A non-invasive real-time monitoring and intervention system for preventing lower extremity venous thrombosis

Abstract

This invention discloses a non-invasive real-time monitoring and intervention system for preventing and treating lower extremity deep vein thrombosis (DVT). It is characterized by comprising an impedance monitoring module, a microcurrent stimulation module, and a data analysis and display module. The impedance monitoring module is connected to the data analysis and display module and includes an impedance measurement circuit and a ring-shaped flexible electrode patch. The ring-shaped flexible electrode patch employs a ring-shaped multi-electrode array composed of multiple sets of electrodes, with current injected into the distal electrodes and voltage difference detected at the proximal electrodes. The impedance monitoring module measures changes in lower extremity impedance through the ring-shaped multi-electrode array. The microcurrent stimulation module is connected to the data analysis and display module and uses a constant current source output. The data analysis and display module includes a main controller and a display. The main controller combines a machine learning model to predict DVT risk, achieving closed-loop control. This invention exhibits good stability, achieving non-invasive, real-time, continuous monitoring and active intervention, overcoming the shortcomings of existing monitoring and prevention technologies.

Description

Technical Field

[0001] This utility model relates to the field of medical device technology, and more specifically to a monitoring system for the prevention and treatment of lower extremity venous thrombosis. Background Technology

[0002] Deep vein thrombosis (DVT) of the lower extremities is a common complication in postoperative patients, long-term bedridden individuals, and in the field of aerospace medicine, seriously threatening patients' health. Current technologies mainly include: ultrasound examination: requires professional operation, cannot provide real-time continuous monitoring, and is costly, hindering widespread application. Mechanical compression stockings: lack comfort, prolonged wear can easily lead to skin damage, and cannot monitor the risk of thrombosis in real time. Anticoagulants: pose a bleeding risk, require regular monitoring of coagulation function, causing inconvenience to patients. Single impedance monitoring: can only detect abnormal blood flow, lacks intervention methods, and cannot effectively prevent thrombosis. These existing technologies have many shortcomings and cannot meet the clinical needs for non-invasive, real-time, continuous monitoring and proactive intervention for deep vein thrombosis of the lower extremities. Summary of the Invention

[0003] The purpose of this invention is to provide a stable, reliable, easy-to-operate, and real-time non-invasive real-time monitoring and intervention system for the prevention and treatment of lower extremity venous thrombosis, in order to overcome the shortcomings of existing technologies.

[0004] This utility model achieves the above-mentioned objectives using the following technical solution: a non-invasive real-time monitoring and intervention system for preventing and treating lower extremity venous thrombosis, characterized in that it includes an impedance monitoring module, a microcurrent stimulation module, and a data analysis and display module.

[0005] The impedance monitoring module is connected to the data analysis and display module, and includes an impedance measurement circuit and a ring-shaped flexible electrode patch. The ring-shaped flexible electrode patch uses a ring-shaped multi-electrode array composed of multiple sets of electrodes. Current is injected into the distal electrode, and the voltage difference is detected by the proximal electrode. The impedance monitoring module measures the impedance changes of the lower limbs through the ring-shaped multi-electrode array, and uses a 50kHz-1MHz multi-frequency scan to improve the signal-to-noise ratio. The two-end method measurement ensures accuracy. The system completes a multi-frequency scan (50kHz / 100kHz / 1MHz) every 5 minutes to avoid motion artifacts.

[0006] The microcurrent stimulation module is connected to the data analysis and display module, and uses a constant current source output with adjustable frequency. Low frequency is used for endothelial regulation, and high frequency is used for muscle pump activation.

[0007] The data analysis and display module includes a main controller and a display. The main controller combines a machine learning model to predict the risk of DVT and realize closed-loop control. The algorithm process includes impedance data acquisition, signal preprocessing, time-frequency feature extraction, thrombosis risk model analysis, risk level judgment, and triggering microcurrent stimulation.

[0008] As a further explanation of the above scheme, the annular flexible electrode patch is respectively attached to the proximal and distal ends of the thigh and the proximal and distal ends of the calf. The annular flexible electrode patch includes a flexible conductive patch and a fixing buckle, and more than 8 electrodes are arranged on the flexible conductive patch.

