Method, device and equipment for estimating consciousness state category based on heartbeat-induced electroencephalogram

Abstract

The invention provides a method, a device and equipment for estimating a consciousness state category based on heartbeat-induced electroencephalogram, which can be applied to the technical field of electroencephalogram signal detection. The method for estimating the consciousness state category based on the heartbeat-induced electroencephalogram comprises the steps that focused ultrasonic signals are transmitted to a plurality of coding sites in the somatosensory cortex and the island leaf of the brain of a target object, and a plurality of coding electroencephalogram signals are obtained; on the basis of a preset electroencephalogram period interval and the ultrasonic frequency of the focused ultrasonic signal, the multiple coded electroencephalogram signals are decoded, multiple target electroencephalogram data are obtained, the preset electroencephalogram period interval is determined on the basis of the peak value of R waves in the electrocardiosignals of the target object, and the preset electroencephalogram period interval is determined on the basis of the peak value of the R waves in the electrocardiosignals of the target object; the target electroencephalogram data comprises multiple pieces of envelope data corresponding to the coding sites; performing variance calculation on the multiple pieces of target electroencephalogram data to obtain a target electroencephalogram variance index; and obtaining consciousness state category information of the target object according to a preset consciousness state threshold value and the target electroencephalogram variance index.

Description

Technical Field

[0001] The present disclosure relates to the technical field of electroencephalogram (EEG) signal detection, and more specifically to a method, device and apparatus for estimating the category of consciousness state based on heartbeat-induced EEG. Background Art

[0002] Obtaining the consciousness level of patients with consciousness disorders can be used as an intermediate auxiliary information to help relevant professionals make targeted clinical diagnoses or formulate targeted clinical plans, which is of great significance. The acquisition of existing consciousness level information is usually based on manual judgment. However, manual judgment is subjective and inefficient, and cannot effectively judge the patient's consciousness level.

[0003] In the process of realizing the above-mentioned inventive concept, the inventors found that due to the weakness and conduction characteristics of heartbeat evoked potentials, it is difficult to extract them accurately, and because it is difficult to extract the conversion relationship between heartbeat evoked potentials and consciousness level category information, it is difficult to evaluate the target object's consciousness level category information based on heartbeat evoked potentials. Summary of the invention

[0004] In view of the above problems, the present disclosure provides a method, apparatus and device for estimating the category of consciousness state based on heartbeat-induced EEG.

[0005] According to the first aspect of the present disclosure, a method for estimating the category of consciousness state based on heartbeat-induced EEG is provided, comprising: transmitting a focused ultrasonic signal to a plurality of coding sites in the somatosensory cortex and the insula of the brain of a target object to obtain a plurality of coded EEG signals, wherein the coded EEG signals are obtained by the EEG signals generated by the somatosensory cortex or the insula and the focused ultrasonic signal; decoding the plurality of coded EEG signals based on a predetermined EEG cycle interval and the ultrasonic frequency of the focused ultrasonic signal to obtain a plurality of target EEG data, wherein the predetermined EEG cycle interval is determined based on the peak value of the R wave in the electrocardiogram signal of the target object, and the target EEG data includes a plurality of envelope data corresponding to the coding sites; performing variance calculation on the plurality of target EEG data to obtain a target EEG variance index; obtaining the category information of the target object's consciousness state based on a predetermined consciousness state threshold and the target EEG variance index.

[0006] According to an embodiment of the present disclosure, based on a predetermined EEG cycle interval and the ultrasonic frequency of a focused ultrasonic signal, a plurality of coded EEG signals are decoded and processed to obtain a plurality of target EEG data, including: determining a plurality of EEG time points based on a plurality of R-wave peaks in an electrocardiogram signal; determining a plurality of predetermined EEG time periods based on a predetermined EEG cycle interval and a plurality of EEG time points; intercepting and processing each coded EEG signal based on a plurality of predetermined EEG time periods to obtain a plurality of intermediate induced EEG signals; performing bandpass filtering on a plurality of intermediate induced EEG signals according to the ultrasonic frequency of a focused ultrasonic signal and a predetermined filtering bandwidth to obtain a plurality of target induced EEG signals; and respectively performing envelope extraction processing on a plurality of target induced EEG signals to obtain a plurality of target EEG data.

[0007] According to an embodiment of the present disclosure, variance calculation is performed on multiple target EEG data to obtain a target EEG variance index, including: calculating multiple envelope data in each target EEG data to obtain multiple target EEG indices corresponding to each encoding site; normalizing the multiple target EEG indices corresponding to each encoding site to obtain a target EEG variance index.

[0008] According to an embodiment of the present disclosure, the target EEG index includes EEG variance, EEG mean and EEG standard deviation; multiple target EEG indices corresponding to each coding site are normalized to obtain a target EEG variance index, including: based on a predetermined normalization function, according to multiple EEG variances, multiple EEG means and multiple EEG standard deviations corresponding to each coding site, multiple intermediate EEG variances corresponding to each coding site are obtained; multiple target EEG variances are selected from the multiple intermediate EEG variances corresponding to each coding site; and multiple target EEG variances are averaged to obtain a target EEG variance index.

[0009] According to an embodiment of the present disclosure, focused ultrasonic signals are emitted to multiple encoding sites in the somatosensory cortex and insula of the target object's brain to obtain multiple encoded EEG signals, including: acquiring the target object's electrocardiogram (ECG) signal; when the ECG signal satisfies a first preset condition, focused ultrasonic signals are emitted to multiple encoding sites in the somatosensory cortex and insula, wherein the first preset condition characterizes the presence of Q waves in the target object's ECG signal; and encoding and processing the multiple EEG signals and focused ultrasonic signals generated by the somatosensory cortex and insula to obtain multiple encoded EEG signals.

[0010] According to an embodiment of the present disclosure, when an electrocardiogram signal satisfies a first preset condition, a focused ultrasonic signal is transmitted to multiple coding sites in the somatosensory cortex and the insula, including: determining multiple three-dimensional spatial coordinate information corresponding to the multiple coding sites in the somatosensory cortex and the insula, wherein the three-dimensional spatial coordinate information represents position information of the coding sites in a three-dimensional space established with the left temporal lobe in the brain as the origin; based on the multiple three-dimensional spatial coordinate information, a focused ultrasonic signal is transmitted to the multiple coding sites.

[0011] According to an embodiment of the present disclosure, based on a predetermined consciousness state threshold and a target EEG variance index, consciousness state category information of the target object is obtained, including: when the target EEG variance index is greater than the predetermined consciousness state threshold, consciousness state category information characterizing that the target object's consciousness state is a first degree consciousness state is obtained; when the target EEG variance index is less than the predetermined consciousness state threshold, consciousness state category information characterizing that the target object's consciousness state is a second degree consciousness state is obtained.

[0012] According to an embodiment of the present disclosure, the predetermined consciousness state threshold is obtained by the following operations, including: acquiring a sample data set, wherein the sample data set includes a plurality of first sample coded EEG signals and a plurality of first sample electrocardiogram signals corresponding to a first degree of consciousness state, and a plurality of second sample coded EEG signals and a plurality of second sample electrocardiogram signals corresponding to a second degree of consciousness state; decoding and processing the plurality of first sample coded EEG signals based on a first predetermined EEG cycle interval and an ultrasonic frequency of a focused ultrasonic signal to obtain a plurality of first sample EEG data, wherein the first predetermined EEG cycle interval is determined based on the peak value of the R wave in the first sample electrocardiogram signal corresponding to the first sample coded EEG signal; decoding and processing the plurality of second sample coded EEG signals based on a second predetermined EEG cycle interval and an ultrasonic frequency of a focused ultrasonic signal Decoding processing is performed to obtain multiple second sample EEG data, wherein the second predetermined EEG cycle interval is determined based on the peak value of the R wave in the second sample electrocardiogram signal corresponding to the second sample encoded EEG signal; variance calculation is performed on the multiple first sample EEG data and the multiple second sample EEG data respectively to obtain multiple first sample target EEG variance indices and multiple second sample target EEG variance indices; based on the multiple first sample target EEG variance indices and the multiple second sample target EEG variance indices, a first sample curve corresponding to the first degree of consciousness state and a second sample curve corresponding to the second degree of consciousness state are obtained; when the values ​​of the ordinates of the first sample curve and the second sample curve are the same, the sample target EEG variance index of the same abscissa in the first sample curve and the second sample curve is determined to be the predetermined consciousness state threshold.

[0013] The second aspect of the present disclosure provides a device for estimating consciousness state classification based on heartbeat-induced EEG, comprising: a transmitting module, for transmitting focused ultrasonic signals to multiple coding sites in the somatosensory cortex and insula of the brain of a target object, to obtain multiple coded EEG signals, wherein the coded EEG signals are obtained by the EEG signals generated by the somatosensory cortex or the insula and the focused ultrasonic signals; a processing module, for decoding the multiple coded EEG signals based on a predetermined EEG cycle interval and the ultrasonic frequency of the focused ultrasonic signal, to obtain multiple target EEG data, wherein the predetermined EEG cycle interval is determined based on the peak value of the R wave in the electrocardiogram signal of the target object, and the target EEG data includes multiple envelope data corresponding to the coding sites; a calculating module, for performing variance calculation on the multiple target EEG data to obtain a target EEG variance index; an obtaining module, for obtaining the consciousness state category information of the target object according to a predetermined consciousness state threshold and the target EEG variance index.

