Anesthesia efficacy evaluation system and method based on artificial intelligence

Abstract

The present invention discloses an anesthesia drug effect evaluation system and method based on artificial intelligence, belonging to the field of drug effect evaluation, including: collecting all heart rate data of a patient during the anesthesia process, dividing time nodes for the entire process of the patient from the anesthesia induction period to the anesthesia recovery period, and constructing a time node heart rate data set; calculating the average value of the time node heart rate data and calculating the heart rate change rate; calculating the comprehensive heart rate value and the comprehensive heart rate change degree; designing and analyzing the evaluation value based on the heart rate change rate and the comprehensive heart rate change degree; the present invention realizes comprehensive and phased monitoring and analysis of the heart rate by collecting the heart rate data during the anesthesia process and combining the time node heart rate data set; further calculating the comprehensive heart rate value and its change degree, accurately reflecting the influence of anesthetic drugs on the heart rate at different stages; quantitatively evaluating the anesthesia effect by designing the evaluation value and combining with a preset threshold, making anesthesia management more accurate and safe.

Description

Technical Field

[0001] The present invention relates to the technical field of drug efficacy evaluation, and particularly to an artificial intelligence-based anesthesia drug efficacy evaluation system and method. Background Art

[0002] In modern surgical and medical procedures, anesthesia is one of the important means to ensure the safety and comfort of patients. With the development of medical technology, the types and usage methods of anesthetic drugs have become increasingly diverse, and the evaluation of anesthesia effects has become more complex and important. Traditional anesthesia evaluation mainly relies on the experience of anesthesiologists and some physiological indicators of patients, such as heart rate, blood pressure, and respiratory rate. However, due to individual differences and environmental factors, relying solely on these indicators cannot fully and accurately reflect the actual effects of anesthetic drugs. With the rapid development of artificial intelligence (AI) technology, AI-based anesthesia drug efficacy evaluation methods have gradually become a research hotspot. These new methods can more precisely evaluate the effects of anesthetic drugs and improve the safety and effectiveness of anesthesia by analyzing a large amount of physiological data collected during the patient's surgery and combining machine learning and deep learning algorithms.

[0003] However, the existing AI-based anesthesia drug efficacy evaluation methods still have some deficiencies. First, most methods only focus on single or a few physiological indicators and ignore the complexity and multi-dimensionality of the human physiological system. Second, most current models are based on static data analysis and lack the ability to dynamically process time series data, making it difficult to accurately reflect the changes in the patient's physiological state during anesthesia. In addition, since the training of the models in the existing methods depends on a large amount of labeled data, the quality of the data and the accuracy of the labeling also have a significant impact on the evaluation results. In practical applications, how to analyze multi-dimensional data in real-time and efficiently during anesthesia and then give accurate anesthesia drug efficacy evaluation results is a major challenge faced by the existing technologies. Summary of the Invention

[0004] The purpose of the present invention is to provide an artificial intelligence-based anesthesia drug efficacy evaluation system and method to solve the problems raised in the above background art.

[0005] To solve the above technical problems, the present invention provides the following technical solutions:

[0006] An artificial intelligence-based method for evaluating anesthetic efficacy, the method comprising the following steps: constructing a set of heart rate sensors, collecting all heart rate data detected and recorded by the heart rate sensors during the anesthesia of the patient, and constructing a heart rate data set; dividing the entire process of the patient from the anesthesia induction period to the anesthesia recovery period into time nodes, and constructing a time node heart rate data set based on the heart rate data set; calculating the average value of the time node heart rate data detected by the same heart rate sensor at the same time node, and calculating the heart rate change rate of the same heart rate sensor at different time nodes based on the average value; calculating the comprehensive heart rate value of the heart rate data collected by all heart rate sensors at the same time node, and calculating the comprehensive heart rate change degree at different time nodes; designing and analyzing an evaluation value based on the heart rate change rate and the comprehensive heart rate change degree.

[0007] As a preferred embodiment of the artificial intelligence-based method for evaluating anesthetic efficacy according to the present invention, constructing a set of heart rate sensors BS = {BS a |a ∈ [1, A]}, where BS a represents the a-th heart rate sensor, and A represents the total number of heart rate sensors; the heart rate sensor is used to detect and record the heart rate data of the human body.

[0008] Further, collect all heart rate data detected and recorded by the heart rate sensor BS a during the anesthesia of the patient, and construct a heart rate data set PD = {PD ab |a ∈ [1, A], b ∈ [1, B]}, where PD ab represents the b-th heart rate data detected by the a-th heart rate sensor during the anesthesia, and B represents the total number of heart rate data.

