Subthreshold cardiac communication methods, systems, devices, media, and applications

Abstract

The application provides a sub-threshold heart communication method, system, device, medium and application, and belongs to the technical field of biomedical engineering. The method comprises the following steps: constructing a communication channel formed by coupling of multiple three-dimensional myocardial cell models; modulating physiological information into a sub-threshold electrical signal and injecting the signal into the channel for transmission; demodulating the signal at a receiving end to recover the information; wherein a physiological noise model is integrated in the channel to simulate a real physiological environment. The system corresponds to a transmitting end, a communication channel and a receiving end. The application adopts the sub-threshold heart communication method, system, device, medium and application, the three-dimensional myocardial cell model is used to more truly simulate a heart tissue structure, the complete communication process simulation of the sub-threshold electrical signal in a noise environment is realized for the first time, the existing research gap is filled, and an innovative technical platform is provided for research on cardiac electrophysiological mechanisms, performance optimization and evaluation of implantable medical devices such as a cardiac pacemaker.

Description

Technical Field

[0001] This invention relates to the field of biomedical engineering technology, and in particular to a subthreshold cardiac communication method, system, device, medium, and application. Background Technology

[0002] In the fields of cardiac electrophysiology research and biomimetic communication, the mechanism of electrical signal conduction between cardiomyocytes is a core issue. Current technologies generally employ simplified models based on one-dimensional cable theory to simulate this process. While these models can describe basic electrical signal attenuation and conduction velocity, they cannot reproduce the true three-dimensional anisotropic structure of cardiac tissue, nor can they accurately depict the complex propagation, diffusion, and interaction patterns of signals in three-dimensional space. This results in a significant discrepancy between the simulation results and the actual physiological reality of the living heart, limiting their application value in precision medicine and the development of high-end equipment.

[0003] Furthermore, current research focuses heavily on the analysis of the strong electrical signal, the action potential, while generally neglecting the crucial role of subthreshold membrane potentials in intercellular communication. Although subthreshold signals do not reach the intensity required to trigger an action potential, they play a vital role in cardiac rhythm fine-tuning, intercellular synchronization, and early pathological responses. Currently, systematic research on how subthreshold signals encode physiological information and how they are reliably transmitted within three-dimensional cellular networks is lacking. This creates a significant gap in our understanding of cardiac function integrity and misses a potential opportunity to develop ultra-low-power bioelectronic interfaces using this mechanism.

[0004] A more prominent problem is that most existing models are theoretically extrapolated in idealized, noise-free static environments, severely neglecting the complex physiological environment of the real heart, which is filled with ion channel thermal noise and electromagnetic interference. While this simplification reduces computational complexity, it causes the models to lose their physiological realism. Noise is not merely interference; it is an integral part of the living system and can have a decisive impact on the detection, decoding, and system stability of subthreshold signals. The lack of systematic modeling and integration of physiological noise prevents the current technical framework from assessing the feasibility and robustness of subthreshold communication in real in vivo environments, constituting a fundamental obstacle to its clinical translation and application. Summary of the Invention

[0005] The purpose of this invention is to provide a subthreshold cardiac communication method, system, device, medium, and application. It aims to solve key problems in the prior art, such as the simplification of cardiac electrical signal conduction models, insufficient research on subthreshold communication mechanisms, and lack of simulation of physiological noise environments, by constructing a simulation system that is closer to the real physiological environment.

[0006] To achieve the above objectives, the present invention provides a subthreshold cardiac communication method, comprising the following steps: Step S1: Configure the cardiac communication system, including: The transmitting end is used to generate subthreshold electrical signals; The communication channel, consisting of multiple electrically coupled three-dimensional cardiomyocyte models, is used to transmit subthreshold electrical signals, and the communication channel is configured to simulate an electrical signal transmission environment containing physiological noise. The receiving end is used to demodulate subthreshold electrical signals; Step S2: At the transmitting end, a subthreshold electrical signal is generated based on the physiological information to be transmitted and applied to the communication channel; Step S3: The subthreshold electrical signal propagates in the communication channel and is captured at the receiving end; Step S4: At the receiving end, the captured electrical signals are processed to reconstruct physiological information.

