Teleoperative nerve electrophysiology monitoring method and system

Abstract

The invention provides a remote intraoperative neural electrophysiology monitoring method and system, and the method comprises the steps: firstly collecting a continuous neural electrophysiology waveform signal set synchronously recorded by a patient in an operation through a plurality of monitoring leads, and then carrying out the feature extraction of the signal set, and obtaining a neural function state feature containing a waveform feature and a rhythm feature; then, remote real-time analysis is carried out on the neural function state characteristics, an intraoperative neural function monitoring result containing function stability information and abnormal fluctuation information is generated, the intraoperative neural function monitoring result is transmitted to a remote monitoring terminal to be dynamically displayed, and finally intraoperative intervention prompting operation is triggered based on the abnormal fluctuation information. Therefore, the space limitation of nerve electrophysiology monitoring in a traditional operation is broken through, high-quality medical resources can be fully utilized, decision support is provided for the operator in time, and the risk of nerve injury in the operation is reduced.

Description

Technical Field

[0001] The present invention relates to the field of medical monitoring technology, and in particular to a remote intraoperative neuroelectrophysiological monitoring method and system. Background Art

[0002] During surgery, real-time and accurate monitoring of the patient's neurological function is crucial to ensuring the success of the operation and the patient's postoperative neurological function recovery. Traditional intraoperative neuroelectrophysiological monitoring methods mostly involve equipping the operating room with professional monitoring equipment and personnel to collect and analyze the patient's neuroelectrophysiological signals. However, the above methods have many limitations. On the one hand, the space at the operating room is limited, and additional monitoring equipment and personnel may cause a certain degree of interference with the surgical operation, affecting the smoothness and efficiency of the operation. On the other hand, in some complex or large operations, on-site monitoring may not be able to fully utilize a wider range of medical resources, such as the lack of real-time consultation and comprehensive judgment by multidisciplinary experts.

[0003] Furthermore, existing neuroelectrophysiological monitoring technologies also have shortcomings in signal processing and analysis. They typically only capture basic information about neuroelectrophysiological signals, providing an incomplete and in-depth assessment of neurological function. This makes it difficult to accurately capture subtle changes in neurological function and potential risks, hindering timely and effective decision-making for surgeons and increasing the risk of intraoperative neurological injury. Summary of the Invention

[0004] In view of the above-mentioned problems, in combination with the first aspect of the present invention, an embodiment of the present invention provides a remote intraoperative neuroelectrophysiological monitoring method, the method comprising: Acquiring a neuroelectrophysiological signal set from an intraoperative patient, wherein the neuroelectrophysiological signal set includes continuous neuroelectrophysiological waveform signals synchronously recorded through a plurality of monitoring leads; Performing feature extraction processing on the neuroelectrophysiological signal set to obtain a neurological function state feature of the neuroelectrophysiological waveform signal, wherein the neurological function state feature includes a waveform feature and a rhythm feature; Performing remote real-time analysis and processing on the neurological function status characteristics to generate intraoperative neurological function monitoring results, wherein the intraoperative neurological function monitoring results include functional stability information and abnormal fluctuation information; Transmitting the intraoperative neurological function monitoring results to a remote monitoring terminal, wherein the remote monitoring terminal is used to dynamically display the functional stability information and abnormal fluctuation information; An intraoperative intervention prompt operation is triggered based on abnormal fluctuation information in the intraoperative neurological function monitoring result, and the intraoperative intervention prompt operation is used to assist the surgeon in adjusting the surgical operation.

[0005] On the other hand, an embodiment of the present invention also provides a remote intraoperative neuroelectrophysiological monitoring system, including a processor and a machine-readable storage medium, wherein the machine-readable storage medium is connected to the processor, the machine-readable storage medium is used to store programs, instructions or codes, and the processor is used to execute the programs, instructions or codes in the machine-readable storage medium to implement the above method.

[0006] Based on the above aspects, the embodiment of the present invention first collects a set of continuous neuroelectrophysiological waveform signals recorded synchronously by multiple monitoring leads, performs feature extraction processing on the continuous neuroelectrophysiological waveform signal set, and obtains neurological function status characteristics including waveform characteristics and rhythm characteristics. It can deeply explore the intrinsic information of neuroelectrophysiological signals, more accurately reflect the neurological function status, remotely analyze the neurological function status characteristics in real time and generate intraoperative neurological function monitoring results including functional stability information and abnormal fluctuation information, breaking the spatial limitations and enabling experts to participate in monitoring and diagnosis remotely, making full use of high-quality medical resources, and transmitting the monitoring results to the remote monitoring terminal for dynamic display, so that relevant personnel can grasp the patient's neurological function status in real time. It triggers intraoperative intervention prompt operations based on abnormal fluctuation information, which can provide decision-making basis for the surgeon in a timely manner, assist in adjusting the surgical operation, and effectively reduce the risk of nerve damage during surgery. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] Figure 1 1 is a schematic diagram of the execution flow of the remote intraoperative neuroelectrophysiological monitoring method provided by an embodiment of the present invention.

[0008] Figure 2 Schematic diagram of exemplary hardware and software components of a remote intraoperative neuroelectrophysiological monitoring system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0009] The present invention will be described in detail below with reference to the accompanying drawings. Figure 1 FIG1 is a flow chart of a remote intraoperative neuroelectrophysiological monitoring method provided by an embodiment of the present invention. The remote intraoperative neuroelectrophysiological monitoring method is introduced in detail below.

[0010] Step S110: Acquire a neuroelectrophysiological signal set of the patient during surgery, where the neuroelectrophysiological signal set includes continuous neuroelectrophysiological waveform signals synchronously recorded through multiple monitoring leads.

[0011] During neurosurgery, neuroelectrophysiological signal collection is necessary to comprehensively and in real time monitor the patient's neurological function during surgery. The operating room is equipped with a neuroelectrophysiological monitoring system equipped with multiple monitoring leads. These monitoring leads are placed in specific areas of the patient's scalp based on the location of the cerebral aneurysm and the distribution of surrounding nerves.

[0012] For example, if a cerebral aneurysm is located near the left temporal lobe of the brain, monitoring leads will be densely arranged on the scalp corresponding to the left temporal lobe to focus on capturing the electrophysiological activity of the nerves in that area. At the same time, a certain number of monitoring leads will be reasonably distributed in other relevant areas of the brain to ensure that extensive and comprehensive neuroelectrophysiological information can be recorded. Each monitoring lead continuously records neuroelectrophysiological waveform signals. Moreover, all monitoring leads will work strictly synchronously, which means that they will start recording signals at the same time point to ensure that the recorded neuroelectrophysiological signals are continuous and synchronous, forming a complete neuroelectrophysiological signal set. The signals in this neuroelectrophysiological signal set reflect the real-time activity of nerves in different brain regions during surgery.

[0013] Step S120: performing feature extraction processing on the neuroelectrophysiological signal set to obtain the neurological function state features of the neuroelectrophysiological waveform signal, wherein the neurological function state features include waveform features and rhythm features.

[0014] After acquiring a collection of neuroelectrophysiological signals, feature extraction is required to extract key information reflecting neurological function. Waveform and rhythm features are important characteristic indicators of neuroelectrophysiological signals, reflecting the activity pattern and functional status of nerves. By extracting and analyzing these waveform and rhythm features, we can more accurately understand changes in a patient's neurological function during surgery.

[0015] Step S121: performing signal preprocessing on the continuous electrophysiological waveform signal in the neural electrophysiological signal set to obtain an electrophysiological waveform signal after interference removal.

[0016] During the actual signal acquisition process, the collected continuous electrophysiological waveform signals are inevitably subject to various interferences due to the complexity of the surgical environment and the patient's own physiological activities. These interferences affect the subsequent accurate extraction of signal features. Therefore, signal preprocessing is required to remove these interferences and obtain pure electrophysiological waveform signals that can truly reflect neural activity.

[0017] Step S1211: performing power frequency interference suppression processing on the continuous electrophysiological waveform signal, using a notch filtering method to filter out fixed frequency interference components in the continuous electrophysiological waveform signal, and retaining effective frequency components of the neural electrophysiological signal.

[0018] During cerebral aneurysm clipping surgery, various electrical devices in the operating room generate power frequency interference. This power frequency interference typically manifests as a fixed-frequency electrical signal that can be superimposed on the neuroelectrophysiological waveform signal, affecting signal quality. To remove this power frequency interference, a notch filter can be used. The notch filter's parameters are precisely set based on the power frequency interference commonly found in surgical environments. When the continuous electrophysiological waveform signal passes through the notch filter, it identifies the fixed-frequency interference component and filters it out. However, the effective frequency components of the neuroelectrophysiological signal pass smoothly through the filter, resulting in a preliminarily purified signal. This ensures that the signal for subsequent analysis is unaffected by power frequency interference and more accurately reflects the true electrophysiological activity of the nerve.