[0009] As a preferred embodiment of the artificial intelligence-based method for evaluating anesthetic efficacy according to the present invention, divide the entire process of the patient from the anesthesia induction period to the anesthesia recovery period into x time nodes, and define a time node set TS = {TS t |t ∈ [1, x]}, where TS t represents the t-th time node; the anesthesia induction period refers to the initial stage of the anesthesia process that makes the patient enter the anesthesia state; the anesthesia recovery period refers to the end stage of the patient's recovery of consciousness after the anesthesia is stopped.

[0010] Further, based on the heart rate data set and the time node set, construct a time node heart rate data set TPD = {TPD ab (TS t )|a ∈ [1, A], b ∈ [1, B], t ∈ [1, x]}, where TPD ab (TS t ) represents the time node heart rate data at the time node TS t .

[0011] As a preferred solution of the artificial intelligence-based anesthetic efficacy evaluation method described in the present invention, calculate the time-node heart rate data TPD detected by the heart rate sensor BS a at the time node TS t and the average value of TPD ab (TS t ) is calculated as follows:

[0012]

[0013] where μ ab (TS t ) represents the average value of the time-node heart rate data TPD ab (TS t ).

[0014] Furthermore, based on the average value of the time-node heart rate data TPD ab (TS t ), calculate the heart rate change rate of the time-node heart rate data TPD a detected by the heart rate sensor BS from the time node TS t to the time node TS t+1 , and the calculation formula is as follows: ab (TS t ) is calculated as follows:

[0015]

[0016] where μ ab (TS t+1 ) represents the average value of the time-node heart rate data TPD at the time node TS t+1 , and Δμ ab (TS t+1 →TS ab (TS t →TS t+1 ) represents the heart rate change rate.

[0017] In the present invention, if Δμ ab (TS t →TS t+1 )>0, it means that the heart rate increases relative to the time node TS t+1 within the time node TS t . If Δμ ab (TS t →TS t+1 )<0, it means that the heart rate decreases relative to the time node TS t+1 within the time node TS tIt has decreased. By monitoring this rate of change, the impact of anesthetic drugs on the heart rate can be further analyzed, thereby evaluating the anesthetic effect.

[0018] Furthermore, calculate at the time node TS t the combined heart rate value of the heart rate data collected by all heart rate sensors, and the calculation formula is as follows:

[0019]

[0020] where, μ com (TS t ) represents the combined heart rate value, and ω a represents the preset coefficient of the a-th heart rate sensor BS a .

[0021] In the present invention, heart rate sensors are usually placed at different body parts (such as the chest, wrist, ankle, etc.), and each sensor monitors heart rate data of different parts, and these data may vary due to the different positions of the sensors; the weight ω a reflects the importance of each sensor and is determined according to the sensor position. For example, sensors close to the heart may obtain a higher weight because these positions more directly reflect heart activities, and the data of sensors with higher weights have a greater impact on the final combined heart rate value.

[0022] Furthermore, based on the combined heart rate value μ com (TS t ), calculate the combined heart rate change degree from the time node TS t to the time node TS t+1 , and the calculation formula is as follows:

[0023] Δμ com (TSS t →TS t+1 ) = μ com (TS t+1 ) - μ com (TS t ) ;

[0024] where, μ com (TS t+1 ) represents the combined heart rate value at the time node TS t+1 , and Δμ com (TS t →TS t+1 ) represents the combined heart rate change degree.

[0025] In the present invention, if the comprehensive heart rate change degree is positive, it indicates that the patient's heart rate is rising, which means that the depth of anesthesia is weakening, and the patient may be gradually turning from a deep anesthesia state to a superficial anesthesia or awakening state. If the heart rate increase persists, it may indicate that the efficacy of the anesthetic drug is gradually weakening, suggesting that drug supplementation or adjustment is needed; if the comprehensive heart rate change degree is negative, it indicates that the patient's heart rate is falling, which means that the depth of anesthesia is deepening, and the patient has entered a deeper anesthesia state. If the heart rate drops too fast or too much, it may indicate that the patient has entered an over-anesthesia state, which may have an adverse impact on the patient's physiological functions, such as inhibiting breathing or lowering blood pressure. At this time, it may be necessary to reduce the dose of the anesthetic drug or take other measures to adjust the depth of anesthesia.

[0026] As a preferred embodiment of the anesthetic efficacy evaluation method based on artificial intelligence according to the present invention, an evaluation value is designed based on the heart rate change rate and the comprehensive heart rate change degree, and the calculation formula is as follows:

[0027] E(TS t →TS t+1 )=α·Δμ ab (TS t →TS t+1 )+β·Δμ com (TS t →TS t+1 );

[0028] Wherein, α and β are preset change coefficients, and E(TS t →TS t+1 ) represents the evaluation value.

