Method and apparatus for evaluating consciousness level based on TEP data

Abstract

The present application discloses a method and device for evaluating the consciousness level based on TEP data. The method includes: obtaining the target state of a target object and the corresponding target duration of the target state; determining target stimulation parameters according to the target state and the target duration; within a first preset time period, performing periodic stimulation on the target object with the target stimulation parameters through a transcranial magnetic stimulation device to obtain n first TEP data; removing interference from the n first TEP data to obtain n second TEP data; determining the second TEP data that meet the first preset condition among the n second TEP data to obtain m second TEP data; determining m consciousness index values according to the m second TEP data; and determining the target consciousness state of the target object according to the m consciousness index values. By adopting the embodiments of the present application, it is possible to evaluate the consciousness level of a target object when the target object cannot interact with the environment.

Description

Technical Field

[0001] This application relates to the field of medical technology, and in particular, to a method and device for evaluating the level of consciousness based on TEP data. Background Art

[0002] In clinical practice, there has always been a lack of objective and reliable methods for evaluating the level of consciousness. For an object that can interact with the surrounding environment (e.g., a comatose person), the level of consciousness can be evaluated by observing their activity status; however, for an object in an anesthetic state or a special brain injury state, they may be conscious but unable to interact with the environment, and in this case, it is impossible to evaluate their level of consciousness. Therefore, how to evaluate the level of consciousness of an object when the object cannot interact with the environment has become an urgent problem to be solved. Summary of the Invention

[0003] Embodiments of this application provide a method and device for evaluating the level of consciousness based on TEP data, which can evaluate the level of consciousness of a target object when the target object cannot interact with the environment.

[0004] In a first aspect, embodiments of this application provide a method for evaluating the level of consciousness based on TEP data, which is applied to an electronic device. The electronic device is connected to a transcranial magnetic stimulation device. The method includes:

[0005] Obtain the target state of the target object and the corresponding target duration; the target state includes any one of the following: anesthetic state, brain injury state;

[0006] Determine target stimulation parameters according to the target state and the target duration;

[0007] Within a first preset time period, perform periodic stimulation on the target object through the transcranial magnetic stimulation device with the target stimulation parameters to obtain n first TEP data; each stimulation corresponds to one first TEP data; n is an integer greater than 1;

[0008] Remove interference from the n first TEP data to obtain n second TEP data;

[0009] Determine the second TEP data that meet the first preset condition among the n second TEP data to obtain m second TEP data; m is a positive integer less than or equal to n;

[0010] Determine m consciousness index values according to the m second TEP data;

[0011] Determine the target consciousness state of the target object according to the m consciousness index values; the target consciousness state includes any one of the following: awake, drowsy, confused, stuporous, comatose.

[0012] In a second aspect, an awareness level assessment device based on TEP data provided by an embodiment of the present application is applied to an electronic device, which is connected to a transcranial magnetic stimulation device. The device includes: an acquisition unit, a control unit, and an evaluation unit, where:

[0013] The acquisition unit is configured to acquire the target state of a target object and the target duration corresponding to the target state; the target state includes any one of the following: anesthetic state, brain injury state;

[0014] The control unit is configured to determine target stimulation parameters according to the target state and the target duration; within a first preset time period, perform periodic stimulation on the target object through the transcranial magnetic stimulation device with the target stimulation parameters to obtain n first TEP data; each stimulation corresponds to one first TEP data; n is an integer greater than 1; perform interference removal on the n first TEP data to obtain n second TEP data; determine the second TEP data that meet the first preset condition among the n second TEP data to obtain m second TEP data; m is a positive integer less than or equal to n;

[0015] The evaluation unit is configured to determine m awareness index values according to the m second TEP data; determine the target awareness state of the target object according to the m awareness index values; the target awareness state includes any one of the following: awake, drowsy, confused, stuporous, comatose.

[0016] In a third aspect, the present application provides an electronic device, including: a processor and a memory. The memory is used to store one or more programs. Among them, the above one or more programs are stored in the above memory and are configured to be executed by the above processor. The above programs include instructions for performing the steps in the first aspect of the present application.

[0017] In a fourth aspect, the present application provides a computer-readable storage medium. Among them, the above computer-readable storage medium stores a computer program for electronic data exchange. Among them, the above computer program enables a computer to execute some or all of the steps described in the first aspect of the present application.

[0018] In a fifth aspect, the present application provides a computer program product. Among them, the above computer program product includes a non-transitory computer-readable storage medium storing a computer program. The above computer program is operable to enable a computer to execute some or all of the steps described in the first aspect of the present application. The computer program product can be a software installation package.

[0019] Implementing the present application has the following beneficial effects:

[0020] It can be seen that the method for evaluating the consciousness level based on TEP data described in the embodiments of the present application includes: obtaining the target state of the target object and the corresponding target duration of the target state; determining the target stimulation parameter according to the target state and the target duration; within the first preset time period, performing periodic stimulation on the target object through a transcranial magnetic stimulation device with the target stimulation parameter to obtain n first TEP data; performing interference removal on the n first TEP data to obtain n second TEP data; determining the second TEP data that meets the first preset condition among the n second TEP data to obtain m second TEP data; determining m consciousness index values according to the m second TEP data; determining the target consciousness state of the target object according to the m consciousness index values; by collecting the TEP data of the target object, the TEP data is the direct electrophysiological response of the brain to the stimulation, which can more truly reflect the consciousness state of people, analyzing the TEP data to obtain the consciousness index value, and determining the consciousness level of the target object according to the consciousness index value, thereby realizing the evaluation of the consciousness level of the target object in the case where the target object cannot interact with the environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the background art, the following will describe the drawings required to be used in the embodiments of the present application or the background art.

[0022] Figure 1 It is a schematic structural diagram of a consciousness level evaluation device provided by an embodiment of the present application;

[0023] Figure 2 It is a scene application diagram of a consciousness level evaluation device provided by an embodiment of the present application;

[0024] Figure 3 It is a flowchart of a method for evaluating the consciousness level based on TEP data provided by an embodiment of the present application;

[0025] Figure 4 It is a schematic diagram of a TMS control page provided by an embodiment of the present application;

[0026] Figure 5 It is a waveform diagram of a TEP waveform provided by an embodiment of the present application;

[0027] Figure 6 It is a schematic diagram of a first straight line provided by an embodiment of the present application;

[0028] Figure 7 It is a block diagram of the functional units of a device for evaluating the consciousness level based on TEP data provided by an embodiment of the present application;

[0029] Figure 8It is a schematic structural diagram of an electronic device provided by an embodiment of the present application. Detailed implementation manners

[0030] In order to enable those skilled in the art to better understand the solution of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0031] The terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally further includes steps or units not listed, or optionally further includes other steps or units inherent to these processes, methods, products or devices.

[0032] Referring to "embodiments" herein means that the specific features, structures or characteristics described in connection with the embodiments can be included in at least one embodiment of the present application. The phrase appears in various places in the specification and does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive with other embodiments. Those skilled in the art will explicitly and implicitly understand that the embodiments described herein can be combined with other embodiments.

[0033] The electronic device described in the embodiments of the present application may include a smart phone (such as an Android phone, an iOS phone, a Windows Phone phone, etc.), a tablet computer, a personal digital assistant, a laptop computer, a video matrix, a monitoring platform, a mobile internet device (MID) or a wearable device, etc. The above are only examples, not an exhaustive list, including but not limited to the above devices. Of course, the above electronic device may also be a server, for example, a cloud server.

[0034] Some professional terms involved in the present application will be explained below:

[0035] Transcranial Magnetic Stimulation (TMS) technology: It is a magnetic stimulation technology that uses pulsed magnetic fields to act on the central nervous system, changes the membrane potential of cortical nerve cells, generates induced current, affects brain metabolism and nerve electrical activities, and thus causes a series of physiological and biochemical reactions.

[0036] Stimulation parameters of TMS: Refer to the various operating indicators of the TMS device during operation. For example, stimulation intensity, stimulation frequency, number of stimulation pulses, etc.

[0037] Transcranial magnetic stimulation-evoked potential (TEP): It refers to the potential change generated by the electroactivity of brain neurons recorded on the scalp surface after TMS acts on a specific area of the cerebral cortex. It reflects the brain's response to TMS stimulation and is an important electrophysiological signal for studying brain function and neurophysiological activities. The TEP waveform usually contains multiple components, and these components may correspond to different brain nerve activity processes. By analyzing the waveform complexity and components, the neural mechanisms of the brain in different cognitive, motor, or sensory functions can be understood, and thus, the level of brain consciousness can be evaluated.

[0038] Depth of anesthesia: It refers to the degree of inhibition of the human central nervous system by anesthetic drugs during general anesthesia. It is a comprehensive concept used to measure the degree of a patient's transition from a conscious state to complete loss of consciousness and non-responsiveness to noxious stimuli. The depth of anesthesia is not a simple linear measure but a description of a complex state involving the interaction of multiple physiological and pharmacological factors.

