Method, system, device and medium for delirium assessment based on wakeful closed-eye occipital alpha waves

Abstract

The application belongs to the technical field of postoperative delirium evaluation, and discloses a delirium evaluation method, system, device and medium based on wakeful closed-eye occipital lobe Alpha wave, which comprises the following steps: determining the preoperative brain function reserve score of a patient according to the preoperative wakeful Alpha peak frequency and the frequency judgment interval; calculating the brain function reserve consumption rate according to the preoperative wakeful Alpha peak frequency, the postoperative wakeful Alpha peak frequency and the frequency lower limit extreme value, obtaining the anesthesia brain impact judgment interval, and determining the anesthesia brain impact score according to the brain function reserve consumption rate and the anesthesia brain impact judgment interval; using the postoperative delirium risk index matrix to evaluate the postoperative delirium of the patient, and obtaining the postoperative delirium risk index. The delirium evaluation is more accurate, the accuracy and objectivity of the postoperative delirium evaluation of the patient are improved, the preoperative brain function reserve score and the postoperative anesthesia brain impact score are used for joint decision-making, the postoperative delirium risk index of the patient is determined, and the evaluation is more intuitive.

Description

[0001] This invention relates to the field of postoperative delirium assessment technology, and in particular to a method, system, device and medium for delirium assessment based on occipital alpha waves in the awake, closed eyes. Background Technology

[0002] With the deepening of global population aging and the evolution of modern surgical techniques, a large number of elderly patients have gained access to surgery. As people age, the brain undergoes structural and functional decline, reducing its tolerance to anesthesia and surgical stress. Postoperative delirium (POD) and postoperative cognitive impairment (POCD), as major perioperative neurocognitive disorders (PNDs), have a high incidence in elderly patients. These perioperative brain injuries not only directly lead to prolonged hospital stays and increased iatrogenic complications, but also induce long-term chronic brain decline and even accelerate the progression of dementia. Perioperative brain health management has become a core issue in evaluating the safety of modern surgical procedures, optimizing the prognosis of elderly patients, and reducing the consumption of medical resources.

[0003] Currently, postoperative delirium (POD) and postoperative cognitive impairment (POCD) are mainly assessed in clinical practice using subjective medical scales (such as the CAM scale). This method is highly subjective, requires patients to answer a large number of questionnaires, is time-consuming, and has low accuracy. Summary of the Invention

[0004] The purpose of this application is to propose a method, system, device, and medium for assessing delirium based on the Alpha wave in the occipital lobe when the patient is awake and eyes are closed, in order to solve the problems of time-consuming, subjective, and inaccurate current postoperative delirium assessment.

[0005] Firstly, a method for assessing delirium based on occipital alpha waves during awake, closed-eye sleep, includes:

[0006] Step 100: Obtain the patient's awake and closed-eye EEG signal, extract the preoperative awake Alpha peak frequency based on the awake and closed-eye EEG signal, obtain the frequency judgment interval corresponding to the brain function reserve, and determine the patient's preoperative brain function reserve score based on the preoperative awake Alpha peak frequency and the frequency judgment interval.

[0007] Step 200: Obtain the postoperative awake Alpha peak frequency and the lower limit extreme value of the human brain awake occipital lobe Alpha peak frequency within a specified time after anesthesia. Calculate the brain function reserve consumption rate based on the preoperative awake Alpha peak frequency, postoperative awake Alpha peak frequency, and lower limit extreme value. Obtain the anesthesia brain impact judgment interval. Determine the anesthesia brain impact score based on the brain function reserve consumption rate and the anesthesia brain impact judgment interval.

[0008] Step 300: Based on the preoperative brain function reserve score and anesthesia brain impact score, the patient's postoperative delirium risk is stratified and a postoperative delirium risk index matrix is ​​constructed. The postoperative delirium risk index matrix is ​​used to assess the patient's postoperative delirium and obtain the postoperative delirium risk index.

[0009] Furthermore, step 100 includes:

[0010] Step 110: Place EEG electrodes in the occipital lobe of the patient's brain and use the EEG electrodes to collect the patient's awake, closed-eye EEG signals.

[0011] Step 120: Perform rate spectral density analysis on the awake, closed-eye EEG signal to extract the preoperative awake Alpha peak frequency.

[0012] Furthermore, after step 120, step 100 also includes:

[0013] Step 130: Set multiple frequency thresholds for the preoperative awake alpha peak frequency;

[0014] Step 140: According to the frequency magnitude, the preoperative awake Alpha peak frequency is sequentially divided into multiple frequency judgment intervals based on the frequency threshold.

[0015] Furthermore, after step 140, step 100 also includes:

[0016] Step 150: Set the preoperative brain function reserve score for each frequency judgment interval;

[0017] Step 160: Based on the frequency judgment interval corresponding to the extracted preoperative awake Alpha peak frequency, the preoperative brain function reserve score is obtained by mapping.

[0018] Furthermore, step 200 includes:

[0019] Step 210: Obtain the lower limit extreme value of the peak frequency of the Alpha wave in the occipital lobe of a awake, closed-eye human brain.

[0020] Step 220: Calculate the brain reserve safety margin parameter using the preoperative awake Alpha peak frequency and the lower limit extreme value of the frequency;

[0021] Step 230: Calculate the brain function reserve consumption parameters using the preoperative awake alpha peak frequency and the postoperative awake alpha peak frequency;

[0022] Step 240: Calculate the brain function reserve consumption rate based on the brain function reserve consumption parameter and the brain reserve safety margin parameter.

[0023] Furthermore, after step 240, step 200 further includes:

[0024] Step 250: Set multiple consumption rate thresholds for the brain function reserve consumption rate;

[0025] Step 260: According to the consumption rate, the brain impact is divided into multiple anesthesia brain impact judgment intervals according to the consumption rate threshold or the postoperative awake alpha peak frequency, and an anesthesia brain impact score is set for each anesthesia brain impact judgment interval.

[0026] Step 270: Based on the anesthesia brain impact judgment interval corresponding to the patient's current brain function reserve depletion rate, the patient's current anesthesia brain impact score is mapped to the interval.

[0027] Furthermore, step 300 includes:

[0028] Step 310: Stratify the risk of postoperative delirium in patients based on the preoperative brain function reserve score and anesthesia brain impact score;

[0029] Step 320: Assign values ​​to each matrix layer, with equal values ​​assigned to layers within the same matrix, to obtain the postoperative delirium risk index matrix.