[0019] Step S1212: performing baseline drift correction processing on the continuous electrophysiological waveform signal, eliminating slow baseline fluctuations in the continuous electrophysiological waveform signal by a sliding average filtering method, so that the waveform baseline is maintained at a preset stable level.

[0020] During the acquisition process, continuous electrophysiological waveform signals may exhibit slow baseline fluctuations. This baseline drift may be caused by factors such as slight movements of the patient's body and small changes in electrode-skin contact. Baseline drift can cause an overall shift in the signal, affecting the accurate assessment of waveform characteristics. To eliminate this baseline drift, a sliding average filtering method can be used. Specifically, a time window of fixed length is set within which the signal is averaged. Over time, this time window slides along the signal's time axis, continuously performing the average calculation. This method produces a smooth baseline estimate. This baseline estimate is then subtracted from the original signal to eliminate slow baseline fluctuations, maintaining the waveform baseline at a preset stable level. This allows subsequent analysis of the signal's waveform characteristics to be unaffected by baseline drift, enabling more accurate identification of the waveform's shape and changes.

[0021] Step S1213: performing myoelectric interference removal processing on the continuous electrophysiological waveform signal, using an adaptive filtering algorithm to identify and suppress high-frequency myoelectric interference pulses in the continuous electrophysiological waveform signal, and retaining low-frequency neural electrophysiological signal components.

[0022] During cerebral aneurysm clipping surgery, the patient's muscle activity also generates electrical signals, which can interfere with the acquisition of neuroelectrophysiological waveform signals. Myoelectric signals typically have higher frequencies, while neuroelectrophysiological signals have relatively lower frequencies. To remove myoelectric interference, an adaptive filtering algorithm can be used. An adaptive filter automatically adjusts its parameters based on the characteristics of the input signal to effectively identify and suppress myoelectric interference. It analyzes the signal's frequency components, identifies high-frequency myoelectric interference pulses, and subtracts these interference pulses from the continuous electrophysiological waveform signal by adjusting the filter weights. This preserves the low-frequency neuroelectrophysiological signal components, resulting in a purer signal that more accurately reflects neural activity.

[0023] Step S1214: performing electrode noise filtering processing on the continuous electrophysiological waveform signal, identifying and removing sudden electrode noise spikes in the continuous electrophysiological waveform signal by a threshold judgment method.

[0024] During the signal acquisition process, the contact between the electrode and the skin may generate some sudden noise spikes, which will seriously affect the signal quality. In order to remove these electrode noise spikes, a threshold judgment method can be used. First, a reasonable threshold can be set based on the amplitude range of normal neuroelectrophysiological signals. When a certain amplitude in the continuous electrophysiological waveform signal exceeds the threshold, it is considered that the signal corresponding to the amplitude is a sudden electrode noise spike. Then, these noise spikes can be removed from the signal and replaced with a suitable substitute value. This substitute value can be obtained by interpolation calculation based on the normal signal before and after the noise spike to ensure the continuity and integrity of the signal. In this way, the electrode noise spikes can be effectively filtered out and the signal quality can be improved.

[0025] Step S1215: Integrate the signals after the above signal preprocessing to obtain an electrophysiological waveform signal after interference removal from physiological interference and external environmental interference, wherein the electrophysiological waveform signal after interference removal retains the original characteristics of the neural electrophysiological activity.

[0026] After completing preprocessing steps such as power frequency interference suppression, baseline drift correction, myoelectric interference removal, and electrode noise filtering, the resulting processed signals need to be integrated. This integration process ensures that the results of each processing step are coordinated and does not introduce new interference or errors. Specifically, the signals obtained from different processing steps are matched and fused in time and amplitude to form a unified signal. The resulting de-interferenced electrophysiological waveform signal eliminates physiological and environmental interference while retaining the original characteristics of neural electrophysiological activity.

[0027] Step S122: performing time dimension segmentation processing on the electrophysiological waveform signal after interference removal, dividing the electrophysiological waveform signal after interference removal into multiple continuous waveform time segments according to preset time intervals, each waveform time segment having a continuous time series relationship.

[0028] After obtaining the electrophysiological waveform signal after interference removal, in order to analyze the characteristics of the signal more carefully, it is necessary to perform segmentation processing on the time dimension. In cerebral aneurysm clipping surgery, a suitable time interval can be preset according to the progress of the operation and the characteristics of the neuroelectrophysiological signal. For example, the time interval can be determined according to the time period of the key operation in the operation. For example, the operation of clipping the aneurysm may take a certain amount of time, so the time interval can be set to match the operation time. Then, according to the preset time interval, the electrophysiological waveform signal after interference removal is divided into multiple continuous waveform time segments. Each waveform time segment is continuous in time and has a clear order. Through the above segmentation processing, the long signal can be decomposed into multiple shorter, easy-to-analyze segments, which facilitates the subsequent in-depth study of the waveform characteristics and rhythm characteristics of each segment. Each waveform time segment contains detailed information about the neuroelectrophysiological signal within that time period.

[0029] Step S123: extract waveform features from each waveform time segment, identify characteristic waveform forms in the waveform time segment, the characteristic waveform forms include the fluctuation pattern of the waveform and the connection relationship between waveforms, and obtain waveform features of each waveform time segment based on the characteristic waveform forms.

[0030] After completing the time dimension segmentation processing, waveform feature extraction is required for each waveform time segment. Waveform features can reflect the waveform change characteristics of the neuroelectrophysiological signal within a specific time segment.

[0031] Step S1231: performing waveform morphology recognition on each waveform time segment, comparing the waveform time segment with a preset standard neuroelectrophysiological waveform template through a template matching method, and identifying a characteristic waveform in the waveform time segment that meets the standard template.

[0032] In the scenario of cerebral aneurysm clipping surgery, a series of standard neuroelectrophysiological waveform templates can be established in advance. These templates are obtained based on the analysis and summary of a large number of normal neuroelectrophysiological signals, and represent the typical waveform morphology under different neurological functional states. For each waveform time segment, a template matching method can be used to compare it with a preset standard template. The specific operation is to slide the waveform time segment on the time axis and compare it with each standard template one by one. By calculating the similarity index between the two, such as the correlation coefficient, it is determined whether there is a characteristic waveform that meets the standard template in the waveform time segment. If the similarity index exceeds the preset threshold, it is considered that the corresponding characteristic waveform exists in the waveform time segment. In the above way, representative characteristic waveforms can be identified from complex waveform time segments.

[0033] Step S1232: extracting the fluctuation pattern of the characteristic waveform, analyzing the rising segment change trend and the falling segment change trend of the characteristic waveform, and determining the overall morphological characteristics of the characteristic waveform.

[0034] After identifying the characteristic waveform, it is necessary to further extract its fluctuation pattern. This includes detailed analysis of the rising and falling segments of the characteristic waveform. The rising segment refers to the part of the waveform that rises from a lower amplitude to a higher amplitude, while the falling segment refers to the part that falls from a higher amplitude to a lower amplitude. For the rising segment, its rising speed, slope and other changing trends can be analyzed. For example, a faster rising speed may indicate a higher level of nerve excitation, while a slower rising speed may indicate a slower nerve response. For the falling segment, its falling speed and slope and other characteristics are also analyzed. By comprehensively analyzing the changing trends of the rising and falling segments, the overall morphological characteristics of the characteristic waveform can be determined. The above-mentioned overall morphological characteristics can reflect the activity pattern and functional status of the nerve during that time period.

[0035] Step S1233: Analyze the connection relationship between adjacent characteristic waveforms in the waveform time segment, identify the interval pattern and superposition pattern between the characteristic waveforms, and determine the structural characteristics of the waveform sequence.

[0036] In addition to analyzing the morphological characteristics of individual characteristic waveforms, it is also necessary to analyze the connectivity between adjacent characteristic waveforms within a waveform's time segment. Adjacent characteristic waveforms may exhibit different interval patterns and overlay patterns. Interval patterns refer to the time intervals between adjacent characteristic waveforms. For example, longer intervals may indicate a slower rhythm of neural activity, while shorter intervals may indicate more frequent neural activity. Overlay patterns refer to whether adjacent characteristic waveforms overlap with each other. The degree and manner of this overlap can also reflect the complexity of neural activity. By analyzing these interval and overlay patterns, the structural characteristics of the waveform sequence can be determined. The structural characteristics of a waveform sequence reflect the organization and arrangement of neural electrophysiological signals along the temporal dimension.

[0037] Step S1234: constructing a waveform feature descriptor based on the overall morphological features and structural features, wherein the waveform feature descriptor includes morphological parameters of the characteristic waveform and structural parameters of the waveform sequence.

[0038] After determining the overall morphological characteristics of the characteristic waveform and the structural characteristics of the waveform sequence, these characteristics need to be integrated to construct a waveform feature descriptor. A waveform feature descriptor is a comprehensive feature representation that incorporates the morphological parameters of the characteristic waveform and the structural parameters of the waveform sequence. Morphological parameters can include the rise rate, fall rate, and amplitude of the characteristic waveform, while structural parameters can include the interval time and degree of overlap between adjacent characteristic waveforms. By combining these parameters, a multidimensional feature vector is formed, namely the waveform feature descriptor. This waveform feature descriptor can comprehensively describe the waveform characteristics of a waveform time segment.

