Cardiac electrophysiological data visualization processing method, device and electronic equipment

Abstract

This disclosure relates to a method, apparatus, and electronic device for visualizing cardiac electrophysiological data. The embodiments provided in this disclosure effectively solve the technical problems of low efficiency and poor consistency in traditional purely manual labeling by combining automatic identification with human-computer interaction correction for feature point calibration. The embodiments of this disclosure can automatically and quickly complete the initial feature point localization, significantly reducing the workload of manual operation. The human-computer interaction correction mechanism of this embodiment ensures the accuracy of key feature points and avoids misjudgments that may occur in complex waveform situations with purely automatic identification. The combination of the two improves efficiency while ensuring accuracy, achieving a balance between efficiency and accuracy.

Description

Technical Field

[0001] This disclosure relates to the fields of image processing and computer vision technology, and in particular to a method, apparatus and electronic device for visualizing cardiac electrophysiological data. Background Technology

[0002] In catheter ablation procedures for atrial fibrillation and other arrhythmias, accurate identification of the driving focus (such as rotor or focal activation) is crucial. Current theory suggests that the key driving regions maintaining atrial fibrillation typically exhibit high-frequency, short-cycle electrical activity. Therefore, accurately measuring and visually displaying the activation cycle length at each electrode site is of significant clinical guiding importance for surgeons to quickly locate potential ablation targets. Three-dimensional mapping systems, as core equipment in cardiac electrophysiological surgery, primarily function to acquire electrophysiological signals through intracardiac catheters and present the acquired electrophysiological parameters in a visual manner. This data can serve as intermediate data for reference only, used to determine the mechanism of arrhythmia and formulate ablation strategies.

[0003] However, feature point identification in existing 3D mapping systems relies on manual intervention, resulting in low efficiency and significant errors. Traditional systems often require doctors to manually mark feature points on the waveform frame by frame when calculating perimeter. Faced with hundreds of mapping points and complex fragmented potentials, manual operation is not only time-consuming but also prone to inconsistencies due to visual fatigue, directly affecting the accuracy of perimeter calculations. During the mapping of complex arrhythmias, surgeons need to frequently switch between viewing the waveform and observing the 3D model; manual feature point marking significantly increases data processing time and poses substantial safety risks. Current mainstream 3D mapping systems simply overlay data onto the anatomical model surface as a two-dimensional color map. This method has obvious visual blind spots, color transitions easily mask subtle gradient differences in the data, and it cannot provide accurate quantitative readings while preserving the anatomical view.

[0004] Therefore, there is an urgent need for a cardiac electrophysiological data visualization processing solution that can improve the efficiency of feature point calibration and intuitively display the circumference parameters through multi-dimensional visualization. Summary of the Invention

[0005] This disclosure provides several embodiments of a method, apparatus, and electronic device for visualizing cardiac electrophysiological data. At least one embodiment can solve the technical problems of traditional mapping systems, such as reliance on manual feature point identification and a single visualization paradigm. It achieves fully automated linkage of waveform correction, data calculation, and multidimensional visualization, significantly improving the accuracy and efficiency of arrhythmia mapping. The technical solution of this disclosure includes the following:

[0006] According to a first aspect of the present disclosure, a method for visualizing cardiac electrophysiological data is provided, comprising:

[0007] Electrophysiological waveform data and spatial location information of multipolar catheters were collected to construct a three-dimensional anatomical model of the cardiac chambers;

[0008] The electrophysiological waveform data is displayed on the interactive interface of the three-dimensional anatomical model of the heart chamber, and the feature points of the electrophysiological waveform data are identified.

[0009] Receive a correction instruction for the feature point, execute the correction instruction, and update the average perimeter parameter of the target waveform based on the corrected feature point sequence. The target waveform includes the electrophysiological waveform corresponding to the feature point or lead associated with the correction instruction.

[0010] Based on a pre-built linkage rendering mechanism, in response to the updated circumference data after the average circumference parameter is updated, a three-dimensional quantitative bar chart that maps one-to-one with each electrode of the multi-polar catheter is dynamically drawn in a dedicated display area independent of the three-dimensional anatomical model of the heart cavity, and a circumference mapping map is rendered in real time on the surface of the three-dimensional anatomical model of the heart cavity.

[0011] In some embodiments of the method, the geometric height and axial color gradient of the three-dimensional quantized histogram are nonlinearly mapped to the average perimeter parameter, wherein the height of the column decreases as the value of the average perimeter parameter increases, and the color changes along the axis according to a preset color mapping table.

[0012] In some embodiments of the method, the geometric height and axial color gradient of the three-dimensional quantized histogram are non-linearly mapped to the average perimeter parameter, including: the height of each color bar in the three-dimensional quantized histogram is calculated as follows:

[0013]

[0014] in, This represents the maximum value among the average circumferences of all leads measured in this study. Indicates the first The average circumference of each lead measured in this study. This is a preset fixed value, which can represent the minimum drawing height of the bars in a three-dimensional quantized bar chart.

[0015] In some embodiments of the method, the geometric height and axial color gradient of the three-dimensional quantized histogram are non-linearly mapped to the average perimeter parameter, wherein the coloring process for each color bar of the three-dimensional quantized histogram includes:

[0016] A three-primary-color mapping table is pre-constructed, wherein the size of the three-primary-color mapping table and the step size in the traversal assignment process are determined based on the minimum and maximum values ​​of the average perimeter of each lead measured;

[0017] Calculate the index of the histogram corresponding to each lead in the three primary color mapping table, and determine the color set of the histogram corresponding to each lead based on the index;

[0018] Using the color set as the data source for the gradient color, draw the gradient color in the corresponding histogram in the height direction.

[0019] In some embodiments of the method, identifying feature points of the electrophysiological waveform data includes:

[0020] Baseline estimation and baseline correction are performed on the original electrophysiological waveform signal to eliminate baseline drift. The baseline estimation uses the median of the signal within the sliding window as the true baseline.

[0021] Calculate the background noise level data within the current sliding window using the median absolute difference;

[0022] A detection threshold is dynamically generated based on the background noise level data. The corrected signal is traversed, and extreme points with differences exceeding the detection threshold are used as candidate feature points.

[0023] Within a preset time window of the candidate feature points, the first derivative is calculated, and the point with the maximum negative slope is taken as the excitation starting point.

[0024] In some embodiments of the method, each acquisition and measurement of electrophysiological waveform data forms a measurement record, which includes a record index, a waveform timeline index, a waveform feature point set, a catheter number, a circumference set, and a set of surface mapping points on the three-dimensional anatomical model of the heart chamber; wherein, the waveform timeline index and the set of surface mapping points on the three-dimensional anatomical model of the heart chamber correspond one-to-one through the system time index;

[0025] The linked rendering mechanism also includes a two-way linked positioning step:

[0026] In response to the operation of selecting feature points in the waveform interface, the corresponding record index is determined by querying the waveform timeline index, and the corresponding mapping point is highlighted on the three-dimensional anatomical model of the heart chamber based on the determined record index.

[0027] In response to the operation of selecting a mapping point on the three-dimensional anatomical model of the heart cavity, the corresponding record index is determined by querying the set of mapping points on the surface of the three-dimensional anatomical model of the heart cavity, and then jumps to the corresponding waveform position.

[0028] In response to the operation of selecting measurement record P in the measurement records, the system simultaneously jumps to the waveform position of the corresponding measurement record P and highlights the three-dimensional calibration point by querying the record index. P is the selected measurement record in the set of measurement records.

[0029] In some embodiments of the method, during the process of identifying feature points of electrophysiological waveform data or calculating the average circumference parameter, a selection instruction for the leads involved in the identification or calculation is received; in response to the selection instruction, the average circumference parameter of the corresponding lead is calculated only based on the waveform feature points of the selected lead, and the average circumference data of the selected lead is synchronously updated to cover the three-dimensional quantitative histogram and the circumference mapping of the three-dimensional anatomical model of the heart chamber.

[0030] In some embodiments of the method, in the linked rendering mechanism, the upper limit of the time delay from updating the average perimeter parameter to synchronously updating the dual views of the three-dimensional quantized histogram and the perimeter mapping map of the three-dimensional anatomical model of the heart cavity is 15ms; wherein, the three-dimensional quantized histogram adopts a real-time update mechanism, and the perimeter mapping map of the three-dimensional anatomical model of the heart cavity adopts a merged drawing mechanism. The merged drawing mechanism refers to merging multiple batches of drawing requests within a preset time window into a single rendering action, and only performing color mapping and surface rendering on the mapping points located within the visible area, while caching the mapping point data outside the visible area without performing real-time rendering.

[0031] According to a second aspect of the present disclosure, a cardiac electrophysiological data visualization processing apparatus is provided, comprising:

[0032] The data acquisition module is used to acquire electrophysiological waveform data and spatial location information of the multipolar catheter to construct a three-dimensional anatomical model of the heart chambers.

[0033] The feature point recognition module is used to display the electrophysiological waveform data on the interactive interface of the three-dimensional anatomical model of the heart chamber and to identify the feature points of the electrophysiological waveform data.