[0014] A third aspect of the present disclosure provides an electronic device, comprising: one or more processors; and a memory for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors execute the above method.

[0015] The fourth aspect of the present disclosure also provides a computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, causes the processor to execute the above method.

[0016] The fifth aspect of the present disclosure also provides a computer program product, including a computer program, which implements the above method when executed by a processor.

[0017] According to the method, device and equipment for estimating the category of consciousness state based on heartbeat-induced EEG disclosed in the present invention, based on the features in the target object's EEG signal, a focused ultrasonic signal is emitted to multiple coding sites in the somatosensory cortex and insula of the target object's brain during a predetermined time period to obtain multiple coded EEG signals, and then based on a predetermined EEG cycle interval and the ultrasonic frequency of the focused ultrasonic signal, the multiple coded EEG signals are decoded in a time-locked and space-locked manner to obtain multiple target EEG data and perform variance calculation to obtain a target EEG variance index, and then based on the pre-established feature association of EEG signal-EEG data-consciousness state category information, according to the predetermined consciousness state threshold and the target EEG variance index, the consciousness state category information of the target object is classified and confirmed, thereby obtaining intermediate auxiliary information that can assist relevant professionals in making judgments, thereby achieving the acquisition of accurate classification auxiliary information on the target object's current state of consciousness. In combination with the features in the target object's electrocardiogram (ECG) signal, a focused ultrasonic signal is emitted to the somatosensory cortex and insula of the target object, which may produce microvolt-level heart-evoked potentials, at a specific moment, so that the tiny EEG signal (heart-evoked potential) reacts with the ultrasonic signal. This allows accurate coded EEG signals containing microvolt-level heart-evoked potentials to be extracted in a non-invasive environment, reducing the difficulty of extracting coded EEG signals while allowing the extracted coded EEG signals to include complete and accurate EEG signals associated with the state of consciousness.

[0018] According to the embodiments of the present disclosure, further, the features between the EEG signal-consciousness state category information are analyzed and relevant feature associations are established to determine a predetermined consciousness state threshold that can be used for auxiliary judgment, and the variance of the acquired target EEG data of the target object is calculated to obtain a target EEG variance index. The current state of the target object is judged based on the target EEG variance index obtained from the encoded EEG signal of the target object and the feature association between the EEG signal and the consciousness state category (predetermined consciousness state threshold) to obtain the consciousness state category information of the target object, thereby providing a basis for objectively evaluating the brain consciousness level through feature association, so that relevant professionals can make judgments based on the auxiliary information obtained from the evaluation, thereby improving efficiency and reducing costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The above contents and other objects, features and advantages of the present disclosure will become more apparent through the following description of the embodiments of the present disclosure with reference to the accompanying drawings, in which:

[0020] Figure 1 An application scenario diagram of the method for estimating the category of the state of consciousness based on heartbeat-induced EEG according to an embodiment of the present disclosure is shown;

[0021] Figure 2A flow chart showing a method for estimating the category of consciousness state based on heartbeat-induced EEG according to an embodiment of the present disclosure is shown;

[0022] Figure 3 A flowchart of obtaining multiple encoded EEG signals according to an embodiment of the present disclosure is shown;

[0023] Figure 4 A flowchart of obtaining multiple target EEG data according to an embodiment of the present disclosure is shown;

[0024] Figure 5 A flow chart of obtaining a target EEG variance index according to an embodiment of the present disclosure is shown;

[0025] Figure 6 A structural block diagram of a device for estimating the category of a state of consciousness based on heartbeat-induced electroencephalogram according to an embodiment of the present disclosure is shown;

[0026] Figure 7 A block diagram of an electronic device is shown according to an embodiment of the present disclosure, which implements a method for estimating the category of the state of consciousness based on heartbeat-induced EEG. DETAILED DESCRIPTION

[0027] Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the present disclosure. In the following detailed description, for ease of explanation, many specific details are set forth to provide a comprehensive understanding of the embodiments of the present disclosure. However, it is apparent that one or more embodiments may also be implemented without these specific details. In addition, in the following description, descriptions of known structures and technologies are omitted to avoid unnecessary confusion of the concepts of the present disclosure.

[0028] The terms used herein are only for describing specific embodiments and are not intended to limit the present disclosure. The terms "comprise", "include", etc. used herein indicate the existence of the features, steps, operations and / or components, but do not exclude the existence or addition of one or more other features, steps, operations or components.

[0029] All terms (including technical and scientific terms) used herein have the meanings commonly understood by those skilled in the art unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification and should not be interpreted in an idealized or overly rigid manner.

[0030] When using expressions such as "at least one of A, B, and C, etc.", they should generally be interpreted according to the meaning of the expression commonly understood by those skilled in the art (for example, "a system having at least one of A, B, and C" should include but is not limited to a system having A alone, B alone, C alone, A and B, A and C, B and C, and / or A, B, C, etc.).

[0031] In the technical solution of the present disclosure, the user information (including but not limited to user personal information, user image information, user device information, such as location information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved are all information and data authorized by the user or fully authorized by all parties, and the collection, storage, use, processing, transmission, provision, disclosure and application of the relevant data comply with relevant laws, regulations and standards, take necessary confidentiality measures, do not violate public order and good morals, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0032] Obtaining intermediate auxiliary information about the consciousness level of patients with consciousness disorders is of great significance for helping relevant professionals to make targeted clinical diagnoses or formulate targeted clinical plans. The acquisition of existing consciousness level information is usually based on manual judgment. However, manual judgment is subjective and inefficient, and cannot effectively judge the patient's consciousness level.

[0033] Heartbeat evoked potential is a potential component related to the level of consciousness, that is, a phase-locked neural response with the heartbeat time can be observed about 300ms after the R peak of the electrocardiogram, which can usually be generated in deep brain structures. However, heartbeat evoked potentials of the microvolt level are generated in deep brain locations such as the somatosensory cortex and insula, and are often submerged in the scalp electroencephalogram during the conduction process of each layer of brain tissue, resulting in the difficulty in extracting heartbeat evoked potentials and the inability to obtain intermediate auxiliary information based on the characteristics of heartbeat evoked potentials. In the process of realizing the above-mentioned inventive concept, it was found that due to the weakness and conduction characteristics of heartbeat evoked potentials, it is difficult to accurately extract them, and because it is difficult to extract the conversion relationship between heartbeat evoked potentials and consciousness level category information, it is difficult to evaluate the target object's consciousness level category information based on heartbeat evoked potentials.

[0034] In view of this, an embodiment of the present disclosure provides a method for estimating the category of consciousness state based on heartbeat-induced EEG, comprising: transmitting a focused ultrasonic signal to multiple coding sites in the somatosensory cortex and insula of the target object's brain to obtain multiple coded EEG signals, wherein the coded EEG signals are obtained by the EEG signals generated by the somatosensory cortex or the insula and the focused ultrasonic signals; decoding the multiple coded EEG signals based on a predetermined EEG cycle interval and the ultrasonic frequency of the focused ultrasonic signal to obtain multiple target EEG data, wherein the predetermined EEG cycle interval is determined based on the peak value of the R wave in the electrocardiogram signal of the target object, and the target EEG data includes multiple envelope data corresponding to the coding sites; performing variance calculation on the multiple target EEG data to obtain a target EEG variance index; obtaining the consciousness state category information of the target object according to the predetermined consciousness state threshold and the target EEG variance index.

[0035] Figure 1 An application scenario diagram of a method for estimating the category of consciousness state based on heartbeat-induced EEG according to an embodiment of the present disclosure is shown.

[0036] like Figure 1 As shown, the application scenario according to this embodiment may include a first ultrasound device 101, a target object 102, and a receiver 103. The first ultrasound device 101 is used to send an ultrasound signal to the target object 102.

[0037] The user may use the first ultrasound device 101 to interact with the target object 102 and the receiver 103 to receive or send signals, etc.

[0038] The receiver 103 may be a receiver for receiving various acoustic and electrical echo signals, for example, receiving and processing the ultrasonic signal sent by the first ultrasonic device 101 or the acoustic and electrical echo signal generated by the target object 102 (for example only). The receiver 103 may analyze and process the received acoustic and electrical echo signal and other data, and feed back the processing result (for example, obtaining or generating relevant measurement data according to a user request, etc.) to the terminal device.

[0039] It should be noted that the method for estimating the category of the state of consciousness based on heartbeat-induced EEG provided in the embodiment of the present invention can generally be executed by the receiver 103. Accordingly, the device for estimating the category of the state of consciousness based on heartbeat-induced EEG provided in the embodiment of the present invention can generally be arranged in the receiver 103. The method for estimating the category of the state of consciousness based on heartbeat-induced EEG provided in the embodiment of the present invention can also be executed by a receiver or a receiver cluster that is different from the receiver 103 and can communicate with the first ultrasound device 101 and / or the receiver 103. Accordingly, the device for estimating the category of the state of consciousness based on heartbeat-induced EEG provided in the embodiment of the present invention can also be arranged in a receiver or a receiver cluster that is different from the receiver 103 and can communicate with the first ultrasound device 101 and / or the receiver 103.

