Pacemaker follow-up visit data management system

Abstract

The invention relates to the technical field of exception management, and discloses a pacemaker follow-up data management system, and the system comprises the steps: collecting a direction modal parameter, an electrical modal parameter, a sensing threshold modal parameter and a mechanical load modal parameter when a pacemaker is implanted into a patient, so as to construct a parameter baseline; in the monitoring time period, multi-mode body position load sampling is carried out on a patient, and the method comprises the steps that the patient is controlled to execute multi-mode body positions in sequence, and monitoring data is collected for each body position; detecting and processing the monitoring data based on a preset detection rule and a parameter baseline to obtain a detection result; a first processing module or a data monitoring module is performed according to the detection result; on the basis of the improved micro-flexure contact impedance model, the double-body-position comprehensive coupling degree of the monitoring data is determined in combination with the monitoring data; based on the double-body-position comprehensive coupling degree of the monitoring data, the electrode micro-dislocation index of the patient in the monitoring time period is determined.

Description

Technical Field

[0001] The present invention relates to the technical field of abnormality management, and more particularly, to a pacemaker follow-up data management system. Background Art

[0002] A pacemaker, an electronic heart that maintains a patient's life, achieves both pacing and sensing functions through electrode leads that contact the myocardium. However, microdislocation of the lead (a common complication after surgery) can lead to elevated pacing thresholds, impaired sensing, and, in severe cases, syncope, heart failure, and even sudden death. Existing monitoring methods that rely on periodic X-rays or a single parameter (impedance) are prone to missed diagnoses, resulting in significant displacement by the time it is discovered.

[0003] Therefore, the existing pacemaker management system that determines whether a pacemaker has electrode micro-dislocation based on pacemaker follow-up data has a poor ability to identify the risk of electrode micro-dislocation. Summary of the Invention

[0004] The present invention provides a pacemaker follow-up data management system to solve the technical problems raised in the background technology.

[0005] The present invention provides a pacemaker follow-up data management system, comprising:

[0006] A baseline building module is used to collect directional modal parameters, electrical modal parameters, sensing threshold modal parameters, and mechanical load modal parameters when a patient is implanted with a pacemaker to build a parameter baseline;

[0007] The directional modal parameters include: initial direction vector n0 of the conductor;

[0008] The electrical modal parameters include: initial impedance Z0;

[0009] The perception threshold modal parameters include: initial perception amplitude A0 and initial threshold current I0 in supine position;

[0010] Mechanical load modal parameters include: initial chest wall pressure P0;

[0011] The data monitoring module is used to perform multimodal body load sampling on the patient during the monitoring period, including: controlling the patient to perform multimodal body positions in sequence and collecting monitoring data for each body position;

[0012] A data detection module is used to detect and process the monitoring data based on preset detection rules and parameter baselines to obtain detection results; and to perform a first processing module or a data monitoring module according to the detection results;

[0013] The data processing module is used to determine the dual-position comprehensive coupling degree of the monitoring data based on the improved micro-flexure contact impedance model and the monitoring data; and to determine the electrode micro-dislocation index of the patient during the monitoring period based on the dual-position comprehensive coupling degree of the monitoring data.

[0014] The abnormality judgment module is used to compare the electrode micro-dislocation index with the electrode micro-dislocation threshold. If the electrode micro-dislocation index is greater than or equal to the electrode micro-dislocation threshold, it is determined that the patient is at risk of electrode micro-dislocation.

[0015] Furthermore, the patient is controlled to perform multimodal postures in sequence, including:

[0016] Control the patient to perform supine position a, sitting position b, and standing position c in sequence, and maintain each position for a preset time, and collect monitoring data for each position;

[0017] Among them, the monitoring data includes: monitoring direction modal parameters n i , monitor electrical modal parameters Z i , monitoring perception threshold modal parameter A i , I i and monitoring mechanical load modal parameters P i , i∈{a,b,c}.

