CN-120788518-B - Intraoperative nerve electrophysiology monitoring method and system combined with anesthesia depth monitoring

CN120788518BCN 120788518 BCN120788518 BCN 120788518BCN-120788518-B

Abstract

The invention provides an intraoperative nerve electrophysiological monitoring method and system combining anesthesia depth monitoring, which comprises the steps of firstly collecting intraoperative anesthesia depth data of a patient in real time through anesthesia depth monitoring equipment, starting a nerve electrophysiological monitoring parameter dynamic adaptation flow based on the intraoperative anesthesia depth data of the patient, adjusting the signal collection configuration of the nerve electrophysiological monitoring equipment to generate a monitoring configuration parameter adapting to the current anesthesia state, continuously collecting the intraoperative nerve electrophysiological signal of the patient according to the parameter control equipment, then executing a synchronous correlation analysis flow of signals and the anesthesia depth data, carrying out time sequence correlation processing to generate a correlation analysis result, finally starting an intraoperative nerve state evaluation flow based on the correlation analysis result, analyzing the corresponding relation between waveform signal characteristics and anesthesia depth change, and generating an intraoperative nerve state evaluation report containing nerve function state indication information, thereby improving the accuracy of intraoperative nerve function monitoring.

Inventors

LI QIANG
LI LI
LIU JIANMIN
DUAN GUOLI
XU YI
Lv nan
ZHANG GUANGHAO
WU YINA

Assignees

中国人民解放军海军军医大学第一附属医院

Dates

Publication Date: 20260512
Application Date: 20250728

Claims (7)