[0040] It should be understood that Figure 1 The number of the first ultrasound device, the target object and the receiver in the embodiment is only for illustration. According to the implementation requirements, there may be any number of ultrasound devices, observation objects and receivers.

[0041] The following will be based on Figure 1 The scene described by Figure 2~Figure 5 The method of estimating the category of consciousness state based on heartbeat-induced EEG in the disclosed embodiment is described in detail.

[0042] Figure 2 A flowchart of a method for estimating the category of consciousness state based on heartbeat-induced EEG according to an embodiment of the present disclosure is shown.

[0043] like Figure 2 As shown, the method for estimating the category of the state of consciousness based on heartbeat-induced EEG in this embodiment includes operations S210 to S240.

[0044] In operation S210, focused ultrasonic signals are transmitted to a plurality of encoding sites in the somatosensory cortex and the insula of the brain of the target object to obtain a plurality of encoded electroencephalogram signals.

[0045] According to an embodiment of the present disclosure, the encoded EEG signal is obtained from the EEG signal generated by the somatosensory cortex or the insula and the focused ultrasonic signal.

[0046] According to an embodiment of the present disclosure, before acquiring the coded EEG signal of the target object, the ECG signal of the target object is synchronously detected in real time, and a focused ultrasonic signal is emitted to the brain of the target object in combination with the QRS wave characteristics in the ECG signal of the target object. The somatosensory cortex and insula in the brain of the target object respectively include a plurality of preset coding sites, and each coding site can be encoded and processed according to the EEG signal generated in the somatosensory cortex and insula and the received focused ultrasonic signal to obtain a coded EEG signal corresponding to each coding site, that is, a plurality of coded EEG signals.

[0047] In operation S220, based on a predetermined EEG cycle interval and an ultrasonic frequency of a focused ultrasonic signal, a plurality of encoded EEG signals are decoded to obtain a plurality of target EEG data.

[0048] According to an embodiment of the present disclosure, the predetermined brain electrical cycle interval is determined based on the peak value of the R wave in the electrocardiogram signal of the target object.

[0049] According to an embodiment of the present disclosure, the target EEG data includes a plurality of envelope data corresponding to the encoding sites.

[0050] According to an embodiment of the present disclosure, based on a predetermined EEG cycle interval (time information) determined by the electrocardiogram signal of the target object and the ultrasonic frequency (frequency information) of the focused ultrasonic signal, each of the multiple encoded EEG signals is decoded and processed in accordance with the time-space information to obtain multiple target EEG data of the target object, wherein the multiple target EEG data include target EEG data corresponding to each encoding site, and each target EEG data includes multiple envelope data, i.e., multiple envelope data corresponding to the encoding site.

[0051] In operation S230, variance calculation is performed on a plurality of target EEG data to obtain a target EEG variance index.

[0052] According to the embodiments of the present disclosure, variance calculations are performed on a plurality of target EEG data respectively, and a variance index corresponding to each target EEG data can be obtained, thereby obtaining a target EEG variance index of the target object based on the plurality of variance indices.

[0053] In operation S240, the consciousness state category information of the target object is obtained according to the predetermined consciousness state threshold and the target EEG variance index.

[0054] According to an embodiment of the present disclosure, by pre-establishing feature associations from EEG signals-EEG data-consciousness state category information, the target object's consciousness state category information is discriminated according to the target EEG variance index of the target object and a predetermined consciousness state threshold, thereby obtaining the target object's consciousness state category information based on the established feature association information and using it as intermediate auxiliary information to assist relevant professionals in making professional judgments, so as to facilitate subsequent operations on the target object.

[0055] According to an embodiment of the present disclosure, the consciousness state category information may include whether the consciousness state of the target object belongs to the first predetermined category. For example, in the case of belonging to the first predetermined category, the consciousness state category information obtained may include that the target object may be conscious, and in the case of not belonging to the first predetermined category, the consciousness state category information obtained may include that the target object may not be conscious. Here, it should be noted that the consciousness state category information obtained here is only used as reference information for medical staff to make a diagnosis, and is not used as a direct diagnosis result.

[0056] For example, by synchronously acquiring the target object's electrocardiogram signal in real time, based on the characteristics of the target object's electrocardiogram signal, focused ultrasonic signals are emitted to multiple coding sites in the somatosensory cortex and insula of the brain, thereby obtaining multiple coded EEG signals, and time-space related decoding processing is performed on the multiple coded EEG signals to obtain target EEG data corresponding to each coding site, thereby calculating the target EEG variance index of the target object, and analyzing the current consciousness state category information of the target object based on the characteristic relationship between the pre-established consciousness state threshold and the EEG variance index and the target EEG variance index of the target object, so as to facilitate scientists or professionals in related fields to identify the current state of the target object based on the current consciousness state category information of the target object, and thus make a judgment suitable for the target object.

[0057] According to an embodiment of the present disclosure, based on features in the target object's electrocardiogram signal, a focused ultrasonic signal is emitted to multiple coding sites in the somatosensory cortex and insula of the target object's brain during a predetermined time period to obtain multiple coded EEG signals, and then based on a predetermined EEG cycle interval and the ultrasonic frequency of the focused ultrasonic signal, the multiple coded EEG signals are decoded in a time-locked and space-locked manner to obtain multiple target EEG data and perform variance calculation to obtain a target EEG variance index, and then based on a pre-established feature association of EEG signal-EEG data-consciousness state category information, the target object's consciousness state category information is classified and confirmed according to a predetermined consciousness state threshold and the target EEG variance index, thereby obtaining intermediate auxiliary information that can assist relevant professionals in making judgments, thereby achieving accurate classification auxiliary information acquisition of the target object's current state of consciousness. In combination with the features in the target object's electrocardiogram (ECG) signal, a focused ultrasonic signal is emitted to the somatosensory cortex and insula of the target object, which may produce microvolt-level heart-evoked potentials, at a specific moment, so that the tiny EEG signal (heart-evoked potential) reacts with the ultrasonic signal. This allows accurate coded EEG signals containing microvolt-level heart-evoked potentials to be extracted in a non-invasive environment, reducing the difficulty of extracting coded EEG signals while allowing the extracted coded EEG signals to include complete and accurate EEG signals associated with the state of consciousness.

[0058] According to the embodiments of the present disclosure, further, the features between the EEG signal-consciousness state category information are analyzed and relevant feature associations are established to determine a predetermined consciousness state threshold that can be used for auxiliary judgment, and the variance of the acquired target EEG data of the target object is calculated to obtain a target EEG variance index. The current state of the target object is judged based on the target EEG variance index obtained from the encoded EEG signal of the target object and the feature association between the EEG signal and the consciousness state category (predetermined consciousness state threshold) to obtain the consciousness state category information of the target object, thereby providing a basis for objectively evaluating the brain consciousness level through feature association, so that relevant professionals can make judgments based on the auxiliary information obtained from the evaluation, thereby improving efficiency and reducing costs.

[0059] Figure 3 A flow chart of obtaining multiple encoded EEG signals according to an embodiment of the present disclosure is shown.

[0060] like Figure 3 As shown, the method of this embodiment of transmitting focused ultrasonic signals to multiple encoding sites in the somatosensory cortex and insula of the target object's brain to obtain multiple encoded electroencephalogram signals includes operations S310~S330.

[0061] In operation S310 , an electrocardiogram signal of a target object is acquired.

[0062] According to an embodiment of the present disclosure, in the process of acquiring the consciousness state category information of the target object, multiple physiological acquisition devices can be used to record the target object's electrocardiogram signal in real time, so as to perform corresponding operations according to the characteristic peaks in the target object's electrocardiogram signal.

[0063] In operation S320 , when the electrocardiogram signal satisfies a first preset condition, a focused ultrasonic wave signal is transmitted to a plurality of encoding sites in the somatosensory cortex and the insula.

[0064] According to an embodiment of the present disclosure, the first preset condition represents the presence of a Q wave in the target object's electrocardiogram signal.

[0065] According to an embodiment of the present disclosure, when an electrocardiogram signal satisfies a first preset condition, a method of transmitting a focused ultrasonic signal to multiple encoding sites in a somatosensory cortex and an insular lobe includes the following operations.

[0066] According to an embodiment of the present disclosure, a plurality of three-dimensional spatial coordinate information corresponding to a plurality of encoding sites in the somatosensory cortex and the insula is determined.

[0067] According to an embodiment of the present disclosure, the three-dimensional spatial coordinate information represents the position information of the encoding site in the three-dimensional space established with the left temporal lobe in the brain as the origin.

[0068] According to an embodiment of the present disclosure, multiple encoding sites are randomly defined in the somatosensory cortex and three-dimensional spatial coordinate information of the multiple encoding sites in the somatosensory cortex is obtained. The three-dimensional spatial coordinate information set of the multiple encoding sites in the somatosensory cortex can be expressed according to formula (1).

