Dynamic intervention feedback-oriented pacemaker remote intelligent management platform

Abstract

The invention relates to the technical field of pacemaker remote intelligent management, in particular to a pacemaker remote intelligent management platform for dynamic intervention feedback, which comprises a heart rate rhythm extraction module for collecting signals and extracting variation trend, a rhythm fluctuation classification module for identifying section features to form subareas, and a data processing module for processing the subareas. The output response level calibration module analyzes the track to distinguish the response level, the intervention response coordination identification module distinguishes the behavior offset according to the intervention content, the remote management level judgment module tracks the offset track and analyzes the offset, and the remote intelligent management result of the pacemaker is obtained through merging. According to the method, a rhythm evolution structure is constructed by organizing the heart rate direction, clear state boundaries and associations are presented, hierarchical description is established based on response differences, an intervention and feedback contrast mechanism is formed, continuous identification and merging judgment of offset are supported, and it is ensured that management has rhythm consistency, hierarchical discrimination and decision continuity; and therefore, the stable judgment and abnormity identification capability of remote regulation and control can be enhanced.

Description

Technical Field

[0001] This invention relates to the field of pacemaker remote intelligent management technology, and in particular to a pacemaker remote intelligent management platform oriented towards dynamic intervention feedback. Background Technology

[0002] The field of remote intelligent pacemaker management technology encompasses systematic methods for non-contact data transmission and management of implantable cardiac pacemakers through information communication. The core of this technology is to achieve continuous monitoring, data acquisition, and remote analysis of the pacemaker's operational status within the patient, assisting medical personnel in scientific management and dynamic intervention. This field typically covers multiple aspects, including remote data communication, embedded device management, electrocardiogram signal acquisition and transmission, and doctor-patient data interaction. Physiological data generated by the pacemaker is transmitted to a medical management platform via wireless communication technology, where it is interpreted and processed by doctors or the system. The systematic characteristic of this field lies in constructing a linkage mechanism between the pacemaker, data receiving device, communication network, and medical terminal, thereby enabling remote perception, analysis, and medical feedback of the patient's cardiac status.

[0003] Among them, the pacemaker remote intelligent management platform for dynamic intervention feedback refers to a remote management architecture used to realize the synchronous linkage of proactive data acquisition and real-time intervention decision-making in the pacemaker management process. It mainly addresses the technical problems of traditional remote monitoring systems in passive information collection and lack of real-time response mechanisms. It covers multiple technical aspects, including a data reading control mechanism based on periodic automatic wake-up, an active data screening process based on intervention conditions set by doctors, a hierarchical information transmission channel combined with network transmission timing settings, and remote command triggering logic for connecting to the intelligent intervention feedback module of the medical end. Generally, it controls the pacemaker status reading frequency by setting time thresholds, starts the data transmission program based on parameter anomaly judgment model, and realizes data interaction with the medical platform by building a standardized interaction protocol to complete the dynamic acquisition and processing of management decision-making basis.

[0004] Existing technologies rely primarily on status data feedback and condition triggering during operation, focusing on parameter performance within a single moment or independent interval. When heart rate changes exhibit alternating phases or repetitive directions, the relevant data is easily fragmented. Intervention judgments depend on combinations of static information, making it difficult to reflect continuous deviations during the response process. In scenarios with frequent remote operations or overlapping management commands, issues such as inconsistent intervention effects and the accumulation of abnormal behaviors going unnoticed can arise, thus limiting the consistency and reliability of remote management judgments. Summary of the Invention

[0005] The purpose of this invention is to address the shortcomings of existing technologies by proposing a remote intelligent management platform for pacemakers that is oriented towards dynamic intervention feedback.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: a remote intelligent management platform for pacemakers oriented towards dynamic intervention feedback, the platform comprising: The heart rate rhythm extraction module acquires the pacemaker's operating heart rate signal, reads the direction of heart rate changes, connects them to form a heart rate fluctuation path, identifies the starting position of the change from stability and the ending position of the return to stability, extracts the continuity and switching characteristics of the direction change, and obtains the heart rate rhythm change trend content. The rhythm fluctuation classification module identifies fluctuation segments with consistent heart rate direction and frequent switching based on the heart rate direction change characteristics in the heart rate rhythm change trend content. It extracts the heart rate direction maintenance time and the number of switching occurrences, and merges fluctuation segments with the same change mode according to differences to form rhythm fluctuation partitioning results. The output response level calibration module calls the time period range corresponding to each fluctuation partition in the rhythm fluctuation partitioning result, reads the output signal change trajectory during pacemaker operation, extracts the difference in the initial and final trend morphology, distinguishes the response level based on the difference between the trajectories, and forms a pacing output response level description. The intervention response coordination and identification module extracts the response rhythm, action time range and intervention action area description from the remote intervention content based on the response level information in the pacing output response level description, and compares it item by item with the response level information to distinguish intervention behaviors that are consistent in direction and have deviations, thus forming intervention behavior comparison information.

[0007] As a further aspect of the present invention, the heart rate rhythm variation trend content includes the heart rate direction change interval division results, heart rate direction switching density distribution, and heart rate direction continuous segment length set; the rhythm fluctuation partitioning results include direction stable fluctuation partitioning category, direction high-frequency switching fluctuation partitioning category, and direction mixed change fluctuation partitioning category; the pacing output response level description includes pacing output initial response level, pacing output adjustment response level, and pacing output abnormal response level; and the intervention behavior comparison information includes intervention rhythm consistency identifier, intervention time range matching identifier, and intervention effect area matching identifier.

[0008] As a further embodiment of the present invention, the heart rate rhythm extraction module includes a signal acquisition submodule, a change positioning submodule, and a rhythm extraction submodule; The signal acquisition submodule acquires the heart rate signal recorded by the heart rate acquisition electrode in the chest cavity during the continuous operation of the pacemaker. The continuous heart rate signal is divided into multiple adjacent time periods according to the time sequence of the signal recording. The corresponding heart rate change trend and time position identifier are extracted for each time period. The change trends of each time period are arranged in chronological order to establish a set of change segments on the continuous time axis and generate a heart rate change segment sequence. The change positioning submodule calls the change trend information and time position identifier of adjacent change segments in the heart rate change segment sequence, judges whether the change trend between adjacent segments has changed segment by segment, marks the time position of the first change of trend as the change start position, and marks the time position of the change trend returning to consistency as the change end position. All change start positions and change end positions are paired and organized in chronological order to form a corresponding change segment identifier set, and generate a heart rate change segment position set. The rhythm extraction submodule calls upon the distribution of each heart rate change segment in the heart rate change segment location set on the time axis, continuously judges the time connection status and trend continuity status between adjacent change segments, distinguishes between continuous time segments with consistent trends and time segments with multiple trend changes, organizes the corresponding change path structure according to the trend maintenance status and change rhythm within each time segment, and sequentially integrates all time segments on a unified time axis to generate heart rate rhythm change trend content.

[0009] As a further aspect of the present invention, the rhythm fluctuation classification module includes a fluctuation identification submodule, a feature extraction submodule, and a type merging submodule; The fluctuation identification submodule, based on the change direction characteristics of each time period in the heart rate rhythm change trend content, sequentially extracts the directional consistent segments and the frequently changing direction segments within a continuous time range, determines whether the time segments with consistent direction have continuity, and extracts the number of switching between adjacent time periods in the frequently changing direction segments, and marks all identified directional consistent segments and direction changing segments respectively to establish a fluctuation segment distribution sequence. The feature extraction submodule calls the start and end time information of each segment in the fluctuation segment distribution sequence, extracts the corresponding time span for segments with consistent direction, counts the number of switching for segments with frequent direction switching, establishes a set of maintenance time for segments with consistent direction and a set of switching frequency for segments with frequent switching, combines the contents of the two sets in chronological order to form a comparison structure, and generates a set of rhythm change performance. The type merging submodule determines whether adjacent segments are consistent in terms of temporal continuity and change patterns based on the time span and switching frequency of each segment in the rhythm change performance set. It filters segments that meet the same directional change pattern and categorizes them into the same type. It integrates the corresponding time period range according to the classification results, marks the segment boundaries of each type on the time axis in sequence, and obtains the rhythm fluctuation partitioning results.