[0039] Step S1235: Using the waveform feature descriptor as the waveform feature of each waveform time segment, the waveform feature is used to characterize the waveform variation characteristics of the neuroelectrophysiological signal within the time segment.

[0040] Finally, the constructed waveform feature descriptor is used as the waveform feature of each waveform time segment. This waveform feature can accurately characterize the waveform variation characteristics of the neuroelectrophysiological signal within that time segment. By analyzing and comparing the waveform features of each waveform time segment, we can understand the waveform variation of the neuroelectrophysiological signal in different time periods, and then infer whether the functional state of the nerve has changed. For example, if the waveform features in a certain time period are significantly different from the normal state, it may indicate that the nerve was affected by the surgical operation during that time period and has functional abnormalities.

[0041] Step S124: extract rhythm features from the electrophysiological waveform signal after interference removal, analyze the repetitive change pattern of the electrophysiological waveform signal after interference removal in a continuous time interval, identify rhythm patterns with periodic occurrence characteristics, and obtain the rhythm features of the electrophysiological waveform signal after interference removal based on the rhythm patterns.

[0042] In addition to waveform features, rhythmic characteristics are also important indicators of neural electrophysiological signals. Rhythmic characteristics reflect the periodicity and regularity of neural activity. During cerebral aneurysm clipping surgery, rhythmic features must be extracted from the electrophysiological waveform signals after noise removal.

[0043] First, the interference-free electrophysiological waveform signal can be analyzed over continuous time intervals to identify recurring patterns. This can be achieved through methods such as spectral analysis. Spectral analysis converts the signal from the time domain to the frequency domain, allowing for a clearer observation of the signal's frequency components. In the frequency domain, periodic signals appear as peaks at specific frequencies. By identifying these peaks, the presence of rhythmic patterns in the signal can be determined. For example, if a clear peak is found at a certain frequency in the spectrum, and this peak persists over a continuous time interval, the signal corresponding to that frequency can be considered periodic, indicating the presence of a corresponding rhythmic pattern.

[0044] After identifying the rhythm pattern, features related to the rhythm pattern can be further extracted. These features may include the frequency, amplitude, and phase of the rhythm. Frequency indicates the speed of the rhythm, amplitude indicates the strength of the rhythm, and phase indicates the relative position of the rhythm in time. By comprehensively analyzing these features, the rhythm characteristics of the electrophysiological waveform signal after interference removal can be obtained. Rhythm characteristics can reflect the activity rhythm and stability of nerves over a period of time and are of great reference value for judging changes in the functional state of nerves. For example, if there are significant changes in rhythm characteristics, such as changes in rhythm frequency or fluctuations in amplitude, it may indicate that the functional state of the nerve has been affected and requires further attention and analysis.

[0045] Step S125: performing time axis association processing on the waveform features and the rhythm features, establishing a corresponding relationship between the waveform features and the rhythm features in the same time interval, and generating an association feature set including a time tag.

[0046] After extracting waveform and rhythm features, they need to be correlated on the time axis to more comprehensively understand the characteristics of the neuroelectrophysiological signals and the functional status of the nerves. In the context of cerebral aneurysm clipping surgery, since waveform features are extracted based on waveform time segments, while rhythm features are extracted based on continuous time intervals, they need to be aligned and correlated on the time axis.

[0047] The specific operation is to arrange the waveform features and rhythm features in chronological order, and then establish a corresponding relationship between them within the same time interval. For example, for a specific time interval, find the corresponding waveform features and rhythm features within the time interval and associate them. In order to facilitate subsequent analysis and processing, a time stamp can be added to each associated waveform feature and rhythm feature to record their corresponding time interval. In the above manner, an associated feature set containing a time stamp is generated. The associated feature set integrates the waveform features and rhythm features in the time dimension, so that the characteristics of the neuroelectrophysiological signals at different time points can be analyzed from both the waveform and rhythm aspects, so as to more comprehensively understand the functional status of the nerves. For example, within the time interval of a critical surgical operation, the changes in the waveform features and rhythm features within the time period can be observed simultaneously, so as to more accurately judge the impact of the surgical operation on the nerve function.

[0048] Step S126: Obtaining the neural function state feature of the neural electrophysiological waveform signal based on the integration of the associated feature set, wherein the neural function state feature includes a combination of waveform features and rhythm features corresponding to different time intervals.

[0049] After generating a set of associated features containing time stamps, it is necessary to integrate the associated feature set to obtain the neural function state features of the neuroelectrophysiological waveform signal. The neural function state features are a combination of waveform features and rhythm features corresponding to different time intervals.

[0050] During cerebral aneurysm clipping surgery, the waveform features and rhythm features in the associated feature set can be grouped and integrated by time interval. For each time interval, the corresponding waveform features and rhythm features within that interval are combined to form a multidimensional feature vector, which represents the functional status of the nerve during that time interval. By analyzing and comparing the feature vectors of different time intervals, it is possible to understand how the functional status of the nerve changes over time. For example, the functional status of the nerve may vary at different stages of the surgery. During the aneurysm clipping process, the waveform features and rhythm features may fluctuate significantly. By analyzing these changes, it is possible to promptly identify whether neurological function has been affected. Ultimately, the feature vectors of all time intervals are integrated to form the functional status feature of the nerve electrophysiological waveform signal. This functional status feature can comprehensively and accurately reflect the functional status of the nerve throughout the entire surgical process.

[0051] Step S130: remotely and in real time analyze and process the neurological function status characteristics to generate intraoperative neurological function monitoring results, wherein the intraoperative neurological function monitoring results include functional stability information and abnormal fluctuation information.

[0052] After obtaining the neurological function status characteristics, in order to timely know the patient's neurological function status during surgery, these characteristics need to be remotely analyzed and processed in real time to generate intraoperative neurological function monitoring results.

[0053] Step S131: Send the neural function status characteristics to the remote analysis system so that the remote analysis system performs time series modeling on the neural function status characteristics and constructs a dynamic model of the neural function status characteristics changing with monitoring time. The dynamic model is used to describe the continuous change process of waveform characteristics and rhythm characteristics.

[0054] During a cerebral aneurysm clipping surgery, neurological status characteristics are transmitted from the surgical site to a remote analysis system. After receiving these neurological status characteristics, the remote analysis system can perform time series modeling on them. Time series modeling is a method for analyzing data that changes over time, capturing characteristics such as trends, periodicity, and seasonality.

[0055] For neurological status characteristics, the remote analysis system arranges waveform and rhythm characteristics according to monitoring time, forming two time series. These two time series are then modeled using appropriate time series modeling methods, such as the autoregressive integrated moving average (ARIMA) model or long short-term memory (LSTM) network. These models can predict future data based on historical data, thereby constructing a dynamic model of neurological status characteristics over monitoring time. This dynamic model describes how waveform and rhythm characteristics change over the continuous monitoring period, including their upward and downward trends, and periodic changes. This dynamic model provides a deeper understanding of the dynamic patterns of neurological status.

[0056] Step S132: Analyze the change trend of the neural function state characteristics based on the dynamic model, extract the change pattern of the waveform characteristics and the change pattern of the rhythm characteristics, and obtain the overall trend change information of the neural function state characteristics.

[0057] After constructing the dynamic model, it is necessary to analyze the changing trend of the neural function status characteristics based on the dynamic model.

[0058] Step S1321: Based on the waveform feature time series output by the dynamic model, analyze the change amplitude and change direction of the waveform feature during the continuous monitoring time to determine the short-term change pattern and long-term change pattern of the waveform feature.

[0059] During cerebral aneurysm clipping surgery, the dynamic model outputs a time series of waveform feature changes over monitoring time. Analysis of the amplitude of waveform feature changes focuses on differences in waveform feature amplitude within different short time intervals. For example, within a short time segment, the waveform amplitude is observed to see whether it increases or decreases, and to what extent. The direction of change refers to whether the waveform feature exhibits an upward or downward trend. By continuously observing and analyzing these amplitude and direction of change, the short-term variation pattern of the waveform feature can be determined. For example, if the waveform amplitude continues to rise within several adjacent short time segments, and the amplitude of the increase gradually increases, it can be determined that the waveform feature exhibited an upward and increasing variation pattern within that short time period.

[0060] To determine long-term patterns of change, the observation timeframe is expanded to a longer period. By integrating changes in multiple short time segments, the overall trend of the waveform characteristics over a longer period of time is analyzed. For example, during the first half of the surgery, the waveform characteristics exhibited an overall fluctuating upward trend, but the magnitude of the increase was uneven, sometimes large and sometimes small. This allows us to determine the long-term pattern of change in the waveform characteristics over this longer period of time.

