CN110265143B - Intelligent auxiliary diagnosis system based on electroencephalogram - Google Patents
Intelligent auxiliary diagnosis system based on electroencephalogram Download PDFInfo
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Abstract
The invention relates to an electroencephalogram-based intelligent auxiliary diagnosis system which comprises eleven subsystems of electroencephalogram preprocessing, waveform model construction, waveform identification, appearance model construction, appearance mode identification, distribution model construction, distribution mode identification, state model construction, patient state identification, diagnosis model construction and diagnosis. The method comprises the following steps that five subsystems, namely waveform model construction, appearance model construction, distribution model construction, state model construction and diagnosis model construction, are used for respectively constructing models in different modes; after the electroencephalogram source signal is preprocessed by the electroencephalogram preprocessing subsystem through filtering, artifact removing, labeling, reference transformation and the like, the electroencephalogram data are processed by the waveform recognition subsystem, the appearance mode recognition subsystem, the distribution mode recognition subsystem, the patient state recognition subsystem and the diagnosis subsystem sequentially through corresponding models to form diagnosis opinions which are provided for doctors to serve as references. The invention can improve the efficiency and quality of electroencephalogram medical diagnosis.
Description
Technical Field
The invention relates to an intelligent auxiliary diagnosis system based on electroencephalogram.
Background
At present, the brain diseases are diagnosed by depending on the electroencephalogram checked by medical staff manually, the diagnosis result is limited by experience level and knowledge accumulation of the medical staff, different doctors give different diagnoses to the same electroencephalogram, more adverse effects are generated on treatment of patients, and in order to capture effective information, the medical diagnosis based on the electroencephalogram needs to record electroencephalogram signals for a long time. In the face of long-time electroencephalogram signals, doctors need to read screen by screen to make diagnosis, time is consumed, and reading quality is difficult to guarantee.
Disclosure of Invention
The invention aims to provide an intelligent auxiliary diagnosis system based on electroencephalogram, aiming at solving the problem of the existing intelligent auxiliary diagnosis based on electroencephalogram, and the system for diagnosing various brain diseases is constructed by utilizing the thought of an intelligent technology according to an acquired data set and is applied to other patients, so that the adverse effect of doctors on the understanding of the disease conditions due to personal limitations is reduced, and the disease diagnosis efficiency is improved.
In order to achieve the purpose, the technical scheme of the invention is as follows: an electroencephalogram-based intelligent auxiliary diagnosis system comprises eleven subsystems, namely an electroencephalogram preprocessing subsystem, a waveform model building subsystem, a waveform identification subsystem, an appearance model building subsystem, an appearance mode identification subsystem, a distribution model building subsystem, a distribution mode identification subsystem, a state model building subsystem, a patient state identification subsystem, a diagnosis model building subsystem and a diagnosis subsystem;
the electroencephalogram preprocessing subsystem receives an electroencephalogram source signal as input, and outputs preprocessed electroencephalogram data after filtering, artifact removing, labeling and reference transformation; the electroencephalogram preprocessing subsystem removes noise in the electroencephalogram signals through filtering, removes signal segments affected by artifacts through threshold limitation, and removes the artifacts mixed on each lead through independent component analysis; the marking function of the electroencephalogram preprocessing subsystem allows a user to supplement unmarked events in the marking source data; the marking function of the brain electrical preprocessing subsystem also allows a user to mark brain electrical waveforms; the reference transformation function of the electroencephalogram preprocessing subsystem can switch the source signals, namely the source signals can be switched into electroencephalogram signals of other reference modes through calculation;
the waveform model construction subsystem extracts a sample of each brain waveform from the preprocessed brain electrical data labeled with the brain waveform, constructs a training set for each brain waveform by using the extracted sample, constructs a waveform model for each brain waveform by a method of training on the training set, and outputs the waveform model to the waveform recognition subsystem; the waveform model construction subsystem has the functions of continuously accumulating preferred brain waveform samples, expanding a training set and reconstructing an improved optimized waveform model;
