Qualitative analysis method and system for the correlation between transcutaneous auricular vagus nerve stimulation and disorders of consciousness

Abstract

The present invention belongs to the technical field of diagnostic data processing, and specifically relates to a method and system for qualitative analysis of the correlation between transcutaneous auricular vagus nerve stimulation and disorders of consciousness. By constructing the characteristic information of the data distribution of standard consciousness evaluation index data, the invention can more comprehensively reflect the consciousness state of patients. In addition, through the corresponding verification of the abnormal fluctuation value of the electrical stimulation signal, the influencing factors of drug treatment and disease lesions can be more accurately identified and excluded, so as to improve the accuracy and effectiveness of the etiology analysis of patients, and enable the rational allocation and use of medical resources; the invention also provides a system and an electronic device for qualitative analysis of the correlation between transcutaneous auricular vagus nerve stimulation and disorders of consciousness.

Description

Technical Field

[0001] The present invention belongs to the technical field of diagnostic data processing, and particularly relates to a method and system for qualitative analysis of the correlation between transcutaneous auricular vagus nerve stimulation and disorders of consciousness. Background Art

[0002] In recent years, the research on disorders of consciousness and nerve stimulation has attracted extensive attention. There are significant differences in the neural activities between patients with disorders of consciousness and normal individuals. Studying these differences can help distinguish different types of disorders of consciousness. At the same time, non-invasive brain nerve regulation technologies, such as mechanical stimulation, electrical stimulation, and magnetic stimulation, have achieved more and more applications. Specifically, transcutaneous auricular vagus nerve stimulation has achieved certain results in recent years and has attracted much attention because it uses electrical stimulation to regulate the body state, and has broad clinical application prospects. However, in the actual application process, the correlation between nerve electrical stimulation and disorders of consciousness and its specific impact on the patient's consciousness state still lack clear qualitative analysis and correlation evaluation.

[0003] Therefore, how to accurately analyze the correlation qualitatively through scientific methods and then improve the clinical application effect of nerve electrical stimulation has become a key issue in current research. Based on this, the present solution has carried out corresponding research and design. Summary of the Invention

[0004] The purpose of the present invention is to provide a method for qualitative analysis of the correlation between transcutaneous auricular vagus nerve stimulation and disorders of consciousness, which can accurately analyze the relationship between transcutaneous auricular vagus nerve stimulation and disorders of consciousness qualitatively.

[0005] The technical solutions adopted by the present invention are specifically as follows:

[0006] A method for qualitative analysis of the correlation between transcutaneous auricular vagus nerve stimulation and disorders of consciousness includes:

[0007] Obtain the physiological data of the patient in the normal consciousness state, and perform preprocessing to remove noise and outliers to obtain the physiological parameters in the normal consciousness state;

[0008] Obtain the physiological data of the patient in the state of disorders of consciousness, and compare it with the physiological parameters in the normal consciousness state to obtain the consciousness evaluation index;

[0009] Obtain the electrical stimulation signal, obtain the corresponding abnormal fluctuation value according to the electrical stimulation signal, and then obtain the corresponding fluctuation risk according to the abnormal fluctuation value;

[0010] Judge the correlation between transcutaneous auricular vagus nerve stimulation and disorders of consciousness according to the consciousness evaluation index and the fluctuation risk, and generate an association report;

[0011] Wherein, the association report includes the consciousness evaluation index, the fluctuation risk, and the correlation conclusion.

[0012] In a preferred embodiment, the step of obtaining physiological data of a patient in a normal conscious state, preprocessing it to remove noise and outliers, and obtaining physiological parameters in the normal conscious state includes:

[0013] Obtain physiological data of a patient in a normal conscious state and perform normalization processing;

[0014] Perform denoising processing on the normalized physiological data to remove the noise components in the physiological data;

[0015] Perform duplicate item screening on the denoised physiological data to eliminate duplicate parameters in the physiological data and obtain physiological parameters in the normal conscious state.

[0016] In a preferred embodiment, the step of obtaining physiological data of a patient in a state of impaired consciousness, comparing it with the physiological parameters in the normal conscious state, and obtaining a consciousness evaluation index includes:

[0017] Obtain physiological data of a patient in a state of impaired consciousness and perform synchronous screening with the physiological parameters in the normal conscious state to obtain comparison parameters in the state of impaired consciousness;

[0018] Perform a difference operation on the physiological parameters in the normal conscious state and the comparison parameters in the state of impaired consciousness to obtain difference parameters;

[0019] Compare the difference parameters with a preset evaluation threshold;

[0020] When the difference parameter is greater than the evaluation threshold, it indicates that the difference between the physiological parameters in the normal conscious state and the comparison parameters in the state of impaired consciousness is too large, and it is determined that the consciousness information of the patient in the state of impaired consciousness is abnormal;

[0021] When the difference parameter is less than or equal to the evaluation threshold, it indicates that the difference between the physiological parameters in the normal conscious state and the comparison parameters in the state of impaired consciousness is acceptable, and it is determined that the consciousness information of the patient in the state of impaired consciousness is normal.

[0022] In a preferred embodiment, the step of obtaining an electrical stimulation signal, obtaining a corresponding abnormal fluctuation value according to the electrical stimulation signal, and then obtaining a corresponding fluctuation risk according to the abnormal fluctuation value includes:

[0023] Obtain an electrical stimulation signal and record the first fluctuation duration of the electrical stimulation signal;

[0024] Obtain the standard waveform function of the electrical stimulation signal and compare it with the electrical stimulation signal to obtain a plurality of abnormal nodes;

[0025] Obtain the fluctuation values of the electrical stimulation signals under multiple of the abnormal nodes, and record them as abnormal fluctuation values;

[0026] Obtain the fluctuation delay parameter of the abnormal fluctuation value, and compare the fluctuation delay parameter with a preset delay threshold;

[0027] When the fluctuation delay parameter is less than the preset delay threshold, it indicates that the duration of the abnormal fluctuation is short, the electrical stimulation signal is stable, and the corresponding abnormal fluctuation value is determined as a short-term fluctuation risk;

[0028] When the fluctuation delay parameter is greater than or equal to the delay threshold, it indicates that the duration of the abnormal fluctuation is long, the electrical stimulation signal is unstable, and the corresponding abnormal fluctuation value is determined as a long-term fluctuation risk.