[0030] Furthermore, after step 320, step 300 further includes:

[0031] Step 330: The patient's preoperative brain function reserve score and anesthesia brain impact score;

[0032] Step 340: Determine the target matrix elements of the postoperative delirium risk index matrix by using the preoperative brain function reserve score as the row number of the postoperative delirium risk index matrix and the anesthesia brain impact score as the column number of the postoperative delirium risk index matrix.

[0033] Step 350: Use the elements of the target matrix as the postoperative delirium risk index.

[0034] To address the aforementioned technical problems, embodiments of this application also provide a delirium assessment system based on awake, closed-eye occipital alpha waves, employing the delirium assessment method based on awake, closed-eye occipital alpha waves described in the first aspect, comprising:

[0035] The preoperative brain function reserve scoring module is used to acquire the patient's awake and closed-eye EEG signal, extract the preoperative awake Alpha peak frequency based on the awake and closed-eye EEG signal, obtain the frequency judgment interval corresponding to the brain function reserve, and determine the patient's preoperative brain function reserve score based on the preoperative awake Alpha peak frequency and the frequency judgment interval.

[0036] The anesthesia brain impact scoring module is used to obtain the postoperative awake Alpha peak frequency and the lower limit extreme value of the human brain awake occipital lobe Alpha peak frequency within a specified time after anesthesia. Based on the preoperative awake Alpha peak frequency, postoperative awake Alpha peak frequency, and lower limit extreme value, the brain function reserve consumption rate is calculated to obtain the anesthesia brain impact judgment interval. The anesthesia brain impact score is determined based on the brain function reserve consumption rate and the anesthesia brain impact judgment interval.

[0037] The postoperative delirium assessment module is used to stratify the risk of postoperative delirium in patients based on the preoperative brain function reserve score and anesthesia brain impact score, and to construct a postoperative delirium risk index matrix. The postoperative delirium risk index matrix is ​​used to assess the postoperative delirium in patients and obtain the postoperative delirium risk index.

[0038] To address the aforementioned technical problems, embodiments of this application also provide a computer device, including a memory and a processor. The memory stores computer-readable instructions, and the processor executes the computer-readable instructions to implement the steps of the delirium assessment method based on awake, closed-eye occipital alpha waves as described in the first aspect.

[0039] To address the aforementioned technical problems, embodiments of this application also provide a computer-readable storage medium storing computer-readable instructions. When executed by a processor, the computer-readable instructions implement the steps of the delirium assessment method based on awake, closed-eye occipital alpha waves as described in the first aspect.

[0040] Compared with the prior art, the embodiments of this application have the following main technical effects: By determining the preoperative brain function reserve score and the postoperative anesthesia brain impact score, the risk of postoperative delirium in patients is stratified according to the preoperative brain function reserve score and the anesthesia brain impact score, and a postoperative delirium risk index matrix is ​​constructed. The postoperative delirium risk index matrix is ​​used to assess the postoperative delirium in patients. The stratification using the preoperative brain function reserve score and the postoperative anesthesia brain impact score makes the delirium assessment more accurate, improves the accuracy and objectivity of the postoperative delirium assessment, and solves the problems of time-consuming, subjective, and inaccurate current postoperative delirium assessment. The joint decision-making using the preoperative brain function reserve score and the postoperative anesthesia brain impact score determines the patient's postoperative delirium risk index, making the assessment more intuitive. Attached Figure Description

[0041] To more clearly illustrate the solutions in this application, the accompanying drawings used in the description of the embodiments of this application will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0042] Figure 1 This is a flowchart of an embodiment of a delirium assessment method based on occipital alpha waves during awake, closed-eye sleep, as described in this application.

[0043] Figure 2 yes Figure 1 A flowchart of one specific implementation of step 100;

[0044] Figure 3 yes Figure 2 A flowchart of a specific implementation method following step 120;

[0045] Figure 4 yes Figure 1 A flowchart of a specific implementation of step 200;

[0046] Figure 5 yes Figure 4 A flowchart of a specific implementation method following step 240;

[0047] Figure 6 yes Figure 5 A flowchart of a specific implementation of step 300;

[0048] Figure 7 yes Figure 6 A flowchart of a specific implementation method following step 320;

[0049] Figure 8 This is a graph showing the relationship between preoperative awake Alpha peak frequency and age in this invention;

[0050] Figure 9 This is a comparison chart of the preoperative awake Alpha peak frequency in young and old patients in this invention;

[0051] Figure 10 This is a comparison chart of the preoperative and postoperative conscious alpha peak frequencies in the population of this invention.

[0052] Figure 11 This is a comparison chart of the preoperative and postoperative conscious alpha peak frequencies of individuals in this invention.

[0053] Figure 12This is a graph showing the preoperative awake Alpha peak frequency results in this invention;

[0054] Figure 13 This is a graph showing the postoperative awake Alpha peak frequency results in this invention;

[0055] Figure 14 This is a schematic diagram of the postoperative delirium risk index matrix in this invention;

[0056] Figure 15 This is a schematic diagram of the matrix layering of the postoperative delirium risk index matrix in this invention;

[0057] Figure 16 This is a schematic diagram of an embodiment of a delirium assessment system based on occipital alpha waves in a conscious, closed-eye state, according to this application.

[0058] Figure 17 This is a schematic diagram of the structure of an embodiment of a computer device according to this application. Detailed Implementation

[0059] The technical solutions of this invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this invention, and not all of them. Other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are all within the scope of protection of this invention.

[0060] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0061] Unless otherwise defined, 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 application pertains; the terminology used herein in the specification of the application is for the purpose of describing particular embodiments only and is not intended to limit the application; the terms “comprising” and “having”, and any variations thereof, in the specification, claims, and foregoing description of the drawings of this application are intended to cover non-exclusive inclusion.

[0062] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this application, "multiple" means two or more, unless otherwise explicitly specified.

[0063] Currently, there is no objective and efficient method for assessing the risk of postoperative delirium. Clinically, postoperative delirium (POD) and postoperative cognitive impairment (POCD) are primarily assessed using subjective medical scales (such as the CAM scale). This method is highly subjective, requires patients to complete numerous questionnaires, is time-consuming, and has low accuracy.