[0069] (1);

[0070] Among them, C SC It can be represented as a set of three-dimensional spatial coordinate information of multiple encoding sites in the somatosensory cortex. It can be represented as the three-dimensional spatial coordinate information of the first encoding site in the somatosensory cortex. It can be represented as the three-dimensional spatial coordinate information of the second encoding site in the somatosensory cortex. It can be represented as the three-dimensional spatial coordinate information of the mth coding site in the somatosensory cortex, where there may be a total of m coding sites in the somatosensory cortex.

[0071] According to an embodiment of the present disclosure, a plurality of coding sites are randomly defined in the insula and the three-dimensional spatial coordinate information of the plurality of coding sites in the insula is obtained. The three-dimensional spatial coordinate information set of the plurality of coding sites in the insula can be expressed according to formula (2).

[0072] (2);

[0073] Among them, C IL It can be represented as a set of three-dimensional spatial coordinate information of multiple coding sites in the insula. It can be represented as the three-dimensional spatial coordinate information of the first coding site in the insula, It can be represented as the three-dimensional spatial coordinate information of the second coding site in the insula, It can be represented as the three-dimensional spatial coordinate information of the nth coding site in the insula, where there can be a total of n coding sites in the insula.

[0074] According to an embodiment of the present disclosure, focused ultrasonic signals are transmitted to a plurality of encoding sites based on a plurality of three-dimensional spatial coordinate information.

[0075] According to an embodiment of the present disclosure, when a Q wave is detected in the ECG signal of the target object, a trigger signal is output to a signal generator through a serial port. After receiving the trigger signal, the signal generator transmits an ultrasonic excitation pulse to the ultrasonic probe, and combines the three-dimensional spatial coordinate information of the encoding site, that is, the three-dimensional spatial coordinate information set C of multiple encoding sites in the somatosensory cortex. SC and the three-dimensional spatial coordinate information set C of multiple coding sites in the insula ILThe three-dimensional motion system is used to control the mobile ultrasound probe to aim at multiple encoding sites in the somatosensory cortex and insula of the target object, and then the ultrasound probe emits a focused ultrasound signal, where the ultrasound frequency of the focused ultrasound signal is f FUS .

[0076] In operation S330, a plurality of electroencephalogram signals and focused ultrasonic wave signals generated by the somatosensory cortex and the insula are coded to obtain a plurality of coded electroencephalogram signals.

[0077] According to the embodiments of the present disclosure, each coding site in the somatosensory cortex and the insula can generate an electroencephalogram (EEG) signal, which is encoded and processed with the focused ultrasonic signal received by the coding sites of the somatosensory cortex and the insula to obtain an encoded electroencephalogram (EEG) signal corresponding to each coding site. Multiple encoded electroencephalogram (EEG) signals can be expressed according to formula (3), and multiple electrocardiogram (ECG) signals of the target object collected at the same time as the multiple encoded electroencephalogram (EEG) signals can be expressed according to formula (4).

[0078] (3);

[0079] in, It can be represented as a collection of multiple coded EEG signals. can be characterized as the coded EEG signal of the first coding site in the somatosensory cortex, can be characterized as an encoded EEG signal at a second encoding site in the somatosensory cortex, It can be represented as the coded EEG signal of the mth coding site in the somatosensory cortex, It can be characterized as the coded EEG signal of the first coding site in the insula, The encoded EEG signal can be characterized as a second encoding site in the insula, It can be characterized as the encoded EEG signal of the nth encoding site in the insula.

[0080] (4);

[0081] in, It can be represented as a collection of multiple ECG signals. The electrocardiogram signal can be characterized as the first coding site in the somatosensory cortex. The ECG signal can be characterized as a second encoding site in the somatosensory cortex, It can be represented as the ECG signal of the mth coding site in the somatosensory cortex, The electrocardiographic signal can be characterized as the first coding site in the insula, The ECG signal can be characterized as a second coding site in the insula, It can be represented as the ECG signal of the nth coding site in the insula.

[0082] According to the embodiments of the present disclosure, multiple three-dimensional spatial coordinate information corresponding to multiple coding sites in the somatosensory cortex and the insula can be predetermined, and the electrocardiogram (ECG) signal of the target object can be acquired by real-time monitoring. When a Q wave appears in the ECG signal, based on the multiple three-dimensional spatial coordinate information, focused ultrasonic signals are transmitted to multiple coding sites in the somatosensory cortex and the insula, and then multiple EEG signals and focused ultrasonic signals generated by the somatosensory cortex and the insula are encoded and processed to obtain multiple encoded EEG signals. This realizes transmitting focused ultrasonic signals to multiple coding sites based on the occurrence time of the heartbeat evoked potential, combined with the Q wave characteristics in the ECG signal of the target object and the position information of the encoding sites, so that the EEG signals generated at the multiple encoding sites in the somatosensory cortex and the insula change with the focused ultrasonic signals, and an accurate encoded EEG signal containing the EEG signal (heartbeat evoked potential) of the target object is obtained, thereby completing the accurate and complete extraction of the heartbeat evoked potential at the microvolt level.

[0083] Figure 4 A flow chart of obtaining multiple target EEG data according to an embodiment of the present disclosure is shown.

[0084] like Figure 4 As shown, the method of this embodiment decodes multiple encoded EEG signals based on a predetermined EEG cycle interval and the ultrasonic frequency of the focused ultrasonic signal to obtain multiple target EEG data includes operations S410~S450.

[0085] In operation S410, a plurality of electroencephalogram time points are determined according to a plurality of R-wave peaks in the electrocardiogram signal.

[0086] According to an embodiment of the present disclosure, an electrocardiogram signal may include multiple QRS waves, and the time corresponding to the peak of each R wave is determined as the EEG time point. The multiple EEG time points can be expressed according to formula (5).

[0087] (5);

[0088] Among them, t R It can be represented as a collection of multiple EEG time points, t 1 R It can be represented as the first EEG time point, that is, the time corresponding to the peak of the first R wave, t 2 R It can be represented as the second EEG time point, that is, the time corresponding to the peak of the second R wave, t nT R It can be represented as the nTth EEG time point, that is, the time corresponding to the peak value of the nTth R wave.

[0089] In operation S420, a plurality of predetermined EEG time periods are determined based on the predetermined EEG cycle interval and the plurality of EEG time points.

[0090] According to an embodiment of the present disclosure, the predetermined EEG cycle interval can be based on the EEG time point, and the period between 200ms before and 600ms after each EEG time point is selected as the predetermined EEG period, that is, , based on the predetermined EEG cycle interval, each EEG time point is brought into the predetermined EEG cycle interval to obtain multiple predetermined EEG time periods. R =(201,204,208,212,216), based on the predetermined EEG cycle interval and the 5 EEG time points, the 5 predetermined EEG time periods are (1ms,801ms), (4ms,804ms), (8ms,808ms), (12ms,812ms), and (16ms,816ms).

[0091] In operation S430, based on a plurality of predetermined EEG time periods, each encoded EEG signal is intercepted and processed to obtain a plurality of intermediate induced EEG signals.

[0092] According to an embodiment of the present disclosure, based on multiple predetermined EEG time periods, the coded EEG signals corresponding to each coding site are intercepted and processed, and the intercepted multiple time period signals are used as multiple intermediate induced EEG signals corresponding to the coding site, thereby obtaining multiple intermediate induced EEG signals corresponding to multiple coding sites. The set of multiple intermediate induced EEG signals can be characterized as , time-locked processing of the encoded EEG signals is realized to obtain time period signals related to the consciousness level categories.

[0093] For example, there are 3 coding sites and 5 predetermined EEG time periods, namely (10ms, 810ms), (20ms, 820ms), (30ms, 830ms), (40ms, 840ms), and (50ms, 850ms). Based on the 5 predetermined EEG time periods, the coded EEG signal of the first coding site is intercepted to obtain the intermediate evoked EEG signal of (10ms, 810ms), the intermediate evoked EEG signal of (20ms, 820ms), the intermediate evoked EEG signal of (30ms, 830ms), the intermediate evoked EEG signal of (40ms, 840ms), and the intermediate evoked EEG signal of (50ms, 850ms) corresponding to the first coding site. The coded EEG signal of the second coding site is intercepted to obtain The intermediate evoked EEG signals of (10ms, 810ms), (20ms, 820ms), (30ms, 830ms), (40ms, 840ms) and (50ms, 850ms) corresponding to the second coding site, and the coding EEG signals of the third coding site were intercepted to obtain the intermediate evoked EEG signals of (10ms, 810ms), (20ms, 820ms), (30ms, 830ms), (40ms, 840ms) and (50ms, 850ms) corresponding to the third coding site.

[0094] In operation S440, a plurality of intermediate evoked EEG signals are subjected to bandpass filtering according to the ultrasound frequency of the focused ultrasound signal and a predetermined filter bandwidth to obtain a plurality of target evoked EEG signals.

[0095] According to an embodiment of the present disclosure, a Butterworth filter may be used to perform bandpass filtering on a plurality of intermediate evoked EEG signals, and the relationship between the obtained target evoked EEG signals and the intermediate evoked EEG signals may be expressed according to formula (6).

[0096] (6);

[0097] in, can be characterized as target-evoked brain electrical activity, can be characterized as a Butterworth filter, can be characterized as a predetermined filter bandwidth, It can be characterized as the filter order.

[0098] According to an embodiment of the present disclosure, a filter is used to perform bandpass filtering on multiple intermediate evoked EEG signals according to the ultrasonic frequency of the focused ultrasonic signal and a pre-set predetermined filter bandwidth, thereby obtaining multiple target evoked EEG signals corresponding to each encoding site, thereby achieving space-locked (frequency-locked) processing of the intermediate evoked EEG signals to obtain frequency band signals related to the consciousness level category.

[0099] For example, there are 3 coding sites, and each coding site corresponds to 5 intermediate evoked EEG signals. The ultrasonic frequency of the focused ultrasonic signal is 10 Hz, and the predetermined filtering bandwidth is 5 Hz. Then, the frequency of 5 Hz-15 Hz needs to be retained, and these 15 intermediate evoked EEG signals are all subjected to band-pass filtering to obtain target evoked EEG signals with a frequency of 5 Hz-15 Hz (10 ms, 810 ms), a target evoked EEG signal with a frequency of 5 Hz-15 Hz (20 ms, 820 ms), a target evoked EEG signal with a frequency of 5 Hz-15 Hz (30 ms, 830 ms), a target evoked EEG signal with a frequency of 5 Hz-15 Hz (40 ms, 840 ms), and a target evoked EEG signal with a frequency of 5 Hz-15 Hz (50 ms, 850 ms) corresponding to the first coding site. The frequency corresponding to the second coding site is 5 Hz-15 Hz (10 ms, 810 ms). s), target evoked EEG signals of 5Hz-15Hz (20ms, 820ms), target evoked EEG signals of 5Hz-15Hz (30ms, 830ms), target evoked EEG signals of 5Hz-15Hz (40ms, 840ms) and target evoked EEG signals of 5Hz-15Hz (50ms, 850ms); the frequencies corresponding to the third encoding site are target evoked EEG signals of 5Hz-15Hz (10ms, 810ms), target evoked EEG signals of 5Hz-15Hz (20ms, 820ms), target evoked EEG signals of 5Hz-15Hz (30ms, 830ms), target evoked EEG signals of 5Hz-15Hz (40ms, 840ms) and target evoked EEG signals of 5Hz-15Hz (50ms, 850ms).

[0100] In operation S450, envelope extraction processing is performed on the plurality of target evoked EEG signals respectively to obtain a plurality of target EEG data.

[0101] According to an embodiment of the present disclosure, a plurality of target EEG data obtained after envelope extraction processing can be expressed according to formula (7) and formula (8).

[0102] (7);

[0103] Among them, S HEPIt can be characterized as multiple target EEG data obtained after envelope extraction, and envelope can be characterized as envelope extraction processing.

[0104] (8);

[0105] in, It can be represented as multiple target EEG data corresponding to the first encoding site in the somatosensory cortex, can be represented as multiple target EEG data corresponding to the second encoding site in the somatosensory cortex, It can be represented as multiple target EEG data corresponding to the mth encoding site in the somatosensory cortex, It can be represented as multiple target EEG data corresponding to the first encoding site in the insula, can be represented as multiple target EEG data corresponding to the second encoding site in the insula, It can be represented as a plurality of target EEG data corresponding to the nth encoding site in the insula.

[0106] For example, there are two coding sites in the somatosensory cortex and two coding sites in the insula. The five target evoked EEG signals corresponding to the first coding site in the somatosensory cortex are subjected to envelope extraction processing to obtain five target EEG data corresponding to the first coding site in the somatosensory cortex, and the five target evoked EEG signals corresponding to the second coding site in the somatosensory cortex are subjected to envelope extraction processing to obtain five target EEG data corresponding to the second coding site in the somatosensory cortex; the five target evoked EEG signals corresponding to the first coding site in the insula are subjected to envelope extraction processing to obtain five target EEG data corresponding to the first coding site in the insula, and the five target EEG data corresponding to the second coding site in the insula are subjected to envelope extraction processing. The five target evoked EEG signals corresponding to the first coding site were subjected to envelope extraction processing to obtain the five target EEG data corresponding to the second coding site in the insula. Among them, since the five target evoked EEG signals are signals of the time periods of (10ms, 810ms), (20ms, 820ms), (30ms, 830ms), (40ms, 840ms), and (50ms, 850ms), the five target EEG data obtained also correspond one-to-one to (10ms, 810ms), (20ms, 820ms), (30ms, 830ms), (40ms, 840ms), and (50ms, 850ms).

[0107] According to the embodiments of the present disclosure, multiple EEG time points are determined based on the peak values ​​of multiple R waves in the electrocardiogram signal, and then multiple predetermined EEG time periods for time-locking the coded EEG signal are obtained based on the predetermined EEG cycle interval and the multiple EEG time points, so that each coded EEG signal is intercepted and processed based on the multiple predetermined EEG time periods to obtain multiple intermediate induced EEG signals, and then the multiple intermediate induced EEG signals are band-pass filtered according to the ultrasonic frequency of the focused ultrasonic signal and the predetermined filtering bandwidth to obtain multiple time-locked and space-locked target induced EEG signals, and finally Then, envelope extraction is performed on multiple target evoked EEG signals respectively to obtain multiple target EEG data, realizing time-locked and space-locked processing of the coded EEG signals of each coding site, thereby obtaining signals of multiple predetermined EEG time periods of each coding site at a predetermined frequency (multiple target evoked EEG signals), and then the time-locked and space-locked heartbeat evoked potentials are accurately extracted (multiple target EEG data) by means of envelope extraction, so that microvolt-level heartbeat evoked EEG can be accurately extracted, so as to facilitate the determination of the target object's consciousness state category based on multiple target EEG data.

[0108] According to an embodiment of the present disclosure, a method for performing variance calculation on a plurality of target EEG data to obtain a target EEG variance index includes the following operations.

[0109] According to an embodiment of the present disclosure, multiple envelope data in each target EEG data are calculated to obtain multiple target EEG indexes corresponding to each encoding site.

[0110] According to an embodiment of the present disclosure, variance, mean, standard deviation, etc. are calculated for multiple envelope data in each target EEG data, and the obtained target EEG index may include EEG variance, EEG mean and EEG standard deviation, but is not limited to the above EEG indices.

[0111] According to an embodiment of the present disclosure, a plurality of target EEG indices corresponding to each encoding site are normalized to obtain a target EEG variance index.

[0112] Figure 5 A flow chart for obtaining a target EEG variance index according to an embodiment of the present disclosure is shown.

[0113] like Figure 5 As shown, the method of normalizing multiple target EEG indices corresponding to each encoding site to obtain a target EEG variance index in this embodiment includes operations S510 to S530.

[0114] In operation S510, based on a predetermined normalization function, a plurality of intermediate EEG variances corresponding to each encoding site are obtained according to a plurality of EEG variances, a plurality of EEG means, and a plurality of EEG standard deviations corresponding to each encoding site.

[0115] According to an embodiment of the present disclosure, since each coding site corresponds to envelope data of multiple predetermined EEG time periods at a predetermined frequency, multiple envelope data corresponding to each coding site are calculated to obtain preliminary EEG variance, EEG mean and EEG standard deviation corresponding to each predetermined EEG time period, and then normalized to obtain the intermediate EEG variance corresponding to each predetermined EEG time period in each coding site. For example, the first encoding site in the somatosensory cortex corresponds to the envelope data of (10ms, 810ms), (20ms, 820ms), (30ms, 830ms), (40ms, 840ms), and (50ms, 850ms) at 5Hz-15Hz, thereby calculating the EEG variance, EEG mean, and EEG standard deviation corresponding to (10ms, 810ms), the EEG variance, EEG mean, and EEG standard deviation corresponding to (20ms, 820ms), the EEG variance, EEG mean, and EEG standard deviation corresponding to (30ms, 830ms), and the EEG variance, EEG mean, and EEG standard deviation corresponding to (40ms, 840ms). The target EEG index (EEG variance, EEG mean and EEG standard deviation) at each coding site was then normalized to obtain the intermediate EEG variance corresponding to (10ms, 810ms) of the first coding site in the somatosensory cortex, the intermediate EEG variance corresponding to (20ms, 820ms), the intermediate EEG variance corresponding to (30ms, 830ms), the intermediate EEG variance corresponding to (40ms, 840ms), and the intermediate EEG variance corresponding to (50ms, 850ms).

[0116] In operation S520, a plurality of target EEG variances are selected from a plurality of intermediate EEG variances corresponding to each encoding site.

[0117] According to an embodiment of the present disclosure, the intermediate EEG variance corresponding to the period of 200-400 ms may be selected as the target EEG variance, and the target EEG variance may be calculated according to formula (9).

[0118] (9);

[0119] in, It can be represented as the target EEG variance, It can be represented as the EEG variance, It can be represented as the mean value of EEG. It can be represented as the EEG standard deviation, where, through formula (9), all the intermediate EEG variances corresponding to the period of 200-400ms can be selected as multiple target EEG variances while performing normalization processing.

[0120] In operation S530, a plurality of target EEG variances are averaged to obtain a target EEG variance index.

[0121] For example, the somatosensory cortex includes two coding sites, the insula includes two coding sites, the two target EEG variances of 200~400ms corresponding to the first coding site in the somatosensory cortex, the two target EEG variances of 200~400ms corresponding to the second coding site, and the two target EEG variances of 200~400ms corresponding to the first coding site in the insula, and the two target EEG variances of 200~400ms corresponding to the second coding site, a total of eight target EEG variances are averaged, and the average value obtained is the target EEG variance index.

