Data time synchronization method and system

Abstract

The application relates to a data time synchronization method and system. The method comprises the following steps: performing pulse modulation processing by using an implanted pulse generator to obtain a modulated target pulse, and applying the modulated target pulse to a target object; collecting a local field potential signal of the target object by using a deep brain stimulation electrode; and collecting a body surface detection signal by using a body surface potential sensor; wherein the body surface detection signal is a signal formed by the target pulse propagating in the human body tissue of the target object; determining a synchronization correction parameter between the local field potential signal and the body surface detection signal by using the body surface potential sensor, and applying the synchronization correction parameter to the body surface detection signal to realize data time synchronization between the local field potential signal and the body surface detection signal. The method can improve data synchronization accuracy.

Description

Technical Field

[0001] This application relates to the field of neuromodulation medical device technology, and in particular to a data time synchronization method and system. Background Technology

[0002] In the clinical research and treatment optimization of deep brain stimulation (DBS), it is necessary to perform fusion analysis on the in vivo neural signals (such as local field potentials, LFP) collected by the DBS device and the in vitro physiological data collected by external sensors, such as electroencephalography (EEG) and electromyography (EMG).

[0003] However, existing technologies still suffer from poor data synchronization accuracy in these types of data applications. Summary of the Invention

[0004] Therefore, it is necessary to provide a data time synchronization method and system that can improve the accuracy of data synchronization in response to the above-mentioned technical problems.

[0005] Firstly, this application provides a data time synchronization method, which includes:

[0006] An implanted pulse generator is used to perform pulse modulation processing to obtain a modulated target pulse, which is then applied to the target object.

[0007] The local field potential signal of the target object is acquired using deep brain stimulation electrodes; and the body surface detection signal is acquired using a body surface potential sensor; wherein the body surface detection signal is the signal formed by the target pulse propagating in the human tissue of the target object;

[0008] The synchronization correction parameters between the local field potential signal and the body surface detection signal are determined using the body surface potential sensor, and the synchronization correction parameters are applied to the body surface detection signal to achieve data time synchronization between the local field potential signal and the body surface detection signal.

[0009] Secondly, this application also provides a data time synchronization system, which includes an implantable pulse generator, a deep brain stimulation electrode, and a body surface potential sensor.

[0010] The implantable pulse generator is used to output a modulated target pulse and apply the modulated target pulse to the target object.

[0011] The deep brain stimulation electrode is used to acquire the local field potential signal of the target object;

[0012] The body surface potential sensor is used to collect body surface detection signals, determine the synchronization correction parameters between the local field potential signal and the body surface detection signal, and apply the synchronization correction parameters to the body surface detection signal to achieve data time synchronization between the local field potential signal and the body surface detection signal. The body surface detection signal is the signal formed by the target pulse propagating in the target human tissue.

[0013] The aforementioned data time synchronization method and system first utilizes an implantable pulse generator to perform pulse modulation processing, obtaining a modulated target pulse to provide a benchmark for data synchronization and reduce source synchronization deviation. Next, deep brain stimulation electrodes acquire local field potential signals, while a body surface potential sensor acquires the body surface detection signal formed by the propagation of the target pulse within the body. Finally, the body surface potential sensor determines synchronization correction parameters and applies them to the body surface detection signal, achieving high-precision alignment of the time axes of the two types of signals, solving the problem of data asynchrony between the body and the outside world, and improving data synchronization accuracy. Attached Figure Description

[0014] Figure 1 This is a diagram illustrating the application environment of a data time synchronization method in one embodiment.

[0015] Figure 2 This is a flowchart illustrating a data time synchronization method in one embodiment;

[0016] Figure 3 Here is a waveform diagram of the target pulse before and after modulation in one embodiment;

[0017] Figure 4 This is a flowchart illustrating a data time synchronization method in another embodiment;

[0018] Figure 5 This is a flowchart illustrating a data time synchronization method in another embodiment;

[0019] Figure 6 This is a flowchart illustrating a data time synchronization method in another embodiment;

[0020] Figure 7 This is a scenario environment diagram of a data time synchronization method in one embodiment;

[0021] Figure 8 This is a flowchart illustrating a data time synchronization method in another embodiment. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0023] Deep brain stimulation (DBS) technology, as a core tool in the field of neuromodulation, has become one of the "gold standards" for treating movement disorders such as Parkinson's disease, essential tremor, and dystonia. It involves implanting microelectrodes into specific nuclei in the patient's brain (such as the subthalamic nucleus and the medial part of the globus pallidus) to release precise and controllable low-frequency or high-frequency electrical pulses. This directly modulates abnormally excited neural circuits, effectively improving core symptoms such as tremor, rigidity, and bradykinesia, and significantly enhancing patients' quality of life. Clinical data shows that standardized DBS treatment can improve motor function scores in Parkinson's disease patients by 40%-70%, and can also reduce the dosage of anti-Parkinson's drugs, lowering the risk of drug side effects.

[0024] With technological advancements, modern deep brain stimulation (DBS) devices have evolved from the traditional "single stimulation" model to an integrated "stimulation-sensing-recording" system. In addition to continuously delivering therapeutic electrical stimulation, their built-in high-precision sensing modules can collect local brain field potentials in real time—low-frequency electrical signals originating from neural cluster activity. These signals contain crucial information about the functional state of brain nuclei; for example, elevated β-wave power in the LFP (low-field epithelial function) of the subthalamus in Parkinson's disease patients is highly correlated with worsening motor symptoms. This function lays the foundation for "closed-loop regulation" in DBS: by monitoring in vivo neural signals such as LFP, stimulation parameters (such as frequency, amplitude, and pulse width) can be dynamically adjusted to achieve "on-demand stimulation," avoiding the problems of "overstimulation" or "understimulation" in traditional open-loop stimulation, and promoting the application of precision medicine in the field of neuromodulation.

[0025] In clinical research, relying solely on in vivo neural signals collected by DBS devices is insufficient to comprehensively assess a patient's neurophysiological state and actual functional performance. Therefore, clinical teams need to perform multimodal fusion analysis with in vitro physiological data collected by external sensors, covering multiple dimensions: for example, monitoring the electrical activity of the entire cerebral cortex using surface electroencephalography (EEG) to analyze the synergy of the brain's overall neural circuits; recording muscle electrical activity using electromyography (EMG) to quantify the frequency, amplitude, and degree of muscle rigidity of limb tremors; capturing the trajectory and speed of gait and hand movements using motion sensors such as inertial measurement units to assess the actual improvement in motor function; and even combining vital signs data such as heart rate and blood oxygenation to determine the impact of stimulation parameters on the patient's overall physiological state. This multimodal data fusion can construct a complete assessment chain of "brain nuclei-cortex-muscle-motor behavior," which can not only be used to verify the effectiveness of DBS stimulation parameters but also provide comprehensive data support for the development of individualized treatment plans—for example, for patients with predominantly tremor symptoms, the stimulation frequency can be optimized to achieve precise inhibition by combining tremor-related frequency band signals from LFP with tremor amplitude data from EMG.

