CN113208629A - Alzheimer disease screening method and system based on EEG signal - Google Patents

Alzheimer disease screening method and system based on EEG signal Download PDF

Info

Publication number
CN113208629A
CN113208629A CN202110365783.3A CN202110365783A CN113208629A CN 113208629 A CN113208629 A CN 113208629A CN 202110365783 A CN202110365783 A CN 202110365783A CN 113208629 A CN113208629 A CN 113208629A
Authority
CN
China
Prior art keywords
eye
disease
electroencephalogram signals
alzheimer
state characteristics
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
Application number
CN202110365783.3A
Other languages
Chinese (zh)
Inventor
沈阳
卢树强
王晓岸
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Beijing Brain Up Technology Co ltd
Original Assignee
Beijing Brain Up Technology Co ltd
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Beijing Brain Up Technology Co ltd filed Critical Beijing Brain Up Technology Co ltd
Priority to CN202110365783.3A priority Critical patent/CN113208629A/en
Publication of CN113208629A publication Critical patent/CN113208629A/en
Pending legal-status Critical Current

Links

Images

Classifications

    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Measuring for diagnostic purposes; Identification of persons
    • A61B5/40Detecting, measuring or recording for evaluating the nervous system
    • A61B5/4076Diagnosing or monitoring particular conditions of the nervous system
    • A61B5/4088Diagnosing of monitoring cognitive diseases, e.g. Alzheimer, prion diseases or dementia
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Measuring for diagnostic purposes; Identification of persons
    • A61B5/72Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235Details of waveform analysis
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Measuring for diagnostic purposes; Identification of persons
    • A61B5/72Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235Details of waveform analysis
    • A61B5/7246Details of waveform analysis using correlation, e.g. template matching or determination of similarity
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Measuring for diagnostic purposes; Identification of persons
    • A61B5/72Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235Details of waveform analysis
    • A61B5/7264Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
    • A61B5/7267Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems involving training the classification device

Abstract

The invention discloses an Alzheimer's disease screening method and system based on an EEG signal, wherein the method comprises the following steps: BCI equipment collects electroencephalogram signals of a user, preprocesses and codes the electroencephalogram signals, and transmits the electroencephalogram signals to a detection algorithm system; the detection algorithm system decodes the received electroencephalogram signals, extracts the eye opening state characteristics and the eye closing state characteristics of the user from the decoded electroencephalogram signals, inputs the eye opening state characteristics and the eye closing state characteristics to the convolutional neural network, and obtains the Alzheimer disease incidence probability rating. By adopting the method, the disease incidence condition can be predicted and early warned 3-5 years in advance, and multi-dimensional prediction can be carried out according to the disease incidence probability, the disease incidence time and the disease degree. And the method only needs to wear dry electrode inspection equipment to carry out short-time resting EEG acquisition, is rapid and efficient, and is easy for large-scale popularization.

Description

Alzheimer disease screening method and system based on EEG signal
Technical Field
The invention relates to the technical field of EEG signal identification, in particular to an Alzheimer's disease screening method and system based on EEG signals.
Background
Alzheimer's Disease (AD) is a progressive degenerative disease of the nervous system with occult onset. Clinically, the overall dementia such as dysmnesia, aphasia, disuse, agnosia, impairment of visual spatial skills, dysfunction in execution, and personality and behavior changes are characterized, and the etiology is unknown. Patients who are older than 65 years are called presenile dementia; the patient after 65 years old is called senile dementia. The clinical causes of AD mainly include family history, body disease induction, head trauma, immune function and other factors; the clinical manifestations are divided into three stages, which are mild dementia, hypomnesis, moderate dementia, severe hypomnesis, severe dementia and severe memory loss.
The current clinical examination methods for alzheimer's disease are: neuropsychological tests, mainly including the simple mental scale of memory levels (MMSE), daily living capacity Assessment (ADL), behavioral and mental state assessment (BPSD) scale, etc.; blood and cerebrospinal fluid examinations to screen organic pathogens; neuroimaging examination to screen for neurological organic lesions and trauma; electroencephalogram spectrum inspection; screening genes, and the like. The current inspection method has the problems that the neuropsychological scale test is a subjective questionnaire test, and the accuracy and the credibility are low; the gene detection cost is high, the implementation is not easy, and people carrying the genes which are easy to attack do not necessarily form the diagnosis factor of the early AD, so that unnecessary early intervention can be guided; the main common defects of the examination methods of neuropsychological scale test, blood and cerebrospinal fluid examination, neuroimaging examination and electroencephalogram spectrum examination are that the examination methods can only be applied to determining the degree of disease occurrence when AD has already suffered from diseases, namely clinical symptoms are already shown, and early screening and early treatment mediation cannot be achieved. Later examination does not reflect the screening significance of early intervention, so that the patient misses the optimal early intervention prediction.
In summary, in any of the above existing methods, manual work is required to be matched with a long time for examination, and rapid (10-minute level) examination cannot be achieved, so that the method is difficult to apply to large-scale physical examination and large-scale population screening, and the screening efficiency is low.
Disclosure of Invention
The invention aims to provide an Alzheimer's disease screening method and system based on an EEG signal, so that rapid screening of Alzheimer's disease is realized, and screening efficiency is improved.
In order to solve the above technical problem, the present invention provides an alzheimer's disease screening method based on EEG signals, comprising:
BCI equipment collects electroencephalogram signals of a user, preprocesses and codes the electroencephalogram signals, and transmits the electroencephalogram signals to a detection algorithm system;
the detection algorithm system decodes the received electroencephalogram signals, extracts the eye opening state characteristics and the eye closing state characteristics of the user from the decoded electroencephalogram signals, inputs the eye opening state characteristics and the eye closing state characteristics to the convolutional neural network, and obtains the Alzheimer disease incidence probability rating.
Preferably, the method further comprises:
and the detection algorithm system inputs the incidence probability rating of the Alzheimer's disease into the generation model to obtain the incidence probability, incidence time and incidence severity of the Alzheimer's disease.
Preferably, the time for acquiring the brain electrical signal of the user by the BCI equipment comprises an eye-open time period and an eye-close time period.
Preferably, the eye-open state features are frequency distribution, time-domain feature waveforms, time-domain sequence relations and repetition patterns of the electroencephalogram signals in the eye-open time period; the closed-eye state features are frequency distribution, time domain characteristic waveforms, time domain sequence relations and repetitive patterns of the electroencephalogram signals in the closed-eye time period.
Preferably, the method further comprises:
the detection algorithm system sends the incidence probability, the incidence time and the incidence severity degree to an early screening report system;
the early screening report system generates a corresponding chart and a graphic report according to the morbidity probability, the morbidity time and the morbidity severity.
Preferably, the screening report system comprises a data analysis module and a document automatic generation module, and is used for collecting and sorting the early characteristics of the alzheimer disease, automatically drawing and generating a graphic report.
The invention also provides an Alzheimer's disease screening system based on the EEG signal, which is used for realizing the method and comprises the following steps:
the BCI equipment is used for acquiring the electroencephalogram signals of a user, preprocessing and coding the electroencephalogram signals and transmitting the electroencephalogram signals to the detection algorithm system;
and the detection algorithm system is used for decoding the received electroencephalogram signals, extracting the eye opening state characteristics and the eye closing state characteristics of the user from the decoded electroencephalogram signals, and inputting the eye opening state characteristics and the eye closing state characteristics to the convolutional neural network to obtain the Alzheimer disease incidence probability rating.
According to the Alzheimer's disease screening method and system based on the EEG signals, the disease incidence probability rating of the user is extracted from the EEG signals, so that the judgment result of the Alzheimer's disease of the user is obtained, the automatic identification of early EEG characteristics of the Alzheimer's disease by using an artificial intelligence algorithm can be realized, the disease incidence can be predicted and early warned 3-5 years in advance, and the multi-dimensional prediction can be further performed according to the disease incidence probability, the disease incidence time and the disease incidence degree. And only need wear dry electrode check-out set and carry out the resting EEG of short time and gather, high efficiency easily extensive popularization.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.
FIG. 1 is a flow chart of a method for screening for Alzheimer's disease based on EEG signals according to the present invention;
FIG. 2 is a front view of a portable BCI apparatus;
FIG. 3 is a top view of a portable BCI apparatus;
FIG. 4 is an overall flowchart of Alzheimer's disease screening;
FIG. 5 is a schematic diagram of an Alzheimers disease detection algorithm provided by the present invention;
fig. 6 is a schematic structural diagram of an alzheimer's disease screening system based on EEG signals provided by the present invention.
Detailed Description
The core of the invention is to provide the Alzheimer's disease screening method and system based on the EEG signal, so that the rapid screening of the Alzheimer's disease is realized, and the screening efficiency is improved.
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, fig. 1 is a flowchart of an EEG signal-based alzheimer screening method provided in the present invention, where the method includes the following steps:
s11: BCI equipment collects electroencephalogram signals of a user, preprocesses and codes the electroencephalogram signals, and transmits the electroencephalogram signals to a detection algorithm system;
s12: the detection algorithm system decodes the received electroencephalogram signals, extracts the eye opening state characteristics and the eye closing state characteristics of the user from the decoded electroencephalogram signals, inputs the eye opening state characteristics and the eye closing state characteristics to the convolutional neural network, and obtains the Alzheimer disease incidence probability rating.
Therefore, in the method, the BCI is used for collecting the electroencephalogram signals to perform signal processing and transmitting the electroencephalogram signals to the detection algorithm system, the detection algorithm system is used for extracting the eye opening state characteristics and the eye closing state characteristics of the user from the decoded electroencephalogram signals, the Alzheimer disease incidence probability rating is obtained by utilizing the eye opening state characteristics, the eye closing state characteristics and the convolutional neural network, namely the Alzheimer disease screening is completed by extracting the incidence probability rating of the user from the EEG signals, the quick screening of the Alzheimer disease is realized, and the screening efficiency is improved.
The method can realize the automatic recognition of the early EEG characteristics of the Alzheimer's disease by using an artificial intelligence algorithm, and can predict and early warn the onset condition 3-5 years ahead. And the method only needs to wear dry electrode inspection equipment to carry out short-time resting EEG acquisition, is rapid and efficient, and is easy for large-scale popularization.
Among them, EEG (Electroencephalogram) signals are patterns obtained by recording spontaneous biopotentials of the brain from the scalp by amplifying them with a precision electronic instrument, and are spontaneous and rhythmic electrical activities of brain cell groups recorded by electrodes. There are conventional electroencephalograms, dynamic electroencephalogram monitoring, video electroencephalogram monitoring. BCI (Brain Computer Interface) equipment is capable of acquiring Brain wave signals, i.e., EEG signals.
In step S11, the preprocessing of the electroencephalogram signal is specifically performed by processing the electroencephalogram signal with a threshold screening, common mode rejection, and reference subtraction algorithm to obtain a processed electroencephalogram signal, and then encoding and transmitting the electroencephalogram signal.
The BCI equipment acquires the electroencephalogram signals of the user, wherein the time for acquiring the electroencephalogram signals of the user comprises an eye opening time period and an eye closing time period. The eye opening state features are frequency distribution, time domain feature waveforms, time domain sequence relations and repetition patterns of the electroencephalogram signals in the eye opening time period. The closed-eye state features are frequency distribution, time domain characteristic waveforms, time domain sequence relations and repetitive patterns of the electroencephalogram signals in the closed-eye time period.
Based on the above method, further, after step S12, the detection algorithm system inputs the alzheimer disease incidence probability rating into the generative model to obtain the incidence probability, incidence time and incidence severity of the alzheimer disease.
The probability generation Model is a generation Model (generic Model) for short, is an important Model in probability statistics and machine learning, and refers to a series of models for randomly generating observable data. The generated model has wide application, can be used for modeling different data, such as images, texts, sounds and the like in machine learning, can be used for directly modeling data, for example, sampling data according to a probability density function of a certain variable, and can also be used for establishing conditional probability distribution among the variables. The conditional probability distribution may be formed by a generative model according to bayesian theorem.
Further, the method comprises the following steps:
s21: the detection algorithm system sends the incidence probability, the incidence time and the incidence severity degree to an early screening report system;
s22: the early screening report system generates a corresponding chart and a graphic report according to the morbidity probability, the morbidity time and the morbidity severity.
The screening report system comprises a data analysis module and a document automatic generation module, and is used for collecting and sorting the early features of the Alzheimer's disease, automatically drawing and generating a graphic report.
Referring to fig. 2 and 3, in acquiring raw EEG data, the device used in this embodiment may be a portable BCI device, which belongs to a dry electrode high-precision EEG acquisition apparatus, where electrodes are symmetrically distributed in the frontal lobe or frontal lobe, electrode points are symmetrically distributed left and right, and the electrodes have a higher sampling rate, and can meet requirements for accurate depiction, denoising, filtering, amplifying, encoding and transmission of electroencephalogram signals; the device mainly comprises a biosensor, a front-end circuit device and the like.
In this embodiment, the detection algorithm system includes a data transmission and storage module and a data analysis algorithm module; the method realizes the functions of carrying out feature extraction and algorithm discrimination on EEG signal data and identifying AD early-stage features. The system modalities include, but are not limited to: cloud platform system, terminal equipment display system.
In this embodiment, the screening report system includes a data analysis module and a document automatic generation module, which implement the functions of collecting, sorting, automatically drawing and generating an image-text report of the AD early stage features, so that the patient and the medical staff can more clearly understand the screening result. The system mainly comprises a software system and a printing device.
Based on the method, more specifically, the specific implementation flow refers to fig. 4 and 5, and the specific process is as follows:
firstly, the portable BCI equipment is worn, positioned and impedance is adjusted, so that the portable BCI equipment can acquire and obtain the forehead and the stable and spontaneous scalp electroencephalogram of the forehead, and the portable BCI equipment acquires resting EEG data of a user for 10 minutes;
wherein the acquisition time is 10 minutes, the user sits still, keeps the head and the body from shaking as much as possible, and keeps the eye opening state in the first 5 minutes and the eye closing state in the last 5 minutes; the ambient temperature and noise are kept at normal suitable levels throughout.
Secondly, preprocessing and coding the data, namely amplifying the electroencephalogram signal, then simply processing the signal by running a threshold screening, common mode rejection and reference subtraction algorithm deployed at the front end, converting the processed 6-channel signal data into a digital signal by using an A/D converter, and then denoising and filtering the electroencephalogram signal. Filtering the original data by adopting a filtering algorithm, and filtering high-frequency artifacts and low-frequency artifacts in the original data, power frequency interference, eye electrical noise and other noises to obtain a pure electroencephalogram signal; the processing carrier is not limited to cloud computing and front-end computing; preferably, the scheme adopts a front-end intelligent chip for calculation.
The electroencephalogram signal amplification uses an analog method circuit to amplify electroencephalogram data of 10 channels (including 1 grounding channel and 1 reference channel); the specific amplification factor is 1000 times, and a 20-stage amplification circuit is adopted, wherein the first stage amplification is 2 times, and the second stage amplification is 50 times;
wherein, signals with relative voltage value above 100uV are screened and removed by adopting a threshold value; improving the signal-to-noise ratio by adopting a common mode rejection algorithm; the reference subtraction algorithm specifically averages the reference potentials of A1 and A2, and subtracts the data of the 6 measurement channels after common mode suppression.
Thirdly, the processed digital signal data is compressed and then transmitted to the Alzheimer's disease detection algorithm system in real time. The transmission mode can be any wireless connection mode, including but not limited to bluetooth, data traffic and WiFi, and preferably, the scheme adopts the bluetooth mode to transmit data.
Fourthly, decoding, feature extraction, quality judgment and rough analysis, incidence probability judgment and incidence condition prediction are carried out on the data through an Alzheimer's disease detection algorithm system, and the method specifically comprises the following steps:
1. restoring the compressed high-frequency digital signal data into 8-signal channel waveform data;
2. extracting characteristics such as an eye-opening time domain characteristic waveform, a time domain sequence relation and a repetitive pattern, a closed eye time domain characteristic waveform, a cognitive formation characteristic waveform, a memory reconstruction characteristic waveform and the like, wherein the characteristics are obtained by analyzing a large amount of experimental data;
the first five minutes are eye-open states, and the last five minutes are eye-closed states.
3. Grading and labeling the early AD symptoms, respectively representing the AD morbidity probability, applying a convolutional neural network, taking the extracted characteristics as input, and judging to obtain a generated result model so as to judge the risk probability of the early AD characteristics;
the early signs of AD are labeled in a grading way and are divided into 0-5 grades, and the 0%, 20%, 40%, 60%, 80% and 100% grading values of the AD incidence probability are represented respectively.
4. And applying a generation model, and taking the incidence probability grade obtained by the model evaluation as input to obtain continuous incidence probability, incidence time and incidence severity estimation values, wherein the model input can be labeled by adopting a case analysis method.
Fifthly, through an early screening report system, the predicted characteristic data is collated, a chart or a graphic report is automatically generated, and the chart or the graphic report is printed and presented to users and medical staff. The chart can be a disease incidence probability and time relation chart, a disease condition degree prediction chart, a cognitive function evaluation index chart, a disease condition prediction chart and the like, the report comprises medical diagnosis suggestions, scientific interventions, cognitive interventions suggestions and the like, and the result is fed back to the user, a doctor and other detection personnel.
Referring to fig. 6, fig. 6 is a system for screening alzheimer's disease based on EEG signals, which is provided by the present invention and is used for implementing the method, including:
the BCI equipment 101 is used for collecting electroencephalogram signals of a user, preprocessing and coding the electroencephalogram signals and transmitting the electroencephalogram signals to the detection algorithm system;
and the detection algorithm system 102 is used for decoding the received electroencephalogram signal, extracting the eye opening state characteristics and the eye closing state characteristics of the user from the decoded electroencephalogram signal, and inputting the eye opening state characteristics and the eye closing state characteristics to the convolutional neural network to obtain the Alzheimer disease incidence probability rating.
Therefore, in the system, the BCI acquires the electroencephalogram signals, processes the signals and transmits the signals to the detection algorithm system, the detection algorithm system extracts the eye opening state characteristics and the eye closing state characteristics of the user from the decoded electroencephalogram signals, the probability rating of the onset of the Alzheimer's disease is obtained by using the eye opening state characteristics, the eye closing state characteristics and the convolutional neural network, namely, the screening of the Alzheimer's disease is completed by extracting the probability rating of the onset of the user from the EEG signals, the rapid screening of the Alzheimer's disease is realized, the screening efficiency is improved, the automatic identification of the early EEG characteristics of the Alzheimer's disease by using an artificial intelligence algorithm can be realized, and the prediction and early warning of the onset of the Alzheimer's disease can be realized 3-5 years in advance.
For the introduction of the system for screening alzheimer's disease based on EEG signals provided by the present invention, please refer to the foregoing embodiments of the method for screening alzheimer's disease based on EEG signals, which are not repeated herein. The embodiments are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same or similar parts among the embodiments are referred to each other. For the system disclosed by the embodiment, the description is relatively simple because the system corresponds to the method disclosed by the embodiment, and the relevant points can be referred to the method part for description.
Those of skill would further appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the various illustrative components and steps have been described above generally in terms of their functionality in order to clearly illustrate this interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may reside in Random Access Memory (RAM), memory, Read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.
The method and system for screening alzheimer's disease based on EEG signals provided by the present invention are described in detail above. The principles and embodiments of the present invention are explained herein using specific examples, which are presented only to assist in understanding the method and its core concepts. It should be noted that, for those skilled in the art, it is possible to make various improvements and modifications to the present invention without departing from the principle of the present invention, and those improvements and modifications also fall within the scope of the claims of the present invention.

Claims (7)

1. A method for screening Alzheimer's disease based on EEG signals, comprising:
BCI equipment collects electroencephalogram signals of a user, preprocesses and codes the electroencephalogram signals, and transmits the electroencephalogram signals to a detection algorithm system;
the detection algorithm system decodes the received electroencephalogram signals, extracts the eye opening state characteristics and the eye closing state characteristics of the user from the decoded electroencephalogram signals, inputs the eye opening state characteristics and the eye closing state characteristics to the convolutional neural network, and obtains the Alzheimer disease incidence probability rating.
2. The method of claim 1, further comprising:
and the detection algorithm system inputs the incidence probability rating of the Alzheimer's disease into the generation model to obtain the incidence probability, incidence time and incidence severity of the Alzheimer's disease.
3. The method of claim 1, wherein the BCI device acquires a user's brain electrical signal for a time comprising an open eye period and a closed eye period.
4. The method of claim 3, wherein the eye-open state characteristic is a frequency distribution, a time-domain characteristic waveform, a time-domain sequence relationship, a repetitive pattern of the brain electrical signal over an eye-open period; the closed-eye state features are frequency distribution, time domain characteristic waveforms, time domain sequence relations and repetitive patterns of the electroencephalogram signals in the closed-eye time period.
5. The method of claim 2, further comprising:
the detection algorithm system sends the incidence probability, the incidence time and the incidence severity degree to an early screening report system;
the early screening report system generates a corresponding chart and a graphic report according to the morbidity probability, the morbidity time and the morbidity severity.
6. The method of claim 5, wherein the screening report system comprises a data analysis module, a document auto-generation module for collating, auto-plotting and generating a textual report of the early stage feature collection of Alzheimer's disease.
7. An EEG signal based alzheimer's disease screening system for implementing the method of any of claims 1 to 6, comprising:
the BCI equipment is used for acquiring the electroencephalogram signals of a user, preprocessing and coding the electroencephalogram signals and transmitting the electroencephalogram signals to the detection algorithm system;
and the detection algorithm system is used for decoding the received electroencephalogram signals, extracting the eye opening state characteristics and the eye closing state characteristics of the user from the decoded electroencephalogram signals, and inputting the eye opening state characteristics and the eye closing state characteristics to the convolutional neural network to obtain the Alzheimer disease incidence probability rating.
CN202110365783.3A 2021-04-06 2021-04-06 Alzheimer disease screening method and system based on EEG signal Pending CN113208629A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202110365783.3A CN113208629A (en) 2021-04-06 2021-04-06 Alzheimer disease screening method and system based on EEG signal

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202110365783.3A CN113208629A (en) 2021-04-06 2021-04-06 Alzheimer disease screening method and system based on EEG signal

Publications (1)

Publication Number Publication Date
CN113208629A true CN113208629A (en) 2021-08-06

Family

ID=77086530

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202110365783.3A Pending CN113208629A (en) 2021-04-06 2021-04-06 Alzheimer disease screening method and system based on EEG signal

Country Status (1)

Country Link
CN (1) CN113208629A (en)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113729709A (en) * 2021-09-23 2021-12-03 中国科学技术大学先进技术研究院 Neurofeedback apparatus, neurofeedback method, and computer-readable storage medium
CN115346657A (en) * 2022-07-05 2022-11-15 深圳市镜象科技有限公司 Training method and device for improving senile dementia recognition effect by transfer learning

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20070299360A1 (en) * 2006-06-21 2007-12-27 Lexicor Medical Technology, Llc Systems and Methods for Analyzing and Assessing Dementia and Dementia-Type Disorders
CN103150611A (en) * 2013-03-08 2013-06-12 北京理工大学 Hierarchical prediction method of II type diabetes mellitus incidence probability
CN110781751A (en) * 2019-09-27 2020-02-11 杭州电子科技大学 Emotional electroencephalogram signal classification method based on cross-connection convolutional neural network
CN111407231A (en) * 2020-03-30 2020-07-14 河北省科学院应用数学研究所 Method and device for detecting risk of Alzheimer's disease and terminal equipment
JP2021045208A (en) * 2019-09-14 2021-03-25 卓成 桂 Dementia risk determination system

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20070299360A1 (en) * 2006-06-21 2007-12-27 Lexicor Medical Technology, Llc Systems and Methods for Analyzing and Assessing Dementia and Dementia-Type Disorders
CN103150611A (en) * 2013-03-08 2013-06-12 北京理工大学 Hierarchical prediction method of II type diabetes mellitus incidence probability
JP2021045208A (en) * 2019-09-14 2021-03-25 卓成 桂 Dementia risk determination system
CN110781751A (en) * 2019-09-27 2020-02-11 杭州电子科技大学 Emotional electroencephalogram signal classification method based on cross-connection convolutional neural network
CN111407231A (en) * 2020-03-30 2020-07-14 河北省科学院应用数学研究所 Method and device for detecting risk of Alzheimer's disease and terminal equipment

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113729709A (en) * 2021-09-23 2021-12-03 中国科学技术大学先进技术研究院 Neurofeedback apparatus, neurofeedback method, and computer-readable storage medium
CN113729709B (en) * 2021-09-23 2023-08-11 中科效隆(深圳)科技有限公司 Nerve feedback device, nerve feedback method, and computer-readable storage medium
CN115346657A (en) * 2022-07-05 2022-11-15 深圳市镜象科技有限公司 Training method and device for improving senile dementia recognition effect by transfer learning

Similar Documents

Publication Publication Date Title
Li et al. Seizure onset detection using empirical mode decomposition and common spatial pattern
Islam et al. A wavelet-based artifact reduction from scalp EEG for epileptic seizure detection
Naik et al. Single-channel EMG classification with ensemble-empirical-mode-decomposition-based ICA for diagnosing neuromuscular disorders
CN110013250B (en) Multi-mode characteristic information fusion prediction method for suicidal behavior of depression
CN109498041B (en) Driver road rage state identification method based on electroencephalogram and pulse information
CN112741638B (en) Medical diagnosis auxiliary system based on EEG signal
CN110960191A (en) Epilepsia electroencephalogram signal classification method based on frequency spectrum energy diagram
CN113208629A (en) Alzheimer disease screening method and system based on EEG signal
Lopes et al. Automatic electroencephalogram artifact removal using deep convolutional neural networks
CN114190944B (en) Robust emotion recognition method based on electroencephalogram signals
Kehri et al. EMG signal analysis for diagnosis of muscular dystrophy using wavelet transform, SVM and ANN
WO2022135449A1 (en) Interictal epileptiform discharge activity detection apparatus and method for epileptic patient
Ibrahim et al. Formulation of a novel classification indices for classification of human hearing abilities according to cortical auditory event potential signals
Fickling et al. Good data? The EEG quality index for automated assessment of signal quality
CN115188448A (en) Traditional Chinese medicine doctor diagnosis and treatment experience recording method based on brain waves
Chavan et al. Effective epileptic seizure detection by classifying focal and non-focal EEG signals using human learning optimization-based hidden Markov model
CN111067513A (en) Sleep quality detection key brain area judgment method based on characteristic weight self-learning
Anastasiadou et al. Detection and removal of muscle artifacts from scalp EEG recordings in patients with epilepsy
JP2017042562A (en) Music hearing experience presence/absence estimation method, music hearing experience presence/absence estimation device and music hearing experience presence/absence estimation program
Li et al. A segmentation-denoising network for artifact removal from single-channel EEG
CN113208620A (en) Sleep stage based Alzheimer disease screening method and system
Antunes et al. A morphology-based feature set for automated Amyotrophic Lateral Sclerosis diagnosis on surface electromyography
Paithane et al. Electroencephalogram signal analysis using wavelet transform and support vector machine for human stress recognition
CN114699093A (en) Electroencephalogram seizure signal detection method based on convolutional neural network and long-term and short-term memory
CN114569141A (en) Epilepsy prediction system based on feature channel fusion and deep learning

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
WD01 Invention patent application deemed withdrawn after publication
WD01 Invention patent application deemed withdrawn after publication

Application publication date: 20210806