[0029] Furthermore, a preset evaluation threshold τ is set, wherein τ is 0.

[0030] If E(TS t →TS t+1 )>τ, it is determined that the anesthetic effect is weakened and the drug efficacy is poor.

[0031] If E(TS t →TS t+1 )≤τ, it is determined that the anesthetic effect is effective and the drug efficacy is good.

[0032] In the present invention, if the evaluation value is positive, it generally indicates that the patient's physiological response becomes more active, which may mean that the anesthetic effect weakens, the patient may be gradually waking up from the anesthetic state or the anesthetic depth is insufficient, so the drug effect is not good, and the dose or concentration of the anesthetic drug may need to be adjusted; if the evaluation value is negative, it generally indicates that the patient's physiological response becomes more stable and the heart rate decreases, which may mean that the anesthetic drug is effectively taking effect and the patient is in a controlled anesthetic state, so the drug effect is good and the anesthetic dose is appropriate; if the evaluation value is zero, it indicates that the influence of the anesthetic drug on the heart rate is in a stable state without significant fluctuations, meaning that the drug effect is stable during this time period, and the patient may be in a stable anesthetic state, but still needs to be continuously monitored to ensure that the patient's state remains controlled.

[0033] An artificial intelligence-based anesthetic drug effect evaluation system, which includes: a heart rate data acquisition module, a time node data processing module, a heart rate data analysis module, and an anesthetic drug effect evaluation module.

[0034] The heart rate data acquisition module constructs and manages a set of heart rate sensors and collects the data of each sensor into a unified heart rate dataset.

[0035] The time node data processing module divides the anesthetic process into multiple time nodes and associates the collected heart rate data with these time nodes.

[0036] The heart rate data analysis module calculates the average heart rate, heart rate change rate, and comprehensive heart rate value based on the time node data, and identifies the relationship between the heart rate change and the anesthetic depth.

[0037] The anesthetic drug effect evaluation module determines whether the anesthetic drug effect is good by calculating the evaluation value and comparing it with a preset threshold.

[0038] Furthermore, the heart rate data acquisition module further includes a heart rate sensor set construction unit and a heart rate data acquisition unit.

[0039] The heart rate sensor set construction unit constructs a heart rate sensor set BS = {BS a |a ∈ [1, A]}, where BS a represents the a-th heart rate sensor, and A represents the total number of heart rate sensors; the heart rate sensors are used to detect and record the heart rate data of the human body.

[0040] The heart rate data acquisition unit collects all the heart rate data detected and recorded by the heart rate sensor BS during the anesthetic process of the patient and constructs a heart rate dataset PD = {PD a |a ∈ [1, A], b ∈ [1, B]}, where PD ab |a ∈ [1, A], b ∈ [1, B]}, where PD abDenote the b-th heart rate data detected by the a-th heart rate sensor during anesthesia, and B represents the total number of heart rate data.

[0041] Furthermore, the time node data processing module further includes a time node division unit and a time node heart rate data set construction unit.

[0042] The time node division unit divides the entire process of the patient from the anesthesia induction period to the anesthesia recovery period into x time nodes, and defines the time node set TS = {TS t |t ∈ [1, x]}, where TS t represents the t-th time node; the anesthesia induction period refers to the initial stage that makes the patient enter the anesthesia state during the anesthesia process; the anesthesia recovery period refers to the end stage when the patient regains consciousness after the anesthesia is stopped.

[0043] The time node heart rate data set construction unit constructs a time node heart rate data set TPD = {TPD ab (TS t )|a ∈ [1, A], b ∈ [1, B], t ∈ [1, x]} based on the heart rate data set and the time node set, where TPD ab (TS t ) represents the time node heart rate data at the time node TS t .

[0044] Furthermore, the heart rate data analysis module further includes a heart rate average calculation unit and a comprehensive heart rate value calculation unit.

[0045] The heart rate average calculation unit calculates the average value of the time node heart rate data TPD a detected by the heart rate sensor BS t at the time node TS ab (TS t ); based on the average value of the time node heart rate data TPD ab (TS t ), calculates the heart rate change rate of the time node heart rate data TPD a detected by the heart rate sensor BS t from the time node TS t+1 to the time node TS ab (TS t ).