1. A method of intraoperative neurophysiologic monitoring in combination with anesthesia depth monitoring, the method comprising: collecting intraoperative anesthesia depth data of a patient in real time through anesthesia depth monitoring equipment, wherein the intraoperative anesthesia depth data is a continuous monitoring sequence reflecting the current anesthesia inhibition degree of the patient; Based on the intraoperative anesthesia depth data, starting a dynamic adaptation flow of the nerve electrophysiological monitoring parameters, and adjusting signal acquisition configuration of the nerve electrophysiological monitoring equipment according to the change characteristics of the intraoperative anesthesia depth data to generate nerve electrophysiological monitoring configuration parameters adapting to the current anesthesia state, wherein the method specifically comprises the following steps: Continuously monitoring the real-time change trend of the intraoperative anesthesia depth data, and identifying whether a preset parameter adjustment triggering event occurs in the intraoperative anesthesia depth data, wherein the parameter adjustment triggering event is an event that the change amplitude of the intraoperative anesthesia depth data exceeds a preset threshold value; When a parameter adjustment triggering event is identified, extracting intra-operative anesthesia depth data change characteristics corresponding to the parameter adjustment triggering event, wherein the change characteristics comprise change direction characteristics and change rate characteristics; According to the change direction characteristics and the change rate characteristics, matching corresponding parameter adjustment schemes from a preset monitoring parameter adjustment rule base, wherein the parameter adjustment schemes comprise signal acquisition configuration items to be adjusted and corresponding adjustment operation instructions; According to the adjustment operation instruction in the parameter adjustment scheme, the current signal acquisition configuration of the nerve electrophysiology monitoring equipment is adjusted item by item, wherein the signal acquisition configuration comprises signal amplification configuration, filtering frequency band configuration and sampling interval configuration; After parameter adjustment is completed, starting a configuration verification flow of the nerve electrophysiological monitoring equipment, and verifying whether the adjusted signal acquisition configuration can stably acquire the intraoperative nerve electrophysiological signals meeting the quality requirements by acquiring a section of test signals; If the verification is not passed, the matching and the adjustment flow of the parameter adjustment scheme are re-executed until the configuration verification is passed; controlling the nerve electrophysiological monitoring equipment to continuously collect the intraoperative nerve electrophysiological signals of the patient according to the nerve electrophysiological monitoring configuration parameters, wherein the intraoperative nerve electrophysiological signals are waveform signal sequences reflecting nerve conduction functions; Executing a synchronous correlation analysis flow of the intraoperative nerve electrophysiological signal and the intraoperative anesthesia depth data, and performing time sequence correlation processing on the intraoperative nerve electrophysiological signal and the intraoperative anesthesia depth data in the same time window to generate a correlation analysis result, wherein the method specifically comprises the following steps of: Setting a correlation analysis time window with fixed duration, and dividing the intraoperative nerve electrophysiological signal and the intraoperative anesthesia depth data into a signal segment unit and a data segment unit which correspond to the correlation analysis time window, wherein each signal segment unit and each data segment unit have the same timestamp range; Executing a waveform characteristic extraction flow on each signal segment unit, and identifying a characteristic waveform form and a characteristic waveform appearance time in the signal segment unit, wherein the characteristic waveform form comprises a waveform amplitude form, a waveform duration form and a waveform interval form; executing a trend feature extraction flow on each data fragment unit, and identifying the anesthesia depth change trend and trend turning points in the data fragment units, wherein the change trend comprises a stable trend, an ascending trend and a descending trend; carrying out time sequence alignment association on the characteristic waveform form and the characteristic waveform appearance time of the signal segment unit and the anesthesia depth change trend and trend turning point of the data segment unit within the same time stamp range, and establishing a corresponding relation between the characteristic waveform and the anesthesia depth change; summarizing and analyzing the corresponding relation of all time windows, and identifying a regular association mode of the characteristic waveform form along with the variation trend of the anesthesia depth, wherein the regular association mode is a stable corresponding relation of the characteristic waveform form variation and the variation trend of the anesthesia depth; combining the regular association mode and the corresponding timestamp range into an association analysis result; Based on the association analysis result, starting an intraoperative nerve state evaluation flow, and generating an intraoperative nerve state evaluation report containing nerve function state indication information by analyzing the corresponding relation between waveform signal characteristics and anesthesia depth change in the association analysis result, wherein the method specifically comprises the following steps: analyzing the association analysis result, extracting all the regular association modes and the corresponding time stamp ranges, and determining the duration time duty ratio of each regular association mode in operation; Comparing the extracted regular association mode with a preset normal association mode library, and identifying whether an abnormal association mode deviating from the normal association mode exists or not, wherein the abnormal association mode is a mode in which the corresponding relation between the characteristic waveform form change and the anesthesia depth change trend does not accord with the normal association mode; Classifying the identified abnormal association mode, and determining the nerve function influence type corresponding to the abnormal association mode according to the characteristic waveform morphological abnormality type and the anesthesia depth change trend abnormality type of the abnormal association mode; Counting occurrence frequency and duration time of abnormal association modes of different nerve function influence types in operation, and calculating influence degree parameters of the nerve function influence types, wherein the influence degree parameters are comprehensive statistical results of the occurrence frequency and duration time of the abnormal association modes; According to the influence degree parameters of each nerve function influence type, referring to a preset nerve function state evaluation standard, determining the current nerve function state grade of a patient, wherein the nerve function state grade comprises a normal state grade, a mild abnormal state grade and a moderate abnormal state grade; The neural function status level, detailed information of abnormal associated mode and influence degree parameters are integrated into an intraoperative neural status assessment report containing neural function status indication information.
2. The method of claim 1, wherein continuously monitoring the real-time trend of the intra-operative depth of anesthesia data, identifying whether a preset parameter adjustment trigger event occurs in the intra-operative depth of anesthesia data comprises: setting a real-time monitoring period, and periodically sampling the intra-operative anesthesia depth data according to the real-time monitoring period to obtain an anesthesia depth data sampling value of each monitoring period; calculating the difference value of the anesthesia depth data sampling values of two adjacent monitoring periods to obtain the period variation of the anesthesia depth data; comparing the absolute value of the periodic variation with a preset variation amplitude threshold, and if the absolute value of the periodic variation is larger than the preset variation amplitude threshold, marking that a potential parameter adjustment triggering event occurs in the current monitoring period; Continuously monitoring a plurality of monitoring periods, counting the continuous occurrence times of the potential parameter adjustment triggering events, and if the continuous occurrence times reach a preset triggering time threshold value, determining that the parameter adjustment triggering events occur; recording a starting time stamp, an ending time stamp and a corresponding periodic variation sequence of the parameter adjustment trigger event as complete description information of the parameter adjustment trigger event.