[0007] Preferably, in step S1, the construction of the communication channel includes: Constructing a cell geometry model: Based on the calcium release unit network, a topological structure of a single three-dimensional cardiomyocyte is constructed. A single cardiomyocyte is defined as a three-dimensional discrete mesh composed of several calcium release units arranged in the longitudinal, transverse and depth directions. Define electrophysiological parameters: Establish a set of ordinary differential equations including membrane capacitance and multiple ion channel currents to calculate the local electrophysiological state of each calcium release unit node. The multiple ion channel currents include sodium ion current, calcium ion current and potassium ion current. Establishing a gap connection model and numerical solution: The reaction-diffusion equation is used to describe the conduction of electrical signals between and within cells, and the diffusion coefficient is set to characterize the gap connection strength; the finite difference method is used to solve the reaction-diffusion equation in a spatiotemporal discretization to simulate the evolution of subthreshold electrical signals in the communication channel.

[0008] Preferably, in step S2, the signal modulation algorithm uses on-off keying modulation: mapping binary information bit 1 to a stimulation current of a specific intensity, and mapping binary information bit 0 to a current without stimulation; and controlling the intensity of the stimulation current so that the membrane potential fluctuations it causes when propagating in the communication channel are always lower than the action potential threshold of myocardial cells.

[0009] Preferably, step S4, processing the captured electrical signal specifically includes the following steps: Signal sampling: Based on the signal propagation delay time, determine the optimal sampling time within each bit transmission cycle, and obtain the instantaneous membrane potential value of the myocardial cells at the receiving end at that time; Threshold decision: The instantaneous membrane potential value is compared with a preset decision threshold; Information restoration: If the instantaneous membrane potential value is greater than or equal to the decision threshold, the received information bit is determined to be logic 1; if it is less than the decision threshold, it is determined to be logic 0.

[0010] Preferably, in step S1, simulating an electrical signal transmission environment containing physiological noise specifically involves introducing an additive Gaussian white noise term into the response-diffusion equation describing changes in myocardial cell membrane potential. ;in Standard Gaussian white noise, As the reference noise intensity, This is used as a noise scaling factor to simulate membrane potential fluctuations caused by random gating of ion channels.

[0011] The present invention also provides a subthreshold cardiac communication system for implementing the method described above, comprising: The transmitting end is used to generate subthreshold electrical signals based on physiological information; The communication channel, which consists of multiple electrically coupled three-dimensional cardiomyocyte models, is used to transmit subthreshold electrical signals in an environment simulating physiological noise. The receiving end is used to receive and process electrical signals from the communication channel to reconstruct physiological information.

[0012] Preferably, the communication channel integrates a noise model unit for applying interference signals characterizing physiological random electrical activity to the three-dimensional cardiomyocyte model.

[0013] The present invention also provides a computer device, comprising: a memory and a processor; the memory stores a computer program, and the processor executes the computer program to implement the steps of the subthreshold cardiac communication method described above.

[0014] The present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the subthreshold cardiac communication method described above.

[0015] The present invention also provides a simulation test method for cardiac pacemaker stimulation parameters, which uses the subthreshold cardiac communication system as described above, or runs on the computer equipment described above, to simulate the subthreshold stimulation parameters of the cardiac pacemaker, and evaluates and optimizes the stimulation parameters by analyzing the signal transmission effect in the system.

[0016] Therefore, the present invention employs the above-described subthreshold cardiac communication method, system, device, medium, and application, and the beneficial technical effects are as follows: (1) This invention constructs a communication channel composed of multiple electrically coupled three-dimensional cardiomyocyte models, thus abandoning the simplified one-dimensional / two-dimensional models in the prior art, thereby enabling a more realistic simulation of signal propagation, attenuation, and interaction in the three-dimensional spatial structure of the heart. This improvement directly overcomes the defect in the prior art that the model cannot reproduce the real three-dimensional structure.

[0017] (2) The core of the method of this invention lies in generating, transmitting and demodulating subthreshold electrical signals (whose strength is insufficient to trigger action potentials), and a complete communication link including modulation and decision mechanisms is designed for this purpose. This specifically fills the gap in the existing technology where research on communication mechanisms for subthreshold signals is still lacking.