[0061] Step S1322: Based on the rhythm feature time series output by the dynamic model, analyze the occurrence frequency change and intensity change of the rhythm feature during the continuous monitoring time to determine the short-term change pattern and long-term change pattern of the rhythm feature.

[0062] Similarly, the dynamic model also outputs a time series of rhythmic features. To analyze the frequency changes of rhythmic features, the number of rhythm occurrences is counted over a continuous monitoring period. If the frequency of a rhythm suddenly increases or decreases over a short period of time, this frequency change can be captured. For example, if a rhythm originally occurs stably at a certain frequency over a short period of time, and then suddenly increases significantly over the next short period of time, this is a short-term manifestation of frequency change. Intensity change refers to changes in the amplitude of the rhythm. By observing the increase and decrease in the rhythm amplitude over a short period of time, the short-term intensity change pattern of the rhythmic feature can be determined.

[0063] Long-term patterns of change can be determined by analyzing the frequency and intensity of rhythmic features over a longer period of time. For example, during surgery, the frequency of a rhythmic feature gradually decreases, then slowly increases at some point, with corresponding fluctuations in intensity. These combined patterns can be used to determine the long-term pattern of rhythmic changes.

[0064] Step S1323: Fusing the short-term change pattern and the long-term change pattern of the waveform feature to obtain a comprehensive change pattern of the waveform feature, where the comprehensive change pattern reflects the overall evolution of the waveform feature over time.

[0065] After determining the short-term and long-term change patterns of the waveform characteristics, they need to be fused to gain a more comprehensive understanding of the waveform characteristics' changes. The fusion process comprehensively considers the rapid fluctuations in the short-term change pattern and the overall trend in the long-term change pattern. For example, the short-term change pattern may have some localized fluctuations, but the long-term change pattern shows an overall upward or downward trend. This information is then combined during fusion. Through this fusion, a comprehensive change pattern of the waveform characteristics can be obtained, which can reflect how the waveform characteristics have evolved throughout the monitoring period, such as whether they have gradually stabilized, gradually changed, or exhibited complex fluctuations.

[0066] Step S1324: The short-term change pattern and the long-term change pattern of the rhythmic feature are integrated to obtain a comprehensive change pattern of the rhythmic feature, which reflects the overall evolution law of the rhythmic feature over time.

[0067] For rhythmic features, their short-term and long-term change patterns must also be fused. During the fusion process, the different short-term and long-term manifestations of the frequency and intensity of the rhythmic features will be considered. For example, there may be rapid fluctuations in frequency in the short term, but in the long term, there is a slow downward trend in frequency. This information will be integrated during the fusion process. As a result, the final comprehensive change pattern of rhythmic features can clearly show the overall evolution of the rhythmic features during the entire monitoring time, whether it has become more regular, more disordered, or has other changes.

[0068] Step S1325: Integrate the comprehensive change pattern of the waveform characteristics and the comprehensive change pattern of the rhythm characteristics to generate overall trend change information of the neural function state characteristics, and the overall trend change information is used to describe the continuous change process of the neural function state with monitoring time.

[0069] Finally, the combined change patterns of waveform and rhythmic features are integrated, taking into account their temporal synchrony and interrelationships. For example, waveform and rhythmic features may change simultaneously during certain time periods, or changes in one may trigger changes in the other. By integrating these two combined change patterns, overall trend change information for the neurological function status characteristics is generated, which provides a comprehensive and detailed description of how the neurological function status continuously changes throughout the monitoring period.

[0070] Step S133: Compare the trend change information with a preset neurological function safety reference range, where the neurological function safety reference range includes a waveform feature range and a rhythm feature range under normal physiological conditions, and identify feature change intervals that exceed the neurological function safety reference range.

[0071] After obtaining the overall trend change information of the neurological function status characteristics, it is necessary to compare it with the preset neurological function safety reference range. The neurological function safety reference range is obtained based on the analysis of a large number of neuroelectrophysiological signals under normal physiological conditions, which includes the waveform characteristic range and rhythm characteristic range under normal conditions.

[0072] During cerebral aneurysm clipping surgery, the waveform and rhythm characteristics in the overall trend change information are compared with their corresponding safety reference ranges. For waveform characteristics, the change trend is checked to see if it is within the normal waveform characteristic range, such as whether the amplitude and frequency of the waveform are within a reasonable range. For rhythm characteristics, the frequency, amplitude, phase, and other characteristics are checked to see if they are within the normal rhythm characteristic range.

[0073] If the waveform characteristics or rhythm characteristics within a certain time interval are found to exceed the safety reference range, the time interval is considered to be a characteristic change interval. Through the above comparison and identification, possible abnormalities in the neurological function state can be discovered in a timely manner. For example, if during surgery it is found that the amplitude of the waveform characteristics within a certain time period suddenly increases and exceeds the normal range, or the frequency of the rhythm characteristics has changed significantly and is not within the normal range, then it can be judged that the neurological function within this time period may have been affected and requires further attention and analysis.

[0074] Step S134: Continuously verify the characteristic change interval to determine whether the waveform characteristics and rhythm characteristics within the characteristic change interval continue to exceed the neurological function safety reference range. If they continue to exceed, it is determined to be an abnormal fluctuation interval, and abnormal fluctuation information is generated based on the abnormal fluctuation interval.

[0075] After identifying characteristic change intervals, continuous verification of these intervals is necessary to avoid misjudgment. In the context of cerebral aneurysm clipping surgery, characteristic change intervals may occur due to brief disturbances or accidental factors, but these changes do not necessarily indicate a true abnormality in neurological function. Therefore, it is necessary to determine whether the waveform and rhythm characteristics within the characteristic change intervals consistently exceed the neurological function safety reference range.

[0076] Specifically, a time threshold is set. If the waveform and rhythm characteristics within a characteristic change interval continuously exceed the safety reference range within the time threshold, the characteristic change interval is determined to be an abnormal fluctuation interval. For example, if the time threshold is set to a specific duration, and during this duration, the amplitude of the waveform characteristic is consistently above the normal range, and the frequency of the rhythm characteristic consistently deviates from the normal range, then this time period can be considered an abnormal fluctuation interval.

[0077] After determining the abnormal fluctuation interval, abnormal fluctuation information can be generated based on this interval. Abnormal fluctuation information can include the start and end time of the abnormal fluctuation, the type of abnormal fluctuation (such as abnormal waveform characteristics, abnormal rhythm characteristics, etc.), and the degree of abnormal fluctuation. This abnormal fluctuation information can accurately describe abnormal conditions in neurological function status.

[0078] Step S135: Integrate the trend change information and the abnormal fluctuation information to generate intraoperative neurological function monitoring results including functional stability information and abnormal fluctuation information. The functional stability information is used to describe the overall stability of the neurological function state, and the abnormal fluctuation information is used to mark the abnormal change interval of the neurological function state.

[0079] After completing the continuous verification of the feature change interval and the generation of abnormal fluctuation information, the trend change information and abnormal fluctuation information need to be integrated to generate intraoperative neurological function monitoring results. In cerebral aneurysm clipping surgery, functional stability information can be determined by analyzing trend change information. If the trend change information shows that the waveform characteristics and rhythm characteristics fluctuate within the normal range for most of the time and the change trend is relatively stable, then it can be considered that the overall stability of the neurological function state is relatively high; conversely, if the trend change information shows that the characteristics change more drastically and frequently exceed the normal range, then the overall stability of the neurological function state is low.

[0080] Abnormal fluctuation information is reflected by marking the abnormal change intervals in the neurological function status. The abnormal fluctuation information is integrated with the functional stability information to form a complete intraoperative neurological function monitoring result. The intraoperative neurological function monitoring result can not only reflect the overall stability of the neurological function status, but also clearly mark the time period and type of abnormalities in the neurological function status. For example, it can be clearly seen in the monitoring results that the neurological function status is relatively stable at a certain stage of the operation, while abnormal fluctuations occur at another stage, and the specific circumstances of the abnormal fluctuations can be known, such as the starting time, duration and fluctuation type of the abnormal fluctuations. Therefore, the surgical team can promptly learn about the changes in the neurological function status based on the intraoperative neurological function monitoring results and take corresponding measures to ensure the safety of the operation.

[0081] Step S140: Transmitting the intraoperative neurological function monitoring results to a remote monitoring terminal, wherein the remote monitoring terminal is used to dynamically display the functional stability information and abnormal fluctuation information.

[0082] After generating intraoperative neurological function monitoring results, they need to be transmitted to a remote monitoring terminal to provide the surgical team with real-time and intuitive information about the neurological function status. The remote monitoring terminal is typically located within the operating room or other relevant monitoring location, allowing the surgical team to view the results at any time.

[0083] During cerebral aneurysm clipping surgery, intraoperative neurological function monitoring results are transmitted from a remote analysis system to a remote monitoring terminal via a high-speed, stable network. Upon receiving the results, the remote monitoring terminal analyzes and processes them to dynamically display information on functional stability and abnormal fluctuations.