[0009] Furthermore, the electrode uses an Ag / AgCl electrode with a diameter of 10 mm. After using the Ag / AgCl electrode, the contact impedance can be reduced to below 5 kΩ, which is a significant improvement compared to ordinary metal electrodes (which typically have a contact impedance of over 20 kΩ), thereby improving the signal-to-noise ratio of the measurement signal.

[0010] Furthermore, the machine learning model is a lightweight random forest classifier. The input features include ΔZ, PFR, waveform entropy, and historical trend, and the output is a risk probability of 0%-100%. When the risk probability exceeds 30%, microcurrent stimulation is triggered. The stimulation mode is selected according to the PFR value: when PFR < 0.15, a low-frequency stimulation of 1Hz and 0.5mA is used for endothelial modulation; otherwise, a high-frequency stimulation of 30Hz and 2mA is used to activate the muscle pump. The system evaluates the intervention effect every 2 minutes and dynamically adjusts the current intensity (±0.2mA) according to the ΔZ recovery amplitude.

[0011] Furthermore, the main controller uses an STM32H743 main control chip to process data in real time.

[0012] Furthermore, the impedance measurement circuit uses an AD5933 chip connected to a ring-shaped flexible electrode patch to achieve impedance amplitude / phase measurement.

[0013] Furthermore, the monitor employs different display and output methods, such as data transmission methods like USB and Bluetooth.

[0014] The beneficial effects that this utility model can achieve by adopting the above-mentioned technical solution are:

[0015] This invention employs a multi-band ring electrode array, fitted to the proximal and distal ends of the thigh and calf, enabling more comprehensive monitoring of lower limb impedance changes and improving monitoring accuracy. Through multi-frequency scanning and the ring electrode design, this system significantly improves the signal-to-noise ratio and monitoring range, enabling more accurate identification of blood flow stasis and early detection of the risk of lower limb venous thrombosis, filling the gaps in the accuracy and range of existing technologies. Combined with microcurrent stimulation technology, the stimulation parameters (intensity and frequency) are dynamically adjusted based on impedance monitoring data, achieving closed-loop control of "sensing-analysis-intervention." Low-frequency stimulation improves endothelial function, while high-frequency stimulation activates the muscle pump, preventing thrombosis and overcoming the limitation of existing technologies in real-time intervention. A machine learning classifier fusing impedance time-domain / frequency-domain features dynamically assesses thrombosis risk, outputting risk probabilities (0%-100%), enabling real-time monitoring of thrombosis risk changes and timely triggering of intervention measures to reduce the risk of thrombosis, meeting the clinical needs for real-time monitoring and dynamic intervention. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of the structure of this utility model;

[0017] Figure 2 This is a structural diagram of the annular electrode of this utility model;

[0018] Figure 3 This is a schematic diagram of the impedance analysis module structure;

[0019] Figure 4 This is a system architecture diagram of the present invention.

[0020] Figure labeling: 1. Impedance monitoring module 1-1. Impedance measurement circuit 1-2. Ring flexible electrode patch 1-21. Flexible conductive patch 1-22. Fixing buckle 1-23. Electrode 2. Microcurrent stimulation module 3. Data analysis and display module. Detailed Implementation

[0021] In the description of this utility model, it should be noted that the directional terms such as "center", "lateral", "longitudinal", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", and "counterclockwise" are based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this utility model and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. They should not be construed as limiting the specific protection scope of this utility model.

[0022] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features. Thus, the use of "first" and "second" to define a feature may explicitly or implicitly include one or more of that feature, and in this description of the utility model, "at least" means one or more, unless otherwise explicitly specified.

[0023] In this utility model, unless otherwise explicitly specified and limited, the terms "assembly," "connection," and "joining" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can also refer to a mechanical connection; they can refer to a direct connection or a connection through an intermediate medium; or they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this utility model according to the specific circumstances.

[0024] In this utility model, unless otherwise specified and limited, "above" or "below" the second feature can include direct contact between the first and second features, or contact between the first and second features through another feature between them. Furthermore, "above," "below," and "over" the second feature includes the first feature being directly above or diagonally above the second feature, or simply indicates that the first feature is at a higher horizontal level than the second feature. "Above," "below," and "under" the second feature includes the first feature being directly below or diagonally below the second feature, or simply indicates that the first feature is at a lower horizontal level than the second feature.