[0029] In a preferred solution, the step of obtaining the standard waveform function of the electrical stimulation signal and comparing it with the electrical stimulation signal to obtain multiple abnormal nodes includes:

[0030] Obtain the standard waveform function of the electrical stimulation signal;

[0031] Perform a Fourier transform on the standard waveform function to obtain standard waveform frequency domain characteristic parameters;

[0032] Obtain a sample waveform, and perform a Fourier transform on the sample waveform to obtain the frequency domain characteristic parameters of the sample waveform;

[0033] Perform a difference operation on the frequency domain characteristic parameters of the standard waveform and the frequency domain characteristic parameters of the sample waveform to obtain frequency domain difference parameters;

[0034] Obtain the electroencephalogram waveform of the patient, obtain a standard time period according to the electroencephalogram waveform, and obtain the electrical stimulation signal that matches the frequency domain characteristic parameters of the standard waveform within the standard time period, and record it as a calibration waveform;

[0035] Perform the same processing on the time of the calibration waveform and the standard waveform, and output it as a comparison time parameter;

[0036] When the frequency domain difference parameter is within a preset first comparison threshold, and the comparison time parameter is greater than or equal to a preset second comparison threshold, then calibrate the calibration waveform as the standard waveform of the electrical stimulation signal, and record the standard time period as the sample time period;

[0037] When the frequency domain difference parameter is not within the preset first comparison threshold, or the comparison time parameter is less than or equal to the preset second comparison threshold, then reselect the calibration waveform, and compare the standard waveform and the calibration waveform;

[0038] Compare the electrical stimulation waveform within the sample time period with the standard waveform, and record the nodes with abnormal comparison as abnormal nodes.

[0039] In a preferred embodiment, each of the abnormal fluctuation values includes an abnormal duration, a fluctuation amplitude, and a fluctuation frequency. Among them, the abnormal duration is used to determine short-term fluctuations or long-term fluctuations, the fluctuation amplitude is used to evaluate the degree of change of the fluctuations, and the fluctuation frequency is used to analyze the occurrence frequency of the fluctuations.

[0040] In a preferred embodiment, after obtaining the corresponding fluctuation risk according to the abnormal fluctuation value, the short-term fluctuation amplitude of the electrical stimulation signal is obtained based on the short-term fluctuation risk, and is compared with the safety threshold of the short-term fluctuation amplitude;

[0041] When the short-term fluctuation amplitude is greater than the safety threshold, the corresponding short-term fluctuation risk is upgraded to a long-term fluctuation risk;

[0042] When the short-term fluctuation amplitude is less than or equal to the safety threshold, it is determined that the electrical stimulation signal is normal.

[0043] In a preferred embodiment, the step of judging the correlation between transcutaneous auricular vagus nerve stimulation and consciousness disorder according to the consciousness evaluation index and the fluctuation risk, and generating an association report includes:

[0044] Obtain a comprehensive risk assessment model;

[0045] Input the consciousness evaluation index and the fluctuation risk parameters into the comprehensive risk assessment model to obtain a comprehensive risk assessment value;

[0046] Obtain a comprehensive risk assessment threshold and compare it with the comprehensive risk assessment value;

[0047] When the comprehensive risk assessment value is greater than or equal to the comprehensive risk assessment threshold, it is determined that there is an association relationship between transcutaneous auricular vagus nerve stimulation and consciousness disorder, and an association report is generated;

[0048] When the comprehensive risk assessment value is less than the comprehensive risk assessment threshold, it is determined that there is no association relationship between transcutaneous auricular vagus nerve stimulation and consciousness disorder, and a non-correlation report is generated.

[0049] In addition, the present invention also provides a qualitative analysis system for the correlation between transcutaneous auricular vagus nerve stimulation and consciousness disorder, including:

[0050] A parameter acquisition module, which is used to acquire the physiological data of the patient in the normal consciousness state, and perform preprocessing to remove noise and outliers to obtain the physiological parameters in the normal consciousness state;

[0051] A state evaluation module, which is used to acquire the physiological data of the patient in the consciousness disorder state, and perform a comparison process with the physiological parameters in the normal consciousness state to obtain a consciousness evaluation index;

[0052] A stimulation acquisition module, which is used to acquire an electrical stimulation signal, obtain a corresponding abnormal fluctuation value according to the electrical stimulation signal, and then obtain a corresponding fluctuation risk according to the abnormal fluctuation value;

[0053] An association determination module, which is used to judge the correlation between transcutaneous auricular vagus nerve stimulation and consciousness disorder according to the consciousness evaluation index and the fluctuation risk, and generate an association report;

[0054] Wherein, the association report includes the consciousness evaluation index, the fluctuation risk, and the correlation conclusion.

[0055] The present invention also provides an electronic device, including: one or more processors;

[0056] A storage device for storing computer programs executable by one or more processors;

[0057] Wherein, when executing the computer program, the one or more processors implement the method for qualitative analysis of the correlation between transcutaneous auricular vagus nerve stimulation and consciousness disorder.

[0058] By constructing the characteristic information of the standard consciousness evaluation index data distribution data, the present invention can more comprehensively reflect the consciousness state of the patient. In addition, through the corresponding verification of the abnormal fluctuation value of the electrical stimulation signal, the influencing factors of drug treatment and disease lesions can be more accurately identified and excluded, so as to improve the accuracy and effectiveness of the patient's etiology analysis, and enable the rational allocation and use of medical resources. Brief Description of the Drawings

[0059] Figure 1 It is a schematic flowchart of the method of the present invention;

[0060] Figure 2 It is a schematic structural diagram of the system of the present invention. Detailed Embodiments

[0061] In order to more clearly illustrate the technical solution of the present invention, the following will be described in detail in combination with embodiments. This embodiment is based on the basic solution of the present invention, and the difference is that this embodiment provides some specific parameters and data for better understanding and application of the present invention.