[0084] Step S141: the remote monitoring terminal receives the intraoperative neurological function monitoring result, and analyzes the functional stability information and abnormal fluctuation information in the intraoperative neurological function monitoring result.

[0085] After receiving the intraoperative neurological function monitoring results, the remote monitoring terminal first analyzes them. The monitoring results are usually transmitted in a set data format, and the remote monitoring terminal needs to decode and analyze them according to the corresponding protocols and rules.

[0086] For functional stability information, we can extract descriptions of the overall stability of neurological function and related data, such as stability scores and stable time periods. For abnormal fluctuation information, we can extract key information such as the start and end time of the abnormal fluctuation, as well as the type of abnormal fluctuation. Through parsing, we can separate functional stability information from abnormal fluctuation information in the monitoring results.

[0087] Step S142: Generate a functional stability dynamic trend graph based on the functional stability information. The functional stability dynamic trend graph uses the monitoring time as the horizontal axis and the functional stability degree as the vertical axis to draw a stability change curve of the neural function state in real time.

[0088] After analyzing the functional stability information, the remote monitoring terminal generates a dynamic functional stability trend chart based on this information. A coordinate system is established with monitoring time as the horizontal axis and the degree of functional stability as the vertical axis. The degree of functional stability can be expressed using a quantitative indicator, such as a stability score.

[0089] During cerebral aneurysm clipping surgery, as monitoring time progresses, the remote monitoring terminal updates functional stability data in real time and plots the corresponding points in a coordinate system. These points are then connected to form a stability curve. This curve visually illustrates the stability changes of neurological function throughout the surgery. For example, a relatively smooth curve indicates that neurological function is relatively stable for most of the time; however, significant fluctuations indicate that neurological function may have been affected by factors such as the surgical procedure, leading to instability. By observing this dynamic trend chart, the surgical team can promptly monitor changes in neurological stability so they can take appropriate measures.

[0090] Step S143: marking an abnormality on the functional stability dynamic trend graph based on the abnormal fluctuation information, and adding a visual identifier at the time axis position of the abnormal change interval corresponding to the abnormal fluctuation information, wherein the visual identifier is used to distinguish different types of abnormal fluctuations.

[0091] After generating a dynamic trend graph of functional stability, in order to allow the surgical team to more intuitively identify abnormal fluctuations in the neurological function status, abnormal marking needs to be performed based on the abnormal fluctuation information.

[0092] Step S1431: parsing the abnormal fluctuation type in the abnormal fluctuation information, and determining a visual identifier style corresponding to each abnormal fluctuation type, wherein the visual identifier style includes color, shape, and filling mode.

[0093] During cerebral aneurysm clipping surgery, after receiving abnormal fluctuation information, the remote monitoring terminal first analyzes it. Abnormal fluctuation information includes different types of abnormal fluctuations, such as abnormal waveform characteristics and abnormal rhythm characteristics. For each type of abnormal fluctuation, a corresponding visual identifier style needs to be determined. In terms of color, waveform abnormalities can be set to red, as red generally indicates danger and abnormality, which can attract the surgical team's high attention. Rhythm abnormalities can be set to blue, which is relatively mild and represents different types of abnormalities. In terms of shape, waveform abnormalities can be represented by rectangles, which give people a sense of regularity and clarity; rhythm abnormalities can be represented by triangles, which have a certain uniqueness. The fill pattern can also be different. For example, the rectangle for waveform abnormalities can be filled with solid fill, while the triangle for rhythm abnormalities can be filled with hollow fill. With the above settings, the surgical team can quickly distinguish different types of abnormal fluctuations based on the visual identifier style.

[0094] Step S1432: Locate the start time point and the end time point of the abnormal change interval corresponding to the abnormal fluctuation information on the time axis of the functional stability dynamic trend graph, and determine the time range of the abnormal mark.

[0095] After determining the visual identifier style, the timeline of the functional stability dynamic trend chart needs to be used to accurately identify the abnormal fluctuation interval corresponding to the abnormal fluctuation information. By analyzing the start and end time points of the abnormal fluctuation information, these points can be precisely marked on the timeline. For example, if the abnormal fluctuation begins at a specific time after the start of surgery and ends at another time, the corresponding positions of these two time points will be found on the timeline. Once these two positions are determined, the time range of the abnormal mark is clearly defined, that is, the period between the start and end time points.

[0096] Step S1433: Draw a visual identifier at a corresponding time axis position of the functional stability dynamic trend graph according to the time range and visual identifier style of the abnormal mark, wherein the length of the visual identifier is proportional to the duration of the abnormal change interval.

[0097] Based on the determined abnormality marking time range and visual identifier style, a visual identifier is drawn at the corresponding timeline position on the functional stability dynamic trend chart. The length of the visual identifier is adjusted according to the duration of the abnormal change interval; the longer the duration, the longer the visual identifier. For example, if an abnormal fluctuation persists for a long time, the length of the corresponding visual identifier (such as a rectangle or triangle) on the timeline will be increased accordingly. This allows the surgical team to intuitively understand the duration of the abnormal fluctuation from the length of the visual identifier. Through accurate drawing, the visual identifier can be clearly displayed on the functional stability dynamic trend chart, making it easier for the surgical team to observe.

[0098] Step S1434: performing hierarchical processing on the visual identifiers corresponding to the overlapping abnormal change intervals, determining the display hierarchy of the visual identifiers according to the priority of the abnormal fluctuation type, and displaying the visual identifiers with higher priority at the top.

[0099] In actual situations, there may be overlapping abnormal change intervals, that is, different types of abnormal fluctuations appear in the same time period. At this time, it is necessary to perform hierarchical processing on the visual identifiers corresponding to the overlapping abnormal change intervals. First, the priority will be determined based on the importance of the abnormal fluctuation type and the degree of impact on neurological function. For example, the impact of waveform feature abnormalities on neurological function may be more direct and serious, so the priority of waveform feature abnormalities may be higher than that of rhythm feature abnormalities. According to this priority order, the visual identifiers with higher priority are displayed at the top. In this way, when the surgical team views the dynamic trend chart of functional stability, they can see the abnormal fluctuations with higher priority first, which facilitates them to give priority to and deal with abnormalities that have a greater impact on neurological function.

[0100] Step S1435: adding an interactive prompt function to the visual identifier, so that when the operator clicks or hovers over the visual identifier, detailed information of the abnormal fluctuation is displayed, including the abnormal start time, abnormal duration and abnormal fluctuation type.

[0101] To provide the surgical team with a deeper understanding of abnormal fluctuations, an interactive prompt function is added to the visual identifier. When the surgeon clicks or hovers over the visual identifier, a pop-up window displays detailed information about the abnormal fluctuation. This detailed information includes the abnormality start time, which allows the surgical team to understand when the abnormality began; the abnormality duration, which shows how long the abnormality has lasted; and the abnormality type, which clarifies whether the abnormality is a waveform feature or a rhythm feature. Through this interactive prompt function, the surgical team can obtain detailed information about the abnormal fluctuation when needed.

[0102] Step S144: extract the waveform features and rhythm features corresponding to the abnormal fluctuation interval from the intraoperative neurological function monitoring results, and display the waveform features and rhythm features in the form of a waveform graph in the associated area of ​​the functional stability dynamic trend graph to achieve the linked display of abnormal fluctuations and corresponding waveform and rhythm features.

[0103] After abnormal marking, in order to allow the surgical team to have a more comprehensive understanding of the abnormal fluctuations, it is necessary to extract the waveform characteristics and rhythm characteristics corresponding to the abnormal fluctuation interval from the intraoperative neurological function monitoring results, and display them in the form of a waveform graph in the associated area of ​​the functional stability dynamic trend graph.

[0104] During cerebral aneurysm clipping surgery, for each abnormal fluctuation interval, the corresponding waveform and rhythm characteristics data within that interval are found from the monitoring results. This data is then processed and converted to generate a corresponding waveform graph. The waveform graph can intuitively display the waveform and rhythm changes of the neuroelectrophysiological signals within the abnormal fluctuation interval.

[0105] The generated waveform graph is displayed in an associated area of ​​the functional stability dynamic trend graph, such as next to or below the visual identifier of the abnormality mark. This allows the surgical team to simultaneously observe the specific waveform and rhythm characteristics corresponding to the abnormal fluctuation when viewing the functional stability dynamic trend graph, achieving a linked display of the abnormal fluctuation and the corresponding waveform and rhythm characteristics. This linked display allows the surgical team to more deeply analyze the causes and impacts of the abnormal fluctuation.

[0106] Step S145: The remote monitoring terminal updates the functional stability dynamic trend graph and the associated displayed waveform graph in real time to ensure that the displayed content is synchronized with the latest intraoperative neurological function monitoring results.

[0107] During surgery, neurological function status is constantly changing, and intraoperative neurological function monitoring results are updated in real time. To ensure the timeliness and accuracy of the information displayed by the remote monitoring terminal, it is necessary to update the dynamic trend chart of functional stability and the associated waveform chart in real time.

[0108] The remote monitoring terminal regularly obtains the latest intraoperative neurological function monitoring results from the remote analysis system. Upon receiving new results, the functional stability and abnormal fluctuation information can be re-analyzed. For dynamic trend charts of functional stability, the stability change curve can be updated based on the new functional stability data. For associated waveforms, if new abnormal fluctuation intervals appear or the characteristics of the original abnormal fluctuation intervals change, the corresponding waveforms can be regenerated and displayed.

[0109] Real-time updates ensure that the content displayed on the remote monitoring terminal is synchronized with the latest intraoperative neurological function monitoring results. This allows the surgical team to obtain the latest information on neurological function status at any time, making timely decisions and adjustments to ensure the safety and smooth progress of the operation.

[0110] Step S150: triggering an intraoperative intervention prompt operation based on the abnormal fluctuation information in the intraoperative neurological function monitoring result, wherein the intraoperative intervention prompt operation is used to assist the surgeon in adjusting the surgical operation.

[0111] After the remote monitoring terminal displays the intraoperative neurological function monitoring results and marks the abnormal fluctuation information, in order to respond to abnormal changes in the neurological function status in a timely manner, it is necessary to trigger intraoperative intervention prompt operations based on the abnormal fluctuation information.

[0112] Step S151: parsing the abnormal fluctuation information, extracting the abnormal start time, abnormal duration and abnormal fluctuation type from the abnormal fluctuation information, wherein the abnormal fluctuation type is determined based on the abnormal pattern of waveform characteristics and rhythm characteristics.

[0113] During cerebral aneurysm clipping surgery, after receiving abnormal fluctuation information, the remote monitoring terminal will first analyze it. Abnormal fluctuation information typically contains key information such as the abnormal start time, abnormal duration, and abnormal fluctuation type. The abnormal fluctuation type is determined based on the abnormal patterns of waveform characteristics and rhythm characteristics. For example, if the waveform characteristics show abnormal patterns such as a sudden increase in amplitude or a change in frequency, or if the frequency, amplitude, phase, etc. of the rhythm characteristics undergo abnormal changes, the corresponding abnormal fluctuation type can be determined based on these abnormal patterns, such as abnormal waveform characteristics, abnormal rhythm characteristics, etc.

[0114] Step S152: Evaluate the severity of the abnormal fluctuation according to the abnormal duration and abnormal fluctuation type, wherein the severity is determined based on the length of the abnormal duration and the potential impact of the abnormal pattern on neurological function.

[0115] After extracting key information about abnormal fluctuations, the severity of the abnormal fluctuation needs to be assessed based on its duration and type. The duration of the abnormality is a key factor in assessing severity. Generally speaking, the longer the abnormality lasts, the longer neurological function has been affected and the greater the potential harm.

[0116] The type of abnormal fluctuation also influences severity assessment. Different types of abnormal fluctuations have different potential impacts on neurological function. For example, abnormal waveform characteristics may affect the transmission and processing of neural signals, while abnormal rhythm characteristics may affect neural synchronization and coordination. Based on the potential impact of the abnormal pattern on neurological function, the abnormal fluctuation type can be classified into different levels, such as mild, moderate, and severe.

[0117] The severity of the abnormal fluctuation is determined by comprehensively considering the abnormal duration and abnormal fluctuation type. For example, if the abnormal duration is long and the abnormal fluctuation type is severe, then the severity of the abnormal fluctuation can be considered high; if the abnormal duration is short and the abnormal fluctuation type is mild, then the severity is relatively low.

[0118] Step S153: matching a corresponding intervention prompt rule from a preset intervention rule library based on the severity, wherein the intervention prompt rule includes a prompt method and prompt content, and the prompt method is positively correlated with the severity.

[0119] After assessing the severity of the abnormal fluctuation, it is necessary to match the corresponding intervention prompt rules from the preset intervention rule library. The intervention rule library is pre-established and contains intervention prompt rules corresponding to different severities.

[0120] Intervention prompt rules include prompt method and prompt content. The prompt method is positively correlated with severity; that is, the higher the severity, the more obvious and intense the prompt method. For example, for mild abnormalities, the prompt method may be a slight flashing prompt on the display interface of the remote monitoring terminal; for moderate abnormalities, the prompt method may be a large prompt window popping up on the display interface, accompanied by a slight prompt tone; for severe abnormalities, the prompt method may be a striking red prompt window popping up on the display interface, accompanied by a strong prompt tone.

[0121] The prompt content is customized according to the type and severity of the abnormal fluctuation. It may include a brief description of the abnormality, an analysis of possible causes, and preliminary suggestions. For example, for a mild abnormality with abnormal waveform characteristics, the prompt content may be "A slight abnormality in waveform characteristics has been detected. This may be a temporary effect of the surgical procedure. Please pay close attention." For a severe abnormality with abnormal rhythm characteristics, the prompt content may be "Serious abnormalities in rhythm characteristics may affect neurological function. It is recommended to adjust the surgical procedure immediately."

[0122] By matching the corresponding intervention prompt rules from the intervention rule library, it can be ensured that appropriate prompt methods and prompt contents are provided according to the severity of abnormal fluctuations, and the surgical team can be reminded in time to pay attention to abnormal changes in neurological function status.

[0123] Step S154: triggering a multimodal prompt operation of the remote monitoring terminal based on the prompt method, wherein the multimodal prompt operation includes a visual prompt operation and an auditory prompt operation, wherein the visual prompt operation generates an abnormal prompt window on the display interface, and the auditory prompt operation plays a prompt sound through the audio output module.

[0124] After matching the corresponding intervention prompt rules, the remote monitoring terminal triggers a multimodal prompt operation based on the prompt method. The multimodal prompt operation includes visual prompt operation and auditory prompt operation, alerting the surgical team in multiple ways at the same time.

[0125] For visual prompts, an abnormality prompt window is generated on the remote monitoring terminal's display interface, based on the prompt method requirements. The size, color, and content of the prompt window are adjusted according to the severity of the abnormal fluctuation. For example, for mild abnormalities, the prompt window can be smaller and lighter in color; for severe abnormalities, the prompt window will be larger and bright red. The prompt window will display the prompt content, allowing the surgical team to intuitively understand the abnormality and provide suggestions.

[0126] For auditory prompts, a tone is played through the remote monitoring terminal's audio output module. The tone's intensity and frequency are adjusted based on the severity of the anomaly. For example, for a mild anomaly, the tone might be a gentle ticking sound; for a severe anomaly, the tone might be a loud alarm.

[0127] Through multimodal prompt operations, it can ensure that the surgical team can receive prompt information of abnormal fluctuations in a timely manner under any circumstances, and increase attention to abnormal changes in neurological function status.

[0128] Step S155: Generate surgical operation adjustment suggestions based on the prompt content and the abnormal fluctuation type. The surgical operation adjustment suggestions include surgical operation links and adjustment directions related to the abnormal fluctuation type. The adjustment direction is determined based on the changing trend of the abnormal fluctuation.

[0129] After triggering the multimodal prompt operation, in order to help the surgeon adjust the surgical operation in time to cope with abnormal fluctuations in neurological function, it is necessary to generate surgical operation adjustment suggestions based on the prompt content and the type of abnormal fluctuation.

[0130] Step S1551: parse the prompt content and extract the neurological function influencing factors related to the abnormal fluctuation type, wherein the neurological function influencing factors include mechanical stimulation and electrical stimulation of the nerve tissue caused by the surgical operation.

[0131] During cerebral aneurysm clipping surgery, the prompts displayed by the remote monitoring terminal contain important information related to the type of abnormal fluctuation. By analyzing the prompts, factors affecting neurological function related to the abnormal fluctuation type are extracted. For example, if the abnormal fluctuation type is an abnormal waveform characteristic, the prompt may mention that it is caused by excessive mechanical stimulation of the nerve tissue by the surgical instrument; if the abnormal rhythm characteristic is an abnormal rhythm, it may be that the electrical stimulation during the surgery interfered with the normal rhythm of the nerve. This clarifies the specific factors affecting neurological function.

[0132] Step S1552: Query the corresponding surgical operation link from the preset operation link association library according to the neurological function influencing factors, and the surgical operation link includes the instrument operation area, operation force control and operation speed adjustment.

[0133] The preset operation link association library is a pre-established database that records the surgical operation links corresponding to different neurological function influencing factors. After determining the neurological function influencing factors, the corresponding surgical operation links can be queried from the operation link association library based on these factors. For example, if the neurological function influencing factor is mechanical stimulation, the corresponding surgical operation link will be displayed in the association library, which may include instrument operation area, operation force control, and operation speed adjustment. The instrument operation area may need to be adjusted to avoid excessive proximity of the instrument to or compression of the nerve tissue; in terms of operation force control, the operation force may need to be reduced to reduce the mechanical stimulation to the nerves; in terms of operation speed adjustment, the operation speed may need to be reduced to give the nerve tissue enough recovery time. By querying the operation link association library, the specific surgical operation links that may need to be adjusted are clarified.

[0134] Step S1553: Analyze the changing trend in the abnormal fluctuation information to determine whether the abnormal fluctuation is in an aggravating trend or a slowing trend. The aggravating trend indicates that the abnormality increases over time, and the slowing trend indicates that the abnormality decreases over time.

[0135] After determining the surgical procedure, it's necessary to analyze the changing trends in the abnormal fluctuation information. By comparing and analyzing abnormal data at different time points within the abnormal fluctuation information, it's possible to determine whether the abnormal fluctuation is increasing or decreasing. For example, if the amplitude, frequency, and other indicators of the abnormal fluctuation increase over several consecutive time points, this indicates an increasing trend; if these indicators decrease, this indicates a decreasing trend. Clarifying the changing trends of abnormal fluctuations is crucial for determining the direction of adjustment.

[0136] Step S1554: Determine the adjustment direction based on the surgical operation links and the changing trend of the abnormal fluctuations. If the abnormal fluctuations tend to intensify, the adjustment direction is to reduce the stimulation intensity on the nerve tissue. If the abnormal fluctuations tend to slow down, the adjustment direction is to maintain the current operation or moderately adjust the stimulation intensity.

[0137] The direction of adjustment should be determined based on the surgical operation process and the changing trend of abnormal fluctuations. If the abnormal fluctuations show an intensifying trend, it means that the current surgical operation is stimulating the nerve tissue too much, and measures need to be taken to reduce the intensity of stimulation. For example, for the instrument operation area, the instrument can be moved farther away from the nerve tissue; for operation force control, the operation force can be further reduced; for operation speed adjustment, the operation speed can be reduced. If the abnormal fluctuations show a slowing trend, it means that the current operation may be effective, and the current operation can be maintained, or the stimulation intensity can be appropriately adjusted according to the situation, such as fine-tuning the operation force or speed, to ensure that the nerve function can be continuously and stably restored.

[0138] Step S1555: The surgical operation steps and corresponding adjustment directions are combined into surgical operation adjustment suggestions, and the surgical operation adjustment suggestions are displayed in the prompt window of the remote monitoring terminal in the form of text descriptions to assist the surgeon in making surgical operation adjustments.

[0139] Finally, the surgical operation links and the corresponding adjustment directions are combined into surgical operation adjustment suggestions. For example, each surgical operation link (such as instrument operation area, operation force control, operation speed adjustment) and the corresponding adjustment direction (such as reducing stimulation intensity, maintaining or moderately adjusting stimulation intensity) are described in clear and unambiguous text. For example, "The current waveform feature abnormality is aggravated. It is recommended to adjust the instrument operation area away from the nerve tissue, while reducing the operation force and speed." The surgical operation adjustment suggestions are displayed in the prompt window of the remote monitoring terminal in the form of text descriptions. The surgeon can directly see the specific adjustment suggestions in the prompt window and adjust the surgical operation in time according to these suggestions to ensure the patient's neurological function safety.

[0140] Figure 2 The following is a schematic diagram showing exemplary hardware and software components of a remote intraoperative neuroelectrophysiological monitoring system 100 that can implement the concepts of the present application, as provided in some embodiments of the present application. For example, a processor 120 can be used in the remote intraoperative neuroelectrophysiological monitoring system 100 to perform the functions of the present application.

[0141] The remote intraoperative neuroelectrophysiological monitoring system 100 can be a general-purpose server or a special-purpose server, both of which can be used to implement the remote intraoperative neuroelectrophysiological monitoring method of the present application. Although only one server is shown in this application, for convenience, the functions described in this application can be implemented in a distributed manner on multiple similar platforms to balance the processing load.

[0142] For example, the remote intraoperative neuroelectrophysiological monitoring system 100 may include a network port 110 connected to a network, one or more processors 120 for executing program instructions, a communication bus 130, and different forms of storage media 140, such as a disk, ROM, or RAM, or any combination thereof. Exemplarily, the remote intraoperative neuroelectrophysiological monitoring system 100 may also include program instructions stored in ROM, RAM, or other types of non-transitory storage media, or any combination thereof. The method of the present application can be implemented according to these program instructions. The remote intraoperative neuroelectrophysiological monitoring system 100 also includes an I / O interface 150 between the computer and other input and output devices.

[0143] For ease of explanation, only one processor is described in the remote intraoperative neuroelectrophysiological monitoring system 100. However, it should be noted that the remote intraoperative neuroelectrophysiological monitoring system 100 in the present application may also include multiple processors, so the steps performed by one processor described in the present application may also be performed jointly or individually by multiple processors. For example, if the processor of the remote intraoperative neuroelectrophysiological monitoring system 100 performs step A and step B, it should be understood that step A and step B may also be performed jointly by two different processors or individually in one processor. For example, the first processor performs step A and the second processor performs step B, or the first processor and the second processor perform steps A and B together.

[0144] In addition, an embodiment of the present invention further provides a readable storage medium, in which computer-executable instructions are preset. When a processor executes the computer-executable instructions, the above-mentioned remote intraoperative neuroelectrophysiological monitoring method is implemented.

[0145] It should be noted that in order to simplify the description of the present invention and thus help understand one or more embodiments of the invention, in the foregoing description of the embodiments of the present invention, multiple features are sometimes combined into one embodiment, figure or description thereof.

Claims

1. A remote intraoperative neuroelectrophysiological monitoring method, characterized in that: The method comprises: Acquiring a neurophysiological signal set from an intraoperative patient, wherein the neurophysiological signal set includes continuous neurophysiological waveform signals synchronously recorded through a plurality of monitoring leads; Performing feature extraction processing on the neuroelectrophysiological signal set to obtain a neurological function state feature of the neuroelectrophysiological waveform signal, wherein the neurological function state feature includes a waveform feature and a rhythm feature; Performing remote real-time analysis and processing on the neurological function status characteristics to generate intraoperative neurological function monitoring results, wherein the intraoperative neurological function monitoring results include functional stability information and abnormal fluctuation information; Transmitting the intraoperative neurological function monitoring results to a remote monitoring terminal, wherein the remote monitoring terminal is used to dynamically display the functional stability information and abnormal fluctuation information; An intraoperative intervention prompt operation is triggered based on abnormal fluctuation information in the intraoperative neurological function monitoring result, and the intraoperative intervention prompt operation is used to assist the surgeon in adjusting the surgical operation.

2. The remote intraoperative neuroelectrophysiological monitoring method according to claim 1, characterized in that: The performing feature extraction processing on the neural electrophysiological signal set to obtain neural function state features of the neural electrophysiological waveform signal includes: performing signal preprocessing on the continuous electrophysiological waveform signal in the neural electrophysiological signal set to obtain an electrophysiological waveform signal after interference removal; Performing time dimension segmentation processing on the electrophysiological waveform signal after interference removal, dividing the electrophysiological waveform signal after interference removal into a plurality of continuous waveform time segments according to preset time intervals, each waveform time segment having a continuous time series relationship; Extracting waveform features from each waveform time segment to identify characteristic waveform forms in the waveform time segment, wherein the characteristic waveform forms include an undulating change pattern of the waveform and a connection relationship between waveforms, and obtaining waveform features of each waveform time segment based on the characteristic waveform forms; Extracting rhythm features from the electrophysiological waveform signal after interference removal, analyzing the repetitive variation pattern of the electrophysiological waveform signal after interference removal within a continuous time interval, identifying a rhythm pattern with periodic occurrence characteristics, and obtaining the rhythm features of the electrophysiological waveform signal after interference removal based on the rhythm pattern; Performing time axis correlation processing on the waveform features and the rhythm features, establishing a corresponding relationship between the waveform features and the rhythm features in the same time interval, and generating a correlation feature set including a time mark; The neural function state feature of the neural electrophysiological waveform signal is obtained based on the integration of the associated feature set, and the neural function state feature includes a combination of waveform features and rhythm features corresponding to different time intervals.

3. The remote intraoperative neuroelectrophysiological monitoring method according to claim 2, characterized in that: The performing signal preprocessing on the continuous electrophysiological waveform signal in the neural electrophysiological signal set to obtain the electrophysiological waveform signal after interference removal includes: Performing power frequency interference suppression processing on the continuous electrophysiological waveform signal, using a notch filtering method to filter fixed frequency interference components in the continuous electrophysiological waveform signal, and retaining effective frequency components of the neural electrophysiological signal; Performing baseline drift correction processing on the continuous electrophysiological waveform signal, eliminating slow baseline fluctuations in the continuous electrophysiological waveform signal by a sliding average filtering method, so that the waveform baseline is maintained at a preset stable level; Performing myoelectric interference removal processing on the continuous electrophysiological waveform signal, using an adaptive filtering algorithm to identify and suppress high-frequency myoelectric interference pulses in the continuous electrophysiological waveform signal, and retaining low-frequency neural electrophysiological signal components; Performing electrode noise filtering on the continuous electrophysiological waveform signal, identifying and removing sudden electrode noise spikes in the continuous electrophysiological waveform signal by a threshold judgment method; The signals after the above signal preprocessing are integrated to obtain an electrophysiological waveform signal after interference removal from physiological interference and external environmental interference, wherein the electrophysiological waveform signal after interference removal retains the original characteristics of the neural electrophysiological activity.