[0046] The comprehensive heart rate value calculation unit calculates the comprehensive heart rate value of the heart rate data collected by all heart rate sensors at the time node TS t , and based on the comprehensive heart rate value μ com (TS t ), calculates at the time node TS tUp to time node TS t+1 The comprehensive heart rate change degree at this time.

[0047] Furthermore, the anesthesia efficacy evaluation module further includes an evaluation value calculation unit and an anesthesia effect determination unit.

[0048] The evaluation value calculation unit designs an evaluation value E(TS t →TS t+1 ) based on the heart rate change rate and the comprehensive heart rate change degree.

[0049] The anesthesia effect determination unit presets an evaluation threshold τ. If E(TS t →TS t+1 ) > τ, it is determined that the anesthesia effect weakens and the drug efficacy is poor; if E(TS t →TS t+1 ) ≤ τ, it is determined that the anesthesia effect is effective and the drug efficacy is good.

[0050] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: By constructing a heart rate sensor set and collecting heart rate data during anesthesia, and combining the division of the anesthesia process into multiple time nodes to construct a time node heart rate data set, the comprehensive and phased monitoring and analysis of the heart rate are realized. Based on the average value and change rate of the heart rate data, the comprehensive heart rate value and its change degree are further calculated, which can accurately reflect the influence of anesthetic drugs on the heart rate at different stages. Finally, by designing an evaluation value and combining a preset threshold, the anesthesia effect is quantitatively evaluated, making anesthesia management more accurate and safe, and effectively reducing the risks during the anesthesia process. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0052] Among them:

[0053] Figure 1 is a flowchart of a method for evaluating anesthesia efficacy based on artificial intelligence according to an embodiment of the present invention;

[0054] Figure 2 is a structural diagram of a system for evaluating anesthesia efficacy based on artificial intelligence according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0055] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0056] Please refer to Figure 1 - Figure 2 , the present invention provides a technical solution:

[0057] Please refer to Figure 1 , in the first embodiment: A method for evaluating anesthetic efficacy based on artificial intelligence is provided, and the method includes the following steps:

[0058] Step S1: Construct a set of heart rate sensors, and collect all the heart rate data detected and recorded by the heart rate sensors during the anesthesia process of the patient to construct a heart rate data set.

[0059] Specifically, construct a set of heart rate sensors BS = {BS a |a ∈ [1, a]}, where BS a represents the a-th heart rate sensor, and A represents the total number of heart rate sensors; the heart rate sensor is used to detect and record the heart rate data of the human body.

[0060] Furthermore, collect all the heart rate data detected and recorded by the heart rate sensor BS a during the anesthesia process of the patient to construct a heart rate data set PD = {PD ab |a ∈ [1, A], b ∈ [1, B]}, where PD ab represents the b-th heart rate data detected by the a-th heart rate sensor during the anesthesia process, and B represents the total number of heart rate data.

[0061] Step S2: Divide the entire process of the patient from the anesthesia induction period to the anesthesia recovery period into time nodes, and construct a time node heart rate data set based on the heart rate data set.

[0062] Specifically, divide the entire process of the patient from the anesthesia induction period to the anesthesia recovery period into x time nodes, and define a time node set TS = {TS t |t ∈ [1, x]}, where TS t represents the t-th time node; the anesthesia induction period refers to the initial stage of making the patient enter the anesthesia state during the anesthesia process; the anesthesia recovery period refers to the end stage of the patient recovering consciousness after the anesthesia is stopped.

[0063] Furthermore, based on the heart rate data set and the time node set, construct a time node heart rate data set TPD = {TPD ab (TSt ) | a ∈ [1, A], b ∈ [1, B], t ∈ [1, x]}, where TPD ab (TS t ) represents the time node heart rate data at the time node TS t when.

[0064] Step S3: Calculate the average value of the time node heart rate data detected by the same heart rate sensor at the same time node. Based on the average value, calculate the heart rate change rate of the same heart rate sensor at different time nodes; calculate the comprehensive heart rate value of the heart rate data collected by all heart rate sensors at the same time node, and calculate the comprehensive heart rate change degree at different time nodes.

[0065] Specifically, calculate the time node heart rate data TPD a detected by the heart rate sensor BS t at the time node TS ab (TS t ) The average value is calculated as follows:

[0066]

[0067] where μ ab (TS t ) represents the average value of the time node heart rate data TPD ab (TS t ).

[0068] For example, assume that the total number of heart rate sensors is 2, i.e., A = 2, and the total number of heart rate data is 2, i.e., B = 2. Then the first time node heart rate data TPD ab (TS t ) is 3, and the second time node heart rate data TPD ab (TS t ) is 5. Substituting into the formula, we get μ ab (TS t ) = 0.5 * (3 + 5) = 4.

[0069] Furthermore, based on the average value of the time node heart rate data TPD ab (TS t ), calculate the heart rate change rate of the time node heart rate data TPDah(TS a ) detected by the heart rate sensor BS t from the time node TS t+1 to the time node TS t as follows:

[0070]

[0071] Among them, μ ab (TS t+1 ) represents the average value of the heart rate data TPD t+1 at the time node TS ab (TS t+1 ), and Δμ ab (TS t →TS t+1 ) represents the heart rate change rate.

[0072] For example, assume that the first heart rate data at the time node TS t+1 collected by the first heart rate sensor is 2, and the second first heart rate data is 4. Then, at the time node TS t+1 , the heart rate data at the time node is 0.5*(2 + 4) = 3. Substituting into the formula, we get Δμ ab (TS t →TS t+1 ) to be -0.25.

[0073] In the present invention, if Δμ ab (TS t →TS t+1 )>0, it indicates that the heart rate has increased relative to the time node TS t+1 within the time node TS t . If Δμ ab (TS t →TS t+1 )<0, it indicates that the heart rate has decreased relative to the time node TS t+1 within the time node TS t . By monitoring this change rate, the effect of the anesthetic drug on the heart rate can be further analyzed, thereby evaluating the anesthetic efficacy.

[0074] Calculate the comprehensive heart rate value of the heart rate data collected by all heart rate sensors at the time node TS t . The calculation formula is as follows:

[0075]

[0076] Among them, μ com (TS t ) represents the comprehensive heart rate value, and ω a represents the coefficient preset for the a-th heart rate sensor a.

[0077] For example, assume that the average value of the heart rate data TPD ab (TS t ) collected by the second heart rate sensor is 2, and the preset coefficient ω a is 1. Substituting into the formula, we get the comprehensive heart rate value μ com (TS t) is 0.5 * (4 + 2) = 3.

[0078] In the present invention, the heart rate sensors are usually placed at different body parts (such as the chest, wrist, ankle, etc.). Each sensor monitors the heart rate data of different parts, and these data may vary due to the different positions of the sensors; the weight ω a reflects the importance of each sensor and is determined according to the sensor position. For example, the sensors close to the heart may obtain a higher weight because these positions more directly reflect the heart activity, and the data of the sensors with higher weights have a greater impact on the final comprehensive heart rate value.

[0079] Furthermore, based on the comprehensive heart rate value μ com (TS t ), calculate the comprehensive heart rate change degree at the time node TS t to the time node TS t+1 . The calculation formula is as follows:

[0080] Δμ com ((TS t →TS t+1 ) = μ com (TS t+1 ) - μ com ((TS t );

[0081] where, μ com (TS t+1 ) represents the comprehensive heart rate value at the time node TS t+1 ,

[0082] Δμ com (TS t →TS t+1 ) represents the comprehensive heart rate change degree.

[0083] For example, assume that the comprehensive heart rate value μ t+1 (TS com ) at the time node TS t+1 is 2. Substituting it into the formula, the comprehensive heart rate change degree Δμ com (TS t →TS t+1 ) is -1.

[0084] In the present invention, if the comprehensive heart rate change degree is positive, it indicates that the patient's heart rate is rising, which means that the depth of anesthesia is weakening, and the patient may be gradually turning from a deep anesthesia state to a superficial anesthesia or awakening state. If the heart rate increase persists, it may indicate that the efficacy of the anesthetic drug is gradually weakening, suggesting that drug supplementation or adjustment is needed; if the comprehensive heart rate change degree is negative, it indicates that the patient's heart rate is decreasing, which means that the depth of anesthesia is deepening, and the patient has entered a deeper anesthesia state. If the heart rate drops too fast or too much, it may indicate that the patient has entered an over-anesthesia state, which may have an adverse impact on the patient's physiological functions, such as inhibiting breathing or lowering blood pressure. At this time, it may be necessary to reduce the dose of the anesthetic drug or take other measures to adjust the depth of anesthesia.

[0085] Step S4: Design and analyze the evaluation value based on the heart rate change rate and the comprehensive heart rate change degree.

[0086] Based on the heart rate change rate and the comprehensive heart rate change degree, design an evaluation value, and the calculation formula is as follows:

[0087] E(TS t →TS t+1 )=α·Δμ ab (TS t →TS t+1 )+β·Δμ com (TS t →TS t+1 );

[0088] Wherein, α and β are preset change coefficients, and E(TS t →TS t+1 ) represents the evaluation value.

[0089] Preset an evaluation threshold τ, wherein τ is 0.