[0026] However, existing multimodal data fusion technologies still face two major bottlenecks that severely restrict their clinical application effectiveness:

[0027] First, the clock systems of implanted devices and external sensors are completely independent, leading to significant inconsistencies in data timestamps. DBS implanted devices (such as pulse generators) use built-in low-power crystal oscillators as their clock source to ensure stability and low power consumption during in vivo operation. Their clock frequency accuracy is typically on the ±50ppm (parts per million) level. External sensors, on the other hand, mostly use commercially available clock chips, achieving an accuracy of ±10ppm, but there is no unified clock calibration mechanism between the two. During long-term data acquisition (such as 24-hour dynamic monitoring), this clock deviation accumulates continuously. For example, with one hour of acquisition, even with a clock accuracy difference of 40ppm, the cumulative time deviation can reach 144 milliseconds. For neural signals requiring millisecond-level time alignment, this deviation directly leads to errors in the temporal correlation between "neural events and physiological responses"—for example, mismatching the stimulus response of brain nuclei with subsequent muscle activity, rendering the fusion analysis results clinically unreliable. Second, traditional wireless communication synchronization methods increase system complexity and are difficult to avoid synchronization error disturbances. To address the timestamp inconsistency issue, existing technologies often employ wireless communication for time calibration between implanted devices and external sensors. For example, an external controller periodically sends synchronization signals to the implanted device, which then adjusts its timestamp accordingly. However, this approach has several drawbacks: First, the addition of a wireless communication module increases the hardware complexity of the DBS system and raises power consumption—and since implanted devices have limited battery capacity, this extra power consumption shortens device lifespan and increases the frequency of device replacements during surgery. Second, unavoidable signal delays, packet loss, and signal attenuation during wireless transmission cause random disturbances in the synchronization signal, resulting in millisecond-level errors in the calibrated timestamps. For neurophysiological data fusion requiring microsecond or sub-millisecond synchronization accuracy, this error is completely unacceptable, ultimately preventing precise matching of multimodal data and impacting the accuracy of treatment efficacy assessment and parameter optimization.

[0028] Based on this, this application provides a data time synchronization method and system to solve the above problems.

[0029] This method can be applied to Figure 1 In application environments, such as Figure 1As shown, the deep brain stimulation electrode corresponds to the DBS electrode; the implantable pulse generator corresponds to the G106RS stimulator; and the body surface potential sensor corresponds to the sEMG sensor. The implantable pulse generator and deep brain stimulation electrode are placed inside the target subject, while the body surface potential sensor is placed outside the target subject and along the line connecting the implantable pulse generator and the deep brain stimulation electrode. The G106RS stimulator performs pulse modulation processing to obtain a modulated target pulse, which is then applied to the target subject. Next, the DBS electrode acquires the local field potential signal of the target subject, while the sEMG sensor acquires the body surface detection signal, which is formed by the propagation of the target pulse within the target subject's tissue. Finally, the sEMG sensor determines the synchronization correction parameter between the local field potential signal and the body surface detection signal, and this parameter is applied to the body surface detection signal to achieve data time synchronization between the two signals.

[0030] In one embodiment, a data time synchronization method is provided, such as... Figure 2 As shown, it includes the following steps:

[0031] Step 201: Use an implanted pulse generator to perform pulse modulation processing to obtain a modulated target pulse, and apply the modulated target pulse to the target object.

[0032] Among them, the implantable pulse generator (IPG) can be implanted under the skin of the target patient's chest and connected to deep brain stimulation electrodes in the brain via extension wires. Its function is to generate and output precise electrical pulses according to preset parameters to provide therapeutic stimulation to specific brain nuclei.

[0033] The target population specifically refers to patients receiving deep brain stimulation (DBS) therapy (such as patients with Parkinson's disease or essential tremor). It is important to note that the target population comprises two parts: first, the target nuclei within the brain (such as the subthalamic nucleus in Parkinson's patients, which is the implantation site for DBS electrodes and the source of local field potential signals); and second, the body surface tissues (such as the scalp and limb skin), which are the areas where surface potential sensors are placed.

[0034] In this embodiment, an implantable pulse generator is used to perform pulse modulation processing to obtain a modulated target pulse, which is then applied to the target object.

[0035] In one specific embodiment, an external programming device sends a control command to the implantable pulse generator, triggering its pulse modulation function. Upon receiving the command, the implantable pulse generator modulates its initial baseline pulse output, ensuring the stability of the core therapeutic parameters while adding a preset time signature to the pulse to generate a modulated target pulse. Subsequently, the implantable pulse generator transmits the modulated target pulse to a pre-implanted deep brain stimulation electrode in the target area via an internal extension wire. The electrode then releases the pulse to a specific physiological location on the target patient, completing the pulse's action.

[0036] Step 202: Acquire local field potential signals of the target object using deep brain stimulation electrodes. Also, acquire surface detection signals using a body surface potential sensor.

[0037] Among them, the body surface detection signal is the signal formed by the propagation of the target pulse in the human tissue of the target object.

[0038] Deep brain stimulation (DBS) electrodes are signal input / output units that come into direct contact with brain tissue. They can be a flexible wire only a few millimeters in diameter with multiple tiny electrode contacts at the end, and are surgically implanted near the target nucleus in the patient's brain.

[0039] Local field potential (LFP) refers to the low-frequency electrical signal generated when a large number of nerve cells in a specific region of the brain are active synchronously. Its amplitude is much smaller than that of a single neuron's action potential, but it can reflect the overall functional state of the neural circuit in that region.

[0040] In this embodiment, a deep brain stimulation electrode is used to acquire local field potential signals of the target object. Additionally, a body surface potential sensor is used to acquire surface detection signals. These surface detection signals are signals generated by the propagation of the target pulse within the target object's human tissue.

[0041] In one specific embodiment, while the modulated target pulse is applied to a specific physiological site of the target object, the deep brain stimulation electrode switches to signal acquisition mode to capture the local field potential signal generated around the target site in real time. The acquired signal is then transmitted back to the implantable pulse generator for temporary storage via an extension wire. Simultaneously, a body surface potential sensor is deployed at a designated location on the target object's body surface. Through contact with the body surface tissue, the sensor detects in real time the body surface electrical signal (i.e., the body surface detection signal) formed by the modulated target pulse propagating along the human tissue, and transmits this signal to an external data acquisition terminal for recording.

[0042] Step 203: Use a body surface potential sensor to determine the synchronization correction parameters between the local field potential signal and the body surface detection signal, and apply the synchronization correction parameters to the body surface detection signal to achieve data time synchronization between the local field potential signal and the body surface detection signal.

[0043] Among them, the body surface potential sensor is a non-invasive signal acquisition device placed on the surface of the target object (such as the scalp, trunk skin, and limb muscle surface). Its core function is to detect weak electrical signal changes on the human body surface. It is usually composed of conductive electrode plates, signal amplifiers, and data transmission modules.