[0062] Embodiment 1

[0063] Please refer to Figure 1 As shown, it is a flowchart of the method provided by the present invention, specifically as follows:

[0064] S1. Acquire the physiological data of the patient in the normal consciousness state, and perform preprocessing to remove noise and outliers, so as to obtain the physiological parameters in the normal consciousness state;

[0065] S2. Obtain the physiological data of the patient in the state of consciousness disorder, and compare it with the physiological parameters in the normal consciousness state to obtain the consciousness evaluation index;

[0066] S3. Obtain the electrical stimulation signal, obtain the corresponding abnormal fluctuation value according to the electrical stimulation signal, and then obtain the corresponding fluctuation risk according to the abnormal fluctuation value;

[0067] S4. Judge the correlation between transcutaneous auricular vagus nerve stimulation and consciousness disorder according to the consciousness evaluation index and the fluctuation risk, and generate an association report; wherein, the association report includes the consciousness evaluation index, the fluctuation risk and the correlation conclusion.

[0068] In this embodiment, the fluctuation range of blood pressure specifically refers to ±20 mmHg, the fluctuation range of the electrical stimulation signal specifically refers to ±10%, the normal consciousness state means that the patient is conscious, with normal thinking and behavior, and the consciousness disorder means that the patient is unconscious and has abnormal thinking and behavior.

[0069] For a certain volunteer patient: In his normal consciousness state, his physiological data such as heart rate, brain waves, etc. are heart rate: 65 beats / min, blood pressure: 90 - 100 mmHg (±20 mmHg), brain waves (α waves): 8 - 12 Hz; after the occurrence of consciousness disorder, his synchronous physiological data is measured as the heart rate fluctuates between 85 - 100 beats / min, the blood pressure fluctuates between 120 - 160 mmHg, and the abnormal fluctuation frequency is 20 times / min.

[0070] The normal consciousness state means that the body state is normal, such as being conscious, with normal thinking and behavior, and no abnormal interference is taken. The recording period can be adjusted according to the doctor's advice, such as 1 - 2 days. Among them, the determination of the sampling period in the normal consciousness state is carried out when the patient is conscious and there is no abnormality. Based on this, it can be known that the physiological data of the patient in the normal consciousness state can be used to construct a corresponding characteristic information template to facilitate real-time comparison of the physiological data of the patient in the consciousness disorder state.

[0071] The physiological data of the patient in the consciousness disorder state is the original information recorded in the consciousness disorder state. The confirmation of the abnormal information lies in that the physiological data in the normal consciousness state lies in the waking state of the body state, such as heart rate, brain waves, etc. The measurement method is that weak fluctuations can be obtained by conventional measurement methods. After the comparison is completed, the fluctuation parameter of the nerve electrical stimulation is recorded as ±10%, and the duration of the abnormal fluctuation is 0.5 hours.

[0072] The steps of obtaining the physiological data of the patient in the normal consciousness state and performing preprocessing to remove noise and outliers to obtain the physiological parameters in the normal consciousness state include:

[0073] Obtain the physiological data of the patient in the normal conscious state and perform normalization processing;

[0074] Perform denoising processing on the normalized physiological data to remove the noise components in the physiological data;

[0075] Screen out duplicate items from the denoised physiological data, eliminate the duplicate parameters in the physiological data, and obtain the physiological parameters in the normal conscious state.

[0076] For the physiological data of the patient in the normal conscious state, preprocessing is required to remove the noise and outliers therein. This process includes the following steps:

[0077] First, perform normalization processing on the collected physiological data to eliminate the scale differences between different data.

[0078] Secondly, perform denoising processing on the normalized physiological data to filter out noise components such as electromagnetic interference.

[0079] Screen out duplicate items from the denoised data, eliminate the physiological parameters that are repeatedly recorded, and finally obtain the physiological parameters reflecting the patient's normal conscious state.

[0080] The steps of obtaining the physiological data of the patient in the state of consciousness disorder and comparing it with the physiological parameters in the normal conscious state to obtain the consciousness evaluation index include:

[0081] Obtain the physiological data of the patient in the state of consciousness disorder and perform synchronous screening with the physiological parameters in the normal conscious state to obtain the comparison parameters in the state of consciousness disorder;

[0082] Perform a difference operation on the physiological parameters in the normal conscious state and the comparison parameters in the state of consciousness disorder to obtain the difference parameters;

[0083] Compare the difference parameters with the preset evaluation threshold;

[0084] When the difference parameter is greater than the evaluation threshold, it indicates that the difference between the physiological parameters in the normal conscious state and the comparison parameters in the state of consciousness disorder is too large, and it is determined that the consciousness information of the patient in the state of consciousness disorder is abnormal;

[0085] When the difference parameter is less than or equal to the evaluation threshold, it indicates that the difference between the physiological parameters in the normal conscious state and the comparison parameters in the state of consciousness disorder is acceptable, and it is determined that the consciousness information of the patient in the state of consciousness disorder is normal.

[0086] Under the state of the patient's unconsciousness, synchronously record the patient's physiological data to ensure data consistency, and then output the paired parameters such as temperature under the unconsciousness state, and compare the physiological and psychological parameters (brain waves, heart rate, blood pressure, etc.) under the normal and abnormal consciousness states.

[0087] Calculate the difference between each pair of parameters to obtain a difference parameter table, including a series of values.

[0088] Set an evaluation threshold (based on clinical data or expert opinions). Assume the threshold is: heart rate: ±5 beats per minute, blood pressure: ±10 mmHg, brain wave (α wave): 1 Hz.

