CN120458518A

CN120458518A - Anesthetic efficacy evaluation method and system based on multiparameter physiological signals

Info

Publication number: CN120458518A
Application number: CN202510839404.8A
Authority: CN
Inventors: 魏霞; 张兵; 周萌
Original assignee: Harbin Medical University
Current assignee: Harbin Medical University
Priority date: 2025-06-23
Filing date: 2025-06-23
Publication date: 2025-08-12

Abstract

The invention relates to the technical field of anesthesia monitoring, in particular to an anesthesia efficacy evaluation method and system based on a multi-parameter physiological signal, wherein the method comprises the steps of acquiring brain waves, blood oxygen saturation, respiratory frequency, blood pressure and heart rate of a monitored person, and determining anesthesia efficiency indexes of the monitored person according to data characteristics of frequency segment curves of a plurality of waveforms corresponding to the brain waves; the method comprises the steps of analyzing respiratory resistance conditions in a current period according to monitoring curves of blood oxygen saturation and respiratory frequency, determining respiratory inhibition indexes of a monitored person by combining anesthesia efficiency indexes, analyzing abnormal change conditions of blood pressure and heart rate according to monitoring curves of the blood pressure and the heart rate, determining anesthesia stress degree of the monitored person, and determining anesthesia efficacy evaluation values in the current period according to the respiratory inhibition indexes and the anesthesia stress degree. The invention overcomes the defect of fusion analysis of static multidimensional physiological parameters to a certain extent, and further improves the accuracy of the anesthetic efficacy evaluation result.

Description

Anesthetic efficacy evaluation method and system based on multiparameter physiological signals

Technical Field

The invention relates to the technical field of anesthesia monitoring, in particular to an anesthesia efficacy evaluation method and system based on a multi-parameter physiological signal.

Background

The use of anesthetics plays a critical role in modern medicine, especially in surgical procedures and pain management. However, drug efficacy assessment and monitoring of anesthetics has been a major challenge in clinical anesthesiology due to individual differences and the effects of various external factors. The existing anesthesia monitoring system collects physiological signals through multi-parameter sensing, and generates characteristics through preprocessing steps such as wavelet denoising, motion artifact removal and the like.

However, the existing evaluation accuracy is limited by the independent analysis of parameters, independent feature extraction processes, neglecting physiological relations among parameters, static fusion defects, incapability of expressing pathological coupling due to linear combination such as the existing BIS index (Bispectral Index, brain electricity double-frequency index) and the like, and incapability of adapting to dynamic pathological states. Therefore, the lack of an effective data fusion strategy in the existing anesthetic efficacy evaluation may result in low accuracy and reliability of anesthetic efficacy evaluation results.

Disclosure of Invention

In order to solve the technical problem of low accuracy of the existing anesthetic efficacy evaluation result, the invention aims to provide an anesthetic efficacy evaluation method and system based on multi-parameter physiological signals, and the adopted technical scheme is as follows:

One embodiment of the invention provides an anesthetic efficacy evaluation method based on a multi-parameter physiological signal, which comprises the following steps:

Acquiring physiological parameters of a plurality of dimensions corresponding to a monitored person in a current period when the monitored person is in a target anesthesia stage, wherein the physiological parameters comprise brain waves, blood oxygen saturation, respiratory rate, blood pressure and heart rate, and the target anesthesia stage is a core stage for evaluating the efficacy of the drugs;

analyzing the sedative depth condition of the current period according to the data characteristics of the frequency segment curves of a plurality of waveforms corresponding to the brain waves, and determining the anesthesia efficiency index of the monitored person in the current period;

Analyzing the respiratory resistance condition of the current period according to the monitoring curve of the blood oxygen saturation and the respiratory frequency, and determining the respiratory inhibition index of the anesthesia efficacy of the current period to the monitored person by combining the anesthesia efficiency index of the current period;

Analyzing abnormal change conditions of the blood pressure and the heart rate according to the monitoring curves of the blood pressure and the heart rate, and determining the anesthesia stress degree of a monitored person in the current period;

and determining an anesthetic efficacy evaluation value in the current period according to the respiratory depression index and the anesthetic stress degree.

Further, analyzing the sedative depth condition of the current period according to the data characteristics of the frequency band curves of the waveforms corresponding to the brain waves, and determining the anesthesia efficiency index of the monitored person in the current period, including:

Performing Fourier transformation on brain waves in the current period, and further extracting curves of frequency bands corresponding to a plurality of waveforms in the brain wave frequency domain to obtain frequency band curves of all the waveforms, wherein the waveforms comprise beta waves and theta waves;

Determining a sedation depth index of a monitored person in the current period according to the difference condition of a previous slope and a next slope in a frequency band curve of the beta wave and the correlation condition between an amplitude data set and a frequency data set of the frequency band curve of the theta wave;

Acquiring BIS values of the monitored person in the current period, and determining anesthesia efficiency indexes of the monitored person in the current period by combining the sedation depth indexes;

wherein the BIS value and the anesthesia efficiency index show a negative correlation, and the sedation depth and the anesthesia efficiency index show a positive correlation.

Further, the determining a sedation depth indicator for the monitored subject over the current period of time includes:

Calculating the slope between two adjacent data points in a frequency band curve of the beta wave to obtain a slope sequence;

Calculating the difference between the previous slope and the next slope in the slope sequence, and taking the average value of all the difference values as the trend reduction degree;

acquiring correlation coefficients of the amplitude data set and the frequency data set;

Determining a sedation depth index of the monitored person in the current period by combining the correlation coefficient and the trend reduction degree;

Wherein the correlation coefficient is inversely correlated with the sedation depth indicator, and the trend reduction levels are positively correlated with the sedation depth indicator.

Further, the analyzing the respiratory resistance condition of the current period according to the monitoring curve of the blood oxygen saturation and the respiratory frequency, and combining the anesthesia efficiency index of the current period, determining the respiratory inhibition index of the anesthesia efficacy to the monitored person in the current period includes:

Analyzing fluctuation conditions of monitoring data according to the monitoring curve of the blood oxygen saturation, and determining a first respiratory depression factor;

Analyzing the distribution condition and the numerical abnormality condition of the respiratory rate data according to the respiratory rate monitoring curve, and determining a second respiratory suppression factor;

Combining the first respiratory depression factor, the second respiratory depression factor and the anesthesia efficiency index, and determining respiratory depression indexes of the anesthesia efficacy to the monitored person in the current period;

Wherein the first respiratory depression factor, the second respiratory depression factor, and the anesthetic efficiency indicator are all positively correlated with the respiratory depression indicator.

Further, the determining the second respiratory depression factor comprises:

Uniformly dividing the monitoring curve of the respiratory rate into a plurality of local time periods, and calculating the quality index of the respiratory rate in all the local time periods of the current time period;

Acquiring a lower limit value of a normal respiratory rate range, analyzing the difference condition of the respiratory rate value of each data point in a respiratory rate monitoring curve and the lower limit value, and determining the numerical value abnormality degree of the respiratory rate in the current period;

Determining a second respiratory depression factor in combination with said abnormality index and said numerical abnormality extent;

Wherein the dysplasmic index is inversely related to the second respiratory depression factor, and the numerical abnormality degree and the second respiratory depression factor are positively related.

Further, the analyzing abnormal changes of the blood pressure and the heart rate according to the monitoring curves of the blood pressure and the heart rate, and determining the anesthesia stress degree of the monitored person in the current period, includes:

obtaining a first blood pressure data subset and a second blood pressure data subset after dividing according to a blood pressure data set of the blood pressure monitoring curve;

determining the abnormal rising degree of the blood pressure according to the blood pressure difference between the first blood pressure data subset and the second blood pressure data subset;

Selecting a heart rate value corresponding to the maximum slope as a target heart rate value by calculating the slope of each two adjacent data points in the heart rate monitoring curve;

Determining the abnormal degree of heart rate performance according to the difference condition between the target heart rate value and the heart rate value of each data point in the heart rate monitoring curve;

determining the anesthesia stress degree of the monitored person in the current period by combining the abnormal rise degree of the blood pressure and the abnormal expression degree of the heart rate;

wherein the degree of abnormal rise in blood pressure and the degree of abnormal heart rate performance are both positively correlated with the degree of anesthesia stress.

Further, the obtaining the divided first blood pressure data subset and the divided second blood pressure data subset according to the blood pressure data set of the blood pressure monitoring curve includes:

Arranging the blood pressure data sets according to a preset sequence to obtain a new blood pressure data set;

calculating the difference value between every two adjacent blood pressure data in the new blood pressure data set, and taking the blood pressure data corresponding to the maximum difference value as a division point;

dividing the new blood pressure data set by using the dividing points to obtain a first blood pressure data subset and a second blood pressure data subset after division.

Further, the determining the anesthetic efficacy evaluation value of the current period according to the respiratory depression index and the anesthetic stress degree includes:

carrying out negative correlation treatment on the anesthesia stress degree to obtain a negative correlation value of the anesthesia stress degree;

And calculating the product of the negative correlation value and the respiratory depression index, normalizing the product to obtain a normalized value, and taking the normalized value as an anesthetic efficacy evaluation value in the current period.

Further, after the anesthetic efficacy evaluation value of the current period is determined, the method further comprises the step of adjusting the current anesthetic dosage according to the anesthetic efficacy evaluation value of the current period;

When the anesthetic efficacy evaluation value is larger than a first evaluation threshold value, determining that the product of the anesthetic efficacy evaluation value and the current anesthetic dosage is a reduction amount of the current anesthetic dosage, and reducing and adjusting the current anesthetic dosage by using the reduction amount;

When the anesthetic efficacy evaluation value is smaller than a second evaluation threshold value, determining that the product of the negative correlation value of the anesthetic efficacy evaluation value and the current anesthetic dose is the increment of the current anesthetic dose, and increasing and adjusting the current anesthetic dose by using the increment;

When the anesthetic efficacy evaluation value is greater than or equal to the second evaluation threshold and less than or equal to the first evaluation threshold, keeping the current anesthetic dose unchanged;

wherein the first evaluation threshold is greater than the second evaluation threshold.

The invention also provides an anesthetic efficacy evaluation system based on the multi-parameter physiological signals, which comprises a processor and a memory, wherein the processor is used for processing instructions stored in the memory so as to realize an anesthetic efficacy evaluation method based on the multi-parameter physiological signals.

The invention has the following beneficial effects:

in the existing anesthetic efficacy evaluation method, most of physiological parameters are independently analyzed, an effective data fusion strategy is lacked, but the accuracy and reliability of anesthetic efficacy evaluation results are limited due to the fact that the existing multidimensional physiological data fusion realized by machine learning are adopted, so that the invention provides an anesthetic efficacy evaluation method and system based on multi-parameter physiological signals.

Firstly, obtaining the physiological parameters of a plurality of dimensions corresponding to a core stage of evaluating the drug effect, obtaining the physiological parameters of the core stage to provide more reliable data support, secondly, analyzing the sedative depth condition of the current period through brain wave data, wherein the determined anesthesia efficiency index can comprehensively reflect sedative depth characteristics of a plurality of aspects, the numerical accuracy is stronger, then, determining the respiratory depression index of a monitored person through combining the blood oxygen saturation and the respiratory frequency with the anesthesia efficiency index, wherein the respiratory depression index can reflect the excessive risk degree of the current anesthesia agent, combining the physiological parameters of different dimensions to analyze, considering the correlation characteristics among the physiological parameters of different dimensions, obviously improving the numerical accuracy of the respiratory depression index, and then, for the anesthesia drug effect evaluation, not only considering the condition of excessive anesthesia dose, but also considering the physical stress condition caused by the insufficient anesthesia agent, so that the anesthesia stress degree of the monitored person in the current period needs to be determined by combining the blood pressure and the heart rate, and finally, quantifying the respiratory depression index and the anesthesia stress degree determined in the two aspects, and statically evaluating the drug effect evaluation value of the current period, thereby effectively improving the physiological parameters of the anesthesia effect evaluation result, and further overcoming the accuracy of the analysis of the multiple dimensions. Meanwhile, reliable data support is provided for anesthesia management, timely adjustment of anesthesia dosage in the operation process is facilitated, and low risk requirements in the operation process are guaranteed.

Drawings

In order to more clearly illustrate the embodiments of the invention or the technical solutions and advantages of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are only some embodiments of the invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.

FIG. 1 is a flowchart showing steps of an anesthetic efficacy evaluation method based on a multi-parameter physiological signal according to an embodiment of the present invention;

FIG. 2 is a schematic diagram of an electroencephalogram signal frequency domain curve;

FIG. 3 is a schematic view of a blood oxygen saturation monitoring curve;

FIG. 4 is a schematic view of a patient respiratory rate monitoring profile during surgery;

fig. 5 is a schematic diagram of a heart rate monitoring curve.

Detailed Description

In order to further describe the technical means and effects adopted by the present invention to achieve the preset purpose, the following detailed description is given below of the specific implementation, structure, features and effects of the technical solution according to the present invention with reference to the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" means that the embodiments are not necessarily the same. Furthermore, the particular features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.

Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

The application scenario aimed by the invention can be:

The invention mainly uses anesthesia management in the operation process as an application scene of anesthesia efficacy evaluation. In particular, in modern operating rooms, anesthesiologists often need to monitor patient vital signs in real time to ensure the effectiveness and safety of the anesthetic drugs. The system integrates various sensors, can monitor physiological parameters of different dimensions in real time, provides comprehensive state information of patients, evaluates the efficacy of the anesthetic drug, analyzes clinical effects, and needs an anesthesiologist to adjust the drug dosage in time if the efficacy evaluation is abnormal. However, most of physiological signals in the existing evaluation methods are independently analyzed, physiological correlations among parameters are ignored, and an effective data fusion strategy is lacked, so that accurate anesthetic efficacy evaluation is not facilitated.

In order to perform more accurate anesthetic efficacy evaluation, an embodiment of the present invention provides an anesthetic efficacy evaluation method based on a multiparameter physiological signal, as shown in fig. 1, comprising the steps of:

S1, acquiring physiological parameters of a plurality of dimensions corresponding to a monitored person in a current period when the monitored person is in a target anesthesia stage.

Here, the monitored person is a patient who needs to be subjected to anesthesia treatment, the target anesthesia stage is a core stage for evaluating the efficacy of drugs, and the physiological parameters include brain waves, blood oxygen saturation, respiratory rate, blood pressure and heart rate, and the current period is a current period in the core stage, such as 10 minutes at present.

The anesthetic process is generally divided into three phases, an induction phase, a maintenance phase and a recovery phase. The maintenance period refers to dynamic adjustment in operation to stabilize the anesthesia state, and is a core stage suitable for evaluating the drug effect based on multi-parameter physiological signals according to the existing literature and clinical requirements. Of course, the practitioner may also collect physiological parameters of different dimensions in real time to perform the anesthetic efficacy evaluation, which is not particularly limited herein.

The maintenance period needs to balance the sedation depth, the analgesia effect and the physiological stability in real time, and the intensity and the frequency of the operation stimulus (such as skin cutting and organ traction) are continuously changed, so as to monitor the multidimensional physiological data. For example, multi-mode general anesthesia requires simultaneous monitoring of antinociception and unconsciousness levels, and conventional single physiological parameters are not satisfactory for dynamic needs in surgery. Therefore, physiological parameters of several dimensions of the monitored person during the current period of the maintenance period need to be acquired.

In this embodiment, a suitable monitoring device is selected to ensure that the device is able to monitor the desired physiological parameter. For example, ECG electrodes are placed on the chest of a monitored person according to standard positions to ensure good contact between the electrodes and the skin, a probe of a pulse oximeter is placed on the fingertips, earlobes or toes of the patient to ensure that light can pass through the skin to detect blood oxygen saturation, a wearable respiration sensor is used to monitor the respiration rate, or a self-contained respiration monitoring function of an anesthesia machine is used.

It should be noted that, before the monitoring device is turned on, the monitoring device needs to be checked to ensure its normal operation and perform calibration, so as to improve the accuracy of the values of the collected physiological parameters in different dimensions.