[0064] To address the aforementioned issues, a method, system, device, and medium for assessing delirium based on alpha waves in the occipital lobe during awake, closed-eye sleep are proposed.

[0065] Firstly, a delirium assessment method based on occipital alpha waves during awake, closed-eye sleep, such as... Figure 1 , Figure 1 This is a flowchart of an embodiment of a delirium assessment method based on occipital alpha waves during awake, closed-eye sleep, as described in this application; including:

[0066] Step 100: Obtain the patient's awake and closed-eye EEG signal, extract the preoperative awake Alpha peak frequency based on the awake and closed-eye EEG signal, obtain the frequency judgment interval corresponding to the brain function reserve, and determine the patient's preoperative brain function reserve score based on the preoperative awake Alpha peak frequency and frequency judgment interval.

[0067] In this embodiment, the relationship between the preoperative awake alpha peak frequency and age was studied in the awake closed-eye EEG signal:

[0068] In a conscious, closed-eye state, alpha waves of 7-13 Hz appear in the occipital visual cortex. The frequency with the strongest alpha wave energy is called the alpha peak frequency (APF). Numerous previous psychological studies have confirmed a strong positive correlation between APF and cognitive ability. This study applies the occipital APF index, used in psychological research, to surgical anesthesia patients to explore its potential as a tool for preoperative brain function assessment in these patients.

[0069] Methods: An eight-channel wearable EEG acquisition device was used, with all EEG electrodes located in the occipital lobe. Patients performed a 20-second eye-closing and eye-opening sequence based on voice commands, repeated three times. A self-developed program automatically analyzed the EEG during the eye-closing period across the eight channels. A multi-taper multi-window spectral estimation algorithm was used for time-frequency analysis (specific parameters: window size 4 seconds, step size 1 second, number of taper windows 5) to detect the presence of peaks in the alpha band (7-13Hz) during the eye-closing period, and the frequency of the peak location (i.e., alpha peak frequency, APF) was calculated. Preoperative APF measurements were performed on 140 patients. Due to limitations in clinical acquisition conditions (excessive EEG electrode impedance, excessive electromagnetic noise interference in the clinical environment, patient non-cooperation, etc.), valid preoperative APF values ​​were obtained in 98 cases. APF values ​​exceeding 7.5-12Hz were excluded as outliers (4 cases). A total of 94 preoperative APF measurements were ultimately included in the analysis.

[0070] Result: As Figure 8 and 9 , Figure 8 This is a graph showing the relationship between preoperative awake alpha peak frequency and age in this invention. Figure 9 This is a comparison of preoperative awake Alpha peak frequencies between young and older patients in this invention. The patients' awake, closed-eye occipital lobe APF was highly negatively correlated with age, with a correlation coefficient R of -0.59 and p=0<0.01. The APF (mean=9.62, std=0.74) in the older group (>=60 years) was lower than that in the younger group (<60 years) (mean=10.63, std=0.63), P=0<0.01. The linear fit between awake, closed-eye occipital lobe APF and age was: AFP=-0.025*Age+11.4, meaning that APF decreased by 0.25Hz every 10 years.

[0071] Conclusions and Discussion: Peak frequency of alpha waves in the occipital lobe during awake, eye-closed surgery is strongly negatively correlated with age, with lower APF in the elderly compared to younger individuals, suggesting it as a potential objective tool for assessing brain function. Individuals with APF significantly deviating from the normal age-related trend may indicate preoperative brain vulnerability and may be at high risk for postoperative delirium and neurological disorders.

[0072] In this embodiment, the effects of anesthetic drugs and surgery on the human brain were also studied:

[0073] Methods: An eight-channel wearable EEG acquisition device was used, with all EEG electrodes located in the occipital lobe. Patients closed and opened their eyes for 20 seconds according to voice commands, repeated three times. A self-developed program automatically analyzed the EEG during the eye-closed period across the eight channels, detecting the presence of alpha wave peaks and calculating the APF value. Preoperative and postoperative APF values ​​were collected. Preoperative values ​​were collected several hours before surgery, and postoperative values ​​were collected one or two days after surgery. A total of 11 pairs of complete preoperative and postoperative APF measurements were included in the analysis.

[0074] Result: As Figure 10 and 11 , Figure 10 This is a comparison chart of the preoperative and postoperative awake alpha peak frequencies in the population presented in this invention. Figure 11 This is a comparison chart of the preoperative and postoperative awake Alpha peak frequencies in this invention. The postoperative awake occipital lobe APF (mean=8.85, std=0.79) of surgical anesthesia patients was significantly lower than that before surgery (mean=9.70, std=0.75), P=0.0018<0.01.

[0075] Conclusions and Discussion: Previous psychological studies on awake, eye-closed occipital lobe APF have generally agreed that it reflects a person's cognitive abilities. This study found that after surgical anesthesia, patients' awake, eye-closed occipital lobe APF decreased significantly compared to pre-operative levels, suggesting that surgical anesthesia can cause a decline in brain and cognitive functions. Figure 12 and 13 , Figure 12 This is a graph showing the preoperative awake Alpha peak frequency results in this invention. Figure 13 This is a graph showing the postoperative awake alpha peak frequency (APF) results from this invention. The decrease in APF was statistically significant not only at the group level but also at the individual level. This study reveals that surgical anesthesia causes a decline in patients' cognitive function, and this impact is not recoverable for at least two days after surgical anesthesia.

[0076] In one possible implementation, such as Figure 2 , Figure 2 yes Figure 1 A flowchart of a specific implementation of step 100; step 100 includes:

[0077] Step 110: Place EEG electrodes in the occipital lobe of the patient's brain and use the EEG electrodes to collect the patient's awake and closed-eye EEG signals; Step 120: Perform rate spectral density analysis on the awake and closed-eye EEG signals and extract the preoperative awake Alpha peak frequency.

[0078] In this embodiment, EEG signal measurement is performed according to a calibration protocol.

[0079] The calibration protocol is as follows: The patient sits quietly in a ward environment within 24 hours before the operation and wears a portable EEG headband to perform a standard 3-minute awake resting-state EEG measurement protocol with eyes closed.

