CN111938671A - Anxiety trait quantification method based on multi-dimensional internal perception features - Google Patents
Anxiety trait quantification method based on multi-dimensional internal perception features Download PDFInfo
- Publication number
- CN111938671A CN111938671A CN202010815620.6A CN202010815620A CN111938671A CN 111938671 A CN111938671 A CN 111938671A CN 202010815620 A CN202010815620 A CN 202010815620A CN 111938671 A CN111938671 A CN 111938671A
- Authority
- CN
- China
- Prior art keywords
- anxiety
- perception
- heartbeat
- trait
- preprocessing
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/16—Devices for psychotechnics; Testing reaction times ; Devices for evaluating the psychological state
- A61B5/165—Evaluating the state of mind, e.g. depression, anxiety
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7235—Details of waveform analysis
- A61B5/7246—Details of waveform analysis using correlation, e.g. template matching or determination of similarity
Landscapes
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Engineering & Computer Science (AREA)
- Psychiatry (AREA)
- Public Health (AREA)
- Surgery (AREA)
- Veterinary Medicine (AREA)
- Physics & Mathematics (AREA)
- General Health & Medical Sciences (AREA)
- Biophysics (AREA)
- Pathology (AREA)
- Biomedical Technology (AREA)
- Heart & Thoracic Surgery (AREA)
- Medical Informatics (AREA)
- Molecular Biology (AREA)
- Animal Behavior & Ethology (AREA)
- Physiology (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Artificial Intelligence (AREA)
- Signal Processing (AREA)
- Child & Adolescent Psychology (AREA)
- Developmental Disabilities (AREA)
- Educational Technology (AREA)
- Hospice & Palliative Care (AREA)
- Psychology (AREA)
- Social Psychology (AREA)
- Measurement And Recording Of Electrical Phenomena And Electrical Characteristics Of The Living Body (AREA)
Abstract
The invention relates to the technical field of electronic informatization of biological indexes, in particular to an anxiety trait quantification method based on multi-dimensional internal perception characteristics, which is used for acquiring multi-dimensional data of tested behaviours, electroencephalogram physiology and nuclear magnetic resonance images; preprocessing the data of behavioral heartbeat perception sensitivity, heartbeat perception potential, brain structure and task state; the method is characterized by constructing a multi-dimensional deep learning network, establishing an automatic quantification system, combining the multi-dimensional characteristics of the behavioral science, the electroencephalogram physiology and the nuclear magnetic resonance images under an internal perception paradigm, performing characteristic learning by virtue of the automatic learning and the advantages of a nonlinear hierarchy system through the deep network learning, extracting characteristic values for modeling, obtaining an individualized anxiety trait level scoring result, providing a tool for quantitatively and objectively evaluating the anxiety trait level, realizing accurate quantification of the anxiety level, effectively identifying the anxiety disorder in the ultra-early stage, having a huge biological objective diagnosis effect, and assisting diagnosis and treatment of the anxiety disorder.
Description
Technical Field
The invention relates to the technical field of electronic informatization of biological indexes, in particular to an anxiety trait quantification method based on multi-dimensional internal perception characteristics.
Background
Anxiety Disorder (AD) is the highest prevalence among psychiatric disorders, with a prevalence of 5.0% for 12 months and a lifetime prevalence of 7.6%. Anxiety disorders are not only the most common and affecting a large population of people, but also have considerable functional impairment, directly affecting the working ability and social function of the patient.
The high anxiety-characterized population is at increased risk of developing anxiety disorders, and multiple studies indicate a poorer prognosis outcome for anxiety with psychotic disorders. In the prior art, whether anxiety exists in people is judged by means of answers of subjective questions, the judgment method is high in subjectivity, and whether anxiety exists and the degree of anxiety traits cannot be accurately and objectively judged.
Therefore, a method for evaluating the anxiety trait in a quantitative manner with great accuracy is needed.
Disclosure of Invention
The invention breaks through the difficult problems in the prior art, designs the anxiety trait quantification method based on the multi-dimensional internal perception characteristics, can accurately and objectively quantify and evaluate the anxiety trait by an informationized artificial intelligence means, achieves the ultra-early identification of anxiety disorder, and provides a basis for subsequent accurate and personalized treatment.