Thus, the embodiment obtains the physiological parameters of each dimension corresponding to the current period when the monitored person is in the maintenance period.

S2, analyzing the sedative depth condition of the current period according to the data characteristics of the frequency band curves of a plurality of waveforms corresponding to the brain waves, and determining the anesthesia efficiency index of the monitored person in the current period.

Here, the sedative depth condition of the monitored person has important clinical significance for the evaluation of the anesthetic efficacy, so that the sedative depth condition of the current period needs to be analyzed to determine the anesthetic efficiency index, so as to provide data support for the accurate determination of the subsequent anesthetic efficacy evaluation value.

As an exemplary embodiment, the above step S2 may be implemented by steps S21 to S23 (not shown in the drawings):

S21, carrying out Fourier transformation on the brain waves in the current time period, and further extracting curves of frequency bands corresponding to a plurality of waveforms in the brain wave frequency domain to obtain frequency band curves of all the waveforms.

Brain wave data is typically shown in the form of a waveform image, with the abscissa generally representing time and the ordinate generally representing voltage in mV. Also, brain wave data generally includes a plurality of waveforms such as an alpha wave, a beta wave, a delta wave, and a theta wave.

In this embodiment, the brain wave in the current period is converted into the frequency domain by fourier transformation to obtain the brain wave frequency domain, and then the bands with different frequencies, such as an α wave (8-12 Hz), a β wave (12-30 Hz), a δ wave (1-4 Hz) and a θ wave (4-8 Hz), are extracted from the brain wave frequency domain. Each waveform may represent a certain physical logic, and this embodiment only analyzes the data characteristics of the frequency band curves of the β wave and the θ wave.

The frequency domain curve of the brain electrical signal is shown in fig. 2, the abscissa of the graph in fig. 2 is the frequency, the ordinate is the amplitude, and the frequencies of different waveforms are not randomly set, which is the existing setting method and is equivalent to the known technology. In addition, the implementation process of fourier transform is the prior art, and is not in the scope of the present invention, and will not be described in detail herein.

S22, determining a sedation depth index of the monitored person in the current period according to the difference condition of the previous slope and the next slope in the frequency segment curve of the beta wave and the correlation condition between the amplitude data set and the frequency data set of the frequency segment curve of the theta wave.

Under the deep anesthesia state, the beta wave amplitude shows a decreasing trend along with the deepening of the sedation depth, and the anesthesia exerts the drug effect along with the deepening of the sedation depth, and the theta wave amplitude shows a decreasing trend along with the increasing of the frequency, namely on the premise of larger sedation depth, the correlation between the theta wave amplitude and the frequency is poor. Therefore, by analyzing the frequency band curves of the beta wave and the theta wave, the sedation depth index of the monitored person in the current period can be quantified and determined.

As an exemplary embodiment, the above step S22 may be implemented by steps 221 to S224 (not shown in the drawings):

s221, calculating the slope between two adjacent data points in the frequency band curve of the beta wave to obtain a slope sequence.

S222, calculating the difference between the previous slope and the next slope in the slope sequence, and taking the average value of all the differences as the trend reduction degree.

S223, obtaining the correlation coefficient of the amplitude data set and the frequency data set.

S224, determining the sedation depth index of the monitored person in the current period by combining the correlation coefficient and the trend reduction degree.

As an example, the sedation depth index described above may be achieved by the following calculation formula:

Wherein f _e represents a sedative depth index of a monitored person in a current period, e represents the current period, H represents a correlation coefficient of an amplitude data set and a frequency data set of a theta wave, n represents the number of slopes in a slope sequence corresponding to a frequency segment curve of a beta wave, k _i represents an ith slope in the slope sequence, k _i+1 represents an (i+1) th slope in the slope sequence, The degree of trend reduction corresponding to the frequency band curve of the beta wave is shown.

The correlation coefficient may be an absolute value of the pearson correlation coefficient, and if the correlation coefficient is zero, a non-zero constant, for example, 0.01, may be added at the position of the denominator to avoid the situation that the denominator is zero, and in addition, the calculation process of the pearson correlation coefficient is in the prior art, which is not in the scope of the present invention, and will not be described in detail herein.

S23, acquiring BIS values of the monitored person in the current period, and determining anesthesia efficiency indexes of the monitored person in the current period by combining the sedation depth indexes.

Here, the anesthesia efficiency index refers to the degree of anesthesia of the monitored subject receiving the anesthesia treatment in the current period.

Besides the frequency band curves of the beta wave and the theta wave can be used for analyzing the sedation depth, the BIS value of the monitored person in the current period can also be used for representing the sedation depth, the lower the BIS value is, the deeper the sedation is, so the BIS value and the anesthesia efficiency index show negative correlation, and the sedation depth determined by the frequency band curves of the beta wave and the theta wave and the anesthesia efficiency index show positive correlation. Here, positive correlation means that the corresponding index shows an increasing trend as the data increases, and negative correlation means that the corresponding index shows a decreasing trend as the data increases. Therefore, to improve the numerical accuracy of the anesthesia efficiency index, the BIS value is also used as one of the key calculation factors for analyzing the sedation depth for determining the anesthesia efficiency index.

In this embodiment, the brain activity of the monitored person in the current period is continuously recorded by the BIS monitoring device, and the BIS value is calculated, and the BIS value in the current period is recorded as T.

As an example, the above step S23 may be implemented by the following calculation formula:

Wherein F _e represents the anesthesia efficiency index of the monitored person in the current period, F _e represents the sedation depth index of the monitored person in the current period, and T represents the BIS value of the current period.