[0080] Awake EEG signals were acquired from the occipital lobe leads (preferably O1 and O2 leads) of the patient's head. Characteristic preoperative awake alpha peak frequencies (defined as...) were extracted using classical power spectral density analysis. ).

[0081] In one possible implementation, after step 120, as Figure 3 , Figure 3 yes Figure 2 A flowchart of a specific implementation method following step 120; step 100 also includes:

[0082] Step 130: Set multiple frequency thresholds for the preoperative awake Alpha peak frequency; Step 140: Divide the preoperative awake Alpha peak frequency into multiple frequency judgment intervals according to the frequency magnitude and frequency thresholds.

[0083] In this embodiment, 9.0Hz and 10.0Hz are defined as frequency thresholds to divide the frequency judgment interval:

[0084] like ;

[0085] like ;

[0086] like .

[0087] In one possible implementation, after step 140, step 100 further includes:

[0088] Step 150: Set the Preoperative Brain Function Reserve Score (PBFRS) for each frequency judgment interval; Step 160: Map the PBFRS to the frequency judgment interval corresponding to the extracted preoperative awake alpha peak frequency.

[0089] In this embodiment, PBFRS is set based on the extracted... Within the physical frequency range, a three-level ladder mapping is implemented to output a static PBFR index (range 1–3 points, with higher scores indicating poorer reserves and greater brain fragility). The PBFRS is then:

[0090] (1),

[0091] Step 200: Obtain the postoperative awake Alpha peak frequency and the lower limit extreme value of the human brain awake occipital lobe Alpha peak frequency within a specified time after anesthesia. Calculate the brain function reserve depletion rate based on the preoperative awake Alpha peak frequency, postoperative awake Alpha peak frequency, and lower limit extreme value. Obtain the anesthesia brain impact judgment interval. Determine the anesthesia brain impact score based on the brain function reserve depletion rate and the anesthesia brain impact judgment interval.

[0092] In this embodiment, the limitations of using peak alpha frequency (APF) to characterize electroencephalogram (EEG) signals were investigated. APF values ​​were measured in the surgical preparation room before surgery. Preoperative anxiety may be a confounding factor affecting APF values. APF values ​​primarily reflect the relatively stable portion of the brain state, but also fluctuate slightly under the influence of physiological and psychological factors. Postoperative pain and sleep may be another confounding factor affecting APF values. Based on these limitations, the Anesthesia Brain Impact Score (ABIS) was introduced to dynamically and quantitatively assess the degree of substantial slowing of the central thalamic-cortical circuit caused by perioperative anesthesia and other negative pathological and physiological stresses.

[0093] In one possible implementation, such as Figure 4 , Figure 4 yes Figure 1 A flowchart of a specific implementation of step 200; step 200 includes:

[0094] Step 210: Obtain the lower limit extreme value of the peak frequency of the occipital lobe alpha wave in the human brain when awake and with eyes closed; Step 220: Calculate the brain reserve safety margin parameter using the preoperative awake alpha peak frequency and the lower limit extreme value; Step 230: Calculate the brain function reserve consumption parameter using the preoperative awake alpha peak frequency and the postoperative awake alpha peak frequency; Step 240: Calculate the brain function reserve consumption rate (BRCR) based on the brain function reserve consumption parameter and the brain reserve safety margin parameter.

[0095] In this embodiment, EEG signals were acquired according to a defined follow-up protocol. The follow-up protocol was as follows: from postoperative day 1 to day 7, the patient underwent the exact same 3-minute awake, resting-state EEG measurement protocol with eyes closed in the ward. EEG signals from the occipital lobe leads were also acquired, and the awake alpha peak frequency (defined as...) one week postoperatively was extracted. ).

[0096] The lower limit of the peak frequency of Alpha waves in the occipital lobe of the human brain when the brain is awake and eyes are closed is set to [value missing]. Therefore, the defining term The "Brain Reserve Margin" (APF) is the inherent slowing range of a patient's brain, representing the maximum extent of slowing that the brain can withstand before a breakdown. A higher APF indicates a greater potential for this value to decrease; in other words, it means the individual has ample brain reserves and a greater capacity to withstand surgical and anesthetic disturbances. Definition This represents the actual "brain reserve consumption" experienced by the patient after the perioperative trauma. Dividing the brain reserve consumption by the brain reserve safety margin yields the core brain reserve consumption rate (BRCR) model.

[0097] (2).

[0098] In one possible implementation, after step 240, as Figure 5 , Figure 5 yes Figure 4 A flowchart of a specific implementation following step 240; step 200 also includes:

[0099] Step 250: Set multiple consumption rate thresholds for brain function reserve depletion rate; Step 260: Divide the brain into multiple anesthesia brain impact judgment intervals according to the consumption rate thresholds or the postoperative awake alpha peak frequency, and set an anesthesia brain impact score for each anesthesia brain impact judgment interval; Step 270: Map the patient's current anesthesia brain impact score to the anesthesia brain impact judgment interval corresponding to the patient's current brain function reserve depletion rate.

[0100] In this embodiment, 15% and 45% are defined as consumption rate thresholds, and the judgment interval for anesthesia brain impact is:

[0101]

[0102]

[0103]

[0104] The formula for calculating the anesthesia brain impact score is:

[0105] (3),

[0106] according to The calculated result range outputs a dynamic anesthesia brain impact score (ABIS) of 1–3 points (higher scores indicate more severe brain impact). In particular, besides... In addition to the brain reserve depletion rate, postoperative This threshold is another sufficient condition for an ABIS score of level 3.

[0107] Step 300: Based on the preoperative brain function reserve score and anesthesia brain impact score, the risk of postoperative delirium in patients is stratified and a postoperative delirium risk index matrix is ​​constructed. The postoperative delirium risk index matrix is ​​used to assess the postoperative delirium in patients and obtain the postoperative delirium risk index.

[0108] In one possible implementation, such as Figure 6 , Figure 6 yes Figure 5 A flowchart of a specific implementation of step 300; step 300 includes:

[0109] Step 310: Stratify the risk of postoperative delirium based on the preoperative brain function reserve score and anesthesia brain impact score; Step 320: Assign values ​​to each matrix stratum, with equal values ​​assigned to strata within the same matrix, to obtain the postoperative delirium risk index matrix, such as... Figure 14 and 15 , Figure 14 This is a schematic diagram of the postoperative delirium risk index matrix in this invention. Figure 15 This is a schematic diagram of the matrix layering of the postoperative delirium risk index matrix in this invention.

[0110] In this embodiment, based on a complete exhaustive combination of preoperative brain functional reserve scores and anesthesia brain impact scores, and according to the above, the postoperative delirium risk index matrix includes... This provides a complete exhaustive list of control combinations. The postoperative delirium risk index matrix is ​​divided into three layers: Layer 1 (all elements are 1), Layer 2 (all elements are 2), and Layer 3 (all elements are 3), corresponding to the postoperative delirium risk index (DRI) of 1, 2, and 3, respectively. DRI values ​​of 1, 2, and 3 correspond to three risk levels: low (Green), medium (Yellow), and high (Red). The red, yellow, and green grading visual light strips on the bedside terminal can be directly driven based on these risk levels.

[0111] In one possible implementation, after step 320, as Figure 7 , Figure 7 yes Figure 6 A flowchart of a specific implementation method following step 320; step 300 also includes:

[0112] Step 330: Obtain the patient's preoperative brain function reserve score and anesthesia brain impact score; Step 340: Determine the target matrix element of the postoperative delirium risk index matrix by using the preoperative brain function reserve score as the row number of the postoperative delirium risk index matrix and the anesthesia brain impact score as the column number of the postoperative delirium risk index matrix; Step 350: Use the target matrix element as the postoperative delirium risk index.

[0113] In this embodiment, the value of the PBFRS index corresponds to the number of rows in the matrix; the value of the ABIS index corresponds to the number of columns in the matrix. For example, when PBFRS=2 and ABIS=3, the DRI value corresponding to the 2nd row and 3rd column in the matrix is ​​found. At this time, DRI=3, which corresponds to a high risk of delirium.

[0114] Analysis of postoperative delirium: The following is a detailed explanation of nine scenarios:

[0115] Case 01 (PBFR=1, ABIS=1) Output DRI = Low Risk (Green)

[0116] Conditions for determining status: The patient's preoperative static brain reserve score (PBFRS) was detected to be 1 (i.e., Furthermore, the dynamic anesthesia brain impact score (ABIS) at the one-week follow-up postoperatively was 1 (i.e., marginal cost ratio). ).

[0117] The patient was located in Matrix[1][1], the main light strip on the visual interactive screen was green (Green always on), and the patient was marked as "perfectly preserved central rhythm" in the software system.

[0118] Electrophysiological Mechanisms and Clinical Prognosis: A Dually Superior Physiological State. This type of patient is commonly found in young and middle-aged adults or elderly subjects with excellent cerebrovascular and cognitive health. Prior to the surgery, the physical infrastructure of the thalamic-cortical feedback loop in their brains was extremely intact, with high initial synaptic density and rapid neural conduction velocity. Simultaneously, with proper perioperative management, the brain did not suffer significant iatrogenic or pathological acute trauma. One week post-surgery, the central nervous system's master clock frequency was perfectly maintained at a high frequency above 10Hz, with no pathophysiological basis for postoperative delirium or acute cognitive impairment.

[0119] Case 02 (PBFR=1, ABIS=2) Output DRI = Low Risk (Green)

[0120] The patient's preoperative static brain reserve score (PBFR) was detected to be 1 (i.e. Furthermore, the dynamic anesthesia brain impact score (ABIS) obtained one week post-surgery was 2 (i.e., the marginal depletion rate was in the moderate range). ).

[0121] Locate the array Matrix[1][2] and determine that although the patient's brain function has been greatly affected, the overall resilience is still within the safe compensation period, so maintain the output of green low-risk signal.

[0122] Electrophysiological Mechanisms and Clinical Prognosis: High-Reserve Compensatory Phenotype. These patients have an extremely robust brain foundation. Although the perioperative period involves complex drug infusions or systemic inflammation that cause a certain degree of acute shock to the central nervous system (e.g., a drop in awake APF from 10.5 Hz to 9.5 Hz one week post-surgery, a significant absolute drop of 1.0 Hz), the absolute frequency after the drop (9.5 Hz) is still far higher than the extreme lower limit of human APF due to the large primary denominator and wide safety margin. This indicates that the nervous system exhibits extremely strong fault tolerance, resulting in a good clinical prognosis and a low probability of delirium.

[0123] Case 03 (PBFR=1, ABIS=3) Output DRI = Medium Risk (Yellow)

[0124] The patient's preoperative static brain reserve score (PBFR) was detected to be 1 (i.e. Furthermore, the dynamic anesthesia brain impact score (ABIS) obtained one week post-surgery was 3 (i.e., the marginal rate of expenditure was severely depleted). ,or ).

[0125] Locate the array Matrix[1][3], adjust the risk index to yellow, and trigger a transient beep on the bedside terminal to alert the caregiver.

[0126] Electrophysiological Mechanisms and Clinical Prognosis: A Resilient Brain Encounters Extreme Shock. This situation is clinically common in highly educated / young patients with excellent initial brain reserves who have undergone highly destructive surgical trauma (such as prolonged cardiopulmonary bypass surgery, cerebral hypoperfusion caused by massive hemorrhage, or severe postoperative sepsis). Despite their strong preoperative brain reserves, a safety margin depletion rate exceeding 45% (e.g., an APF plummeting from 10.5 Hz to around 8.0 Hz) clearly reveals a brain-wide organic blockage in signal transmission between the thalamus and cortex synapses. Although the patient's compensatory abilities prevent immediate onset of typical acute delirium, they exhibit a significant tendency toward postoperative cognitive impairment (POCD) one week post-surgery, necessitating follow-up cognitive rehabilitation in the ward.

[0127] Case 04 (PBFR=2, ABIS=1) Output DRI = Low Risk (Green)

[0128] The patient's preoperative static brain reserve score (PBFR) was detected to be 2 (i.e. Furthermore, the dynamic anesthesia brain impact score (ABIS) obtained one week post-surgery was 1 (i.e., marginal cost ratio). ).

[0129] Locate the array Matrix[2][1]. The system outputs a standard green safety control signal.

[0130] Electrophysiological Mechanisms and Clinical Prognosis: The patient was in a normal, uninjured state in a middle-aged or elderly patient. This matches the optimal electrophysiological trajectory of a typical middle-aged or elderly patient undergoing minimally invasive, short-duration, non-hemorrhagic surgery. Although the patient's initial brain reserve score was at a moderate decline (Level 2) due to normal age, the extremely precise control of intraoperative anesthesia depth (completely avoiding deep burst suppression and iatrogenic drug overdose) and the absence of severe systemic inflammation in the postoperative subacute phase resulted in a relatively stable brain function.

[0131] Case 05 (PBFR=2, ABIS=2) Output DRI = Medium Risk (Yellow)

[0132] The patient's preoperative static brain reserve score (PBFR) was detected to be 2 (i.e. Furthermore, the dynamic anesthesia brain impact score (ABIS) obtained one week post-surgery was 2 (i.e., the marginal depletion rate was in the moderate range). ).

[0133] Locate the array Matrix[2][2] and display the yellow indicator block.

[0134] Electrophysiological Mechanisms and Clinical Prognosis: This is the most typical postoperative manifestation in elderly patients. This condition is widespread in routine elderly surgical patients in general surgery and orthopedics. These patients have moderate initial physical reserves, accompanied by standard perioperative EEG slowing. At this point, their thalamic-cortical EEG safety margin has been gradually eroded and depleted, and the compensatory capacity of the entire brain network is on the verge of dynamic equilibrium. If these patients are further subjected to environmental stress in the surgical ward (such as severe noise interference at night, ICU environmental deprivation, and severe postoperative wound pain), they are highly susceptible to crossing the red line and inducing acute delirium. This group belongs to the target population for routine clinical behavioral interventions (such as multidimensional orientation correction and sleep rhythm optimization) in the ward.

[0135] Case 06 (PBFR=2, ABIS=3) Output DRI = High Risk (Red)

[0136] The patient's preoperative static brain reserve score (PBFR) was detected to be 2 (i.e. Furthermore, the dynamic anesthesia brain impact score (ABIS) obtained one week post-surgery was 3 (i.e., marginal depletion rate). ,or ).

[0137] Locate the array Matrix[2][3], determine the patient's status as red, and mark the patient as "critical state of severe brain function impairment" in the software system.

[0138] Electrophysiological Mechanism and Clinical Prognosis: Acute decompensation occurred in the normal brain under the impact of anesthesia. The patient's original brain matrix was at the level of a typical middle-aged or elderly person, but due to an unexpectedly severe systemic shock during the perioperative period (e.g., prolonged and extensive EEG burst inhibition during surgery, leading to deep hypersynchronization inhibition of neuronal synapses), a follow-up test one week postoperatively showed a precipitous decline in peak alpha frequency. Clinically, this manifested as an extremely fragile state of brain function, with a surge in the incidence of postoperative delirium.

[0139] "Decompensation" refers to the situation where, when an organ or system is damaged or overloaded, the body's self-regulation and compensatory abilities are exhausted, making it unable to maintain normal physiological functions, leading to a complete collapse of the balance of the entire system.

[0140] Case 07 (PBFR=3, ABIS=1) Output DRI = Medium Risk (Yellow)

[0141] The patient's preoperative static brain reserve score (PBFR) was detected to be 3 (i.e. The dynamic anesthesia brain impact score (ABIS) obtained one week post-surgery was 1 (i.e., marginal cost ratio). ).

[0142] Locate the array Matrix[3][1], which is yellow (medium risk).

[0143] Electrophysiological Mechanism and Clinical Prognosis: Baseline-Dominated High Risk. This situation fully demonstrates the rigor of this two-dimensional mechanism design. Although the frequency of follow-up tests in the first week after surgery hardly decreased, the patient's occipital lobe awake alpha baseline was already significantly low before entering the operating room, suggesting that the patient already had severe intrinsic brain aging, microvascular sclerosis, or early-stage latent multiple dementia. Such brain hardware has extremely high and unpredictable sensitivity to any slight environmental disturbance (such as transient hypoxia or medication change stimulation). Therefore, the algorithm classified it as yellow medium risk to ensure the prudence of clinical protection throughout the perioperative period.

[0144] Case 08 (PBFR=3, ABIS=2) Output DRI = High Risk (Red)

[0145] The patient's preoperative static brain reserve score (PBFR) was detected to be 3 (i.e. Furthermore, the dynamic anesthesia brain impact score (ABIS) obtained one week post-surgery was 2 (i.e., the marginal depletion rate was in the moderate range). ).

[0146] Locate the array Matrix[3][2]. It was detected that the patient was in a dual high-risk state of "weak static foundation" and "large dynamic impact", and the control state machine output the highest level red high-risk warning.

[0147] Electrophysiological Mechanisms and Clinical Prognosis: The Definite Collapse of a Fragile Brain Due to Re-injury. Patients already have extremely limited initial brain reserves (a very narrow denominator), and after the trauma of surgery and anesthesia, they experience a further moderate rhythm deficit. Under the combined influence of these two malignant factors, patients in the clinical ward are highly likely to present with highly apathetic, somnolent, and concealed hypoactive delirium (Hypoactive Delirium) or acute generalized cognitive disorder, with impaired cognitive function.

[0148] Case 09 (PBFR=3, ABIS=3) Output DRI = High Risk (Red)

[0149] The patient's preoperative static brain reserve score was detected to be at the highest risk level, PBFR=3 (i.e., Furthermore, the dynamic anesthesia brain impact score, which was degraded one week post-surgery, also reached its peak at ABIS=3 (i.e., marginal depletion rate). ,or ).

[0150] Locate the array Matrix[3][3]. This is the ultimate critical value node of this finite state machine. The central processing unit triggers a red danger flashing across all channels and sends the highest level bedside neurological critical value notification to the medical IoT main network, strongly driving the intervention of the multidisciplinary advanced brain protection team.

[0151] Electrophysiological mechanisms and clinical prognosis: In this case, elderly patients with severe cognitive decline suffer the most severe perioperative central nervous system injury.