[0090] If E(TS t →TS t+1 )>τ, it is determined that the anesthetic effect is weakened and the drug efficacy is poor.

[0091] If E(TS t →TS t+1 )≤τ, it is determined that the anesthetic effect is effective and the drug efficacy is good.

[0092] For example, assume that α and β are 0.2 and 0.4 respectively. Substituting into the formula, the evaluation value E(TS t →TS t+1 ) is 0.2*(-0.25)+0.4*(-1)=(-0.05)+(-0.4)=-0.45. Since the evaluation value is less than the evaluation threshold, it is determined that the anesthetic effect is effective and the drug efficacy is good.

[0093] In the present invention, if the evaluation value is positive, it generally indicates that the patient's physiological response becomes more active, which may mean that the anesthetic effect weakens, the patient may be gradually waking up from the anesthetic state or the anesthetic depth is insufficient, so the drug effect is not good, and the dose or concentration of the anesthetic drug may need to be adjusted; if the evaluation value is negative, it generally indicates that the patient's physiological response becomes more stable and the heart rate decreases, which may mean that the anesthetic drug is effectively taking effect and the patient is in a controlled anesthetic state, so the drug effect is good and the anesthetic dose is appropriate; if the evaluation value is zero, it indicates that the influence of the anesthetic drug on the heart rate is in a stable state without significant fluctuations, meaning that the drug effect is stable during this time period, and the patient may be in a stable anesthetic state, but still needs to be continuously monitored to ensure that the patient's state remains controlled.

[0094] Please refer to Figure 2 , in the second embodiment: Provide an artificial intelligence-based anesthetic drug effect evaluation system, which includes: a heart rate data acquisition module, a time node data processing module, a heart rate data analysis module, and an anesthetic drug effect evaluation module.

[0095] The heart rate data acquisition module collects the data of each sensor into a unified heart rate dataset by constructing and managing a set of heart rate sensors.

[0096] The time node data processing module divides the anesthetic process into multiple time nodes and associates the collected heart rate data with these time nodes.

[0097] The heart rate data analysis module calculates the average heart rate, heart rate change rate, and comprehensive heart rate value based on the time node data, and identifies the relationship between the heart rate change and the anesthetic depth.

[0098] The anesthetic drug effect evaluation module determines whether the anesthetic drug effect is good by calculating the evaluation value and comparing it with a preset threshold.

[0099] Further, the heart rate data acquisition module further includes a heart rate sensor set construction unit and a heart rate data acquisition unit.

[0100] The heart rate sensor set construction unit constructs a heart rate sensor set BS = {BS a |a ∈ [1, A]}, where BS a represents the a-th heart rate sensor, and A represents the total number of heart rate sensors; the heart rate sensor is used to detect and record the heart rate data of the human body.

[0101] The heart rate data acquisition unit collects all the heart rate data detected and recorded by the heart rate sensor BS during the anesthetic process of the patient and constructs a heart rate dataset PD = {PD a ab ​|a ∈ [1, A], b ∈ [1, B]}, where PD ab represents the b-th heart rate data detected by the a-th heart rate sensor during the anesthesia process, and B represents the total number of heart rate data.

[0102] Furthermore, the time node data processing module further includes a time node division unit and a time node heart rate data set construction unit.

[0103] The time node division unit divides the entire process of the patient from the anesthesia induction period to the anesthesia recovery period into x time nodes, and defines the time node set TS = {TS t |t ∈ [1, x]}, where TS t represents the t-th time node; the anesthesia induction period refers to the initial stage that makes the patient enter the anesthesia state during the anesthesia process; the anesthesia recovery period refers to the end stage when the patient regains consciousness after the anesthesia is stopped.

[0104] The time node heart rate data set construction unit constructs a time node heart rate data set TPD = {TPD ab (TS t )|a ∈ [1, A], b ∈ [1, B], t ∈ [1, x]}, where TPD ab (TS t ) represents the time node heart rate data at the time node TS t .

[0105] Furthermore, the heart rate data analysis module further includes a heart rate average calculation unit and a comprehensive heart rate value calculation unit.

[0106] The heart rate average calculation unit calculates the average value of the time node heart rate data TPD a detected by the heart rate sensor BS t at the time node TS ab (TS t ); based on the average value of the time node heart rate data TPD ab (TS t ), calculates the heart rate change rate of the time node heart rate data TPD a detected by the heart rate sensor BS t from the time node TS t+1 to the time node TS ab (TS t ).