[0025] The specific embodiments of this utility model will be further described below with reference to the accompanying drawings, making the technical solution and beneficial effects of this utility model clearer and more explicit. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this utility model, and should not be construed as limiting this utility model.

[0026] like Figures 1-4 As shown, this utility model is a non-invasive real-time monitoring and intervention system for the prevention and treatment of lower extremity venous thrombosis. It adopts a closed-loop architecture of "sensing-analysis-intervention" and includes an impedance monitoring module 1, a microcurrent stimulation module 2, and a data analysis and display module 3.

[0027] The impedance monitoring module 1 measures lower limb impedance changes and identifies blood stasis through a ring-shaped multi-electrode array. Structurally, it includes an impedance measurement circuit 1-1 and a ring-shaped flexible electrode patch 1-2. The ring-shaped flexible electrode patch 1-2 employs a ring-shaped multi-electrode array composed of multiple electrode groups. The impedance measurement circuit 1-1 uses an AD5933 chip to measure impedance amplitude / phase with a resolution of 0.1Ω; it employs a two-terminal method, injecting current (<100μA) at the distal electrode and detecting the voltage difference at the proximal electrode. The ring-shaped flexible electrode patch 1-2 includes a flexible conductive patch 1-21 and a fixing clip 1-22, with eight electrodes 1-23 arranged on the flexible conductive patch. The electrodes are 10mm diameter Ag / AgCl electrodes. Using Ag / AgCl electrodes reduces the contact impedance to below 5kΩ, a significant improvement compared to ordinary metal electrodes, thereby increasing the signal-to-noise ratio of the measurement signal. Operating frequency: 50kHz~1MHz (multi-frequency scanning to improve signal-to-noise ratio).

[0028] The microcurrent stimulation module 2 obtains real-time impedance characteristics (such as PFR value, ΔZ change, and risk probability) and control commands (such as a risk threshold for triggering stimulation > 30%) from the data analysis module. For example, if PFR < 0.15 (blood stasis characteristic), endothelial modulation is required, and a low-frequency mode is selected; if PFR ≥ 0.15, muscle pump activation is required, and a high-frequency mode is selected. Structurally, it includes a control unit, a signal generation unit, a power amplification unit, a protection unit, and an output interface. The control unit uses an STM32 coprocessor to receive commands from the data analysis module and generate parameter control signals. The signal generation unit includes a frequency generator and a bidirectional wave synthesis circuit. The frequency generator generates low-frequency / high-frequency square wave signals, and the bidirectional wave synthesis circuit outputs positive and negative pulses alternately. The biphasic square wave avoids electrode polarization. The principle of the biphasic wave is that positive pulses (such as +2mA, 200μs) and negative pulses (-2mA, 200μs) are output alternately, balancing the total positive and negative charges, avoiding electrochemical reactions (polarization) on the electrode surface, and protecting the skin and electrode lifespan. The PWM signal is generated by the STM32's built-in timer, and after filtering and level conversion, it becomes a square wave drive signal.

[0029] Low-frequency mode: 1Hz square wave (1s period, 50% duty cycle), used to stimulate vascular endothelial cells to release nitric oxide and improve vasodilation function.

[0030] High-frequency mode: 30Hz square wave (33.3ms period, 50% duty cycle), triggers muscle fiber contraction, simulating the "muscle pump" effect to promote venous return.

[0031] The power amplification unit includes a constant current source circuit (based on an operational amplifier such as OPA549 + power transistor, ensuring that the output current is stable and adjustable within the range of 0.1~5mA, unaffected by fluctuations in electrode-skin contact impedance (the current remains constant when impedance changes)) and an impedance matching network (ensuring efficient signal transmission to the electrode). The protection unit uses a MAX30004 overcurrent detection chip and a contact impedance detection circuit (voltage divider resistor + ADC sampling). Impedance matching detection: verify the electrode-skin contact impedance (<10kΩ) before stimulation. When the current exceeds 5mA (hardware current limiting threshold), immediately cut off the power transistor drive signal to prevent excessive local current from causing skin burns.