In order to achieve the above object, the present invention provides a method for quantifying anxiety trait based on multidimensional intrinsic perception features, comprising: the method comprises the following steps:
s1, acquiring multi-dimensional data of the tested behavioural study, the electroencephalogram physiology and the nuclear magnetic resonance image;
s2, preprocessing the behavioral heartbeat perception sensitivity, heartbeat perception potential, brain structure and task state data;
s3, a multi-dimensional deep learning network is constructed, and an automatic quantification system is established.
Further, S3 constructs a multidimensional deep learning network, and the specific method for establishing the automatic quantization system includes:
s31, training the region and edge characteristics of the independent learning significance object of a single task model;
s32, generating a large number of candidate regions by using the detected edges, and optimizing and recombining by using a conditional random field;
s33, extracting the result of the saliency region detection, sorting the candidate regions and calculating the weight;
s34 generates an anxiety trait score based on the weights.
Further, the specific method for preprocessing the behavioral heartbeat perception sensitivity, the heartbeat perception potential, the brain structure and the task state data in S2 is as follows:
s21, calculating the heartbeat perception score of the tested person according to the heartbeat sensed by the tested person and the actual heartbeat number, wherein the specific calculation formula is as follows:
wherein, P represents the score for evaluating the heart beat perception capability, and the full value is 1; o isiRepresenting the number of heartbeats actually recorded, GiRepresenting the number of heartbeats experienced by the subject; k represents the total number of tests tested;
s22, performing off-line preprocessing on the electroencephalogram physiological data by using an EEGLAB tool box;
s23, preprocessing the brain imaging data by using SPM8 software, and reserving voxel volume information;
s24 uses AFNI software to process data preprocessing and statistic analysis for the heartbeat perception task state image.
Further, the specific method of performing offline preprocessing on the electroencephalogram physiological data by using the EEGLAB toolbox in S22 is as follows: firstly, detecting the R peak value of an electrocardio channel by using a combined self-adaptive threshold value realized by an FMRIB plug-in; then, carrying out low-pass filtering of 30hz on the electroencephalogram signal by utilizing a basic FIR filter of EEGLAB; then, carrying out independent component analysis by using a runica function to remove artifacts; segmenting the electroencephalogram without artifacts, forming an epoch of 1000ms by using the R wave front for 200ms, and adopting an EEGLAB Planar combination method for the electroencephalogram components of +/-100 Muv; finally, HEP extraction is carried out on different individuals, time points and different spaces (or electrodes).
Further, the specific method for preprocessing the brain imaging data by using the SPM8 software in S23 is as follows:
s231, image segmentation, namely performing gray matter, white matter and cerebrospinal fluid segmentation on the converted image by using a unified segmentation method, and registering a template by using a DARTEL (direct detection and reconstruction);
s232 screening gray matter study specific templates using DARTEL algorithm;
s233 spatially normalizes each gray matter to be tested with three sets of templates generated by the test, and then smoothes the normalized gray matter.
Further, the specific method for performing data preprocessing and statistical analysis on the heartbeat perception task state image by using the AFNI software in S24 is as follows: firstly, data format conversion, scalp removal, structural image and functional image alignment, head motion correction and space smoothing are carried out on the functional image; and then, carrying out spatial standardization on the structural image by taking TT _ N27 as a template, storing the generated transformation matrix, and carrying out statistical analysis on the individual level by using a GLM model to obtain beta coefficients of the digital heartbeat and the pure-sounding. It is then normalized with the already saved transformation matrix.
The invention also provides an anxiety trait quantification apparatus, comprising a processor, a memory, and an anxiety trait quantification program stored on the memory and executable by the processor, wherein when the anxiety trait quantification program is executed, the anxiety trait quantification apparatus implements an anxiety trait quantification method as follows: collecting multidimensional data of the experimented behaviours, electroencephalogram physiology and nuclear magnetic resonance images; preprocessing data of behavioral heartbeat perception sensitivity, heartbeat perception related potential, brain structure and task state; based on the constructed multi-dimensional deep learning network, a series of biological characteristics of the anxiety quality level are automatically extracted and jointly predicted, and an individual anxiety quality index is formed.