the waveform identification subsystem receives the pre-processed electroencephalogram data to be diagnosed, which are output by the electroencephalogram pre-processing subsystem, identifies all electroencephalograms from the electroencephalogram data by adopting a waveform model provided by the waveform model construction subsystem, marks the identified electroencephalograms, the spatial positions and the time positions of the electroencephalograms in the electroencephalogram data, and outputs the marked electroencephalogram data;
the appearance model construction subsystem, the distribution model construction subsystem, the state model construction subsystem and the diagnosis model construction subsystem respectively receive experience rules of doctors in a mode of interacting with users, express the experience rules by using a normalized data structure, eliminate logic contradictions among the rules by using logic reasoning, construct a model by using a group of optimized rules and output the model; the appearance model construction subsystem receives the experience rule of the doctor for judging the appearance mode of the brain waveform in time and outputs an appearance model for identifying the appearance mode; the distribution model construction subsystem receives the empirical rule of the doctor for judging the spatial distribution mode of the brain waveform and outputs a distribution model for identifying the distribution mode; the state model building subsystem receives an experience rule that a doctor judges the state of a patient according to a brain waveform, a brain waveform occurrence mode and a brain waveform distribution mode, and outputs a state model for identifying the state of the patient; the diagnosis model construction subsystem receives the experience rules of doctors for diagnosing according to the brain waveforms, the brain waveform occurrence modes and the brain waveform distribution modes of patients in different states, and outputs a diagnosis model for diagnosing diseases;
the appearance mode identification subsystem receives brain waveform data output by the waveform identification subsystem, identifies the appearance mode of the brain waveform by adopting an appearance model provided by the appearance model construction subsystem, marks the identified appearance mode in the brain wave data, and outputs the marked brain wave data;
the distribution mode identification subsystem receives brain waveform data output by the waveform identification subsystem, identifies the brain waveform distribution mode by adopting a distribution model provided by the distribution model construction subsystem, marks the identified distribution mode in the brain electrical data, and outputs the marked brain electrical data;
the patient state identification subsystem receives the electroencephalogram data marked with the electroencephalogram waveform, the appearance mode and the distribution mode, processes the electroencephalogram data by adopting the state model provided by the state model construction subsystem, identifies the states of the patient at different time intervals, and outputs the electroencephalogram data after adding state marks;
the diagnosis subsystem receives the electroencephalogram data marked with the electroencephalogram waveform, the appearance mode, the distribution mode and the patient state, processes the electroencephalogram data by using the diagnosis model provided by the diagnosis model construction subsystem, and gives a diagnosis suggestion for the patient; the diagnosis opinions consist of qualitative opinions, description about electroencephalogram and electroencephalogram data indexes corresponding to the electroencephalogram description; each qualitative opinion is supported by a set of descriptions about electroencephalograms, each electroencephalogram description corresponding to a set of electroencephalogram data; the diagnostic subsystem supports retrieval of user data from the qualitative opinion to the electroencephalogram description to the electroencephalogram data.
Compared with the prior art, the invention has the following beneficial effects: according to the electroencephalogram-based intelligent auxiliary diagnosis system, the system for diagnosing various brain diseases is constructed by utilizing the thought of an intelligent technology according to the collected data set and is applied to other patients, so that the adverse effect of doctors on the understanding of the disease conditions due to personal limitations is reduced, and the disease diagnosis efficiency is improved.
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Fig. 1 is a block diagram of an electroencephalogram-based intelligent auxiliary diagnosis system according to the present invention.
Detailed Description
The technical scheme of the invention is specifically explained in the following by combining the attached drawings.
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the present application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
As shown in fig. 1, the present embodiment provides an intelligent auxiliary diagnosis system based on electroencephalogram, which includes eleven sub-systems, namely an electroencephalogram preprocessing sub-system, a waveform model building sub-system, a waveform identification sub-system, an appearance model building sub-system, an appearance mode identification sub-system, a distribution model building sub-system, a distribution mode identification sub-system, a state model building sub-system, a patient state identification sub-system, a diagnosis model building sub-system, and a diagnosis sub-system;
the electroencephalogram preprocessing subsystem receives an electroencephalogram source signal as input, and outputs preprocessed electroencephalogram data after filtering, artifact removing, labeling and reference transformation; the electroencephalogram preprocessing subsystem removes noise in the electroencephalogram signals through filtering, limits a threshold value to remove signal segments influenced by artifacts, and analyzes independent components to remove the artifacts mixed on each lead; the marking function of the electroencephalogram preprocessing subsystem allows a user to supplement unmarked events in the marking source data; the marking function of the brain electrical preprocessing subsystem also allows a user to mark brain electrical waveforms; the reference transformation function of the electroencephalogram preprocessing subsystem can switch the source signals into electroencephalogram signals of other reference modes through calculation; in this embodiment, the unlabelled events include known epileptic seizure, open-closed eye test, eye state sensitivity test, hyperventilation test, flash stimulation, sleep induction, drug induction and events deemed to be labeled by a doctor in the electroencephalogram recording process; the brain wave comprises sine-like wave, alpha rhythm, beta rhythm, gamma rhythm, delta rhythm, theta rhythm, bow wave, incisional wave, diphasic wave, triphase wave, multiposition phasic wave, spike wave, multiple spike wave, complex wave, K complex wave and polymorphism wave; other reference patterns include ipsilateral ear reference, binaural reference, mean reference, and bipolar leads;
the waveform model construction subsystem extracts a sample of each brain waveform from the preprocessed brain electrical data labeled with the brain waveform, constructs a training set for each brain waveform by using the extracted sample, constructs a waveform model for each brain waveform by a method of training on the training set, and outputs the waveform model to the waveform recognition subsystem; the waveform model construction subsystem has the functions of continuously accumulating preferred brain waveform samples, expanding a training set and reconstructing an improved optimized waveform model;
the waveform identification subsystem receives the pre-processed electroencephalogram data to be diagnosed, which are output by the electroencephalogram pre-processing subsystem, identifies all electroencephalograms from the electroencephalogram data by adopting a waveform model provided by the waveform model construction subsystem, marks the identified electroencephalograms, the spatial positions and the time positions of the electroencephalograms in the electroencephalogram data, and outputs the marked electroencephalogram data;
the occurrence model construction subsystem, the distribution model construction subsystem, the state model construction subsystem and the diagnosis model construction subsystem respectively receive experience rules of doctors in a mode of interacting with users, represent the experience rules by using a standardized data structure, eliminate logical contradictions among the rules by using logical reasoning, construct a model by using a group of optimized rules and output the model; the appearance model construction subsystem receives the experience rule of the doctor for judging the appearance mode of the brain waveform in time and outputs an appearance model for identifying the appearance mode; the distribution model construction subsystem receives the empirical rule of the doctor for judging the spatial distribution mode of the brain waveform and outputs a distribution model for identifying the distribution mode; the state model building subsystem receives an experience rule that a doctor judges the state of a patient according to a brain waveform, a brain waveform occurrence mode and a brain waveform distribution mode, and outputs a state model for identifying the state of the patient; the diagnosis model construction subsystem receives the experience rules of doctors for diagnosing according to the brain waveforms, the brain waveform occurrence modes and the brain waveform distribution modes of patients in different states, and outputs a diagnosis model for diagnosing diseases;
the appearance mode identification subsystem receives brain waveform data output by the waveform identification subsystem, identifies the appearance mode of the brain waveform by adopting an appearance model provided by the appearance model construction subsystem, marks the identified appearance mode in the brain wave data, and outputs the marked brain wave data; in this embodiment, the brain waveforms appear in a pattern including activity, rhythm, burst, periodicity, sporadic, transient, and synchronous;
the distribution mode identification subsystem receives brain waveform data output by the waveform identification subsystem, identifies the brain waveform distribution mode by adopting a distribution model provided by the distribution model construction subsystem, marks the identified distribution mode in the brain electrical data, and outputs the marked brain electrical data; in this embodiment, the brain waveform distribution pattern includes broad, diffuse, lateral, local, multifocal, wandering, and symmetrical;
the patient state identification subsystem receives the electroencephalogram data marked with the electroencephalogram waveform, the appearance mode and the distribution mode, processes the electroencephalogram data by adopting the state model provided by the state model construction subsystem, identifies the states of the patient at different time intervals, and outputs the electroencephalogram data after adding state marks; in this embodiment, the patient state includes a normal waking period, a normal sleeping period, a sleeping cycle, a background activity abnormality, and a paroxysmal abnormality;
the diagnosis subsystem receives the electroencephalogram data marked with the electroencephalogram waveform, the appearance mode, the distribution mode and the patient state, processes the electroencephalogram data by using the diagnosis model provided by the diagnosis model construction subsystem, and gives a diagnosis suggestion for the patient; the diagnosis opinions consist of qualitative opinions, description about electroencephalogram and electroencephalogram data indexes corresponding to electroencephalogram description; each qualitative opinion is supported by a set of descriptions about electroencephalograms, each electroencephalogram description corresponding to a set of electroencephalogram data; the diagnosis subsystem supports the retrieval of the user from qualitative opinions, electroencephalogram description and electroencephalogram data; in this embodiment, the qualitative opinions are classified into the grades of normal, normal range, borderline, mild abnormality, moderate abnormality, and severe abnormality; descriptions about electroencephalograms include background activity, evoked trials, sleep, abnormal waves, clinical episodes, drug effects, and interfering waves;
while the invention has been described with reference to a preferred embodiment, it will be understood by those skilled in the art that various changes may be made and equivalents may be substituted for elements thereof without departing from the scope of the invention. However, any simple modification, equivalent change and modification of the above embodiments according to the technical essence of the present invention are within the protection scope of the technical solution of the present invention.
Claims (1)
1. An electroencephalogram-based intelligent auxiliary diagnosis system is characterized by comprising eleven subsystems, namely an electroencephalogram preprocessing subsystem, a waveform model building subsystem, a waveform identification subsystem, an appearance model building subsystem, an appearance mode identification subsystem, a distribution model building subsystem, a distribution mode identification subsystem, a state model building subsystem, a patient state identification subsystem, a diagnosis model building subsystem and a diagnosis subsystem;
the electroencephalogram preprocessing subsystem receives an electroencephalogram source signal as input, and outputs preprocessed electroencephalogram data after filtering, artifact removing, labeling and reference transformation; the electroencephalogram preprocessing subsystem removes noise in an electroencephalogram source signal through filtering, removes signal segments influenced by artifacts through threshold limitation, and removes the artifacts mixed on each lead through independent component analysis; the label of the brain electrical preprocessing subsystem is used for allowing a user to supplement and label events which are not labeled in the brain electrical source signal; the marking of the brain electrical preprocessing subsystem is also used for allowing a user to mark brain electrical waveforms; the reference transformation of the brain electrical pre-processing subsystem is used for switching the brain electrical source signal into brain electrical signals of other reference modes; the unmarked events comprise known epileptic seizure, open-close eye tests, eye state sensitivity tests, hyperventilation tests, flash stimulation, sleep induction and drug induction in the electroencephalogram recording process; the brain wave comprises sine-like wave, alpha rhythm, beta rhythm, gamma rhythm, delta rhythm, theta rhythm, bow wave, incisional wave, diphasic wave, triphase wave, multiposition phasic wave, spike wave, multiple spike wave, complex wave, K complex wave and polymorphism wave; other reference patterns include ipsilateral ear reference, binaural reference, mean reference, and bipolar leads;
the waveform model construction subsystem extracts a sample of each brain waveform from the preprocessed brain electrical data labeled with the brain waveform, constructs a training set for each brain waveform by using the extracted sample, constructs a waveform model for each brain waveform by a method of training on the training set, and outputs the waveform model to the waveform recognition subsystem; the waveform model construction subsystem is used for continuously accumulating brain waveform samples, expanding a training set and reconstructing an improved and optimized waveform model;
the waveform identification subsystem receives the preprocessed electroencephalogram data to be diagnosed, which are output by the electroencephalogram preprocessing subsystem, identifies all electroencephalogram waveforms from the electroencephalogram data by adopting a waveform model provided by the waveform model construction subsystem, marks the identified electroencephalogram waveforms, the spatial positions and the temporal positions of the electroencephalogram waveforms in the electroencephalogram data, and outputs the marked electroencephalogram data;
the appearance model construction subsystem, the distribution model construction subsystem, the state model construction subsystem and the diagnosis model construction subsystem respectively receive experience rules of doctors in a mode of interacting with users, express the experience rules by using a normalized data structure, eliminate logic contradictions among the rules by using logic reasoning, construct corresponding models by using a group of optimized rules and output the models; the appearance model construction subsystem receives an empirical rule that a doctor judges the appearance mode of the brain waveform in time and outputs an appearance model for identifying the appearance mode; the distribution model construction subsystem receives an empirical rule that a doctor judges the spatial distribution mode of the brain waveform and outputs a distribution model for identifying the distribution mode; the state model building subsystem receives an experience rule that a doctor judges the state of a patient according to a brain waveform, a brain waveform occurrence mode and a brain waveform distribution mode, and outputs a state model for identifying the state of the patient; the diagnosis model construction subsystem receives the experience rules of doctors for diagnosing according to the brain waveforms, the brain waveform occurrence modes and the brain waveform distribution modes of patients in different states, and outputs a diagnosis model for diagnosing diseases;
the appearance mode identification subsystem receives the brain waveforms output by the waveform identification subsystem, identifies the appearance modes of the brain waveforms by adopting the appearance models provided by the appearance model construction subsystem, marks the identified appearance modes in the brain electrical data, and outputs the marked brain electrical data; wherein, the appearance modes of the brain wave comprise activity, rhythm, outbreak, paroxysmal, periodicity, sporadic, transient and synchronicity;
the distribution mode identification subsystem receives the brain waveforms output by the waveform identification subsystem, identifies the brain waveform distribution mode by adopting a distribution model provided by the distribution model construction subsystem, marks the identified distribution mode in the brain electrical data and outputs the marked brain electrical data; wherein, the brain waveform distribution mode comprises universality, diffusivity, sidedness, locality, multifocal, wandering and symmetry;
the patient state identification subsystem receives the electroencephalogram data marked with the electroencephalogram waveform, the appearance mode and the distribution mode, processes the electroencephalogram data by adopting the state model provided by the state model construction subsystem, identifies the states of the patient at different time intervals, and outputs the electroencephalogram data after adding state marks; wherein the patient state comprises normal waking period, normal sleep period, sleep cycle, background activity abnormality, and paroxysmal abnormality;
the diagnosis subsystem receives the electroencephalogram data marked with the electroencephalogram waveform, the appearance mode, the distribution mode and the patient state, processes the electroencephalogram data by using the diagnosis model provided by the diagnosis model construction subsystem, and gives a diagnosis suggestion for the patient; the diagnosis opinions consist of qualitative opinions, description about electroencephalogram and electroencephalogram data indexes corresponding to the electroencephalogram description; each qualitative opinion is supported by a set of descriptions about electroencephalograms, each electroencephalogram description corresponding to a set of electroencephalogram data; the diagnosis subsystem supports the retrieval of the user from qualitative opinions, electroencephalogram description and electroencephalogram data; wherein, the qualitative opinions are divided into the grades of normal, normal range, borderline, mild abnormity, moderate abnormity and severe abnormity; the description about electroencephalogram includes background activity, evoked potential, sleep, abnormal waves, clinical attacks, drug action, and interfering waves.
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