In general, BIS value is not zero, so the embodiment does not need to consider the special case of zero of denominator, BIS value is a common index, which comprehensively considers the information of various frequency waveforms to calculate an index value of 0 to 100, and generally BIS value is between 40 and 60 to represent proper anesthesia depth, and the lower BIS value represents deeper sedation.

To this end, the present example obtains an anesthesia efficiency indicator that characterizes the depth of sedation of a monitored subject undergoing anesthesia treatment over a current period of time.

In the process of anesthesia management in operation, the higher the anesthesia efficiency of a patient is, the more the anesthesia efficiency of the patient meets the operation requirement of the patient, the more or less anesthesia is likely to influence actual operation risks, and in the anesthesia process, errors can be caused in the evaluation of the anesthesia efficacy due to individual variability of monitored people. Therefore, the individual difference characteristics of the physiological state of the subject are analyzed in combination with the calculated anesthesia efficiency index, and the following step S3 is performed.

S3, analyzing the respiratory resistance condition of the current period according to the monitoring curve of the blood oxygen saturation and the respiratory frequency, and determining the respiratory inhibition index of the anesthesia efficacy of the current period to the monitored person by combining the anesthesia efficiency index of the current period.

The ideal anesthetic state is one in which the monitored remains sufficiently sedated during surgery to avoid pain and discomfort, while avoiding complications resulting from excessive sedation. The respiratory depression index determined by the individuation state analysis of the monitored person can improve the safety of anesthetic medication, reduce adverse reactions and can more pertinently manage the anesthesia of the patient in the operation process.

As an exemplary embodiment, the above step S3 may be implemented by steps S31 to S33 (not shown in the drawings):

S31, analyzing fluctuation conditions of monitoring data according to a monitoring curve of blood oxygen saturation, and determining a first respiratory depression factor.

In this embodiment, the fluctuation variance is calculated according to the monitoring curve of the blood oxygen saturation, and the fluctuation variance can be used to measure the fluctuation degree of the monitoring curve of the blood oxygen saturation, which indicates that the blood oxygen saturation continuously fluctuates in the current period, so as to indicate that the respiration or circulation of the monitored person has a problem, so that the fluctuation variance can be used as the first respiration suppression factor. Of course, other methods may be used by the practitioner to analyze the fluctuation of the monitored curve, such as calculating standard deviation.

The schematic diagram of the blood oxygen saturation monitoring curve is shown in fig. 3, the abscissa is time, and the ordinate is blood oxygen saturation, and whether the fluctuation variance or the fluctuation standard deviation of the monitoring curve is calculated, both the fluctuation variance and the fluctuation standard deviation are not in the protection scope of the present invention, and are not described in detail herein.

S32, analyzing the distribution condition and the numerical abnormality condition of the respiratory rate data according to the respiratory rate monitoring curve, and determining a second respiratory suppression factor.

The more uniform the distribution of the respiratory frequency data is, the more the respiration of the monitored person can be in a regular state, otherwise, the respiration of the monitored person is in a state not conforming to the expected state, and when the respiratory frequency data is obviously lower than the normal range, the respiratory inhibition condition is indicated.

As an exemplary embodiment, the above step S32 may be implemented by steps S321 to S323 (not shown in the drawings):

S321, uniformly dividing a monitoring curve of the respiratory rate into a plurality of local time periods, and calculating the quality index of the respiratory rate in all the local time periods of the current time period.

For the monitoring curve of the respiratory rate, the chest and abdomen motion can be converted into resistance or capacitance change through the wearable respiratory sensor, and an analog voltage signal is output so as to reflect the respiratory rate characteristic of the current period. The schematic diagram of the respiratory rate monitoring curve of the patient during the operation is shown in fig. 4, wherein in fig. 4, the abscissa represents time, and the ordinate represents pressure value.

In this embodiment, the quality index may represent the distribution of the respiratory rate data in all the partial segments, when the quality index is more similar to 1, the data points on the monitoring curve representing the respiratory rate represent a uniform distribution, i.e. the respiratory state of the monitored person represents a regular state, whereas when the quality index is more similar to 0, the data points on the monitoring curve representing the respiratory rate represent a non-uniform distribution, which indicates that the respiratory state of the monitored person does not conform to the expected state.

The number of the local time periods may be set to 10, and the calculation process of the quality index is the prior art, which is not in the scope of the present invention and will not be described in detail herein.

S322, obtaining the lower limit value of the normal respiratory rate range, analyzing the difference condition of the respiratory rate value and the lower limit value of each data point in the respiratory rate monitoring curve, and determining the numerical value abnormality degree of the respiratory rate in the current period.

Here, the lower limit value of the normal respiratory rate range may be set by the practitioner based on historical experience, and is not particularly limited here.

In this embodiment, the difference between the lower limit value and the respiratory rate value of each data point is calculated, and the average value of all the differences is used as the numerical abnormality degree. Of course, the ratio of the lower limit value of each data point to the respiratory rate value can be calculated, and the average value of all the ratios can be used as the numerical abnormality degree. The calculation method of the numerical value abnormality degree is not particularly limited here.

S323, determining a second respiratory depression factor by combining the heteroplasmy index and the numerical abnormality degree.

Here, the dysplasmatic index is inversely related to the second respiratory depression factor, and the numerical abnormality degree and the second respiratory depression factor are positively related.