[0044] The surface detection signal is a weak electrical signal formed on the body surface after the modulated target pulse acts on the brain nuclei and propagates outward along human tissues (such as nerve fibers, blood vessels, and connective tissues). Its essence is "an attenuated version of the target pulse after propagation in human tissues", but it retains the "temporal characteristic mark" of the target pulse.

[0045] Synchronization correction parameters refer to the values ​​calculated by the body surface potential sensor to eliminate time deviations between the local field potential signal and the body surface detection signal, based on the analysis of their temporal relationship. These parameters include time offset (the time difference between the peak and start points of the two signals) and signal delay coefficient (the difference in the speed of pulse propagation inside and outside the body). Because the implanted device and the body surface sensor have independent clocks, there will be a discrepancy between the timestamps of the local field potential signal (in vivo) and the body surface detection signal (out vivo). Synchronization correction parameters act as a "yardstick for correcting this discrepancy"—by applying them to the timestamp of the body surface detection signal, the timestamps of the body surface detection signal and the local field potential signal are perfectly aligned, achieving "signal correspondence at the same time point," thus removing time obstacles for subsequent multimodal data fusion.

[0046] In this embodiment, a body surface potential sensor is used to determine the synchronization correction parameters between the local field potential signal and the body surface detection signal, and the synchronization correction parameters are applied to the body surface detection signal to achieve data time synchronization between the local field potential signal and the body surface detection signal.

[0047] In one specific embodiment, the in vitro data acquisition terminal retrieves the local field potential signal stored in the implanted pulse generator and performs correlation analysis with the surface detection signal transmitted by the surface potential sensor. By identifying the characteristic waveforms triggered by the same modulated target pulse in both types of signals, the signal correlation point is determined. Based on the correlation point, the matching analysis module of the surface potential sensor compares the timestamps of the two types of signals, calculates the time deviation between them, and then generates synchronization correction parameters. Finally, the synchronization correction parameters are imported into the time calibration module of the surface detection signal to adjust the timestamp of the surface detection signal, so that the adjusted surface detection signal and the local field potential signal are consistent in the time dimension, achieving time synchronization of the two types of data.

[0048] In the above data time synchronization method, firstly, an implantable pulse generator is used to perform pulse modulation processing to obtain a modulated target pulse, which provides a reference for data synchronization and reduces source synchronization deviation. Next, a deep brain stimulation electrode collects local field potential signals, and a body surface potential sensor collects the body surface detection signal formed by the propagation of the target pulse in the body. Finally, the body surface potential sensor determines the synchronization correction parameters and applies them to the body surface detection signal to achieve high-precision alignment of the time axes of the two types of signals, solve the problem of data asynchrony between the body and the outside, and improve the data synchronization accuracy.

[0049] In one embodiment, the above-mentioned "using an implanted pulse generator to perform pulse modulation processing to obtain a modulated target pulse" includes:

[0050] An implanted pulse generator is used to superimpose a carrier signal of a preset frequency onto the output initial pulse to obtain a modulated target pulse; the total charge transfer of the target pulse remains constant during the superposition of the carrier signal.

[0051] The carrier signal with the preset frequency refers to the high-frequency electrical signal that is pre-set with frequency parameters by the implantable pulse generator during the pulse modulation process and is used to superimpose on the initial pulse. Its function is to add a recognizable time feature mark to the initial pulse without interfering with the therapeutic function of the initial pulse.

[0052] Total charge transfer refers to the total amount of charge delivered to the target tissue per unit time by the electrical pulses output by the implanted pulse generator through the deep brain stimulation electrodes. It is one of the core indicators for measuring the safety and therapeutic effectiveness of deep brain stimulation. From a physiological perspective, there is a safe threshold for the tolerance of neural tissue to the amount of charge: if the total charge transfer is too high, it may lead to damage or abnormal excitation of the tissues surrounding the electrodes; if it is too low, the therapeutic effect of regulating neural circuits cannot be achieved.

[0053] In this embodiment of the application, an implantable pulse generator is used to superimpose a carrier signal of a preset frequency onto the output initial pulse to obtain a modulated target pulse.

[0054] In one specific embodiment, the external programming system establishes a communication connection with the implantable pulse generator, sends a "carrier superposition modulation" command to it, and synchronously transmits preset carrier signal parameters. Upon receiving the command, the implantable pulse generator automatically activates the initial pulse generation module, outputting a basic initial pulse for treatment. Subsequently, its internal carrier superposition unit autonomously generates a corresponding high-frequency carrier signal according to preset parameters and precisely superimposes this carrier signal onto the waveform of the initial pulse. During the superposition process, the carrier signal is integrated into the initial pulse in an attached manner, without altering the overall therapeutic waveform framework of the initial pulse, only adding high-frequency characteristics in the details to form a preliminary modulated pulse.

[0055] Simultaneously, the charge balance monitoring module built into the implanted pulse generator automatically starts: this module continuously collects the current change data of the superimposed pulse through the electrode current detection unit, and automatically calculates the real-time total charge transfer based on the integral relationship between current and time. When a slight deviation in the total charge transfer caused by the superimposed carrier signal is detected (such as the carrier signal bringing additional charge input, making the total charge slightly higher than the standard value of the initial pulse), the charge balance module immediately sends a correction command to the initial pulse parameter adjustment unit. After receiving the command, the adjustment unit autonomously makes a fine adjustment to the amplitude parameter of the initial pulse (such as slightly reducing the amplitude of the initial pulse). By reducing the charge of the initial pulse itself, it offsets the additional charge brought by the carrier signal, so that the total charge transfer of the modulated target pulse formed after superposition is always maintained within the same constant range as the initial pulse before the carrier was superimposed.

[0056] While completing the superposition of carrier signals and generating time-stamped modulated target pulses, the total charge transfer is strictly kept stable. This satisfies the requirements of subsequent data synchronization for pulse time characteristics, avoids safety risks to the target's neural tissue due to charge fluctuations, and ensures that the treatment function is not affected.

[0057] In one embodiment, the above-mentioned "using an implanted pulse generator to perform pulse modulation processing to obtain a modulated target pulse" includes:

[0058] An implanted pulse generator is used to perform timing fine-tuning on the output initial pulse. By changing the time interval between adjacent initial pulses, the modulated target pulse is obtained.

[0059] In this embodiment of the application, an implantable pulse generator is used to perform timing fine-tuning on the output initial pulse. By changing the time interval between adjacent initial pulses, a modulated target pulse is obtained.

[0060] In one specific embodiment, the external programming system establishes a communication connection with the implantable pulse generator, sending timing fine-tuning modulation commands to it, and simultaneously transmitting preset time interval adjustment rules (such as interval change patterns, adjustment amplitude ranges, etc.). After receiving the commands, the implantable pulse generator automatically starts the initial pulse generation module, outputting a continuous basic initial pulse sequence according to conventional treatment parameters, at which time a fixed time interval is maintained between adjacent initial pulses.