[0107] The comprehensive heart rate value calculation unit calculates the comprehensive heart rate value of the heart rate data collected by all heart rate sensors at the time node TS t , and based on the comprehensive heart rate value μ com(TS t ), calculate the comprehensive heart rate change degree at time node TS t to time node TS t+1 .

[0108] Furthermore, the anesthesia efficacy evaluation module further includes an evaluation value calculation unit and an anesthesia effect determination unit.

[0109] The evaluation value calculation unit designs an evaluation value E(TS t →TS t+1 ) based on the heart rate change rate and the comprehensive heart rate change degree.

[0110] The anesthesia effect determination unit presets an evaluation threshold τ. If E(TS t →TS t+1 ) > τ, it is determined that the anesthesia effect weakens and the drug efficacy is poor; if E(TS t →TS t+1 ) ≤ τ, it is determined that the anesthesia effect is effective and the drug efficacy is good.

[0111] It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or also includes elements inherent to such process, method, article or device.

[0112] Finally, it should be noted that the above are only preferred embodiments of the present invention and are not used to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. An artificial intelligence-based anesthetic efficacy evaluation method, characterized in that: The method comprises the following steps: Step S1: construct a heart rate sensor set, and collect all heart rate data detected and recorded by the heart rate sensor during the anesthesia process of the patient to construct a heart rate data set; Step S2: dividing the entire process from the anesthesia induction period to the anesthesia recovery period of the patient into time nodes, and constructing a time node heart rate data set based on the heart rate data set; Step S3: Calculate the average value of the time node heart rate data detected by the same heart rate sensor at the same time node, and calculate the heart rate change rate of the same heart rate sensor at different time nodes based on the average value; calculate the comprehensive heart rate value of the heart rate data collected by all heart rate sensors at the same time node, and calculate the comprehensive heart rate change degree at different time nodes; Step S4: designing and analyzing evaluation values ​​based on the heart rate variability and the comprehensive heart rate variability; The step S1 comprises the following steps: Construct heart rate sensor set BS = {BS a |a∈[1,A]}, where BS a represents the ath heart rate sensor, and A represents the total number of heart rate sensors; the heart rate sensor is used to detect and record the heart rate data of a human body; Collect the patient's heart rate sensor during anesthesia BS a All the heart rate data detected and recorded are used to construct the heart rate data set PD = {PD ab |a∈[1,A],b∈[1,B]}, where PD ab represents the bth heart rate data detected by the ath heart rate sensor during anesthesia, and B represents the total number of heart rate data; The step S2 comprises the following steps: The whole process from anesthesia induction to anesthesia recovery is divided into x time nodes, and the time node set TS = {TS t |t∈[1,x]}, where TS t represents the tth time node; the anesthesia induction period refers to the initial stage of the anesthesia process in which the patient enters the anesthesia state; the anesthesia recovery period refers to the end stage in which the patient recovers consciousness after the anesthesia is stopped; Based on the heart rate data set and the time node set, a time node heart rate data set TPD is constructed. ab (TS t )|a∈[1,A],b∈[1,B],t∈[1,x]},where TPD ab (TS t ) indicates that at time node TS t Heart rate data at the time node; The step S3 comprises the following steps: Calculate heart rate sensor BS a At time node TS t Heart rate data TPD at the time point of detection ab (TS t ), calculated as follows: Among them, μ ab (TS t ) represents the time node heart rate data TPD ab (TS t )’s average value; Based on the heart rate data TPD at the time node ab (TS t ) to calculate the heart rate sensor BS a At time node TS t To time node TS t+1 Heart rate data TPD at the time point of detection ab (TS t ) is calculated by the following formula: Among them, μ ab (TS t+1 ) represents the time node TS t+1 Time node heart rate data TPD ab (TS t+1 ) average value, △μ ab (TS t →TS t+1 ) represents the heart rate change rate; Calculated at time node TS t When the heart rate data collected by all heart rate sensors is calculated, the comprehensive heart rate value is calculated as follows: Among them, μ com (TS t ) represents the comprehensive heart rate value, ω a Indicates the ath heart rate sensor BS a Preset coefficients; Based on the comprehensive heart rate value μ com (TS t ), calculated at time node TS t To time node TS t+1 The comprehensive heart rate variability at 1000 Hz is calculated as follows: △μ com (TS t →TS t+1 )=μ com (TS t+1 )-μ com (TS t ); Among them, μ com (TS t+1 ) represents the time node TS t+1 The comprehensive heart rate value at the time, △μ com (TS t →TS t+1 ) represents the comprehensive heart rate variability; The step S4 comprises the following steps: Based on the heart rate change rate and the comprehensive heart rate change degree, an evaluation value is designed, and the calculation formula is as follows: E(TS t →TS t+1 )=α·△μ ab (TS t →TS t+1 )+β·△μ com (TS t →TS t+1 ); Among them, α and β are the preset coefficients of variation, E(TS t →TS t+1 ) represents the evaluation value; Preset evaluation threshold τ; If E(TS t →TS t+1 )>τ, it is judged that the anesthetic effect is weakened and the drug efficacy is poor; If E(TS t →TS t+1 )≤τ, the anesthetic effect is judged to be effective and the drug efficacy is good.