4. The remote intraoperative neuroelectrophysiological monitoring method according to claim 2, characterized in that: The step of extracting waveform features from each waveform time segment and identifying characteristic waveform forms in the waveform time segment includes: Performing waveform morphology recognition on each waveform time segment, comparing the waveform time segment with a preset standard neuroelectrophysiological waveform template using a template matching method, and identifying a characteristic waveform in the waveform time segment that meets the standard template; Extracting the fluctuation pattern of the characteristic waveform, analyzing the rising and falling segment change trends of the characteristic waveform, and determining the overall morphological characteristics of the characteristic waveform; Analyzing the connection relationship between adjacent characteristic waveforms in the waveform time segment, identifying the interval pattern and superposition pattern between the characteristic waveforms, and determining the structural characteristics of the waveform sequence; Constructing a waveform feature descriptor based on the overall morphological features and structural features, wherein the waveform feature descriptor includes morphological parameters of the characteristic waveform and structural parameters of the waveform sequence; The waveform feature descriptor is used as the waveform feature of each waveform time segment, and the waveform feature is used to characterize the waveform change characteristics of the neural electrophysiological signal within the time segment.

5. The remote intraoperative neuroelectrophysiological monitoring method according to claim 1, characterized in that: The remote real-time analysis and processing of the neurological function status characteristics to generate intraoperative neurological function monitoring results includes: Sending the neurological function status characteristics to a remote analysis system so that the remote analysis system performs time series modeling on the neurological function status characteristics and constructs a dynamic model of the neurological function status characteristics changing over monitoring time, wherein the dynamic model is used to describe the continuous change process of waveform characteristics and rhythm characteristics; Analyzing the change trend of the neural function state characteristics based on the dynamic model, extracting the change pattern of the waveform characteristics and the change pattern of the rhythm characteristics, and obtaining the overall trend change information of the neural function state characteristics; Comparing the trend change information with a preset neurological function safety reference range, wherein the neurological function safety reference range includes a waveform characteristic range and a rhythm characteristic range under a normal physiological state, and identifying a characteristic change interval that exceeds the neurological function safety reference range; Continuously verifying the characteristic change interval to determine whether the waveform characteristics and rhythm characteristics within the characteristic change interval continuously exceed the neurological function safety reference range; if so, determining it as an abnormal fluctuation interval, and generating abnormal fluctuation information based on the abnormal fluctuation interval; The trend change information and the abnormal fluctuation information are integrated to generate intraoperative neurological function monitoring results containing functional stability information and abnormal fluctuation information. The functional stability information is used to describe the overall stability of the neurological function state, and the abnormal fluctuation information is used to mark the abnormal change interval of the neurological function state.

6. The remote intraoperative neuroelectrophysiological monitoring method according to claim 5, characterized in that: The analyzing the change trend of the neural function state characteristics based on the dynamic model, extracting the change pattern of the waveform characteristics and the change pattern of the rhythm characteristics, and obtaining the overall trend change information of the neural function state characteristics includes: Based on the waveform feature time series output by the dynamic model, analyzing the change amplitude and change direction of the waveform feature during the continuous monitoring time, and determining the short-term change pattern and long-term change pattern of the waveform feature; Based on the rhythm feature time series output by the dynamic model, analyzing the frequency and intensity changes of the rhythm features during the continuous monitoring time, and determining the short-term change pattern and long-term change pattern of the rhythm features; The short-term change pattern and the long-term change pattern of the waveform feature are integrated to obtain a comprehensive change pattern of the waveform feature, wherein the comprehensive change pattern reflects the overall evolution law of the waveform feature over time; fusing the short-term change pattern and the long-term change pattern of the rhythmic characteristics to obtain a comprehensive change pattern of the rhythmic characteristics, wherein the comprehensive change pattern reflects the overall evolution law of the rhythmic characteristics over time; The comprehensive change pattern of the waveform characteristics and the comprehensive change pattern of the rhythm characteristics are integrated to generate overall trend change information of the neural function state characteristics, and the overall trend change information is used to describe the continuous change process of the neural function state with monitoring time.

7. The remote intraoperative neuroelectrophysiological monitoring method according to claim 1, characterized in that: The transmitting of the intraoperative neurological function monitoring results to a remote monitoring terminal, wherein the remote monitoring terminal is used to dynamically display the functional stability information and abnormal fluctuation information, includes: The remote monitoring terminal receives the intraoperative neurological function monitoring result and analyzes the functional stability information and abnormal fluctuation information in the intraoperative neurological function monitoring result; Generate a functional stability dynamic trend graph based on the functional stability information, wherein the functional stability dynamic trend graph uses the monitoring time as the horizontal axis and the functional stability degree as the vertical axis to draw a stability change curve of the neural function state in real time; Marking an abnormality on the functional stability dynamic trend graph based on the abnormal fluctuation information, and adding a visual identifier at the time axis position of the abnormal change interval corresponding to the abnormal fluctuation information, wherein the visual identifier is used to distinguish different types of abnormal fluctuations; Extracting waveform features and rhythm features corresponding to abnormal fluctuation intervals from the intraoperative neurological function monitoring results, and displaying the waveform features and rhythm features in the form of waveform graphs in associated areas of the functional stability dynamic trend graph, thereby realizing a linked display of abnormal fluctuations and corresponding waveform and rhythm features; The remote monitoring terminal updates the functional stability dynamic trend graph and the associated displayed waveform graph in real time, ensuring that the displayed content is synchronized with the latest intraoperative neurological function monitoring results.

8. The remote intraoperative neuroelectrophysiological monitoring method according to claim 7, characterized in that: The abnormal marking on the functional stability dynamic trend graph based on the abnormal fluctuation information and adding a visual identifier at the time axis position of the abnormal change interval corresponding to the abnormal fluctuation information include: parsing the abnormal fluctuation type in the abnormal fluctuation information, and determining a visual identifier style corresponding to each abnormal fluctuation type, wherein the visual identifier style includes color, shape, and fill mode; Locating the start time point and the end time point of the abnormal change interval corresponding to the abnormal fluctuation information on the time axis of the functional stability dynamic trend graph, and determining the time range of the abnormal mark; Drawing a visual identifier at a corresponding time axis position on the functional stability dynamic trend graph according to the time range and visual identifier style of the abnormal mark, wherein the length of the visual identifier is proportional to the duration of the abnormal change interval; Perform hierarchical processing on the visual identifiers corresponding to the overlapping abnormal change intervals, and determine the display level of the visual identifiers according to the priority of the abnormal fluctuation type, with the visual identifiers with higher priority displayed at the top; An interactive prompt function is added to the visual identifier. When the operator clicks or hovers over the visual identifier, detailed information of the abnormal fluctuation is displayed, and the detailed information includes the abnormal start time, abnormal duration and abnormal fluctuation type.

9. The remote intraoperative neuroelectrophysiological monitoring method according to claim 1, characterized in that: The triggering of an intraoperative intervention prompt operation based on abnormal fluctuation information in the intraoperative neurological function monitoring result includes: parsing the abnormal fluctuation information, extracting the abnormal start time, abnormal duration, and abnormal fluctuation type from the abnormal fluctuation information, wherein the abnormal fluctuation type is determined based on an abnormal pattern of waveform features and rhythm features; assessing the severity of the abnormal fluctuations according to the abnormal duration and abnormal fluctuation type, wherein the severity is determined based on the length of the abnormal duration and the potential impact of the abnormal pattern on neurological function; Matching corresponding intervention prompt rules from a preset intervention rule library based on the severity, wherein the intervention prompt rules include a prompt method and a prompt content, and the prompt method is positively correlated with the severity; triggering a multimodal prompt operation of the remote monitoring terminal based on the prompt mode, wherein the multimodal prompt operation includes a visual prompt operation and an auditory prompt operation, wherein the visual prompt operation generates an abnormal prompt window on the display interface, and the auditory prompt operation plays a prompt sound through the audio output module; A surgical operation adjustment suggestion is generated based on the prompt content and the abnormal fluctuation type. The surgical operation adjustment suggestion includes a surgical operation link and an adjustment direction related to the abnormal fluctuation type. The adjustment direction is determined based on a changing trend of the abnormal fluctuation.

10. A remote intraoperative neuroelectrophysiological monitoring system, characterized in that: It includes a processor and a memory, the memory is connected to the processor, the memory is used to store programs, instructions or codes, and the processor is used to execute the programs, instructions or codes in the memory to implement the remote intraoperative neuroelectrophysiological monitoring method described in any one of claims 1 to 9.

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