[0061] Subsequently, the timing control unit inside the implanted pulse generator is automatically activated. This unit dynamically fine-tunes the initial pulse sequence according to preset time interval adjustment rules: by calculating the timestamps of adjacent initial pulses in real time, it slightly alters the time intervals between some adjacent pulses according to the rules (for example, while maintaining the overall pulse frequency stability, slightly shortening the intervals of some groups of adjacent pulses and slightly lengthening others, forming a pulse sequence with characteristic timing markers). During the fine-tuning process, the timing control unit maintains real-time data interaction with the initial pulse generation module, ensuring that each change in time interval accurately affects the corresponding initial pulse, avoiding pulse loss or overlap, and gradually transforming the conventional initial pulse sequence into a preliminary modulated pulse sequence with unique timing characteristics.

[0062] Simultaneously, the pulse integrity monitoring module built into the implantable pulse generator operates automatically. By acquiring the output pulse sequence data in real time, it verifies whether the time interval between adjacent pulses after timing fine-tuning conforms to preset rules, and whether the pulse treatment parameters (such as amplitude and width) have deviated. If the time interval of an adjacent pulse deviates from the preset range, the monitoring module immediately sends a correction signal to the timing control unit. The timing control unit quickly adjusts the time interval parameters of subsequent pulses to ensure that the finally generated modulated target pulse has both the required timing characteristics (for subsequent data synchronization) and maintains stable treatment parameters, enabling it to act normally on the target's neural tissue and ensuring that the treatment function is not affected by the timing fine-tuning operation.

[0063] In one specific embodiment, refer to Figure 3 As shown, Figure 3 The second picture is Figure 3 The first image shows the modulated target pulse obtained by superimposing the initial pulse with a carrier signal of a preset frequency. Figure 3 The third picture in the picture is... Figure 3 The modulated target pulse is obtained by performing timing fine-tuning on the initial pulse in the first image.

[0064] In one embodiment, such as Figure 4As shown, the above-mentioned "using an implanted pulse generator to perform timing fine-tuning on the output initial pulse, and obtaining the modulated target pulse by changing the time interval between adjacent initial pulses" includes:

[0065] Step 301: Use an implanted pulse generator to determine consecutive initial pulse pairs; the initial pulse pair is two adjacent initial pulses in the time series.

[0066] In this embodiment of the application, an implanted pulse generator is used to determine consecutive initial pulse pairs; the initial pulse pair is two adjacent initial pulses in the time sequence.

[0067] In one specific embodiment, the external programming system establishes a communication connection with the implantable pulse generator, sending a pulse pair symmetry time shift modulation command to it, and synchronously transmitting the pulse pair identification rules and symmetry time shift parameters (such as time shift direction, time shift amplitude upper limit, etc.). After receiving the command, the implantable pulse generator automatically starts the initial pulse generation module, outputs a continuous basic initial pulse sequence according to conventional treatment parameters, and simultaneously activates the internal pulse pair identification unit.

[0068] The pulse pair identification unit performs real-time analysis on the output initial pulse sequence according to the preset pulse pair identification rules: by capturing the start and end timestamps of each initial pulse, it automatically determines two adjacent initial pulses in the time sequence as a "continuous initial pulse pair", and numbers each group of pulse pairs according to the generation order, such as the first group being pulse 1 and pulse 2, the second group being pulse 2 and pulse 3, etc.

[0069] Step 302: Perform a symmetrical time-shift operation on the continuous initial pulse pairs to obtain the modulated target pulse.

[0070] In this embodiment of the application, a symmetrical time-shift operation is performed on a continuous initial pulse pair to obtain a modulated target pulse.

[0071] In one specific embodiment, the symmetric time-shift control unit inside the implanted pulse generator is automatically activated. This unit calls the predetermined initial pulse pair data and, according to preset symmetric time-shift parameters, performs a symmetric time-shift operation on each initial pulse pair: using the original midpoint of the two initial pulses in each pulse pair as a reference, the first initial pulse is slightly shifted towards the reference point (e.g., shortening the time distance between the first pulse and the reference point), while the second initial pulse is shifted by the same magnitude in the opposite direction to the reference point (e.g., lengthening the time distance between the second pulse and the reference point), thus creating a symmetrical relationship between the two time-shift operations. For example, if the first pulse shifts Δt towards the reference point, the second pulse shifts Δt away from the reference point, ensuring that the total time span of each pulse pair remains unchanged, only changing the relative positions of the two internal pulses.

[0072] During the time-shifting operation, the time-shifting accuracy monitoring module built into the implantable pulse generator operates synchronously and automatically. By collecting the timestamp data of the pulses after time-shifting in real time, it verifies whether the time-shifting amplitude of each pulse pair meets the preset parameters, whether the time-shifting operation maintains symmetry, and whether the core treatment parameters of the pulses (such as amplitude, width, and total charge transfer) have shifted. If the time-shifting amplitude of a certain pulse pair is detected to be out of range or the symmetry is unbalanced, the monitoring module immediately sends a correction signal to the symmetry time-shifting control unit. The control unit quickly adjusts the time-shifting parameters of that pulse pair and subsequent pulse pairs, ultimately generating a modulated target pulse with symmetrical timing characteristics. This satisfies the timing marking requirements for subsequent data synchronization and ensures the stability of the treatment effect on the target nerve tissue.

[0073] In one embodiment, such as Figure 5 As shown, the aforementioned "determining the synchronization correction parameters between the local field potential signal and the body surface detection signal" includes:

[0074] Step 401: Based on the fluctuation state of the local field potential signal and the intensity information of the body surface detection signal, adjust the window length in real time to obtain the correlation analysis window; the window length is used to calculate the correlation between the local field potential signal and the body surface detection signal.

[0075] In this embodiment, the window length is adjusted in real time based on the fluctuation state of the local field potential signal and the intensity information of the body surface detection signal to obtain a correlation analysis window. The window length is used to calculate the correlation between the local field potential signal and the body surface detection signal.

[0076] In one specific embodiment, the implantable pulse generator establishes a real-time data interaction link with the body surface potential sensor. The body surface potential sensor automatically receives the local field potential signal collected by the deep brain stimulation electrode and transmitted through the implantable pulse generator, while simultaneously collecting the body surface detection signal captured by itself, and then activates the internal window length adjustment unit.

[0077] The window length adjustment unit dynamically monitors two types of signals received in real time according to preset signal analysis rules: on the one hand, it captures the fluctuation state of the local field potential signal (such as the frequency of signal amplitude changes and the interval between peaks and troughs). If an increase in the frequency of signal fluctuations is detected (such as increased fluctuations due to enhanced β-wave components), it determines that the window length needs to be reduced to improve analysis sensitivity. On the other hand, it extracts the intensity information of the body surface detection signal (such as the absolute value of the signal peak and the signal-to-noise ratio). If a decrease in signal intensity is detected (such as signal attenuation due to slight contact deviation of the body surface sensor), it determines that the window length needs to be appropriately extended to ensure the integrity of signal acquisition. Based on the monitoring results of the two types of signals, the window length adjustment unit calculates and outputs an appropriate correlation analysis window length in real time, ensuring that the window can fully cover the effective fluctuation period of the local field potential signal and also include the clear features of the body surface detection signal, thus completing the dynamic adjustment of the correlation analysis window.