2. An artificial intelligence-based anesthetic efficacy evaluation system, which executes an artificial intelligence-based anesthetic efficacy evaluation method as claimed in claim 1, characterized in that: The system includes: a heart rate data acquisition module, a time node data processing module, a heart rate data analysis module and an anesthetic efficacy evaluation module; The heart rate data collection module constructs and manages a set of heart rate sensors and collects data from each sensor into a unified heart rate data set; The time node data processing module divides the anesthesia process into multiple time nodes and associates the collected heart rate data with these time nodes; The heart rate data analysis module calculates the average heart rate, heart rate change rate and comprehensive heart rate value based on the time node data, and identifies the relationship between the heart rate change and the depth of anesthesia; The anesthetic efficacy evaluation module determines whether the anesthetic efficacy is good by calculating the evaluation value and comparing it with a preset threshold value.

3. The artificial intelligence-based anesthetic efficacy evaluation system according to claim 2, characterized in that: The heart rate data acquisition module also includes a heart rate sensor set construction unit and a heart rate data acquisition unit; The heart rate sensor set building unit builds a heart rate sensor set BS={BS a |a∈[1,A]}, where BS a represents the ath heart rate sensor, and A represents the total number of heart rate sensors; the heart rate sensor is used to detect and record the heart rate data of a human body; The heart rate data acquisition unit collects the heart rate data of the patient during anesthesia. a All the heart rate data detected and recorded are used to construct the heart rate data set PD = {PD ab |a∈[1,A],b∈[1,B]}, where PD ab It represents the bth heart rate data detected by the ath heart rate sensor during the anesthesia process, and B represents the total number of heart rate data.

4. The artificial intelligence-based anesthetic efficacy evaluation system according to claim 3, characterized in that: The time node data processing module also includes a time node division unit and a time node heart rate data set construction unit; The time node division unit divides the entire process from the anesthesia induction period to the anesthesia awakening period of the patient into x time nodes, and defines the time node set TS = {TS t |t∈[1,x]}, where TS t represents the tth time node; the anesthesia induction period refers to the initial stage of the anesthesia process in which the patient enters the anesthesia state; the anesthesia recovery period refers to the end stage in which the patient recovers consciousness after the anesthesia is stopped; The time node heart rate data set construction unit constructs a time node heart rate data set TPD based on the heart rate data set and the time node set. ab (TS t )|a∈[1,A],b∈[1,B],t∈[1,x]},where TPD ab (TS t ) indicates that at time node TS t Heart rate data at the time node.

5. The artificial intelligence-based anesthetic efficacy evaluation system according to claim 4, characterized in that: The heart rate data analysis module also includes a heart rate average value calculation unit and a comprehensive heart rate value calculation unit; The heart rate average value calculation unit calculates the heart rate sensor BS a At time node TS t Heart rate data TPD at the time point of detection ab (TS t ) average value; based on the heart rate data TPD at the time node ab (TS t ) to calculate the heart rate sensor BS a At time node TS t To time node TS t+1 Heart rate data TPD at the time point of detection ab (TS t )’s heart rate variability; The comprehensive heart rate value calculation unit calculates the time node TS t When the heart rate data collected by all heart rate sensors is a comprehensive heart rate value, based on the comprehensive heart rate value μ com (TS t ), calculated at time node TS t To time node TS t+1 The comprehensive heart rate variability over time.

6. The artificial intelligence-based anesthetic efficacy evaluation system according to claim 5, characterized in that: The anesthetic efficacy evaluation module also includes an evaluation value calculation unit and an anesthetic effect determination unit; The evaluation value calculation unit designs an evaluation value E(TS) based on the heart rate change rate and the comprehensive heart rate change degree. t →TS t+1 ); The anesthetic effect determination unit presets an evaluation threshold τ, if E(TS t →TS t+1 )>τ, it is judged that the anesthetic effect is weakened and the drug efficacy is poor; if E(TS t →TS t+1 )≤τ, the anesthetic effect is judged to be effective and the drug efficacy is good.

Images