[0039] Perturbational Complexity Index (PCI): It is an index used to measure the complexity of a system (especially the brain system in the field of neuroscience). It is mainly used to evaluate the complexity of the neural activity pattern of the brain when it is subjected to external stimuli (such as TMS) or internal state changes (such as the transition between different conscious states). The calculation of this index is usually based on the analysis of brain electrophysiological signals (such as electroencephalogram EEG, transcranial magnetic stimulation-evoked potential TEP). Its calculation process is complex and time-consuming, resulting in its inapplicability as an algorithm index for real-time online detection in clinical practice, which limits the popularization and application of this index.

[0040] Perturbational Complexity Index of TEP data in the short-term (PCIst): It is an index used to quantify the complexity of TEP data in a short period (usually within a short time window after transcranial magnetic stimulation). It is obtained through a series of complex mathematical processes on TEP data. Generally, it is used to evaluate the differences in the complexity of neural activities of the brain under different conscious states, degrees of brain injury, etc. Compared with PCI, the calculation of PCIst is accurate, reliable, and time-consuming, making it suitable as an algorithm index for online detection.

[0041] Please refer toFigure 1 , Figure 1 is a schematic structural diagram of a consciousness level evaluation device provided by an embodiment of the present application. As Figure 1 shown, the consciousness level evaluation device may include an electronic device and a transcranial magnetic stimulation device (hereinafter referred to as a TMS device), where:

[0042] The specific composition of the TMS device may include a stimulation coil and a pulse generator. The pulse generator is used to generate high-intensity current pulses, and these current pulses are converted into magnetic field pulses through the stimulation coil to stimulate the cerebral cortex, induce the brain to generate TEP data, and collect the TEP data. Then, the TEP data is transmitted to the electronic device. The pulse generator can adjust parameters such as the intensity, frequency, and duration of the pulses to meet different stimulation requirements. These TEP data reflect the neural activities of the brain under stimulation and are important bases for evaluating the consciousness level. Different stimulation parameters (such as intensity, frequency, etc.) can trigger different brain responses. Therefore, the excitability and consciousness state of the brain can be judged based on these responses.

[0043] The electronic device is the core device for processing and analyzing data. It can run specialized signal processing software and can perform operations such as filtering, feature extraction, and data analysis on the raw data (TEP data) collected by the TMS device. For example, the software can remove noises such as power frequency interference and electromyogram artifacts in the TEP data to improve the data quality and extract effective information related to the consciousness level. For example, the level of consciousness can be judged by calculating various characteristic parameters such as the latency and amplitude of the TEP data. At the same time, the electronic device can also be used to control the parameter settings and stimulation process of the TMS device to achieve synchronous operation of the two.

[0044] Please refer to Figure 2 , Figure 2 is a scenario application diagram of a consciousness level evaluation device provided by an embodiment of the present application. When TMS stimulation needs to be performed on a target object, the stimulation coil can be placed above the head of the target object. The TMS device can receive the control instructions issued by the electronic device, and the pulse generator generates corresponding electrical pulse signals according to the control instructions. The electrical pulse signals are transmitted to the stimulation coil, and the stimulation coil converts the electrical pulses into magnetic field pulses. This magnetic field pulse can penetrate the scalp and skull and act on the cerebral cortex, thereby inducing the neural electrical activities of the brain and generating TEP data. Then, the electronic device can analyze these TEP data by using the consciousness level evaluation method based on TEP data provided by the embodiment of the present application to evaluate the level of consciousness of the target object.

[0045] Please refer to Figure 3 , Figure 3It is a flowchart of a method for evaluating the level of consciousness based on TEP data provided by an embodiment of the present application. This method is applied to an electronic device, and the electronic device is connected to a transcranial magnetic stimulation device. The method may include the following steps:

[0046] S301. Obtain the target state of the target object and the target duration corresponding to the target state; the target state includes any one of the following: anesthetic state, brain injury state.

[0047] In an embodiment of the present application, detailed communication can be carried out with the target object himself / herself (if conscious and able to communicate), family members, caregivers or other relevant personnel to inquire about the recent physical condition of the target object, whether he / she has experienced surgery or trauma, etc. At the same time, medical record data of the target object in the target hospital can also be obtained, including admission records, progress records, surgical records, diagnostic reports, etc. Through the inquiry of the medical history, it can be learned whether the target object has received anesthesia and the relevant situation of the anesthesia, such as the anesthesia method, anesthesia time, etc. If the target object has received anesthesia at the current moment or before the current moment, the target state can be determined as the anesthetic state. Then, the injection moment of the anesthetic drug can be obtained, and by subtracting the injection moment from the current moment, the target duration can be obtained; on the contrary, if the medical record data records that the target object has suffered a brain injury, the target state can be determined as the brain injury state, and the starting moment when the target object is diagnosed with a brain injury can be obtained. By subtracting the injection moment from the starting moment of the brain injury, the target duration can be obtained. Among them, the target hospital is the hospital where the target object seeks medical treatment.

[0048] S302. Determine the target stimulation parameters according to the target state and the target duration.

[0049] In an embodiment of the present application, the target stimulation parameters may include at least one of the following: stimulation intensity, stimulation frequency, number of stimulation pulses, stimulation site, etc., which are not limited herein.

[0050] In a specific embodiment, analysis can be carried out according to the target state and the target duration to obtain stimulation parameters that conform to the actual situation of the target object, that is, the target stimulation parameters.

[0051] Optionally, in step S302, when the target state includes the anesthetic state, the determining the target stimulation parameters according to the target state and the target duration may include the following steps:

[0052] A1. Obtain the target drug type and target anesthetic drug dosage of the anesthetic drug injected into the target object;

[0053] A2. Estimate the first drug onset duration required for the target object to reach the preset anesthetic depth according to the target drug type and the target anesthetic drug dosage;

[0054] A3. Estimate the target anesthesia depth of the target object at the current moment according to the first drug onset duration, the target duration, and the preset anesthesia depth; the target duration is the duration between the injection moment of the anesthetic drug and the current moment;

[0055] A4. Determine the first stimulation parameter corresponding to the target anesthesia depth;

[0056] A5. Obtain the target physical health parameters of the target object;

[0057] A6. Determine the first influence factor corresponding to the target physical health parameters;

[0058] A7. Adjust the first stimulation parameter according to the first influence factor to obtain a second stimulation parameter;

[0059] A8. In a second preset time period, perform periodic stimulation on the target object with the second stimulation parameter through the transcranial magnetic stimulation device to obtain a reference TEP data; a is a positive integer less than n; each stimulation corresponds to a reference TEP data; the end moment of the second preset time period is earlier than the start moment of the first preset time period;

[0060] A9. Remove interference from the a reference TEP data to obtain a TEP data;

[0061] A10. When the a TEP data meet the second preset condition, determine the target stimulation parameter according to the second stimulation parameter.

[0062] In the embodiments of the present application, the target drug type may include one of the following: intravenous anesthetics, inhaled anesthetics, opioid analgesics, muscle relaxants, etc., which are not limited herein; the preset anesthesia depth and the second preset condition can both be preset in advance or default, and the second preset condition is used to judge whether the data quality of the a obtained TEP data is qualified; the target physical health parameters may include one of the following: blood oxygen concentration, blood pressure, blood routine indexes, cardiopulmonary function indexes (such as, maximum oxygen uptake), immune function indexes (such as, immunoglobulin level), etc., which are not limited herein.

[0063] In specific embodiments, the target drug type and the target anesthetic dosage of the anesthetic drug injected into the target object can be obtained first. Specifically, the anesthetic record sheet of the target object can be obtained. The anesthetic record sheet will detail information such as the type of anesthetic drug used, the administration route (such as intravenous injection, inhalation, etc.), the injection dosage, and the time nodes of drug use. Thus, the target drug type and the target anesthetic dosage can be obtained. Or, the electronic device can be connected to the anesthetic information management system of the target hospital to query the anesthetic information of the target object from the anesthetic information management system. Thus, the target drug type and the target anesthetic dosage can be obtained.