[0152] Typical patient case: One week after surgery, a patient's awake, closed-eye APF only dropped to 7.4 Hz. Compared to the preoperative baseline of 8.0 Hz, this represents a significant absolute decrease. The change was only a seemingly clinically insignificant 0.6 Hz. Because this 0.6 Hz change is extremely small (numerically much smaller than the significantly reduced 2.5 Hz in the young patient in case 03), it could be considered a safe case if only the absolute amount of the decrease was considered. This case was handled using an individualized safety margin leverage formula; however, since the preoperative baseline was only 8.0 Hz, the available margin was very small. Substitute into the formula:

[0153]

[0154] The consumption rate is as high as 60%. The anesthesia brain impact score directly locks in the highest score ABIS=3. The state machine successfully amplified the originally weak latent slowing down several times through this mechanism, classifying it as a red high-risk condition (DRI=3, Red). Clinically, it has been confirmed that patients in this state completely lose the ability to synchronize the thalamic-cortical network information resonance, and the postoperative delirium outbreak rate is close to 100%.

[0155] To address the aforementioned technical problems, this application also provides a delirium assessment system 400 based on occipital alpha waves during awake, closed-eye occipital lobe examination. Figure 16 , Figure 16 This is a schematic diagram of an embodiment of a delirium assessment system based on awake, closed-eye occipital alpha waves according to the first aspect of this application, comprising:

[0156] The preoperative brain function reserve scoring module 401 is used to acquire the patient's awake and closed-eye EEG signal, extract the preoperative awake Alpha peak frequency based on the awake and closed-eye EEG signal, obtain the frequency judgment interval corresponding to the brain function reserve, and determine the patient's preoperative brain function reserve score based on the preoperative awake Alpha peak frequency and frequency judgment interval.

[0157] The anesthesia brain impact scoring module 402 is used to obtain the postoperative awake Alpha peak frequency and the lower limit extreme value of the human brain awake occipital lobe Alpha peak frequency within a specified time after anesthesia. The brain function reserve consumption rate is calculated based on the preoperative awake Alpha peak frequency, the postoperative awake Alpha peak frequency, and the lower limit extreme value of the frequency. The anesthesia brain impact judgment interval is obtained, and the anesthesia brain impact score is determined based on the brain function reserve consumption rate and the anesthesia brain impact judgment interval.

[0158] The postoperative delirium assessment module 403 is used to stratify the risk of postoperative delirium in patients based on the preoperative brain function reserve score and the anesthesia brain impact score, and to construct a postoperative delirium risk index matrix. The postoperative delirium risk index matrix is ​​used to assess the postoperative delirium in patients and obtain the postoperative delirium risk index.

[0159] To address the aforementioned technical problems, embodiments of this application also provide a computer device, including a memory and a processor. The memory stores computer-readable instructions, and when the processor executes the computer-readable instructions, it implements the steps of the delirium assessment method based on awake, closed-eye occipital alpha waves as described in the first aspect.

[0160] This computer device can be a computer, server, workstation, or other similar device; it can also be a mobile device such as a mobile phone, tablet, or in-vehicle mobile terminal; or other devices with program execution capabilities. Its internal structure diagram can be as follows: Figure 17 As shown, Figure 17 This is a schematic diagram of the structure of a computer device according to an embodiment of the present application. The computer device includes a processor, a memory, and a network module. The processor of the computer device provides computing and control capabilities. The memory of the computer device includes non-volatile and / or volatile storage media and internal memory. The non-volatile storage media stores an operating system, instructions, or code. The internal memory provides an environment for the operation of the operating system and instructions or code in the non-volatile storage media. When the instructions or code are executed by the processor, they implement the functions or steps of a delirium assessment method based on awake, closed-eye occipital alpha waves. The network module of the computer device may include a network interface and / or a wireless network module, through which the computer device can communicate with other devices or service platforms. Furthermore, the computer device may also include a display screen and input devices, etc.

[0161] The memory is used to store computer programs, which include program instructions. The processor is configured to call the program instructions, and when the processor executes the instructions or code, it implements the steps of the Gram-angle field-based anesthesia depth assessment method as described above.

[0162] To address the aforementioned technical problems, embodiments of this application also provide a computer-readable storage medium storing computer-readable instructions. When executed by a processor, the computer-readable instructions implement the steps of the delirium assessment method based on awake, closed-eye occipital alpha waves as described in the first aspect.

[0163] The computer-readable storage medium stores a computer program, which includes program instructions that are implemented when executed by a processor. Figures 1 to 7 The delirium assessment methods based on the occipital alpha wave provided in each step of the above process can be found in the implementation methods provided in each step, and will not be repeated here.

[0164] The aforementioned computer-readable storage medium may be an apparatus for assessing delirium based on occipital alpha waves during awake, closed-eye sleep, as provided in any of the foregoing embodiments, or an internal storage unit of the aforementioned terminal device, such as a hard drive or memory of a computer device. The computer-readable storage medium may also be an external storage device of the computer device, such as a plug-in hard drive, smart media card (SMC), secure digital (SD) card, flash card, etc., provided on the computer device.

[0165] Furthermore, the computer-readable storage medium may include both internal storage units and external storage devices of the computer device. The computer-readable storage medium is used to store the computer program and other programs and data required by the computer device. The computer-readable storage medium can also be used to temporarily store data that has been output or will be output.

[0166] However, it should be understood that it is not required to implement all the components shown; more or fewer components may be implemented instead. Those skilled in the art will understand that the computer device described herein is a device capable of automatically performing numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes, but is not limited to, microprocessors, application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), digital signal processors (DSPs), embedded devices, etc.

[0167] The computer device can be a desktop computer, laptop, handheld computer, or cloud server, etc. The computer device can interact with the user via a keyboard, mouse, remote control, touchpad, or voice control.

[0168] Compared with the prior art, the embodiments of this application have the following main technical effects: By determining the preoperative brain function reserve score and the postoperative anesthesia brain impact score, the risk of postoperative delirium in patients is stratified according to the preoperative brain function reserve score and the anesthesia brain impact score, and a postoperative delirium risk index matrix is ​​constructed. The postoperative delirium risk index matrix is ​​used to assess the postoperative delirium in patients. The stratification using the preoperative brain function reserve score and the postoperative anesthesia brain impact score makes the delirium assessment more accurate, improves the accuracy and objectivity of the postoperative delirium assessment, and solves the problems of time-consuming, subjective, and inaccurate current postoperative delirium assessment. The joint decision-making using the preoperative brain function reserve score and the postoperative anesthesia brain impact score determines the patient's postoperative delirium risk index, making the assessment more intuitive.