[0010] As a further aspect of the present invention, the output response level calibration module includes a trajectory extraction submodule, a morphology recognition submodule, and a level determination submodule; The trajectory extraction submodule calls the time period range corresponding to each fluctuation partition in the rhythm fluctuation partitioning result, sequentially reads the output signal content that matches each time period during the continuous operation of the pacemaker, extracts the recording points and change segments of each signal content in time order, organizes the signal arrangement order under each partition, constructs a set of continuous output trajectories that distinguish time periods, and generates a set of partitioned signal trajectory fragments. The morphology recognition submodule extracts the signal value change trend of the starting and ending stages of the trajectory based on the arrangement order of each trajectory on the time axis in the partitioned signal trajectory segment set. It classifies the morphological features of the two stages according to the continuous direction and turning amplitude of the trend, organizes the trend combination methods corresponding to each trajectory according to the trajectory number, establishes the mapping relationship between the partition and the trend, and obtains the list of starting and ending morphological features. The grade determination submodule calls the trend combination methods corresponding to each fluctuation partition in the start and end morphology feature list, judges the degree of difference in the response trend structure of different combination methods, cross-classifies the combination methods according to three items: trend change direction, duration span and response amplitude, and groups partitions with similar response performance into a unified grade category, sequentially marks the time period number corresponding to each grade, and generates a pacing output response grade description.

[0011] As a further aspect of the present invention, the intervention response coordination and identification module includes a level calling submodule, an element comparison submodule, and a behavior classification submodule; The grade call submodule extracts the response rhythm content, action time range, and intervention area location identifier corresponding to each response grade based on the response level information reflected in the pacing output response grade description. It also establishes a numbered index structure in chronological order and forms a matching set by combining the grade label and attribute fields of each record. This set is used to match the three types of field content in the remote intervention information and generate a response grade mapping list. The element comparison submodule obtains the intervention information content sent by the remote operation terminal, extracts the intervention rhythm description, the time range description, and the area description, and calls the corresponding three fields in the response level mapping list to compare each piece of intervention information item by item in the three field dimensions, respectively marking whether the rhythm is consistent, whether the time overlaps, and whether the area corresponds to the matching status, establishing an intervention comparison structure item by item, and obtaining an intervention consistency comparison list. The behavior classification submodule identifies whether a behavior belongs to a combination of rhythm consistency, time consistency, and regional consistency based on the matching status combination of each intervention record in the intervention consistency comparison list. Behaviors that are consistent in all three aspects are classified into the direction consistency category, and the rest are classified into the direction offset category. The module also organizes the time index and the area of ​​action identification number corresponding to each type of intervention behavior to obtain the intervention behavior comparison information.

[0012] As a further aspect of the present invention, the platform also includes: The remote management level determination module extracts the heart rate response trajectory within the corresponding time period based on the intervention behavior that deviates from the intervention behavior comparison information and continuously tracks the change trend over time. It then performs segmentation and merging analysis on the heart rate response trajectory segments that deviate from the indicated trend to obtain the pacemaker remote intelligent management results. The results of the pacemaker remote intelligent management include the results of the remote intervention suitability assessment, the cumulative status of pacing response deviation, and the conclusion of the remote management effectiveness evaluation.

[0013] As a further embodiment of the present invention, the remote management level determination module includes a trajectory extraction submodule, an offset tracking submodule, and a segment merging submodule; The trajectory extraction submodule extracts the heart rate response signal content recorded during the continuous operation of the pacemaker in the corresponding time period based on the intervention behavior comparison information where the direction is offset. It reads the heart rate response trajectory in each time period in sequence according to the intervention behavior number, splices and arranges all heart rate response trajectories in chronological order, and organizes them into a structured set by combining the time label corresponding to each trajectory segment to generate an offset-related trajectory sequence. The offset tracking submodule calls the arrangement structure of each trajectory in the offset associated trajectory sequence on the time axis, extracts the direction performance of each trajectory in three dimensions: response rhythm, action time range, and intervention action area, and compares the continuous change path of the trajectory in the three dimensions one by one according to the change direction field recorded in the intervention behavior comparison information, and filters out the trajectory segments that do not keep in line with the intervention direction to obtain the set of deviation direction segments. The segment merging submodule merges multiple segments with the same deviation direction within adjacent time periods based on the temporal position index of each segment in the deviation direction segment set. It integrates three pieces of information corresponding to the response rhythm, time range, and action area of ​​consecutive deviation segments to establish a multi-dimensional merged deviation behavior performance structure. The structure is then organized and classified according to the trajectory number and intervention area number to obtain the pacemaker remote intelligent management results.

[0014] As a further aspect of the present invention, the process of splicing and arranging all heart rate response trajectories in chronological order is specifically as follows: Based on the start and end times of the time period corresponding to the intervention behavior number, the heart rate response trajectory is timestamped and aligned, and a continuity check segment is inserted between adjacent heart rate response trajectories to determine whether there are any interval breaks on the time axis between adjacent heart rate response trajectories. The process of comparing each continuous change path of the trajectory in three dimensions—response rhythm, duration of action, and area of ​​intervention—is as follows: In each heart rate response trajectory, a set of changing parameters corresponding to the response rhythm, the action time range, and the intervention area are extracted. The difference between the set of changing parameters is calculated within a continuous time window. When the change amplitude of any dimension continuously exceeds a preset offset threshold, the corresponding time window is marked as a trajectory segment that is not consistent with the intervention direction.

[0015] As a further aspect of the present invention, the process of merging multiple segments with the same deviation direction within adjacent time periods specifically involves: Based on the adjacency of trajectory segments that do not align with the intervention direction on the time axis and the consistency of offset in the response rhythm, the action time range, and the intervention action area, trajectory segments that satisfy the condition that the continuous time interval is less than the preset merging time interval and the same offset direction are merged, and corresponding time span records are generated to obtain the offset behavior performance results after multidimensional merging.

[0016] Compared with the prior art, the advantages and positive effects of the present invention are as follows: In this invention, by continuously organizing the direction of heart rate changes and forming a rhythm evolution structure, the heart rate state presents clear stage boundaries and change correlations. Based on the differences in output response under different rhythm states, a hierarchical descriptive relationship is established, enabling a comparable response structure between remote intervention behavior and actual heart rate feedback. This supports the continuous identification and merging of intervention deviations, promoting consistency in rhythm perception, distinguishability of response levels, and continuity of management decisions in the remote management process. Consequently, it enhances the ability to make stable judgments and identify anomalies during remote control. Attached Figure Description

[0017] Figure 1 This is a platform flowchart of the present invention; Figure 2 This is a flowchart illustrating the acquisition process of the heart rate rhythm extraction module of the present invention. Figure 3 This is a flowchart illustrating the acquisition process of the rhythm fluctuation classification module of the present invention. Figure 4 This is a flowchart illustrating the acquisition process of the output response level calibration module of this invention. Figure 5 This is a flowchart illustrating the acquisition process of the intervention response coordination and identification module of the present invention. Figure 6 This is a flowchart of the remote management level determination module of the present invention. Detailed Implementation

[0018] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0019] In embodiments of the present invention, words such as "exemplarily," "for example," etc., are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one.

[0020] In the embodiments of this invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning. Similarly, the terms "of," "corresponding (relevant)," and "corresponding" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning.

[0021] In this embodiment of the invention, sometimes a subscript such as W1 may be written in a non-subscript form such as W1. When the difference is not emphasized, the meaning they express is the same.

[0022] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0023] Please see Figure 1 This invention provides a technical solution: a remote intelligent management platform for pacemakers oriented towards dynamic intervention feedback, the platform comprising: The heart rate rhythm extraction module continuously acquires heart rate signals during pacemaker operation through heart rate acquisition electrodes in the chest cavity. It reads the direction of heart rate change segment by segment in chronological order, connects the direction of heart rate change between adjacent time periods to form a complete heart rate fluctuation path, and identifies the starting position of the change in heart rate direction from stable to stable and the ending position of the change in heart rate direction returning to stable in adjacent time periods in the heart rate fluctuation path. Combining the distribution of the starting and ending positions on the time axis, it extracts the continuity and switching characteristics of the change in heart rate direction to obtain the content of heart rate rhythm change trend. The rhythm fluctuation classification module identifies fluctuation segments with consistent heart rate direction changes and frequent switching characteristics within a continuous time range, based on the continuity and switching characteristics of heart rate direction changes presented in the heart rate rhythm variation trend content. It extracts the heart rate direction maintenance time performance in the fluctuation segments with consistent heart rate direction changes and the number of switching occurrences in the fluctuation segments with frequent switching heart rate direction changes. Based on the differences between the heart rate direction maintenance time performance and the number of switching occurrences, it performs merging processing to classify fluctuation segments with consistent heart rate direction changes into the same type, forming the rhythm fluctuation partitioning result. The output response level calibration module calls the time period range corresponding to each fluctuation partition in the rhythm fluctuation partition result, reads the output signal change trajectory within the time period corresponding to each fluctuation partition during the continuous operation of the pacemaker, unfolds each signal change trajectory in chronological order, extracts the trend pattern presented in the beginning and end stages of each output signal change trajectory, and distinguishes the response level of the pacing output under each fluctuation partition based on the difference in trend pattern between different output signal change trajectories, thus forming a pacing output response level description. The intervention response coordination and identification module reads the remote intervention content sent by the remote operation terminal based on the response level information reflected in the pacing output response level description, extracts the response rhythm description, action time range description, and intervention action area description in the remote intervention content, and compares them one by one with the response rhythm, action time range, and intervention action area presented in the response level information. In the comparison process, it distinguishes between intervention behaviors that are consistent in direction and intervention behaviors that are deviated in direction, thus forming intervention behavior comparison information. The remote management level assessment module extracts the heart rate response trajectory within the time period corresponding to the intervention behavior with directional deviation in the intervention behavior comparison information during the continuous operation of the pacemaker. It continuously tracks the changes in the heart rate response trajectory in terms of response rhythm, duration of action, and intervention area in chronological order. It performs segmented analysis on the heart rate response trajectory segments that deviate from the changes indicated in the intervention behavior comparison information, and merges multiple deviation changes in chronological order to obtain the pacemaker remote intelligent management results.

[0024] The heart rate rhythm variation trend includes the results of heart rate direction change interval division, heart rate direction switching density distribution, and heart rate direction continuous segment length set. The rhythm fluctuation zoning results include the zoning categories of stable directional fluctuations, high-frequency directional switching fluctuations, and mixed directional fluctuations. The pacing output response level description includes the pacing output initial response level, pacing output adjustment response level, and pacing output abnormal response level. The intervention behavior comparison information includes the intervention rhythm consistency indicator, intervention time range matching indicator, and intervention effect area matching indicator. The pacemaker remote intelligent management results include the remote intervention fit degree judgment result, pacing response deviation cumulative state, and remote management effectiveness evaluation conclusion.

[0025] Please see Figure 2 The heart rate rhythm extraction module includes a signal acquisition submodule, a change positioning submodule, and a rhythm extraction submodule. The signal acquisition submodule acquires the heart rate signal recorded by the heart rate acquisition electrode in the chest cavity during the continuous operation of the pacemaker. The continuous heart rate signal is divided into multiple adjacent time periods according to the time sequence of the signal recording. The corresponding heart rate change trend and time position identifier are extracted for each time period. The change trends of each time period are arranged in chronological order to establish a set of change segments on the continuous time axis and generate a heart rate change segment sequence. The heart rate signal recorded by the heart rate acquisition electrode in the chest cavity during continuous pacemaker operation is acquired. The voltage amplitude data stream transmitted by the implanted endocardial or epicardial electrode at a set sampling frequency of 1000Hz is read. A single time window of 10 seconds is set, and the voltage amplitude data stream is segmented along the positive time axis to obtain the voltage dataset within the first time window (0 to 10 seconds), the second voltage dataset (10 to 20 seconds), and so on, forming several consecutive voltage datasets. For each voltage dataset, a differential... The thresholding method iterates through all recorded voltage amplitude points, identifying peaks where the voltage amplitude exceeds a set threshold (e.g., 2.5 mV) as R-wave positions. The time difference between two adjacent R-wave positions is calculated as the RR interval. For example, if 12 R-waves are detected in the first time window, 11 RR interval values ​​are calculated: 0.8 seconds, 0.81 seconds, 0.79 seconds, etc. The average of all RR intervals within the window is calculated to obtain the average heart rate cycle. The reciprocal of the average heart rate cycle is then multiplied by 60 to obtain the average heart rate value for that time window (e.g., ...). (beats / min), and simultaneously calculate the difference between the average heart rate in the second half of the window and the average heart rate in the first half of the window in the first 5 seconds. Divide this difference by the time span of 5 seconds to obtain the slope of the heart rate change. (Unit: times / minute / second), if the calculated slope The value is 0.8. The center time point of the window (5 seconds) is recorded as the time position marker, and the slope (0.8) is recorded as the trend value. The time period from 0 to 10 seconds is marked as the slight increase in heart rate. Then, the second time window is processed, and its center time point (15 seconds) and corresponding trend value are calculated. The calculation of all time windows is completed in sequence. The center time point (5 seconds) of the first time window and its trend value (0.8) are placed first, and the center time point (15 seconds) of the second time window and its trend value (1.2) are placed second. All the calculated time points and trend values ​​are arranged into ordered two-dimensional data pairs according to the time sequence. These two-dimensional data pairs are connected end to end to generate a sequence of heart rate change segments.

[0026] The change positioning submodule calls the change trend information and time position identifier of adjacent change segments in the heart rate change segment sequence, determines whether the change trend between adjacent segments has changed segment by segment, marks the time position of the first change as the change start position, and marks the time position of the change trend returning to consistency as the change end position. All change start positions and change end positions are paired and organized in chronological order to form a corresponding change segment identifier set, and generate a heart rate change segment location set. Retrieve trend information and time position markers of adjacent heart rate change segments in the heart rate change sequence, and set a trend change judgment threshold. The value is 0.5 (unit: times / minute / second), which is the trend value of the first segment in the sequence. The trend value of the second segment Calculate the absolute value of the difference between the two. Since this value is less than the threshold of 0.5, it is determined that the trend has not changed, and the second segment will continue to be read. With the third segment The absolute value of the difference is calculated to be 1.2, which is greater than the threshold of 0.5. Therefore, a trend reversal is determined to have occurred between the second and third segments. The end time of the second segment, 20 seconds prior, is extracted as the time point of the first trend reversal and marked as the starting point of the change. Then continue traversing backwards, monitoring the trend value difference between subsequent adjacent segments, until the Nth segment is detected. With the (N+1)th segment When the absolute value of the difference is less than the threshold of 0.5 three times consecutively, the heart rate change trend is considered to have returned to consistency and stabilized, and the start time point of the Nth segment is extracted as the termination position of the change. For example, during the traversal, if a violent fluctuation is detected starting at the 5th segment, the recording time is set to 45 seconds as the starting position. If the fluctuation disappears at the 12th segment, the recording time is set to 115 seconds as the ending position. Combining 45 seconds and 115 seconds forms a closed interval. The interval is assigned a unique ID-01, and the search continues to find the next starting position of the change. Assuming that the difference exceeds the limit again at 200 seconds, 200 seconds is marked as the new starting position, and if the difference returns to a low value at 240 seconds, 240 seconds is marked as the new ending position, thus forming the interval. The sequence is numbered ID-02. If the corresponding termination position is not found before the end of the traversal, the last time point of the sequence is marked as a temporary termination position. All identified closed intervals are stored in a list in chronological order of their start times to generate a set of heart rate change segment locations.

[0027] The rhythm extraction submodule calls the heart rate change segment location set to find the distribution of each change segment on the time axis, continuously judges the time connection and trend continuity between adjacent change segments, distinguishes between continuous time segments with consistent trends and time segments with multiple trend changes, organizes the corresponding change path structure according to the trend maintenance state and change rhythm within each time segment, and sequentially integrates all time segments on a unified time axis to generate heart rate rhythm change trend content. Retrieve the distribution of each heart rate variation segment in the heart rate variation segment location set on the time axis, and read the time range of the first variation segment ID-01 in the list. Time range of the second change segment ID-02 Calculate the time interval between two segments. The continuity judgment time interval threshold is set to 10 seconds. Since 85 seconds is greater than 10 seconds, these two segments are judged as independent discontinuous segments. If the interval is less than 10 seconds, it is necessary to further judge whether the trend characteristics of the two are similar to determine whether to merge them (the merging step is skipped here due to the large interval). Read the trend values ​​of all original heart rate change segments contained within the ID-01 segment. If the trend values ​​of the 7 segments contained within the segment are respectively If all values ​​are greater than 0 and the differences between them are within 1.0, this segment is determined to be a continuous upward trend segment with a consistent trend, and its characteristic is recorded as "unidirectional continuous rise". If the trend value sequence of another segment ID-03 is read as follows: The numerical values ​​alternate between positive and negative signs, indicating multiple changes in direction. With four sign reversals, this segment is identified as a period of oscillation with multiple trend changes, and its characteristic is recorded as "high-frequency bidirectional switching." For the unidirectionally rising ID-01 segment, its average slope of 1.8 is calculated as the intensity of change. For the oscillating ID-03 segment, its oscillation frequency is calculated as the number of sign reversals divided by the total duration of the segment (e.g., ...). The description of ID-01 as "unidirectional continuous rise, intensity 1.8" is integrated with its time axis position, and the description of ID-03 as "high frequency bidirectional switching, frequency 0.2Hz" is integrated with its time axis position. Following the direction from 0 seconds to infinity on the time axis, these segment information with specific feature descriptions and numerical quantifications are spliced ​​together end to end. For the blank stable period between two segments, the "stable maintenance" feature is automatically filled in to generate the content of heart rate rhythm change trend.

[0028] Please see Figure 3 The rhythm fluctuation classification module includes a fluctuation identification submodule, a feature extraction submodule, and a type merging submodule; The fluctuation identification submodule extracts the directional features of each time period in the heart rate rhythm change trend content, sequentially extracts the directional consistent segments and the frequently switching segments within a continuous time range, determines whether the time segments with consistent direction have continuity, and extracts the number of switching between adjacent time periods in the frequently switching segments. All identified directional consistent segments and switching segments are marked to establish a fluctuation segment distribution sequence. Based on the directional characteristics of heart rate rhythm variation trends across different time periods, the heart rate rhythm trend data stream generated in the previous steps is read. A 1-minute scan cycle is set, and within each scan cycle, the directional indicator of the smallest time unit (e.g., 10 seconds) is read sequentially. If the directional indicators of five consecutive time units (i.e., within 50 seconds) are all "+" (representing a slope > 0) or all "-" (representing a slope < 0), then this 50-second range is determined to be a time segment with a consistent direction, and the start time of this segment is extracted. With end time Calculate its duration If the continuously detected direction change indicators show an alternating pattern of "+, -, +, -", and the number of alternations exceeds 3 within 30 seconds, then the 30-second range is determined to be a segment with frequent direction switching. For each identified direction-consistent segment, its end time is checked. Start time of the segment that is consistent with the next direction Time difference between Set a continuity determination threshold For 5 seconds, if If two segments have the same direction indicator (both are "+" or both are "-"), then these two segments are considered continuous and treated as the same large segment. If the direction indicators are opposite, they are not merged. For segments with frequent direction switching, the direction indicators of all adjacent time units within the segment are traversed, and the total number of times the direction indicator flips (from "+" to "-" or from "-" to "+") is counted. For example, if the direction sequence recorded in a frequently switching segment is "+, -, +, -, +", then the number of switching times is counted as 4. A unique index number is assigned to each identified segment (such as Seg-01, Seg-02), and segments with consistent directions are marked as Type-A, and segments with frequent direction switching are marked as Type-B. The index numbers such as Seg-01 (Type-A), Seg-02 (Type-B) and their corresponding type labels are stored in a list in chronological order to establish a fluctuating segment distribution sequence.

[0029] The feature extraction submodule calls the start and end time information of each segment in the fluctuation segment distribution sequence, extracts the corresponding time span for segments with consistent direction, counts the number of switching for segments with frequent direction switching, establishes a set of maintenance time for segments with consistent direction and a set of switching frequency for segments with frequent switching, combines the contents of the two sets in chronological order to form a comparison structure, and generates a set of rhythm change performance. The start and end times of each segment in the fluctuation segment distribution sequence are retrieved. Each record in the sequence is traversed, and for segments marked as Type-A (same direction), their start times are read. and end time Calculate the time span For example, if the start time of Seg-01 is read as 10:00:00 and the end time as 10:00:45, then the calculated time span is 45 seconds. This value of 45 seconds is stored in the set of durations of the segment with the same direction. In the Chinese, the corresponding record format is: For segments marked as Type-B (frequent direction switching), read the number of switching records for that segment. Simultaneously read the time length of this segment. Calculate the switching frequency For example, if the time duration for reading Seg-02 is 30 seconds and the number of switching operations is 6, then the switching frequency is calculated as follows: The number of times per second (unit: Hz) is stored in the handover frequency set of the frequently switched sections. In the Chinese, the corresponding record format is: After processing all segments, create a comparison structure table with two columns. The first column sorts all segment IDs in chronological order, and the second column is filled with the corresponding feature values. For Type-A segments, the maintenance time value (e.g., 45) is entered; for Type-B segments, the handover frequency value (e.g., 0.2) is entered. If two adjacent segments are Type-A and Type-B respectively, calculate the ratio or difference of their feature values ​​to quantify the degree of feature jump. For example, calculate the maintenance time of Seg-01 (45) and the reciprocal of the frequency of Seg-02 (i.e., the average handover interval). ratio It is used to describe the severity of the transition from stability to oscillation, and encapsulates all the calculated duration, switching frequency and characteristic comparison values ​​of adjacent segments to generate a set of rhythm change performance.

[0030] The type merging submodule determines whether adjacent segments are consistent in terms of temporal continuity and change patterns based on the time span and switching frequency of each segment in the rhythm change performance set. It filters segments that meet the same directional change pattern and categorizes them into the same category. It integrates the corresponding time period range according to the classification results, marks the segment boundaries of each type on the time axis in turn, and obtains the rhythm fluctuation partitioning results. Based on the time span and switching frequency of each segment in the rhythmic change performance set, read the data from two adjacent segments in the performance set. and The feature parameters are used to set the time span threshold for merging discrimination. Frequency difference threshold ,like and Both are of Type-A type, and the time interval between them is less than 5 seconds (based on the continuity judgment of the preceding steps), so they are directly merged. It is a Type-B type and the switching frequency is 0.2Hz. Both are Type-B and have a switching frequency of 0.22Hz. Calculate the frequency difference between them. ,because If the two changes are determined to be in the same way, then... and They are merged into the same type of oscillation segment and assigned a new unified type identifier, Class-Oscillation-1. It is of type Type-A and lasts for 60 seconds. For Type-B, with a switching frequency of 0.5Hz, the two are determined to have inconsistent change methods. Their original type identifiers, Class-Stable-1 and Class-Oscillation-2, are maintained. After completing pairwise comparisons and merging of all adjacent segments, for each merged unified type identifier, the earliest start time of all original segments it contains is extracted. With the latest end time For example, the time range of the merged Class-Oscillation-1 is determined as The time axis is then redefined to divide the range into several continuous, non-overlapping functional zones, such as "Long-term Stability Zone I", "High-Frequency Oscillation Zone II", and "Short-term Fluctuation Zone III", to obtain the rhythmic fluctuation partitioning results.

[0031] Please see Figure 4 The output response level calibration module includes a trajectory extraction submodule, a morphology recognition submodule, and a level determination submodule; The trajectory extraction submodule calls the time period range corresponding to each fluctuation partition in the rhythm fluctuation partitioning result, sequentially reads the output signal content that matches each time period during the continuous operation of the pacemaker, extracts the recording points and change segments of each signal content in chronological order, organizes the signal arrangement order under each partition, constructs a set of continuous output trajectories that distinguish time periods, and generates a set of partitioned signal trajectory fragments. Retrieve the time period range corresponding to each fluctuation partition in the rhythm fluctuation partitioning result, and read the time period of the first partition "Long-term Stable Region I" recorded in the fluctuation partitioning result. The pulse delivery records within that time period are read from the event counter register inside the pacemaker, and the timestamp of each pulse delivery is extracted. With the corresponding output voltage amplitude and pulse width For example, a pulse with a voltage of 2.5V and a pulse width of 0.4ms is emitted every 100.5 seconds, and another pulse with a voltage of 2.5V and a pulse width of 0.4ms is emitted every 101.5 seconds, and so on, to construct a set of pulse sequences within this time period: Read the time period of the second partition, "High-Frequency Oscillation Zone II". Extract the pulse sequence that the pacemaker automatically adjusts based on feedback from the accelerometer or minute ventilation sensor during that time period. If the pacemaker performs rate adaptive adjustments during this period, the recorded pulse interval may shorten from 1000ms to 800ms, and the voltage amplitude may increase to 2.8V. The extracted pulses will then... , Discrete recording points in the sequence are sorted in the forward direction of the time axis. For each sequence, the voltage amplitude and frequency value (frequency is the reciprocal of the time interval between adjacent pulses) of adjacent pulse points are connected to plot a continuous parameter variation curve. For example, for... Connect (200s, 60ppm) and (201s, 65ppm) (where 65ppm corresponds to an interval of approximately 0.92s) to form a frequency rising line segment with a slope of 5ppm / s. Traverse all partitions and establish corresponding voltage change trajectory and frequency change trajectory for each partition. Associate and bind these trajectory data with their respective partition IDs (such as Zone-I, Zone-II) to generate a set of partition signal trajectory segments.

[0032] The morphology recognition submodule extracts the signal value change trend of the starting and ending stages of the trajectory based on the arrangement order of each trajectory on the time axis in the partition signal trajectory segment set. It classifies the morphological features of the two stages according to the continuous direction and turning amplitude of the trend, organizes the trend combination methods corresponding to each trajectory according to the trajectory number, establishes the mapping relationship between the partition and the trend, and obtains the list of starting and ending morphological features. Based on the time-axis arrangement of each trajectory in the zone signal trajectory segment set, for each zone's signal trajectory (taking frequency change trajectory as an example), the first 5 seconds of data from its beginning are extracted as the starting stage, and the last 5 seconds of data from its end are extracted as the ending stage. For example, for the Zone-II frequency trajectory, 200s to 205s is extracted as the starting stage, and 245s to 250s as the ending stage. Linear regression fitting is performed on the data points of the starting stage, and the slope of the regression line is calculated. ,like (Unit: ppm / s), the initial trend is determined to be "rapidly rising". The condition was determined to be "stable and maintained". Similarly, the regression slope at the end of the phase was calculated. ,like The trend at the end of the phase is determined to be "rapid decline". The average amplitude of the initial phase is calculated. Average amplitude of the final stage The difference: ,like ppm (this value is the frequency increment, with correct dimensions), determining the transition amplitude as "significant gain", combining these features, for example, the feature combination for Zone-II is {start: rapid rise, end: rapid fall, transition: significant gain}, assigning it a combination code Code-A. For Zone-I, if its start and end slopes are both near 0, and ppm; The feature combination is {start: stationary, end: stationary, transition: no change}, assigned a combination code Code-B, and iterates through all partition trajectories to calculate... , , Numerical values ​​and corresponding qualitative descriptions (such as "rapid rise") are entered into a table indexed by partition ID, establishing a one-to-one correspondence between partition ID and trend feature codes (Code-A, Code-B, etc.) to obtain a list of start and end pattern features.

[0033] The grade determination submodule calls the trend combination method corresponding to each fluctuation partition in the start and end pattern feature list, judges the degree of difference in the response trend structure of different combination methods, cross-classifies the combination methods according to three items: trend change direction, duration span and response amplitude, classifies partitions with similar response performance into a unified grade category, sequentially marks the time period number corresponding to each grade, and generates a pacing output response grade description. Call the trend combination methods corresponding to each fluctuation partition in the start and end pattern feature list, and set the difference judgment rule table. The rule definition is: if the trend change direction of two partitions is the same (such as both rising or both stable), and the difference in the duration is within a certain range... Within this range, the difference in response amplitude is within Within ppm, similar response performance is considered. Reading the characteristics of Zone-II (Code-A, amplitude gain 15ppm, duration 50s) and Zone-IV (Code-A', amplitude gain 14ppm, duration 48s), a comparison reveals that both have a "rise then fall" structure, with a significant amplitude difference. Duration difference relative error Zone-II and Zone-IV are determined to belong to the same response level and are classified into the "Level-1 Dynamic Response" category. The characteristics of Zone-I (Code-B, stable, lasting 100s) and Zone-III (Code-B', stable, lasting 120s) are read and they are classified into the "Level-0 Baseline Maintenance" category. If a Zone-V is found to have the characteristic of "continuous rise without fall", it is classified into the "Level-2 Continuous Intervention" category. The classification of all zones is checked in turn, and a level label (Level-0, Level-1, Level-2...) is assigned to each category. These labels are then filled into the corresponding time period records. For example, 100s-200s is marked as Level-0 and 200s-250s is marked as Level-1. Finally, a detailed document containing the time axis, zone ID and corresponding response level label (such as "100s-200s: Static Maintenance Level; 200s-250s: Dynamic Adjustment Level") is generated, and a pacing output response level description is produced.

[0034] Please see Figure 5 The intervention response coordination and identification module includes a level call submodule, an element comparison submodule, and a behavior classification submodule; The grade call submodule extracts the response rhythm content, action time range, and intervention area location identifier corresponding to each response grade based on the response level information reflected in the pacing output response grade description. It also establishes a numbered index structure in chronological order and forms a matching set by combining the grade label and attribute fields of each record. This set is used to match the three types of field content in the remote intervention information and generate a response grade mapping list. Based on the response hierarchy information reflected in the pacing output response level description, each record in the response level description document is traversed, and three key fields are extracted: response rhythm (e.g., "frequency increases by 5 ppm / min"), action time range (e.g., "10:30:00-10:35:00"), and intervention area (e.g., "right ventricle RV"). Each record is then processed according to its timestamp. Sort the records in ascending order and assign them unique serial numbers (e.g., L-001, L-002). For the record with ID L-001, extract its level label "Level-1" and the corresponding attribute set. ={Rhythm:"Increase",Time:[100s,200s],Area:"RV"}, converts the text descriptions in the attribute set into standardized numerical values ​​or codes. For example, "increase frequency" is converted into code 1, and "right ventricle" is converted into code 01, thus constructing a standardized feature vector. For the record "Level-0" with ID L-002, if its attribute is "frequency maintenance", it is converted to code 0 and a vector is constructed. All transformed feature vectors are packaged with their original IDs and level labels and stored in a hash mapping table or structured array in the system memory. The key is the sequence number ID, and the value is the data packet containing these three standardized fields, so that subsequent modules can quickly retrieve and compare them by ID to generate a response level mapping list.

[0035] The element comparison submodule obtains the intervention information content sent by the remote operation terminal, extracts the intervention rhythm description, the time range description, and the area description, and calls the corresponding three fields in the response level mapping list to compare each piece of intervention information item by item in the three field dimensions, and marks the matching status of whether the rhythm is consistent, whether the time overlaps, and whether the area corresponds, and establishes an intervention comparison structure to obtain an intervention consistency comparison list. The system acquires intervention information from the remote control terminal, parses the received JSON or XML format intervention instruction data packets via an RF communication module or Bluetooth telemetry module, and extracts the intervention rhythm parameters. (For example, the setting value is "+5ppm"), preset action time window (e.g., "10:30:00-10:35:00") and target scope code (For example, "RV"), invoke the response level mapping list, and read each record in the list sequentially. Compare the intervention rhythm and calculate the command parameters. The degree of match with the actual response rhythm code recorded in the list; if the instruction is "accelerate" and the actual response is also code 1 (accelerate), mark it. Otherwise, mark Secondly, compare the time range and calculate the instruction time window. Time range of the list overlap rate The formula is (The denominator uses the instruction time window length as a reference), if ,mark By comparing the region codes, the instruction can be directly judged. Is it exactly equal to the area code in the list? If they are equal, mark them. Combine these three state bits into a state tuple. or The results are then appended to the corresponding intervention instruction ID. For example, for instruction CMD-001, the comparison result is {rhythm: consistent, time: overlap 95%, area: correspondence}, and for instruction CMD-002, the comparison result is {rhythm: opposite, time: overlap 100%, area: correspondence}. All comparison results are arranged in the order of instruction receipt to obtain the intervention consistency comparison list.

[0036] The behavior classification submodule identifies whether a behavior belongs to a combination of rhythm consistency, time consistency, and regional consistency based on the matching status combination of each intervention record in the intervention consistency comparison list. Behaviors that are consistent in all three aspects are classified into the direction consistency category, and the rest are classified into the direction offset category. The module also organizes the time index and the area of ​​action identification number corresponding to each type of intervention behavior to obtain the intervention behavior comparison information. Based on the matching status combinations of each intervention record in the intervention consistency comparison list, iterate through each comparison result tuple in the list and check its three status bits. , , Are they all (or the overlap rate satisfies) (And the region code is consistent). If all three conditions are true, the intervention behavior is determined to belong to the "direction consistency category", and its ID is added to the consistency set. In the middle, and record its corresponding time index. and the area of ​​action identifier If any one of the three items is (For example, if the rhythm is mismatched, or the time overlap rate is only 0.5), then the intervention behavior is determined to belong to the "direction offset category", and its ID is added to the offset set. In the example, all three states of CMD-001 are "Match", and it is classified as... CMD-002 is classified as [unclear] because of its opposite rhythm. For inclusion The records are further analyzed to extract specific mismatches, which are then labeled as subtypes such as "rhythm offset" or "time lag" for subsequent analysis. Finally, two sets are compiled and output, one of which is... Includes all executed instructions recorded. It contains records of all instructions with deviations and details of those deviations, providing information on intervention behavior comparisons.

[0037] Please see Figure 6The remote management level determination module includes a trajectory extraction submodule, an offset tracking submodule, and a segment merging submodule; The trajectory extraction submodule extracts the heart rate response signal content recorded during the continuous operation of the pacemaker in the corresponding time period based on the intervention behavior comparison information where the direction is offset. It reads the heart rate response trajectory in each time period in sequence according to the intervention behavior number, splices and arranges all heart rate response trajectories in chronological order, and organizes them into a structured set by combining the time label corresponding to each trajectory segment to generate an offset-related trajectory sequence. The specific process of splicing and arranging all heart rate response trajectories in chronological order is as follows: Based on the start and end times of the time period corresponding to the intervention behavior number, the heart rate response trajectory is timestamped and aligned, and a continuity check segment is inserted between adjacent heart rate response trajectories to determine whether there are any interval breaks on the time axis between adjacent heart rate response trajectories. Based on the intervention behaviors that show a directional deviation in the intervention behavior comparison information, read the status field from the intervention behavior comparison information list, filter out the intervention behavior records marked as "directional deviation" or "inconsistent", and extract the unique intervention behavior ID corresponding to each record: (e.g., Intervention-03, Intervention-07), for Intervention-03, read its defined time period range. (For example ), retrieves the raw heart rate response signal data within that time range from the pacemaker's storage module. This data consists of a series of discrete timestamps and heart rate value pairs (e.g. Similarly, extract the time period corresponding to Intervention-07. The heart rate data within the data was processed, and all extracted heart rate response signal segments were arranged in chronological order according to their corresponding intervention behavior IDs. The time boundary between two adjacent segments was read, i.e., the end time of the previous segment, Intervention-03. The start time of the next segment, Intervention-07 Calculate the time difference between the two. The time threshold for continuity verification is set to 2 seconds, because The system determines that there are interval breakpoints on the time axis between the two response trajectories. A verification segment object marked "discontinuous breakpoint" is inserted between the two trajectory data blocks, and the breakpoint duration is recorded as 50 seconds. If a "continuous connection" identifier is inserted, and if time overlap is detected (i.e., the difference is negative), the end point of the previous trajectory is corrected according to the timestamp of the next trajectory. All original heart rate data segments and the inserted continuity verification segments are repackaged according to the time axis to construct a linear data stream containing a complete time context. Each record contains three fields: "trajectory data", "corresponding intervention ID", and "connection status with the previous trajectory", generating an offset associated trajectory sequence.

[0038] The offset tracking submodule calls the arrangement structure of each trajectory in the offset-related trajectory sequence on the time axis, extracts the trend performance of each trajectory in three dimensions: response rhythm, action time range, and intervention area, and compares the continuous change path of the trajectory in the three dimensions one by one according to the change trend field recorded in the intervention behavior comparison information, and filters out the trajectory segments that do not keep in line with the intervention direction to obtain the set of deviation trend segments. The process of comparing each continuous change path of the trajectory in three dimensions—response rhythm, duration of action, and area of ​​intervention—is as follows: In each heart rate response trajectory, the set of changing parameters corresponding to the response rhythm, the time range of action, and the area of ​​intervention are extracted. The difference of the set of changing parameters is calculated within a continuous time window. When the change amplitude of any dimension continuously exceeds the preset offset threshold, the corresponding time window is marked as a trajectory segment that is not consistent with the intervention direction. The system retrieves the time-axis arrangement structure of each trajectory in the offset-related trajectory sequence. For each trajectory segment associated with a specific intervention ID in the sequence, it reads the preset target change trajectory parameters for that ID from the intervention behavior control information, including the target heart rate change rate. (e.g., +5 bpm / min), target action time window and the target electrode region code (e.g., RV-Lead) Set the sliding time window length to 5 seconds, and slide the window with a step size of 1 second on the current heart rate response trajectory, calculating the actual heart rate change rate within each window. The calculation formula is: heart rate at the end of the window minus heart rate at the beginning, divided by the window duration, to calculate the rate of change deviation. Set the response rhythm offset threshold The target speed is 2 bpm / min. If the deviation exceeds this threshold for three consecutive windows, the time period corresponding to these windows is marked as a "rhythm offset segment". At the same time, it is checked whether the timestamp of the current trajectory falls within the target's time window. In addition, if the duration of the out-of-range segment exceeds the set tolerance value of 10 seconds, this segment is marked as a "time lag segment," and the source code of the electrode channel currently recording the heart rate signal is further read. ,judge With target area code If they are inconsistent (e.g., the target is the left ventricle LV but the signal comes from the right ventricle RV), the entire segment is marked as a "regional misalignment segment". All trajectory time slices marked as offset, lag, or misalignment and their specific deviation values ​​(e.g., deviation amplitude 3 bpm / min) are extracted to form a series of discrete time slices with specific error attributes, resulting in a set of deviation trajectory segments.

[0039] The segment merging submodule merges multiple segments with the same deviation direction within adjacent time periods based on the temporal position index of each segment in the deviation direction segment set. It integrates three pieces of information corresponding to the response rhythm, time range, and area of ​​action of consecutive deviation segments, establishes a multi-dimensional deviation behavior performance structure after merging, and sorts and classifies them according to trajectory number and intervention area number to obtain the pacemaker remote intelligent management results. The process of merging multiple segments with the same deviation direction within adjacent time periods is as follows: Based on the adjacency of trajectory segments that do not align with the intervention direction on the time axis and the consistency of their offset in the three aspects of response rhythm, action time range, and intervention action area, trajectory segments that meet the conditions of continuous time interval less than the preset merging time interval and the same offset direction are merged, and corresponding time span records are generated to obtain the offset behavior performance results after multidimensional merging. Based on the temporal index of each segment in the deviation segment set, the segments in the set are sorted in ascending order by their start time, and the end time of the first deviation segment Seg-A is read. And its offset type identifier (such as "rhythm offset - too fast"), read the start time of the next adjacent offset segment Seg-B. and its offset type identifier; Calculate the time interval between two segments Set a preset merging time interval threshold. For 15 seconds, if If the offset type identifiers of Seg-A and Seg-B are completely identical (both are "rhythm offset - too fast"), then they are determined to belong to the same continuous offset behavior fluctuation. A merging operation is performed to merge the two segments into a new large segment. The start time of the new segment is taken as the start time of Seg-A, and the end time is taken as the end time of Seg-B. The average offset amplitude within the merged segment is recalculated (using a weighted average method, with the weight being the duration of each segment). If the types are inconsistent or the interval exceeds the timeout, they remain independent and are not merged. After traversing the entire set to complete all possible merging, a unique management event number (such as Event-Dev-01) is assigned to each finally merged offset behavior segment. The key indicators such as the intervention area number (such as RV zone), total duration, and maximum offset amplitude corresponding to the event are structured and encapsulated. These events are classified according to the severity of the offset (such as amplitude) and frequency (for example, those with a deviation greater than 5 bpm / min and lasting for more than 30 seconds are classified as "requiring manual intervention"). The results of remote intelligent management of the pacemaker are obtained.

[0040] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A remote intelligent management platform for pacemakers oriented towards dynamic intervention feedback, characterized in that, The platform includes: The heart rate rhythm extraction module acquires the pacemaker's operating heart rate signal, reads the direction of heart rate changes, connects them to form a heart rate fluctuation path, identifies the starting position of the change from stability and the ending position of the return to stability, extracts the continuity and switching characteristics of the direction change, and obtains the heart rate rhythm change trend content. The rhythm fluctuation classification module identifies fluctuation segments with consistent heart rate direction and frequent switching based on the heart rate direction change characteristics in the heart rate rhythm change trend content. It extracts the heart rate direction maintenance time and the number of switching occurrences, and merges fluctuation segments with the same change mode according to differences to form rhythm fluctuation partitioning results. The output response level calibration module calls the time period range corresponding to each fluctuation partition in the rhythm fluctuation partitioning result, reads the output signal change trajectory during pacemaker operation, extracts the difference in the initial and final trend morphology, distinguishes the response level based on the difference between the trajectories, and forms a pacing output response level description. The intervention response coordination and identification module extracts the response rhythm, action time range and intervention action area description from the remote intervention content based on the response level information in the pacing output response level description, and compares it item by item with the response level information to distinguish intervention behaviors that are consistent in direction and have deviations, thus forming intervention behavior comparison information.

2. The pacemaker remote intelligent management platform for dynamic intervention feedback as described in claim 1, characterized in that: The heart rate rhythm variation trend content includes the heart rate direction change interval division results, heart rate direction switching density distribution, and heart rate direction continuous segment length set. The rhythm fluctuation partitioning results include direction stable fluctuation partitioning category, direction high-frequency switching fluctuation partitioning category, and direction mixed change fluctuation partitioning category. The pacing output response level description includes pacing output initial response level, pacing output adjustment response level, and pacing output abnormal response level. The intervention behavior comparison information includes intervention rhythm consistency indicator, intervention time range matching indicator, and intervention effect area matching indicator.

3. The pacemaker remote intelligent management platform for dynamic intervention feedback according to claim 1, characterized in that: The heart rate rhythm extraction module includes a signal acquisition submodule, a change positioning submodule, and a rhythm extraction submodule; The signal acquisition submodule acquires the heart rate signal recorded by the heart rate acquisition electrode in the chest cavity during the continuous operation of the pacemaker. The continuous heart rate signal is divided into multiple adjacent time periods according to the time sequence of the signal recording. The corresponding heart rate change trend and time position identifier are extracted for each time period. The change trends of each time period are arranged in chronological order to establish a set of change segments on the continuous time axis and generate a heart rate change segment sequence. The change positioning submodule calls the change trend information and time position identifier of adjacent change segments in the heart rate change segment sequence, judges whether the change trend between adjacent segments has changed segment by segment, marks the time position of the first change of trend as the change start position, and marks the time position of the change trend returning to consistency as the change end position. All change start positions and change end positions are paired and organized in chronological order to form a corresponding change segment identifier set, and generate a heart rate change segment position set. The rhythm extraction submodule calls upon the distribution of each heart rate change segment in the heart rate change segment location set on the time axis, continuously judges the time connection status and trend continuity status between adjacent change segments, distinguishes between continuous time segments with consistent trends and time segments with multiple trend changes, organizes the corresponding change path structure according to the trend maintenance status and change rhythm within each time segment, and sequentially integrates all time segments on a unified time axis to generate heart rate rhythm change trend content.

4. The pacemaker remote intelligent management platform for dynamic intervention feedback as described in claim 1, characterized in that: The rhythm fluctuation classification module includes a fluctuation recognition submodule, a feature extraction submodule, and a type merging submodule; The fluctuation identification submodule, based on the change direction characteristics of each time period in the heart rate rhythm change trend content, sequentially extracts the directional consistent segments and the frequently changing direction segments within a continuous time range, determines whether the time segments with consistent direction have continuity, and extracts the number of switching between adjacent time periods in the frequently changing direction segments, and marks all identified directional consistent segments and direction changing segments respectively to establish a fluctuation segment distribution sequence. The feature extraction submodule calls the start and end time information of each segment in the fluctuation segment distribution sequence, extracts the corresponding time span for segments with consistent direction, counts the number of switching for segments with frequent direction switching, establishes a set of maintenance time for segments with consistent direction and a set of switching frequency for segments with frequent switching, combines the contents of the two sets in chronological order to form a comparison structure, and generates a set of rhythm change performance. The type merging submodule determines whether adjacent segments are consistent in terms of temporal continuity and change patterns based on the time span and switching frequency of each segment in the rhythm change performance set. It filters segments that meet the same directional change pattern and categorizes them into the same type. It integrates the corresponding time period range according to the classification results, marks the segment boundaries of each type on the time axis in sequence, and obtains the rhythm fluctuation partitioning results.

5. The pacemaker remote intelligent management platform for dynamic intervention feedback according to claim 1, characterized in that: The output response level calibration module includes a trajectory extraction submodule, a shape recognition submodule, and a level determination submodule; The trajectory extraction submodule calls the time period range corresponding to each fluctuation partition in the rhythm fluctuation partitioning result, sequentially reads the output signal content that matches each time period during the continuous operation of the pacemaker, extracts the recording points and change segments of each signal content in time order, organizes the signal arrangement order under each partition, constructs a set of continuous output trajectories that distinguish time periods, and generates a set of partitioned signal trajectory fragments. The morphology recognition submodule extracts the signal value change trend of the starting and ending stages of the trajectory based on the arrangement order of each trajectory on the time axis in the partitioned signal trajectory segment set. It classifies the morphological features of the two stages according to the continuous direction and turning amplitude of the trend, organizes the trend combination methods corresponding to each trajectory according to the trajectory number, establishes the mapping relationship between the partition and the trend, and obtains the list of starting and ending morphological features. The grade determination submodule calls the trend combination methods corresponding to each fluctuation partition in the start and end morphology feature list, judges the degree of difference in the response trend structure of different combination methods, cross-classifies the combination methods according to three items: trend change direction, duration span and response amplitude, and groups partitions with similar response performance into a unified grade category, sequentially marks the time period number corresponding to each grade, and generates a pacing output response grade description.

6. The pacemaker remote intelligent management platform for dynamic intervention feedback according to claim 1, characterized in that: The intervention response coordination and identification module includes a level call submodule, an element comparison submodule, and a behavior classification submodule; The grade call submodule extracts the response rhythm content, action time range, and intervention area location identifier corresponding to each response grade based on the response level information reflected in the pacing output response grade description. It also establishes a numbered index structure in chronological order and forms a matching set by combining the grade label and attribute fields of each record. This set is used to match the three types of field content in the remote intervention information and generate a response grade mapping list. The element comparison submodule obtains the intervention information content sent by the remote operation terminal, extracts the intervention rhythm description, the time range description, and the area description, and calls the corresponding three fields in the response level mapping list to compare each piece of intervention information item by item in the three field dimensions, respectively marking whether the rhythm is consistent, whether the time overlaps, and whether the area corresponds to the matching status, establishing an intervention comparison structure item by item, and obtaining an intervention consistency comparison list. The behavior classification submodule identifies whether a behavior belongs to a combination of rhythm consistency, time consistency, and regional consistency based on the matching status combination of each intervention record in the intervention consistency comparison list. Behaviors that are consistent in all three aspects are classified into the direction consistency category, and the rest are classified into the direction offset category. The module also organizes the time index and the area of ​​action identification number corresponding to each type of intervention behavior to obtain intervention behavior comparison information.

7. The pacemaker remote intelligent management platform for dynamic intervention feedback according to claim 1, characterized in that: The platform also includes: The remote management level determination module extracts the heart rate response trajectory within the corresponding time period based on the intervention behavior that deviates from the intervention behavior comparison information and continuously tracks the change trend over time. It then performs segmentation and merging analysis on the heart rate response trajectory segments that deviate from the indicated trend to obtain the pacemaker remote intelligent management results. The results of the pacemaker remote intelligent management include the results of the remote intervention suitability assessment, the cumulative status of pacing response deviation, and the conclusion of the remote management effectiveness evaluation.

8. The pacemaker remote intelligent management platform for dynamic intervention feedback according to claim 7, characterized in that: The remote management level determination module includes a trajectory extraction submodule, an offset tracking submodule, and a segment merging submodule; The trajectory extraction submodule extracts the heart rate response signal content recorded during the continuous operation of the pacemaker in the corresponding time period based on the intervention behavior comparison information where the direction is offset. It reads the heart rate response trajectory in each time period in sequence according to the intervention behavior number, splices and arranges all heart rate response trajectories in chronological order, and organizes them into a structured set by combining the time label corresponding to each trajectory segment to generate an offset-related trajectory sequence. The offset tracking submodule calls the arrangement structure of each trajectory in the offset associated trajectory sequence on the time axis, extracts the direction performance of each trajectory in three dimensions: response rhythm, action time range, and intervention action area, and compares the continuous change path of the trajectory in the three dimensions one by one according to the change direction field recorded in the intervention behavior comparison information, and filters out the trajectory segments that do not keep in line with the intervention direction to obtain the set of deviation direction segments. The segment merging submodule merges multiple segments with the same deviation direction within adjacent time periods based on the temporal position index of each segment in the deviation direction segment set. It integrates three pieces of information corresponding to the response rhythm, time range, and action area of ​​consecutive deviation segments to establish a multi-dimensional merged deviation behavior performance structure. The structure is then organized and classified according to the trajectory number and intervention area number to obtain the pacemaker remote intelligent management results.

9. The pacemaker remote intelligent management platform for dynamic intervention feedback according to claim 8, characterized in that: The process of stitching and arranging all heart rate response trajectories in chronological order is as follows: Based on the start and end times of the time period corresponding to the intervention behavior number, the heart rate response trajectory is timestamped and aligned, and a continuity check segment is inserted between adjacent heart rate response trajectories to determine whether there are any interval breaks on the time axis between adjacent heart rate response trajectories. The process of comparing each continuous change path of the trajectory in three dimensions—response rhythm, duration of action, and area of ​​intervention—is as follows: In each heart rate response trajectory, a set of changing parameters corresponding to the response rhythm, the action time range, and the intervention area are extracted. The difference between the set of changing parameters is calculated within a continuous time window. When the change amplitude of any dimension continuously exceeds a preset offset threshold, the corresponding time window is marked as a trajectory segment that is not consistent with the intervention direction.

10. The pacemaker remote intelligent management platform for dynamic intervention feedback according to claim 8, characterized in that: The process of merging multiple segments that deviate in the same direction within adjacent time periods is specifically as follows: Based on the adjacency of trajectory segments that do not align with the intervention direction on the time axis and the consistency of offset in the response rhythm, the action time range, and the intervention action area, trajectory segments that satisfy the condition that the continuous time interval is less than the preset merging time interval and the same offset direction are merged, and corresponding time span records are generated to obtain the offset behavior performance results after multidimensional merging.

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