[0032] Output interface: Conductive contacts connected to the annular flexible electrode patch 1-1 (supports quick plugging and unplugging). This module, through its design of "dynamic parameter adjustment + dual-mode output + multi-level protection," achieves precise stimulation of the vascular endothelium and muscle pump while ensuring safety, serving as the execution core in closed-loop control.

[0033] After each stimulation, the data analysis module evaluates the recovery magnitude of ΔZ (impedance change): if the recovery is insufficient (e.g., <5%), the current is automatically increased by 0.2mA; if the recovery is too strong (e.g., >15%), the current is reduced by 0.2mA, forming a closed loop of "stimulation-detection-adjustment" to ensure personalized adaptation of stimulation intensity.

[0034] Data Analysis and Display Module 3: Combines machine learning models to predict DVT risk and achieve closed-loop control. It uses the STM32H743 (ARM Cortex-M7) main control chip for real-time data processing. The algorithm flow includes impedance data acquisition, signal preprocessing, time-frequency feature extraction, thrombosis risk model analysis, risk level judgment, and triggering microcurrent stimulation.

[0035] This system uses a closed-loop control principle of "sensing-analysis-intervention" to monitor and prevent DVT.

[0036] 1. Sensing Phase: The impedance monitoring module measures lower limb impedance changes through a ring-shaped multi-electrode array. Multi-frequency scanning (50kHz~1MHz) is used to improve the signal-to-noise ratio, and two-end measurement ensures accuracy. The system completes a multi-frequency scan every 5 minutes with a sampling rate of 200Hz to avoid motion artifacts.

[0037] 2. Analysis Phase: The data analysis module preprocesses the collected impedance data, including bandpass filtering and baseline correction. Then, it extracts time-domain features (ΔZ, waveform entropy) and frequency-domain features (power ratio (PFR) in the 0.1-0.3 Hz band). Based on these features, a machine learning model (lightweight random forest classifier) ​​is used to dynamically assess the risk of thrombosis, outputting a risk probability of 0%-100%.

[0038] 3. Intervention Phase: When the risk probability exceeds 30%, the system triggers microcurrent stimulation. Different stimulation modes are selected based on the PFR value.

[0039] If PFR < 0.15, use low-frequency stimulation of 1 Hz and 0.5 mA for endothelial modulation.

[0040] Otherwise, a high-frequency stimulation of 30Hz and 2mA is used to activate the muscle pump. The system assesses the intervention effect every 2 minutes and dynamically adjusts the current intensity (±0.2mA) based on the ΔZ recovery amplitude. Through this closed-loop control, the system can achieve non-invasive real-time monitoring, personalized intervention, and adaptive adjustment of DVT, effectively solving the problems existing in current technologies.

[0041] The following is a specific example illustrating how this system can be used in postoperative patients:

[0042] 1. System Deployment: After the patient's surgery, attach eight ring-shaped flexible electrode patches to the proximal and distal ends of the patient's thigh and calf, respectively. Connect the main controller to the electrodes and start the system.

[0043] 2. Monitoring Phase: The system begins performing multi-frequency scans (50kHz, 100kHz, 1MHz) every 5 minutes to continuously monitor changes in lower limb impedance. Assuming that 2 hours post-surgery, the system detects an 18% decrease in ΔZ in the patient's right leg, with a significant reduction in energy in the 0.1–0.3Hz frequency band.

[0044] 3. Risk Assessment: The data analysis module inputs these features into a pre-trained machine learning model. The model outputs a thrombosis risk probability of 45%, exceeding the 30% intervention threshold.

[0045] 4. Intervention phase: The system automatically triggers microcurrent stimulation. Since PFR < 0.15 was detected, indicating insufficient low-frequency blood flow oscillation, the system selects a low-frequency stimulation mode of 1 Hz and 0.5 mA to improve endothelial function.

[0046] 5. Effect Evaluation and Adjustment: After 2 minutes, the system reassesses the impedance change. If the ΔZ recovery is not significant, the system will increase the current intensity by 0.2mA. If the PFR improves but is still not ideal, the system may switch to a high-frequency stimulation mode of 30Hz, 2mA to activate the muscle pump.