[0018] Furthermore, based on the preset detection rules, the monitoring data is detected and processed, including:

[0019] Preset detection rules include: direction anomaly detection, electrical anomaly detection, and mechanical anomaly detection;

[0020] Directional anomaly detection includes: calculating n i and the arc cosine value of n0. If the arc cosine value is ≥ the preset offset threshold, the direction detection is abnormal;

[0021] Electrical anomaly detection includes: calculating Z i and Z0; if the absolute ratio of the difference and Z0 is greater than or equal to the electrical modal parameter threshold, an electrical detection anomaly is obtained;

[0022] Mechanical anomaly detection includes: determining the value range P of the mechanical load modal parameter based on experts; judging Then the mechanical detection is abnormal;

[0023] If the monitoring data is detected as abnormal for any preset detection rule, it returns to the data monitoring module to re-acquire the monitoring data; otherwise, it enters the data processing module.

[0024] Furthermore, the improved micro-flexure contact impedance model is as follows:

[0025]

[0026] Among them, Z G represents the predicted impedance value of the improved model, K G Denotes the improved coupling coefficient, Δθ i Indicates the posture deviation angle corresponding to the i-th posture, which is n i and the arc cosine of n0.

[0027] Furthermore, the dual-position comprehensive coupling degree of the monitoring data is determined, including:

[0028] Step 51, for the adjacent body position a→b in the multi-modal body position, obtain a simplified micro-flexure contact impedance model based on impedance change;

[0029] Step 52: Determine the improved coupling coefficient K between adjacent positions a→b based on the simplified micro-flexure contact impedance model. G (a→b);

[0030] Step 53: Repeat steps 51 and 52 for the adjacent position b→c to determine the improved coupling coefficient K G (b→c);

[0031] Step 54, calculate K G (b→c) and K G The root mean square of (a→b) is used to obtain the comprehensive coupling degree of the two positions.

[0032] Furthermore, based on the dual-posture comprehensive coupling degree of the monitoring data, the electrode micro-dislocation index of the patient during the monitoring period is determined. The electrode micro-dislocation index is the product of the structural resistance coupling index, the posture threshold perception coupling index and the dual-posture comprehensive coupling degree.

[0033] Furthermore, the structural resistance coupling index includes:

[0034] Based on the improved micro-flexure contact impedance model Δθ i and Z G The linear relationship based on Δθ i and Z i Construct the structural resistance energy E of the i-th body position i , E i =Z i ×|Δθ i |;

[0035] The structural resistance energy E corresponding to each body position i After accumulation, normalization is performed to obtain the structural resistance coupling index ESCE.

[0036] Furthermore, the attitude threshold perception coupling indicators include:

[0037] Based on the linear relationship between contact area and threshold current, the A i and I i Normalize the ratio with A0 and I0 to get the normalized ratio r i ;

[0038] Normalized ratio r based on each body position i , calculate the normalized ratio r i The root mean square error is used as the attitude threshold perception coupling indicator STC.

[0039] The beneficial effects of this invention are: through multimodal body position load sampling and structure-function coupling modeling, it achieves high sensitivity, low missed diagnosis, and accurate identification of early abnormalities such as electrode microdislocation. This avoids the existing monitoring methods that rely on a single parameter. By comprehensively analyzing multiple data such as direction, impedance, perception, threshold, and mechanical load, and using an improved micro-flexure contact impedance model and three-position coupling analysis, the accuracy of abnormality detection of electrode microdislocation is improved, ensuring patient safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 This is a module diagram of a pacemaker follow-up data management system of the present invention. DETAILED DESCRIPTION

[0041] The subject matter described herein will now be discussed with reference to exemplary embodiments. It should be understood that these embodiments are discussed solely to enable those skilled in the art to better understand and implement the subject matter described herein, and that the functions and arrangements of the elements discussed may be varied without departing from the scope of this specification. Various examples may omit, substitute, or add various processes or components as needed. In addition, features described with respect to some examples may also be combined in other examples.