[0078] Step 402: Based on the correlation analysis window, the local field potential signal and the body surface detection signal are registered to obtain the time feature region directly related to the modulated target pulse.

[0079] In this embodiment of the application, based on the correlation analysis window, the local field potential signal and the body surface detection signal are registered to obtain the time feature region directly related to the modulated target pulse.

[0080] In one specific embodiment, the signal registration unit built into the body surface potential sensor is automatically activated, and the adjusted correlation analysis window is invoked to perform synchronous registration processing on the local field potential signal and the body surface detection signal: taking the correlation analysis window as the unit, the two types of signals are segmented along the time axis, and by calculating the waveform similarity of the two types of signals in each window segment (such as the Pearson correlation coefficient), window segments with similarity higher than a preset threshold are selected. These segments mean that they contain associated signals triggered by the same modulated target pulse, and are thus determined as time feature regions directly related to the modulated target pulse, realizing the preliminary correlation and positioning of the two types of signals in the time dimension.

[0081] Step 403: Within the time feature region, extract the first feature segment related to the modulated target pulse from the local field potential signal and the second feature segment corresponding to the modulated target pulse from the body surface detection signal.

[0082] In this embodiment of the application, within the time feature region, a first feature segment related to the modulated target pulse in the local field potential signal and a second feature segment corresponding to the modulated target pulse in the body surface detection signal are extracted respectively.

[0083] In a specific embodiment, the signal feature extraction unit of the body surface potential sensor automatically performs fine processing on the signal within the time feature region: for the local field potential signal, after removing background noise through a filtering algorithm, it identifies waveform segments that match the timing characteristics of the modulated target pulse, such as a specific phase offset wave that appears after the pulse is applied, and determines it as the first feature segment; for the body surface detection signal, after noise suppression processing, it captures signal segments that correspond to the propagation characteristics of the modulated target pulse, such as a pulse response wave carrying a carrier marker, and determines it as the second feature segment, ensuring that both types of feature segments are directly associated with the same modulated target pulse.

[0084] Step 404: Determine the time difference between the first feature segments and use the time difference as the synchronization correction parameter.

[0085] In this embodiment of the application, the time difference between the first feature segments is determined, and the time difference is determined as the synchronization correction parameter.

[0086] In one specific embodiment, the time difference calculation unit of the body surface potential sensor automatically reads the start timestamps of the first feature segment and the second feature segment, and obtains the time difference between the two feature segments by calculating the difference between their timestamps. At the same time, the unit also performs mean filtering on the time difference of multiple consecutive time feature regions to eliminate deviations caused by random noise. Finally, the processed time difference is determined as the synchronization correction parameter, providing an accurate basis for the time calibration of the subsequent body surface detection signal, and ensuring high-precision time synchronization between the local field potential signal and the body surface detection signal.

[0087] In one embodiment, such as Figure 6 As shown, the above-mentioned "registration processing of local field potential signals and body surface detection signals based on correlation analysis windows to obtain time feature regions directly related to the modulated target pulse" includes:

[0088] Step 501: Based on the correlation analysis window, the local field potential signal and the body surface detection signal are synchronously divided according to the time axis to obtain multiple sets of corresponding signal segments with matching time lengths.

[0089] In this embodiment, based on the correlation analysis window, the local field potential signal and the body surface detection signal are synchronously divided according to the time axis to obtain multiple sets of corresponding signal segments with matching time lengths.

[0090] In one specific embodiment, after the body surface potential sensor completes the dynamic adjustment of the correlation analysis window, it activates the internal signal segmentation unit. This unit uses the adjusted correlation analysis window as a time reference and synchronously divides the received local field potential signal and the body surface detection signal according to the time axis. By reading the timestamps of the two types of signals, it ensures that the time length of each segment of local field potential signal is completely matched with the corresponding segment of body surface detection signal. For example, if the correlation analysis window is 50ms, then the duration of each segmented signal is 50ms. Finally, multiple sets of corresponding signal segments with the same time length are generated, laying the foundation for subsequent correlation analysis.

[0091] Step 502: Calculate the first cross-correlation coefficient of each corresponding signal segment using a cross-correlation analysis algorithm, and remove signal segment groups whose first cross-correlation coefficient is less than a first preset threshold to obtain coarsely registered signal segment groups.

[0092] In this embodiment, a cross-correlation analysis algorithm is used to calculate the first cross-correlation coefficient of each corresponding signal segment, and signal segment groups with a first cross-correlation coefficient less than a first preset threshold are eliminated to obtain coarsely registered signal segment groups.

[0093] In one specific embodiment, the cross-correlation calculation unit of the body surface potential sensor employs a preset cross-correlation analysis algorithm to perform calculations on the corresponding signal segments with matched time lengths for each group: by calculating the similarity between the two types of signal segments at different time offsets, the first cross-correlation coefficient of each group of segments is output. Simultaneously, the unit calls a built-in first preset threshold (pre-set based on clinical signal characteristics) to classify signal segment groups with a first cross-correlation coefficient less than the threshold as "non-associated segment groups" and automatically removes them—these segment groups, due to their low signal similarity, are highly unlikely to contain associated signals from the same modulated target pulse; the remaining signal segment groups with a first cross-correlation coefficient greater than or equal to the first preset threshold are marked as coarsely registered signal segment groups, completing the initial signal screening.

[0094] Step 503: Based on the correlation analysis window, interpolation processing is performed on the coarse registration signal segment group to obtain the processed coarse registration signal segment group.

[0095] In this embodiment of the application, based on the correlation analysis window, the coarse registration signal segment group is interpolated to obtain the processed coarse registration signal segment group.

[0096] In one specific embodiment, the interpolation processing unit of the body surface potential sensor performs interpolation calculations on the coarse registration signal segment group based on the previously determined correlation analysis window time accuracy. To address the potential problem of uneven sampling point intervals in each coarse registration signal segment group (such as slight differences between the sampling frequency of the local field potential signal and the body surface detection signal), the signal sampling points are supplemented through linear interpolation or cubic spline interpolation algorithms. This ensures that the sampling density of each segment is consistent and the time axis accuracy is improved, ultimately resulting in the processed coarse registration signal segment group, reducing subsequent analysis errors caused by sampling differences.

[0097] Step 504: Recalculate the second cross-correlation coefficient of the processed coarse registration signal segment group, and select signal segment subgroups with a value greater than the second preset threshold from the second cross-correlation coefficient to obtain the fine registration signal segment group.

[0098] In this embodiment of the application, the second cross-correlation coefficient of the coarsely registered signal segment group is recalculated, and a subgroup of signal segments with a value greater than a second preset threshold is selected from the second cross-correlation coefficient to obtain the finely registered signal segment group.

[0099] In one specific embodiment, after the interpolation process is completed, the secondary cross-correlation calculation unit of the body surface potential sensor recalculates the second cross-correlation coefficient of the coarsely registered signal segment group. Because the interpolation process optimizes signal accuracy, the second cross-correlation coefficient more accurately reflects the true similarity between the two types of signals. Subsequently, the unit calls a second preset threshold (higher than the first preset threshold, used to further filter high-similarity segments) to select signal segment subgroups that meet the threshold requirements from the second cross-correlation coefficient, identifying them as finely registered signal segment groups, thus achieving high-precision signal filtering.

[0100] Step 505: In the finely registered signal segment group, extract the signal interval that matches the characteristics of the modulated target pulse, and determine the signal interval as the time feature region directly related to the modulated target pulse.

[0101] In this embodiment of the application, in the finely registered signal segment group, a signal interval matching the characteristics of the modulated target pulse is extracted, and the signal interval is determined as a time feature region directly related to the modulated target pulse.

[0102] In one specific embodiment, the feature matching of the body surface potential sensor reads the preset features of the modulated target pulse (such as carrier signal frequency, timing fine-tuning rules, etc.) and compares these features with each signal segment in the finely registered signal segment group: it identifies the signal interval in the segment that completely matches the features of the target pulse (such as the body surface detection signal interval containing a specific frequency carrier mark, the local field potential signal response interval corresponding to the pulse timing), and marks this interval as the time feature region directly related to the modulated target pulse, providing accurate time positioning for subsequent feature segment extraction and synchronization correction parameter calculation.

[0103] In one embodiment, the above method further includes:

[0104] When the target object moves autonomously within a preset range, causing micro-displacement of the deep brain stimulation electrode wires in the target object's body, the surface potential sensor is used to obtain the additional potential disturbance naturally formed in the human tissue by the micro-displacement, and the additional potential disturbance is determined as a local field potential signal.

[0105] In this embodiment of the application, when the target object moves autonomously within a preset range, causing micro-displacement of the deep brain stimulation electrode wires in the target object's body, the surface potential sensor is used to obtain the additional potential disturbance naturally formed in the human tissue by the micro-displacement, and the additional potential disturbance is determined as a local field potential signal.

[0106] In one specific embodiment, when the target subject performs actions within a preset range of activity (such as voluntary movements like limb lifting or slow walking), the contraction of muscles and the movement of joints cause slight deformation of surrounding tissues, which in turn leads to a minute displacement (i.e., micro-displacement) of the implanted deep brain stimulation electrode leads. This micro-displacement causes a subtle change in the contact state between the electrode contacts and the surrounding nerve tissue, triggering weak fluctuations in local neural electrical activity. These fluctuations naturally propagate through the human tissue, forming additional potential disturbances.

[0107] At this time, the surface potential sensor continuously collects surface electrical signal data through stable contact with the target object's surface and automatically activates the internal disturbance signal monitoring unit. This unit performs dynamic comparative analysis of the real-time collected surface electrical signals based on a preset signal baseline (i.e., the surface electrical signal reference when the target object is stationary). When a slight fluctuation deviating from the baseline is detected in the electrical signal, and the time of the fluctuation highly coincides with the time axis of the target object's autonomous movement, the fluctuation is determined to be an additional potential disturbance caused by the micro-displacement of the deep brain stimulation electrode wire.

[0108] Subsequently, the signal classification unit of the body surface potential sensor is activated, calling the preset signal feature library (containing typical features of local field potential signals, body surface detection signals, and interference signals) to perform feature matching on the identified additional potential disturbances: by analyzing the frequency range of the disturbance signal (such as conforming to the low-frequency characteristics of local field potential signals) and waveform morphology (such as periodic fluctuations related to neural electrical activity), irrelevant interference items such as environmental electromagnetic interference and electrical signals directly generated by muscle activity are eliminated, confirming that the additional potential disturbance originates from the interaction between the nerve tissue in the target object and the micro-displacement of the electrode, and can reflect the local neural activity state.

[0109] Finally, the signal marking unit of the body surface potential sensor automatically identifies the feature-verified additional potential disturbance as a local field potential signal, adds a corresponding timestamp (recording the moment of micro-displacement), and synchronously transmits it to the internal data storage module and implanted pulse generator. This provides accurate local field potential data support for subsequent time synchronization with the body surface detection signal and analysis of the impact of micro-displacement on nerve signals, ensuring high-quality nerve signal acquisition and synchronization can still be achieved even in dynamic scenarios where the target object moves autonomously.

[0110] In one specific embodiment, refer to Figure 7 As shown, in Figure 7In this study, when a target subject moves voluntarily within a preset range, the DBSlead (deep brain stimulation electrode lead) within the body will generate micro-displacements. These micro-displacements naturally create additional potential disturbances in the body tissue, which can be detected by a surface potential sensor (shown in the figure through a relevant acquisition device used to collect surface electrical signals). The figure also shows waveforms of signals such as PSG-EEG (polysomnography-electroencephalography), DBS-LFP (deep brain stimulation-local field potential), and chin EMG (chin EMG). The DBS-LFP signal contains the additional potential disturbances generated by the micro-displacements of the deep brain stimulation electrode leads. These disturbance signals are identified as local field potential signals for subsequent analysis and research. For example, by observing characteristics such as the "onset of vibration" and "peak point," the influence of micro-displacements on local field potential signals can be analyzed. Among them, EEGreference refers to: EEG reference electrode, used to provide a reference potential for EEG signal acquisition and ensure the accuracy of EEG signal measurement; DBSlead is a deep brain stimulation electrode lead, which is an implanted lead used to transmit deep brain stimulation pulses; chin EMG is mandibular electromyography, used to collect electromyographic signals in the mandibular region to reflect the electrical activity of muscles; onset of vibration refers to the vibration initiation point, which is the moment when the vibration signal begins; peak point refers to the peak point, which is the point where the signal reaches its maximum value; PSG-EEG is polysomnography-EEG, which is an EEG that combines the monitoring of multiple physiological signals and is often used for EEG research in sleep and other states; DBS-LFP is deep brain stimulation-local field potential, which refers to the local field potential signal collected during deep brain stimulation.

[0111] In one embodiment, such as Figure 8 As shown, the above method also includes:

[0112] Step 601: With the target subject in a natural physiological state, record the in vivo electrocardiogram signals collected by the deep brain stimulation electrode using an external electrocardiogram sensor.

[0113] In this embodiment of the application, when the target object is in a natural physiological state, an external electrocardiogram sensor is used to record the in vivo electrocardiogram signals collected by deep brain stimulation electrodes.

[0114] In one specific embodiment, the target subject is in a natural physiological state (such as sitting still, normal breathing, or no strenuous activity). The external ECG sensor establishes a signal interaction link with the internal deep brain stimulation electrode through stable contact with a specific part of the target subject's body surface (such as the chest, wrist, or other common ECG signal acquisition locations). The deep brain stimulation electrode acquires the target subject's internal ECG signals in real time without affecting neural regulation functions, and synchronously transmits these signals to the external ECG sensor via the signal transmission module of an implanted pulse generator. Simultaneously, the external ECG sensor receives the internal ECG signals and activates its own surface ECG acquisition function to record the target subject's external ECG signals in real time.

[0115] Step 602: Use an external ECG sensor to extract the characteristic waves from the in vivo ECG signal and the external ECG signal collected by the external ECG sensor, and determine the time offset of the characteristic waves in the in vivo ECG signal and the external ECG signal.

[0116] In this embodiment, an external electrocardiogram (ECG) sensor is used to extract characteristic waves from the in vivo and external ECG signals, and the time offset of the characteristic waves in the in vivo and external ECG signals is determined.

[0117] In one specific embodiment, the feature wave extraction unit built into the external ECG sensor is automatically activated. This unit calls a preset ECG signal feature recognition algorithm to process the acquired in vivo and external ECG signals respectively: for the in vivo ECG signal, it filters out the weak interference that may be caused by deep brain stimulation pulses and identifies the feature waves in the signal; for the external ECG signal, it similarly removes environmental electromagnetic interference and electromyographic interference through a noise suppression algorithm and extracts the corresponding feature waves of the same type, ensuring that the feature wave types in the two types of signals are consistent, and providing a unified benchmark for subsequent time offset calculation.

[0118] After the feature wave extraction is completed, the time offset calculation unit of the external ECG sensor is activated. This unit reads the timestamp data of the same type of feature wave in the in vivo ECG signal and the external ECG signal: using multiple consecutive feature waves as analysis samples, it calculates the time difference between each in vivo ECG signal feature wave and the corresponding external ECG signal feature wave, and then performs an average calculation on these time differences to eliminate the error caused by single signal fluctuations and obtain a stable time offset.

[0119] Step 603: Determine the time offset as the synchronization correction parameter between the in vivo ECG signal and the external ECG signal.

[0120] In this embodiment, the time offset is determined as a synchronization correction parameter between in vivo and external electrocardiogram signals.

[0121] In one specific embodiment, the synchronization correction parameter determination unit of the external ECG sensor automatically marks the calculated time offset as the synchronization correction parameter between the in vivo ECG signal and the external ECG signal, and stores the parameter in the internal data cache module. At the same time, it is synchronously sent to the implantable pulse generator and the external data analysis system to provide a basis for the subsequent time calibration of the two types of ECG signals. By applying the synchronization correction parameter to the timestamp adjustment of the external ECG signal or the in vivo ECG signal, high-precision time synchronization between the in vivo ECG signal and the external ECG signal can be achieved, providing an accurate time reference for subsequent analysis of the correlation between cardiac and neural functions combined with deep brain stimulation data.

[0122] In one embodiment, the above method further includes:

[0123] Step 1: Using an implantable pulse generator, a carrier signal of a preset frequency is superimposed on the output initial pulse to obtain a modulated target pulse; during the superposition of the carrier signal, the total charge transfer of the target pulse remains constant.

[0124] Step 2: Use an implanted pulse generator to determine consecutive initial pulse pairs; the initial pulse pair is two adjacent initial pulses in the time series.

[0125] Step 3: Perform a symmetrical time-shift operation on the continuous initial pulse pairs to obtain the modulated target pulse.

[0126] Step 4: Acquire the local field potential signal of the target object using deep brain stimulation electrodes; or, when the target object moves autonomously within a preset range, causing micro-displacement of the deep brain stimulation electrode wires within the target object's body, use a body surface potential sensor to acquire the additional potential disturbance naturally formed in the human tissue by the micro-displacement, and determine the additional potential disturbance as the local field potential signal.

[0127] Step 5: Collect surface detection signals using a surface potential sensor; whereby the surface detection signal is the signal formed by the propagation of the target pulse in the target human tissue.

[0128] Step 6: Using a body surface potential sensor, the window length is adjusted in real time based on the fluctuation state of the local field potential signal and the intensity information of the body surface detection signal to obtain a correlation analysis window; the window length is used to calculate the correlation between the local field potential signal and the body surface detection signal.

[0129] Step 7: Using the body surface potential sensor based on the correlation analysis window, the local field potential signal and the body surface detection signal are synchronously divided according to the time axis to obtain multiple sets of corresponding signal segments with matching time lengths.

[0130] Step 8: Using a body surface potential sensor, a cross-correlation analysis algorithm is used to calculate the first cross-correlation coefficient of each corresponding signal segment. Signal segment groups with a first cross-correlation coefficient less than a first preset threshold are removed to obtain coarse registration signal segment groups.

[0131] Step 9: Using a body surface potential sensor based on a correlation analysis window, interpolate the coarse registration signal segment group to obtain the processed coarse registration signal segment group.

[0132] Step 10: Recalculate the second cross-correlation coefficient of the processed coarse registration signal segment group using a body surface potential sensor, and select signal segment subgroups with a value greater than the second preset threshold from the second cross-correlation coefficient to obtain the fine registration signal segment group.

[0133] Step 11: Using a body surface potential sensor, extract the signal interval that matches the characteristics of the modulated target pulse in the finely registered signal segment group, and determine the signal interval as the time feature region directly related to the modulated target pulse.

[0134] Step 12: Within the time feature region, the body surface potential sensor is used to extract the first feature segment related to the modulated target pulse in the local field potential signal and the second feature segment corresponding to the modulated target pulse in the body surface detection signal.

[0135] Step 13: Use a body surface potential sensor to determine the time difference between the first feature segments and use the time difference as a synchronization correction parameter.

[0136] Step 14: With the target subject in a natural physiological state, record the in vivo electrocardiogram signals collected by the deep brain stimulation electrode using an external electrocardiogram sensor.

[0137] Step 15: Use an external ECG sensor to extract the characteristic waves from the in vivo ECG signal and the external ECG signal collected by the external ECG sensor, and determine the time offset of the characteristic waves in the in vivo ECG signal and the external ECG signal.

[0138] Step 16: Determine the time offset as the synchronization correction parameter between the in vivo ECG signal and the external ECG signal.

[0139] Step 17: Apply the synchronization correction parameters to the body surface detection signal to achieve data time synchronization between the local field potential signal and the body surface detection signal.

[0140] It should be noted that the execution order of step 1 and steps 2-3 is not limited in this application embodiment. In actual execution, step 1 or steps 2-3 can be executed according to the actual situation.

[0141] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly 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 steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0142] In one embodiment, a data time synchronization system is provided, comprising an implantable pulse generator, a deep brain stimulation electrode, and a body surface potential sensor; the implantable pulse generator is used to output a modulated target pulse and apply the modulated target pulse to a target object; the deep brain stimulation electrode is used to acquire local field potential signals of the target object; the body surface potential sensor is used to acquire body surface detection signals, determine synchronization correction parameters between the local field potential signals and the body surface detection signals, and apply the synchronization correction parameters to the body surface detection signals to achieve data time synchronization between the local field potential signals and the body surface detection signals. The body surface detection signals are signals formed by the propagation of the target pulse in the human tissue of the target object.