[0034] The perimeter update module is used to receive correction instructions for the feature points, execute the correction instructions, and update the average perimeter parameter of the target waveform based on the corrected feature point sequence. The target waveform includes the electrophysiological waveform corresponding to the feature points or leads associated with the correction instructions.

[0035] The linkage rendering module is used to dynamically draw a three-dimensional quantitative bar chart that maps one-to-one with each electrode of the multi-polar catheter in a dedicated display area independent of the three-dimensional anatomical model of the heart cavity, based on a pre-built linkage rendering mechanism and in response to the circumference data after the average circumference parameter is updated, and to render a circumference mapping map on the surface of the three-dimensional anatomical model of the heart cavity in real time.

[0036] According to a third aspect of the present disclosure, an electronic device is also provided, comprising:

[0037] At least one processor;

[0038] Memory used to store the processor's executable instructions;

[0039] The processor is configured to execute the instructions to implement the cardiac electrophysiological data visualization processing method according to any embodiment of the present disclosure;

[0040] According to a fourth aspect of the present disclosure, an image processing terminal device is also provided, including:

[0041] At least one processor;

[0042] Memory used to store the processor's executable instructions;

[0043] The processor is configured to execute the instructions to implement the cardiac electrophysiological data visualization processing method according to any embodiment of the present disclosure.

[0044] According to a fifth aspect of the present disclosure, a computer program product is also provided, including a computer program that, when executed by a processor, implements the cardiac electrophysiological data visualization processing method described in any embodiment of the present disclosure.

[0045] The cardiac electrophysiological data visualization processing method provided in this disclosure effectively solves the technical problems of low efficiency and poor consistency in traditional purely manual labeling by combining automatic identification with human-computer interaction correction for feature point calibration. This disclosure allows for automatic and rapid initial feature point localization, significantly reducing the workload of manual operations. The human-computer interaction correction mechanism ensures the accuracy of key feature points, avoiding potential misjudgments that may occur with purely automatic identification in complex waveform situations. The combination of these two methods improves efficiency while maintaining accuracy, achieving a balance between the two. Furthermore, the "target waveform" introduced in this embodiment allows for recalculation only of the waveform corresponding to the feature points or leads associated with the correction command, rather than a full recalculation. This clarifies the scope of the correction operation. This design significantly improves computational efficiency, ensures real-time response to interactive operations, and provides a performance foundation for subsequent linked rendering.

[0046] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0047] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure, and are not intended to unduly limit this disclosure.

[0048] Figure 1 This is a schematic diagram illustrating a scenario of implementing a method for visualizing cardiac electrophysiological data, according to an exemplary embodiment.

[0049] Figure 2This is a flowchart illustrating a method for visualizing cardiac electrophysiological data according to an exemplary embodiment;

[0050] Figure 3 This is a schematic diagram illustrating waveform feature point correction based on interactive operation according to an exemplary embodiment;

[0051] Figure 4 This is a schematic diagram of a three-dimensional quantized histogram according to an exemplary embodiment;

[0052] Figure 5 This is a schematic diagram illustrating the perimeter mapping of a three-dimensional anatomical model of the heart cavity according to an exemplary embodiment;

[0053] Figure 6 This is a schematic block diagram illustrating a cardiac electrophysiological data visualization processing device according to an exemplary embodiment;

[0054] Figure 7 This is a schematic diagram of the internal structure of an electronic device according to an exemplary embodiment. Detailed Implementation

[0055] To enable those skilled in the art to better understand the technical solutions of this disclosure, the technical solutions in the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings.

[0056] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this disclosure are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this disclosure described herein can be implemented in orders other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this disclosure. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this disclosure. The terms "comprising," "including," or any other variations thereof are intended to cover a non-exclusive inclusion, such that a process, method, product, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, product, or apparatus. Without further limitation, the presence of other identical or equivalent elements in a process, method, product, or apparatus that includes said elements is not excluded. For example, the use of terms such as "first," "second," etc., is to denote names and does not indicate any specific order.

[0057] This disclosure provides a method for visualizing cardiac electrophysiological data, which can be applied to, for example... Figure 1 In the implementation scenario shown. Figure 1A schematic diagram illustrating a scenario for implementing a method for visualizing cardiac electrophysiological data, according to an exemplary embodiment. Figure 1 The electronic device 120 can be a three-dimensional mapping system, which can be divided into multiple modules or units according to physical or logical functions, or a certain functional module can also include multiple units that implement specific functions. In some specific embodiments, the steps of the method can be implemented in... Figure 1 The illustrated electronic device 120 may include a processor that can store / load / execute executable instructions of a pre-built mapping processing algorithm to implement the method steps described in any embodiment of this disclosure. The processor may have functions such as data reception, processing, storage, and transmission, and may also interact with other devices or units. The processor may include a processor in a local terminal, such as an MCU (Microcontroller Unit), or a processor in a remote server. In some embodiments, a local processor and a processor in a remote server may work together to complete the visualization processing of cardiac electrophysiological data. The processor may be integrated into various terminal devices, including but not limited to in-vehicle devices, personal computers, laptops, smartphones, tablets, wearable devices, medical devices, VR (Virtual Reality) devices, etc. The server may also be a single server, a server cluster, a distributed subsystem, a cloud processing platform, a server containing blockchain nodes, or a combination thereof. The processors described in this disclosure may include various control units capable of performing logic processing functions, including but not limited to CPU (Central Processing Unit), PLC (Programmable Logic Controller), ECU (Electronic Control Unit), MCU (Microcontroller Unit), and controllers composed of one or more logic function units, chips, etc.

[0058] To more clearly illustrate the technical solution of this disclosure, the following is combined with... Figure 1The illustrated implementation scenarios provide insights into some embodiments provided in this disclosure. The multi-electrode catheters described in some embodiments of this disclosure can be circular mapping catheters, basket catheters, or other types of multi-electrode catheters, used to acquire intracardiac electrophysiological signals at various electrode sites within the heart chambers. In some embodiments of this solution, unipolar navigation can also be applied, with the acquired and processed electrophysiological waveforms being unipolar signals. This embodiment can be implemented on a host computer for 3D mapping processing, capable of executing core algorithms such as 3D modeling, feature point recognition, perimeter calculation, and linked rendering. It can include or be connected to an interactive display terminal, providing a human-computer interaction interface, including waveform display windows, 3D model display windows, 3D quantized histogram display windows, and operation control panels. Operators can interact with the system using input devices such as a mouse, keyboard, or touchscreen.

[0059] As mentioned earlier, one of the main technical challenges of existing 3D mapping systems in the mapping of arrhythmias such as atrial fibrillation is that feature point identification relies on manual marking, which is inefficient and inconsistent. Furthermore, the visualization paradigm is limited, relying solely on surface color mapping, which cannot provide accurate quantitative comparisons. Current technologies often require manually marking waveform feature points one by one among hundreds of mapping points. This is not only time-consuming and labor-intensive, but prolonged fine-tuning can also lead to visual fatigue, thus affecting the accuracy of the marking. In terms of visualization, traditional 2D color mapping methods have significant visual blind spots. The gradual transition of colors makes subtle differences in perimeter difficult to perceive with the naked eye. Operators often need to repeatedly compare the colors of different areas to roughly judge the relative length of the perimeter, failing to obtain intuitive and accurate quantitative perception. These problems are particularly prominent in the mapping of complex atrial fibrillation, seriously affecting surgical efficiency and the accuracy of ablation target localization.

[0060] This disclosure provides a method for visualizing cardiac electrophysiological data, which improves upon existing technologies in at least two dimensions: First, it combines automatic identification with human-computer interaction correction, leveraging the efficiency of algorithms while retaining the accuracy of manual intervention, significantly improving the efficiency and reliability of feature point calibration. Second, it employs a unique three-dimensional quantized bar graph visualization paradigm, intuitively presenting the perimeter parameters of each electrode in the form of three-dimensional geometric entities within an independent window. It also establishes a full-link linkage rendering mechanism from waveform correction to synchronous updates of dual views, enabling real-time interactive experiences where operators can correct local features while the global display is updated. These two improvements work together to solve the technical problems of low efficiency, poor accuracy, and unintuitive visualization in traditional mapping systems, providing strong technical support for accurate mapping and efficient treatment of arrhythmias.

[0061] like Figure 2 As shown, Figure 2This is a flowchart illustrating a method for visualizing cardiac electrophysiological data according to an exemplary embodiment. In some embodiments provided in this disclosure, the method may include:

[0062] S20: Collect electrophysiological waveform data and spatial location information of the multipolar catheter to construct a three-dimensional anatomical model of the heart chamber.

[0063] The multipolar catheters described in this disclosure typically refer to mapping catheters with multiple electrodes, such as Lasso catheters, basket catheters, and mesh catheters. The number of electrodes in a multipolar catheter can be 10, 20, etc., and this disclosure does not specify a particular number. During the movement of the multipolar catheter within the heart chambers, each electrode can independently acquire local intracardiac electrophysiological signals at its location. These signals reflect the electrical activity of myocardial cells and are fundamental data for analyzing the mechanisms of arrhythmias.