Wherein the content of the first and second substances,the specific method for preprocessing the behavioural heartbeat perception sensitivity, the heartbeat perception potential, the brain structure and the task state data comprises the following steps: firstly, calculating the heartbeat perception score of a tested person according to the heartbeat sensed by the tested person and the actual heartbeat number, wherein the specific calculation formula is as follows:
wherein, P represents the score for evaluating the heart beat perception capability, and the full value is 1; o isiRepresenting the number of heartbeats actually recorded, GiRepresenting the number of heartbeats experienced by the subject; k represents the total number of tests tested; then, carrying out off-line preprocessing on the electroencephalogram physiological data by using an EEGLAB tool box; then, preprocessing the brain imaging data by using SPM8 software, and reserving voxel volume information; and finally, carrying out data preprocessing and statistical analysis on the heartbeat perception task state image by using AFNI software.
The invention also provides a computer-readable storage medium, which is characterized in that: a computer readable storage medium having stored thereon an anxiety trait quantification program, wherein the anxiety trait quantification program, when executed by a processor, implements a method of anxiety trait quantification as follows: collecting multidimensional data of the experimented behaviours, electroencephalogram physiology and nuclear magnetic resonance images; preprocessing data of behavioral heartbeat perception sensitivity, heartbeat perception related potential, brain structure and task state; based on the constructed multi-dimensional deep learning network, a series of biological characteristics of the anxiety quality level are automatically extracted and jointly predicted, and an individual anxiety quality index is formed.
The specific method for preprocessing the behavioural heartbeat perception sensitivity, the heartbeat perception potential, the brain structure and the task state data comprises the following steps: firstly, calculating the heartbeat perception score of a tested person according to the heartbeat sensed by the tested person and the actual heartbeat number, wherein the specific calculation formula is as follows:
wherein, P represents the score for evaluating the heart beat perception capability, and the full value is 1; o isiRepresenting the number of heartbeats actually recorded, GiRepresenting the number of heartbeats experienced by the subject; k represents the total number of tests tested; then, carrying out off-line preprocessing on the electroencephalogram physiological data by using an EEGLAB tool box; then, preprocessing the brain imaging data by using SPM8 software, and reserving voxel volume information; and finally, carrying out data preprocessing and statistical analysis on the heartbeat perception task state image by using AFNI software.
Compared with the prior art, the method combines the multi-dimensional characteristics of the ethology, the electroencephalogram physiology and the nuclear magnetic resonance image under the internal perception paradigm, performs characteristic learning by virtue of the automatic learning and the advantages of a nonlinear hierarchy system through deep network learning, extracts characteristic values for modeling, obtains an individualized anxiety trait level scoring result, provides a tool for quantitatively and objectively evaluating the anxiety trait level, realizes accurate quantification of the anxiety level, effectively identifies the anxiety disorder in the ultra-early stage, has a huge biological objective diagnosis effect, and can assist diagnosis and treatment of the anxiety disorder.
Drawings
Fig. 1 is a flowchart illustrating a method for quantifying anxiety trait based on perceptual features in multiple dimensions according to an embodiment.
Fig. 2 is a schematic flow chart illustrating the establishment of a multidimensional deep learning network in an anxiety trait quantification method based on multidimensional internal perception features in an embodiment.
Detailed Description
The invention will be further described with reference to the accompanying drawings, but is not to be construed as being limited thereto.
Referring to fig. 1, in an embodiment of the present invention, a method for quantifying anxiety trait based on multi-dimensional inner perception features is designed, comprising the following steps:
s1, acquiring the multi-dimensional data of the tested behavioural index, the electroencephalogram physiological index and the nuclear magnetic resonance image.
For obtaining the tested behavioural index, the intra-evaluation perception of the measurement-psychological Tracking Paradigm (Mental Tracking Paradigm) of the heartbeat perception is adopted, and the Paradigm needs to be tested to quietly sense own heartbeats in different time intervals and report the number of heartbeats sensed in each time interval.