3. The method for monitoring the intra-operative nerve electrophysiology combined with anesthesia depth monitoring according to claim 1, wherein after parameter adjustment is completed, a configuration verification process of the nerve electrophysiology monitoring device is started, and whether the adjusted signal acquisition configuration can stably acquire the intra-operative nerve electrophysiology signal meeting the quality requirement is verified by acquiring a section of test signal, comprising: Controlling the nerve electrophysiological monitoring equipment to acquire test signals with preset time length according to the adjusted signal acquisition configuration, wherein the test signals are nerve electrophysiological signals of a patient in the current state; Executing a signal quality evaluation flow on the test signal, and extracting a noise duty ratio parameter, a signal integrity parameter and a waveform identifiable degree parameter from the test signal, wherein the noise duty ratio parameter is the ratio of noise signal energy to total signal energy, the signal integrity parameter is the ratio of the complete waveform to the total waveform quantity, and the waveform identifiable degree parameter is the ratio of the clearly identifiable characteristic waveform to the total waveform quantity; Comparing the noise duty ratio parameter, the signal integrity parameter and the waveform identifiable degree parameter with corresponding quality thresholds respectively, and judging whether all the parameters meet the quality requirements, wherein the quality requirements are that the noise duty ratio parameter is smaller than the noise threshold, the signal integrity parameter is larger than the integrity threshold, and the waveform identifiable degree parameter is larger than the identifiable degree threshold; if at least one parameter does not meet the quality requirement, the configuration verification is judged to be failed, and the unqualified parameter names and the corresponding parameter values are recorded; And when the configuration verification fails, generating a configuration adjustment optimization suggestion, wherein the configuration adjustment optimization suggestion is a correction direction prompt of an adjustment operation instruction based on the parameters which do not reach the standard.
4. The method for intra-operative neurophysiologic monitoring in combination with anesthesia depth monitoring according to claim 1, wherein the step of performing a waveform feature extraction procedure on each signal segment unit, and identifying a feature waveform form and a feature waveform occurrence time in the signal segment unit includes: Carrying out waveform segmentation processing on the signal segment units, and dividing a continuous waveform signal into a plurality of independent single waveform units, wherein each single waveform unit is a complete waveform period; For each single waveform unit, identifying the starting point time and the ending point time of the waveform, calculating the difference value between the starting point time and the ending point time as the duration time of the single waveform unit, and determining the waveform duration time form of the signal segment unit according to the duration time distribution characteristics of all the single waveform units; Identifying the wave crest moment and the wave trough moment of each single wave form unit, extracting the signal amplitude corresponding to the wave crest moment as the wave crest amplitude, extracting the signal amplitude corresponding to the wave trough moment as the wave trough amplitude, and determining the wave form amplitude form of the signal segment units according to the distribution characteristics of the wave crest amplitude and the wave trough amplitude of all the single wave form units; calculating the starting point moment difference value of two adjacent single waveform units to obtain waveform interval time, and determining the waveform interval form of the signal segment units according to the distribution characteristics of all waveform interval time; The starting time of each single waveform unit is recorded as the characteristic waveform appearance time, and the waveform amplitude form, the waveform duration form, the waveform interval form and the corresponding characteristic waveform appearance time are combined to form a waveform characteristic extraction result of the signal segment unit.
5. The method for intra-operative nerve electrophysiological monitoring combined with anesthesia depth monitoring according to claim 4, wherein the time-series alignment correlation between the waveform and the occurrence time of the waveform of the signal segment unit and the anesthesia depth change trend and trend turning point of the data segment unit within the same time stamp range, and the establishment of the correspondence between the waveform and the anesthesia depth change, comprises: Extracting a characteristic waveform appearance time sequence of the signal segment unit, and converting each characteristic waveform appearance time into a relative time relative to the starting time of the signal segment unit; Extracting a trend turning point sequence of a data segment unit, and converting each trend turning point into a relative moment relative to the starting time of the data segment unit, wherein the data segment unit and the signal segment unit have the same starting time; Comparing the relative time of the occurrence time of the characteristic waveform with the relative time of the trend turning point, identifying the occurrence time of the characteristic waveform and the trend turning point with the time difference within a preset range, and determining the characteristic waveform occurrence time and the trend turning point as potential association pairs; For each potential association pair, analyzing the association of waveform amplitude morphology, waveform duration morphology, waveform interval morphology and anesthesia depth change trend before and after the turning point of the corresponding trend of the corresponding characteristic waveform, and judging whether the characteristic waveform morphology change has an accompanying relationship with the anesthesia depth change trend; determining potential association pairs with accompanying relations as effective association pairs, and recording characteristic waveform forms, characteristic waveform occurrence moments, anesthesia depth change trends and trend turning points in the effective association pairs; And constructing a corresponding relation table of characteristic waveforms and anesthesia depth changes between the signal segment units and the data segment units according to all the effective association pairs.
6. The method for intra-operative neurophysiologic monitoring in combination with anesthesia depth monitoring according to claim 1, wherein said comparing the extracted regular correlation pattern with a preset normal correlation pattern library, identifying whether an abnormal correlation pattern deviating from the normal correlation pattern exists, comprises: A normal association mode subset matched with the current operation type and the basic condition of a patient is called from a preset normal association mode library, and the normal association mode subset comprises corresponding relation templates of characteristic waveform morphological changes and anesthesia depth change trends of various standards; Comparing the characteristic waveform form change parameters and anesthesia depth change trend parameters in the extracted regularity association modes with standard parameters of each corresponding relation template in the normal association mode subset item by item, and calculating parameter deviation degree, wherein the parameter deviation degree is the difference degree between actual parameters and standard parameters; Counting the average value of all parameter deviation degrees, and judging that the regular association mode accords with the normal association mode if the average value is smaller than a preset deviation degree threshold value, and judging that the regular association mode is an abnormal association mode if the average value is larger than or equal to the preset deviation degree threshold value; further analyzing the distribution condition of the deviation degree of each parameter for the regular association mode judged to be the abnormal association mode, and determining main deviation parameters, wherein the main deviation parameters are parameters with the deviation degree exceeding a single parameter deviation threshold; And recording main deviation parameters and corresponding deviation degrees of the abnormal association mode as characteristic description information of the abnormal association mode.
7. An intra-operative neurophysiologic monitoring system incorporating anesthesia depth monitoring, comprising a processor and a memory, the memory being connected to the processor, the memory being for storing a program, instructions or code, the processor being for executing the program, instructions or code in the memory to implement the intra-operative neurophysiologic monitoring method incorporating anesthesia depth monitoring as set forth in any of the preceding claims 1-6.