[0018] (3) This invention actively integrates and simulates an electrical signal transmission environment containing physiological noise in the communication channel. This design acknowledges the characteristic of noise as an inherent component of physiological systems, enabling the communication system to assess the feasibility and stability of subthreshold communication links under near-realistic complex physiological environments. This overcomes the obstacle of existing models ignoring noise and being unable to assess the feasibility of real in vivo environments, thus enhancing the value of research results in clinical translation. Attached Figure Description

[0019] Figure 1 This is a hierarchical representation of cardiomyocyte action potentials and a communication diagram of "subthreshold" membrane potentials, in which... Figure 1 (a) in the diagram is a longitudinal section of the heart. Figure 1 (b) in the image is a magnified view of a cluster of cardiomyocytes. Figure 1 (c) in the figure represents a typical action potential curve of single-cell membrane potential changing over time; the green dashed line represents the resting membrane potential, and the orange dashed line represents the threshold potential; subthreshold membrane potential communication is the transmission of signals when the membrane potential is between the two dashed lines. Figure 2 A three-dimensional geometric model of cardiomyocytes; Figure 3 This provides a general modeling framework for a communication system based on three-dimensional cardiomyocytes. Figure 3 (a) is a schematic diagram of the system physics, in which the transmitting nano-actuator injects a stimulating current through a membrane attached to a specific region of a cardiomyocyte. ) induces cell depolarization, the channel is composed of three-dimensional cardiomyocytes connected in series, and the receiving-end nanoactuator reads the target cell membrane potential through the corresponding membrane. V ); Figure 3 (b) in the figure is the classic communication equivalent model of the system, which consists of a signal transmitter, a myocardial cell channel and a receiver, to realize information transmission within biological tissues. Detailed Implementation

[0020] The technical solution of the present invention will be further described below with reference to the accompanying drawings and embodiments.

[0021] Unless otherwise defined, the technical or scientific terms used in this invention shall have the ordinary meaning as understood by one of ordinary skill in the art to which this invention pertains.

[0022] Example 1 This embodiment combines Figures 1 to 3This document describes in detail the construction and simulation process of the system described in this invention under a high-performance computing environment. This embodiment is implemented through computer simulation, using C++ with a unified computing device architecture (CUDA) as the programming language, Visual Studio Code as the development environment, and a hardware platform based on a graphics processor, specifically an NVIDIA GPU for accelerated computing.

[0023] Step 1: System configuration and 3D channel modeling.

[0024] 1. Construct a three-dimensional geometric model of cardiomyocytes (combined with...) Figure 2 ): Using Visual Studio Code to write CUDA C++ programs, based on Figure 2 The geometric model of the cardiomyocyte shown is used to construct a computational model of a single three-dimensional cardiomyocyte. Unlike the traditional one-dimensional cable model, this embodiment uses a calcium release unit, i.e., a CRU network, to construct a three-dimensional discrete mesh.

[0025] Grid partitioning: A single cardiomyocyte is logically composed of 20,000 CRU nodes, arranged in a horizontal array of 100 CRUs. 20 CRUs in the vertical direction The depth is 10 CRUs. Each CRU node is an independent functional computing unit, containing cytoplasmic space, dual space, and sarcoplasmic reticulum structure.

[0026] Parameter definition: Defines the distance step size Cell membrane capacitance .

[0027] 2. Constructing a communication channel (combined with...) Figure 3 ):according to Figure 3 The system framework shown connects multiple three-dimensional cardiomyocyte models along the vertical axis, and the intercellular voltage diffusion coefficient is set. To characterize the electrical coupling strength of the gap connection, a communication channel consisting of multiple electrically coupled three-dimensional cardiomyocyte models is constructed.

[0028] 3. Integrated Physiological Noise Model: An additive Gaussian white noise term is introduced into the reaction-diffusion equation describing membrane potential changes to simulate potential fluctuations caused by random gating of ion channels under real physiological conditions. In the CUDA kernel function, for each CRU node... At time step membrane potential Discretization is performed to solve the problem, and the formula is as follows: ; in, For the first The CRU node in the ... The membrane potential at each time step; For time step; This is the spatial step size, i.e., the distance between CRU nodes; For the first The CRU node in the ... The sum of ion channel currents at each time step, including sodium current ( ), L-type calcium current ( ) and various ion channel currents, including multiple potassium currents, among which the multiple potassium currents specifically include: inward rectified potassium current ( ), fast activation delayed rectified potassium current ( ) and slow-activation delayed rectified potassium current ( ); For the first The CRU node in the ... External stimulation current applied at each time step; The reference noise level is set to 0.00194mV; The noise scaling factor is set to 0.15. These are random numbers that follow a standard normal distribution.

[0029] Step 2, Signal Modulation and Transmission (combined) Figure 1 , Figure 3 ).

[0030] 1. Information Encoding: The transmitting end encodes the myocardial pathology-related physiological information to be transmitted (such as "normal sinus rhythm" or "ventricular premature beats") into a binary bit stream.

[0031] 2. OOK modulation: such as Figure 3 As shown in (a), the transmitter nanoactuator adopts the OOK modulation strategy.