[0047] 6. Continuous Monitoring: The system continues the closed-loop process of "monitoring-assessment-intervention" until the risk of thrombosis is reduced to a safe level (e.g., <10%). Throughout the process, medical staff can view the patient's risk status and the effectiveness of the intervention in real time through the display module.

[0048] This embodiment demonstrates how the system can effectively reduce the risk of DVT in postoperative patients through non-invasive, real-time, continuous monitoring and personalized proactive intervention, overcoming the limitations of existing technologies.

[0049] In this embodiment, a multi-band impedance monitoring method is employed: impedance measurement is performed using multi-frequency scanning technology ranging from 50kHz to 1MHz, with a multi-frequency scan (50kHz / 100kHz / 1MHz) completed every 5 minutes. Multi-band scanning significantly improves the signal-to-noise ratio and monitoring accuracy of impedance measurements. Different frequencies of current have varying penetration depths and sensitivities to human tissues; multi-frequency scanning provides more comprehensive tissue impedance information, helping to more accurately identify blood stasis and early thrombosis risks. During impedance measurement, the system sequentially scans at frequencies of 50kHz, 100kHz, and 1MHz. For example, the low-frequency current at 50kHz primarily reflects changes in extracellular fluid impedance, while the high-frequency current at 1MHz can penetrate cell membranes, reflecting the total impedance of intracellular and extracellular fluids. By comparing impedance data at different frequencies, the system can more accurately determine the presence of tissue edema or blood stasis, thereby improving the accuracy of thrombosis risk assessment.

[0050] An adaptive microcurrent stimulation adjustment strategy is employed: the system assesses the intervention effect (ΔZ rise) every 2 minutes and dynamically adjusts the current intensity (±0.2mA). This adaptive adjustment strategy adjusts stimulation parameters based on the patient's real-time response, avoiding overstimulation or understimulation. This personalized adjustment improves intervention effectiveness while reducing the risk of side effects, enhancing patient comfort and treatment adherence. Assuming the initial microcurrent intensity is set to 1mA, after the first 2 minutes of stimulation, if the ΔZ rise is not significant (e.g., less than 5%), the system will increase the current intensity by 0.2mA to 1.2mA. Conversely, if the ΔZ rise is significant (e.g., greater than 15%), the system may decrease the current intensity by 0.2mA to 0.8mA to avoid overstimulation. In this way, the system can find the optimal stimulation parameters for each patient.

[0051] This technical solution employs a machine learning-based dynamic risk assessment model using a lightweight random forest classifier. Input features include ΔZ, PFR (power ratio in the 0.1-0.3Hz band), waveform entropy, and historical trends (sliding window of 10 minutes), outputting a risk probability (0%-100%). The machine learning model integrates multi-dimensional features to achieve more accurate thrombosis risk prediction. Compared to single indicators or empirical judgment, this method can capture more complex physiological change patterns, improving the sensitivity and specificity of early warnings. Furthermore, its lightweight design makes it suitable for real-time operation in embedded systems. The system calculates the risk probability every 5 minutes. For example, in a certain assessment, the system detects the following features: ΔZ decreased by 12%, PFR was 0.18, waveform entropy increased by 5%, and ΔZ showed a downward trend over the past 10 minutes. The machine learning model integrates these features and outputs a thrombosis risk probability of 35%. Since this exceeds the 30% threshold, the system triggers microcurrent stimulation intervention. This dynamic assessment can promptly capture subtle changes in thrombosis risk, enabling early intervention.

[0052] This technical solution employs a dual-mode microcurrent stimulation strategy. The system selects different stimulation modes based on the PFR value: when PFR < 0.15, low-frequency stimulation (1Hz, 0.5mA) is used for endothelial modulation; otherwise, high-frequency stimulation (30Hz, 2mA) is used for muscle pump activation. This dual-mode stimulation strategy provides more precise intervention for different physiological states. Low-frequency stimulation primarily acts on vascular endothelial cells, promoting nitric oxide release and improving vascular function; high-frequency stimulation simulates muscle contraction, activating muscle pump function and promoting venous return. This differentiated stimulation protocol can more comprehensively prevent thrombosis. For example, if the system detects a PFR value of 0.12 during monitoring, below the threshold of 0.15, this indicates insufficient low-frequency blood flow oscillations and potential endothelial dysfunction. The system will automatically select the 1Hz, 0.5mA low-frequency stimulation mode to improve endothelial function. If, after 30 minutes, the PFR value rises to 0.20, the system will switch to the 30Hz, 2mA high-frequency stimulation mode to further activate the muscle pump and promote venous return.

[0053] Compared with existing technologies, this invention employs non-invasive real-time monitoring to replace intermittent ultrasound examinations; proactive intervention to prevent thrombosis through microcurrent stimulation; and personalized control to adaptively adjust stimulation parameters and avoid over-intervention.

[0054] The above description is only a preferred embodiment of the present utility model. It should be noted that for those skilled in the art, several modifications and improvements can be made without departing from the inventive concept of the present utility model, and these all fall within the protection scope of the present utility model.

Claims

1. A non-invasive real-time monitoring and intervention system for the prevention of lower extremity venous thrombosis, characterized in that, It includes an impedance monitoring module, a microcurrent stimulation module, and a data analysis and display module. The impedance monitoring module is connected to the data analysis and display module, including an impedance measurement circuit and a ring-shaped flexible electrode patch; The ring-shaped flexible electrode patch uses a ring-shaped multi-electrode array composed of multiple sets of electrodes. Current is injected into the distal electrode, and the voltage difference is detected by the proximal electrode. The impedance monitoring module measures the impedance change of the lower limb through the ring-shaped multi-electrode array. It uses a 50kHz-1MHz multi-frequency scan to improve the signal-to-noise ratio, and a two-end method to ensure accuracy. The system completes a multi-frequency scan every 5 minutes. The microcurrent stimulation module is connected to the data analysis and display module, and uses a constant current source output with adjustable frequency. Low frequency is used for endothelial regulation, and high frequency is used for muscle pump activation. The data analysis and display module includes a main controller and a display. The main controller combines a machine learning model to predict the risk of DVT and realize closed-loop control. The algorithm process includes impedance data acquisition, signal preprocessing, time-frequency feature extraction, thrombosis risk model analysis, risk level judgment, and triggering microcurrent stimulation.

2. A non-invasive real-time monitoring and intervention system for preventing lower extremity venous thrombosis according to claim 1, characterized in that, The annular flexible electrode patch is attached to the proximal and distal ends of the thigh and the proximal and distal ends of the calf, respectively. The annular flexible electrode patch includes a flexible conductive patch and a fixing buckle, and more than 8 electrodes are arranged on the flexible conductive patch.

3. A non-invasive real-time monitoring and intervention system for preventing lower extremity venous thrombosis according to claim 2, characterized in that, The electrode used is an Ag / AgCl electrode with a diameter of 10 mm.

4. The non-invasive real-time monitoring and intervention system for preventing lower extremity venous thrombosis according to claim 1, wherein, The machine learning model is a lightweight random forest classifier. The input features include ΔZ, PFR, waveform entropy, and historical trend, and the output is a risk probability of 0%-100%. When the risk probability exceeds 30%, microcurrent stimulation is triggered. The stimulation mode is selected according to the PFR value: when PFR < 0.15, a low-frequency stimulation of 1Hz and 0.5mA is used for endothelial modulation; otherwise, a high-frequency stimulation of 30Hz and 2mA is used to activate the muscle pump. The system evaluates the intervention effect every 2 minutes and dynamically adjusts the current intensity according to the ΔZ recovery amplitude.

5. A non-invasive real-time monitoring and intervention system for preventing and treating lower extremity venous thrombosis according to claim 1, characterized in that, The main controller uses an STM32H743 main control chip to process data in real time.

6. The non-invasive real-time monitoring and intervention system for preventing lower extremity venous thrombosis according to claim 1, wherein, The impedance measurement circuit uses an AD5933 chip connected to a ring-shaped flexible electrode patch to achieve impedance amplitude / phase measurement.

7. The non-invasive real-time monitoring and intervention system for preventing lower extremity venous thrombosis according to claim 1, wherein, The monitors use different display and output methods.

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