In specific implementation, the experimental paradigm is performed for 3 rounds, the time period of each round of the default is 21S, 26S and 36S, and the number of occurrences of R waves is recorded by using EKG during the trial default heartbeat.
The EEG physiological index is detected by 64-lead EEG/ERP of Brain Products, and is a Brain Amp standard amplifier.
In a specific implementation, the sampling rate of each channel is set to 5 kHz/sec.
For the brain imaging index, Siemens 3.0T Trio Tim magnetic resonance scanner is adopted, and the visual and auditory stimulation device is IFIS-SA manufactured by Invivo manufacturer.
In a specific implementation, the scan parameters are set as follows:
structural phase scanning parameters: pulse repetition time/echo time (TR/TE) 1900ms/2.46ms, Flip Angle (FA)9 °, field of view (FOV) 240mm × 240mm, matrix (matrix) 256 × 256, layer thickness 1mm, layer spacing 0.5mm, layer number 192, acquisition time 5 minutes, excitation Number (NEX) 1.
Task state BOLD-EPI functional imaging scanning parameters: TR 2000ms, TE 35ms, FA 70 °, FOV 240mm × 240mm, matrix 64 × 64, layer thickness 5mm, layer spacing 0mm, layer number 30, acquisition time 8 min 18 sec, NEX 1.
S2, preprocessing the behavioral heartbeat perception sensitivity, heartbeat perception potential, brain structure and task state data:
s21, calculating the heartbeat perception score of the tested person according to the heartbeat sensed by the tested person and the actual heartbeat number, wherein the specific calculation formula is as follows:
wherein, P represents the score for evaluating the heart beat perception capability, and the full value is 1; o isiRepresenting the actual recorded heart beatNumber of times, GiRepresenting the number of heartbeats experienced by the subject; k represents the total number of tests tested;
s22 off-line pre-processing of electroencephalography data using the EEGLAB toolbox: firstly, detecting the R peak value of an electrocardio channel by using a combined self-adaptive threshold value realized by an FMRIB plug-in; then, carrying out low-pass filtering of 30hz on the electroencephalogram signal by utilizing a basic FIR filter of EEGLAB; then, carrying out independent component analysis by using a runica function to remove artifacts; segmenting the electroencephalogram without artifacts, forming an epoch of 1000ms by using the R wave front for 200ms, and adopting an EEGLAB Planar combination method for the electroencephalogram components of +/-100 Muv; finally, HEP extraction is carried out on different individuals, time points and different spaces (or electrodes).
S23 pre-process the brain imaging data using SPM8 software, preserving voxel volume information: firstly, carrying out image segmentation, carrying out gray matter, white matter and cerebrospinal fluid segmentation on the converted image by using a uniform segmentation method, and estimating an optimal template by utilizing DARTEL registration so that the generated segmented image achieves optimal registration through nonlinear transformation; then, a research specific template of gray matter is examined by adopting a DARTEL algorithm; finally, aligning each gray matter to be tested with all templates generated by the test, carrying out spatial standardization, and standardizing the gray matter to MNI space, wherein affine transformation from the generated average image (template space) to a TPM image (standard adult brain MNI space of Montreal institute of neurology) and smoothing processing on data are included, and isotropic 6mm FWHM Gaussian kernels are used for smoothing processing.
S24, using AFNI software to perform data preprocessing and statistical analysis on the heartbeat perception task state image: firstly, data format conversion, scalp removal, structural image alignment with functional image, head motion correction of the functional image, namely, removing individuals larger than 0.8 by using 12 parameters of head absolute and relative three-dimensional translation and rotation, and then performing space smoothing by using a 6mm full-width half-maximum Gaussian kernel; and then, carrying out spatial standardization on the structural image by taking TT _ N27 as a template, storing the generated transformation matrix, and carrying out statistical analysis on the individual level by using a GLM model to obtain beta coefficients of the digital heartbeat and the pure-sounding. It is then normalized with the already saved transformation matrix.