Description

Intraoperative nerve electrophysiology monitoring method and system combined with anesthesia depth monitoring Technical Field The invention relates to the technical field of operation monitoring, in particular to an intraoperative nerve electrophysiological monitoring method and system combined with anesthesia depth monitoring. Background In the operation process, the monitoring of the nerve function of the patient and the control of the anesthesia depth are key links for ensuring the success of the operation and the safety of the patient. Currently, in the field of intraoperative neurophysiologic monitoring, it is common practice to acquire neurophysiologic signals of a patient by adopting fixed signal acquisition configuration parameters. However, the anesthetic state of the patient during the operation is dynamically changed, and different anesthetic depths can have a significant effect on the neuro-electrophysiologic signals. For example, as the depth of anesthesia increases, nerve conduction velocity may slow, and the amplitude and waveform characteristics of the neuro-electrophysiologic signal may change. While existing anesthesia depth monitoring techniques are capable of acquiring patient anesthesia depth data in real time, these data are typically only used to guide anesthesiologists in adjusting the anesthetic drug dosage to maintain the proper anesthesia depth and are not effectively combined with neuroelectrophysiology monitoring. In the operation process, the nerve electrophysiological monitoring equipment cannot dynamically adjust the signal acquisition configuration according to the actual anesthesia state of the patient, so that the acquired nerve electrophysiological signal may not accurately reflect the actual nerve function state of the patient, thereby affecting the judgment of the operator on the nerve condition of the patient and increasing the operation risk. Therefore, how to effectively combine anesthesia depth monitoring with neuroelectrophysiology monitoring to improve accuracy of intraoperative nerve function monitoring is a technical problem to be solved in the current surgical monitoring field. Disclosure of Invention 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 method for intra-operative neurophysiologic monitoring in combination with anesthesia depth monitoring, the method comprising: collecting intraoperative anesthesia depth data of a patient in real time through anesthesia depth monitoring equipment, wherein the intraoperative anesthesia depth data is a continuous monitoring sequence reflecting the current anesthesia inhibition degree of the patient; starting a dynamic adaptation flow of the nerve electrophysiology monitoring parameters based on the intraoperative anesthesia depth data, and adjusting signal acquisition configuration of the nerve electrophysiology monitoring equipment according to the change characteristics of the intraoperative anesthesia depth data to generate nerve electrophysiology monitoring configuration parameters adapting to the current anesthesia state; controlling the nerve electrophysiological monitoring equipment to continuously collect the intraoperative nerve electrophysiological signals of the patient according to the nerve electrophysiological monitoring configuration parameters, wherein the intraoperative nerve electrophysiological signals are waveform signal sequences reflecting nerve conduction functions; Executing a synchronous correlation analysis flow of the intraoperative nerve electrophysiological signals and the intraoperative anesthesia depth data, and carrying out time sequence correlation processing on the intraoperative nerve electrophysiological signals and the intraoperative anesthesia depth data in the same time window to generate a correlation analysis result; and starting an intraoperative nerve state evaluation flow based on the association analysis result, and generating an intraoperative nerve state evaluation report containing nerve function state indication information by analyzing the corresponding relation between waveform signal characteristics and anesthesia depth change in the association analysis result. In yet another aspect, an embodiment of the present invention further provides an intra-operative neurophysiologic monitoring system in combination with anesthesia depth monitoring, including a processor, a machine-readable storage medium, the machine-readable storage medium being connected to the processor, the machine-readable storage medium storing a program, instructions or code, the processor being configured to execute the program, instructions or code in the machine-readable storage medium, so as to implement the method described above. Based on the above aspects, the embodiment of the invention realizes an omnibearing and dynamic operation monitoring mode by organically combining anesthesia depth m