[0122] According to an embodiment of the present disclosure, multiple envelope data in each target EEG data are calculated to obtain multiple EEG variances, multiple EEG means and multiple EEG standard deviations corresponding to each coding site, and then based on a predetermined normalization function, multiple intermediate EEG variances corresponding to each coding site are obtained according to the multiple EEG variances, multiple EEG means and multiple EEG standard deviations corresponding to each coding site. Based on a time period of 200-400ms, multiple target EEG variances are selected from the multiple intermediate EEG variances corresponding to each coding site, and the multiple target EEG variances are averaged to obtain a target EEG variance index, thereby realizing the calculation of the envelope data in the target EEG data using variance-related operations to obtain a target EEG variance index related to the consciousness state category characteristics, and in the process of obtaining the target EEG variance index, the time period for processing the target EEG variance to obtain the target EEG variance index is limited, thereby improving the accuracy of the target EEG variance index, so as to obtain the relevant and accurate score (target EEG variance index) of the target object's consciousness state category at this time under a relatively stable and balanced condition.

[0123] According to an embodiment of the present disclosure, a method for obtaining the consciousness state category information of a target object according to a predetermined consciousness state threshold and a target EEG variance index includes the following operations.

[0124] According to an embodiment of the present disclosure, when the target EEG variance index is greater than a predetermined consciousness state threshold, consciousness state category information characterizing that the consciousness state of the target object is a first degree consciousness state is obtained.

[0125] According to an embodiment of the present disclosure, the first degree of consciousness state can be characterized as intermediate auxiliary information of the target object's consciousness state, that is, >Z th In the case of, confirming that the target object's consciousness state is the first degree consciousness state, wherein, It can be represented as the target EEG variance index, Zth Can be characterized as a predetermined state of consciousness threshold.

[0126] According to an embodiment of the present disclosure, when the target EEG variance index is less than a predetermined consciousness state threshold, consciousness state category information characterizing that the consciousness state of the target object is a second degree consciousness state is obtained.

[0127] According to an embodiment of the present disclosure, the second degree of consciousness state can be characterized as intermediate auxiliary information that the target object does not have a consciousness state, that is, <Z th In this case, it is confirmed that the target object's state of consciousness is the second degree of consciousness.

[0128] According to an embodiment of the present disclosure, the predetermined consciousness state threshold is obtained through the following operations.

[0129] According to an embodiment of the present disclosure, a sample data set is acquired.

[0130] According to an embodiment of the present disclosure, the sample data set includes multiple first sample encoded EEG signals and multiple first sample ECG signals corresponding to a first degree of consciousness and multiple second sample encoded EEG signals and multiple second sample ECG signals corresponding to a second degree of consciousness.

[0131] According to an embodiment of the present disclosure, based on a first predetermined EEG cycle interval and the ultrasonic frequency of a focused ultrasonic signal, a plurality of first sample encoded EEG signals are decoded to obtain a plurality of first sample EEG data.

[0132] According to an embodiment of the present disclosure, the first predetermined EEG cycle interval is determined based on the peak value of the R wave in the first sample electrocardiogram signal corresponding to the first sample encoded EEG signal.

[0133] According to an embodiment of the present disclosure, the first sample coded EEG signal and the first sample ECG signal in the first degree of consciousness state classification correspond one to one, and the peak of the R wave in each first sample ECG signal corresponds to a different time point, so that according to each first sample ECG signal and the first predetermined EEG cycle interval, a plurality of sets of sample predetermined EEG time periods are obtained, so that the first sample coded EEG signal corresponding to the first sample ECG signal is time-locked, and then the ultrasonic frequency of the focused ultrasonic signal is used to perform frequency-locking processing on the sample intermediate induced EEG signal obtained after the time-locking processing, and then envelope extraction processing is performed to obtain a plurality of first sample EEG data corresponding to each first sample coded EEG signal.

[0134] According to an embodiment of the present disclosure, based on the second predetermined EEG cycle interval and the ultrasonic frequency of the focused ultrasonic signal, a plurality of second sample encoded EEG signals are decoded to obtain a plurality of second sample EEG data.

[0135] According to an embodiment of the present disclosure, the second predetermined EEG cycle interval is determined based on the peak value of the R wave in the second sample electrocardiogram signal corresponding to the second sample encoded EEG signal.

[0136] According to an embodiment of the present disclosure, the second sample coded EEG signal and the second sample ECG signal in the second degree of consciousness state classification correspond one to one, and the peak of the R wave in each second sample ECG signal corresponds to a different time point, so that according to each second sample ECG signal and the second predetermined EEG cycle interval, a plurality of sets of sample predetermined EEG time periods are obtained, so that the second sample coded EEG signal corresponding to the second sample ECG signal is time-locked, and then the ultrasonic frequency of the focused ultrasonic signal is used to perform frequency-locking processing on the sample intermediate induced EEG signal obtained after the time-locking processing, and then envelope extraction processing is performed to obtain a plurality of second sample EEG data corresponding to each second sample coded EEG signal.

[0137] According to an embodiment of the present disclosure, variance calculations are performed on a plurality of first sample EEG data and a plurality of second sample EEG data, respectively, to obtain a plurality of first sample target EEG variance indices and a plurality of second sample target EEG variance indices.

[0138] According to an embodiment of the present disclosure, the sample EEG variance, sample EEG mean and sample EEG standard deviation are calculated as described above for multiple first sample EEG data and multiple second sample EEG data, and then based on a predetermined normalization function, multiple sample intermediate EEG variances are obtained according to the multiple sample EEG variances, the multiple sample EEG means and the multiple sample EEG standard deviations, and then based on the signal time of 200~400ms, multiple sample target EEG variances are selected, and then averaged to obtain a first sample target EEG variance index and a second sample target EEG variance index.

[0139] According to an embodiment of the present disclosure, a first sample curve corresponding to a first degree of consciousness state and a second sample curve corresponding to a second degree of consciousness state are obtained based on multiple first sample target EEG variance indices and multiple second sample target EEG variance indices.

[0140] According to an embodiment of the present disclosure, both the first sample curve and the second sample curve are normal distribution curves.

[0141] According to an embodiment of the present disclosure, when the values ​​of the ordinates of the first sample curve and the second sample curve are the same, the sample target EEG variance index of the same abscissa in the first sample curve and the second sample curve is determined as the predetermined consciousness state threshold.

[0142] According to an embodiment of the present disclosure, the first sample curve and the second sample curve are placed in the same coordinate system, and the intersection point (sample target EEG variance index) of the two curves is the predetermined consciousness state threshold.

[0143] According to an embodiment of the present disclosure, a sample data set corresponding to a first degree of consciousness state and a sample data set corresponding to a second degree of consciousness state are obtained, and then the sample data set corresponding to the first degree of consciousness state and the sample data set corresponding to the second degree of consciousness state are processed respectively based on a first predetermined EEG cycle interval, a second predetermined EEG cycle interval and the ultrasonic frequency of a focused ultrasonic signal to obtain a plurality of first sample EEG data and a plurality of second sample EEG data, and then variance calculation is performed to obtain a plurality of first sample target EEG variance indices and a plurality of second sample target EEG variance indices, thereby generating a first sample curve and a second sample curve, and determining the intersection of the first sample curve and the second sample curve as a predetermined consciousness state threshold, so that the predetermined consciousness state threshold and the target EEG variance index of the target object can be used for comparison to determine the consciousness state category information of the target object, and training simulation processing is achieved through a large number of data samples corresponding to the first degree of consciousness state and the second degree of consciousness state, thereby obtaining a measurement threshold in the feature association of EEG signal-EEG data-consciousness state category information, so that the predetermined consciousness state threshold can be directly used to analyze the consciousness state of the target object during the application process, and intermediate auxiliary information of the consciousness state can be obtained to assist relevant professionals in making judgments and operations.

[0144] Figure 6 A structural block diagram of a device for estimating the category of consciousness state based on heartbeat-induced EEG according to an embodiment of the present disclosure is shown.

[0145] like Figure 6 As shown, the device for estimating the category of consciousness state based on heartbeat-induced EEG in this embodiment includes: a transmitting module 610, a processing module 620, a calculating module 630 and an obtaining module 640.

[0146] The transmitting module 610 is used to transmit focused ultrasonic signals to multiple coding sites in the somatosensory cortex and insula of the target object's brain to obtain multiple coded EEG signals, wherein the coded EEG signals are obtained from the EEG signals generated by the somatosensory cortex or insula and the focused ultrasonic signals. The transmitting module 610 can be used to perform the operation S210 described above, which will not be described in detail here.

[0147] The processing module 620 is used to decode the multiple coded EEG signals based on the predetermined EEG cycle interval and the ultrasonic frequency of the focused ultrasonic signal to obtain multiple target EEG data, wherein the predetermined EEG cycle interval is determined based on the peak value of the R wave in the electrocardiogram signal of the target object, and the target EEG data includes multiple envelope data corresponding to the coding site. The processing module 620 can be used to perform the operation S220 described above, which will not be repeated here.