[0064] The electrophysiological waveform data typically refers to electrical signal data acquired by each electrode of a multipolar catheter, reflecting changes in myocardial electrical activity over time. Electrophysiological waveform data can include bipolar and unipolar signals. Bipolar signals are obtained through the potential difference between two adjacent electrodes, featuring high signal-to-noise ratio and accurate local localization. Unipolar signals are the potential of a single electrode relative to a reference electrode; their waveform contains richer information, such as the direction of activation and conduction velocity. The scheme disclosed in this paper can support the processing of both bipolar and unipolar signals simultaneously, or only one signal type, depending on the specific scenario requirements and system configuration.

[0065] During the acquisition of electrophysiological waveform data, coordinate data reflecting the position of the catheter electrode in three-dimensional space can also be acquired to obtain the spatial position information of the catheter electrode.

[0066] The aforementioned three-dimensional anatomical model of the heart chambers typically refers to a three-dimensional geometric model reflecting the internal anatomical structure of the heart chambers, constructed based on the spatial position information of the catheter within the heart chambers. During cardiac electrophysiological surgery, the operator can manipulate the mapping catheter to move within the heart chambers, collecting the spatial coordinates of various points within the heart chambers through positioning sensors at the catheter tip. The system then generates a three-dimensional anatomical model of the heart chambers based on the collected spatial point cloud data using a surface reconstruction algorithm. This three-dimensional anatomical model can be a three-dimensional model of any heart chamber, such as the left atrium, right atrium, left ventricle, or right ventricle, or it can be a complete heart model containing multiple chambers. The construction method of the three-dimensional anatomical model of the heart chambers can adopt methods currently applicable to three-dimensional modeling of internal organs in clinical practice; this disclosure does not specifically limit this method.

[0067] During the acquisition of electrophysiological waveform data and spatial location information, timestamp information corresponding to each sampling point can also be recorded. The timestamp information can be used to ensure that data collected by different electrodes at different times can be accurately aligned and compared in the time dimension, providing a unified and accurate time reference for subsequent perimeter calculation, linkage positioning, and rendering.

[0068] S22: Display the electrophysiological waveform data on the interactive interface of the three-dimensional anatomical model of the heart chamber, and identify the feature points of the electrophysiological waveform data.

[0069] The interactive interface described in this disclosure typically refers to a graphical user interface provided by the system for operators to perform operations and view information. The interactive interface uses a three-dimensional anatomical model of the cardiac chambers as its core carrier, integrating multiple functions such as waveform display, parameter display, and operation control. In some embodiments of this disclosure, the interactive interface may include multiple display areas, such as a waveform display area, a three-dimensional model display area, a three-dimensional quantitative histogram display area, a parameter display area, and an operation control area. The waveform display area is used to display the electrophysiological waveform data of each lead, allowing users to view waveform morphology, identify feature points, and perform correction operations within this area. The interactive interface can be displayed in a single-screen multi-window layout or a multi-screen split-screen layout; this disclosure does not specifically limit this.

[0070] The characteristic points of the waveform typically refer to time points on the intracardiac electrophysiological waveform that have specific physiological significance, such as the starting point of a local potential (i.e., the starting point of the QRS wave), peak point (peak), trough point (lowest point), maximum negative slope point, and end point. The identification of these characteristic points can usually be achieved automatically through algorithms, eliminating the need for manual point-by-point marking. In atrial fibrillation cycle mapping, the characteristic points typically include the starting point of local excitation, i.e., the time point at which myocardial cells begin depolarization. For example, if the starting point of the QRS wave (the QRS complex is the most prominent group of waveforms on an electrocardiogram, representing the electrical excitation process of the ventricular muscle before contraction, consisting of a downward Q wave, an upward tall and peaked R wave, and a downward broad and deep S wave, with a normal width not exceeding 0.11 seconds) is used as the characteristic point, then the time interval from the starting point of one QRS wave to the starting point of the next QRS wave is the cycle length. The range of atrial fibrillation cycle length is typically between 120 ms and 250 ms, with the specific value depending on the heart rate and the type of arrhythmia. Accurate identification of feature points is the prerequisite and foundation for calculating the perimeter of an excitation, and the accuracy of feature point identification directly determines the accuracy of perimeter calculation.

[0071] Algorithms for automatic feature point recognition can employ waveform detection methods from this field, such as thresholding, slope methods, wavelet transform, template matching, and deep learning methods. In some preferred embodiments of this disclosure, an adaptive dynamic thresholding algorithm can be used for automatic feature point recognition. This algorithm can dynamically adjust the detection threshold based on the local noise level, exhibiting high accuracy and robustness. The results of the automatic recognition are superimposed on the waveform in the form of visual markers, such as marking the positions of feature points with vertical lines or dots of different colors, facilitating intuitive viewing and judgment.

[0072] S24: Receive a correction instruction for the feature point, execute the correction instruction, and update the average perimeter parameter of the target waveform based on the corrected feature point sequence. The target waveform includes the electrophysiological waveform corresponding to the feature point or lead associated with the correction instruction.

[0073] The correction instructions described in this embodiment typically refer to user commands to adjust or modify automatically identified feature points. These correction operations may include, but are not limited to: dragging feature points to change their position, deleting misidentified feature points, manually adding missing feature points, and adjusting the type of feature points. Users can input correction instructions through various methods such as mouse dragging, keyboard shortcuts, and touch operations. The system receives user correction instructions in real time and executes corresponding correction operations to update the feature point sequence.

[0074] The target waveform typically refers to the electrophysiological waveform corresponding to the feature point or lead associated with the correction command. For example, when a user corrects a feature point, in some embodiments, only the waveform of the lead to which that feature point belongs may be affected, and the average circumference parameter of that lead needs to be recalculated, while the waveforms and circumference parameters of other uncorrected leads may remain unchanged and do not need to be recalculated. Thus, in some embodiments of this disclosure, the correction operation only affects the target waveform and does not trigger a recalculation of all data, thereby significantly improving computational efficiency and ensuring real-time response of interactive operations.

[0075] The feature point sequence typically refers to a sequence formed by arranging all feature points in a waveform data in chronological order. The feature point sequence is the basic data for calculating the circumference; each feature point corresponds to one myocardial excitation, and the time difference between two adjacent feature points is the circumference of one excitation.

[0076] The mean cycle length parameter typically refers to the statistical average of the time interval between two adjacent excitations in a segment of waveform data. In tachyarrhythmias such as atrial fibrillation, the excitation cycle length is an important parameter reflecting the frequency of myocardial electrical activity, and short cycle length regions usually correspond to the driving focus or key maintenance matrix of atrial fibrillation. The mean cycle length parameter can be calculated in various ways, such as the arithmetic mean, median, and mode. This disclosure does not limit the specific calculation method for the mean cycle length.

[0077] The updated average perimeter parameter of the target waveform typically refers to the system's ability to recalculate the average perimeter parameter of the leads corresponding to the target waveform after receiving and executing a correction command, replacing the old result with the new one. The update process is usually required to be completed within a very short time, so that the user can barely perceive the delay. For example, in some embodiments of this disclosure, the upper limit of the latency from updating the average perimeter parameter to synchronously updating the dual views of the three-dimensional quantized histogram and the perimeter mapping map of the three-dimensional anatomical model of the heart chambers can be set to 15ms. Real-time updates are a prerequisite for achieving linked rendering; the calculation speed must be fast enough to ensure that subsequent visualization updates have good real-time performance and smoothness. Therefore, in some embodiments of this disclosure, the recalculation time of the average perimeter of the target waveform after feature point correction is controlled at the millisecond level, ensuring immediate response to interactive operations.

[0078] S26: Based on a pre-built linkage rendering mechanism, in response to the updated circumference data after the average circumference parameter is applied, a three-dimensional quantitative bar chart that maps one-to-one with each electrode of the multi-polar catheter is dynamically drawn in a dedicated display area independent of the three-dimensional anatomical model of the heart cavity, and a circumference mapping map is rendered in real time on the surface of the three-dimensional anatomical model of the heart cavity.

[0079] The dedicated display area described in this embodiment typically refers to an independent area within the interactive interface specifically used for displaying three-dimensional quantitative histograms. This area is independent of the display area of ​​the three-dimensional cardiac anatomical model. The dedicated display area ensures that the three-dimensional quantitative histograms do not interfere with the display of the three-dimensional anatomical model, allowing users to view both views simultaneously without mutual interference. The dedicated display area can be located at the bottom, side, or other suitable positions on the interface, and its size can be adjusted as needed; this disclosure does not impose specific limitations on this.