[0064] Next, based on the target drug type and the target anesthetic dosage, the first drug onset duration required for the target object to reach the preset anesthetic depth can be estimated. Specifically, the onset time range of the anesthetic drug of the target drug type from the injection moment to the target object reaching the preset anesthetic depth can be obtained from a preset database (for example, the anesthetic sharing database). For example, assuming that the onset time range of the intravenous anesthetic from the injection moment to the target object reaching the preset anesthetic depth is 2 minutes to 5 minutes. Also, since the onset duration of the anesthetic drug is affected by the dosage, the first drug onset duration can be determined from the onset time range according to the target anesthetic dosage. Generally speaking, within the safe dosage range, the larger the anesthetic dosage, the shorter the onset time. For example, the mapping relationship between the preset anesthetic dosage and the onset factor can be pre-stored, and the target onset factor corresponding to the target anesthetic dosage can be determined based on this mapping relationship. The value range of the target onset factor is -0.3 to 0. The calculation can be performed based on the target onset factor and the onset time range. The calculation formula is as follows:

[0065] The first drug onset duration = the lower limit of the onset time range + (the upper limit of the onset time range - the lower limit of the onset time range) × (1 + the target onset factor);

[0066] According to the above formula, the first drug onset duration can be obtained. Next, based on the first drug onset duration, the target duration, and the preset anesthetic depth, the target anesthetic depth of the target object at the current moment can be estimated. Specifically, the target duration difference can be obtained by subtracting the first drug onset duration from the target duration. Then, the calculation can be performed according to the target duration difference and the preset anesthetic depth. The calculation formula is as follows:

[0067] The target anesthetic depth = the preset anesthetic depth + the preset anesthetic depth × (1 + the target duration difference / the first drug onset duration) × 100%;

[0068] Based on the above formula, the target anesthesia depth can be obtained. Then, the first stimulation parameter corresponding to the target anesthesia depth can be determined. For example, a mapping relationship between the preset anesthesia depth and the stimulation parameter can be pre-stored, and based on this mapping relationship, the first stimulation parameter corresponding to the target anesthesia depth can be determined. Next, the target physical health parameter of the target object can be obtained. Specifically, the target physical health parameter can be blood pressure, and the blood pressure of the target object can be measured by a blood pressure monitor, that is, the target physical health parameter. Further, the first influencing factor corresponding to the target physical health parameter can be determined. For example, a mapping relationship between the physical health parameter and the influencing factor can be pre-stored, and based on this mapping relationship, the first influencing factor corresponding to the target physical health parameter can be determined. The value range of the first influencing factor can be -0.5 to 0.5, and the first stimulation parameter can be adjusted according to the first influencing factor. The specific calculation formula is as follows:

[0069] The second stimulation parameter = the first stimulation parameter × (1 + the first influencing factor);

[0070] According to the above formula, the second stimulation parameter can be obtained. During the second preset time period, the target object can be periodically stimulated by the TMS device with the second stimulation parameter to obtain a reference TEP data. It should be noted that the stimulation period can be preset or default in advance. For example, the stimulation period can be 2.5 seconds.

[0071] Next, the a reference TEP data can be de-noised to obtain a TEP data. The specific de-noising steps can be the same as those of S304, which will be described in detail below and will not be elaborated here. When the a TEP data meet the second preset condition, the second stimulation parameter can be directly used as the target stimulation parameter. Then, the stimulation parameter of the transcranial magnetic stimulation device can be adjusted to the target stimulation parameter through the electronic device. For example, please refer to Figure 4 , Figure 4 is a schematic diagram of a TMS control page provided by an embodiment of the present application. The TMS control page is an operation page of the electronic device. As Figure 4 shown, the TMS control page includes: serial port number, transcranial magnetic stimulator, disconnect connection, current intensity 50%, "+", "-", preparation button, start stimulation button, fast stimulation button, port number 9230, whether to receive external commands (in Figure 4 , there is a "√" on the left side of receiving external commands, indicating that the electronic device can receive external commands. If there is an "×" on the left side of receiving external commands, it means that the electronic device does not receive external commands) and other information.

[0072] Among them, the transcranial magnetic stimulator represents the device type; the "+" and "-" are used to increase or decrease the stimulation intensity; click the preparation button to start the preparation work; click the start stimulation button to start stimulating the target object; click the fast stimulation button, which means not to perform preparation and directly stimulate the target object. When the target stimulation parameter is the stimulation intensity, the stimulation parameter of the transcranial magnetic stimulation device can be adjusted to the target stimulation parameter through the "+" and "-" buttons; of course, if the target stimulation parameter is not the stimulation intensity, it can also be controlled and adjusted through other operation pages of the electronic device.

[0073] When a TEP data does not meet the second preset condition, the second stimulation intensity can be adjusted according to a preset ratio. The specific calculation formula is as follows:

[0074] The updated second stimulation intensity = the second stimulation intensity × (1 + preset ratio);

[0075] Among them, the preset ratio can be preset in advance or default; the updated second stimulation intensity is obtained according to the above formula; the target object is periodically stimulated again by the transcranial magnetic stimulation device with the updated second stimulation intensity to obtain the updated a TEP data, and it is judged whether the updated a TEP data meet the second preset condition. If the updated a TEP data still do not meet the above second preset condition, continue to adjust the updated second stimulation intensity according to the preset ratio until the latest a TEP data meet the above second preset condition, and use the latest second stimulation parameter as the target stimulation parameter.

[0076] In this way, by obtaining the target drug type and the target anesthetic dose, estimating the first drug onset duration required for the target object to reach the preset anesthetic depth, and then estimating the target anesthetic depth at the current moment to determine the corresponding first stimulation parameter, the influence of the anesthetic drug on the brain state is considered. Because the excitability of the brain is different at different anesthetic depths, and the response to TMS stimulation is also different. For example, at a relatively shallow anesthetic depth, the brain may have a relatively strong response to TMS, and parameters such as the stimulation intensity need to be appropriately reduced; while at a relatively deep anesthetic depth, a stronger stimulation may be required to cause an effective response. Therefore, the stimulation parameters determined in this way are more in line with the brain state of the target object at that time, providing appropriate stimulation conditions for obtaining valuable TEP data subsequently, thereby improving the accuracy of consciousness level assessment.

[0077] Optionally, the method may further include the following steps:

[0078] B1. Obtain the signal-to-noise ratio of each TEP data in the a TEP data to obtain a signal-to-noise ratios;

[0079] B2. Determine the signal-to-noise ratios among the a signal-to-noise ratios that are not less than the first preset signal-to-noise ratio, and obtain b signal-to-noise ratios; b is a natural number less than a;

[0080] B3. Determine the first qualification rate according to the b signal-to-noise ratios and the a signal-to-noise ratios;

[0081] B4. When the first qualification rate is less than the preset qualification rate, determine that the a TEP data do not meet the second preset condition;

[0082] B5. When the first qualification rate is not less than the preset qualification rate, determine the waveform corresponding to each TEP data among the a TEP data, and obtain a TEP waveforms;

[0083] B6. Determine the latency and amplitude corresponding to each TEP waveform among the a TEP waveforms, and obtain a latencies and a amplitudes;

[0084] B7. Determine the target latency duration range corresponding to the a latencies;

[0085] B8. Determine the target overlap degree between the target latency duration range and the preset latency duration range;

[0086] B9. When the target overlap degree is less than the preset overlap degree, determine that the a TEP data do not meet the second preset condition;

[0087] B10. When the target overlap degree is not less than the preset overlap degree, perform fitting based on the a latencies and the start time corresponding to each latency to obtain a first straight line; the abscissa is time, and the ordinate is the latency duration;

[0088] B11. Obtain the first slope corresponding to the first straight line;

[0089] B12. Determine the amplitudes among the a amplitudes that are within the preset amplitude range, and obtain c amplitudes; c is a natural number less than a;

[0090] B13. Determine the second qualification rate according to the c amplitudes and the a amplitudes;

[0091] B14. When the first slope is not less than the preset slope, and / or, the second qualification rate is less than the preset qualification rate, determine that the a TEP data do not meet the second preset condition;

[0092] B15. When the first slope is less than the preset slope, and the second qualification rate is not less than the preset qualification rate, determine that the a TEP data meet the second preset condition.

[0093] In the embodiments of the present application, the first preset signal-to-noise ratio, the preset qualification rate, the preset latency duration range, the preset overlap degree, the preset amplitude range, and the preset slope can all be preset in advance or default.

[0094] In a specific embodiment, the signal-to-noise ratio of each of the a TEP data can be obtained to obtain a signal-to-noise ratios. Specifically, the preset signal-to-noise ratio calculation method can be used to calculate the signal-to-noise ratio of the a TEP data to obtain a signal-to-noise ratios, where the preset signal-to-noise ratio calculation method can be any one of the following: power spectrum estimation method, root mean square method, peak signal-to-noise ratio method; then, the signal-to-noise ratios not less than the first preset signal-to-noise ratio (for example, 3 decibels) among the a signal-to-noise ratios can be found to obtain b signal-to-noise ratios; the first qualification rate is determined according to the b signal-to-noise ratios and the a signal-to-noise ratios, and the specific calculation formula is as follows:

[0095] The first qualification rate = b / a × 100%;

[0096] The first qualification rate can be obtained according to the above formula; when the first qualification rate is less than the preset qualification rate, it can be determined that the a TEP data do not meet the second preset condition;

[0097] When the first qualification rate is not less than the preset qualification rate, the a TEP data can be converted into corresponding waveforms to obtain a TEP waveforms. Specifically, each TEP data contains a series of electrophysiological signal intensity values, which reflect the potential change of the brain after being stimulated by TMS. Professional drawing software (for example, MATLAB software) can be used to draw a graph according to the a TEP data to obtain a TEP waveforms.

[0098] For example, please refer to Figure 5 , Figure 5 is a waveform diagram of a TEP waveform provided by an embodiment of the present application. As Figure 5 shown, the horizontal axis x of this waveform diagram is time, with the unit of millisecond (ms), and the vertical axis y is the potential magnitude, and the unit can be microvolt (μV).

[0099] Among them, the coordinate origin (0, 0) is the starting moment when the TMS device just starts to stimulate. The latency is the time interval from the starting moment (coordinate origin) to the appearance of a specific peak or trough in the TEP waveform. It reflects the time required for a nerve impulse to conduct from the stimulation point to the brain area where the recording electrode is located, and can be used to evaluate the functional state of the nerve conduction pathway. A peak is a point in the TEP waveform where the potential positively deviates from the baseline (the zero potential line where the abscissa is located) to reach the maximum value, representing the positive potential change of brain electrical activity. A trough is a point in the TEP waveform where the potential negatively deviates from the baseline to reach the maximum value, representing the negative potential change of brain electrical activity. The amplitude is the potential difference from the baseline (i.e., the x-axis of the coordinate) to the peak (or trough), and is used to measure the intensity of a certain waveform component in the TEP signal. It reflects information such as the number of activated neurons and the synchrony of neuron activities. The peak-to-peak value is the potential difference between the peak and the trough, comprehensively considering the positive and negative maximum changes of the TEP waveform, and can more comprehensively reflect the dynamic range of the potential change.

[0100] Next, the latency and amplitude corresponding to each of the a TEP waveforms can be determined, obtaining a latencies and a amplitudes. Specifically, these a TEP waveforms can be analyzed through professional drawing software to obtain the characteristic data of each TEP waveform, and a latencies and a amplitudes can be extracted from this characteristic data; then, the target latency duration range corresponding to the a latencies can be determined. Specifically, the maximum and minimum values among the a latencies can be found, and the target latency duration range can be obtained by subtracting the minimum value from the maximum value. Next, the target overlap degree between the target latency duration range and the preset latency duration range can be determined. Specifically, the overlapping duration range between the target latency duration range and the preset latency duration range can be found first. For example, assuming the target latency duration range is 5 milliseconds to 20 milliseconds and the preset latency duration range is 5 milliseconds to 15 milliseconds, then the overlapping duration range is 5 milliseconds to 15 milliseconds. Calculations are performed based on this overlapping duration range and the preset latency duration range. The specific calculation formula is as follows:

[0101] Target overlap degree = overlapping duration range / preset latency duration range × 100%;

[0102] The target overlap degree can be obtained according to the above formula; when the target overlap degree is less than the preset overlap degree, it can be determined that the a TEP data do not meet the second preset condition.

[0103] When the target coincidence degree is not less than the preset coincidence degree, fitting can be performed based on the a latency periods and the start time corresponding to each latency period to obtain a first straight line. Specifically, the start time corresponding to each latency period among the a latency periods can be obtained first to obtain a start times, and these a start times are combined with the corresponding latency periods among the a latency periods to obtain a coordinate points. The least squares method can be used to fit these a coordinate points, thereby obtaining the first straight line.

[0104] For example, please refer to Figure 6 , Figure 6 which is a schematic diagram of a first straight line L1 provided by an embodiment of the present application. Figure 6 Each black dot in represents one of the above-mentioned a coordinate points. Fitting can be performed based on these black dots to obtain the first straight line L1.

[0105] Next, the first slope corresponding to the first straight line can be obtained; further, the amplitudes within the preset amplitude range among the a amplitudes can be found to obtain c amplitudes; then, the second qualification rate can be determined according to the c amplitudes and the a amplitudes. The specific calculation formula is as follows:

[0106] Second qualification rate = c / a × 100%;

[0107] The second qualification rate can be obtained according to the above formula; when the first slope is not less than the preset slope, and / or, the second qualification rate is less than the preset qualification rate, it is determined that the a TEP data do not meet the second preset condition;

[0108] When the first slope is less than the preset slope and the second qualification rate is not less than the preset qualification rate, it is determined that the a TEP data meet the second preset condition.

[0109] In this way, by obtaining the signal-to-noise ratio of each TEP data and screening out the data not less than the first preset signal-to-noise ratio, data with large noise interference can be removed, ensuring that the retained data has high reliability, reducing the influence of noise on subsequent analysis, and thus improving the credibility of TEP data analysis.

[0110] Optionally, in step S302, when the target state includes the brain injury state, the determining the target stimulation parameter according to the target state and the target duration may include the following steps:

[0111] C1. Determine the target injury cause corresponding to the brain injury state;

[0112] C2. Determine the third stimulation parameter corresponding to the target injury cause;

[0113] C3. Determine the first stimulation tolerance parameter of the target object according to the target physical health parameter;

[0114] C4. Determine the parameter difference between the first stimulation tolerance parameter and the preset stimulation tolerance parameter to obtain a first parameter difference;

[0115] C5. Based on the target damage cause, estimate the corresponding parameter difference of the target object after the target duration to obtain a second parameter difference; the target duration is the duration between the starting moment when the target object is determined to be in the brain damage state and the current moment;

[0116] C6. Determine the target deviation degree between the first parameter difference and the second parameter difference;

[0117] C7. Determine the second influence factor corresponding to the target deviation degree;

[0118] C8. Adjust the third stimulation parameter according to the second influence factor to obtain a fourth stimulation parameter;

[0119] C9. When the fourth stimulation parameter is within the preset stimulation parameter range, determine the target stimulation parameter according to the fourth stimulation parameter.

[0120] In the embodiments of the present application, the preset stimulation tolerance parameter can be preset in advance or by default. The stimulation tolerance parameter is used to represent the high or low tolerance degree of the human body to TMS stimulation. The larger the preset stimulation tolerance parameter, the higher the tolerance degree of the human body to TMS stimulation.

[0121] In a specific embodiment, the target damage cause corresponding to the brain damage state can be determined. Specifically, the medical record data of the target object can be obtained, and the target damage cause can be determined according to the medical record data. For example, if the medical record data records that the target object "had cardiac arrest and had impaired consciousness and limb convulsions after resuscitation", combined with the clinical manifestations and relevant examinations, it can be determined that the brain damage is caused by hypoxia. Another example is that if the medical record records that "the patient (i.e., the target object) had a rear-end collision when riding in a car and hit the windshield of the car violently", then the target damage cause of the brain damage can be determined as head trauma caused by a traffic accident; then, the third stimulation parameter corresponding to the target damage cause can be determined. Specifically, a mapping relationship between the preset damage cause and the stimulation parameter can be pre-stored, and the third stimulation parameter corresponding to the target damage cause can be determined based on this mapping relationship.

[0122] Next, the first stimulation tolerance parameter of the target object can be determined based on the target physical health parameter. Specifically, a mapping relationship between the preset physical health parameter and the stimulation tolerance parameter can be pre-stored, and the first stimulation tolerance parameter corresponding to the target physical health parameter can be determined based on this mapping relationship. Next, the first parameter difference can be obtained by subtracting the preset stimulation tolerance parameter from the first stimulation tolerance parameter. Then, the change value of the stimulation tolerance parameter of the target object after the target duration can be estimated based on the target injury cause, that is, the second parameter difference. Specifically, clinical research papers, case reports, treatment guidelines, etc. related to the target injury cause can be searched. These documents may contain data on the brain receiving TMS stimulation after a certain period of time (e.g., the target duration) in a similar brain injury situation. The second parameter difference can be determined based on these data. For example, in the literature on neurorehabilitation after craniocerebral trauma, it is mentioned that one week after the injury (assuming the target duration is one week), the maximum tolerance threshold (stimulation parameter) of the patient's brain to TMS stimulation increased by about 15% on average. Then, 15% can be used as the second parameter difference. It should be noted that both the first parameter difference and the second parameter difference can be percentages. Further, the target deviation degree between the first parameter difference and the second parameter difference can be calculated. The specific calculation formula is as follows:

[0123] Target deviation degree = |First parameter difference - Second parameter difference| / Second parameter difference × 100%;

[0124] According to the above formula, the target deviation degree can be obtained. Next, the second influence factor corresponding to the target deviation degree can be determined. Specifically, a mapping relationship between the preset deviation degree and the influence factor can be pre-stored, and the second influence factor corresponding to the target deviation degree can be determined based on this mapping relationship. The value range of the second influence factor can be -0.25 to 0.25. Next, the third stimulation parameter can be adjusted according to the second influence factor. The specific calculation formula is as follows:

[0125] Fourth stimulation parameter = Third stimulation parameter × (1 + Second influence factor);

[0126] According to the above formula, the fourth stimulation parameter can be obtained. When the fourth stimulation parameter is within the preset stimulation parameter range, the fourth stimulation parameter can be directly used as the target stimulation parameter.

[0127] When the fourth stimulation parameter is not within the preset stimulation parameter range, the upper limit and lower limit of the preset stimulation parameter of the preset stimulation parameter range can be obtained, and it can be judged whether the fourth stimulation parameter is closer to the upper limit of the preset stimulation parameter or the lower limit of the preset stimulation parameter. If the fourth stimulation parameter is closer to the upper limit of the preset stimulation parameter, the upper limit of the preset stimulation parameter is used as the target stimulation parameter. If the fourth stimulation parameter is closer to the lower limit of the preset stimulation parameter, the lower limit of the preset stimulation parameter is used as the target stimulation parameter.

[0128] Thus, by determining the target cause of injury corresponding to the brain injury state and then finding the corresponding third stimulation parameter, this is a personalized treatment idea based on the cause of the disease. Different causes of injury will lead to different pathophysiological states of the brain and different responses to TMS stimulation. For example, brain injury caused by trauma may be accompanied by physical injury and local inflammation of brain tissue, which is very different from brain injury caused by cerebrovascular diseases (such as hematoma compression and local ischemia after cerebral hemorrhage) in terms of changes in the brain microenvironment and nerve function. Formulating the initial third stimulation parameter for these different causes of injury can make the TMS stimulation more targeted and improve the stimulation effect.

[0129] S303. During the first preset time period, use the transcranial magnetic stimulation device to perform periodic stimulation on the target object with the target stimulation parameter to obtain n first TEP data; each stimulation corresponds to one first TEP data; n is an integer greater than 1.

[0130] In the embodiment of the present application, the first preset time period can be preset in advance or by default.

[0131] In a specific embodiment, during the first preset time period, use the transcranial magnetic stimulation device to perform periodic stimulation on the target object with the target stimulation parameter, and synchronously record the TEP data generated by the brain after each stimulation through a data recording device (for example, an electroencephalograph), so as to obtain n first TEP data.

[0132] S304. Remove interference from the n first TEP data to obtain n second TEP data.

[0133] In the embodiment of the present application, interference can be removed from each of the n first TEP data (for example, removing artifacts, filtering, etc.) to obtain n second TEP data.

[0134] Optionally, step S304, removing interference from the n first TEP data to obtain n second TEP data, may include the following steps:

[0135] S41. Obtain target first TEP data; the target first TEP data is any one of the n first TEP data;

[0136] S42. During the third preset time period, collect third TEP data of the target object; the start time of the third preset time period is later than the end time of the second preset time period, and the end time of the third preset time period is earlier than the start time of the first preset time period;

[0137] S43. Remove the maximum and minimum values of the third TEP data to obtain the fourth TEP data;

[0138] S44. Determine the target mean value corresponding to the fourth TEP data;

[0139] S45. Perform baseline correction on the target first TEP data based on the target mean value to obtain the fifth TEP data;

[0140] S46. Filter the fifth TEP data through a preset filtering algorithm to obtain the second TEP data corresponding to the target first TEP data.

[0141] In the embodiments of the present application, both the third preset time period and the preset filtering algorithm can be preset in advance or by default. The preset filtering algorithm may include at least one of the following: wavelet filtering algorithm, Butterworth filtering algorithm, Chebyshev filtering algorithm, etc., which are not limited herein.

[0142] In a specific embodiment, the target first TEP data can be obtained first; then, within the third preset time period, the third TEP data of the target object can be collected through a TMS device. Then, the maximum and minimum values of the third TEP data can be found and deleted from the third TEP data to obtain the fourth TEP data; then, the average value of the fourth TEP data, that is, the target mean value, can be calculated; then, baseline correction can be performed on the target first TEP data based on the target mean value to obtain the fifth TEP data. Specifically, the potential value at each time point in the target first TEP data can be subtracted by the target mean value to obtain the fifth TEP data, which can eliminate the baseline shift caused by factors such as electrode polarization and slow potential drift, so that the potential change of the fifth TEP data can more truly reflect the brain's response to TMS stimulation; finally, the fifth TEP data can be filtered through a preset filtering algorithm, thereby obtaining the second TEP data corresponding to the target first TEP data.

[0143] It should be noted that the third preset time period is a short period of time before the TMS stimulation of the target object (such as 100 - 200 milliseconds before the stimulation). During this period, the brain's electrical activity is relatively stable, and the potential at this time can be used as a baseline for baseline correction.

[0144] Thus, by performing baseline correction on the target first TEP data using the target mean, the baseline drift in the data can be eliminated. Baseline drift may be caused by electrode polarization, slow physiological changes (such as body temperature changes, blood circulation fluctuations, etc.) or external environmental factors (such as electromagnetic interference). The corrected fifth TEP data is referenced to a more stable baseline, enabling the potential changes in the TEP data to more accurately reflect the brain's response to TMS stimulation, rather than being masked or distorted by baseline changes. Thus, the accuracy of the TEP data is improved.

[0145] S305. Determine the second TEP data among the n second TEP data that meet the first preset condition, obtaining m second TEP data; m is a positive integer less than or equal to n.

[0146] In the embodiments of the present application, the first preset condition can be preset in advance or by default, and the first preset condition is used to determine whether the TEP data is normal data.

[0147] In a specific embodiment, the n second TEP data can be sequentially matched with the first preset condition, removing those second TEP data that do not meet the first preset condition, thereby obtaining m second TEP data.

[0148] S306. Determine m consciousness index values based on the m second TEP data.

[0149] In the embodiments of the present application, the index value of each of the m second TEP data can be calculated to obtain m consciousness index values, and each second TEP data corresponds to a consciousness index value.

[0150] Optionally, in step S306, the determining m consciousness index values based on the m second TEP data may include the following steps:

[0151] D1. Obtain target second TEP data; the target second TEP data is any one of the m second TEP data;

[0152] D2. Perform singular value decomposition on the target second TEP data, obtaining i eigenvalues and i eigenvectors; each eigenvalue corresponds to an eigenvector; i is a positive integer;

[0153] D3. Determine the proportion of the energy of each of the i eigenvalues in the sum of the energies of the i eigenvalues, obtaining i proportions;

[0154] Determine the signal-to-noise ratio corresponding to each of the i eigenvectors, obtaining i signal-to-noise ratios;

[0155] D4. Sort the i proportions from largest to smallest, obtaining a first proportion sequence;

[0156] D5. Select eigenvectors from the i eigenvectors based on the i signal-to-noise ratios and the first weight sequence to obtain j eigenvectors; j is a positive integer less than or equal to i;

[0157] D6. Determine the normalized spatio-temporal complexity corresponding to each eigenvector among the j eigenvectors to obtain j normalized spatio-temporal complexities;

[0158] D7. Determine the awareness index value corresponding to the target second TEP data according to the j normalized spatio-temporal complexities.

[0159] In the embodiments of the present application, the awareness index value can be PCIst.

[0160] In a specific embodiment, the target second TEP data can be obtained first, denoted as A(P, t), where P is the number of data channels, that is, the number of electrode channels used when recording the TEP data, reflecting the spatial dimension of data acquisition; t is the number of time samples, representing the number of points for sampling the TEP signal in the time dimension, reflecting the time resolution of data acquisition; then, the target second TEP data can be subjected to singular value decomposition, and the specific calculation formula is as follows:

[0161] ;

[0162] Among them, A represents the TEP data matrix corresponding to the target second TEP data; represents the data component obtained by projecting the TEP data matrix onto the direction represented by the eigenvector represents the data component obtained by projecting the TEP data matrix onto the direction represented by the eigenvector is the i-th eigenvalue, which measures the variance of the data in different principal component directions and reflects the importance of the data in this direction; is the corresponding eigenvector, representing the distribution pattern of the data in different principal component directions; since the singular value decomposition is a conventional technique, the specific singular value decomposition process will not be elaborated here; according to the above formula, i eigenvalues and i eigenvectors can be obtained; then, the ratio of the energy of each of the i eigenvalues to the sum of the energies of the i eigenvalues can be calculated, and the specific calculation formula is as follows:

[0163] ;

[0164] Among them, represents the i-th ratio among the i ratios, is the i-th eigenvalue, and by calculating according to the above formula i times, i ratios can be obtained; further, the signal-to-noise ratio corresponding to each of the i eigenvectors can be determined, and the specific calculation formula is as follows:

[0165] ;

[0166] where t represents time, is the signal-to-noise ratio of the i-th eigenvector, represents the time before TMS stimulation, represents the time after TMS stimulation; calculating according to the above formula i times, i signal-to-noise ratios are obtained; then, these i ratios can be sorted from largest to smallest to obtain the first ratio sequence; next, based on the i signal-to-noise ratios and the first ratio sequence, eigenvectors can be selected from the i eigenvectors to obtain j eigenvectors; then, the normalized spatio-temporal complexity corresponding to each eigenvector among the j eigenvectors can be determined to obtain j normalized spatio-temporal complexities. Specifically, a target eigenvector can be obtained, and the target eigenvector is any one of the j eigenvectors. The amplitude change complexity of its (i.e., the target eigenvector) time domain can be quantified using recursive analysis, and the absolute value of the amplitude difference between two time points is calculated to obtain the distance matrix and , corresponding to the situations before and after TMS stimulation respectively. The specific calculation formulas are as follows:

[0167] ;

[0168] ;

[0169] where "||·||" in the above formula represents taking the norm, here referring to the norm of a vector. j and k respectively represent different time points. These two distance matrices are used to measure the degree of difference in the amplitudes of the principal components at different time points; then, according to a preset threshold, the distance matrices and are binarized to obtain the binarized matrices and , and the normalized result of the difference between these two binarized matrices is calculated. The specific calculation formula is as follows:

[0170] ;

[0171] where represents the sum of all elements in the binarized matrix ; represents the sum of all elements in the binarized matrix ; represents the normalized spatio-temporal complexity of the target eigenvector. Calculating according to the above formula j times, j normalized spatio-temporal complexities can be obtained. Finally, these j normalized spatio-temporal complexities can be accumulated to obtain the consciousness index value corresponding to the target second TEP data.

[0172] In this way, by comprehensively considering multiple parameters such as eigenvalue and signal-to-noise ratio to select eigenvectors, it is possible to adapt to TEP data with different characteristics. For example, for TEP data with different noise levels and different electroencephalogram activity patterns, the most relevant information can be extracted by adjusting the criteria for selecting eigenvectors (based on signal-to-noise ratio and proportion sequence), so as to ensure the accuracy and reliability of the calculation of the consciousness index value and enable it to play a role in different application scenarios.

[0173] Optionally, in step D5, the step of selecting eigenvectors from the i eigenvectors based on the i signal-to-noise ratios and the first proportion sequence to obtain j eigenvectors may include the following steps:

[0174] E1. Determine the signal-to-noise ratios greater than the second preset signal-to-noise ratio among the i signal-to-noise ratios to obtain k signal-to-noise ratios; k is a positive integer not greater than i and not less than j;

[0175] E2. Select k proportions from the first proportion sequence based on the k signal-to-noise ratios, and sort the k proportions from largest to smallest to obtain a second proportion sequence;

[0176] E3. Select proportions from the second proportion sequence in order until the sum of the selected proportions is greater than the preset proportion to obtain j proportions;

[0177] E4. Determine the eigenvectors corresponding to each of the j proportions to obtain the j eigenvectors.

[0178] In the embodiments of the present application, both the second preset signal-to-noise ratio and the preset proportion can be preset in advance or by default.

[0179] In a specific embodiment, each of the i signal-to-noise ratios can be compared with a second preset signal-to-noise ratio (e.g., 1.2 dB) to obtain k signal-to-noise ratios greater than the second preset signal-to-noise ratio. Then, the weights corresponding to each of the k signal-to-noise ratios can be found from the first weight sequence to obtain k weights, and the k weights can be sorted from largest to smallest to obtain a second weight sequence. Further, the weights can be sequentially selected from the second weight sequence until the sum of the selected weights is greater than a preset weight (e.g., 0.99) to obtain j weights. Specifically, the sum of the first weights can be initialized to 0, and the weights can be sequentially selected from the second weight sequence. Each time a weight is selected, it is added to the sum of the first weights. Then, the sum of the first weights is compared with the preset weight to obtain the size relationship between the two. If the sum of the first weights is greater than the preset weight, all the selected weights can be used as the j weights. Conversely, if the sum of the first weights is not greater than the preset weight, weights continue to be selected from the second weight sequence and the selected weights are added to the sum of the first weights until the sum of the first weights is greater than the preset weight to obtain j weights. Finally, the eigenvectors corresponding to each of the j weights in the i eigenvectors can be determined to obtain j eigenvectors.

[0180] In this way, by setting the second preset signal-to-noise ratio for screening, those eigenvectors with higher signal quality can be preferentially retained, making the subsequent analysis more focused on the true and reliable electroencephalogram activity information, thereby improving the accuracy of the consciousness level assessment.

[0181] S307. Determine the target consciousness state of the target object according to the m consciousness index values; the target consciousness state includes any one of the following: awake, drowsy, confused, stuporous, and comatose.

[0182] In the embodiment of the present application, the average value of the m consciousness index values can be calculated to obtain the target consciousness index mean value. Then, the target consciousness state can be determined according to the target consciousness index mean value. For example, a mapping relationship between the preset consciousness index mean value and the consciousness state can be pre-stored, and the target consciousness state corresponding to the target consciousness index mean value can be determined based on this mapping relationship.

[0183] Implementing the present application has the following beneficial effects:

[0184] It can be seen that the method for evaluating the consciousness level based on TEP data described in the embodiments of the present application includes: obtaining the target state of the target object and the target duration corresponding to the target state; determining the target stimulation parameter according to the target state and the target duration; within the first preset time period, performing periodic stimulation on the target object with the target stimulation parameter through a transcranial magnetic stimulation device to obtain n first TEP data; performing interference removal on the n first TEP data to obtain n second TEP data; determining the second TEP data that meets the first preset condition among the n second TEP data to obtain m second TEP data; determining m consciousness index values according to the m second TEP data; determining the target consciousness state of the target object according to the m consciousness index values; by collecting the TEP data of the target object, the TEP data is the direct electrophysiological response of the brain to the stimulation and can more truly reflect the consciousness state of a person, analyzing the TEP data to obtain the consciousness index value, and determining the consciousness level of the target object according to the consciousness index value, thereby realizing the evaluation of the consciousness level of the target object when the target object cannot interact with the environment.

[0185] Please refer to Figure 7 , Figure 7 which is a functional unit composition block diagram of a consciousness level evaluation device 700 based on TEP data provided by an embodiment of the present application, applied to an electronic device, the electronic device is connected to a transcranial magnetic stimulation device, and the consciousness level evaluation device 700 based on TEP data includes: an acquisition unit 701, a control unit 702, and an evaluation unit 703, where:

[0186] The acquisition unit 701 is configured to obtain the target state of the target object and the target duration corresponding to the target state; the target state includes any one of the following: anesthetic state, brain injury state;

[0187] The control unit 702 is configured to determine the target stimulation parameter according to the target state and the target duration; within the first preset time period, perform periodic stimulation on the target object with the target stimulation parameter through the transcranial magnetic stimulation device to obtain n first TEP data; each stimulation corresponds to one first TEP data; n is an integer greater than 1; perform interference removal on the n first TEP data to obtain n second TEP data; determine the second TEP data that meets the first preset condition among the n second TEP data to obtain m second TEP data; m is a positive integer less than or equal to n;

[0188] The evaluation unit 703 is configured to determine m consciousness index values according to the m second TEP data; determine the target consciousness state of the target object according to the m consciousness index values; the target consciousness state includes any one of the following: awake, drowsy, confused, stuporous, comatose.

[0189] Optionally, when the target state includes the anesthetic state, in determining the target stimulation parameter according to the target state and the target duration, the control unit 702 is specifically configured to:

[0190] Obtain the target drug type and the target anesthetic drug amount of the anesthetic drug injected into the target object;

[0191] Estimate the first drug onset duration required for the target object to reach a preset anesthetic depth according to the target drug type and the target anesthetic drug amount;

[0192] Estimate the target anesthetic depth of the target object at the current moment according to the first drug onset duration, the target duration, and the preset anesthetic depth; the target duration is the duration between the injection moment of injecting the anesthetic drug and the current moment;

[0193] Determine the first stimulation parameter corresponding to the target anesthetic depth;

[0194] Obtain the target physical health parameter of the target object;

[0195] Determine the first influence factor corresponding to the target physical health parameter;

[0196] Adjust the first stimulation parameter according to the first influence factor to obtain a second stimulation parameter;

[0197] Within a second preset time period, perform periodic stimulation on the target object with the second stimulation parameter through the transcranial magnetic stimulation device to obtain a reference TEP data; a is a positive integer less than n; each stimulation corresponds to a reference TEP data; the end moment of the second preset time period is earlier than the start moment of the first preset time period;

[0198] Remove interference from the a reference TEP data to obtain a TEP data;

[0199] When the a TEP data meet the second preset condition, determine the target stimulation parameter according to the second stimulation parameter.

[0200] Optionally, the consciousness level evaluation device 700 based on TEP data is further specifically configured to:

[0201] Obtain the signal-to-noise ratio of each TEP data in the a TEP data to obtain a signal-to-noise ratio;

[0202] Determine the signal-to-noise ratio that is not less than the first preset signal-to-noise ratio among the a signal-to-noise ratios to obtain b signal-to-noise ratios; b is a natural number less than a;

[0203] Determine a first qualification rate based on the b signal-to-noise ratios and the a signal-to-noise ratios;

[0204] When the first qualification rate is less than a preset qualification rate, determine that the a TEP data do not meet the second preset condition;

[0205] When the first qualification rate is not less than the preset qualification rate, determine the waveform corresponding to each TEP data among the a TEP data to obtain a TEP waveforms;

[0206] Determine the latency and amplitude corresponding to each TEP waveform among the a TEP waveforms to obtain a latencies and a amplitudes;

[0207] Determine the target latency duration range corresponding to the a latencies;

[0208] Determine the target overlap degree between the target latency duration range and the preset latency duration range;

[0209] When the target overlap degree is less than the preset overlap degree, determine that the a TEP data do not meet the second preset condition;

[0210] When the target overlap degree is not less than the preset overlap degree, perform fitting based on the a latencies and the start time corresponding to each latency to obtain a first straight line; the abscissa is time and the ordinate is the latency duration;

[0211] Obtain the first slope corresponding to the first straight line;

[0212] Determine the amplitudes within a preset amplitude range among the a amplitudes to obtain c amplitudes; c is a natural number less than a;

[0213] Determine a second qualification rate based on the c amplitudes and the a amplitudes;

[0214] When the first slope is not less than a preset slope, and / or, the second qualification rate is less than the preset qualification rate, determine that the a TEP data do not meet the second preset condition;

[0215] When the first slope is less than the preset slope, and the second qualification rate is not less than the preset qualification rate, determine that the a TEP data meet the second preset condition.

[0216] Optionally, when the target state includes the brain injury state, in terms of determining the target stimulation parameters according to the target state and the target duration, the control unit 702 is specifically configured to:

[0217] Determine the target injury cause corresponding to the brain injury state;

[0218] Determine the third stimulation parameter corresponding to the target damage cause;

[0219] Determine the first stimulation tolerance parameter of the target object according to the target physical health parameter;

[0220] Determine the parameter difference between the first stimulation tolerance parameter and the preset stimulation tolerance parameter to obtain the first parameter difference;

[0221] Estimate the corresponding parameter difference of the target object after the target duration based on the target damage cause to obtain the second parameter difference; the target duration is the duration between the starting moment when the target object is determined to be in the brain damage state and the current moment;

[0222] Determine the target deviation degree between the first parameter difference and the second parameter difference;

[0223] Determine the second influence factor corresponding to the target deviation degree;

[0224] Adjust the third stimulation parameter according to the second influence factor to obtain the fourth stimulation parameter;

[0225] When the fourth stimulation parameter is within the preset stimulation parameter range, determine the target stimulation parameter according to the fourth stimulation parameter.

[0226] Optionally, in terms of deinterfering the n first TEP data to obtain n second TEP data, the control unit 702 is specifically configured to:

[0227] Obtain target first TEP data; the target first TEP data is any one of the n first TEP data;

[0228] Collect the third TEP data of the target object within a third preset time period; the starting moment of the third preset time period is later than the ending moment of the second preset time period, and the ending moment of the third preset time period is earlier than the starting moment of the first preset time period;

[0229] Remove the maximum value and the minimum value of the third TEP data to obtain the fourth TEP data;

[0230] Determine the target mean value corresponding to the fourth TEP data;

[0231] Perform baseline correction on the target first TEP data based on the target mean value to obtain the fifth TEP data;

[0232] Filter the fifth TEP data through a preset filtering algorithm to obtain the second TEP data corresponding to the target first TEP data.

[0233] Optionally, in terms of determining the m consciousness index values according to the m second TEP data, the evaluation unit 703 is specifically configured to:

[0234] Obtain target second TEP data; the target second TEP data is any one of the m second TEP data;

[0235] Perform singular value decomposition on the target second TEP data to obtain i eigenvalues and i eigenvectors; each eigenvalue corresponds to an eigenvector; i is a positive integer;

[0236] Determine the proportion of the energy of each of the i eigenvalues in the sum of the energies of the i eigenvalues to obtain i proportions;

[0237] Determine the signal-to-noise ratio corresponding to each of the i eigenvectors to obtain i signal-to-noise ratios;

[0238] Sort the i proportions from largest to smallest to obtain a first proportion sequence;

[0239] Select eigenvectors from the i eigenvectors based on the i signal-to-noise ratios and the first proportion sequence to obtain j eigenvectors; j is a positive integer less than or equal to i;

[0240] Determine the normalized spatio-temporal complexity corresponding to each of the j eigenvectors to obtain j normalized spatio-temporal complexities;

[0241] Determine the consciousness index value corresponding to the target second TEP data according to the j normalized spatio-temporal complexities.

[0242] Optionally, for the step of selecting eigenvectors from the i eigenvectors based on the i signal-to-noise ratios and the first proportion sequence to obtain j eigenvectors, the evaluation unit 703 is specifically configured to:

[0243] Determine the signal-to-noise ratios greater than a second preset signal-to-noise ratio among the i signal-to-noise ratios to obtain k signal-to-noise ratios; k is a positive integer not greater than i and not less than j;

[0244] Select k proportions from the first proportion sequence based on the k signal-to-noise ratios, and sort the k proportions from largest to smallest to obtain a second proportion sequence;

[0245] Select proportions from the second proportion sequence in order until the sum of the selected proportions is greater than a preset proportion to obtain j proportions;

[0246] Determine the eigenvector corresponding to each of the j proportions to obtain the j eigenvectors.

[0247] It can be understood that the functions of the respective unit modules of the awareness level evaluation device 700 based on TEP data provided in this embodiment can be specifically implemented according to the methods in the above method embodiments. The specific implementation process can refer to the relevant descriptions in the above method embodiments and will not be elaborated here.

[0248] Please refer to Figure 8 , Figure 8 which is a schematic structural diagram of an electronic device provided in an embodiment of the present application. The electronic device includes a processor, a memory, a communication interface, and one or more programs. The processor, the memory, and the communication interface are interconnected through a bus. The above one or more programs are stored in the above memory and are configured to be executed by the above processor. The one or more programs include instructions for performing other implementation manners described in the awareness level evaluation method based on TEP data provided in the embodiments of the present invention, which will not be elaborated here.

[0249] An embodiment of the present invention further provides a computer storage medium. The computer storage medium stores a computer program for electronic data exchange. The computer program enables a computer to execute some or all of the steps of any one of the methods described in the above method embodiments. The above computer includes an electronic device.

[0250] An embodiment of the present application further provides a computer program product. The computer program product includes a non-transitory computer-readable storage medium storing a computer program. The computer program is operable to enable a computer to execute some or all of the steps of any one of the methods described in the above method embodiments. The computer program product can be a software installation package. The above computer includes an electronic device.

[0251] It should be noted that for the foregoing method embodiments, for the sake of simple description, they are all expressed as a series of action combinations. However, those skilled in the art should know that the present application is not limited by the described action sequence, because according to the present application, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to the present application.

[0252] In the above embodiments, the descriptions of the respective embodiments have their own emphases. For the parts not detailed in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0253] In several embodiments provided by the present application, it should be understood that the disclosed device can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the above division of units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling, direct coupling, or communication connection to each other can be through some interfaces. The indirect coupling or communication connection of devices or units can be in electrical or other forms.

[0254] The units described above as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0255] In addition, each functional unit in various embodiments of the present application can be integrated into a processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above integrated unit can be implemented in the form of hardware or in the form of a software functional unit.

[0256] If the above integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on such an understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or all or part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the above methods in various embodiments of the present application. And the aforementioned memory includes: various media such as USB flash drives, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), mobile hard disks, magnetic disks, or optical discs that can store program codes.

[0257] The above has introduced the embodiments of the present application in detail. Specific examples are used in this article to elaborate on the principle and implementation manner of the present application. The description of the above embodiments is only used to help understand the method and its core idea of the present application; at the same time, for those of ordinary skill in the art, according to the idea of the present application, there will be changes in the specific implementation manner and application scope. In summary, the content of this specification should not be construed as a limitation to the present application.

Claims

1. A method for assessing the level of consciousness based on TEP data, characterized in that: Applied to an electronic device, the electronic device is connected to a transcranial magnetic stimulation device, and the method comprises: Acquire a target state of a target object and a target duration corresponding to the target state; the target state includes any one of the following: anesthesia state, brain injury state; Determining target stimulation parameters according to the target state and the target duration; In a first preset time period, the target object is periodically stimulated by the transcranial magnetic stimulation device with the target stimulation parameters to obtain n first TEP data; each stimulation corresponds to one first TEP data; n is an integer greater than 1; De-interference is performed on the n first TEP data to obtain n second TEP data; Determine the second TEP data that meets the first preset condition among the n second TEP data to obtain m second TEP data; m is a positive integer less than or equal to n; Determine m consciousness index values ​​according to the m second TEP data; Determine the target consciousness state of the target object according to the m consciousness index values; the target consciousness state includes any one of the following: awake, drowsy, confused, lethargic, comatose; Wherein, when the target state includes the anesthesia state, determining the target stimulation parameter according to the target state and the target duration includes: obtaining a target drug type and a target anesthetic drug amount of the anesthetic drug to be injected into the target subject; estimating the onset time of the first drug required for the target subject to reach a preset anesthesia depth according to the target drug type and the target anesthetic amount; estimating the target anesthesia depth of the target subject at the current moment according to the first drug onset time, the target duration and the preset anesthesia depth; the target duration is the time between the injection time of the anesthetic drug and the current moment; determining a first stimulation parameter corresponding to the target anesthesia depth; Acquire target physical health parameters of the target object; Determining a first influencing factor corresponding to the target physical health parameter; Adjusting the first stimulation parameter according to the first influencing factor to obtain a second stimulation parameter; In a second preset time period, the target object is periodically stimulated by the transcranial magnetic stimulation device with the second stimulation parameter to obtain a reference TEP data; a is a positive integer less than n; each stimulation corresponds to a reference TEP data; the end time of the second preset time period is earlier than the start time of the first preset time period; De-interference is performed on the a reference TEP data to obtain a TEP data; When the a TEP data meet a second preset condition, determining the target stimulation parameter according to the second stimulation parameter; Wherein, the method further comprises: Obtaining a signal-to-noise ratio of each TEP data in the a TEP data to obtain a signal-to-noise ratios; Determine a signal-to-noise ratio that is not less than a first preset signal-to-noise ratio among the a signal-to-noise ratios, and obtain b signal-to-noise ratios; b is a natural number less than a; Determine a first pass rate according to the b signal-to-noise ratios and the a signal-to-noise ratios; When the first pass rate is less than a preset pass rate, determining that the a TEP data do not meet the second preset condition; When the first qualified rate is not less than the preset qualified rate, determining a waveform corresponding to each TEP data in the a TEP data to obtain a TEP waveforms; Determine the latency and amplitude corresponding to each of the a TEP waveforms to obtain a latency and a amplitude; Determine the target incubation period duration range corresponding to the a incubation periods; Determining a target overlap between the target incubation period duration range and a preset incubation period duration range; When the target overlap is less than the preset overlap, determining that the a TEP data do not meet the second preset condition; When the target overlap is not less than the preset overlap, fitting is performed based on the a latent periods and the starting time corresponding to each latent period to obtain a first straight line; the horizontal axis is time, and the vertical axis is the duration of the latent period; Obtaining a first slope corresponding to the first straight line; Determine the amplitudes within the preset amplitude range among the a amplitudes to obtain c amplitudes; c is a natural number less than a; Determining a second pass rate according to the c amplitudes and the a amplitudes; When the first slope is not less than a preset slope, and / or the second pass rate is less than the preset pass rate, determining that the a TEP data do not meet the second preset condition; When the first slope is smaller than the preset slope and the second pass rate is not smaller than the preset pass rate, it is determined that the a TEP data meet the second preset condition.

2. The method according to claim 1, characterized in that When the target state includes the brain injury state, determining the target stimulation parameter according to the target state and the target duration includes: determining a target injury cause corresponding to the brain injury state; Determining a third stimulation parameter corresponding to the target injury cause; Acquire target physical health parameters of the target object; determining a first stimulation tolerance parameter of the target object according to the target physical health parameter; determining a parameter difference between the first stimulation tolerance parameter and a preset stimulation tolerance parameter to obtain a first parameter difference; estimating a parameter difference corresponding to the target object after the target duration based on the target injury cause, and obtaining a second parameter difference; the target duration is the time between the start time when the target object is determined to be in the brain injury state and the current time; determining a target deviation between the first parameter difference and the second parameter difference; Determining a second influencing factor corresponding to the target deviation; adjusting the third stimulation parameter according to the second influencing factor to obtain a fourth stimulation parameter; When the fourth stimulation parameter is within a preset stimulation parameter range, the target stimulation parameter is determined according to the fourth stimulation parameter.

3. The method according to claim 1 or 2, characterized in that The step of removing interference from the n first TEP data to obtain n second TEP data includes: Acquire target first TEP data; the target first TEP data is any first TEP data among the n first TEP data; collecting third TEP data of the target object within a third preset time period; the start time of the third preset time period is later than the end time of the second preset time period, and the end time of the third preset time period is earlier than the start time of the first preset time period; Removing the maximum value and the minimum value of the third TEP data to obtain fourth TEP data; Determining a target mean value corresponding to the fourth TEP data; Baseline-correct the target first TEP data based on the target mean value to obtain fifth TEP data; The fifth TEP data is filtered by a preset filtering algorithm to obtain second TEP data corresponding to the target first TEP data.

4. The method according to claim 1 or 2, characterized in that: The determining m consciousness index values ​​according to the m second TEP data comprises: Acquire target second TEP data; the target second TEP data is any second TEP data among the m second TEP data; Perform singular value decomposition on the target second TEP data to obtain i eigenvalues ​​and i eigenvectors; each eigenvalue corresponds to an eigenvector; i is a positive integer; Determine the proportion of the energy of each of the i eigenvalues ​​to the sum of the energies of the i eigenvalues ​​to obtain i proportions; Determine the signal-to-noise ratio corresponding to each of the i eigenvectors to obtain i signal-to-noise ratios; Sort the i weights from large to small to obtain a first weight sequence; Selecting feature vectors from the i feature vectors based on the i signal-to-noise ratios and the first weight sequence to obtain j feature vectors; j is a positive integer less than or equal to i; Determine the normalized space-time complexity corresponding to each of the j feature vectors to obtain j normalized space-time complexities; Determine the consciousness index value corresponding to the target second TEP data according to the j normalized spatiotemporal complexities.

5. The method according to claim 4, characterized in that The step of selecting feature vectors from the i feature vectors based on the i signal-to-noise ratios and the first weight sequence to obtain j feature vectors includes: Determine a signal-to-noise ratio greater than a second preset signal-to-noise ratio among the i signal-to-noise ratios, and obtain k signal-to-noise ratios; k is a positive integer not greater than i and not less than j; Selecting k weights from the first weight sequence based on the k signal-to-noise ratios, and sorting the k weights from large to small to obtain a second weight sequence; Selecting weights from the second weight sequence in order until the sum of the selected weights is greater than the preset weight, to obtain j weights; Determine a feature vector corresponding to each of the j features to obtain the j feature vectors.

6. A consciousness level assessment device based on TEP data, characterized in that: Applied to an electronic device, the electronic device is connected to a transcranial magnetic stimulation device, the device comprises: an acquisition unit, a control unit, and an evaluation unit, wherein: The acquisition unit is used to acquire a target state of the target object and a target duration corresponding to the target state; the target state includes any one of the following: an anesthesia state and a brain injury state; The control unit is used to determine the target stimulation parameters according to the target state and the target duration; within a first preset time period, the target object is periodically stimulated by the transcranial magnetic stimulation device with the target stimulation parameters to obtain n first TEP data; each stimulation corresponds to one first TEP data; n is an integer greater than 1; the n first TEP data are de-interfered to obtain n second TEP data; the second TEP data that meets the first preset condition among the n second TEP data is determined to obtain m second TEP data; m is a positive integer less than or equal to n; The evaluation unit is used to determine m consciousness index values ​​according to the m second TEP data; determine the target consciousness state of the target object according to the m consciousness index values; the target consciousness state includes any one of the following: awake, drowsy, confused, lethargic, comatose; Wherein, when the target state includes the anesthesia state, the target stimulation parameter is determined according to the target state and the target duration, and the control unit is specifically used for: obtaining a target drug type and a target anesthetic drug amount of the anesthetic drug to be injected into the target subject; estimating the onset time of the first drug required for the target subject to reach a preset anesthesia depth according to the target drug type and the target anesthetic amount; estimating the target anesthesia depth of the target subject at the current moment according to the first drug onset time, the target duration and the preset anesthesia depth; the target duration is the time between the injection time of the anesthetic drug and the current moment; determining a first stimulation parameter corresponding to the target anesthesia depth; Acquire target physical health parameters of the target object; Determining a first influencing factor corresponding to the target physical health parameter; Adjusting the first stimulation parameter according to the first influencing factor to obtain a second stimulation parameter; In a second preset time period, the target object is periodically stimulated by the transcranial magnetic stimulation device with the second stimulation parameter to obtain a reference TEP data; a is a positive integer less than n; each stimulation corresponds to a reference TEP data; the end time of the second preset time period is earlier than the start time of the first preset time period; De-interference is performed on the a reference TEP data to obtain a TEP data; When the a TEP data meet a second preset condition, determining the target stimulation parameter according to the second stimulation parameter; The device is also specifically used for: Obtaining a signal-to-noise ratio of each TEP data in the a TEP data to obtain a signal-to-noise ratios; Determine a signal-to-noise ratio that is not less than a first preset signal-to-noise ratio among the a signal-to-noise ratios, and obtain b signal-to-noise ratios; b is a natural number less than a; Determine a first pass rate according to the b signal-to-noise ratios and the a signal-to-noise ratios; When the first pass rate is less than a preset pass rate, determining that the a TEP data do not meet the second preset condition; When the first qualified rate is not less than the preset qualified rate, determining a waveform corresponding to each TEP data in the a TEP data to obtain a TEP waveforms; Determine the latency and amplitude corresponding to each of the a TEP waveforms to obtain a latency and a amplitude; Determine the target incubation period duration range corresponding to the a incubation periods; Determining a target overlap between the target incubation period duration range and a preset incubation period duration range; When the target overlap is less than the preset overlap, determining that the a TEP data do not meet the second preset condition; When the target overlap is not less than the preset overlap, fitting is performed based on the a latent periods and the starting time corresponding to each latent period to obtain a first straight line; the horizontal axis is time, and the vertical axis is the duration of the latent period; Obtaining a first slope corresponding to the first straight line; Determine the amplitudes within the preset amplitude range among the a amplitudes to obtain c amplitudes; c is a natural number less than a; Determining a second pass rate according to the c amplitudes and the a amplitudes; When the first slope is not less than a preset slope, and / or the second pass rate is less than the preset pass rate, determining that the a TEP data do not meet the second preset condition; When the first slope is smaller than the preset slope and the second pass rate is not smaller than the preset pass rate, it is determined that the a TEP data meet the second preset condition.

7. An electronic device, characterized in that: include: A processor and a memory, wherein the memory is used to store one or more programs and is configured to be executed by the processor, wherein the program includes instructions for executing the steps in the method according to any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that: A computer program for electronic data exchange is stored, wherein the computer program enables a computer to execute the method according to any one of claims 1 to 5.

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