[0089] Compare the difference parameters with the evaluation threshold. The maximum values of the difference parameters are: heart rate: 10 beats per minute, blood pressure: 15 mmHg, brain wave (α wave): 2 Hz. If each index exceeds the evaluation threshold, it can be determined that the patient's consciousness information is abnormal under the unconsciousness state.

[0090] The steps of obtaining an electrical stimulation signal, obtaining a corresponding abnormal fluctuation value according to the electrical stimulation signal, and then obtaining a corresponding fluctuation risk according to the abnormal fluctuation value include:

[0091] Obtain an electrical stimulation signal and record the first fluctuation duration of the electrical stimulation signal;

[0092] Assume that the measured electrical fluctuation curve shows continuous fluctuations for 0.5 hours.

[0093] Obtain the standard waveform function of the electrical stimulation signal and compare it with the electrical stimulation signal to obtain multiple abnormal nodes;

[0094] Record the standard waveform under the patient's normal consciousness state. The determination of the sampling period under the normal consciousness state can be carried out when the patient is conscious and there is no any abnormality. The first fluctuation duration is one fluctuation per minute, lasting for 0.5 hours, and the frequency control result is 90.8%, sampling state.

[0095] Calculate the first fluctuation duration / (total number of abnormal nodes summarized) = 0.5×60×60 / 30 = 60 sec, that is, it means that the electrical stimulation waveform is compared with the standard waveform per minute. The abnormal node refers to the deviation point of the nerve electrical stimulation signal under the patient's unconsciousness state compared with the partial electrical signal under the normal consciousness state (assuming the electrical signal is 60% and the abnormal node identification is 70%).

[0096] Obtain the fluctuation values of the electrical stimulation signal under multiple said abnormal nodes and record them as abnormal fluctuation values;

[0097] Used to detect the instantaneous data at the abnormal nodes, and the deviation amplitude of the electrical stimulation signal from the standard waveform at each abnormal node;

[0098] Suppose three abnormal fluctuation values are defined: abnormal duration, fluctuation amplitude, and fluctuation frequency, which represent the duration, amplitude, and occurrence frequency of the fluctuation respectively. At the abnormal node, the following data may be observed: the abnormal duration is 0.5 seconds, the fluctuation amplitude is 1.2 times (i.e., the stimulation signal is 20% higher than the standard waveform), and the fluctuation frequency is one peak per minute.

[0099] Obtain the fluctuation delay parameter of the abnormal fluctuation value, and compare the fluctuation delay parameter with a preset delay threshold;

[0100] When the fluctuation delay parameter is less than the preset delay threshold, it indicates that the duration of the abnormal fluctuation is short, the electrical stimulation signal is stable, and the corresponding abnormal fluctuation value is determined as a short-term fluctuation risk;

[0101] When the fluctuation delay parameter is greater than or equal to the delay threshold, it indicates that the duration of the abnormal fluctuation is long, the electrical stimulation signal is unstable, and the corresponding abnormal fluctuation value is determined as a long-term fluctuation risk.

[0102] Specific case:

[0103] In human brain wave monitoring, assume that the delay calibration within 0.5 seconds (i.e., 500 milliseconds) of the fluctuation curve of the standard electrical stimulation signal is defined as the quantization value, the delay threshold is set to 100 ms, and the comparison is made:

[0104] Calculate the actual delay quantization value: Calculate the time difference between the time points where each fluctuation value appears in the electrical stimulation signal and the corresponding points in the standard waveform as the quantization value.

[0105] For the first three fluctuation values, assume that the actual delay quantization values are 110 ms, 95 ms, and 105 ms respectively.

[0106] If a certain delay quantization value is greater than or equal to 100 ms, it is marked as a long-term fluctuation risk.

[0107] If it is less than 100 ms, it is marked as a short-term fluctuation risk.

[0108] This step classifies the stability of the signal according to the delay situation of each fluctuation value.

[0109] The steps of obtaining the standard waveform function of the electrical stimulation signal and comparing it with the electrical stimulation signal to obtain multiple abnormal nodes include:

[0110] Obtain the standard waveform function of the electrical stimulation signal;

[0111] Perform Fourier transform on the standard waveform function to obtain the standard waveform frequency domain characteristic parameters;

[0112] Obtain a sample waveform and perform a Fourier transform on the sample waveform to obtain the frequency-domain characteristic parameters of the sample waveform;

[0113] Perform a difference operation on the frequency-domain characteristic parameters of the standard waveform and the frequency-domain characteristic parameters of the sample waveform to obtain the frequency-domain difference parameter;

[0114] Obtain the electroencephalogram waveform of the patient, obtain a standard time period based on the electroencephalogram waveform, and obtain an electrical stimulation signal that matches the frequency-domain characteristic parameters of the standard waveform within the standard time period, and record it as a calibration waveform;

[0115] Perform the same processing on the time of the calibration waveform and the standard waveform, and output it as a comparison time parameter;

[0116] When the frequency-domain difference parameter is within a preset first comparison threshold, and the comparison time parameter is greater than or equal to a preset second comparison threshold, then calibrate the calibration waveform as the standard waveform of the electrical stimulation signal, and record the standard time period as the sample time period;

[0117] When the frequency-domain difference parameter is not within the preset first comparison threshold, or the comparison time parameter is less than or equal to the preset second comparison threshold, then reselect the calibration waveform, and compare the standard waveform and the calibration waveform;

[0118] Compare the electrical stimulation waveform with the standard waveform within the sample time period, and record the nodes with abnormal comparison as abnormal nodes.

[0119] Obtain the standard electrical stimulation waveform function, including performing a Fourier transform on the electrical stimulation standard signal to extract frequency-domain characteristic information and comparing it with the extracted sample waveform.

[0120] Generate frequency-domain characteristic parameters, perform a difference operation, and obtain a frequency-domain characteristic difference table.