[0080] The three-dimensional quantitative histogram typically refers to a visual chart that intuitively displays the perimeter parameters of each electrode in the form of a three-dimensional bar graph; it can also be called a color bar. Each bar in the three-dimensional quantitative histogram corresponds to one electrode in a multi-electrode conduit, and the height and color of the bar reflect the average perimeter value of the corresponding electrode. Unlike traditional two-dimensional color mapping, the three-dimensional quantitative histogram of this embodiment presents data in the form of a three-dimensional geometric entity, possessing stronger visual impact and quantitative perception capabilities. This allows users to clearly see at a glance which electrodes have shorter perimeters and which have longer perimeters by observing the height of the bars, thereby quickly locating potential drive zone areas.

[0081] The one-to-one mapping between the electrodes of a multi-polar catheter typically refers to a one-to-one correspondence between the number and arrangement of the bars in the 3D quantized histogram and the number and spatial arrangement of the electrodes in the multi-polar catheter. For example, for a 10-electrode annular catheter, the 3D quantized histogram also contains 10 bars arranged in a ring, corresponding to the actual layout of the catheter electrodes. This one-to-one mapping relationship allows users to more easily match the bars in the histogram with the electrodes on the catheter, reducing the user's understanding cost.

[0082] In this embodiment, a pre-built linked rendering mechanism can be implemented. Specifically, when a certain data (such as the average perimeter parameter) is updated, the system automatically triggers the synchronous refresh of multiple related views, ensuring that the data displayed in all views remains consistent. This linked rendering mechanism is one of the core innovations of this disclosure. It links waveform correction, data calculation, and visualization display into a complete automated chain, allowing a single user operation to simultaneously affect the display content of multiple views, greatly improving operational efficiency and data consistency. The pre-built linked rendering mechanism typically refers to the association between data updates and view rendering established during the system initialization phase. When a data update event occurs, the corresponding rendering operation is automatically triggered.

[0083] The dynamic rendering typically refers to the real-time change of the shape (height, color, etc.) of a 3D quantized histogram as the average perimeter parameter changes. Dynamic rendering is an important component of the linked rendering mechanism of this disclosure, ensuring that the data displayed in the histogram is always consistent with the latest calculation results. The dynamic rendering process described in some embodiments of this disclosure can be a smooth transition, where the height and color of the bars gradually change to the target value within a few frames to provide a softer visual experience, or it can be a direct jump, updating to the latest value to provide the most timely data feedback.

[0084] When a change in the average perimeter parameter is detected, subsequent rendering operations are automatically triggered. The triggering method can be event-driven, where a notification signal is issued when the parameter changes, and the rendering module executes an update upon receiving the signal; or it can be polling-based, where the rendering module periodically checks for parameter changes and executes an update upon detection. In the preferred embodiment of this disclosure, an event-driven triggering method is used to ensure timely response and efficient resource utilization.

[0085] The perimeter mapping typically refers to a visual image created by plotting the perimeter data of various mapping points onto the surface of a three-dimensional anatomical model using color mapping. Perimeter mapping is a mapping visualization method that can intuitively display the distribution of perimeter in three-dimensional space, showcasing the spatial distribution characteristics of arrhythmias. The color mapping method for perimeter mapping usually employs a color spectrum gradient, such as a gradient from purple (short perimeter) to red (long perimeter), with different colors corresponding to different ranges of perimeter values.

[0086] In this embodiment, after the perimeter data is updated, the system can redraw the perimeter mapping map on the surface of the 3D anatomical model, ensuring that the color distribution of the mapping map is consistent with the latest perimeter data. Real-time rendering performance directly impacts user experience; faster rendering speeds result in smoother operation. Some embodiments provided in this disclosure employ optimization strategies such as merged rendering and view area clipping to improve rendering efficiency, ensuring a smooth interactive experience even with a large number of mapping points.

[0087] The cardiac electrophysiological data visualization processing method provided in this embodiment effectively solves the technical problems of low efficiency and poor consistency in traditional purely manual labeling by combining automatic recognition with human-computer interaction correction for feature point calibration. This embodiment can automatically and quickly complete the initial feature point localization, significantly reducing the workload of manual operations. The human-computer interaction correction mechanism in this embodiment ensures the accuracy of key feature points and avoids misjudgments that may occur in complex waveforms with purely automatic recognition. The combination of the two improves efficiency while ensuring accuracy, achieving a balance between efficiency and accuracy. Furthermore, the "target waveform" introduced in this embodiment can recalculate only the waveform corresponding to the feature points or leads associated with the correction command, rather than recalculating the entire waveform. This clarifies the scope of influence of the correction operation. This design significantly improves computational efficiency, ensures real-time response of interactive operations, and provides a performance foundation for subsequent linked rendering.

[0088] The 3D quantitative histogram visualization and linked rendering mechanism introduced in this embodiment not only achieves full-link automated linkage of waveform correction, perimeter calculation, and multi-dimensional visualization, but also automatically recalculates the average perimeter of the target waveform and synchronously updates the 3D quantitative histogram and the 3D model perimeter mapping map. The entire process requires no manual triggering, is highly responsive, and operates smoothly. The perimeter parameters of each electrode are presented intuitively as 3D geometric entities in an independent window, overcoming the shortcomings of traditional 2D color mapping visualization methods. The 3D quantitative histogram combines height quantization and color gradient dual visual encoding, enabling operators to quickly and accurately identify short-perimeter regions by the height of the bars, providing strong visual support for the localization of atrial fibrillation driving lesions. Simultaneously, the 3D quantitative histogram and the 3D model perimeter mapping map complement and corroborate each other, forming a multi-dimensional, multi-layered visualization system that significantly improves the accuracy and reliability of mapping analysis.

[0089] In some other embodiments of the method described in this disclosure, the geometric height and axial color gradient of the three-dimensional quantized histogram are non-linearly mapped to the average perimeter parameter, wherein the height of the column decreases as the value of the average perimeter parameter increases, and the color changes along the axial direction according to a preset color mapping table.

[0090] The nonlinear function mapping relationship described in this disclosure generally refers to a relationship between the height of a column and its average perimeter that is not a linear proportional relationship, but rather follows a certain nonlinear function relationship.

[0091] For example, in some embodiments, the height of the column can be calculated using the following formula:

[0092] h=h_min+(h_max-h_min)×(CL_max-CL_i) / (CL_max-CL_min);

[0093] Where h represents the height of the bar corresponding to the i-th lead, h_min represents the minimum drawing height of the bar chart (a fixed value), h_max represents the maximum drawing height of the bar chart, CL_max represents the maximum value of the average circumference of all leads in this measurement, CL_i represents the average circumference of the i-th lead in this measurement, and CL_min represents the minimum value of the average circumference of all leads in this measurement. This formula shows that the longer the average circumference of a lead, the lower the bar height; the shorter the average circumference, the higher the bar height. This design pays more attention to the needs of short-circumference leads, with short-circumference areas corresponding to tall bars, making them more visually prominent and better meeting the needs of practical application scenarios, facilitating the rapid discovery and location of short-circumference leads.

[0094] The axial color gradient typically refers to the effect of the column's color gradually changing along the height direction (axial direction). In some embodiments of this disclosure, the color gradient is not a single color, but rather a spectrum formed by multiple colors transitioning according to a certain pattern. For example, in some embodiments of this disclosure, the color map table can be constructed in four segments: the first segment is the transition from blue to cyan, the second segment is the transition from cyan to green, the third segment is the transition from green to yellow, and the fourth segment is the transition from yellow to red. Shorter perimeters correspond to cooler colors (blue, cyan), and longer perimeters correspond to warmer colors (yellow, red), conforming to practical usage habits. The color of the column corresponding to each lead is obtained from the color map table based on its relative position within the entire perimeter range according to its average perimeter.

[0095] The size of the color map table can be set as needed, such as 256 colors or 512 colors. A larger color map table results in smoother and more delicate color transitions, but it also increases the storage space and computing resources required. In practical applications, an appropriate color map table size can be selected based on system performance and display requirements.

[0096] In some embodiments of this disclosure, the geometric height and axial color gradient of the three-dimensional quantized histogram are non-linearly mapped to the average perimeter parameter, including: the height of each color bar in the three-dimensional quantized histogram is calculated using the following method:

[0097] Height_lead=CL_max-CL_i+h_min;

[0098] Wherein, CL_max represents the maximum average circumference among all leads measured in this study, CL_i represents the average circumference of the i-th lead measured in this study, and h_min is a preset fixed value that can represent the minimum height of the bars in the three-dimensional quantitative bar chart. This formula shows that the longer the average circumference of a lead, the lower its height, which better reflects and aligns with the clinical need to focus on leads with shorter circumferences.

[0099] In some embodiments of this disclosure, the geometric height and axial color gradient of the three-dimensional quantized histogram are non-linearly mapped to the average perimeter parameter, wherein the coloring process of each color bar in the three-dimensional quantized histogram includes:

[0100] A three-primary-color mapping table is pre-constructed, wherein the size of the three-primary-color mapping table and the step size in the traversal assignment process are determined based on the minimum and maximum values ​​of the average perimeter of each lead measured;

[0101] Calculate the index of the histogram corresponding to each lead in the three primary color mapping table, and determine the color set of the histogram corresponding to each lead based on the index;

[0102] Using the coloring set as the data source for the color column gradient, draw the gradient on the corresponding bar graph in the height direction.