As an example, the second respiratory depression factor may be implemented by the following calculation formula:

Where g represents a second respiratory depression factor, IQV represents a quality index, a' represents a non-zero constant, such as 00.01, V ^′ represents a lower limit of a normal respiratory rate range, V represents a respiratory rate value for each data point in a respiratory rate monitoring curve, The average value of the ratio of the respiratory rate value to the lower limit value of each data point in the monitoring curve representing the respiratory rate is recorded as the numerical abnormality degree of the respiratory rate in the current period.

For the degree of numerical anomaly,The greater the degree of numerical abnormality, the lower the respiratory rate of the current period is significantly below the normal range, indicating that the respiratory depression condition of the monitored person in the current period is likely to be caused by the excessive anesthetic.

S33, combining the first respiratory depression factor, the second respiratory depression factor and the anesthesia efficiency index, and determining the respiratory depression index of the anesthesia efficacy to the monitored person in the current period.

Here, the first respiratory depression factor, the second respiratory depression factor, and the anesthetic efficiency index all show positive correlation with the respiratory depression index. The respiratory depression index refers to the degree of dyspnea of a monitored person caused by anesthetic efficacy, and the larger the respiratory depression index is, the larger the risk of overdosing of the current anesthetic is.

In this embodiment, the first respiratory depression factor indicating the degree of fluctuation of blood oxygen saturation, the second respiratory depression factor indicating the respiratory rate distribution and the numerical value, and the anesthetic efficiency index indicating the anesthetic depth condition may be multiplied, and the multiplied product may be used as the respiratory depression index of the anesthetic efficacy for the monitored person in the current period.

Of course, on the premise of defining the logic relationship between each calculation factor and the calculation result, other data fusion modes can be adopted to combine the first respiratory suppression factor, the second respiratory suppression factor and the anesthesia efficiency index together so as to quantify and determine the respiratory suppression index of the anesthesia efficacy to the monitored person in the current period. For example, the first respiratory depression factor, the second respiratory depression factor, and the anesthetic efficiency index are normalized and then added.

As an example, the calculation formula of the anesthetic efficacy for the respiratory depression index of the monitored person in the current period may be:

G _e＝F_e Xg XU, wherein G _e represents respiratory inhibitory index of anesthetic effect on monitored person in current period, F _e represents anesthetic efficiency index in current period, G represents second respiratory inhibitory factor, and U represents first respiratory inhibitory factor.

The anesthetic efficiency index indicates that, when the anesthetic efficiency is high, the respiratory disorder is likely to occur, which means that the drug effect of the anesthetic is high for the respiratory depression of the monitored person, and when the respiratory depression of the monitored person is high, the dosage adjustment is required in time for the anesthesia management stage.

Thus, the embodiment obtains the respiratory depression index of the anesthetic efficacy to the monitored person in the current period.

In the anesthetic administration phase, the higher the respiratory depression of the anesthetic to the monitored person, the more likely the anesthetic is excessive during the actual anesthetic dosage treatment, but the anesthetic efficacy during the anesthetic administration cannot be accurately evaluated only according to the change of the respiratory rate, so this embodiment leads to step S4 described below.

S4, analyzing abnormal change conditions of the blood pressure and the heart rate according to the monitoring curves of the blood pressure and the heart rate, and determining the anesthesia stress degree of the monitored person in the current period.

In the actual operation process, after the anesthetic exerts the drug effect, the monitored person can lose the perception capability of body pain, and the physiological signal can tend to be in a more stable state. Therefore, if the physiological signal of the subject fluctuates or abnormally rises with the progress of the operation, the subject may be prompted to feel pain or stress during the operation, and additional anesthesia or adjustment of the anesthesia depth may be required.

As an exemplary embodiment, the above step S4 may be implemented by steps S41 to S45 (not shown in the drawings):

S41, obtaining a first blood pressure data subset and a second blood pressure data subset after division according to a blood pressure data set of a blood pressure monitoring curve.

As an exemplary embodiment, the above step S41 may be implemented by steps S411 to S413 (not shown in the drawings):

S411, arranging the blood pressure data sets according to a preset sequence to obtain a new blood pressure data set.

S412, calculating the difference value between every two adjacent blood pressure data in the new blood pressure data set, and taking the blood pressure data corresponding to the maximum difference value as a division point.

S413, dividing the new blood pressure data set by using the dividing points to obtain a first blood pressure data subset and a second blood pressure data subset after division.

In this embodiment, in order to sort the blood pressure data sets, the blood pressure data sets are sorted. The predetermined sequence may be from small to large or from large to small. When the blood pressure data is arranged in the order from small to large, the first blood pressure data subset is constituted by blood pressure data having a small blood pressure change corresponding to the left part of the new blood pressure data set, and the second blood pressure data subset is constituted by blood pressure data having a large blood pressure change corresponding to the right part of the new blood pressure data set.

Yet another exemplary embodiment includes:

Counting the number of blood pressure data sets, acquiring half of smaller blood pressure data from the blood pressure data sets to form a first blood pressure data subset, and forming the rest blood pressure data in the blood pressure data sets to form a second blood pressure data subset.

It should be noted that, the first blood pressure data subset and the second blood pressure data subset are obtained to analyze the overall trend of the blood pressure data.

S42, determining the abnormal rising degree of the blood pressure according to the blood pressure difference between the first blood pressure data subset and the second blood pressure data subset.

In this embodiment, the average blood pressure value of the first subset of blood pressure data is calculated, and the average blood pressure value of the second subset of blood pressure data is calculated, and the abnormal rising degree of blood pressure can be obtained by subtracting the smaller average blood pressure value from the larger average blood pressure value.

When the abnormal rise degree of the blood pressure is larger, the abnormal rise of the blood pressure in the current period is indicated, and the monitored person can be prompted to feel pain or stress reaction in the operation, and the anesthesia stress degree is larger. Therefore, the degree of abnormal rise of blood pressure is one of the key calculation factors for calculating the degree of anesthesia stress.

S43, calculating the slope of every two adjacent data points in the heart rate monitoring curve, and selecting the heart rate value corresponding to the maximum slope as the target heart rate value.

In this embodiment, in the heart rate monitoring curve, a slope value may be formed between two adjacent data points, where the value of the slope value is positive and negative, the positive slope indicates that the heart rate is in an ascending trend, and the negative slope indicates that the heart rate is in a descending trend. The schematic diagram of the heart rate monitoring curve is shown in fig. 5, in which in fig. 5, the abscissa is time, and the ordinate is heart rate.

It should be noted that, in this embodiment, the maximum slope is a positive value.

S44, determining the abnormal degree of heart rate performance according to the difference between the target heart rate value and the heart rate value of each data point in the heart rate monitoring curve.

As an example, the average value of the difference between the target heart rate value and the heart rate value of each data point in the heart rate monitoring curve is calculated, and the average value of the difference is taken as the abnormal degree of heart rate performance.

Of course, the difference between the heart rate values of each data point of the target heart rate value can also be quantified by means of a ratio, which is not particularly limited herein.

The greater the abnormal heart rate performance degree, the more obvious the heart rate of the monitored person changes in the operation process, and the more the monitored person can feel pain or stress reaction in the operation, the greater the anesthesia stress degree. Therefore, the degree of abnormality in heart rate performance is also one of the key calculation factors for calculating the degree of anesthesia stress.

S45, combining the abnormal rising degree of the blood pressure and the abnormal degree of the heart rate, and determining the anesthesia stress degree of the monitored person in the current period.

Wherein, the abnormal rising degree of blood pressure and the abnormal degree of heart rate performance are positively correlated with the anesthesia stress degree.

As an example, the product of the degree of abnormal rise in blood pressure and the degree of abnormal manifestation of heart rate is taken as the degree of anesthesia stress of the monitored person in the current period.

Of course, the anesthesia stress performance factors of different aspects, namely, the abnormal rising degree of blood pressure and the abnormal degree of heart rate performance, can be combined together by adding and summing, and are not particularly limited herein.

The greater the anesthesia stress level, the stronger the stress of the monitored person, namely, the condition that the anesthesia metering is possibly insufficient, further increasing the risk in the operation process.

Thus far, the present embodiment determines the degree of anesthesia stress of the monitored person in the current period.

S5, determining an anesthetic efficacy evaluation value in the current period according to the respiratory depression index and the anesthetic stress degree.

Here, the anesthetic efficacy evaluation value refers to the effect evaluation of the current anesthetic dose. The anesthetic efficacy evaluation value does not have monotonicity, and can be used for indicating that the clinical effect of the anesthetic is good when the anesthetic efficacy evaluation value is in a reasonable range, indicating that the current anesthetic dosage is at excessive risk when the anesthetic efficacy evaluation value is abnormally large, and indicating that the current anesthetic dosage is at insufficient risk when the anesthetic efficacy evaluation value is abnormally small.

As an exemplary embodiment, the above step S5 may be implemented by steps S51 to S52 (not shown in the drawings):

S51, carrying out negative correlation treatment on the anesthesia stress degree to obtain a negative correlation value of the anesthesia stress degree.

S52, calculating the product of the negative correlation value and the respiratory depression index, normalizing the product to obtain a normalized value, and taking the normalized value as an anesthetic efficacy evaluation value in the current period.

As an example, the calculation formula of the anesthetic efficacy evaluation value of the current period may be:

Wherein p _e represents an anesthetic efficacy evaluation value in the current period, th represents a normalization function such as maximum and minimum normalization, G _e represents an index of respiratory depression of the anesthetic efficacy to the monitored in the current period, X _e represents an anesthetic stress degree of the monitored in the current period, A negative correlation value representing the degree of anesthesia stress.

The anesthetic effect evaluation value p _e is abnormally large, the anesthetic effect is strong for the respiratory depression of a monitored person, the condition that the anesthetic dose is possibly excessive for the monitored person in the current period is indicated, and the condition that the monitored person has an apnea and an unstable circulatory system caused by insufficient anesthetic dose is avoided in the monitoring process, the anesthetic effect evaluation value p _e is abnormally small, the condition that the anesthetic dose is excessive for the monitored person in the current period is indicated, but the condition that the monitored person has a strong anesthetic stress in the monitoring process is indicated, and the condition that the monitored person has an apnea and an unstable circulatory system caused by insufficient anesthetic dose is likely to occur.

So far, based on the characteristics of physiological parameters in different dimensions, data fusion analysis is carried out on the physiological parameters in different dimensions, and the anesthetic efficacy evaluation value in the current period with higher accuracy is obtained.

The anesthetic efficacy evaluation value is abnormally large or abnormally small, is relatively poor for the evaluation result of the anesthetic efficacy in the operation, and needs to be timely adjusted for the poor anesthesia management stage, including adjusting the current anesthetic dosage according to the anesthetic efficacy evaluation value in the current period.

Further, the method specifically comprises the following steps:

When the anesthetic efficacy evaluation value is larger than the first evaluation threshold value, the fact that the current anesthetic dose possibly has excessive risk is indicated, the product of the anesthetic efficacy evaluation value and the current anesthetic dose can be determined to be the reduction amount of the current anesthetic dose, and the reduction amount is utilized to reduce and adjust the current anesthetic dose.

The greater the anesthetic efficacy evaluation value is greater than the first evaluation threshold value, the greater the risk of overdosing is, and the greater the reduction degree of the current anesthetic dosage is, so that the product of the anesthetic efficacy evaluation value and the current anesthetic dosage can be the reduction amount of the current anesthetic dosage.

When the anesthetic efficacy evaluation value is smaller than the second evaluation threshold value, the risk that the current anesthetic dose is possibly insufficient is indicated, the product of the negative correlation value of the anesthetic efficacy evaluation value and the current anesthetic dose is determined to be the increment of the current anesthetic dose, and the current anesthetic dose is increased and adjusted by the increment.

The smaller the anesthetic efficacy evaluation value is smaller than the second evaluation threshold value, the larger the insufficient risk is, and the larger the current anesthetic dose is increased, so that the anesthetic efficacy evaluation value needs to be subjected to negative correlation treatment and then multiplied by the current anesthetic dose to obtain the current anesthetic dose increase. Here, the value of the anesthetic efficacy evaluation value ranges from 0 to 1, so the negative correlation value of the anesthetic efficacy evaluation value may be equal to the difference between 1 and the anesthetic efficacy evaluation value.

When the anesthetic efficacy evaluation value is greater than or equal to the second evaluation threshold value and less than or equal to the first evaluation threshold value, the anesthetic dosage is in a normal range, the current anesthetic dosage is kept unchanged, excessive risks of the anesthetic dosage in the anesthesia management process can be avoided, the stress response of the patient in the operation can be avoided, and the smooth operation is ensured.

It should be noted that, the anesthetic efficacy evaluation value may be used to evaluate whether there is an excessive or insufficient risk of the anesthetic dose, and the practitioner may adaptively adjust the current anesthetic dose according to a priori knowledge, and the adjustment manner of the anesthetic dose is not specifically limited herein.

Wherein the first evaluation threshold is greater than the second evaluation threshold, the first evaluation threshold may be set to 0.7, and the second evaluation threshold may be set to 0.4. The first evaluation threshold and the second evaluation threshold may be set by the practitioner according to the tolerance of the monitored person to the anesthetic, and the setting of the evaluation thresholds is not particularly limited herein, but may be different depending on the tolerance of the monitored person to the anesthetic.

By monitoring the change behavior of the physiological signal of the patient, the safety and the anesthesia effectiveness of the monitored person receiving the anesthesia treatment can be ensured by adopting a reasonable adjustment strategy.

Another embodiment of the present invention provides an anesthetic efficacy evaluation system based on a multi-parameter physiological signal, comprising a processor and a memory, wherein the processor is configured to process instructions stored in the memory to implement an anesthetic efficacy evaluation method based on a multi-parameter physiological signal as described above.

Although the present invention has been described in detail with reference to the foregoing embodiments, it should be understood by those skilled in the art that the foregoing embodiments may be modified or equivalents may be substituted for some of the features thereof, and that the modification or substitution does not depart from the scope of the embodiments of the present invention.

Claims

1. An anesthetic efficacy evaluation method based on multiparameter physiological signals is characterized by comprising the following steps of:

2. The method for evaluating the anesthetic efficacy based on the multi-parameter physiological signals according to claim 1, wherein the analyzing the sedative depth condition of the current period according to the data characteristics of the frequency band curves of the waveforms corresponding to the brain waves, determining the anesthetic efficiency index of the monitored person in the current period comprises:

3. The method of claim 2, wherein determining the sedation depth indicator for the monitored subject during the current period comprises:

4. The method for evaluating the anesthetic efficacy based on the multi-parameter physiological signals according to claim 1, wherein the analyzing the respiratory resistance of the current period according to the monitoring curve of the blood oxygen saturation and the respiratory rate, and the determining the respiratory inhibition index of the anesthetic efficacy to the monitored person in the current period in combination with the anesthetic efficiency index of the current period, comprises:

5. The method for evaluating the efficacy of anesthesia based on a multiparameter physiological signal according to claim 4, wherein said determining a second respiratory depression factor comprises:

6. The method for evaluating the anesthetic efficacy based on the multi-parameter physiological signals according to claim 1, wherein the analyzing abnormal changes of the blood pressure and the heart rate according to the monitoring curve of the blood pressure and the heart rate, determining the anesthetic stress degree of the monitored person in the current period, comprises:

7. The method of claim 6, wherein the obtaining the divided first blood pressure data subset and second blood pressure data subset according to the blood pressure data set of the blood pressure monitoring curve comprises:

8. The method for evaluating the anesthetic efficacy based on the multi-parameter physiological signal according to claim 1, wherein the determining the anesthetic efficacy evaluation value of the current period according to the respiratory depression index and the anesthetic stress degree comprises:

9. The method for evaluating the anesthetic efficacy based on the multi-parameter physiological signal according to claim 1, further comprising adjusting the current anesthetic dosage according to the anesthetic efficacy evaluation value of the current period after determining the anesthetic efficacy evaluation value of the current period;

10. An anesthetic efficacy evaluation system based on a multiparameter physiological signal, characterized by comprising a processor and a memory, said processor being adapted to process instructions stored in said memory to implement an anesthetic efficacy evaluation method based on a multiparameter physiological signal as claimed in any of the claims 1-9.