S3, constructing a multidimensional deep learning network, and establishing an automatic quantization system, which can be specifically shown in fig. 2:
s31, training the region and edge characteristics of the independent learning significance object of a single task model;
s32, generating a large number of candidate regions by using the detected edges, and optimizing and recombining by using a conditional random field;
s33, extracting the result of the saliency region detection, sorting the candidate regions and calculating the weight;
s34 generating anxiety trait score according to the weight value, wherein the score range is 0-10.
In an embodiment, the present invention further provides an anxiety trait quantification apparatus, including a processor, a memory, and an anxiety trait quantification program stored in the memory and executable by the processor, wherein the anxiety trait quantification program, when executed, implements an anxiety trait quantification method as follows: collecting multidimensional data of the experimented behaviours, electroencephalogram physiology and nuclear magnetic resonance images; preprocessing data of behavioral heartbeat perception sensitivity, heartbeat perception related potential, brain structure and task state; based on the constructed multi-dimensional deep learning network, a series of biological characteristics of the anxiety quality level are automatically extracted and jointly predicted, and an individual anxiety quality index is formed.
The specific method for preprocessing the behavioural heartbeat perception sensitivity, the heartbeat perception potential, the brain structure and the task state data comprises the following steps: firstly, calculating the heartbeat perception score of a tested person according to the heartbeat sensed by the tested person and the actual heartbeat number, wherein the specific calculation formula is as follows: p ═
Wherein, P represents the score for evaluating the heart beat perception capability, and the full value is 1; o isiRepresenting the number of heartbeats actually recorded, GiRepresenting the number of heartbeats experienced by the subject; k represents the total number of tests tested; then, carrying out off-line preprocessing on the electroencephalogram physiological data by using an EEGLAB tool box; then, preprocessing the brain imaging data by using SPM8 software, and reserving voxel volume information; finally makeAnd carrying out data preprocessing and statistical analysis on the heartbeat perception task state image by AFNI software.
Preferably, the present invention further provides a computer-readable storage medium, on which an anxiety trait quantification program is stored, wherein the anxiety trait quantification program, when executed by a processor, implements the anxiety trait quantification method as follows: collecting multidimensional data of the experimented behaviours, electroencephalogram physiology and nuclear magnetic resonance images; preprocessing data of behavioral heartbeat perception sensitivity, heartbeat perception related potential, brain structure and task state; based on the constructed multi-dimensional deep learning network, a series of biological characteristics of the anxiety quality level are automatically extracted and jointly predicted, and an individual anxiety quality index is formed.
The specific method for preprocessing the behavioural heartbeat perception sensitivity, the heartbeat perception potential, the brain structure and the task state data comprises the following steps: firstly, calculating the heartbeat perception score of a tested person according to the heartbeat sensed by the tested person and the actual heartbeat number, wherein the specific calculation formula is as follows: p ═
Wherein, P represents the score for evaluating the heart beat perception capability, and the full value is 1; o isiRepresenting the number of heartbeats actually recorded, GiRepresenting the number of heartbeats experienced by the subject; k represents the total number of tests tested; then, carrying out off-line preprocessing on the electroencephalogram physiological data by using an EEGLAB tool box; then, preprocessing the brain imaging data by using SPM8 software, and reserving voxel volume information; and finally, carrying out data preprocessing and statistical analysis on the heartbeat perception task state image by using AFNI software.
It will be understood by those skilled in the art that all or part of the processes of the methods of the above embodiments may be implemented by hardware related to computer program instructions, and the program may be stored in a computer readable storage medium, for example, in the storage medium of a computer system, and executed by at least one processor in the computer system, so as to implement the processes of the embodiments including the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), or the like.
The technical features of the embodiments described above may be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the embodiments described above are not described, but should be considered as being within the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present invention, and the description is specific and detailed, but it should not be understood as the limitation of the scope of the present invention, it should be noted that, for those skilled in the art, many variations and modifications can be made without departing from the concept of the present invention, and these all fall into the protection scope of the present invention.