[0103] In a specific implementation example of the coloring process for each color column of the three-dimensional quantitative bar atlas, the following steps may be included:

[0104] (1) Construct an RGB three-primary color mapping table (array): each element in the mapping table corresponds to one RGB color.

[0105] SizecolorMap=CL_max-CL_min+1;

[0106] SizecolorMap represents the size of the color mapping table; CL_min and CL_max respectively represent the minimum value and the maximum value of the average perimeters of all leads in this measurement.

[0107] Calculate StepcolorMap=1024-BsCL_max-CL_min;

[0108] Bs∈[1, 255], StepcolorMap represents the step size in the process of traversal assignment of the color mapping table.

[0109] The traversal of the color table can be divided into 4 segments:

[0110] The first segment:

[0111] Colork=RGB(255, j, 0);

[0112] where j is the traversal index with a value range from 0 to 255, the initial value of j is 0, and the updated value of j each time is: j=j+StepcolorMap;

[0113] where k is the index of the color table array Color, the initial value of k is 0, and during the traversal process, Color;

[0114] k increases by 1 each time the array is assigned a value, and if k>=SizecolorMap, the assignment of the color table ends;

[0115] If j is updated to 255, that is, k<SizecolorMap when the traversal ends, the traversal of the second segment is continued.

[0116] The second segment:

[0117] Colork=RGB(255-j, 255, j);

[0118] where j is the traversal index with a value range from 0 to 255, the initial value of j is 0, and the updated value of j each time is: j=j+StepcolorMap;

[0119] where k is the value of k at the end of the first segment, k increases by 1 each time the Color array is assigned a value during the traversal process, and if k>=SizecolorMap, the assignment of the color table ends.

[0120] If j is updated to 255, that is, k<SizecolorMap when the traversal ends, then the traversal of the third segment is continued.

[0121] Third segment:

[0122] Colork=RGB(0, 255-j, 255);

[0123] where j is the traversal index with a value range of 0 to 255, the initial value of j is 0, and the updated value of j each time is: j=j+StepcolorMap;

[0124] where k is the value of k at the end of the second segment, k increases by 1 each time the Color array is assigned a value during the traversal process, and if k>=SizecolorMap, the traversal assignment of the color table ends.

[0125] If i is updated to 255, that is, k<SizecolorMap when the traversal ends, then the traversal of the fourth segment is continued.

[0126] Fourth segment:

[0127] Colork=RGB(0, 0, 255-i);

[0128] where j is the traversal index with a value range of 0 to 255, the initial value of j is 0, and the updated value of j each time is: j=j+StepcolorMap;

[0129] where k is the value of k at the end of the third segment, k increases by 1 each time the Color array is assigned a value during the traversal process, and if k>=SizecolorMap or i is updated to 255, the assignment of the color table ends.

[0130] (2) Afterwards, the index of the histogram corresponding to each lead in the color table is calculated, for determining the color of the histogram.

[0131] ColorIndexlead=max(0, min(Cllead-CLmin), SizecolorMap)).

[0132] (3) Calculate the color of the histogram corresponding to each lead:

[0133] Colorlead=ColorColorIndexlead.

[0134] (4) Obtain the histogram coloring set corresponding to each lead:

[0135] A. Constructing a color bar:

[0136] ColorBar0=RGB0,0,156, ColorBar1=RGB(0,0,255), ColorBar2=RGB(0,255,255), ColorBar3=RGB(255,255,0), ColorBar4=RGB(255,0,0);

[0137] B. Traverse the color bar, compare the Colorlead with the color level in the color bar, and obtain the color set:

[0138] ColorSetlead={ColorBari….Colorlead}; i∈[0, 4];

[0139] Define ColorBar0 as the minimum and ColorBar4 as the maximum, and iterate through all color bars. If Colorlead is greater than or equal to ColorBar, then add it to the ColorSetlead set. After the iteration is complete, add Colorlead as the last element to ColorSetlead.

[0140] (5) Use this ColorSetlead as the data source for the gradient color of the color bar and draw the gradient color in the height direction.

[0141] In other embodiments of the method provided in this disclosure, identifying feature points of the electrophysiological waveform data includes:

[0142] Baseline estimation and baseline correction are performed on the original electrophysiological waveform signal to eliminate baseline drift. The baseline estimation uses the median of the signal within the sliding window as the true baseline.

[0143] Calculate the background noise level data within the current sliding window using the median absolute difference;

[0144] A detection threshold is dynamically generated based on the background noise level data. The corrected signal is traversed, and extreme points with differences exceeding the detection threshold are used as candidate feature points.

[0145] Within a preset time window of the candidate feature points, the first derivative is calculated, and the point with the maximum negative slope is taken as the excitation starting point.

[0146] In one specific processing embodiment, the identification of feature points in the electrophysiological waveform data can employ the following adaptive dynamic thresholding algorithm steps:

[0147] S201: Baseline estimation and baseline correction.

[0148] In this embodiment, the median of the signal within the sliding window can be used as the true baseline. This can eliminate baseline drift.

[0149] .

[0150] The original signal, This refers to the length of the sliding window. Baseline drift refers to the phenomenon of a slow change in the signal baseline over time, usually caused by factors such as respiratory movements, catheter movement, and changes in electrode contact. Baseline drift can severely affect the accuracy of feature point identification; therefore, this embodiment performs baseline correction before feature point identification. This embodiment uses the median as the baseline estimate, as the median is not sensitive to outliers (such as spike noise) and is more robust than the average. The length of the sliding window is typically 200~500ms, and can be adjusted according to the characteristics of the signal.

[0151] The baseline correction process involves subtracting the dynamic baseline from the original signal to obtain the baseline-corrected signal, i.e.:

[0152] .

[0153] S202: Calculation of dynamic noise level data.

[0154] Use the median absolute difference (MAD) to calculate the background noise level of the current sliding window:

[0155] .

[0156] MAD (Modular Dispersion) is a robust statistic used to measure the dispersion of data. Compared to standard deviation, MAD is less sensitive to outliers and is more suitable for estimating the noise level of signals containing spikes. Noise levels calculated based on MAD can more accurately reflect the background noise within the current window, providing a reliable basis for subsequent adaptive threshold settings.

[0157] S203: Adaptive dynamic threshold generation.

[0158] This embodiment dynamically generates a detection threshold based on the noise level of the current sliding window, which can reduce or avoid missed detections in scarred areas or false detections in normal areas with a fixed threshold. The formula for calculating the detection threshold can be: Where k is the sensitivity coefficient, typically between 3.0 and 5.0. A higher sensitivity coefficient k results in a higher detection threshold; a lower k results in a lower detection threshold and a lower false negative rate. The sensitivity coefficient can be adjusted according to clinical needs. In this embodiment, the detection threshold is dynamically adjusted based on the local noise level. In noisy scar areas, the threshold automatically increases to avoid misidentifying noise as feature points; in normal areas with less noise, the threshold automatically decreases to ensure that weak potentials are not missed.

[0159] S204: Dynamic threshold filtering.

[0160] By iterating through the corrected signal, we can find the extreme points that exceed the dynamic threshold and obtain a set of candidate feature points.

[0161]

[0162] To prevent the same peak from being repeatedly labeled, a refractory period constraint can be added, meaning the time difference between two candidate points must be greater than a preset refractory period (RP). In atrial fibrillation analysis, the refractory period is typically set to 50-100 ms. The physiological basis of the refractory period constraint is the refractory period characteristic of cardiomyocytes; that is, after depolarization, cardiomyocytes do not respond to stimulation for a period of time, therefore the time interval between two excitations cannot be shorter than the refractory period. This physiological constraint can effectively filter out cases where the same peak is repeatedly detected.

[0163] S205: Precise correction of local slope.

[0164] Within a predefined time window near the candidate point, such as a small time window, calculate the first derivative and find the point with the maximum negative slope as the precise excitation starting point:

[0165] .

[0166] The above formula means that at the candidate point Before and after Within the range (i.e., a small time window), iterate through and examine each time point. Find the slope, and then find the time point where the slope (derivative) is steepest (the negative value is smallest). .

[0167] The purpose of precise local slope correction is to further improve the temporal accuracy of feature points based on coarse localization. The point with the maximum negative slope is considered the true starting point of the local potential because it corresponds to the moment of rapid depolarization of cardiomyocytes. The time window range for fine-tuning is usually set to a few milliseconds to tens of milliseconds before and after the candidate point, and can be adjusted according to the signal sampling rate and waveform morphology.

[0168] Further, S206: Calculate the atrial fibrillation cycle length.

[0169] based on For sequences, calculate the time difference between two consecutive excitations:

[0170]

[0171] S207: Calculate the average perimeter.

[0172] Using the median as the average perimeter can prevent the problem of occasional extreme outliers.

[0173] ;

[0174] N represents the total number of valid atrial fibrillation cycles extracted from this data.

[0175] In this embodiment, baseline estimation and correction steps eliminate the interference of baseline drift on the recognition results, ensuring that feature point recognition is unaffected by slow baseline changes. The refractory period constraint effectively avoids the problem of the same peak being repeatedly marked, ensuring the accuracy of the feature point sequence; the local slope refinement step further improves the temporal accuracy of feature point localization. These steps work together, progressing step by step, enabling the automatic recognition results to approach the level of manual marking by professional physicians, providing a reliable data foundation for subsequent perimeter calculations.

[0176] In other embodiments of the method, each acquisition and measurement of electrophysiological waveform data forms a measurement record, which includes a record index, a waveform timeline index, a waveform feature point set, a catheter number, a circumference set, and a set of surface mapping points on the three-dimensional anatomical model of the heart chamber; wherein, the waveform timeline index and the set of surface mapping points on the three-dimensional anatomical model of the heart chamber correspond one-to-one through the system time index;

[0177] The linked rendering mechanism also includes a two-way linked positioning step:

[0178] In response to the operation of selecting feature points in the waveform interface, the corresponding record index is determined by querying the waveform timeline index, and the corresponding mapping point is highlighted on the three-dimensional anatomical model of the heart chamber based on the determined record index.

[0179] In response to the operation of selecting a mapping point on the three-dimensional anatomical model of the heart cavity, the corresponding record index is determined by querying the set of mapping points on the surface of the three-dimensional anatomical model of the heart cavity, and then jumps to the corresponding waveform position.

[0180] In response to the operation of selecting measurement record P in the measurement records, the system simultaneously jumps to the waveform position of the corresponding measurement record P and highlights the three-dimensional calibration point by querying the record index. P is the selected measurement record in the set of measurement records.

[0181] The measurement record typically refers to the data record generated by the system for a complete waveform measurement operation. Each waveform freeze and measurement generates an independent measurement record, which stores relevant data for this measurement, such as waveform data, feature point positions, perimeter calculation results, and corresponding 3D mapping point positions. Measurement records can be displayed in a list format on the interactive interface, allowing users to view historical measurement records or select a specific record for review and analysis.

[0182] The record index typically refers to a unique identifier for each measurement record, used to distinguish different measurement records. The record index can be a numerical number or other form of unique identifier; this disclosure does not specifically limit its use. Through the record index, the corresponding measurement record and all the data it contains can be quickly located.

[0183] The waveform timeline index typically refers to the index information used to determine the position of the lead waveform on the time axis. The waveform timeline index is a systematic time index, accurate to the millisecond level. When the catheter acquires signals within the heart chambers, under a given time index, the electrophysiological signal data (i.e., lead waveform data) returned by the catheter-end sensor and the positioning signal (mapped point position) correspond one-to-one.

[0184] The waveform feature point set typically refers to the set of position coordinates of the feature points identified (or corrected) in each lead during this measurement. The waveform feature point set contains the specific positions of the feature points of each selected lead on the time axis, serving as the basic data for calculating the perimeter and the basis for displaying feature points in the waveform interface.

[0185] The catheter number typically refers to the identifier of the target catheter, used to distinguish different catheters when multiple catheters are present. When multiple catheter leads are present on the measurement page, the catheter number can be used to identify the catheter to which the target lead belongs, ensuring the correct correspondence between data and catheter.

[0186] The perimeter set typically refers to the collection of average perimeter data from all leads of the target catheter. The average perimeter of unselected leads is set to 0, indicating that the lead was not included in this calculation. The perimeter set is the data source for 3D quantitative histograms and 3D model perimeter mapping.

[0187] The set of 3D model surface mapping points typically refers to the set of position coordinates acquired by each electrode of the target catheter as it moves within the cardiac chamber. Each point in this set corresponds to the position of an electrode in 3D space, serving as the basis for mapping the perimeter data onto the 3D model surface. The 3D model surface mapping point set and the waveform timeline index correspond one-to-one through the system time index, which is the key data foundation for achieving bidirectional linkage positioning.

[0188] Based on the data structure of the above measurement records, this embodiment also provides a two-way linkage positioning function, including:

[0189] (1) From waveform linkage to three-dimensional mapping points: When the user selects a feature point of any target lead with the mouse, the record index of this measurement is determined by querying the waveform timeline index in the current visible waveform page (when there are multiple indexes, waveform feature points can be added to the query), and then the set of mapping points on the surface of the three-dimensional anatomical model of the heart cavity is determined, and the corresponding three-dimensional model mapping points can be highlighted.

[0190] (2) Linking from three-dimensional mapping points to waveforms: When the user selects a mapping point on the three-dimensional model with the mouse, the system determines the record index of this measurement by querying the mapping point set on the surface of the three-dimensional anatomical model of the heart cavity, and then determines the waveform timeline index, so that the user can jump to the corresponding waveform position.

[0191] (3) Selecting the row of a record and linking it to the waveform and three-dimensional measurement points: When the user selects a single record, such as the row of the measurement record P, in the record table, the system queries the record index bound to it through the row data, and then determines the corresponding waveform timeline index and the set of three-dimensional model surface measurement points, so that it can jump to the waveform position and highlight the three-dimensional measurement points at the same time.

[0192] This embodiment achieves data exchange and interactive linkage between the waveform interface, the 3D model interface, and the recording table interface through a two-way linkage positioning mechanism for measurement records. Users can start from any interface and quickly locate the corresponding data in the other two interfaces, greatly facilitating cross-validation and comparative analysis between different views. This multi-view linkage interactive method breaks the situation of independent views and fragmented data in traditional mapping systems, improving the coherence and efficiency of the mapping and analysis process.

[0193] In this embodiment, if a user finds a suspicious short-circuit region on the three-dimensional anatomical model of the cardiac chamber, they can directly click on the mapping point to jump to the corresponding waveform location and carefully examine the waveform morphology to confirm whether it is a real driving lesion. Conversely, if an abnormal feature point is found in the waveform interface, its position in three-dimensional space can be quickly located. This two-way verification capability significantly improves the accuracy of mapping analysis and reduces misjudgments caused by insufficient information from a single view.

[0194] The design of the measurement record in this embodiment ensures that each measurement has a complete data archive, allowing for review of historical measurement records at any time, comparative analysis, and trend observation.

[0195] In other embodiments of the method described in this disclosure, during the process of identifying feature points of electrophysiological waveform data or calculating the average circumference parameter, a selection instruction for the leads involved in identification or calculation is received; in response to the selection instruction, the average circumference parameter of the corresponding lead is calculated only based on the waveform feature points of the selected lead, and the average circumference data of the selected lead is synchronously updated to cover the three-dimensional quantitative histogram and the circumference mapping map of the three-dimensional anatomical model of the heart chamber.

[0196] In some embodiments, the lead selection instruction may include a user's command to select or deselect specific leads for circumference calculation. Users can issue lead selection instructions in various ways, such as checking / unchecking checkboxes in the lead list, clicking lead labels in the waveform interface, or selecting / deselecting corresponding electrodes in the 3D model. Lead selection can be single-selection or multi-selection, allowing users to flexibly choose leads to participate in the calculation as needed. Figure 3 As shown, Figure 3 This is a schematic diagram illustrating waveform feature point correction based on interactive operation according to an exemplary embodiment.

[0197] The phrase "calculating the average perimeter parameter of the corresponding lead based only on the waveform feature points of the selected leads" means that in some embodiments, the system can only calculate the perimeter of the leads selected by the user, while unselected leads are not included in the calculation, and their perimeter data will not be updated. One advantage of this design is that when the signal quality of some leads is poor (e.g., high noise, severe baseline drift), these leads can be temporarily excluded from the calculation, avoiding the influence of interfering data on the overall analysis results.

[0198] The aforementioned overlay update typically refers to the direct replacement of old data with new perimeter data, rather than accumulating or overlaying it on top of old data. Overlay updates ensure that the 3D quantized histogram and 3D model perimeter mapping always display the latest calculation results, preventing confusion caused by residual historical data. When a user reselects a previously deselected lead, the system recalculates the average perimeter of that lead and re-pushes its data to the dual-view display.

[0199] This embodiment provides users with flexible data filtering capabilities through a lead selection mechanism. Users can selectively focus on the cycle length data of specific leads based on clinical needs, excluding leads with poor signal quality or severe interference from affecting the overall analysis. This flexible filtering capability is particularly important in mapping complex arrhythmias, because in real-world applications, not all leads have equally good signal quality. Some leads may have abnormal waveforms due to poor contact, far-field interference, or other reasons. Indiscriminately including all of these leads in the calculation may affect the accuracy of the analysis results. Simultaneously, in this embodiment, the cycle length data of the selected leads is synchronously pushed to the dual-view for overlay updates, ensuring that the 3D quantitative histogram and the 3D model cycle length mapping map always maintain data consistency, avoiding misjudgments caused by data asynchrony. Furthermore, the overlay update method ensures the timeliness and accuracy of the data, ensuring that users always see the latest calculation results. Figure 4 This is a schematic diagram of a three-dimensional quantized histogram according to an exemplary embodiment.

[0200] In other embodiments of the method, in the linked rendering mechanism, the upper limit of the time delay from updating the average perimeter parameter to synchronously updating the dual views of the three-dimensional quantized histogram and the perimeter mapping map of the three-dimensional anatomical model of the heart cavity is 15ms; wherein, the three-dimensional quantized histogram adopts a real-time update mechanism, and the perimeter mapping map of the three-dimensional anatomical model of the heart cavity adopts a merged drawing mechanism. The merged drawing mechanism refers to merging multiple batches of drawing requests within a preset time window into a single rendering action, and only performing color mapping and surface rendering on the mapping points located within the visible area, while caching the mapping point data outside the visible area without performing real-time rendering. Figure 5 This is a schematic diagram illustrating the circumference mapping of a three-dimensional anatomical model of the heart cavity according to an exemplary embodiment.

[0201] Setting a latency cap is an important goal for optimizing the performance of the linked rendering mechanism and a key indicator for measuring system response speed. The latency cap described in this embodiment typically refers to the maximum allowable time interval from data update to view refresh completion. A latency cap of 15ms corresponds to a refresh rate of approximately 66 frames per second, which is higher than the critical refresh rate for smooth human perception (approximately 60 frames per second). This ensures that users do not experience noticeable delays during operation and obtain a smooth real-time interactive experience.

[0202] The real-time update mechanism typically refers to updating the 3D quantized histogram whenever new perimeter data arrives, without delay or merging. Since the data volume of the 3D quantized histogram is relatively small (usually only a few to dozens of bars), the rendering computation is not demanding, thus real-time updates can be used to ensure data immediacy. This real-time update mechanism allows the histogram to closely follow data changes, providing the most timely visual feedback.

[0203] The merged rendering mechanism typically refers to a system that does not immediately respond to every rendering request, but instead collects multiple rendering requests over a period of time and merges them into a single rendering action. The merged rendering time window can be set to 1 second or other suitable values. The merged rendering mechanism is mainly used for rendering perimeter mapping maps of 3D models. Because 3D mapping maps involve color mapping and surface rendering of a large number of mapping points, the computational load is relatively large. If rendering is performed immediately after each data update, it may cause system lag or a drop in frame rate. This embodiment, through merged rendering, can effectively reduce the number of rendering operations, reduce system load, and improve overall performance.

[0204] In some further embodiments of this disclosure, the rendering of the 3D model perimeter mapping map also includes a rendering mechanism that only renders within the visible area. That is, based on the frustum range of the current viewpoint, only the mapping points located within the visible area are color-mapped and surface-rendered, while the mapping point data outside the visible area is cached without real-time rendering. Frustum clipping is a commonly used optimization technique in computer graphics. By removing geometry outside the frustum, the number of primitives that need to be processed can be significantly reduced, improving rendering efficiency.

[0205] In other embodiments, after identifying the feature points of the waveform, leads whose average perimeter deviates from all leads by more than a preset threshold can be filtered out, and the corresponding electrode-collected mapping points are marked as invalid mapping points. These invalid mapping points do not participate in the average perimeter calculation, nor in the rendering of the 3D model perimeter mapping map. Invalid mapping points typically refer to mapping points whose perimeter data is unreliable due to abnormal signal quality or other reasons. In actual surgery, due to poor electrode contact, far-field interference, catheter displacement, respiratory movements, etc., the signals of some leads may be abnormal, causing their perimeter values ​​to deviate significantly from the normal range. If these abnormal data are not filtered, they may interfere with the overall analysis results and even lead to incorrect judgments by the surgeon. Filtering typically refers to the process of the system automatically identifying and excluding abnormal leads. The filtering operation can be performed automatically after feature point identification or manually triggered by the user. After filtering, the mapping points marked as invalid will be displayed in a special way on the 3D model (such as gray, semi-transparent, etc.) to distinguish them from the valid mapping points, allowing the surgeon to intuitively understand which mapping points are reliable and which are unreliable. Invalid calibration points can be excluded from the average perimeter calculation, and the perimeter data of these leads will not affect the overall statistical results; invalid calibration points can also be excluded from the rendering of the perimeter calibration map, and these points will not be displayed in color on the surface of the 3D model, thus avoiding interference from abnormal data on the overall visual effect of the calibration map.

[0206] It is understood that the various embodiments of the methods described in this specification are presented in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on its differences from other embodiments. Relevant details can be found in the descriptions of other method embodiments.

[0207] It should be understood that although the steps in the flowcharts shown in the accompanying drawings are displayed sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise expressly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some of the steps in the accompanying drawings may include multiple steps or stages, which are not necessarily completed at the same time, but may be executed at different times, and the execution order of these steps or stages is not necessarily sequential, but may be performed alternately or in turn with other steps or at least a portion of the steps or stages of other steps.

[0208] Based on the description of the above-described embodiments of the cardiac electrophysiological data visualization processing method, this disclosure also provides a cardiac electrophysiological data visualization processing apparatus. The apparatus may include a system (including a distributed system), software (application), module, component, controller, server, terminal, etc., using the methods described in the embodiments of this specification, combined with necessary hardware implementation. Based on the same innovative concept, the apparatuses in one or more embodiments provided in this disclosure are as described in the following embodiments. Since the implementation schemes and methods for solving the problem by the apparatus are similar, the implementation of the specific apparatus in the embodiments of this specification can refer to the implementation of the foregoing method, and repeated details will not be repeated. As used below, the terms "unit" or "module" can refer to a combination of software and / or hardware that implements a predetermined function. Although the apparatus described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0209] Figure 6 This is a schematic block diagram illustrating a cardiac electrophysiological data visualization processing device according to an exemplary embodiment. The device can be the aforementioned terminal device, or integrated into the terminal device module, component, device, control unit, etc. For details, please refer to... Figure 6 The device 100 may include:

[0210] The data acquisition module 602 is used to acquire electrophysiological waveform data and spatial position information of the multipolar catheter to construct a three-dimensional anatomical model of the heart chamber;

[0211] The feature point recognition module 604 is used to display the electrophysiological waveform data on the interactive interface of the three-dimensional anatomical model of the heart chamber and to recognize the feature points of the electrophysiological waveform data.

[0212] The perimeter update module 606 is used to receive a correction instruction for the feature point, execute the correction instruction, and update the average perimeter parameter of the target waveform based on the corrected feature point sequence. The target waveform includes the electrophysiological waveform corresponding to the feature point or lead associated with the correction instruction.

[0213] The linkage rendering module 608 is used to dynamically draw a three-dimensional quantitative bar chart that maps one-to-one with each electrode of the multi-polar catheter in a dedicated display area independent of the three-dimensional anatomical model of the heart cavity, based on a pre-built linkage rendering mechanism and in response to the circumference data after the average circumference parameter is updated, and to render a circumference mapping map on the surface of the three-dimensional anatomical model of the heart cavity in real time.

[0214] Regarding the apparatus in the above embodiments, the specific methods by which each module performs its operations have been described in detail in the embodiments related to the method, and will not be elaborated upon here. Based on the description of the foregoing method embodiments, the apparatus provided by this disclosure may also include apparatus embodiments corresponding to the various method embodiments of this disclosure, to implement the execution steps or functions of the corresponding methods. For example, it may also include a first module, a second module, a third module, etc., which respectively execute the steps or functions of the corresponding methods. For instance, the feature point recognition module 604 may perform baseline estimation and baseline correction on the original electrophysiological waveform signal to eliminate baseline drift, wherein the baseline estimation uses the median of the signal within the sliding window as the true baseline; calculates the background noise level data within the current sliding window using the absolute difference of the median; dynamically generates a detection threshold based on the background noise level data; traverses the corrected signal, and uses the extreme points where the difference exceeds the detection threshold as candidate feature points; calculates the first derivative within a preset time window of the candidate feature points, and uses the point with the maximum negative slope as the excitation initiation point.

[0215] Based on the foregoing description of the relevant methods and apparatus embodiments, this disclosure also provides a computer-readable storage medium. When the instructions in the computer-readable storage medium are executed by the processor of an electronic device, the electronic device is able to implement the cardiac electrophysiological data visualization processing method as described in any embodiment of this disclosure. Those skilled in the art will understand that all or part of the steps in the methods of the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program can implement the cardiac electrophysiological data visualization processing method as described in any embodiment of this disclosure. The storage medium can be, for example, ROM / RAM, magnetic disk, optical disk, etc.

[0216] In an exemplary embodiment, a computer program product is also provided, including a computer program that, when executed by a processor, implements the cardiac electrophysiological data visualization processing method described in any embodiment of this specification.

[0217] In an exemplary embodiment, an electronic device is also provided, comprising:

[0218] At least one processor;

[0219] Memory used to store the processor's executable instructions;

[0220] The processor is configured to execute the instructions to implement the cardiac electrophysiological data visualization processing method according to any one of the present disclosures.

[0221] In an exemplary embodiment, an image processing terminal device is also provided, comprising: at least one processor; and a memory for storing processor-executable instructions; wherein the processor is configured to execute the instructions to implement the cardiac electrophysiological data visualization processing method according to any one of the present disclosures.

[0222] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to interchangeably. Each embodiment focuses on its differences from other embodiments. In particular, for hardware + program embodiments, since they are basically similar to the method embodiments, the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments.

[0223] In one exemplary embodiment, an electronic device is provided, which may be a controller, a device, or a device within a server. Its internal structure diagram may be as follows: Figure 7As shown. The electronic device may include a processor, memory, input / output interface, communication interface, and input device, and may also include a display unit. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are connected to the system bus via the input / output interface. The processor of the electronic device provides computing and control capabilities. The memory of the electronic device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The input / output interface of the electronic device is used for exchanging information between the processor and external devices. The communication interface of the electronic device is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, Near Field Communication (NFC), or other technologies. When the computer program is executed by the processor, it implements the cardiac electrophysiological data visualization processing method described in any embodiment of this disclosure. The display unit of the electronic device is used to form a visually visible image and may be a display screen, a projection device, or a virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device of the electronic device can be a touch layer covering the display screen, or buttons, trackballs, or touchpads set on the casing of the electronic device, or external keyboards, touchpads, or mice, etc.

[0224] Those skilled in the art will understand that Figure 7 The structure shown is only a block diagram of a part of the structure related to the present application and does not constitute a limitation on the electronic device to which the present application is applied. The specific electronic device may include more or fewer components than shown in the figure, or combine certain components, or have different component arrangements.

[0225] It should be noted that the aforementioned apparatus, devices, electronic devices, etc., as described in the method embodiments, may also include other implementation methods. Specific implementation methods can be found in the descriptions of the relevant method embodiments. Furthermore, new embodiments formed by combinations of features from various methods, apparatuses, devices, and server embodiments still fall within the scope of this disclosure and will not be elaborated upon here.

[0226] For ease of description, the above devices are described in terms of function, divided into various modules. Of course, when implementing one or more of these specifications, the functions of each module can be implemented in the same or different software and / or hardware, or a module that performs the same function can be implemented by a combination of multiple sub-modules or sub-units. The device embodiments described above are merely illustrative. For example, the division of modules or units is only a logical functional division; in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling and communication connections between the devices or units shown or described can be implemented through direct and / or indirect coupling / connection, through standard or custom interfaces or protocols, and can be implemented electrically, mechanically, or in other forms.

[0227] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This disclosure is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated directly or indirectly by the content of the specification.

[0228] It should be understood that this disclosure is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope.

Claims

1. A method for visualizing cardiac electrophysiological data, characterized in that, The method includes: Electrophysiological waveform data and spatial location information of multipolar catheters were collected to construct a three-dimensional anatomical model of the cardiac chambers; The electrophysiological waveform data is displayed on the interactive interface of the three-dimensional anatomical model of the heart chamber, and the feature points of the electrophysiological waveform data are identified. Receive a correction instruction for the feature point, execute the correction instruction, and update the average perimeter parameter of the target waveform based on the corrected feature point sequence. The target waveform includes the electrophysiological waveform corresponding to the feature point or lead associated with the correction instruction. Based on a pre-built linkage rendering mechanism, in response to the updated circumference data after the average circumference parameter is updated, a three-dimensional quantitative bar chart that maps one-to-one with each electrode of the multi-polar catheter is dynamically drawn in a dedicated display area independent of the three-dimensional anatomical model of the heart cavity, and a circumference mapping map is rendered in real time on the surface of the three-dimensional anatomical model of the heart cavity.

2. The method according to claim 1, characterized in that, The geometric height and axial color gradient of the three-dimensional quantized bar chart are non-linearly mapped to the average perimeter parameter. The height of the bar decreases as the value of the average perimeter parameter increases, and the color changes along the axis according to a preset color mapping table.

3. The method according to claim 2, characterized in that, The geometric height and axial color gradient of the three-dimensional quantized histogram have a non-linear functional mapping relationship with the average perimeter parameter, including: the height calculation method of each color bar in the three-dimensional quantized histogram includes: ； in, This represents the maximum value among the average circumferences of all leads measured in this study. Indicates the first The average circumference of each lead measured in this study. This is a preset fixed value, representing the minimum drawing height of the bars in the 3D quantized bar chart.

4. The method according to claim 2, characterized in that, The geometric height and axial color gradient of the three-dimensional quantized histogram have a non-linear functional mapping relationship with the average perimeter parameter. The coloring process for each color bar in the three-dimensional quantized histogram includes: A three-primary-color mapping table is pre-constructed, wherein the size of the three-primary-color mapping table and the step size in the traversal assignment process are determined based on the minimum and maximum values ​​of the average perimeter of each lead measured; Calculate the index of the histogram corresponding to each lead in the three primary color mapping table, and determine the color set of the histogram corresponding to each lead based on the index; Using the color set as the data source for the gradient color, draw the gradient color in the corresponding histogram in the height direction.

5. The method according to claim 1, characterized in that, The identification of feature points in the electrophysiological waveform data includes: Baseline estimation and baseline correction are performed on the original electrophysiological waveform signal to eliminate baseline drift. The baseline estimation uses the median of the signal within the sliding window as the true baseline. Calculate the background noise level data within the current sliding window using the median absolute difference; A detection threshold is dynamically generated based on the background noise level data. The corrected signal is traversed, and extreme points with differences exceeding the detection threshold are used as candidate feature points. Within a preset time window of the candidate feature points, the first derivative is calculated, and the point with the maximum negative slope is taken as the excitation starting point.

6. The method according to claim 1, characterized in that, Each acquisition and measurement of electrophysiological waveform data forms a measurement record, which includes a record index, waveform timeline index, waveform feature point set, catheter number, circumference set, and surface mapping point set of the three-dimensional anatomical model of the heart chamber; wherein, the waveform timeline index and the surface mapping point set of the three-dimensional anatomical model of the heart chamber correspond one-to-one through the system time index; The linked rendering mechanism also includes a two-way linked positioning step: In response to the operation of selecting feature points in the waveform interface, the corresponding record index is determined by querying the waveform timeline index, and the corresponding mapping point is highlighted on the three-dimensional anatomical model of the heart chamber based on the determined record index. In response to the operation of selecting a mapping point on the three-dimensional anatomical model of the heart cavity, the corresponding record index is determined by querying the set of mapping points on the surface of the three-dimensional anatomical model of the heart cavity, and then jumps to the corresponding waveform position. In response to the operation of selecting measurement record P in the measurement records, the system simultaneously jumps to the waveform position of the corresponding measurement record P and highlights the three-dimensional calibration point by querying the record index. P is the selected measurement record in the set of measurement records.

7. The method according to claim 1, characterized in that, During the process of identifying feature points of electrophysiological waveform data or calculating the average circumference parameter, a selection instruction for the leads involved in the identification or calculation is received; in response to the selection instruction, the average circumference parameter of the corresponding lead is calculated only based on the waveform feature points of the selected lead, and the average circumference data of the selected lead is synchronously updated to cover the three-dimensional quantitative histogram and the circumference mapping map of the three-dimensional anatomical model of the heart chamber.

8. The method according to claim 1, characterized in that, In the aforementioned linkage rendering mechanism, the upper limit of the time delay from updating the average perimeter parameter to synchronously updating the dual views of the 3D quantized histogram and the perimeter mapping map of the 3D anatomical model of the heart cavity is 15ms. The 3D quantized histogram adopts a real-time update mechanism, and the perimeter mapping map of the 3D anatomical model of the heart cavity adopts a merged rendering mechanism. The merged rendering mechanism refers to merging multiple batches of rendering requests within a preset time window into a single rendering action, and only performing color mapping and surface rendering on the mapping points located within the visible area, while caching the mapping point data outside the visible area without performing real-time rendering.

9. A device for visualizing and processing cardiac electrophysiological data, characterized in that, include: The data acquisition module is used to acquire electrophysiological waveform data and spatial location information of the multipolar catheter to construct a three-dimensional anatomical model of the heart chambers. The feature point recognition module is used to display the electrophysiological waveform data on the interactive interface of the three-dimensional anatomical model of the heart chamber and to identify the feature points of the electrophysiological waveform data. The perimeter update module is used to receive correction instructions for the feature points, execute the correction instructions, and update the average perimeter parameter of the target waveform based on the corrected feature point sequence. The target waveform includes the electrophysiological waveform corresponding to the feature points or leads associated with the correction instructions. The linkage rendering module is used to dynamically draw a three-dimensional quantitative bar chart that maps one-to-one with each electrode of the multi-polar catheter in a dedicated display area independent of the three-dimensional anatomical model of the heart cavity, based on a pre-built linkage rendering mechanism and in response to the circumference data after the average circumference parameter is updated, and to render a circumference mapping map on the surface of the three-dimensional anatomical model of the heart cavity in real time.

10. An electronic device, characterized in that, include: At least one processor; Memory used to store the processor's executable instructions; The processor is configured to execute the instructions to implement the cardiac electrophysiological data visualization processing method as described in any one of claims 1 to 9.

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