[0032] Logic "1": During a bit transmission cycle Internally, a stimulation current of a specific intensity is applied. .

[0033] Logic "0": No current is applied during the bit transmission cycle. ).

[0034] 3. Subthreshold control: such as Figure 1 As shown in (c), the stimulation current is controlled. The strength (e.g.) ), ensuring that the resulting membrane potential fluctuations remain at the resting potential ( ) and action potential threshold ( Between these values, to avoid triggering cell contraction, signal transmission is carried out using only subthreshold potentials.

[0035] Step 3: Signal transmission and reception.

[0036] 1. Signal transmission: Subthreshold electrical signals propagate through gap junctions in the three-dimensional myocardial cell chain via electrotonic propagation.

[0037] 2. Signal Sampling: The receiver is located at the end of the channel. Based on the pre-calculated signal propagation delay... The optimal sampling time of the system in each bit period Read the instantaneous membrane potential of the receiving cell .in, This represents the index of the currently transmitted binary bit in the sequence (e.g., ); It represents the transmission period of a single binary bit.

[0038] Step 4: Signal demodulation.

[0039] 1. Threshold Decision: Set a voltage signal detection threshold. .

[0040] like If so, the received information bit is determined to be logic "1".

[0041] like If so, the received information bit is determined to be logic "0".

[0042] 2. Information restoration: The demodulated binary sequence is restored to the original physiological information to complete the communication process.

[0043] Example 2 This embodiment illustrates how to use the system described in Embodiment 1 to test and optimize the random facilitation effect of subthreshold stimulation parameters of a cardiac pacemaker.

[0044] Step 1: Construct the parameter scanning test set.

[0045] Configure automated test scripts in Visual Studio Code to leverage the parallel computing advantages of GPUs and set different test variables to simulate different physiological or pathological environments and equipment parameters: 1. Noise scaling factor ( ): The range is set to 0.0-0.5, with a step size of 0.05, to simulate the change from an ideal environment to a high-noise pathological environment.

[0046] 2. Stimulation current intensity ( ): Set the range to This is used to find the optimal power consumption point that satisfies subthreshold transmission.

[0047] Step 2: Evaluation of communication performance indicators.

[0048] Running the simulation program, the system calculates the following core performance indicators for each set of parameter combinations: 1. Bit error rate: The percentage of incorrect receptions after transmitting 1000 random bits, including false positive rate and false negative rate.

[0049] 2. Signal-to-noise ratio: The root mean square (RMS) method is used to calculate the ratio of the signal power at the receiving end (the potential offset RMS when transmitting "1") to the noise power (the potential offset RMS when transmitting "0").

[0050] 3. Channel capacity: Calculate the input X With output Y Mutual information between To evaluate the system's maximum information transmission rate.

[0051] Step 3: Parameter optimization based on stochastic facilitation effect.

[0052] 1. Noise Enhancement Effect Analysis: Analyze simulation data, and when the noise scaling factor... When the SNR is in the range of 0.1-0.2, the system's SNR is lower than that of a noise-free environment ( The signal was improved by approximately 2.0 dB. This confirms the existence of a stochastic facilitation phenomenon in subthreshold communication, where a suitable amount of physiological noise can help weak signals overcome the transmission barrier.

[0053] 2. Determining the optimal operating point: Stimulation current optimization: Results show that when the stimulation current... At that time, BER decreased significantly to near 0.

[0054] Channel capacity maximization: under optimal conditions (such as...) ≈0.15, The system channel capacity can reach over 0.64 bit / use.

[0055] 3. Pacemaker parameter configuration: Based on the above results, pacemakers or nanoactuators can be configured to utilize physiological noise. This is achieved when a suitable amount of background noise is detected in the body (…). When the threshold is approximately 0.1-0.2, the device can use a lower subthreshold stimulation current (e.g., ...). This method enables communication that ensures high SNR and low BER while significantly reducing device power consumption and extending the lifespan of implantable devices.

[0056] It is worth noting that all contents not described in detail in this invention are existing technologies and are well known to those skilled in the art.

[0057] Therefore, this invention employs the aforementioned subthreshold cardiac communication method, system, device, medium, and application. By constructing a three-dimensional myocardial cell communication channel integrating physiological noise and realizing the modulation, transmission, and demodulation of subthreshold electrical signals, it effectively solves the problems of simplified dimensions in existing cardiac electrical signal models, unclear subthreshold communication mechanisms, and lack of realistic physiological noise environments. This achieves a simulation of the cardiac subthreshold communication process that is closer to physiological reality.

[0058] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the technical solutions of the present invention, and these modifications or equivalent substitutions cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A subthreshold cardiac communication method, characterized in that, Includes the following steps: Step S1: Configure the cardiac communication system, including: The transmitting end is used to generate subthreshold electrical signals; The communication channel, consisting of multiple electrically coupled three-dimensional cardiomyocyte models, is used to transmit subthreshold electrical signals, and the communication channel is configured to simulate an electrical signal transmission environment containing physiological noise. The receiving end is used to demodulate subthreshold electrical signals; Step S2: At the transmitting end, a subthreshold electrical signal is generated based on the physiological information to be transmitted and applied to the communication channel; Step S3: The subthreshold electrical signal propagates in the communication channel and is captured at the receiving end; Step S4: At the receiving end, the captured electrical signals are processed to reconstruct physiological information.

2. The subthreshold cardiac communication method according to claim 1, characterized in that, In step S1, the construction of the communication channel includes: Constructing a cell geometry model: Based on the calcium release unit network, a topological structure of a single three-dimensional cardiomyocyte is constructed. A single cardiomyocyte is defined as a three-dimensional discrete mesh composed of several calcium release units arranged in the longitudinal, transverse and depth directions. Define electrophysiological parameters: Establish a set of ordinary differential equations including membrane capacitance and multiple ion channel currents to calculate the local electrophysiological state of each calcium release unit node. The multiple ion channel currents include sodium ion current, calcium ion current and potassium ion current. Establishing a gap connection model and numerical solution: The reaction-diffusion equation is used to describe the conduction of electrical signals between and within cells, and the diffusion coefficient is set to characterize the gap connection strength; the finite difference method is used to solve the reaction-diffusion equation in a spatiotemporal discretization to simulate the evolution of subthreshold electrical signals in the communication channel.

3. The subthreshold cardiac communication method according to claim 1, characterized in that, In step S2, the signal modulation algorithm uses on-off keying modulation: binary information bit 1 is mapped to a stimulation current of a specific intensity, and binary information bit 0 is mapped to a current without stimulation; and the intensity of the stimulation current is controlled so that the membrane potential fluctuation caused by it when propagating in the communication channel is always lower than the action potential threshold of myocardial cells.

4. The subthreshold cardiac communication method according to claim 1, characterized in that, Step S4, processing the captured electrical signal specifically includes the following steps: Signal sampling: Based on the signal propagation delay time, determine the optimal sampling time within each bit transmission cycle, and obtain the instantaneous membrane potential value of the myocardial cells at the receiving end at that time; Threshold decision: The instantaneous membrane potential value is compared with a preset decision threshold; Information restoration: If the instantaneous membrane potential value is greater than or equal to the decision threshold, the received information bit is determined to be logic 1; if it is less than the decision threshold, it is determined to be logic 0.

5. The subthreshold cardiac communication method according to claim 1, characterized in that, In step S1, simulating the electrical signal transmission environment containing physiological noise specifically involves introducing an additive Gaussian white noise term into the response-diffusion equation describing changes in myocardial cell membrane potential. ;in Standard Gaussian white noise, As the reference noise intensity, This is used as a noise scaling factor to simulate membrane potential fluctuations caused by random gating of ion channels.

6. A subthreshold cardiac communication system for implementing the method as described in any one of claims 1-5, characterized in that, include: The transmitting end is used to generate subthreshold electrical signals based on physiological information; The communication channel, which consists of multiple electrically coupled three-dimensional cardiomyocyte models, is used to transmit subthreshold electrical signals in an environment simulating physiological noise. The receiving end is used to receive and process electrical signals from the communication channel to reconstruct physiological information.

7. A subthreshold cardiac communication system according to claim 6, characterized in that, The communication channel integrates a noise model unit, which is used to apply interference signals characterizing physiological random electrical activity to the three-dimensional cardiomyocyte model.

8. A computer device, comprising: Memory and processor; The memory stores a computer program, characterized in that when the processor executes the computer program, it implements the steps of a subthreshold cardiac communication method as described in any one of claims 1-5.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When a computer program is executed by a processor, it implements the steps of a subthreshold cardiac communication method as described in any one of claims 1-5.

10. A simulation testing method for cardiac pacemaker stimulation parameters, characterized in that, Using the subthreshold cardiac communication system as described in any one of claims 6 to 7, or running on the computer device described in claim 8, the subthreshold stimulation parameters of the cardiac pacemaker are simulated, and the stimulation parameters are evaluated and optimized by analyzing the signal transmission effect in the system.

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