[0121] For example, a frequency-domain characteristic is the main frequency. Assume that the main frequency of the standard waveform is 10 Hz, and the main frequency of the sample waveform analyzed by Fourier transform is 9.5 Hz; the frequency-domain difference parameter is recorded as: 10 Hz - 9.5 Hz = 0.5 Hz.

[0122] Obtain the electroencephalogram waveform of the patient, obtain a standard time period based on the electroencephalogram waveform, and obtain an electrical stimulation signal that matches the frequency-domain characteristic parameters of the standard waveform within the standard time period, and record it as a calibration waveform.

[0123] For example: the comparison time parameter is 500 ms. If the main frequency of a standard waveform is 10 Hz in a certain time period, and in the subsequent second cycle, the main frequency of the electrical stimulation waveform continuously remains higher than 10 Hz and reaches 10.5 Hz, it will not be calibrated as the standard waveform. Repeat the selection of the calibration waveform and perform the above comparison until the standard waveform is finally calibrated.

[0124] Among them, it should be recognized that for the standards of specific operating parameters, the statistical time nodes can be determined according to different types of electrical stimulation signals, and the specific period is combined with physiological characteristics, such as the real-time statistical period (the content is combined with the electrical stimulation signals of the understanding type described by the operator, such as wavelength, frequency, etc.) and the autonomic nerve sampling database (such as relative to the sympathetic nerve, vagus nerve, etc.).

[0125] Secondly, it should be clear that the determination of the standard waveform should start from the standard period and sample the time polling state based on the corresponding characteristic information. For example, if 50% of the normal electrical wave pattern appears in one minute and the duration standard is met, it can be determined as the standard waveform, and then the abnormal data of the corresponding period information is compared and fed back.

[0126] Each of the abnormal fluctuation values includes the abnormal duration, the fluctuation amplitude, and the fluctuation frequency. Among them, the abnormal duration is used to determine short-term fluctuations or long-term fluctuations, the fluctuation amplitude is used to evaluate the degree of change of the fluctuation, and the fluctuation frequency is used to analyze the occurrence frequency of the fluctuation.

[0127] In electroencephalogram (EEG) monitoring, a threshold is set such that the abnormal duration is greater than 200 milliseconds. When it is less than 200 milliseconds, it is a short-term fluctuation. When the fluctuation amplitude exceeds 100 microvolts and is less than 50 microvolts, it is a short-term fluctuation. Classification is performed according to the electric field amplitude or other information, and it is recorded as the abnormal state of the fluctuation frequency.

[0128] The classification basis of the electrical stimulation signals is shown in Table 1:

[0129] Table 1 Classification basis table for short-term fluctuations and long-term fluctuations

[0130]

[0131] After obtaining the corresponding fluctuation risk according to the abnormal fluctuation value, the short-term fluctuation amplitude of the electrical stimulation signal is obtained based on the short-term fluctuation risk and compared with the safety threshold of the short-term fluctuation amplitude;

[0132] When the short-term fluctuation amplitude is greater than the safety threshold, the corresponding short-term fluctuation risk is upgraded to a long-term fluctuation risk;

[0133] When the short-term fluctuation amplitude is less than or equal to the safety threshold, it is determined that the electrical stimulation signal is normal.

[0134] For a short-term fluctuation risk, the safety threshold of the short-term fluctuation amplitude is set to 110 microvolts. In this case, if the short-term fluctuation amplitude is 120 microvolts (exceeding the threshold of 110 microvolts), the short-term fluctuation risk is upgraded to a long-term fluctuation risk, and then this brain region is further examined.

[0135] Steps for determining the correlation between transcutaneous auricular vagus nerve stimulation and disorders of consciousness based on consciousness evaluation indicators and fluctuation risks and generating a correlation report, including:

[0136] Obtain a comprehensive risk assessment model;

[0137] Input the consciousness evaluation indicators and fluctuation risk parameters into the comprehensive risk assessment model to obtain a comprehensive risk assessment value;

[0138] Obtain a comprehensive risk assessment threshold and compare it with the comprehensive risk assessment value;

[0139] When the comprehensive risk assessment value is greater than or equal to the comprehensive risk assessment threshold, it is determined that there is an association between transcutaneous auricular vagus nerve stimulation and disorders of consciousness, and a correlation report is generated;

[0140] When the comprehensive risk assessment value is less than the comprehensive risk assessment threshold, it is determined that there is no association between transcutaneous auricular vagus nerve stimulation and disorders of consciousness, and a non-correlation report is generated.

[0141] For example, set a comprehensive risk assessment model: based on clinical data and expert opinions, determine the comprehensive risk assessment threshold. Assume the threshold is 85. Considering the consciousness evaluation indicators and fluctuation risk parameters, assume the patient's evaluation indicator score is 75 (between mild and moderate disorders of consciousness), and the fluctuation risk parameter score is 80.

[0142] Output the comprehensive risk assessment value: by substituting the consciousness evaluation indicators and fluctuation risk parameters into the comprehensive risk assessment model, the comprehensive risk assessment value is obtained.

[0143] Assume that three regions in the brain are detected, and the obtained comprehensive risk assessment values are 90, 88, and 62 respectively.

[0144] Compare the comprehensive risk assessment value with the lipid assessment threshold.

[0145] For the first brain region, the value is 90, which is higher than 85.

[0146] For the second brain region, the value is 88, which is still higher than 85.

[0147] For the third brain region, the value is 62, which is lower than 85.

[0148] Generate a correlation report based on the comprehensive risk assessment value. When the comprehensive risk assessment value obtained in one or more brain regions is higher than the assessment threshold, a correlation report is generated, and the report will include detailed information on which regions are related to disorders of consciousness, and vice versa. For brain regions with a comprehensive risk assessment value lower than the threshold, a non-correlation report is generated to confirm the insufficient association of that brain region with disorders of consciousness.