[0143] In one embodiment, the system further includes an external electrocardiogram (ECG) sensor; a deep brain stimulation electrode, used to collect the target object's internal ECG signals when the target object is in a natural physiological state; and an external ECG sensor, used to record the internal ECG signals collected by the deep brain stimulation electrode, extract characteristic waves from the internal ECG signals and the external ECG signals collected by the external ECG sensor, determine the time offset of the characteristic waves between the internal ECG signals and the external ECG signals, and determine the time offset as a synchronization correction parameter between the internal ECG signals and the external ECG signals.

[0144] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0145] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0146] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A data time synchronization method, characterized in that, The method includes: An implanted pulse generator is used to perform pulse modulation processing to obtain a modulated target pulse, which is then applied to the target object. The local field potential signal of the target object is acquired using deep brain stimulation electrodes; and the body surface detection signal is acquired using a body surface potential sensor; wherein the body surface detection signal is the signal formed by the target pulse propagating in the human tissue of the target object; Based on the fluctuation state of the local field potential signal and the intensity information of the body surface detection signal, the window length is adjusted in real time to obtain a correlation analysis window; the window length is used to calculate the correlation between the local field potential signal and the body surface detection signal. Based on the correlation analysis window, the local field potential signal and the body surface detection signal are registered to obtain the time feature region directly related to the modulated target pulse. Within the time feature region, a first feature segment related to the modulated target pulse in the local field potential signal and a second feature segment corresponding to the modulated target pulse in the body surface detection signal are extracted respectively. The time difference between the first feature segment and the first feature segment is determined, and the time difference is determined as a synchronization correction parameter. The synchronization correction parameter is applied to the body surface detection signal to achieve data time synchronization between the local field potential signal and the body surface detection signal.

2. The method according to claim 1, characterized in that, The process of using an implanted pulse generator to perform pulse modulation processing to obtain a modulated target pulse includes: The implanted pulse generator is used to superimpose a carrier signal of a preset frequency onto the output initial pulse to obtain a modulated target pulse; during the superposition of the carrier signal, the total charge transfer of the target pulse remains constant.

3. The method according to claim 1, characterized in that, The process of using an implanted pulse generator to perform pulse modulation processing to obtain a modulated target pulse includes: The implanted pulse generator is used to perform timing fine-tuning on the output initial pulse. By changing the time interval between adjacent initial pulses, the modulated target pulse is obtained.

4. The method according to claim 3, characterized in that, The step of using the implanted pulse generator to perform timing fine-tuning on the output initial pulse, and obtaining the modulated target pulse by changing the time interval between adjacent initial pulses, includes: The implanted pulse generator is used to determine consecutive initial pulse pairs; the initial pulse pair is two adjacent initial pulses in a time series. A symmetrical time-shift operation is performed on the continuous initial pulse pair to obtain the modulated target pulse.

5. The method according to claim 1, characterized in that, The process of registering the local field potential signal with the body surface detection signal based on the correlation analysis window to obtain a time feature region directly related to the modulated target pulse includes: Based on the correlation analysis window, the local field potential signal and the body surface detection signal are synchronously divided according to the time axis to obtain multiple sets of corresponding signal segments with matching time lengths. The first cross-correlation coefficient of each group of corresponding signal segments is calculated using a cross-correlation analysis algorithm. Signal segment groups with the first cross-correlation coefficient less than a first preset threshold are removed to obtain coarse registration signal segment groups. Based on the correlation analysis window, the coarse registration signal segment group is interpolated to obtain the processed coarse registration signal segment group. The second cross-correlation coefficient of the processed coarse registration signal segment group is recalculated, and a subgroup of signal segments with a value greater than the second preset threshold is selected from the second cross-correlation coefficient to obtain the fine registration signal segment group; In the finely registered signal segment group, a signal interval matching the characteristics of the modulated target pulse is extracted, and the signal interval is determined as a time feature region directly related to the modulated target pulse.

6. The method according to any one of claims 1 to 5, characterized in that, The method further includes: When the target object moves autonomously within a preset range, causing micro-displacement of the deep brain stimulation electrode wires within the target object, the surface potential sensor is used to acquire the additional potential disturbance naturally formed in the human tissue by the micro-displacement, and the additional potential disturbance is determined as the local field potential signal.

7. The method according to any one of claims 1 to 5, characterized in that, The method further includes: While the target object is in a natural physiological state, the in vivo electrocardiogram signals collected by the deep brain stimulation electrodes are recorded using an external electrocardiogram sensor. The characteristic waves in the in vivo electrocardiogram (ECG) signal and the ECG signal acquired by the external ECG sensor are extracted using an external ECG sensor, and the time offset of the characteristic waves in the in vivo ECG signal and the ECG signal acquired by the external ECG sensor is determined. The time offset is determined as the synchronization correction parameter between the in vivo ECG signal and the external ECG signal.

8. A data time synchronization system, characterized in that, The system includes an implantable pulse generator, deep brain stimulation electrodes, and a body surface potential sensor; The implantable pulse generator is used to output a modulated target pulse and apply the modulated target pulse to the target object. The deep brain stimulation electrode is used to acquire the local field potential signal of the target object; The surface potential sensor is used to collect surface detection signals and adjust the window length in real time according to the fluctuation state of the local field potential signal and the intensity information of the surface detection signal to obtain a correlation analysis window; the window length is used to calculate the correlation between the local field potential signal and the surface detection signal. Based on the correlation analysis window, the local field potential signal and the body surface detection signal are registered to obtain the time feature region directly related to the modulated target pulse. Within the time feature region, a first feature segment related to the modulated target pulse in the local field potential signal and a second feature segment corresponding to the modulated target pulse in the body surface detection signal are extracted respectively; the time difference between the first feature segment and the second feature segment is determined, and the time difference is determined as a synchronization correction parameter. The synchronization correction parameter is applied to the body surface detection signal to achieve data time synchronization between the local field potential signal and the body surface detection signal. The body surface detection signal is the signal formed by the target pulse propagating in the target human tissue.

9. The system according to claim 8, characterized in that, The system also includes an external electrocardiogram sensor; The deep brain stimulation electrode is also used to collect the target's internal electrocardiogram signals when the target is in a natural physiological state. The external electrocardiogram sensor is used to record the in vivo electrocardiogram signal acquired by the deep brain stimulation electrode, extract the characteristic waves from the in vivo electrocardiogram signal and the external electrocardiogram signal acquired by the external electrocardiogram sensor, determine the time offset of the characteristic waves in the in vivo electrocardiogram signal and the external electrocardiogram signal, and determine the time offset as the synchronization correction parameter between the in vivo electrocardiogram signal and the external electrocardiogram signal.

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