Claims (8)
1. A method for quantifying anxiety trait based on multi-dimensional internal perception features is characterized by comprising the following steps:
the method comprises the following steps:
s1, acquiring multi-dimensional data of the tested behavioural study, the electroencephalogram physiology and the nuclear magnetic resonance image;
s2, preprocessing behavioural heartbeat perception sensitivity, heartbeat perception potential, brain structure and task state data;
s3, a multi-dimensional deep learning network is constructed, and an automatic quantification system is established.
2. The method according to claim 1, wherein the anxiety trait quantification method based on the multidimensional internal perception features comprises: s3, constructing a multi-dimensional deep learning network, and establishing an automatic quantification system by the specific method:
s31, training the region and edge characteristics of the independent learning significance object of a single task model;
s32, generating a large number of candidate regions by using the detected edges, and optimizing and recombining by using a conditional random field;
s33, extracting the result of the saliency region detection, sorting the candidate regions and calculating the weight;
s34 generates an anxiety trait score based on the weights.
3. The method according to claim 1, wherein the anxiety trait quantification method based on the multidimensional internal perception features comprises: s2 the concrete method for preprocessing the behavioural heartbeat perception sensitivity, heartbeat perception potential, brain structure and task state data is as follows:
s21, calculating the heartbeat perception score of the tested person according to the heartbeat sensed by the tested person and the actual heartbeat number, wherein the specific calculation formula is as follows:
wherein, P represents the score for evaluating the heart beat perception capability, and the full value is 1; o isiRepresenting the number of heartbeats actually recorded, GiRepresenting the number of heartbeats experienced by the subject; k represents the total number of tests tested;
s22, performing off-line preprocessing on the electroencephalogram physiological data by using an EEGLAB tool box;
s23, preprocessing the brain imaging data by using SPM8 software, and reserving voxel volume information;
s24 uses AFNI software to process data preprocessing and statistic analysis for the heartbeat perception task state image.
4. The method according to claim 3, wherein the anxiety trait quantification method based on the multidimensional internal perception features comprises: s22 the specific method for off-line preprocessing of EEGLAB data by using EEGLAB toolbox is as follows: firstly, detecting the R peak value of an electrocardio channel by using a combined self-adaptive threshold value realized by an FMRIB plug-in; then, carrying out low-pass filtering of 30hz on the electroencephalogram signal by utilizing a basic FIR filter of EEGLAB; then, carrying out independent component analysis by using a runica function to remove artifacts; segmenting the electroencephalogram without artifacts, forming an epoch of 1000ms by using the R wave front for 200ms, and adopting an EEGLAB Planar combination method for the electroencephalogram components of +/-100 Muv; finally, feature extraction is carried out on HEPs in different individuals, time points and different spaces.
5. The method according to claim 3, wherein the anxiety trait quantification method based on the multidimensional internal perception features comprises: s23 the concrete method for preprocessing the brain imaging data by using the SPM8 software is as follows:
s231, image segmentation, namely performing gray matter, white matter and cerebrospinal fluid segmentation on the converted image by using a unified segmentation method, and registering a template by using a DARTEL (direct detection and reconstruction);
s232 screening gray matter study specific templates using DARTEL algorithm;
s233 aligns and spatially normalizes the gray matter of each test with all of the templates generated by the test, and then performs smoothing.
6. The method according to claim 3, wherein the anxiety trait quantification method based on the multidimensional internal perception features comprises: S24A specific method for performing data preprocessing and statistical analysis on the heartbeat perception task state image by using AFNI software is as follows: firstly, data format conversion, scalp removal, structural image and functional image alignment, head motion correction and space smoothing are carried out on the functional image; then, carrying out spatial standardization on the structural image by taking TT _ N27 as a template, storing the generated transformation matrix, and carrying out statistical analysis on individual levels by using a GLM model to obtain beta coefficients of the digital heartbeat and the pure-sounding sound; it is then normalized with the already saved transformation matrix.
7. A system for quantifying anxiety trait comprising a processor, a memory, and a program for quantifying anxiety trait stored on the memory and executable by the processor, wherein the program for quantifying anxiety trait when executed implements a method for quantifying anxiety trait as claimed in any one of claims 1 to 6.
8. A computer-readable storage medium characterized by: a computer-readable storage medium having stored thereon a program for quantifying anxiety trait, wherein the program for quantifying anxiety trait, when executed by a processor, implements a method for quantifying anxiety trait according to any one of claims 1-6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010815620.6A CN111938671A (en) | 2020-08-14 | 2020-08-14 | Anxiety trait quantification method based on multi-dimensional internal perception features |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010815620.6A CN111938671A (en) | 2020-08-14 | 2020-08-14 | Anxiety trait quantification method based on multi-dimensional internal perception features |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN111938671A true CN111938671A (en) | 2020-11-17 |
Family
ID=73342165
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010815620.6A Pending CN111938671A (en) | 2020-08-14 | 2020-08-14 | Anxiety trait quantification method based on multi-dimensional internal perception features |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111938671A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114533066A (en) * | 2022-04-28 | 2022-05-27 | 之江实验室 | Social anxiety assessment method and system based on composite expression processing brain network |
| CN116631630A (en) * | 2023-07-21 | 2023-08-22 | 北京中科心研科技有限公司 | Method and device for identifying anxiety disorder and wearable device |
-
2020
- 2020-08-14 CN CN202010815620.6A patent/CN111938671A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114533066A (en) * | 2022-04-28 | 2022-05-27 | 之江实验室 | Social anxiety assessment method and system based on composite expression processing brain network |
| CN116631630A (en) * | 2023-07-21 | 2023-08-22 | 北京中科心研科技有限公司 | Method and device for identifying anxiety disorder and wearable device |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US9020586B2 (en) | Brain activity measuring apparatus, brain activity measuring method, brain activity deducing apparatus, brain activity deducing method, and brain-machine interface apparatus | |
| US20160029965A1 (en) | Artifact as a feature in neuro diagnostics | |
| CN113768461B (en) | Fundus image analysis method, fundus image analysis system and electronic equipment | |
| Lopes et al. | Automatic electroencephalogram artifact removal using deep convolutional neural networks | |
| US20230052100A1 (en) | Systems And Methods For Optical Evaluation Of Pupillary Psychosensory Responses | |
| CN110322554B (en) | Brain dynamic function mode stability calculation method | |
| CN110326054A (en) | Determine the method, apparatus and program for participating in executing at least one brain network of given process | |
| CN111938671A (en) | Anxiety trait quantification method based on multi-dimensional internal perception features | |
| Li et al. | An improved sparse representation over learned dictionary method for seizure detection | |
| CN112348785A (en) | Epileptic focus positioning method and system | |
| CN111568412A (en) | Method and device for reconstructing visual image by utilizing electroencephalogram signal | |
| CN115299947A (en) | Psychological scale confidence evaluation method and system based on multi-modal physiological data | |
| CN114565957A (en) | Consciousness assessment method and system based on micro expression recognition | |
| CN115439920B (en) | Consciousness state detection system and equipment based on emotional audio-visual stimulation and facial expression | |
| KR102317713B1 (en) | Method for generating predictive model based on intra-subject and inter-subject variability using functional connectivity | |
| CN112057068A (en) | Epilepsia pathological data classification method and device and storage medium | |
| CN116469579A (en) | Brain state monitoring method, equipment, storage medium and device | |
| JP6417232B2 (en) | Image evaluation apparatus and program thereof | |
| Gholamipour et al. | Estimation of the independent components reliability of EEG signal in a clinical application | |
| Nancy et al. | A brain EEG classification system for the mild cognitive impairment analysis | |
| Wiedemann et al. | Low-amplitude craniofacial EMG power spectral density and 3D muscle reconstruction from MRI | |
| CN117257303B (en) | Anxiety detection method and device, electronic equipment and storage medium | |
| Ramadan et al. | Computer-aided diagnosis Application by using Nonlinear Dynamics of EEG Signals to detect Depression State | |
| CN113947577B (en) | Method, system, device, processor and storage medium for realizing brain image feature normalization processing based on healthy population distribution | |
| Ferko et al. | Activity in perirhinal and entorhinal cortex predicts observer-specific perceived visual similarities between objects |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |