CN110265143B - Intelligent auxiliary diagnosis system based on EEG - Google Patents

Intelligent auxiliary diagnosis system based on EEG Download PDF

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CN110265143B
CN110265143B CN201910524998.8A CN201910524998A CN110265143B CN 110265143 B CN110265143 B CN 110265143B CN 201910524998 A CN201910524998 A CN 201910524998A CN 110265143 B CN110265143 B CN 110265143B
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黄志华
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Abstract

本发明涉及一种基于脑电图的智能辅助诊断系统,由脑电预处理、波形模型构建、波形识别、出现模型构建、出现方式识别、分布模型构建、分布方式识别、状态模型构建、病人状态识别、诊断模型构建和诊断十一个子系统组成。波形模型构建、出现模型构建、分布模型构建、状态模型构建和诊断模型构建五个子系统采用不同的方式分别构建模型;脑电预处理子系统对脑电源信号进行滤波,伪迹去除,标注和参考变换等预处理后,波形识别子系统、出现方式识别子系统、分布方式识别子系统、病人状态识别子系统和诊断子系统依次运用相应的模型对脑电数据进行处理,形成诊断意见,提供给医生作为参考。本发明能够提高脑电图医疗诊断的效率和质量。

Figure 201910524998

The invention relates to an intelligent auxiliary diagnosis system based on electroencephalogram, which comprises electroencephalogram preprocessing, waveform model construction, waveform identification, appearance model construction, appearance mode identification, distribution model construction, distribution mode identification, state model construction, and patient status It consists of eleven subsystems for identifying, diagnosing model building and diagnosing. The five subsystems of waveform model construction, appearance model construction, distribution model construction, state model construction and diagnosis model construction use different methods to build models respectively; the EEG preprocessing subsystem filters, artifact removal, annotation and reference of brain power signals After preprocessing such as transformation, the waveform identification subsystem, the appearance mode identification subsystem, the distribution mode identification subsystem, the patient state identification subsystem and the diagnosis subsystem successively use the corresponding models to process the EEG data to form diagnostic opinions, which are provided to the patients. doctor as a reference. The invention can improve the efficiency and quality of EEG medical diagnosis.

Figure 201910524998

Description

基于脑电图的智能辅助诊断系统Intelligent auxiliary diagnosis system based on EEG

技术领域technical field

本发明涉及一种基于脑电图的智能辅助诊断系统。The invention relates to an intelligent auxiliary diagnosis system based on electroencephalogram.

背景技术Background technique

目前诊断脑部疾病依赖于医疗人员人工查看脑电图,诊断结果受限医疗人员的经验水平及知识积累,不同的医生对于相同的脑电图给出的诊断不尽相同,对于病人的治疗产生了较多的不利影响,且医生为了捕捉到有效信息,基于脑电图的医疗诊断需要记录很长时间的脑电信号。面对长时程的脑电信号,医生需要逐屏阅读才能作出诊断,非常耗时,也很难保证阅读质量。At present, the diagnosis of brain diseases relies on medical personnel to manually check the EEG, and the diagnosis results are limited by the experience level and knowledge accumulation of medical personnel. Different doctors give different diagnoses for the same EEG, which affects the treatment of patients. In addition, in order to capture effective information, EEG-based medical diagnosis needs to record EEG signals for a long time. In the face of long-term EEG signals, doctors need to read screen by screen to make a diagnosis, which is very time-consuming and difficult to ensure the quality of reading.

发明内容SUMMARY OF THE INVENTION

本发明的目的在于针对现有基于脑电图的智能辅助诊断的问题,提出一种基于脑电图的智能辅助诊断系统,根据已采集的数据集利用智能技术的思想构建用于各类脑疾病诊断的系统应用于其他病人,以减少医生由于个人局限对病情把握造成的不利影响,提高疾病诊断的效率。The purpose of the present invention is to solve the problem of the existing intelligent auxiliary diagnosis based on electroencephalogram, and propose an intelligent auxiliary diagnosis system based on electroencephalogram. The diagnostic system is applied to other patients to reduce the adverse influence of doctors on the grasp of the disease due to personal limitations, and to improve the efficiency of disease diagnosis.

为实现上述目的,本发明的技术方案是:一种基于脑电图的智能辅助诊断系统,包括脑电预处理子系统、波形模型构建子系统、波形识别子系统、出现模型构建子系统、出现方式识别子系统、分布模型构建子系统、分布方式识别子系统、状态模型构建子系统、病人状态识别子系统、诊断模型构建子系统和诊断子系统十一个子系统;In order to achieve the above purpose, the technical scheme of the present invention is: an EEG-based intelligent auxiliary diagnosis system, including an EEG preprocessing subsystem, a waveform model building subsystem, a waveform identification subsystem, an appearance model building subsystem, and an appearance model building subsystem. Mode identification subsystem, distributed model construction subsystem, distributed mode identification subsystem, state model construction subsystem, patient state identification subsystem, diagnosis model construction subsystem and eleven subsystems of diagnosis subsystem;

脑电预处理子系统接收脑电源信号作为输入,经过滤波,伪迹去除,标注和参考变换后输出预处理后的脑电数据;脑电预处理子系统通过滤波去除脑电信号中的噪声,通过阈值限定去除受伪迹影响的信号段,通过独立成分分析去除混合在各导联上的伪迹;脑电预处理子系统的标注功能允许用户补充标注源数据中未标注的事件;脑电预处理子系统的标注功能还允许用户标注脑电波形;脑电预处理子系统的参考变换功能可将源信号切换,即可通过计算把源信号切换为其他参考模式的脑电信号;The EEG preprocessing subsystem receives the brain power signal as input, and outputs the preprocessed EEG data after filtering, artifact removal, labeling and reference transformation; the EEG preprocessing subsystem removes the noise in the EEG signal by filtering, The signal segment affected by the artifact is removed by thresholding, and the artifact mixed on each lead is removed by independent component analysis; the labeling function of the EEG preprocessing subsystem allows users to additionally label unlabeled events in the source data; EEG The labeling function of the preprocessing subsystem also allows users to label the EEG waveform; the reference transformation function of the EEG preprocessing subsystem can switch the source signal, that is, the source signal can be switched to the EEG signal of other reference modes through calculation;

波形模型建构子系统从标注了脑电波形的预处理后的脑电数据中提取每种脑电波形的样本,用提取的样本为每种脑电波形构建训练集,通过在训练集上训练的方法为每个脑电波形构建一个波形模型,将波形模型输出给波形识别子系统;波形模型建构子系统具有不断累积优选脑电波形样本、扩充训练集、重构改进优化波形模型的功能;The waveform model building subsystem extracts samples of each EEG waveform from the preprocessed EEG data marked with EEG waveforms, and uses the extracted samples to construct a training set for each EEG waveform. Methods A waveform model is constructed for each EEG waveform, and the waveform model is output to the waveform identification subsystem; the waveform model construction subsystem has the functions of continuously accumulating and optimizing the EEG waveform samples, expanding the training set, and reconstructing and improving the optimized waveform model;

波形识别子系统接收脑电预处理子系统输出的待诊断的预处理后的脑电数据,采用波形模型建构子系统提供的波形模型从脑电数据中识别出所有的脑电波形,在脑电数据中标注识别出的脑电波形、脑电波形的空间位置和时间位置,并将标注后的脑电数据输出;The waveform identification subsystem receives the preprocessed EEG data to be diagnosed output by the EEG preprocessing subsystem, and uses the waveform model provided by the waveform model construction subsystem to identify all the EEG waveforms from the EEG data. Label the identified EEG waveforms, the spatial and temporal positions of the EEG waveforms in the data, and output the labeled EEG data;

出现模型构建子系统、分布模型构建子系统、状态模型构建子系统、诊断模型构建子系统分别通过与用户交互的方式接收医生的经验规则,并用规范化的数据结构表示它们,用逻辑推理排除规则间的逻辑矛盾,用一组优化的规则构建模型,并将它们输出;出现模型构建子系统接收的是医生判断脑电波形在时间上出现方式的经验规则,输出的是识别出现方式的出现模型;分布模型构建子系统接收的是医生判断脑电波形在空间上分布方式的经验规则,输出的是识别分布方式的分布模型;状态模型构建子系统接收医生依据脑电波形、脑电波形出现方式和脑电波形分布方式来判断病人状态的经验规则,输出用于识别病人状态的状态模型;诊断模型构建子系统接收医生依据病人处于不同状态下的脑电波形、脑电波形出现方式和脑电波形分布方式进行诊断的经验规则,输出用于诊断疾病的诊断模型;Appearance model building subsystem, distribution model building subsystem, state model building subsystem, and diagnosis model building subsystem receive the doctor's empirical rules by interacting with the user, and express them with a normalized data structure, and use logical reasoning to exclude the rules between them. It uses a set of optimized rules to build models and output them; the appearance model building subsystem receives the empirical rules for doctors to judge the appearance of EEG waveforms in time, and outputs the appearance models that identify the appearance patterns; The distribution model building subsystem receives the empirical rules used by doctors to determine the spatial distribution of EEG waveforms, and outputs a distribution model that identifies the distribution patterns; The EEG waveform distribution method is used to determine the empirical rules of the patient's state, and the state model used to identify the patient's state is output; the diagnosis model building subsystem receives the doctor's EEG waveform, the appearance mode of the EEG waveform and the EEG waveform according to the different states of the patient. Empirical rules for diagnosis in a distributed manner, and output diagnostic models for diagnosing diseases;

出现方式识别子系统接收来自波形识别子系统输出的脑电波形数据,采用出现模型构建子系统提供的出现模型识别出脑电波形出现方式,在脑电数据中标注识别出的出现方式,并将标注后的脑电数据输出;The appearance mode identification subsystem receives the EEG waveform data output from the waveform identification subsystem, uses the appearance model provided by the appearance model building subsystem to identify the EEG waveform appearance mode, marks the identified appearance mode in the EEG data, and uses the appearance model provided by the appearance model construction subsystem to identify the EEG waveform appearance mode. Annotated EEG data output;

分布方式识别子系统接收来自波形识别子系统输出的脑电波形数据,采用分布模型构建子系统提供的分布模型识别出脑电波形分布方式,在脑电数据中标注识别出的分布方式,并将标注后的脑电数据输出;The distribution pattern identification subsystem receives the EEG waveform data output from the waveform identification subsystem, uses the distribution model provided by the distribution model building subsystem to identify the EEG waveform distribution pattern, marks the identified distribution pattern in the EEG data, and uses Annotated EEG data output;

病人状态识别子系统接收标注了脑电波形、出现方式和分布方式的脑电数据,采用状态模型构建子系统提供的状态模型进行处理,识别出病人在不同时段的状态,在脑电数据中增加状态标注后再将之输出;The patient state identification subsystem receives the EEG data marked with the EEG waveform, appearance mode and distribution mode, and uses the state model provided by the state model to build the subsystem to process, identify the state of the patient at different time periods, and add the EEG data to the EEG data. After the status is marked, it will be output;

诊断子系统接收标注着脑电波形、出现方式、分布方式和病人状态的脑电数据,运用诊断模型构建子系统提供的诊断模型处理脑电数据,给出对病人的诊断意见;诊断意见由定性意见、关于脑电图的描述、脑电图描述对应的脑电数据索引共同组成;每个定性意见由一组关于脑电图的描述支撑,每个脑电图描述对应着一组脑电数据;诊断子系统支持用户从定性意见到脑电图描述、再到脑电数据的检索。The diagnosis subsystem receives the EEG data marked with the EEG waveform, appearance, distribution and patient status, uses the diagnostic model provided by the diagnostic model to build the subsystem to process the EEG data, and gives a diagnosis opinion for the patient; the diagnosis opinion is determined by qualitative Opinions, descriptions about EEG, and EEG data indexes corresponding to EEG descriptions; each qualitative opinion is supported by a set of EEG descriptions, and each EEG description corresponds to a set of EEG data ; The diagnostic subsystem supports users in the retrieval of EEG data from qualitative opinions to EEG descriptions.

相较于现有技术,本发明具有以下有益效果:本发明提供的一种基于脑电图的智能辅助诊断系统,根据已采集的数据集利用智能技术的思想构建用于各类脑疾病诊断的系统应用于其他病人,以减少医生由于个人局限对病情把握造成的不利影响,提高疾病诊断的效率。Compared with the prior art, the present invention has the following beneficial effects: an EEG-based intelligent auxiliary diagnosis system provided by the present invention uses the idea of intelligent technology to construct a diagnostic system for various brain diseases according to the collected data set. The system is applied to other patients to reduce the adverse influence of doctors on the disease grasp due to personal limitations, and to improve the efficiency of disease diagnosis.

附图说明Description of drawings

图1为本发明基于脑电图的智能辅助诊断系统框图。FIG. 1 is a block diagram of an EEG-based intelligent auxiliary diagnosis system of the present invention.

具体实施方式Detailed ways

下面结合附图,对本发明的技术方案进行具体说明。The technical solutions of the present invention will be described in detail below with reference to the accompanying drawings.

应该指出,以下详细说明都是示例性的,旨在对本申请提供进一步的说明。除非另有指明,本文使用的所有技术和科学术语具有与本申请所属技术领域的普通技术人员通常理解的相同含义。It should be noted that the following detailed description is exemplary and intended to provide further explanation of the application. Unless otherwise defined, 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.

需要注意的是,这里所使用的术语仅是为了描述具体实施方式,而非意图限制根据本申请的示例性实施方式。如在这里所使用的,除非上下文另外明确指出,否则单数形式也意图包括复数形式,此外,还应当理解的是,当在本说明书中使用术语“包含”和/或“包括”时,其指明存在特征、步骤、操作、器件、组件和/或它们的组合。It should be noted that the terminology used herein is for the purpose of describing specific embodiments only, and is not intended to limit the exemplary embodiments according to the present application. As used herein, unless the context clearly dictates otherwise, the singular is intended to include the plural as well, furthermore, it is to be understood that when the terms "comprising" and/or "including" are used in this specification, it indicates that There are features, steps, operations, devices, components and/or combinations thereof.

如图1所示,本实施例提供了一种基于脑电图的智能辅助诊断系统,包括脑电预处理子系统、波形模型构建子系统、波形识别子系统、出现模型构建子系统、出现方式识别子系统、分布模型构建子系统、分布方式识别子系统、状态模型构建子系统、病人状态识别子系统、诊断模型构建子系统和诊断子系统十一个子系统;As shown in FIG. 1 , this embodiment provides an EEG-based intelligent auxiliary diagnosis system, including an EEG preprocessing subsystem, a waveform model construction subsystem, a waveform identification subsystem, an appearance model construction subsystem, and an appearance method. Identification subsystem, distributed model construction subsystem, distributed mode identification subsystem, state model construction subsystem, patient state identification subsystem, diagnosis model construction subsystem and eleven subsystems of diagnosis;

脑电预处理子系统接收脑电源信号作为输入,经过滤波,伪迹去除,标注和参考变换后输出预处理后的脑电数据;脑电预处理子系统通过滤波去除脑电信号中的噪声,阈值限定去除受伪迹影响的信号段,独立成分分析去除混合在各导联上的伪迹;脑电预处理子系统的标注功能允许用户补充标注源数据中未标注的事件;脑电预处理子系统的标注功能还允许用户标注脑电波形;脑电预处理子系统的参考变换功能可以把源信号切换可通过计算把源信号切换为其他参考模式的脑电信号;本实施例中,未标注的事件包括脑电记录过程中已知的癫痫发作、睁闭眼试验、眼状态敏感试验、过度换气试验、闪光刺激、睡眠诱发、药物诱发和医生认为应标注的事件;脑电波形包括正弦样波、α节律、β节律、γ节律、δ节律、θ节律、弓形波、切迹波、双相波、三相波、多位相波、棘波、尖波、棘慢波、多棘波、多棘慢波、复合波、K综合波、多形性波;其他参考模式包括同侧耳参考、双耳参考、平均参考和双极导联;The EEG preprocessing subsystem receives the brain power signal as input, and outputs the preprocessed EEG data after filtering, artifact removal, labeling and reference transformation; the EEG preprocessing subsystem removes the noise in the EEG signal by filtering, Threshold limit removes signal segments affected by artifacts, and independent component analysis removes artifacts mixed in each lead; the labeling function of the EEG preprocessing subsystem allows users to additionally label unlabeled events in the source data; EEG preprocessing The labeling function of the subsystem also allows users to label the EEG waveform; the reference transformation function of the EEG preprocessing subsystem can switch the source signal to the EEG signal of other reference modes through calculation; Annotated events include known epileptic seizures, eye-opening and closing tests, eye-state sensitivity tests, hyperventilation tests, flash stimulation, sleep-inducing, drug-inducing, and events that doctors believe should be labeled during EEG recordings; EEG waveforms include Sine wave, alpha rhythm, beta rhythm, gamma rhythm, delta rhythm, theta rhythm, bow wave, notch wave, biphasic wave, triphasic wave, polyphasic wave, spike wave, sharp wave, spike slow wave, polyspike wave, polyspike and slow wave, complex wave, K complex, polymorphic wave; other reference modes include ipsilateral ear reference, binaural reference, average reference and bipolar lead;

波形模型建构子系统从标注了脑电波形的预处理后的脑电数据中提取每种脑电波形的样本,用提取的样本为每种脑电波形构建训练集,通过在训练集上训练的方法为每个脑电波形构建一个波形模型,将波形模型输出给波形识别子系统;波形模型建构子系统具有不断累积优选脑电波形样本、扩充训练集、重构改进优化波形模型的功能;The waveform model building subsystem extracts samples of each EEG waveform from the preprocessed EEG data marked with EEG waveforms, and uses the extracted samples to construct a training set for each EEG waveform. Methods A waveform model is constructed for each EEG waveform, and the waveform model is output to the waveform identification subsystem; the waveform model construction subsystem has the functions of continuously accumulating and optimizing the EEG waveform samples, expanding the training set, and reconstructing and improving the optimized waveform model;

波形识别子系统接收脑电预处理子系统输出的待诊断的预处理后的脑电数据,采用波形模型建构子系统提供的波形模型从脑电数据中识别出所有的脑电波形,在脑电数据中标注识别出的脑电波形、脑电波形的空间位置和时间位置,并将标注后的脑电数据输出;The waveform identification subsystem receives the preprocessed EEG data to be diagnosed output by the EEG preprocessing subsystem, and uses the waveform model provided by the waveform model construction subsystem to identify all the EEG waveforms from the EEG data. Label the identified EEG waveforms, the spatial and temporal positions of the EEG waveforms in the data, and output the labeled EEG data;

出现模型构建子系统、分布模型构建子系统、状态模型构建子系统、诊断模型构建子系统分别通过与用户交互的方式接收医生的经验规则,用规范化的数据结构表示它们,用逻辑推理排除规则间的逻辑矛盾,用一组优化的规则构建模型,并将它们输出;出现模型构建子系统接收的是医生判断脑电波形在时间上出现方式的经验规则,输出的是识别出现方式的出现模型;分布模型构建子系统接收的是医生判断脑电波形在空间上分布方式的经验规则,输出的是识别分布方式的分布模型;状态模型构建子系统接收医生依据脑电波形、脑电波形出现方式和脑电波形分布方式来判断病人状态的经验规则,输出用于识别病人状态的状态模型;诊断模型构建子系统接收医生依据病人处于不同状态下的脑电波形、脑电波形出现方式和脑电波形分布方式进行诊断的经验规则,输出用于诊断疾病的诊断模型;Appearance model building subsystem, distribution model building subsystem, state model building subsystem, and diagnosis model building subsystem receive the doctor's empirical rules through interaction with users, represent them with a normalized data structure, and use logical reasoning to exclude rules It uses a set of optimized rules to build models and output them; the appearance model building subsystem receives the empirical rules for doctors to judge the appearance of EEG waveforms in time, and outputs the appearance models that identify the appearance patterns; The distribution model building subsystem receives the empirical rules used by doctors to judge the spatial distribution of EEG waveforms, and outputs a distribution model that identifies the distribution patterns; the state model building subsystem receives the doctor’s data based on the EEG waveform, the appearance mode of the EEG waveform, and the distribution model. The EEG waveform distribution method is used to determine the empirical rules of the patient's state, and the state model used to identify the patient's state is output; the diagnostic model building subsystem receives the doctor's EEG waveform, the appearance mode of the EEG waveform and the EEG waveform according to the different states of the patient. Empirical rules for diagnosis in a distributed manner, and output diagnostic models for diagnosing diseases;

出现方式识别子系统接收来自波形识别子系统输出的脑电波形数据,采用出现模型构建子系统提供的出现模型识别出脑电波形出现方式,在脑电数据中标注识别出的出现方式,并将标注后的脑电数据输出;本实施例中,脑电波形出现方式包括活动、节律、暴发、阵发、周期性、散发、一过性、同步性;The appearance mode identification subsystem receives the EEG waveform data output from the waveform identification subsystem, uses the appearance model provided by the appearance model building subsystem to identify the EEG waveform appearance mode, marks the identified appearance mode in the EEG data, and uses the appearance model provided by the appearance model construction subsystem to identify the EEG waveform appearance mode. The marked EEG data output; in this embodiment, the EEG waveform appearance modes include activity, rhythm, outbreak, burst, periodicity, sporadic, transient, and synchronicity;

分布方式识别子系统接收来自波形识别子系统输出的脑电波形数据,采用分布模型构建子系统提供的分布模型识别出脑电波形分布方式,在脑电数据中标注识别出的分布方式,并将标注后的脑电数据输出;本实施例中,脑电波形分布方式包括广泛性、弥漫性、一侧性、局部性、多灶性、游走性、对称性;The distribution pattern identification subsystem receives the EEG waveform data output from the waveform identification subsystem, uses the distribution model provided by the distribution model building subsystem to identify the EEG waveform distribution pattern, marks the identified distribution pattern in the EEG data, and uses The marked EEG data is output; in this embodiment, the EEG waveform distribution modes include extensive, diffuse, one-sided, localized, multifocal, migratory, and symmetrical;

病人状态识别子系统接收标注了脑电波形、出现方式和分布方式的脑电数据,采用状态模型构建子系统提供的状态模型进行处理,识别出病人在不同时段的状态,在脑电数据中增加状态标注后再将之输出;本实施例中,病人状态包括正常清醒期、正常睡眠期、睡眠周期、背景活动异常、阵法性异常;The patient state identification subsystem receives the EEG data marked with the EEG waveform, appearance mode and distribution mode, and uses the state model provided by the state model to build the subsystem to process, identify the state of the patient at different time periods, and add the EEG data to the EEG data. The state is marked and then output; in this embodiment, the patient state includes normal wakefulness, normal sleep, sleep cycle, abnormal background activity, and abnormal formation;

诊断子系统接收标注着脑电波形、出现方式、分布方式和病人状态的脑电数据,运用诊断模型构建子系统提供的诊断模型处理脑电数据,给出对病人的诊断意见;诊断意见由定性意见、关于脑电图的描述、脑电图描述对应的脑电数据索引共同组成;每个定性意见由一组关于脑电图的描述支撑,每个脑电图描述对应着一组脑电数据;诊断子系统支持用户从定性意见到脑电图描述、再到脑电数据的检索;本实施例中,定性意见分为正常、正常范围、界线性、轻度异常、中度异常和重度异常的等级;关于脑电图的描述包括背景活动、诱发试验、睡眠、异常波、临床发作、药物作用和干扰波;The diagnosis subsystem receives the EEG data marked with the EEG waveform, appearance, distribution and patient status, uses the diagnostic model provided by the diagnostic model to build the subsystem to process the EEG data, and gives a diagnosis opinion for the patient; the diagnosis opinion is determined by qualitative Opinions, descriptions about EEG, and EEG data indexes corresponding to EEG descriptions; each qualitative opinion is supported by a set of EEG descriptions, and each EEG description corresponds to a set of EEG data ; The diagnosis subsystem supports users to search from qualitative opinions to EEG descriptions and then to EEG data; in this embodiment, qualitative opinions are divided into normal, normal range, boundary line, mild abnormality, moderate abnormality and severe abnormality grading; descriptions of EEG include background activity, evoked tests, sleep, abnormal waves, clinical seizures, drug effects, and interference waves;

以上所述,仅是本发明的较佳实施例而已,并非是对本发明作其它形式的限制,任何熟悉本专业的技术人员可能利用上述揭示的技术内容加以变更或改型为等同变化的等效实施例。但是凡是未脱离本发明技术方案内容,依据本发明的技术实质对以上实施例所作的任何简单修改、等同变化与改型,仍属于本发明技术方案的保护范围。The above are only preferred embodiments of the present invention, and are not intended to limit the present invention in other forms. Any person skilled in the art may use the technical content disclosed above to make changes or modifications to equivalent changes. Example. However, any simple modifications, equivalent changes and modifications made to the above embodiments according to the technical essence of the present invention without departing from the content of the technical solutions of the present invention still belong to the protection scope of the technical solutions 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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