[0149] Example 2

[0150] As shown Figure 2 in the figure, this embodiment provides a qualitative analysis system for the correlation between transcutaneous auricular vagus nerve stimulation and disorders of consciousness, including:

[0151] A parameter acquisition module, configured to acquire physiological data of a patient in a normal conscious state, and perform normalization processing, denoising processing, and duplicate item screening to remove noise and outliers, so as to obtain physiological parameters in the normal conscious state;

[0152] A state evaluation module, configured to acquire physiological data of a patient in a state of disorder of consciousness, and perform synchronous screening and comparison with physiological parameters in the normal conscious state, specifically including:

[0153] Obtaining comparison parameters in the state of disorder of consciousness;

[0154] Calculating difference parameters between the normal state and the disorder state, and comparing them with an evaluation threshold;

[0155] Judging whether the difference parameter exceeds the threshold, so as to obtain whether the patient's consciousness information is abnormal, and generating a consciousness evaluation index accordingly;

[0156] A stimulation acquisition module, configured to acquire an electrical stimulation signal, and obtain an abnormal fluctuation value and a fluctuation risk therefrom, specifically including:

[0157] Obtaining the electrical stimulation signal and recording the first fluctuation duration;

[0158] Obtaining a standard waveform function of the electrical stimulation signal, and performing Fourier transform to extract frequency domain characteristic parameters;

[0159] Performing Fourier transform on the electrical stimulation sample waveform, and comparing the frequency domain difference with the standard waveform;

[0160] Selecting an electroencephalogram signal calibration waveform in a standard time period, and judging whether it is a standard waveform according to the frequency domain difference parameter and the comparison time parameter;

[0161] Identifying multiple abnormal nodes in the comparison, and obtaining the abnormal fluctuation value of each abnormal node;

[0162] Analyzing the duration, amplitude, and frequency of the abnormal fluctuation value to judge short-term fluctuation or long-term fluctuation;

[0163] Obtaining a fluctuation delay parameter, and comparing it with a delay threshold to preliminarily determine the type of fluctuation risk;

[0164] Obtaining the fluctuation amplitude of the short-term fluctuation, and comparing it with a safety threshold to further judge whether it is upgraded to a long-term fluctuation risk;

[0165] A comprehensive evaluation module, which is used to fuse the consciousness evaluation index and the fluctuation risk parameter, and calculate through a comprehensive risk assessment model, including:

[0166] Obtain the comprehensive risk assessment model;

[0167] Input the consciousness evaluation index and the fluctuation risk parameter to obtain the comprehensive risk assessment value;

[0168] Obtain the comprehensive risk assessment threshold and compare it with the comprehensive risk assessment value;

[0169] Judge whether there is a correlation between consciousness disorder and electrical stimulation;

[0170] An association determination module, which is used to determine the correlation between transcutaneous auricular vagus nerve stimulation and consciousness disorder according to the comprehensive risk assessment value, and output and generate an association report or a non - correlation report including the consciousness evaluation index, the fluctuation risk and the correlation conclusion.

[0171] Embodiment 3

[0172] This embodiment further limits Embodiment 2:

[0173] This embodiment provides a qualitative analysis system for the correlation between transcutaneous auricular vagus nerve stimulation and consciousness disorder. The system integrates multiple functional modules, can comprehensively evaluate the consciousness state from multiple dimensions, and combines the fluctuation characteristics of the electrical stimulation signal to identify the risk and make an association determination for the patient's abnormal consciousness condition. The working principles and specific implementation processes of each module will be described in detail below.

[0174] The whole system includes: a parameter acquisition module, a state evaluation module, a stimulation acquisition module, a comprehensive evaluation module and an association determination module. The system takes the graphical user interface or the control system integration as the platform, and is applicable to scenarios such as neurostimulation experiments, coma evaluation, and follow - up treatment of patients with consciousness disorders.

[0175] The parameter acquisition module is used to obtain the physiological data of the patient when the patient is in a normal consciousness state and perform multi - level pre - processing operations. The types of data collected by this module include, but are not limited to, electrocardiogram (ECG), electroencephalogram (EEG), blood oxygen saturation, skin conductivity, and respiratory rhythm, etc. These data are collected by physiological data acquisition instruments (such as multi - channel monitoring devices) and input into the processing system through data channels.

[0176] After the system receives the original physiological signal, it first normalizes the data to unify the amplitude range and time scale of various signals and avoid misjudgment caused by unit differences. After normalization, the system calls noise filtering algorithms, such as wavelet transform or band - pass filter, to remove noise factors such as power interference and physiological artifacts.

[0177] Furthermore, the system performs duplicate record screening on the denoised data, mainly by comparing the signal change rate within the sampling period to eliminate consecutive duplicate invalid records, thereby ensuring that the "physiological parameters under normal consciousness state" extracted are representative and have information density.

[0178] The state evaluation module is responsible for obtaining the physiological data of the patient when in a state of consciousness disorder and comparing and analyzing it with the normal state to form consciousness evaluation indicators. The system uses the patient's medical records, observation records, and doctor's judgment results to define the time period of the consciousness disorder state as the calibration time period.

[0179] The physiological data collected during this time period will be synchronously screened to maintain the same dimension and sampling frequency as the normal consciousness data obtained by the parameter acquisition module. The comparison and analysis are completed through difference operations. The system calculates the difference values of various physiological parameters in the two states and compares them with the preset "consciousness evaluation threshold".

[0180] For example, if the ratio of alpha waves to theta waves in the EEG is 1.5 in the normal state and 0.6 in the state of consciousness disorder, and the evaluation threshold is set at 0.8, then this parameter will be marked as an abnormal difference and recorded as an "abnormal consciousness point". The system synthesizes the abnormal points of multiple parameters to generate a quantitative "consciousness evaluation indicator".

[0181] When the total number or intensity standard of the differential parameters exceeds the threshold, the system will determine that the consciousness information is abnormal; otherwise, it will be regarded as an acceptable physiological difference and the consciousness state will be determined to be normal.

[0182] The stimulation acquisition module mainly processes the transcutaneous auricular vagus nerve stimulation signals to evaluate the impact of external nerve regulation means on consciousness changes. This system supports directly inputting the signal sequence output by the electrical stimulation device or accessing it through the analog signal acquisition port.

[0183] The system first records the first fluctuation duration of the electrical stimulation signal and automatically matches it with the standard waveform function built into the system. The standard waveform function is derived from the statistical average waveforms of multiple healthy samples. To ensure the accuracy of the comparison, the system performs Fourier transform operations on both the standard waveform and the current electrical stimulation waveform to extract their frequency domain characteristic parameters.

[0184] Then, by comparing the frequency domain characteristic difference parameters (such as main frequency offset, amplitude change, etc.) and the comparison time parameters (such as signal alignment), it is determined whether the current signal deviates from the standard range. If the deviation exceeds the preset first comparison threshold (frequency domain) or the second comparison threshold (time domain), the abnormal points in the current waveform will be marked as "abnormal nodes", and the fluctuation values at these nodes will be extracted as "abnormal fluctuation values".

[0185] For each abnormal fluctuation value, the system further calculates its fluctuation duration, amplitude, and frequency, and these parameters are used to classify the risk level:

[0186] When the fluctuation duration is less than the delay threshold, the system determines it as "short-term fluctuation risk"; if it is greater than or equal to the delay threshold, it is "long-term fluctuation risk".

[0187] In the case of judging as short-term fluctuation risk, the system continues to evaluate whether the amplitude of this fluctuation exceeds the short-term fluctuation safety threshold. If it exceeds, it is upgraded to "long-term fluctuation risk"; otherwise, it is determined that the signal is stable.

[0188] The comprehensive evaluation module introduces a risk assessment model to conduct a comprehensive evaluation on the "consciousness evaluation index" and "fluctuation risk" parameters output by the foregoing modules. The evaluation model adopts multiple linear discriminant analysis or machine learning classification models, such as support vector machine (SVM) or random forest model.

[0189] After receiving the input, the system calculates the comprehensive risk assessment value, which represents the possible relationship strength between electrical stimulation abnormalities and consciousness abnormalities. To ensure the objectivity of the evaluation, the system sets a dynamically adjusted comprehensive risk assessment threshold, which can be adaptively adjusted according to historical samples and patient characteristics.

[0190] When the comprehensive evaluation value is greater than or equal to this threshold, it indicates a high degree of correlation; if it is lower than this threshold, the strong association between the two is initially excluded.

[0191] Integrating the processing results of all modules, the association determination module outputs a qualitative analysis report, including three items:

[0192] Consciousness evaluation index report: listing the comparative difference analysis results and evaluation levels of each physiological signal;

[0193] Fluctuation risk level analysis: showing the fluctuation situation, risk category, and trend change of the electrical stimulation signal;

[0194] Final correlation conclusion: judging whether transcutaneous auricular vagus nerve stimulation may induce or improve the state of consciousness disorder, and outputting a conclusion of "related" or "unrelated".

[0195] If the judgment result is "related", the system will also prompt the doctor to further verify the causal logic according to the delay relationship between the electrical stimulation time point and the consciousness change event point; if it is "unrelated", the system can also help the doctor exclude interference factors and improve the diagnosis efficiency.

[0196] The basic principles, main features and advantages of the present invention have been shown and described above. Those skilled in the art should understand that the present invention is not limited by the above embodiments, and what is described in the above embodiments and the specification only illustrates the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements all fall within the scope of the present invention claimed. The scope of protection claimed by the present invention is defined by the appended claims and their equivalents.

Claims

1. A qualitative analysis method for the correlation between transcutaneous auricular vagus nerve stimulation and disorders of consciousness, characterized in that, Including: S1. Obtain the physiological data of the patient in the normal conscious state, and perform preprocessing to remove noise and outliers, so as to obtain the physiological parameters in the normal conscious state; including: S11. Obtain the physiological data of the patient in the normal conscious state and perform normalization processing; S12. Denoise the normalized physiological data to remove the noise components in the physiological data; S13. Screen out duplicate items from the denoised physiological data to eliminate duplicate parameters in the physiological data, so as to obtain the physiological parameters in the normal conscious state; S2. Obtain the physiological data of the patient in the state of consciousness disorder, and compare it with the physiological parameters in the normal conscious state to obtain the consciousness evaluation index; including: S21. Obtain the physiological data of the patient in the state of consciousness disorder and perform synchronous screening with the physiological parameters in the normal conscious state to obtain the comparison parameters in the state of consciousness disorder; S22. Calculate the difference between the physiological parameters in the normal conscious state and the comparison parameters in the state of consciousness disorder to obtain the difference parameters; S23. Compare the difference parameters with a preset evaluation threshold; S24. When the difference parameter is greater than the evaluation threshold, it indicates that the difference between the physiological parameters in the normal conscious state and the comparison parameters in the state of consciousness disorder is too large, and it is determined that the consciousness information of the patient in the state of consciousness disorder is abnormal; S25. When the difference parameter is less than or equal to the evaluation threshold, it indicates that the difference between the physiological parameters in the normal conscious state and the comparison parameters in the state of consciousness disorder is acceptable, and it is determined that the consciousness information of the patient in the state of consciousness disorder is normal; S3. Obtain the electrical stimulation signal, obtain the corresponding abnormal fluctuation value according to the electrical stimulation signal, and then obtain the corresponding fluctuation risk according to the abnormal fluctuation value; including: S31. Obtain the electrical stimulation signal and record the first fluctuation duration of the electrical stimulation signal; S32. Obtain the standard waveform function of the electrical stimulation signal and compare it with the electrical stimulation signal to obtain multiple abnormal nodes; S33. Obtain the fluctuation values of the electrical stimulation signal under multiple abnormal nodes and record them as abnormal fluctuation values; S34. Obtain the fluctuation delay parameter of the abnormal fluctuation value, and compare the fluctuation delay parameter with a preset delay threshold; S35. When the fluctuation delay parameter is less than the preset delay threshold, it indicates that the duration of the abnormal fluctuation is short and the electrical stimulation signal is stable, and the corresponding abnormal fluctuation value is determined as a short-term fluctuation risk; S36. When the fluctuation delay parameter is greater than or equal to the delay threshold, it indicates that the duration of the abnormal fluctuation is long and the electrical stimulation signal is unstable, and the corresponding abnormal fluctuation value is determined as a long-term fluctuation risk; S4. Judge the correlation between transcutaneous auricular vagus nerve stimulation and consciousness disorder according to the consciousness evaluation index and the fluctuation risk, and generate an association report; wherein, the association report includes the consciousness evaluation index, the fluctuation risk and the correlation conclusion, including: S41. Obtain a comprehensive risk assessment model; S42. Input the consciousness evaluation index and the fluctuation risk parameters into the comprehensive risk assessment model to obtain a comprehensive risk assessment value; S43. Obtain the comprehensive risk assessment threshold and compare it with the comprehensive risk assessment value; S44. When the comprehensive risk assessment value is greater than or equal to the comprehensive risk assessment threshold, it is determined that there is an association between transcutaneous auricular vagus nerve stimulation and disturbance of consciousness, and an association report is generated; S45. When the comprehensive risk assessment value is less than the comprehensive risk assessment threshold, it is determined that there is no association between transcutaneous auricular vagus nerve stimulation and disturbance of consciousness, and a non - relevant report is generated.

2. The qualitative analysis method for the correlation between transcutaneous auricular vagus nerve stimulation and disturbance of consciousness according to claim 1, wherein The steps of obtaining the standard waveform function of the electrical stimulation signal and comparing it with the electrical stimulation signal to obtain multiple abnormal nodes include: S321. Obtain the standard waveform function of the electrical stimulation signal; S322. Perform Fourier transform on the standard waveform function to obtain the standard waveform frequency - domain characteristic parameters; S323. Obtain the sample waveform and perform Fourier transform on the sample waveform to obtain the frequency - domain characteristic parameters of the sample waveform; S324. Perform a difference operation on the frequency - domain characteristic parameters of the standard waveform and the frequency - domain characteristic parameters of the sample waveform to obtain the frequency - domain difference parameters; S325. Obtain the electroencephalogram waveform of the patient, obtain the standard time period according to the electroencephalogram waveform, and obtain the electrical stimulation signal that matches the frequency - domain characteristic parameters of the standard waveform within the standard time period, and record it as the calibration waveform; S326. Perform the same processing on the time of the calibration waveform and the standard waveform, and output it as the comparison time parameter; S327. When the frequency - domain difference parameter is within the preset first comparison threshold, and the comparison time parameter is greater than or equal to the preset second comparison threshold, the calibration waveform is calibrated as the standard waveform of the electrical stimulation signal, and the standard time period is recorded as the sample time period; S328. When the frequency - domain difference parameter is not within the preset first comparison threshold, or the comparison time parameter is less than or equal to the preset second comparison threshold, re - select the calibration waveform and compare the standard waveform and the calibration waveform; S329. Compare the electrical stimulation waveform within the sample time period with the standard waveform, and record the nodes with abnormal comparison as abnormal nodes.

3. The qualitative analysis method for the correlation between transcutaneous auricular vagus nerve stimulation and disorders of consciousness according to claim 2, wherein Each of the abnormal fluctuation values includes abnormal duration, fluctuation amplitude, and fluctuation frequency. Among them, the abnormal duration is used to determine short - term or long - term fluctuations, the fluctuation amplitude is used to evaluate the degree of change of the fluctuation, and the fluctuation frequency is used to analyze the occurrence frequency of the fluctuation.

4. The qualitative analysis method for the correlation between transcutaneous auricular vagus nerve stimulation and disorders of consciousness according to claim 3, wherein After obtaining the corresponding fluctuation risk according to the abnormal fluctuation value, obtain the short - term fluctuation amplitude of the electrical stimulation signal based on the short - term fluctuation risk, and compare it with the safety threshold of the short - term fluctuation amplitude; When the short - term fluctuation amplitude is greater than the safety threshold, upgrade the corresponding short - term fluctuation risk to a long - term fluctuation risk; When the short - term fluctuation amplitude is less than or equal to the safety threshold, it is determined that the electrical stimulation signal is normal.

5. A system for qualitative analysis of the correlation between transcutaneous auricular vagus nerve stimulation and disorders of consciousness, which is used to implement the method according to any one of claims 1-4, and is characterized in that, including: A parameter acquisition module, which is used to acquire the physiological data of the patient in the normal conscious state, and perform pre - processing to remove noise and outliers to obtain the physiological parameters in the normal conscious state; A state evaluation module, which is used to obtain physiological data of a patient in a state of impaired consciousness, and compare it with physiological parameters in a normal conscious state to obtain a consciousness evaluation index; A stimulation acquisition module, which is used to acquire an electrical stimulation signal, obtain a corresponding abnormal fluctuation value according to the electrical stimulation signal, and then obtain a corresponding fluctuation risk according to the abnormal fluctuation value; A correlation determination module, which is used to judge the correlation between transcutaneous auricular vagus nerve stimulation and impaired consciousness according to the consciousness evaluation index and the fluctuation risk, and generate a correlation report; Wherein, the correlation report includes a consciousness evaluation index, a fluctuation risk, and a correlation conclusion.

6. An electronic device, characterized in that, Comprising: One or more processors; A storage device for storing computer programs executable by one or more processors; Wherein, by executing the computer program, the one or more processors implement the method for qualitative analysis of the correlation between transcutaneous auricular vagus nerve stimulation and impaired consciousness according to any one of claims 1-4.

Images