z as the fluid accumulates. By separating these two components, the true change in lean body weight and the excess water that needs to be removed by dialysis can be calculated independently, thus avoiding misdiagnosis of edema due to patient weight gain or dehydration due to patient weight loss, and ultimately outputting an accurate target dry weight value.

[0018] The steps for fitting multi-frequency complex impedance signals using a Cole-Cole model modified based on dialysis population characteristics include: constructing a modified Cole-Cole model equation that incorporates a non-uniform distribution factor; the non-uniform distribution factor is a correction coefficient set for the characteristics of third-space effusion in dialysis patients, used to compensate for non-uniform distortion of the current path caused by ascites or tissue edema; and iteratively calculating model parameters using the least squares method to obtain characteristic parameters that characterize cell membrane capacitance and intracellular / extracellular fluid resistance, thereby establishing the impedance spectrum trajectory.

[0019] In this embodiment, the modified Cole-Cole model is the core means to solve the influence of pathological body fluid distribution on measurement accuracy. The traditional Cole-Cole model is based on the assumption that the human body components are uniformly conductive. However, dialysis patients often have ascites, pleural effusion or severe lower extremity edema. These fluid accumulations, known as the third space, will cause non-uniform distortion of the current path, which will change the low-frequency current flow path and cause abnormal high-frequency current penetration characteristics. Therefore, a non-uniform distribution factor is introduced into the modified model equation constructed in this embodiment. This factor is a dimensionless correction coefficient, and its corresponding exponential term The value range is calibrated through a large amount of clinical data from dialysis patients and is usually between 0.6 and 0.9. This factor is used to correct the diffusion coefficient in the standard model and specifically compensates for the impedance arc flattening phenomenon caused by excessive accumulation of extracellular fluid. When individual calibration data is lacking, the initial value of this factor is preferably set to 0.75. In the specific calculation, the processor 220 uses a nonlinear least squares method to iteratively fit the collected multi-frequency complex impedance data points; the goal of the iteration is to minimize the mean square error between the measured data points and the theoretical model curve; after convergence calculation, three key feature parameters are extracted: Zero-frequency resistance represents extracellular fluid impedance. Infinite frequency resistance represents the total impedance of intracellular and extracellular fluids and its characteristic time constant. Processor 220 utilizes or The film capacitance can be calculated from the equivalent circuit relationship. This set of parameters constitutes an impedance spectrum trajectory that not only reflects water content but also indirectly reflects cell membrane integrity and nutritional status, providing a pathologically corrected physical benchmark for subsequent dry weight assessment. Specifically, the modified Cole-Cole model equation invoked by processor 220 is as follows: in, Indicates angular frequency as Complex impedance at time, The imaginary unit; where, For zero-frequency resistance, For infinite frequency resistors, The characteristic time constant, This refers to the aforementioned non-uniform distribution factor; In the iterative calculation process of the least squares method, the preset... The value range is 0.6 to 0.9, which is used as the initial guess or boundary constraint for the iterative algorithm; in this preferred embodiment, to accommodate individual differences, It is set as the fourth variable to be fitted; processor 220 constructs the objective function, which is the weighted mean square error function of the measured impedance point and the calculated value of the above equation. The Levenberg-Marquardt algorithm is used to find the parameter combination that minimizes the objective function, thereby simultaneously solving for... , , And regarding the patient's current situation value.

[0020] The steps for adaptive filtering of impedance spectrum trajectories using time series analysis techniques include: real-time monitoring of time series data of multi-frequency complex impedance signals to identify nonlinear drift signals; analysis of nonlinear drift signals using wavelet transform or moving average algorithms to separate high-frequency fluctuation features with frequencies higher than a preset physiological fluctuation threshold; identification of high-frequency fluctuation features as contact impedance noise or electrolyte instantaneous fluctuation noise, and filtering them out from the original signal.

[0021] In this embodiment, adaptive filtering aims to improve signal quality in home monitoring scenarios. Since dialysis patients may perform measurements without professional guidance, the contact impedance between the electrode patch and the skin is easily affected by the pressure applied, skin humidity, and ambient temperature, resulting in unstable contact noise. In addition, shortly after dialysis, the body's electrolytes are in the process of rebalancing from the blood to the interstitial space. This intense flow of micro-ions will manifest as nonlinear instantaneous fluctuations in the macroscopic impedance signal. The system monitors the continuously sampled multi-frequency complex impedance signal sequence in real time. When a signal with a mutation rate exceeding the physiological limit is detected, such as a nonlinear drift signal where the impedance value changes by more than 5% within 1 second, the filtering program is started. The wavelet transform algorithm is preferred, and the signal is decomposed into 3 to 5 levels using the Daubechies wavelet basis of type db4 or db6. The preset physiological fluctuation threshold is set based on the natural metabolic rate of human body fluids. For example, the frequency of changes caused by the natural evaporation of human body water or bladder fullness is extremely low. Any signal features with a frequency significantly higher than this threshold are considered non-physiological interferences.

[0022] In this embodiment, the physiological fluctuation threshold is specifically set as follows: This means filtering out fluctuation signals with a period of less than 20 seconds. This threshold setting eliminates extremely low-frequency metabolic drift in the human body while retaining the modulation information of impedance by heart rate and respiratory rate. High-frequency fluctuation characteristic coefficients above this threshold are judged by the system as invalid noise components, such as contact jitter or electrolyte turbulence. By setting these high-frequency coefficients to zero and then performing wavelet reconstruction, or by using a weighted moving average algorithm for smoothing, interference can be effectively filtered out, and low-frequency stable signals reflecting the true state of tissues and body fluids can be retained, ensuring that the input data for dry weight calculation is true and reliable.

[0023] Based on the hierarchical decoupling algorithm, the steps to separate impedance changes into long-term impedance vector drift components and short-term impedance modulus change components include: setting a time threshold to distinguish between physiological tissue growth and fluid retention, with the time threshold set between 24 and 72 hours; extracting the trajectory movement trend of impedance vectors with a time span greater than the time threshold on the complex impedance plane as the long-term impedance vector drift component, and associating it with the muscle or fat growth model; and extracting impedance modulus changes with a time span less than the time threshold and exhibiting periodic recovery characteristics as the short-term impedance modulus change component, and associating it with the extracellular fluid retention model.

[0024] In this embodiment, the layered decoupling algorithm is the key logic for achieving the separation of nutrients and water; the time threshold of 24 to 72 hours is based on the regular cycle of dialysis treatment, which is usually once every other day or every two days, as well as the physiological cycle of human tissue synthesis. For long-term impedance vector drift components: Processor 220 analyzes historical data trends spanning more than a week or a month; if, on the complex impedance plane, the impedance vector as a whole experiences a sustained phase angle shift or a shortening of vector length along the major axis of the first quadrant, this typically does not correspond to water fluctuations during dialysis cycles, but rather to changes in cell membrane integrity or increases / decreases in viable cell mass; the system correlates this trend with muscle or fat growth models, for example, an increase in phase angle typically indicates an increase in muscle mass or an improvement in nutritional status; processor 220 then uses linear regression equations... Calculate the change in tissue quality ,in This represents the phase angle offset at a frequency of 50kHz. A mass conversion constant is set based on statistical data of the dialysis population. This constant can be calibrated according to the patient's gender or BMI index. It is preferably set to 0.5 kg / degree in the initial setting and is used to map changes in angle to changes in mass. For short-term impedance modulus variation components: the processor 220 focuses on data within the dialysis interval, such as within 48 hours; during this period, the impedance modulus... It exhibits a sawtooth-like periodic change—lowest before dialysis, i.e., highest water load, and highest after dialysis, i.e., lowest water load. This signal fluctuation with obvious periodic recovery characteristics is identified by the system as a simple change in fluid volume and associated with the extracellular fluid retention model. Through this dual separation of time and frequency domain characteristics, the system can accurately inform the doctor whether the patient's weight gain is due to long-term drift of the impedance vector or short-term decrease in modulus.

[0025] After calculating the target dry weight for dialysis patients, the process also includes: generating a dialysis dehydration plan for the next dialysis treatment; the total ultrafiltration volume of the dialysis dehydration plan is set as: current weight minus the target dry weight; wherein, when calculating the total ultrafiltration volume, the dry weight baseline value is positively or negatively corrected using the tissue mass increment corresponding to the long-term impedance vector drift component, in order to eliminate misjudgments in dry weight assessment caused by the patient's actual weight gain or loss.

[0026] In this embodiment, the technical solution is ultimately transformed into a dialysis dehydration plan with clinical guidance. Traditional dry weight settings are often static, and doctors may only adjust them every few weeks. This can lead to patients developing hypotension due to excessive dehydration caused by setting the dry weight too low after gaining weight, or heart failure due to water retention caused by setting the dry weight too high after losing weight. The system calculates the baseline ultrafiltration volume (current weight - historical dry weight). For patients using this system, the historical dry weight is initialized by reviewing the most recent clinically set dry weight record or by calculating the initial value using the standard body composition formula. A correction mechanism is introduced: if the long-term impedance vector drift component shows that the patient has recently had a significant increase in tissue mass, such as an increase of 0.5 kg of muscle, the system will automatically increase the dry weight baseline value by 0.5 kg; conversely, if a decrease in tissue mass is detected, the dry weight baseline value will be decreased. The generated ultrafiltration volume = current body weight - (historical dry body weight ± tissue mass correction amount); this calculation result is directly output to medical staff or transmitted to the dialysis machine via an interface as a reference for setting the ultrafiltration volume; this dynamic correction mechanism eliminates misjudgment of dry body weight due to changes in the patient's physical weight, reducing the risk of dialysis complications.

[0027] Example 2: Please see Figure 1-3 The multi-frequency measurement base 100 includes an excitation electrode 110 and a detection electrode 120, which are used to attach to the body surface of a dialysis patient to establish a current loop and a voltage detection point; the signal processing unit 200 is electrically connected to the multi-frequency measurement base 100 and includes a memory 210 and a processor 220; the memory 210 stores a computer program, and when the computer program is executed by the processor 220, it implements the method steps.

[0028] In this embodiment, the assessment device is designed as a portable or wearable device, suitable for home and clinical settings. The multi-frequency measurement base 100 is configured with a four-electrode method, including a pair of excitation electrodes 110 and a pair of detection electrodes 120. The excitation electrodes 110 are typically attached to the distal ends of the patient's back of hand and foot to inject a weak multi-frequency alternating current, such as a safe current below 500 μA. The detection electrodes 120 are attached to the wrist and ankle to measure the voltage drop between the two points. This separate design eliminates the influence of electrode-skin contact resistance on the measurement results. The signal processing unit 200 is the core of the device, integrating a high-precision ADC analog-to-digital converter and a DSP digital signal processor 220. The memory 210 pre-stores the aforementioned modified Cole-Cole model parameter library and the hierarchical decoupling algorithm program. When the processor 220 executes the program, it controls the current source to emit a sweep frequency signal, synchronously acquires the voltage response, and runs adaptive filtering and vector analysis algorithms in real time, ultimately calculating the dry weight data locally. The device can also be equipped with a wireless communication module to upload the processed data to a cloud server or a doctor's terminal.

[0029] The signal processing unit 200 also includes: a dynamic tracking module 230, configured to establish a historical impedance database for each patient and record the historical trajectory of the long-term impedance vector drift component in real time; and a display interface 240, connected to the dynamic tracking module 230, configured to graphically display the long-term trend curve representing changes in tissue mass and the short-term oscillation curve representing fluid fluctuations, and intuitively prompt the recommended dry weight adjustment value.

[0030] In this embodiment, in order to enhance the user's understanding of complex data, the device is equipped with a dynamic tracking module 230 and a visual display interface 240; the dynamic tracking module 230 constructs a structured database indexed by timestamp in the memory 210, which not only stores single measurement values, but also continuously accumulates the patient's historical impedance trajectory to form personalized baseline data. The interactive interface 240, such as an LCD touchscreen or a matching mobile APP interface, transforms the abstract algorithm results into intuitive charts. The interface displays two key curves through a dual-axis coordinate system: one is a smooth, slowly changing nutrition / tissue trend line, corresponding to long-term impedance vector drift, reflecting whether the patient has become thinner or stronger; the other is a sawtooth-shaped fluctuating fluid state line, corresponding to short-term impedance modulus changes, reflecting the current degree of edema. Below or to the side of the chart, the interface will prominently display the recommended dry weight adjustment value, for example: it is recommended to increase the dry weight by 0.3 kg, and use color coding, such as green to indicate that the target has been met, and red to indicate a warning indicating the current fluid overload risk level; this intuitive interactive design allows patients without a deep medical background to understand their own fluid and nutritional status, thereby better cooperating with dialysis treatment management.

[0031] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A bioelectrical impedance based home dry weight management method for dialysis patients, characterized in that, The method comprises the steps of: applying a multi-frequency excitation current covering low frequency to high frequency to a dialysis patient through a multi-frequency signal generation module, and acquiring a multi-frequency complex impedance signal of the dialysis patient through a signal acquisition module; fitting the multi-frequency complex impedance signal by using a Cole-Cole model based on correction of dialysis population characteristics, and constructing an impedance spectrum track containing a resistance component and a reactance component; combining time series analysis technology to perform adaptive filtering on the impedance spectrum track, and removing high-frequency noise interference caused by unstable electrode contact and non-steady-state flow of electrolytes in the body; based on a hierarchical decoupling algorithm, separating the impedance change into a long-term impedance vector drift component and a short-term impedance modulus change component; wherein the long-term impedance vector drift component is a phase angle offset trend of the complex impedance vector extracted based on a preset long-period time window on the complex plane, representing substantial changes in tissue mass; the short-term impedance modulus change component is an impedance modulus oscillation amplitude extracted based on a preset short-period time window, representing volume fluctuations of extracellular fluid; according to the long-term impedance vector drift component, calculating the tissue mass change amount using a preset tissue density conversion coefficient, calculating the excess water removal amount according to the short-term impedance modulus change component, and calculating the dry body weight target value of the dialysis patient based on the current body weight, the change amount of lean body weight and the excess water removal amount.

2. The bioelectrical impedance-based home dry weight management method for a dialysis patient according to claim 1, characterized in that, The step of fitting the multi-frequency complex impedance signal by using a Cole-Cole model based on correction of dialysis population characteristics specifically comprises: constructing a modified Cole-Cole model equation introducing a non-uniform distribution factor; the non-uniform distribution factor is a correction coefficient set for the third space fluid accumulation characteristics of dialysis patients, used to compensate for the non-uniform distortion of the current path caused by ascites or tissue edema; iteratively calculating the model parameters by the least square method to obtain characteristic parameters that can represent the cell membrane capacitance and intracellular / extracellular fluid resistance, thereby establishing the impedance spectrum track.

3. The bioelectrical impedance-based home dry weight management method for a dialysis patient of claim 1, wherein, The step of combining time series analysis technology to perform adaptive filtering on the impedance spectrum track comprises: real-time monitoring of time series data of the multi-frequency complex impedance signal to identify non-linear drift signals; using wavelet transform or moving average algorithm to analyze the non-linear drift signals to separate high-frequency fluctuation characteristics with a frequency higher than a preset physiological fluctuation threshold; determining the high-frequency fluctuation characteristics as contact impedance noise or electrolyte transient fluctuation noise, and filtering them out from the original signal.

4. The bioelectrical impedance-based home dry weight management method for a dialysis patient of claim 1, wherein, The step of separating the impedance change into a long-term impedance vector drift component and a short-term impedance modulus change component based on a hierarchical decoupling algorithm comprises: setting a time threshold for distinguishing between physiological tissue growth and fluid retention, the time threshold being set to between 24 hours and 72 hours; extracting the track movement trend of the impedance vector on the complex impedance plane with a time span greater than the time threshold as the long-term impedance vector drift component, and associating it to a muscle or fat growth model; extracting impedance modulus changes with periodic recovery characteristics with a time span less than the time threshold as the short-term impedance modulus change component, and associating it to a cell extracellular fluid retention model.

5. A bioelectrical impedance based home dry weight management method for dialysis patients according to any one of claims 1 to 4, characterized in that, The step of calculating the dry weight target value of the dialysis patient further comprises: generating a dialysis dehydration plan for the next dialysis treatment; the total ultrafiltration amount of the dialysis dehydration plan is set as the current body weight minus the dry weight target value; wherein, when calculating the total ultrafiltration amount, the dry weight reference value is positively or negatively corrected by the tissue mass increment corresponding to the long-term impedance vector drift component, so as to exclude the dry weight evaluation error caused by the actual muscle gain or weight loss of the patient.

6. A bioelectrical impedance based home dry weight management electronic scale device for dialysis patients, characterized in that, Comprise: a multi-frequency measurement seat comprising an excitation electrode and a detection electrode for attaching to the body surface of a dialysis patient to establish a current loop and a voltage detection point; a signal processing unit electrically connected to the multi-frequency measurement seat, comprising a memory and a processor; The memory stores a computer program, which is executed by the processor to implement the method steps.

7. A bioelectrical impedance based home electronic scale device for home dry weight management of dialysis patients as claimed in claim 6 wherein, The signal processing unit further comprises: a dynamic tracking module configured to establish a historical impedance database of the individual patient and record the historical trajectory of the long-term impedance vector drift component in real time; a display interactive interface connected to the dynamic tracking module and configured to display the long-term trend curve representing the change of tissue mass and the short-term oscillation curve representing the liquid fluctuation in a graphical manner, and intuitively prompt the recommended dry weight adjustment value.

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