[0169] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for assessing delirium based on occipital alpha waves during awake, closed-eye delirium, characterized in that it includes: Step 100: Obtain the patient's awake and closed-eye EEG signal, extract the preoperative awake Alpha peak frequency based on the awake and closed-eye EEG signal, obtain the frequency judgment interval corresponding to the brain function reserve, and determine the patient's preoperative brain function reserve score based on the preoperative awake Alpha peak frequency and the frequency judgment interval. Step 200: Obtain the postoperative awake Alpha peak frequency and the lower limit extreme value of the human brain awake occipital lobe Alpha peak frequency within a specified time after anesthesia. Calculate the brain function reserve consumption rate based on the preoperative awake Alpha peak frequency, postoperative awake Alpha peak frequency, and lower limit extreme value. Obtain the anesthesia brain impact judgment interval. Determine the anesthesia brain impact score based on the brain function reserve consumption rate and the anesthesia brain impact judgment interval. Step 300: Based on the preoperative brain function reserve score and anesthesia brain impact score, the patient's postoperative delirium risk is stratified and a postoperative delirium risk index matrix is ​​constructed. The postoperative delirium risk index matrix is ​​used to assess the patient's postoperative delirium and obtain the postoperative delirium risk index.

2. The delirium assessment method based on occipital alpha waves during awake, closed-eye sleep, as described in claim 1, is characterized in that... Step 100 includes: Step 110: Place EEG electrodes in the occipital lobe of the patient's brain and use the EEG electrodes to collect the patient's awake, closed-eye EEG signals. Step 120: Perform rate spectral density analysis on the awake, closed-eye EEG signal to extract the preoperative awake Alpha peak frequency.

3. The delirium assessment method based on occipital alpha waves during awake, closed-eye sleep, as described in claim 2, is characterized in that... After step 120, step 100 further includes: Step 130: Set multiple frequency thresholds for the preoperative awake alpha peak frequency; Step 140: According to the frequency magnitude, the preoperative awake Alpha peak frequency is sequentially divided into multiple frequency judgment intervals based on the frequency threshold. Step 150: Set the preoperative brain function reserve score for each frequency judgment interval; Step 160: Based on the frequency judgment interval corresponding to the extracted preoperative awake Alpha peak frequency, the preoperative brain function reserve score is obtained by mapping.

4. The delirium assessment method based on occipital alpha waves during awake, closed-eye sleep, as described in claim 1, is characterized in that... Step 200 includes: Step 210: Obtain the lower limit extreme value of the peak frequency of the Alpha wave in the occipital lobe of a awake, closed-eye human brain. Step 220: Calculate the brain reserve safety margin parameter using the preoperative awake Alpha peak frequency and the lower limit extreme value of the frequency; Step 230: Calculate the brain function reserve consumption parameters using the preoperative awake alpha peak frequency and the postoperative awake alpha peak frequency; Step 240: Calculate the brain function reserve consumption rate based on the brain function reserve consumption parameter and the brain reserve safety margin parameter.

5. The delirium assessment method based on occipital alpha waves during awake, closed-eye sleep, as described in claim 4, is characterized in that... After step 240, step 200 further includes: Step 250: Set multiple consumption rate thresholds for the brain function reserve consumption rate; Step 260: According to the consumption rate, the brain impact is divided into multiple anesthesia brain impact judgment intervals according to the consumption rate threshold or the postoperative awake alpha peak frequency, and an anesthesia brain impact score is set for each anesthesia brain impact judgment interval. Step 270: Based on the anesthesia brain impact judgment interval corresponding to the patient's current brain function reserve depletion rate, the patient's current anesthesia brain impact score is mapped to the interval.

6. The delirium assessment method based on occipital alpha waves during awake, closed-eye sleep, as described in claim 1, is characterized in that... Step 300 includes: Step 310: Stratify the risk of postoperative delirium in patients based on the preoperative brain function reserve score and anesthesia brain impact score; Step 320: Assign values ​​to each matrix layer, with equal values ​​assigned to layers within the same matrix, to obtain the postoperative delirium risk index matrix.

7. The delirium assessment method based on occipital alpha waves during awake, closed-eye sleep, as described in claim 6, is characterized in that... After step 320, step 300 further includes: Step 330: The patient's preoperative brain function reserve score and anesthesia brain impact score; Step 340: Determine the target matrix element of the postoperative delirium risk index matrix by using the preoperative brain function reserve score as the row number of the postoperative delirium risk index matrix and the anesthesia brain impact score as the column number of the postoperative delirium risk index matrix. Step 350: Use the elements of the target matrix as the postoperative delirium risk index.

8. A delirium assessment system based on awake, closed-eye occipital alpha waves, employing the delirium assessment method based on awake, closed-eye occipital alpha waves as described in any one of claims 1-7, characterized in that, include: The preoperative brain function reserve scoring module is used to acquire the patient's awake and closed-eye EEG signal, extract the preoperative awake Alpha peak frequency based on the awake and closed-eye EEG signal, obtain the frequency judgment interval corresponding to the brain function reserve, and determine the patient's preoperative brain function reserve score based on the preoperative awake Alpha peak frequency and the frequency judgment interval. The anesthesia brain impact scoring module is used to obtain the postoperative awake Alpha peak frequency and the lower limit extreme value of the human brain awake occipital lobe Alpha peak frequency within a specified time after anesthesia. Based on the preoperative awake Alpha peak frequency, postoperative awake Alpha peak frequency, and lower limit extreme value, the brain function reserve consumption rate is calculated to obtain the anesthesia brain impact judgment interval. The anesthesia brain impact score is determined based on the brain function reserve consumption rate and the anesthesia brain impact judgment interval. The postoperative delirium assessment module is used to stratify the risk of postoperative delirium in patients based on the preoperative brain function reserve score and anesthesia brain impact score, and to construct a postoperative delirium risk index matrix. The postoperative delirium risk index matrix is ​​used to assess the postoperative delirium in patients and obtain the postoperative delirium risk index.

9. A computer device, characterized in that, The device includes a memory and a processor, wherein the memory stores computer-readable instructions, and the processor executes the computer-readable instructions to implement the steps of the delirium assessment method based on the occipital alpha wave of awake eye-closed diaphragm as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-readable instructions that, when executed by a processor, implement the steps of the delirium assessment method based on the occipital alpha wave in awake, closed-eye occipital lobe as described in any one of claims 1 to 7.

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