[0148] The calculation module 630 is used to perform variance calculation on multiple target EEG data to obtain a target EEG variance index. The analysis module 630 can be used to perform the operation S230 described above, which will not be described in detail here.

[0149] The obtaining module 640 is used to obtain the consciousness state category information of the target object according to the predetermined consciousness state threshold and the target EEG variance index. The obtaining module 640 can be used to perform the operation S240 described above, which will not be described in detail here.

[0150] According to an embodiment of the present disclosure, the processing module 620 includes: a first determining submodule, a second determining submodule, a first intercepting submodule, a first filtering submodule, and a first extracting submodule.

[0151] The first determination submodule is used to determine multiple EEG time points according to multiple R wave peaks in the electrocardiogram signal.

[0152] The second determination submodule is used to determine a plurality of predetermined EEG time periods based on a predetermined EEG cycle interval and a plurality of EEG time points.

[0153] The first interception submodule is used to intercept and process each coded EEG signal based on a plurality of predetermined EEG time periods to obtain a plurality of intermediate induced EEG signals.

[0154] The first filtering submodule is used to perform bandpass filtering on the multiple intermediate induced EEG signals according to the ultrasonic frequency of the focused ultrasonic signal and the predetermined filtering bandwidth to obtain multiple target induced EEG signals.

[0155] The first extraction submodule is used to perform envelope extraction processing on multiple target induced EEG signals respectively to obtain multiple target EEG data.

[0156] According to an embodiment of the present disclosure, the calculation module 630 includes: a first calculation submodule and a first normalization submodule.

[0157] The first calculation submodule is used to calculate multiple envelope data in each target EEG data to obtain multiple target EEG indexes corresponding to each encoding site.

[0158] The first normalization submodule is used to normalize multiple target EEG indices corresponding to each encoding site to obtain a target EEG variance index.

[0159] According to an embodiment of the present disclosure, the first normalization submodule includes: a first obtaining unit, a first selecting unit and a first averaging unit.

[0160] The first obtaining unit is used to obtain multiple intermediate EEG variances corresponding to each coding site based on a predetermined normalization function according to multiple EEG variances, multiple EEG means and multiple EEG standard deviations corresponding to each coding site.

[0161] The first selection unit is used to select multiple target EEG variances from multiple intermediate EEG variances corresponding to each encoding site.

[0162] The first averaging unit is used to average multiple target EEG variances to obtain a target EEG variance index.

[0163] According to an embodiment of the present disclosure, the transmitting module 610 includes: a first acquisition submodule, a first transmitting submodule and a first encoding submodule.

[0164] The first acquisition submodule is used to acquire the ECG signal of the target object.

[0165] The first transmitting submodule is used to transmit a focused ultrasonic signal to multiple encoding sites in the somatosensory cortex and the insula when the electrocardiogram signal meets a first preset condition, wherein the first preset condition represents the presence of a Q wave in the electrocardiogram signal of the target object.

[0166] The first encoding submodule is used to encode multiple EEG signals and focused ultrasonic signals generated by the somatosensory cortex and the insular lobe to obtain multiple encoded EEG signals.

[0167] According to an embodiment of the present disclosure, the first transmitting submodule includes: a first determining unit and a first transmitting unit.

[0168] The first determination unit is used to determine multiple three-dimensional spatial coordinate information corresponding to multiple coding sites in the somatosensory cortex and the insula, wherein the three-dimensional spatial coordinate information represents the position information of the coding site in a three-dimensional space established with the left temporal lobe in the brain as the origin.

[0169] The first transmitting unit is used to transmit focused ultrasonic signals to multiple encoding sites based on multiple three-dimensional spatial coordinate information.

[0170] According to an embodiment of the present disclosure, the obtaining module 640 includes: a first obtaining submodule and a second obtaining submodule.

[0171] The first obtaining submodule is used to obtain consciousness state category information characterizing that the consciousness state of the target object is a first degree consciousness state when the target EEG variance index is greater than a predetermined consciousness state threshold.

[0172] The second obtaining submodule is used to obtain consciousness state category information characterizing that the consciousness state of the target object is a second degree consciousness state when the target EEG variance index is less than a predetermined consciousness state threshold.

[0173] According to an embodiment of the present disclosure, the obtaining module 640 further includes: a first obtaining unit, a first decoding unit, a second decoding unit, a first calculating unit, a second obtaining unit and a second determining unit.

[0174] The first acquisition unit is used to acquire a sample data set, wherein the sample data set includes a plurality of first sample coded EEG signals and a plurality of first sample ECG signals corresponding to a first degree of consciousness state, and a plurality of second sample coded EEG signals and a plurality of second sample ECG signals corresponding to a second degree of consciousness state.

[0175] The first decoding unit is used to decode and process multiple first sample encoded EEG signals based on a first predetermined EEG cycle interval and the ultrasonic frequency of the focused ultrasonic signal to obtain multiple first sample EEG data, wherein the first predetermined EEG cycle interval is determined based on the peak value of the R wave in the first sample electrocardiogram signal corresponding to the first sample encoded EEG signal.

[0176] The second decoding unit is used to decode and process multiple second sample encoded EEG signals based on a second predetermined EEG cycle interval and the ultrasonic frequency of the focused ultrasonic signal to obtain multiple second sample EEG data, wherein the second predetermined EEG cycle interval is determined based on the peak value of the R wave in the second sample electrocardiogram signal corresponding to the second sample encoded EEG signal.

[0177] The first calculation unit is used to perform variance calculation on the plurality of first sample EEG data and the plurality of second sample EEG data respectively to obtain a plurality of first sample target EEG variance indices and a plurality of second sample target EEG variance indices.

[0178] The second obtaining unit is used to obtain a first sample curve corresponding to the first degree of consciousness state and a second sample curve corresponding to the second degree of consciousness state according to multiple first sample target EEG variance indices and multiple second sample target EEG variance indices.

[0179] The second determining unit is used to determine, when the values ​​of the ordinates of the first sample curve and the second sample curve are the same, that the sample target EEG variance index of the same abscissa in the first sample curve and the second sample curve is a predetermined consciousness state threshold.

[0180] According to an embodiment of the present disclosure, any multiple modules of the transmitting module 610, the processing module 620, the calculating module 630 and the obtaining module 640 can be combined into one module for implementation, or any one of the modules can be split into multiple modules. Alternatively, at least part of the functions of one or more of these modules can be combined with at least part of the functions of other modules and implemented in one module. According to an embodiment of the present disclosure, at least one of the transmitting module 610, the processing module 620, the calculating module 630 and the obtaining module 640 can be at least partially implemented as a hardware circuit, such as a field programmable gate array (FPGA), a programmable logic array (PLA), a system on a chip, a system on a substrate, a system on a package, an application specific integrated circuit (ASIC), or can be implemented by hardware or firmware such as any other reasonable way of integrating or packaging the circuit, or implemented in any one of the three implementation modes of software, hardware and firmware or in a proper combination of any of them. Alternatively, at least one of the transmitting module 610, the processing module 620, the calculating module 630 and the obtaining module 640 can be at least partially implemented as a computer program module, and when the computer program module is run, the corresponding function can be executed.

[0181] Figure 7 A block diagram of an electronic device is shown according to an embodiment of the present disclosure, which implements a method for estimating the category of the state of consciousness based on heartbeat-induced EEG.

[0182] like Figure 7 As shown, the electronic device according to the embodiment of the present disclosure includes a processor 701, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 702 or the program loaded from the storage part 708 to the random access memory (RAM) 703. The processor 701 may include, for example, a general-purpose microprocessor (such as a CPU), an instruction set processor and / or a related chipset and / or a special-purpose microprocessor (for example, an application-specific integrated circuit (ASIC)), etc. The processor 701 may also include an onboard memory for caching purposes. The processor 701 may include a single processing unit or multiple processing units for performing different actions of the method flow according to the embodiment of the present disclosure.

[0183] In RAM 703, various programs and data required for the operation of the electronic device are stored. The processor 701, ROM 702, and RAM 703 are connected to each other via a bus 704. The processor 701 performs various operations of the method flow according to the embodiment of the present disclosure by executing the programs in ROM 702 and / or RAM 703. It should be noted that the program may also be stored in one or more memories other than ROM 702 and RAM 703. The processor 701 may also perform various operations of the method flow according to the embodiment of the present disclosure by executing the programs stored in the one or more memories.

[0184] According to an embodiment of the present disclosure, the electronic device may further include an input / output (I / O) interface 705, which is also connected to the bus 704. The electronic device may further include one or more of the following components connected to the I / O interface 705: an input portion 706 including a keyboard, a mouse, etc.; an output portion 707 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage portion 708 including a hard disk, etc.; and a communication portion 709 including a network interface card such as a LAN card, a modem, etc. The communication portion 709 performs communication processing via a network such as the Internet. A drive 710 is also connected to the I / O interface 705 as needed. A removable medium 711, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 710 as needed, so that a computer program read therefrom is installed into the storage portion 708 as needed.

[0185] The present disclosure also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments; or may exist independently without being assembled into the device / apparatus / system. The above computer-readable storage medium carries one or more programs, and when the above one or more programs are executed, the method according to the embodiment of the present disclosure is implemented.

[0186] According to an embodiment of the present disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium, for example, it may include but is not limited to: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, an apparatus or a device. For example, according to an embodiment of the present disclosure, the computer-readable storage medium may include the ROM 702 and / or RAM 703 described above and / or one or more memories other than ROM 702 and RAM 703.

[0187] The embodiment of the present disclosure also includes a computer program product, which includes a computer program, and the computer program contains program code for executing the method shown in the flowchart. When the computer program product is run in a computer system, the program code is used to enable the computer system to implement the method for estimating the category of consciousness state based on heartbeat-induced EEG provided in the embodiment of the present disclosure.

[0188] The above functions defined in the system / device of the embodiment of the present disclosure are performed when the computer program is executed by the processor 701. According to the embodiment of the present disclosure, the system, device, module, unit, etc. described above can be implemented by a computer program module.

[0189] In one embodiment, the computer program may rely on tangible storage media such as optical storage devices, magnetic storage devices, etc. In another embodiment, the computer program may also be transmitted and distributed in the form of signals on a network medium, and downloaded and installed through the communication part 709, and / or installed from the removable medium 711. The program code contained in the computer program may be transmitted using any appropriate network medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0190] In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 709, and / or installed from the removable medium 711. When the computer program is executed by the processor 701, the above functions defined in the system of the embodiment of the present disclosure are performed. According to the embodiment of the present disclosure, the system, device, means, module, unit, etc. described above can be implemented by a computer program module.

[0191] It will be appreciated by those skilled in the art that the features described in the various embodiments of the present disclosure may be combined and / or combined in a variety of ways, even if such combinations or combinations are not explicitly described in the present disclosure. In particular, without departing from the spirit and teachings of the present disclosure, the features described in the various embodiments of the present disclosure may be combined and / or combined in a variety of ways. All of these combinations and / or combinations fall within the scope of the present disclosure.

Claims

1. A method for estimating the category of consciousness state based on heartbeat-induced electroencephalogram, comprising: Transmitting focused ultrasonic signals to multiple coding sites in the somatosensory cortex and the insula of the brain of the target subject to obtain multiple coded electroencephalogram signals, wherein the coded electroencephalogram signals are obtained from the electroencephalogram signals generated by the somatosensory cortex or the insula and the focused ultrasonic signals; Based on a predetermined EEG cycle interval and the ultrasonic frequency of the focused ultrasonic signal, the plurality of encoded EEG signals are decoded to obtain a plurality of target EEG data, wherein the predetermined EEG cycle interval is determined based on the peak value of the R wave in the electrocardiogram signal of the target object, and the target EEG data includes a plurality of envelope data corresponding to the encoding sites; Performing variance calculation on the plurality of target EEG data to obtain a target EEG variance index; According to the predetermined consciousness state threshold and the target EEG variance index, the consciousness state category information of the target object is obtained.

2. The method according to claim 1, wherein: The decoding process of the plurality of encoded EEG signals based on the predetermined EEG cycle interval and the ultrasonic frequency of the focused ultrasonic signal to obtain a plurality of target EEG data includes: Determining a plurality of EEG time points according to a plurality of peak values ​​of the R waves in the electrocardiogram signal; Determining a plurality of predetermined EEG time periods based on the predetermined EEG cycle interval and the plurality of EEG time points; Based on the plurality of predetermined EEG time periods, each of the coded EEG signals is intercepted and processed to obtain a plurality of intermediate induced EEG signals; According to the ultrasonic frequency of the focused ultrasonic signal and the predetermined filtering bandwidth, the plurality of intermediate induced EEG signals are subjected to bandpass filtering to obtain a plurality of target induced EEG signals; Envelope extraction processing is performed on the multiple target induced EEG signals respectively to obtain the multiple target EEG data.

3. The method according to claim 1, wherein: The performing variance calculation on the plurality of target EEG data to obtain a target EEG variance index includes: Calculating the plurality of envelope data in each of the target EEG data to obtain a plurality of target EEG indexes corresponding to each of the encoding sites; The multiple target EEG indices corresponding to each of the encoding sites are normalized to obtain the target EEG variance index.

4. The method according to claim 3, wherein: The target EEG index includes EEG variance, EEG mean and EEG standard deviation; The normalizing process of the plurality of target EEG indices corresponding to each of the encoding sites to obtain the target EEG variance index comprises: Based on a predetermined normalization function, a plurality of EEG variances corresponding to each encoding site, a plurality of EEG means and a plurality of EEG standard deviations corresponding to each encoding site are obtained; Selecting a plurality of target EEG variances from the plurality of intermediate EEG variances corresponding to each of the encoding sites; The target EEG variances are averaged to obtain the target EEG variance index.

5. The method according to claim 1, wherein: The method of transmitting focused ultrasonic signals to multiple coding sites in the somatosensory cortex and insula of the target subject's brain to obtain multiple coded EEG signals includes: Acquiring an electrocardiogram signal of the target object; When the electrocardiogram signal satisfies a first preset condition, transmitting a focused ultrasonic signal to the multiple encoding sites in the somatosensory cortex and the insular lobe, wherein the first preset condition indicates that a Q wave appears in the electrocardiogram signal of the target object; The multiple electroencephalogram signals generated by the somatosensory cortex and the insular lobe and the focused ultrasonic signal are coded to obtain the multiple coded electroencephalogram signals.

6. The method according to claim 5, wherein: When the electrocardiogram signal satisfies a first preset condition, transmitting a focused ultrasonic signal to the multiple encoding sites in the somatosensory cortex and the insula includes: Determine a plurality of three-dimensional spatial coordinate information corresponding to the plurality of encoding sites in the somatosensory cortex and the insular lobe, wherein the three-dimensional spatial coordinate information represents position information of the encoding sites in a three-dimensional space established with the left temporal lobe in the brain as the origin; Based on the plurality of three-dimensional space coordinate information, the focused ultrasonic signal is transmitted to the plurality of encoding sites.

7. The method according to claim 1, wherein: The step of obtaining the target object's consciousness state category information according to the predetermined consciousness state threshold and the target EEG variance index includes: When the target EEG variance index is greater than the predetermined consciousness state threshold, obtaining the consciousness state category information indicating that the consciousness state of the target object is a first degree consciousness state; When the target EEG variance index is less than the predetermined consciousness state threshold, the consciousness state category information characterizing that the consciousness state of the target object is a second degree consciousness state is obtained.

8. The method according to claim 7, wherein: The predetermined consciousness state threshold is obtained by the following operations, including: Acquire a sample data set, wherein the sample data set includes a plurality of first sample coded EEG signals and a plurality of first sample ECG signals corresponding to the first degree of consciousness state, and a plurality of second sample coded EEG signals and a plurality of second sample ECG signals corresponding to the second degree of consciousness state; Based on a first predetermined EEG cycle interval and the ultrasonic frequency of the focused ultrasonic signal, the plurality of first sample coded EEG signals are decoded to obtain a plurality of first sample EEG data, wherein the first predetermined EEG cycle interval is determined based on the peak value of the R wave in the first sample electrocardiogram signal corresponding to the first sample coded EEG signal; Based on a second predetermined EEG cycle interval and the ultrasonic frequency of the focused ultrasonic signal, the plurality of second sample coded EEG signals are decoded to obtain a plurality of second sample EEG data, wherein the second predetermined EEG cycle interval is determined based on the peak value of the R wave in the second sample electrocardiogram signal corresponding to the second sample coded EEG signal; Performing variance calculations on the plurality of first sample EEG data and the plurality of second sample EEG data respectively to obtain a plurality of first sample target EEG variance indices and a plurality of second sample target EEG variance indices; Obtaining a first sample curve corresponding to the first degree of consciousness state and a second sample curve corresponding to the second degree of consciousness state according to the plurality of first sample target EEG variance indices and the plurality of second sample target EEG variance indices; In the case where the values ​​of the ordinates of the first sample curve and the second sample curve are the same, the sample target EEG variance index of the same abscissa in the first sample curve and the second sample curve is determined as the predetermined consciousness state threshold.

9. A device for estimating and classifying consciousness states based on heartbeat-induced electroencephalograms, comprising: A transmitting module, used for transmitting focused ultrasonic signals to multiple coding sites in the somatosensory cortex and the insula of the brain of the target object to obtain multiple coded electroencephalogram signals, wherein the coded electroencephalogram signals are obtained from the electroencephalogram signals generated by the somatosensory cortex or the insula and the focused ultrasonic signals; A processing module, configured to decode the plurality of coded EEG signals based on a predetermined EEG cycle interval and the ultrasonic frequency of the focused ultrasonic signal to obtain a plurality of target EEG data, wherein the predetermined EEG cycle interval is determined based on the peak value of the R wave in the electrocardiogram signal of the target object, and the target EEG data includes a plurality of envelope data corresponding to the coding sites; A calculation module, used for performing variance calculation on the plurality of target EEG data to obtain a target EEG variance index; The obtaining module is used to obtain the consciousness state category information of the target object according to a predetermined consciousness state threshold and the target EEG variance index.

10. An electronic device, characterized in that: include: one or more processors; a storage device for storing one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors execute the method according to any one of claims 1 to 8.

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