[0042] like Figure 1 As shown, a pacemaker follow-up data management system includes:

[0043] A baseline building module is used to collect directional modal parameters, electrical modal parameters, sensing threshold modal parameters, and mechanical load modal parameters when a patient is implanted with a pacemaker to build a parameter baseline;

[0044] The directional modal parameters include: initial direction vector n0 of the conductor;

[0045] The electrical modal parameters include: initial impedance Z0;

[0046] The perception threshold modal parameters include: initial perception amplitude A0 and initial threshold current I0 in supine position;

[0047] Mechanical load modal parameters include: initial chest wall pressure P0;

[0048] The data monitoring module is used to perform multimodal body load sampling on the patient during the monitoring period, including: controlling the patient to perform multimodal body positions in sequence and collecting monitoring data for each body position;

[0049] A data detection module is used to detect and process the monitoring data based on preset detection rules and parameter baselines to obtain detection results; and to perform a first processing module or a data monitoring module according to the detection results;

[0050] The data processing module is used to determine the dual-position comprehensive coupling degree of the monitoring data based on the improved micro-flexure contact impedance model and the monitoring data; and to determine the electrode micro-dislocation index of the patient during the monitoring period based on the dual-position comprehensive coupling degree of the monitoring data.

[0051] The abnormality judgment module is used to compare the electrode micro-dislocation index with the electrode micro-dislocation threshold. If the electrode micro-dislocation index is greater than or equal to the electrode micro-dislocation threshold, it is determined that the patient is at risk of electrode micro-dislocation.

[0052] Specifically, the initial lead orientation vector n0 represents the initial spatial orientation of the lead upon implantation. Calibration is performed on the day of implantation using the device's built-in triaxial accelerometer. The orientation vector is determined by detecting gravity, processed, and normalized to the unit vector n0.

[0053] Initial impedance, Z0, represents the electrical impedance (in ohms) measured on the day of implantation when the lead contacts the surrounding tissue. The pacemaker's electrical measurement circuit applies a small test current signal to the lead, measures the response signal, and calculates initial impedance, Z0, using Ohm's law.

[0054] Initial sensing amplitude A0 represents the ECG signal amplitude (in millivolts) detected by the electrodes when the patient is in the supine position. This reflects the electrode's ability to sense ECG signals. The initial sensing amplitude A0 is measured by collecting and processing ECG signals in the supine position using a signal amplification and detection circuit.

[0055] The initial threshold current, I0, represents the minimum current (in milliamperes) that effectively paces the heart when the pulse width is fixed at 0.4 ms. It reflects the energy threshold required for pacing. The pacing current is gradually reduced to observe whether the heart is effectively paced (e.g., confirmed by ECG monitoring). When the current is reduced to a point where pacing is no longer possible, it is slightly increased to determine the minimum current value (initial threshold current, I0) that can effectively capture the heart.

[0056] Initial chest wall pressure (P0) is the pressure (in mmHg) at the point of contact between the chest wall and the electrode on the day of implantation, reflecting the initial mechanical load on the electrode. A calibrated pressure belt is placed around the chest where the electrode is located. The pressure sensor within the belt converts the physical pressure into an electrical signal, which is then processed to produce the initial chest wall pressure (P0).

[0057] In one embodiment of the present invention, controlling a patient to sequentially perform multimodal postures includes:

[0058] Control the patient to perform supine position a, sitting position b, and standing position c in sequence, and maintain each position for a preset time, and collect monitoring data for each position;

[0059] Among them, the monitoring data includes: monitoring direction modal parameters n i , monitor electrical modal parameters Z i , monitoring perception threshold modal parameter A i , I i and monitoring mechanical load modal parameters P i , i∈{a,b,c}.

[0060] The human body experiences a variety of mechanical loads during daily activities. The supine, sitting, and standing positions encompass typical scenarios of rest, partial load, and full gravity load. Electrode microdislocation may manifest differently under different mechanical loads, and a single static position cannot fully reflect the true state of electrodes in dynamic life scenarios. By sequentially switching between these three positions, we can systematically simulate the changes in mechanical load during human activity, providing multi-dimensional data support for accurate assessment of electrode status.

[0061] The supine position a is used as the basic reference position. At this time, the human body is in a resting state and the mechanical load is minimal. The monitoring data of the electrodes can be obtained under stable and low-load conditions.

[0062] Sitting position b can change the chest shape and the direction of electrode force, introducing a moderate mechanical load. In this position, the chest wall pressure P b , electrode direction n b Changes in parameters such as CT scan and MRI can be used to detect electrode adaptability and potential signs of displacement under moderate loads.

[0063] Standing position c puts the human body under full gravity load, and gravity exerts a continuous traction on the electrodes, which can amplify the impact of slight displacement on various monitoring data.

[0064] In one embodiment of the present invention, based on preset detection rules, monitoring data is detected and processed, including:

[0065] Preset detection rules include: direction anomaly detection, electrical anomaly detection, and mechanical anomaly detection;

[0066] Directional anomaly detection includes: calculating n i and the arc cosine value of n0. If the arc cosine value is ≥ the preset offset threshold, the direction detection is abnormal;

[0067] Electrical anomaly detection includes: calculating Z i and Z0; if the absolute ratio of the difference and Z0 is greater than or equal to the electrical modal parameter threshold, an electrical detection anomaly is obtained;

[0068] Mechanical anomaly detection includes: determining the value range P of the mechanical load modal parameter based on experts; judging Then the mechanical detection is abnormal;

[0069] If the monitoring data is detected as abnormal for any preset detection rule, it returns to the data monitoring module to re-acquire the monitoring data; otherwise, it enters the data processing module.

[0070] It should be noted that during data collection, the monitoring data may contain noise or errors due to factors such as patient physiological activity (such as strenuous exercise), equipment interference (such as pressure belt failure), or short-term electrode abnormalities (such as momentary poor contact). If the monitoring data is directly used in subsequent analysis, it will lead to errors in the judgment of micro-dislocation.

[0071] By calculating the current body position direction vector n i The arc cosine value (reflecting the angle difference) of the initial direction vector n0 is used to determine whether the electrode direction has changed significantly. If the arc cosine value is greater than or equal to the preset offset threshold, it indicates that the electrode may have shifted or been subject to strong interference, and data recollection is required.

[0072] Calculate the current body position impedance Z i The difference from the baseline impedance Z0 is measured, and the absolute ratio of this difference to Z0 is evaluated. If the ratio is ≥ the electrical modal parameter threshold, it indicates an impedance anomaly (such as a damaged wire or poor contact), and retesting is required to eliminate interference or faults.

[0073] Determine chest wall pressure P based on expert experience i The reasonable value range P. If P i ∈P, indicating that the pressure measurement is abnormal (such as the pressure belt is worn incorrectly or the sensor is faulty), and the data needs to be recollected to ensure the accuracy of the mechanical load parameters.

[0074] If any detection rule determines that the data is abnormal, the system returns to reacquire the monitoring data, forming a closed-loop verification mechanism for data collection. This prevents abnormal data from interfering with the entire micro-dislocation identification and management process.

[0075] In one embodiment of the present invention, the improved micro-flexure contact impedance model is as follows:

[0076]

[0077] Among them, Z G represents the predicted impedance value of the improved model, K G Denotes the improved coupling coefficient, Δθ i Indicates the posture deviation angle corresponding to the i-th posture, which is n i and the arc cosine of n0.

[0078] It should be noted that the traditional micro-flexure contact impedance model only focuses on the effect of mechanical displacement on impedance, while this model introduces pressure factors to form a multi-factor coupling relationship of displacement → pressure → impedance. By integrating cross-modal parameters, the present invention can adapt to the application scenarios. For example, when a patient changes from a supine position to a standing position, the chest wall pressure P caused by gravity is i changes, and the electrodes shift slightly (Δθ i The traditional micro-flexure contact impedance model cannot explain the corresponding impedance changes.

[0079] By integrating Δθ i and The model can more accurately reflect the actual state of the electrode-tissue contact interface. i or P i ) does not change significantly, but the coupling change Δθ i ×P i Changes can be amplified, providing early warning of microdislocations and improving diagnostic sensitivity and specificity.

[0080] Electrode impedance is affected by mechanical displacement (posture deviation angle Δθ i ) and mechanical load (chest wall pressure P i ) work together. For example, when the electrode is slightly displaced (Δθ i changes), the surrounding tissue pressure (P i ) will further affect the contact state between the electrode and the tissue, thus producing a comprehensive effect on the impedance. The product form of quantifies the synergistic effect of the two, which is more in line with the actual physiological scenario.

[0081] For pressure P i Normalize This eliminates differences in absolute pressure values between patients or under different initial conditions, making the model universal. For example, the initial chest wall pressure P0 may vary between patients, but through normalization, the impact of pressure changes on impedance can be evaluated under the same standard.

[0082] In one embodiment of the present invention, determining the dual-position integrated coupling degree of monitoring data includes:

[0083] Step 51, for the adjacent body position a→b in the multi-modal body position, obtain a simplified micro-flexure contact impedance model based on impedance change;

[0084] Step 52: Determine the improved coupling coefficient K between adjacent positions a→b based on the simplified micro-flexure contact impedance model. G (a→b);

[0085] Step 53: Repeat steps 51 and 52 for the adjacent position b→c to determine the improved coupling coefficient K G (b→c);

[0086] Step 54, calculate K G (b→c) and K G The root mean square of (a→b) is used to obtain the comprehensive coupling degree of the two positions.

[0087] It should be noted that by improving the coupling coefficient K G The impedance-posture-pressure relationship between adjacent body position changes is quantified, and then the combined coupling coefficient is calculated through root mean square (RMS) calculation, which deepens the extraction of electrode microdislocation features. Electrode microdislocation manifests differently at different stages of body position change. By integrating multi-stage information, the ability to identify microdislocation is improved. For example, the coupling coefficient of a mild microdislocation does not change significantly from a to b, but increases significantly from b to c due to the increased gravitational load. RMS calculation integrates information from both stages, avoiding missed diagnoses based on a single-stage analysis.

[0088] A simplified micro-flexure contact impedance model was constructed based on the impedance change for adjacent body positions a→b, which is consistent with the principle that the impedance will change due to mechanical displacement and load when the electrode is slightly dislocated.

[0089] In one embodiment of the present invention, for the adjacent body positions a→b in the multimodal body positions, a simplified micro-flexure contact impedance model is as follows:

[0090]

[0091] Among them, Z b -Z a The predicted impedance value Z corresponding to the improved model G , Z b Indicates the monitoring electrical modal parameters corresponding to the seat, Z a Denotes the monitoring electrical modal parameters corresponding to the supine position, Δθ b Indicates the posture deviation angle corresponding to the sitting position, which is n b and the arc cosine of n0, n b P represents the monitoring direction modal parameter corresponding to the seat, b Denotes the modal parameter of the monitored mechanical load corresponding to the sitting position, Δθ a Indicates the posture deviation angle corresponding to the supine position, which is n aand the arc cosine of n0, n a represents the monitoring direction modal parameter corresponding to the supine position, P a Indicates the modal parameters of the monitored mechanical load corresponding to the supine position.

[0092] In one embodiment of the present invention, based on the dual-posture comprehensive coupling degree of the monitoring data, the electrode micro-dislocation index of the patient during the monitoring period is determined. The electrode micro-dislocation index is the product of the structural resistance coupling index, the posture threshold perception coupling index and the dual-posture comprehensive coupling degree.

[0093] It should be noted that the electrode microdislocation index is the product of the structural resistance coupling index, the posture threshold perception coupling index, and the dual-position comprehensive coupling degree. The structural resistance coupling index reflects the relationship between mechanical displacement (electrode orientation change) and electrical parameters (impedance); the posture threshold perception coupling index reflects the synergy between electrode posture change and perception amplitude and threshold current; and the dual-position comprehensive coupling degree quantifies the correlation between changes in monitoring data between adjacent body positions (supine to sitting, sitting to standing). The product of these three factors forms a comprehensive index that assesses the risk of electrode microdislocation from multiple dimensions: mechanical, electrical, and functional.

[0094] In one embodiment of the present invention, the structural resistance coupling index includes:

[0095] Based on the improved micro-flexure contact impedance model Δθ i and Z G The linear relationship based on Δθ i and Z i Construct the structural resistance energy E of the i-th body position i , E i =Z i ×|Δθ i |;

[0096] The structural resistance energy E corresponding to each body position i After accumulation, normalization is performed to obtain the structural resistance coupling index ESCE.

[0097] It should be noted that the mechanical displacement and impedance are combined and then accumulated and normalized to form the comprehensive index ESCE. ESCE integrates the mechanical and electrical coupling information of multiple body positions and can detect hidden micro-dislocations that are difficult to identify with a single parameter. For example, a slight micro-dislocation makes Δθ i or Z i The change is not significant, but ESCE can be performed through multiple body positions. i Accumulate and amplify abnormal features to improve diagnostic sensitivity.

[0098] When the electrode is slightly dislocated, the mechanical displacement (Δθ i Changes) will inevitably lead to changes in the contact state between the electrode and the tissue, which in turn affects the impedance (Zi E i =Z i ×|Δθ i |Constructed based on the principle that structural changes affect electrical parameters.

[0099] Accumulate E of each body position i , avoiding the one-sidedness of a single static body position. For example, the gravity load in the standing position may amplify the electrical and mechanical coupling characteristics of micro-dislocation. When combined with the supine and sitting position data, it can more comprehensively reflect the state of the electrode under different mechanical loads.

[0100] Normalization eliminates individual differences such as initial impedance Z0, making ESCE applicable to different patients. For example, Z0 is different for different patients, and E i They cannot be compared, but after normalization, the degree of mechanical and electrical coupling anomalies can be evaluated under the same standard.

[0101] It should be noted that the normalization process is to take the structural resistance energy E corresponding to each body position i The ratio of the accumulated value to 3Z0 is used to achieve normalization.

[0102] In one embodiment of the present invention, the attitude threshold perception coupling indicator includes:

[0103] Based on the linear relationship between contact area and threshold current, the A i and I i Normalize the ratio with A0 and I0 to get the normalized ratio r i ;

[0104] Normalized ratio r based on each body position i , calculate the normalized ratio r i The root mean square error is used as the attitude threshold perception coupling indicator STC.

[0105] It should be noted that the contact area-threshold model indicates that changes in contact area will affect the threshold current. The contact area-threshold model is: S t represents the current contact area, S0 represents the initial contact area, I th Represents the threshold current, I rh Indicates the reference current.

[0106] Based on the linear relationship between contact area and threshold current, the perception amplitude A under each body position is i , threshold current I i Perform ratio calculation with the initial values A0 and I0 of the supine position respectively, and then divide the two ratios, that is, This processing eliminates individual differences in initial values, making data from different patients comparable and focusing on the synergistic relationship between the perceived amplitude and the relative changes in threshold current in each body position.

[0107] Based on the r of each body position i , calculate its root mean square error. The root mean square error quantifies r i The smaller the fluctuation, the higher the perception amplitude A. i and threshold current I i The higher the synergy in different body positions, the more stable the electrode-tissue contact interface; conversely, the greater the fluctuation, the worse the synergy, and there is a risk of micro-dislocation.

[0108] The above describes the embodiments of this embodiment, but this embodiment is not limited to the above specific implementation methods. The above specific implementation methods are merely illustrative and not restrictive. Ordinary technicians in this field can also make many forms based on the inspiration of this embodiment, all of which are protected by this embodiment.

Claims

1. A pacemaker follow-up data management system, characterized in that: include: A baseline building module is used to collect directional modal parameters, electrical modal parameters, sensing threshold modal parameters, and mechanical load modal parameters when a patient is implanted with a pacemaker to build a parameter baseline; The directional modal parameters include: initial direction vector n0 of the conductor; The electrical modal parameters include: initial impedance Z0; The perception threshold modal parameters include: initial perception amplitude A0 and initial threshold current I0 in supine position; Mechanical load modal parameters include: initial chest wall pressure P0; The data monitoring module is used to perform multimodal body load sampling on the patient during the monitoring period, including: controlling the patient to perform multimodal body positions in sequence and collecting monitoring data for each body position; A data detection module is used to detect and process the monitoring data based on preset detection rules and parameter baselines to obtain detection results; and to perform a first processing module or a data monitoring module according to the detection results; a data processing module for determining a dual-position integrated coupling degree of the monitoring data based on an improved micro-flexure contact impedance model and in combination with the monitoring data; and determining an electrode micro-dislocation index of the patient during the monitoring period based on the dual-position integrated coupling degree of the monitoring data; The abnormality judgment module is used to compare the electrode micro-dislocation index with the electrode micro-dislocation threshold. If the electrode micro-dislocation index is greater than or equal to the electrode micro-dislocation threshold, it is determined that the patient is at risk of electrode micro-dislocation.

2. A pacemaker follow-up data management system according to claim 1, characterized in that: Control the patient to perform multimodal positions in sequence, including: Control the patient to perform supine position a, sitting position b, and standing position c in sequence, and maintain each position for a preset time, and collect monitoring data for each position; Among them, the monitoring data includes: monitoring direction modal parameters n i , monitor electrical modal parameters Z i , monitoring perception threshold modal parameter A i , I i and monitoring mechanical load modal parameters P i , i∈a,b,c.

3. A pacemaker follow-up data management system according to claim 2, characterized in that: Based on the preset detection rules, the monitoring data is detected and processed, including: Preset detection rules include: direction anomaly detection, electrical anomaly detection, and mechanical anomaly detection; Directional anomaly detection includes: calculating n i and the arc cosine value of n0. If the arc cosine value is ≥ the preset offset threshold, the direction detection is abnormal; Electrical anomaly detection includes: calculating Z i and Z0; if the absolute ratio of the difference and Z0 is greater than or equal to the electrical modal parameter threshold, an electrical detection anomaly is obtained; Mechanical anomaly detection includes: determining the value range P of the mechanical load modal parameter based on experts; judging Then the mechanical detection is abnormal; If the monitoring data is detected as abnormal for any preset detection rule, it returns to the data monitoring module to re-acquire the monitoring data; otherwise, it enters the data processing module.

4. A pacemaker follow-up data management system according to claim 3, characterized in that: The improved micro-flexure contact impedance model is as follows: Among them, Z G represents the predicted impedance value of the improved model, K G Denotes the improved coupling coefficient, Δθ i Indicates the posture deviation angle corresponding to the i-th posture, which is n i and the arc cosine of n0.

5. A pacemaker follow-up data management system according to claim 4, characterized in that: Determine the dual-position comprehensive coupling degree of monitoring data, including: Step 51, for the adjacent body position a→b in the multi-modal body position, obtain a simplified micro-flexure contact impedance model based on impedance change; Step 52: Determine the improved coupling coefficient K between adjacent positions a→b based on the simplified micro-flexure contact impedance model. G (a→b); Step 53: Repeat steps 51 and 52 for the adjacent position b→c to determine the improved coupling coefficient K G (b→c); Step 54, calculate K G (b→c) and K G The root mean square of (a→b) is used to obtain the comprehensive coupling degree of the two positions.

6. A pacemaker follow-up data management system according to claim 5, characterized in that: Based on the dual-posture comprehensive coupling degree of the monitoring data, the electrode micro-dislocation index of the patient during the monitoring period is determined. The electrode micro-dislocation index is the product of the structural resistance coupling index, the posture threshold perception coupling index, and the dual-posture comprehensive coupling degree.

7. A pacemaker follow-up data management system according to claim 6, characterized in that: Structural resistance coupling indicators, including: Based on the improved micro-flexure contact impedance model Δθ i and Z G The linear relationship based on Δθ i and Z i Construct the structural resistance energy E of the i-th body position i , E i =Z i ×|Δθ i |; The structural resistance energy E corresponding to each body position i After accumulation, normalization is performed to obtain the structural resistance coupling index ESCE.

8. A pacemaker follow-up data management system according to claim 6, characterized in that: Attitude threshold perception coupling indicators, including: Based on the linear relationship between contact area and threshold current, the A i and I i Normalize the ratio with A0 and I0 to get the normalized ratio r i ; Normalized ratio r based on each body position i , calculate the normalized ratio r i The root mean square error is used as the attitude threshold perception coupling indicator STC.

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