CN112656367A - System and method for detecting physiological and cognitive functions of brain and neurodegenerative diseases - Google Patents
System and method for detecting physiological and cognitive functions of brain and neurodegenerative diseases Download PDFInfo
- Publication number
- CN112656367A CN112656367A CN202011326958.1A CN202011326958A CN112656367A CN 112656367 A CN112656367 A CN 112656367A CN 202011326958 A CN202011326958 A CN 202011326958A CN 112656367 A CN112656367 A CN 112656367A
- Authority
- CN
- China
- Prior art keywords
- eye
- eye jump
- jump
- score
- under
- 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.)
- Granted
Links
- 230000004770 neurodegeneration Effects 0.000 title claims abstract description 74
- 208000015122 neurodegenerative disease Diseases 0.000 title claims abstract description 74
- 210000004556 brain Anatomy 0.000 title claims abstract description 31
- 230000003920 cognitive function Effects 0.000 title claims abstract description 28
- 230000035790 physiological processes and functions Effects 0.000 title claims abstract description 23
- 238000000034 method Methods 0.000 title claims abstract description 22
- 208000014644 Brain disease Diseases 0.000 title abstract description 5
- 238000001514 detection method Methods 0.000 claims abstract description 55
- 230000004424 eye movement Effects 0.000 claims abstract description 45
- 201000010099 disease Diseases 0.000 claims abstract description 22
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 claims abstract description 22
- 238000000605 extraction Methods 0.000 claims abstract description 10
- 230000006399 behavior Effects 0.000 claims description 47
- 230000000007 visual effect Effects 0.000 claims description 5
- 210000001747 pupil Anatomy 0.000 claims 3
- 230000003542 behavioural effect Effects 0.000 claims 1
- 210000004087 cornea Anatomy 0.000 claims 1
- 238000003745 diagnosis Methods 0.000 abstract description 11
- 238000004364 calculation method Methods 0.000 abstract description 7
- 208000024827 Alzheimer disease Diseases 0.000 description 9
- 208000018737 Parkinson disease Diseases 0.000 description 6
- 230000037185 brain physiology Effects 0.000 description 5
- 201000002212 progressive supranuclear palsy Diseases 0.000 description 5
- 238000011156 evaluation Methods 0.000 description 4
- 230000006870 function Effects 0.000 description 4
- 238000002610 neuroimaging Methods 0.000 description 4
- 230000002490 cerebral effect Effects 0.000 description 3
- 102000013455 Amyloid beta-Peptides Human genes 0.000 description 2
- 108010090849 Amyloid beta-Peptides Proteins 0.000 description 2
- 230000008901 benefit Effects 0.000 description 2
- 230000003925 brain function Effects 0.000 description 2
- 210000001175 cerebrospinal fluid Anatomy 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 230000018109 developmental process Effects 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 206010008027 Cerebellar atrophy Diseases 0.000 description 1
- 230000032683 aging Effects 0.000 description 1
- 239000000090 biomarker Substances 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 230000006378 damage Effects 0.000 description 1
- 230000007850 degeneration Effects 0.000 description 1
- 230000006866 deterioration Effects 0.000 description 1
- 230000036541 health Effects 0.000 description 1
- 238000010253 intravenous injection Methods 0.000 description 1
- 238000009593 lumbar puncture Methods 0.000 description 1
- 230000000877 morphologic effect Effects 0.000 description 1
- 102000004169 proteins and genes Human genes 0.000 description 1
- 108090000623 proteins and genes Proteins 0.000 description 1
- 229940121896 radiopharmaceutical Drugs 0.000 description 1
- 239000012217 radiopharmaceutical Substances 0.000 description 1
- 230000002799 radiopharmaceutical effect Effects 0.000 description 1
- 238000013077 scoring method Methods 0.000 description 1
- 230000035945 sensitivity Effects 0.000 description 1
- 239000000243 solution Substances 0.000 description 1
- 230000002194 synthesizing effect Effects 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
Images
Landscapes
- Eye Examination Apparatus (AREA)
Abstract
The invention discloses a system and a method for detecting physiological and cognitive functions of a brain and neurodegenerative diseases, and belongs to the field of auxiliary diagnosis of the neurodegenerative diseases. The system comprises: the system comprises an eye movement tracking unit, a data acquisition unit, a feature extraction unit and a detection unit; the method comprises the following steps: starting the system and reading the configuration parameters of the system; sequentially acquiring eye movement video images of a tested person under a six-eye jump behavior paradigm according to the sequence of the eye jump behavior paradigm, and extracting eye movement position data; analyzing the eye movement position data, and extracting eye jump characteristic parameters corresponding to each eye jump behavior paradigm; calculating the score of each eye jump characteristic parameter, and calculating the eye jump normal form score under each eye jump behavior normal form according to the weight of each eye jump characteristic parameter under each neurodegenerative disease; and finally, carrying out comprehensive calculation according to the weight of each eye jump normal form score under different disease types to obtain a total score, and outputting a detection result according to a score interval where the total score is located.
Description
Technical Field
The invention relates to the field of evaluation of brain physiological and cognitive function levels and auxiliary diagnosis of neurodegenerative diseases, in particular to a system and a method for detecting brain physiological and cognitive functions and neurodegenerative diseases.
Background
With the increasing aging of society, the deterioration of physiological and cognitive functions of the brain and the occurrence of neurodegenerative diseases have become social problems affecting the health, life quality and economic burden of people. The neurodegenerative diseases can not be reversed, and the early discovery is important for controlling the disease condition and delaying the development of the diseases.
The current diagnosis of neurodegenerative diseases relies on the following methods: 1) scale law or interview law. Although the method is convenient to operate, scales or interviews are often not objective and not comprehensive; 2) and (4) detecting the biomarkers. For example, amyloid beta and Tau are proteins that aggregate abnormally in the brain of patients with alzheimer's disease and help to diagnose alzheimer's disease clinically by observing the levels of amyloid beta and Tau in the cerebrospinal fluid. The detection specimen is cerebrospinal fluid which can be obtained only by lumbar puncture, so the operation difficulty is high, the damage is large, and the wide popularization and application are difficult; 3) and (5) neuroimaging detection.
With the development of neuroimaging, brain imaging methods such as MRI and PET have recently been applied to the diagnosis of neurodegenerative diseases. However, this method can only be performed in a limited hospital, and is expensive, difficult to popularize and widely applicable. More importantly, while MRI is a non-invasive and non-invasive examination, MRI brain images do not provide diagnostic cues until the neurodegenerative disease does not cause morphological (anatomical) changes.
On the other hand, although examination using PET theoretically reveals a change in brain function, PET brain imaging requires intravenous injection of a radiopharmaceutical, which is one of invasive examinations, and it is difficult to convince patients to match examinations. At present, no safe, reliable, rapid, cheap and universal method for detecting physiological and cognitive functions of the brain and neurodegenerative diseases, and assisting in diagnosing and discovering early-stage alzheimer disease, parkinson disease, progressive supranuclear palsy, cerebellar atrophy and other neurodegenerative diseases exists in the market. Obviously, the product has huge market demand and remarkable social benefit.
Disclosure of Invention
The invention aims to provide a system and a method for detecting brain physiology, cognitive function and neurodegenerative diseases, which can carry out comprehensive evaluation through a plurality of eye jump characteristic parameters of a plurality of eye jump behavior paradigms to achieve the purpose of evaluating the brain physiology and cognitive function level of a tested person and quickly judging whether the tested person suffers from the neurodegenerative diseases or not at an early stage.
The invention solves the technical problem, and adopts the technical scheme that:
the invention firstly provides a detection system for brain physiological and cognitive functions and neurodegenerative diseases, which comprises an eye movement tracking unit, a data acquisition unit, a feature extraction unit and a detection unit;
the eye tracking unit is used for providing a physical operation environment for the system;
the data acquisition unit is used for sequentially acquiring eye movement video images of the testee under each eye jump behavior paradigm according to the sequence of the eye jump behavior paradigms, extracting eye movement position data in the eye movement video images and transmitting the eye movement position data to the feature extraction unit;
the characteristic extraction unit is used for analyzing the eye movement position data, extracting eye jump characteristic parameters corresponding to each eye jump behavior paradigm and transmitting the eye jump characteristic parameters to the detection unit;
the detection unit is used for setting different scoring algorithm models based on detection of different disease types, calculating to obtain a score of each eye jump characteristic parameter, calculating an eye jump normal form score under each eye jump behavior normal form according to the weight of each eye jump characteristic parameter under different disease types, calculating to obtain a total score according to the weight of each eye jump normal form score under different disease types, and finally outputting a detection result based on the corresponding disease type according to a scoring interval where the total score is located.
Further, the eye movement tracking unit is an eye movement tracker which comprises an infrared camera, a near infrared light source, a computer host and a display.
Further, the eye jump behavior paradigm sequentially comprises, in order of acquisition: vision, field of vision, forward eye jump, backward eye jump, memory eye jump, and double step eye jump.
Further, the detection result is output in the form of a detection report and a conclusion.
In addition, the invention also provides a method for detecting the physiological and cognitive functions and the neurodegenerative diseases of the brain, which can be applied to the system for detecting the physiological and cognitive functions and the neurodegenerative diseases of the brain and comprises the following steps:
step 1, starting a system and reading configuration parameters of the system;
step 2, sequentially collecting eye movement video images of the tested person in eye movement behavior paradigms of vision, visual field, forward eye movement, backward eye movement, memory eye movement and double-step eye movement according to the sequence of the eye movement behavior paradigms, and extracting eye movement position data in the eye movement video images;
step 3, analyzing the eye movement position data, and extracting eye jump characteristic parameters corresponding to each eye jump behavior paradigm;
step 4, calculating the score of each eye jump characteristic parameter, and calculating the eye jump normal form score under each eye jump behavior normal form according to the weight of each eye jump characteristic parameter under each neurodegenerative disease;
and 5, after all the eye jump normal form scores are calculated, calculating and scoring by integrating the weights of all the eye jump behavior normal form scores to obtain a total score, and outputting a detection result according to a scoring interval where the total score is located.
Further, in step 4, when calculating the score of each eye jump characteristic parameter, different eye jump parameters are calculated by using different scoring formulas.
Further, in step 4, the weight of each eye jump characteristic parameter is different under different neurodegenerative diseases.
Further, in step 4-5, after the scores of all the eye jump characteristic parameters and the scores of the eye jump normal forms under all the eye jump behavior normal forms are calculated, the scores are stored in the database.
Further, in step 5, the step of calculating and scoring by integrating the weights of all the eye jump behavior paradigm scores to obtain a total score, and outputting a detection result according to a scoring interval where the total score is located specifically includes the following steps:
step 501, reading an eye jump normal form score of each eye jump behavior normal form based on each neurodegenerative disease from a database; 502, according to the weight of the corresponding eye jump normal form score under each neurodegenerative disease, calculating by integrating the eye jump normal form scores of the six eye jump behavior normal forms to obtain a total score under the corresponding neurodegenerative disease;
step 503, checking the interval to which the total score under each neurodegenerative disease belongs, if the total score is in the normal score interval, the detection result of the subject under the corresponding neurodegenerative disease is normal; if the total score is in a mild score interval, the detection result of the subject under the corresponding neurodegenerative disease is the mild degree of the current neurodegenerative disease; if the total score is in a moderate score interval, the detection result of the subject under the corresponding neurodegenerative disease is moderate of the current neurodegenerative disease; if the total score is in a severe score interval, the detection result of the subject under the corresponding neurodegenerative disease is the severe degree of the current neurodegenerative disease;
and 504, outputting a corresponding detection result according to the scoring interval where the total score is located, and generating a detection report.
The system and the method have the advantages that the system and the method for detecting the brain physiological and cognitive functions and the neurodegenerative diseases can collect eye jump data based on various specific eye jump behavior paradigms, analyze and extract eye jump characteristic parameters, then carry out diagnosis and evaluation on the eye jump characteristic parameters and give a diagnosis and evaluation conclusion of the brain physiological and cognitive functions and the neurodegenerative diseases such as Alzheimer disease, Parkinson disease and the like.
Drawings
Fig. 1 is a block diagram of a system for detecting physiological and cognitive functions and neurodegenerative diseases in the brain according to embodiment 1 of the present invention.
Detailed Description
The technical solution of the present invention is described in detail below with reference to the accompanying drawings and embodiments.
Example 1
The structural block diagram of the system for detecting physiological and cognitive functions and neurodegenerative diseases of the brain provided by the embodiment is shown in fig. 1, wherein the system comprises an eye movement tracking unit, a data acquisition unit, a feature extraction unit and a detection unit; wherein: the eye tracking unit is used for providing a physical operation environment for the system; the data acquisition unit is used for sequentially acquiring eye movement video images of the testee under each eye jump behavior paradigm according to the sequence of the eye jump behavior paradigms, extracting eye movement position data in the eye movement video images and transmitting the eye movement position data to the feature extraction unit; the characteristic extraction unit is used for analyzing the eye movement position data, extracting eye jump characteristic parameters corresponding to each eye jump behavior paradigm and transmitting the eye jump characteristic parameters to the detection unit; and the detection unit is used for calculating the score of each eye jump characteristic parameter, calculating the comprehensive score under each eye jump behavior paradigm according to the weight of each eye jump characteristic parameter under each neurodegenerative disease, calculating and scoring by synthesizing the weights of all eye jump behavior paradigm scores after the calculation of the comprehensive scores under all eye jump behavior paradigms is finished, obtaining a total score, outputting a detection result according to a scoring interval where the total score is located, and finally outputting the detection result in a detection report and conclusion mode after the calculation of the total score is finished.
In the above system, in order to implement the eye tracking function and provide the necessary physical operating environment for the system, the eye tracking unit may be an eye tracker, and the eye tracker may include an infrared camera, a near-infrared light source, a computer host, a display, and the like.
In this embodiment, the eye jump behavior paradigm sequentially includes according to the order of gathering: vision, field of vision, forward eye jump, backward eye jump, memory eye jump, and double step eye jump.
In practical applications, the detecting unit may be referred to as a neuroscience expert diagnostic subsystem, in which each neurodegenerative disease corresponds to a calculation model.
Firstly, the neuroscience expert diagnosis subsystem can read the collected and extracted eye jump characteristic parameters of more than 200 in the six eye jump behavior paradigms such as visual detection, visual field detection, forward eye jump, backward eye jump, memory eye jump, double step eye jump and the like, then carries out comprehensive scoring on the eye jump characteristic parameters and the eye jump behavior paradigms through a specific calculation formula, and finally judges whether the brain of the testee has early-stage Alzheimer disease, Parkinson disease, progressive supranuclear palsy and other neurodegenerative diseases or not through the section where the scoring result is located.
Here, different disease classes, caused by the degeneration of different brain regions of the brain, exhibit different eye jump parameter characteristics. Therefore, different eye jump paradigms and eye jump parameters have different detection points. Therefore, based on the model, the eye jump normal form and the eye jump parameter have different proportions when diagnosing different disease types, and the calculation formula and the scoring method are also different. Meanwhile, the model also integrates more than 200 eye jump parameters contained in the 6 eye jump normal forms to carry out integrated scoring, and diagnoses and evaluates different disease categories from multiple dimensions, so that the accuracy and specificity of diagnosis can be improved.
Example 2
In this embodiment, on the basis of embodiment 1, a method for detecting physiological and cognitive functions of a brain and neurodegenerative diseases is provided, which includes the following steps:
step 1, starting a system and reading configuration parameters of the system;
step 2, sequentially collecting eye movement video images of the tested person in eye movement behavior paradigms of vision, visual field, forward eye movement, backward eye movement, memory eye movement and double-step eye movement according to the sequence of the eye movement behavior paradigms, and extracting eye movement position data in the eye movement video images;
step 3, analyzing the eye movement position data, and extracting eye jump characteristic parameters corresponding to each eye jump behavior paradigm;
step 4, calculating the score of each eye jump characteristic parameter, and calculating the comprehensive score under each eye jump behavior paradigm according to the proportion of each eye jump characteristic parameter under each neurodegenerative disease model;
and 5, calculating and grading by integrating the weights of all the eye jump behavior paradigm grades to obtain a total grade, and outputting a detection result according to a grading interval where the total grade is located.
Here, the output detection results are the detection findings and reports for various brain functions such as brain physiology, brain cognitive function, cerebellar function, alzheimer disease, parkinson disease, progressive supranuclear palsy, and the like, or neurodegenerative disease models.
In addition, the subject can take the test report to a clinician or a neuroscience expert for reference to assist in diagnosis.
In steps 3 and 4 of the method, after the eye jump characteristic parameters corresponding to each eye jump behavior paradigm are extracted, the eye jump characteristic parameters are uniformly sent to a neuroscience expert diagnosis model, and the model calculates scores of the corresponding eye jump characteristic parameters under each disease model (such as brain physiology, brain cognitive function, cerebellar function, Alzheimer disease, Parkinson disease, progressive supranuclear palsy and the like) according to corresponding algorithms to obtain a scoring result of each eye jump characteristic parameter. The reason for adopting this method for the eye jump characteristic parameter is: the eye jump characteristic parameters of different degrees of the same disease have different characteristics, and the more healthy people are, the more excellent the eye jump characteristic parameters are, and the more serious the disease degree is, the worse the eye jump characteristic parameter characteristics are. Therefore, in order to evaluate the eye jump parameter execution capacity, each eye jump characteristic parameter is scored through a specific formula, and the grade reflects the eye jump execution capacity of the testee on a certain eye jump characteristic parameter. Different eye jump characteristic parameters and different scoring formulas are also adopted.
Further, the comprehensive scoring result of the single eye jump behavior paradigm under the disease model needs to be calculated according to the proportion of each eye jump characteristic parameter under different disease models. The reason for this is that: the sensitivity of different eye jump characteristics for the same disease is different, so the proportion of each eye jump characteristic is different for the diagnosis of different diseases.
It should be noted that, after the scores of all the eye jump characteristic parameters and the scores of the eye jump normal form under all the eye jump behavior normal forms are calculated, the scores can be stored in the database, so that the subsequent steps can be directly read and used.
After the calculation of the eye jump normal form scores under all the eye jump behavior normal forms is completed, in step 5, the weights of all the eye jump normal form scores are integrated to calculate and score, so as to obtain a total score, and a detection result is output according to a scoring interval where the total score is located, which specifically comprises the following steps:
step 501, reading the eye jump normal form score of each eye jump behavior normal form based on each neurodegenerative disease model (such as brain physiology, brain cognitive function, cerebellar function, Alzheimer disease, Parkinson disease, progressive supranuclear palsy and the like) from a database;
502, according to the weight of the corresponding eye jump normal form score under each neurodegenerative disease model, calculating by integrating the eye jump normal form scores of the six eye jump behavior normal forms to obtain a total score under the corresponding neurodegenerative disease model; here, since the emphasis of detection is different for different eye jump patterns, the weights of scores for the eye jump patterns are different for different diseases.
Step 503, checking the section to which the total score of each neurodegenerative disease model belongs, and if the total score is in the normal score section, the detection result of the tested person in the corresponding neurodegenerative disease model is normal; if the total score is in a mild score interval, the detection result of the subject under the corresponding neurodegenerative disease model is mild of the current neurodegenerative disease model (such as early Alzheimer disease); if the total score is in a moderate score interval, the detection result of the subject under the corresponding neurodegenerative disease model is the neutrality of the current neurodegenerative disease model; if the total score is in a severe score interval, the detection result of the tested person under the corresponding neurodegenerative disease model is the severe degree of the current neurodegenerative disease model;
and 504, outputting a corresponding detection result according to the scoring interval where the total score is located, and generating a detection report.
Claims (9)
1. The system for detecting the physiological and cognitive functions of the brain and the neurodegenerative diseases is characterized by comprising an eye movement tracking unit, a data acquisition unit, a feature extraction unit and a detection unit;
the eye tracking unit is used for providing a physical operation environment for the system;
the data acquisition unit is used for sequentially acquiring eye movement video images of the testee under each eye jump behavior paradigm according to the sequence of the eye jump behavior paradigms, extracting eye movement position data in the eye movement video images and transmitting the eye movement position data to the feature extraction unit;
the characteristic extraction unit is used for analyzing the eye movement position data, extracting eye jump characteristic parameters corresponding to each eye jump behavior paradigm and transmitting the eye jump characteristic parameters to the detection unit;
the detection unit is used for setting different scoring algorithm models based on detection of different disease types, calculating to obtain a score of each eye jump characteristic parameter, calculating an eye jump normal form score under each eye jump behavior normal form according to the weight of each eye jump characteristic parameter under different disease types, calculating according to the weight of each eye jump normal form score under different disease types to obtain a total score, and finally outputting a detection result based on the corresponding disease type according to a scoring interval where the total score is located.
2. The system for detecting physiological and cognitive functions and neurodegenerative diseases of the brain according to claim 1, wherein the eye tracking unit is an eye tracker comprising an infrared camera, a near infrared light source, a computer host and a display.
3. The system for detecting physiological and cognitive function and neurodegenerative diseases of the brain as claimed in claim 1, wherein the eye jump behavioral pattern comprises in order of acquisition: vision, field of vision, forward eye jump, backward eye jump, memory eye jump, and double step eye jump.
4. The system for detecting physiological and cognitive function and neurodegenerative diseases in the brain according to claim 1, wherein the detection result is outputted in the form of detection report and conclusion.
5. The method for detecting the physiological and cognitive function and neurodegenerative diseases of the brain is applied to the system for detecting the physiological and cognitive function and neurodegenerative diseases of the brain according to any one of claims 1 to 4, and is characterized by comprising the following steps:
step 1, starting a system and reading configuration parameters of the system;
step 2, sequentially collecting eye movement video images of a tested person in eye jump behavior paradigms of vision, visual field, forward eye jump, backward eye jump, memory eye jump and double step eye jump according to the sequence of the eye jump behavior paradigms, and extracting pupil position, pupil size and cornea reflection point data of each frame in the video images;
step 3, analyzing pupil position data, and extracting eye jump characteristic parameters corresponding to each eye jump behavior paradigm;
step 4, calculating the score of each eye jump characteristic parameter, and calculating the eye jump normal form score under each eye jump behavior normal form according to the weight of each eye jump characteristic parameter under each neurodegenerative disease;
and 5, after all the eye jump normal form scores are calculated, calculating and scoring by integrating the weights of all the eye jump normal form scores to obtain a total score, and outputting a detection result according to a scoring interval where the total score is located.
6. The method as claimed in claim 5, wherein in the step 4, different eye jump parameters are calculated using different scoring formulas when the score of each eye jump characteristic parameter is calculated.
7. The method as claimed in claim 5, wherein in step 4, the weight of each eye jump characteristic parameter is different for different neurodegenerative diseases.
8. The method for detecting physiological and cognitive functions and neurodegenerative diseases in the brain according to any one of claims 5 to 7, wherein in the steps 4 to 5, after the scores of all the eye jump characteristic parameters and the scores of the eye jump normal patterns in all the eye jump behavior normal patterns are calculated, the scores are stored in the database.
9. The method for detecting physiological and cognitive functions and neurodegenerative diseases in the brain according to claim 8, wherein in step 5, the total score is obtained by calculating and scoring by integrating the weights of all the eye jump normal form scores, and the detection result is output according to the scoring interval where the total score is located, which specifically comprises the following steps:
step 501, reading an eye jump normal form score of each eye jump behavior normal form based on each neurodegenerative disease from a database; 502, according to the weight of the corresponding eye jump normal form score under each neurodegenerative disease, calculating by integrating the eye jump normal form scores of the six eye jump behavior normal forms to obtain a total score under the corresponding neurodegenerative disease;
step 503, checking the interval to which the total score under each neurodegenerative disease belongs, if the total score is in the normal score interval, the detection result of the subject under the corresponding neurodegenerative disease is normal; if the total score is in a mild score interval, the detection result of the subject under the corresponding neurodegenerative disease is the mild degree of the current neurodegenerative disease; if the total score is in a moderate score interval, the detection result of the subject under the corresponding neurodegenerative disease is moderate of the current neurodegenerative disease; if the total score is in a severe score interval, the detection result of the subject under the corresponding neurodegenerative disease is the severe degree of the current neurodegenerative disease;
and 504, outputting a corresponding detection result according to the scoring interval where the total score is located, and generating a detection report.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011326958.1A CN112656367B (en) | 2020-11-24 | 2020-11-24 | Detection system and method for brain cognitive function assessment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011326958.1A CN112656367B (en) | 2020-11-24 | 2020-11-24 | Detection system and method for brain cognitive function assessment |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112656367A true CN112656367A (en) | 2021-04-16 |
| CN112656367B CN112656367B (en) | 2024-02-06 |
Family
ID=75403562
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011326958.1A Active CN112656367B (en) | 2020-11-24 | 2020-11-24 | Detection system and method for brain cognitive function assessment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112656367B (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115670373A (en) * | 2022-10-27 | 2023-02-03 | 北京中科睿医信息科技有限公司 | Eye movement detection method, device, equipment and medium |
| CN115778331A (en) * | 2023-02-08 | 2023-03-14 | 四川大学华西医院 | Biomarker group for detecting migraine and application thereof |
| CN117357059A (en) * | 2023-10-16 | 2024-01-09 | 北京清华长庚医院 | System, method, device and storage medium for evaluating dual tasks of motor cognition |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20140154650A1 (en) * | 2012-12-04 | 2014-06-05 | Sync-Think, Inc. | Application of smooth pursuit cognitive testing paradigms to clinical drug development |
| CN106843500A (en) * | 2017-02-27 | 2017-06-13 | 南通大学 | Human-subject test rehabilitation training system based on the dynamic tracer technique of eye |
| CN108922629A (en) * | 2018-06-01 | 2018-11-30 | 中国科学院上海生命科学研究院 | The screening and its application of brain function corelation behaviour normal form index |
| CN109276228A (en) * | 2017-07-21 | 2019-01-29 | 北京集思明智科技有限公司 | A kind of system and its apparatus detecting cerebral function |
-
2020
- 2020-11-24 CN CN202011326958.1A patent/CN112656367B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20140154650A1 (en) * | 2012-12-04 | 2014-06-05 | Sync-Think, Inc. | Application of smooth pursuit cognitive testing paradigms to clinical drug development |
| CN106843500A (en) * | 2017-02-27 | 2017-06-13 | 南通大学 | Human-subject test rehabilitation training system based on the dynamic tracer technique of eye |
| CN109276228A (en) * | 2017-07-21 | 2019-01-29 | 北京集思明智科技有限公司 | A kind of system and its apparatus detecting cerebral function |
| CN108922629A (en) * | 2018-06-01 | 2018-11-30 | 中国科学院上海生命科学研究院 | The screening and its application of brain function corelation behaviour normal form index |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115670373A (en) * | 2022-10-27 | 2023-02-03 | 北京中科睿医信息科技有限公司 | Eye movement detection method, device, equipment and medium |
| CN115778331A (en) * | 2023-02-08 | 2023-03-14 | 四川大学华西医院 | Biomarker group for detecting migraine and application thereof |
| CN117357059A (en) * | 2023-10-16 | 2024-01-09 | 北京清华长庚医院 | System, method, device and storage medium for evaluating dual tasks of motor cognition |
| CN117357059B (en) * | 2023-10-16 | 2024-06-07 | 北京清华长庚医院 | System, method, device and storage medium for evaluating dual tasks of motor cognition |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112656367B (en) | 2024-02-06 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112656367B (en) | Detection system and method for brain cognitive function assessment | |
| US20210251609A1 (en) | Systems and methods for detecting intracranial pressure and volume | |
| CN109276228B (en) | System and device for detecting brain function | |
| JP2009515568A (en) | Apparatus and method for the diagnosis of optically distinguishable ophthalmic symptoms | |
| CN113962311A (en) | Knowledge data and artificial intelligence driven ophthalmic multi-disease identification system | |
| US10098543B2 (en) | Method and system for revealing oculomotor abnormalities | |
| CN111785363A (en) | AI-guidance-based chronic disease auxiliary diagnosis system | |
| US11978208B2 (en) | Trained model, learning method, learning program, medical information acquisition device, medical information acquisition method, and medical information acquisition program | |
| CN114999629A (en) | AD early prediction method, system and device based on multi-feature fusion | |
| CN115691794B (en) | Auxiliary analysis method and system for neural diagnosis | |
| CN112043273B (en) | Brain image data processing method, device, equipment and storage medium | |
| CN113948180A (en) | Method, device, processor and computer readable storage medium for realizing mental disease image report generation processing | |
| CN112911987A (en) | Method for detecting, predicting and monitoring neurological diseases | |
| Temel et al. | Automated Pupillary Light Reflex Test on a Portable Platform | |
| CN114743664A (en) | Gait analysis and determination learning-based spinal cervical spondylosis auxiliary diagnosis system | |
| Winnick et al. | Automated nystagmus detection: Accuracy of slow-phase and quick-phase algorithms to determine the presence of nystagmus | |
| KR20210157444A (en) | Machine Learning-Based Diagnosis Method Of Schizophrenia And System there-of | |
| CN116738352B (en) | Method and device for classifying abnormal rod cells of retinal vascular occlusion disease | |
| TWI854326B (en) | Establishing method of aided estimation program for meniscus tear, aided estimation system for meniscus tear, and aided estimation method for determination of meniscus tear | |
| CN118415588A (en) | Parkinson disease diagnosis system and device based on virtual reality and eye movement tracking technology | |
| CN110192859B (en) | Method for measuring human brain lymph-like pathway clearing efficiency by magnetic resonance | |
| CN113160171A (en) | Elastic ultrasonic imaging image processing method and device | |
| Bidwai et al. | Optical Coherence Tomography Angiography-OCTA dataset for the study of Diabetic Retinopathy | |
| CN116798607A (en) | Whole-process diagnosis prediction system for chronic kidney disease | |
| CN116746884A (en) | Electroencephalogram signal analysis device, electroencephalogram signal analysis method, electronic device, and storage medium |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |