CN112842358A - Brain physiological data processing system, method, device and storage medium - Google Patents
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Abstract
The embodiment of the application provides a system, a method, equipment and a storage medium for processing brain physiological data. In the embodiment of the application, a data processing request for brain physiological data is acquired through a preprocessing component, the brain physiological data is divided into a plurality of data segments, data types corresponding to the data segments are identified, at least two continuous first-type data are determined in the data segments based on the data types corresponding to the data segments, so that the extraction operation of the normally useful brain physiological data is effectively realized, the at least two continuous first-type data can be processed by using a data segment analysis component, specifically, the disease risk prediction processing, the signal abnormality detection processing and the like can be performed, the disease risk prediction result, the signal abnormality detection result and the like of the brain physiological data can be obtained, and the prevention, treatment and the like of brain diseases based on the brain physiological data are effectively realized, Early diagnosis and early intervention.
Description
Technical Field
The present application relates to the field of computer technologies, and in particular, to a system, a method, a device, and a storage medium for processing brain physiological data.
Background
With the coming of the age-old population, neurodegenerative diseases such as alzheimer syndrome, parkinson syndrome and huntington syndrome become health burden of human beings, and at the same time, the number of people with various brain diseases such as stroke and epilepsy in China is the largest in the world, so that research and application of prevention, early diagnosis and early intervention of the brain diseases are particularly urgent.
Disclosure of Invention
Aspects of the present application provide a system, method, apparatus and storage medium for processing brain physiological data to implement prevention, early diagnosis and early intervention for brain diseases based on the brain physiological data.
In a first aspect, an embodiment of the present application provides a system for processing brain physiological data, including:
the system comprises a preprocessing component, a data processing component and a data processing component, wherein the preprocessing component is used for acquiring a data processing request aiming at brain physiological data, dividing the brain physiological data into a plurality of data fragments, identifying data types corresponding to the data fragments respectively, and determining at least two continuous first type data in the data fragments, wherein the first type data are non-artifact data;
and the data segment analysis component is in communication connection with the preprocessing component and is used for acquiring the at least two continuous first-type data and processing the at least two continuous first-type data to obtain a processing result of the brain physiological data.
In a second aspect, an embodiment of the present application provides a method for processing brain physiological data, including:
acquiring a data processing request aiming at brain physiological data;
dividing the brain physiological data into a plurality of data segments;
identifying data types corresponding to the plurality of data fragments respectively;
determining at least two consecutive data of a first type in the plurality of data segments, the data of the first type being non-artifact data;
processing the at least two consecutive first type data to obtain a processing result of the brain physiological data.
In a third aspect, an embodiment of the present application provides an apparatus for processing brain physiological data, including:
the system comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring a data processing request aiming at brain physiological data;
a first dividing module, configured to divide the brain physiological data into a plurality of data segments;
the first identification module is used for identifying the data types corresponding to the data fragments;
a first determining module, configured to determine, in the plurality of data segments, at least two consecutive data of a first type, where the data of the first type is non-artifact data;
the first processing module is used for processing the at least two continuous first type data to obtain a processing result of the brain physiological data.
In a fourth aspect, an embodiment of the present invention provides an electronic device, including: a memory, a processor; wherein the memory is configured to store one or more computer instructions, wherein the one or more computer instructions, when executed by the processor, implement the method for processing brain physiological data in the second aspect.
In a fifth aspect, an embodiment of the present invention provides a computer storage medium for storing a computer program, which makes a computer implement the method for processing brain physiological data in the second aspect when executed.
In the embodiment of the application, the brain physiological data is divided into a plurality of data segments by acquiring a data processing request for the brain physiological data, the data types corresponding to the data segments are identified, at least two consecutive data of the first type are then determined among the plurality of data segments based on the data type to which each of the plurality of data segments corresponds, thereby effectively realizing the extraction operation of normal and useful brain physiological data, then processing at least two continuous first type data, specifically, disease risk prediction processing, signal abnormity detection processing and the like, thereby obtaining the disease risk prediction result, the signal abnormality detection result and the like of the brain physiological data, therefore, the prevention, early diagnosis and early intervention of the brain diseases based on the brain physiological data are effectively realized.
In a sixth aspect, an embodiment of the present application provides a method for acquiring brain physiological data, including:
acquiring brain physiological data of a human body;
filtering the brain physiological data to obtain a filtered data signal;
identifying a data quality of the filtered data signal;
and when the data quality meets a preset requirement, generating the brain physiological data by using the filtered data signal.
In a seventh aspect, an embodiment of the present application provides an apparatus for acquiring brain physiological data, including:
the second acquisition module is used for acquiring the brain physiological data of the human body;
the second filtering module is used for filtering the brain physiological data to obtain a filtered data signal;
a second identification module for identifying the data quality of the filtered data signal;
and the second processing module is used for generating the brain physiological data by using the filtered data signal when the data quality meets a preset requirement.
In an eighth aspect, an embodiment of the present invention provides an electronic device, including: a memory, a processor; wherein the memory is configured to store one or more computer instructions, wherein the one or more computer instructions, when executed by the processor, implement the method for acquiring brain physiological data in the seventh aspect.
In a ninth aspect, an embodiment of the present invention provides a computer storage medium for storing a computer program, which makes a computer implement the method for acquiring brain physiological data in the seventh aspect when executed.
In the embodiment of the application, the brain physiological data of a human body is obtained, then the brain physiological data is filtered, and the data quality of the filtered data signal is identified; when the data quality meets the preset requirement, the filtered data signals are used for generating the brain physiological data, so that the quality of the signals is automatically evaluated when the brain physiological data is acquired, the data quality of the brain physiological data is further ensured, and the accuracy and reliability of analysis processing on the brain physiological data are further improved.
In a tenth aspect, an embodiment of the present application provides a data processing method, including:
acquiring a data processing request aiming at data to be processed;
dividing the data to be processed into a plurality of data fragments;
identifying data types corresponding to the plurality of data fragments respectively;
determining at least two consecutive data of a first type in the plurality of data segments, the data of the first type being non-artifact data;
and processing the at least two continuous first type data to obtain a processing result of the data to be processed.
In an eleventh aspect, an embodiment of the present application provides an apparatus for processing brain physiological data, including:
the third acquisition module is used for acquiring a data processing request aiming at the data to be processed;
the third dividing module is used for dividing the data to be processed into a plurality of data fragments;
the third identification module is used for identifying the data types corresponding to the data fragments;
a third determining module, configured to determine, in the plurality of data segments, at least two consecutive data of a first type, where the data of the first type is non-artifact data;
and the third processing module is used for processing the at least two continuous first-type data to obtain a processing result of the data to be processed.
In a twelfth aspect, an embodiment of the present invention provides an electronic device, including: a memory, a processor; wherein the memory is used for storing one or more computer instructions, wherein the one or more computer instructions, when executed by the processor, implement the data processing method of the tenth aspect.
In a thirteenth aspect, an embodiment of the present invention provides a computer storage medium for storing a computer program, where the computer program is used to make a computer implement the data processing method in the tenth aspect when executed.
In a fourteenth aspect, an embodiment of the present invention provides a detection cap, including:
the detection electrode is used for contacting with the human brain to acquire brain physiological data;
a data transmission module for transmitting the brain physiological data to a server, the server configured to: dividing the brain physiological data into a plurality of data segments; identifying data types corresponding to the plurality of data fragments respectively; determining at least two consecutive data of a first type in the plurality of data segments, the data of the first type being non-artifact data; processing the at least two consecutive first type data to obtain a processing result of the brain physiological data.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
fig. 1 is a first schematic structural diagram of a system for processing brain physiological data according to an exemplary embodiment of the present application;
FIG. 2 is a block diagram of a data segment analysis component according to an exemplary embodiment of the present application;
fig. 3 is a schematic structural diagram ii of a brain physiological data processing system according to an exemplary embodiment of the present application;
fig. 4 is a first schematic view of a first scenario of a method for processing brain physiological data according to an exemplary embodiment of the present application;
fig. 5 is a schematic view of a second scenario of a method for processing brain physiological data according to an exemplary embodiment of the present application;
fig. 6 is a schematic view illustrating a third scenario of a method for processing brain physiological data according to an exemplary embodiment of the present application;
fig. 7 is a schematic view of a scene of a method for processing brain physiological data according to another exemplary embodiment of the present application;
fig. 8 is a schematic diagram of a method for processing brain physiological data according to another exemplary embodiment of the present application;
fig. 9 is a flowchart illustrating a method for processing brain physiological data according to an exemplary embodiment of the present application;
fig. 10 is a first schematic diagram illustrating the determination of target data corresponding to the brain physiological data according to an exemplary embodiment of the present application;
fig. 11 is a diagram two illustrating the determination of target data corresponding to the brain physiological data according to an exemplary embodiment of the present application;
fig. 12 is a flowchart illustrating a method for processing brain physiological data according to another exemplary embodiment of the present application;
fig. 13 is a flowchart illustrating a method for processing brain physiological data according to another exemplary embodiment of the present application;
fig. 14 is a flowchart illustrating a method for acquiring physiological data of a brain according to an exemplary embodiment of the present application;
FIG. 15 is a flow chart illustrating a method for processing data according to an exemplary embodiment of the present application;
fig. 16 is a schematic structural diagram of a device for processing brain physiological data according to an exemplary embodiment of the present application;
fig. 17 is a schematic structural diagram of an electronic device corresponding to the brain physiological data processing device provided in the embodiment shown in fig. 16;
fig. 18 is a schematic structural diagram of an apparatus for acquiring brain physiological data according to an exemplary embodiment of the present application;
fig. 19 is a schematic structural diagram of an electronic device corresponding to the brain physiological data acquisition device provided in the embodiment shown in fig. 18;
fig. 20 is a schematic structural diagram of a data processing apparatus according to an exemplary embodiment of the present application;
fig. 21 is a schematic structural diagram of an electronic device corresponding to the data processing apparatus in the embodiment shown in fig. 20;
fig. 22 is a schematic structural diagram of a detection cap according to an exemplary embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the technical solutions of the present application will be described in detail and completely with reference to the following specific embodiments of the present application and the accompanying drawings. It should be apparent that the described embodiments are only some of the embodiments of the present application, 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 application.
The human brain has approximately 1000 billion neurons, and the specific relationship between how they connect and the connection errors and the neurological diseases that lead to confusion or are severe is not well established. Next, with the advent of the age-related world of the global population, neurodegenerative diseases such as alzheimer's syndrome, parkinson's syndrome, and huntington's syndrome have become a health burden on humans, and at the same time, the number of various brain diseases such as stroke and epilepsy in our country is the largest, which makes research and application of prevention, early diagnosis, and early intervention of brain diseases urgent.
During the course of research into the human brain, brain activity is found to be highly dynamic, with constant changes in the performance of work and even at rest. Therefore, it is extremely difficult to map real-time neural activity in the brain of a living body and understand the meaning, and electroencephalogram (EEG) is a technology for dynamically recording brain electrical activity, not only is the basis of monitoring and diagnosis of chronic diseases such as cerebrovascular diseases, senile dementia and stroke, but also has the characteristics of non-invasion, economy, convenience, flexibility and the like, and is widely applied to clinic.
The embodiment of the application provides a brain electrical physiological data processing system, which comprises a preprocessing component and a data segment analysis component, wherein the preprocessing component can remove artifact data in brain physiological data so as to obtain at least two available continuous first type data, and then the data segment analysis component is used for analyzing and processing the first type data so as to obtain a processing result corresponding to the brain physiological data, so that the quality of the brain physiological data and the analysis efficiency of the brain physiological data are effectively improved. The brain physiological data may include: electroencephalogram signals, cerebral blood flow, intracranial pressure and the like, wherein the electroencephalogram signals refer to electric signals obtained when brain activities are recorded; cerebral blood flow is the flow of blood through a certain cross-sectional area of the cerebral vessels in a unit time; intracranial pressure refers to the pressure of the cerebrospinal fluid in the cranial cavity.
Specifically, after the preprocessing component acquires the brain physiological data, based on the long-range characteristics of the brain physiological data, the brain physiological data can be divided into a plurality of data segments, and the respective corresponding data types of the plurality of data segments are identified, for example: based on the respective data types of all the data segments, one or more key segment data can be extracted from the whole brain physiological data, each key segment data can include at least two continuous first type data, the key segment data is normal and useful brain physiological data, most data segments without significance are filtered out from the key segment data, and then analysis and processing operations can be performed based on all the key segment data, for example: the data abnormality detection processing, the disease prediction processing, the abnormal discharge signal detection processing and the like can be performed, so that the processing results of the brain physiological data, such as the data abnormality detection result, the disease prediction result, the abnormal discharge signal detection result and the like, can be obtained, and the prevention, the early diagnosis and the early intervention of the brain diseases based on the brain physiological data are effectively realized based on the obtained processing results.
The technical solutions provided by the embodiments of the present application are described in detail below with reference to the accompanying drawings.
Fig. 1 is a first schematic structural diagram of a system for processing brain physiological data according to an exemplary embodiment of the present application. As shown in fig. 1, the present embodiment provides a structure of a system for processing brain physiological data, and it should be noted that the brain physiological data may include: electroencephalogram signals, cerebral blood flow, intracranial pressure and the like, wherein the electroencephalogram signals refer to electric signals obtained when brain activities are recorded; cerebral blood flow is the flow of blood through a certain cross-sectional area of the cerebral vessels in a unit time; intracranial pressure refers to the pressure of the cerebrospinal fluid in the cranial cavity. For convenience of explanation, an electroencephalogram signal is taken as an example of brain physiological data.
The processing system comprises: a preprocessing component 1 and a data segment analysis component 2. Specifically, the preprocessing component 1 and the data segment analysis component 2 may perform the following processes:
the device comprises a preprocessing component 1, a data processing module and a data processing module, wherein the preprocessing component 1 is used for acquiring a data processing request aiming at an electroencephalogram signal, dividing the electroencephalogram signal into a plurality of data fragments, identifying data types corresponding to the data fragments respectively, and determining at least two continuous first-type data in the data fragments, wherein the first-type data are non-artifact data;
and the data segment analysis component 2 is in communication connection with the preprocessing component and is used for acquiring the at least two continuous first-type data and processing the at least two continuous first-type data to obtain a processing result of the electroencephalogram signal.
The preprocessing component 1 may remove artifact data in the brain physiological data, for example, when the brain physiological data is an electroencephalogram signal, the preprocessing component 1 may remove artifact signals in the electroencephalogram signal, so as to obtain at least two continuous first-type data. After obtaining at least two consecutive first type data, the preprocessing component 1 may send the at least two consecutive first type data to the data segment analyzing component 2, and the data segment analyzing component 2 may analyze and process the at least two consecutive first type data, specifically, as shown in fig. 2, the data segment analyzing component 2 may include: the first acquisition subassembly 2a, the second acquisition subassembly 2b and the data processing subassembly 2c, the first acquisition subassembly 2a, the second acquisition subassembly 2b and the data processing subassembly 2c may respectively perform the following steps to achieve artifact data removal in the brain electrical signal:
the first obtaining subcomponent 2a is configured to obtain, in all of at least two consecutive first-type data, first proportion information of a data segment of each preset data type;
a second acquisition subcomponent 2b for determining a first operating frequency corresponding to all of the at least two consecutive data of the first type;
and the data processing subassembly 2c is in communication connection with the first acquisition subassembly and the second acquisition subassembly and is used for processing the first proportion information and the first working frequency to obtain a first processing result of the electroencephalogram signal.
In some examples, the preset type data includes at least one of: alpha wave data, fast wave data, slow wave data.
In some examples, when the first obtaining subcomponent 2a obtains the first proportion information of the data segments of each preset data type in all of the at least two consecutive first type data, the first obtaining subcomponent 2a is configured to: acquiring frequency spectrum information of all at least two continuous first type data; and calculating first proportion information of the data segments of the preset type data based on the frequency spectrum information.
In some examples, when the first acquisition subcomponent 2a acquires spectrum information of all of the at least two consecutive data of the first type, the first acquisition subcomponent 2a is configured to: acquiring frequency domain data corresponding to all of the at least two consecutive first type data; and performing power spectral density calculation on the frequency domain data to obtain spectral information of all at least two continuous first type data.
In some examples, when the first obtaining subcomponent 2a calculates first proportion information of data segments of respective preset types of data based on the spectrum information, the first obtaining subcomponent 2a is configured to: determining second proportion information of data segments of each preset type of data in each at least two continuous first types of data based on the frequency spectrum information; and determining the average value of all second proportion information of each preset type of data as the first proportion information of the data segment of each preset type of data aiming at all at least two continuous first type of data.
In some instances, when the second acquisition subcomponent 2b determines a first operating frequency corresponding to all of the at least two consecutive first type data, the second acquisition subcomponent 2b is to: acquiring a second operating frequency corresponding to each of the at least two consecutive first type data; based on all of the second operating frequencies, first operating frequencies corresponding to all of the at least two consecutive data of the first type are calculated.
In some instances, while the second acquisition subcomponent 2b acquires the second operating frequency corresponding to each of the at least two consecutive first type data, the second acquisition subcomponent 2b is to: and acquiring a second working frequency corresponding to each of the at least two continuous first type data based on the frequency spectrum information of each of the at least two continuous first type data.
In some examples, when the second acquisition subcomponent 2b calculates a first operating frequency corresponding to all of the at least two consecutive first type data based on all of the second operating frequencies, the second acquisition subcomponent 1b is to: an average of all the second operating frequencies is determined as the first operating frequency corresponding to all of the at least two consecutive data of the first type.
In some examples, the data processing subcomponent 2c is further configured to: after acquiring the spectrum information of all the at least two continuous first type data, generating a brain topographic map corresponding to the brain electrical signal based on the spectrum information of all the at least two continuous first type data.
In some examples, the data processing subcomponent 2c is further configured to: after determining at least two consecutive first type data, generating a brain topographic map corresponding to the at least two consecutive first type data based on the at least two consecutive first type data.
In some examples, to improve the utility of the system, the system in this embodiment further includes: a disease risk assessment component 3, communicatively coupled to the preprocessing component 1, for: acquiring the at least two continuous data of the first type; acquiring time domain characteristics, frequency domain characteristics and lead connection characteristics of all at least two continuous first types of data; and analyzing and processing based on all the time domain characteristics, the frequency domain characteristics and the lead connection characteristics to obtain disease prediction information corresponding to the electroencephalogram signals.
The disease risk assessment component 3 is trained to perform disease prediction processing based on the time domain features, the frequency domain features, and the lead connection features of the data, so that disease prediction information can be obtained, specifically, the disease risk assessment component 3 can be obtained by performing learning training using the time domain features, the frequency domain features, and the lead connection features of a plurality of data, where the time domain features, the frequency domain features, and the lead connection features of the plurality of data are labeled with different pieces of disease prediction information.
Specifically, when the disease risk assessment component 3 acquires the time domain features, the frequency domain features, and the lead connection features of all of the at least two consecutive first-type data, the acquisition may be performed by a preset feature extractor, that is, the at least two consecutive first-type data are input into the feature extractor, so that the time domain features, the frequency domain features, and the lead connection features of the at least two consecutive first-type data may be acquired. The feature extractor may be trained in advance to extract time-domain features, frequency-domain features, and lead connection features of the data, specifically, the feature extractor may be learned and trained by using a plurality of at least two consecutive first-type data, and the at least two consecutive first-type data are labeled with different time-domain features, frequency-domain features, and lead connection features.
In some examples, when the disease risk assessment component 3 performs analysis processing based on all the time domain features, frequency domain features, and lead connection features to obtain disease prediction information corresponding to the electroencephalogram signal, the disease risk assessment component 3 is configured to: acquiring first prediction information corresponding to all of the at least two continuous first type data based on all of the time domain features, the frequency domain features and the lead connection features; determining disease prediction information corresponding to the brain electrical signal based on all of the first prediction information.
In some examples, the system further comprises: an analysis-by-synthesis component 5, communicatively coupled to the data segment analysis component 2 and the disease risk assessment component 3, for: acquiring the first proportion information, the first working frequency and disease prediction information corresponding to the electroencephalogram signal; and processing the disease prediction information, the first proportion information and the first working frequency corresponding to the electroencephalogram signal to obtain a second processing result corresponding to the electroencephalogram signal.
In some examples, the system further comprises: an abnormal discharge signal detection assembly 4 for: acquiring an electroencephalogram signal; identifying whether the electroencephalogram signals comprise abnormal discharge signals or not; and when the electroencephalogram signals comprise abnormal discharge signals, determining the position information of the abnormal discharge signals.
After the acquisition, the abnormal discharge signal can be input into the abnormal discharge signal detection assembly, and when the electroencephalogram signal comprises the abnormal discharge signal, the abnormal discharge signal detection assembly can output the position information of the abnormal discharge signal and can also output the type information of the abnormal discharge signal according to the requirement. When the abnormal discharge signal detection module can output the type information of the abnormal discharge signal, the abnormal discharge signal detection module can be obtained by using a plurality of trainings, and the abnormal discharge signal is marked with different signal types.
In some examples, the system further comprises: the comprehensive analysis component 5 is connected with the data segment analysis component 2 and the abnormal discharge signal detection component 4 in a communication way and is used for: acquiring the first proportion information, the first working frequency and the position information of the abnormal discharge signal; and processing the position information of the abnormal discharge signal, the first proportion information and the first working frequency to obtain a third processing result of the electroencephalogram signal.
In some examples, the integrated analysis component 5 is communicatively coupled to the disease risk assessment component 3 for: acquiring disease prediction information corresponding to the electroencephalogram signals; and processing the position information of the abnormal discharge signal, disease prediction information corresponding to the electroencephalogram signal, the first proportion information and the first working frequency to obtain a fourth processing result of the electroencephalogram signal.
Specifically, in different application scenarios, a user may have different data processing requests, and the system may perform different data processing operations based on the different data processing requests, for example:
scene one: the user may make a first processing request for the data segment analyzing component 2, and at this time, the data segment analyzing component 2 may process the at least two consecutive first type data based on the first processing request to obtain a first processing result of the electroencephalogram signal.
Scene two: the user can make a second processing request for the data segment analysis component 2 and the disease risk assessment component 3, and at this time, the data segment analysis component 2 can process the at least two continuous first types of data based on the second processing request to obtain a data segment processing result of the electroencephalogram signal; the disease risk assessment component 3 may perform disease prediction processing on at least two consecutive first type data based on the second processing request to obtain a disease prediction result, and at this time, the comprehensive analysis component 5 may perform comprehensive analysis based on the data segment processing result and the disease prediction result to obtain a second processing result of the electroencephalogram signal.
Scene three: the user can make a second processing request for the data segment analysis component 2 and the abnormal discharge signal detection component 4, and at this time, the data segment analysis component 2 can process the at least two continuous first-type data based on the second processing request to obtain a data segment processing result of the electroencephalogram signal; the abnormal discharge signal detection component 4 may identify whether the electroencephalogram signal includes an abnormal discharge signal based on the second processing request, to obtain a detection result of the abnormal discharge signal, and at this time, the comprehensive analysis component 5 may perform comprehensive analysis based on the data segment processing result and the detection result of the abnormal discharge signal, to obtain a third processing result of the electroencephalogram signal.
Scene four: the user can make a second processing request for the data segment analysis component 2, the disease risk assessment component 3 and the abnormal discharge signal detection component 4, and at this time, the data segment analysis component 2 can process the at least two continuous first types of data based on the second processing request to obtain a data segment processing result of the electroencephalogram signal; the disease risk assessment component 3 may perform disease prediction processing on at least two consecutive first type data based on the second processing request to obtain a disease prediction result, and the abnormal discharge signal detection component 4 may identify whether the electroencephalogram signal includes an abnormal discharge signal based on the second processing request to obtain a detection result of the abnormal discharge signal, at this time, the comprehensive analysis component 5 may perform comprehensive analysis based on the data segment processing result, the disease prediction result, and the detection result of the abnormal discharge signal to obtain a fourth processing result of the electroencephalogram signal.
On the basis of the foregoing embodiment, with continued reference to fig. 3, the system in this embodiment may further include: a data acquisition component 6, communicatively coupled to the preprocessing component 4, for:
acquiring a brain data signal of a human body;
filtering the brain data signal to obtain a filtered data signal;
and obtaining the electroencephalogram signal based on the filtered data signal.
In some examples, the data acquisition component 6 is further configured to: identifying data quality of the filtered data signal before obtaining the electroencephalogram signal based on the filtered data signal; and when the data quality meets the preset requirement, generating the electroencephalogram signal based on the filtered data signal.
In some examples, the data acquisition component 6 is further configured to: acquiring data quantity information of all filtered data signals; and when the data volume information is greater than or equal to a preset threshold value, generating the electroencephalogram signal based on the filtered data signal.
In some examples, the data acquisition component 6 is further configured to: and when the data volume information is greater than or equal to a preset threshold value, stopping collecting the brain data signals of the human body.
In some examples, the data acquisition component 6 is further configured to: acquiring the acquisition time of the data signal before the data volume information is greater than or equal to a preset threshold; and when the acquisition time is greater than or equal to a preset time threshold, generating prompt information, wherein the prompt information is used for identifying the operation abnormity of acquiring the data signal.
In some examples, the data acquisition component 6 is further configured to: and when the data quality meets a preset requirement, displaying the filtered data signal.
Specifically, when the data acquisition assembly 6 acquires the electroencephalogram signal, the data acquisition assembly 6 can acquire the multichannel electroencephalogram signal through the connected detection electrodes and transmit the electroencephalogram signal to the amplifier, wherein the amplifier can be independent of the data acquisition assembly 6, or can be integrated in the data acquisition assembly 6, so that an integrated device is formed. After the electroencephalogram signal is acquired by the amplifier, the electroencephalogram signal can be amplified, the amplified electroencephalogram signal is input to the computing device in a wireless or wired mode, the computing device can perform data display and data storage on the electroencephalogram signal after amplification, meanwhile, the data quality of the input electroencephalogram signal can be detected by using an automatic data quality detection algorithm, and whether the electroencephalogram signal meets preset requirements or not is judged based on the data quality. In some instances, the computing device may be an embedded device or a personal computer. In some examples, the computing device and the amplifier are integrated on the data acquisition assembly 6, forming an integrated device.
It should be noted that the brain physiological data processing system in this embodiment may also be applied to an application scenario of driving a vehicle, an application scenario of working by an employee, and a working scenario of learning by a student, and in different application scenarios, different detection results may be obtained.
For example, in an application scenario of driving a vehicle, the system for processing brain physiological data may be integrated on a vehicle-mounted device, so as to obtain brain physiological data of a driver, and then analyze and identify the brain physiological data, so as to detect whether the driver is in a tired state, a concentration degree of attention, or a negative emotion such as anger, so as to obtain a detection result corresponding to the brain physiological data, and generate related prompt information based on the detection result, for example: "fatigue driving, suggesting rest", "inattentive, please pay attention to traffic conditions", "emotional excitement, suggesting to stop driving", and so on. In addition, when the driver is in an exhausted state, is not concentrated in attention or is in negative emotions such as anger and the like, in order to improve traffic safety, the prompt information can be output in a mode of voice and/or flashing of an indicator lamp to remind the driver of paying attention to the traffic safety, and the protection of personal safety of the driver and other people of the motor vehicle is facilitated.
Similarly, in an application scenario of employee work and a work scenario of student study, the system for processing brain physiological data may be integrated on an office device or a learning device, so as to obtain brain physiological data of employees or students, and then analyze and identify the brain physiological data, so as to detect whether employees or students are in fatigue, concentration of attention, or negative emotions such as anger, and the like, so as to obtain a detection result corresponding to the brain physiological data, and generate related prompt information based on the detection result, for example: "suggest rest", "suggest stop work", "suggest stop learning", "suggest relaxation mood", and the like. Specifically, for the staff, when the detection result represents that the brain is in an exhausted state, or is not concentrated in attention or in negative emotion, the staff can stop working in time through the output prompt information, so that the problem that the brain is in the exhausted state, or is not concentrated in attention or in negative emotion to cause injury to the staff is avoided, and personal safety of the staff is protected. For students, when the detection result indicates that the brain is in a tired state, or is not concentrated or is in a negative emotion, the students can adjust the attention or the state in time through the output prompt information, so that the problem of low learning efficiency caused by the fact that the brain is in the tired state, or is not concentrated or is in the negative emotion is avoided, and the learning efficiency of learners is improved.
The following describes in detail an application scenario and a processing procedure of brain physiological data in conjunction with an embodiment of the method.
Fig. 4 is a first scenario diagram illustrating a method for processing brain physiological data according to an exemplary embodiment of the present application. As shown in fig. 4, this embodiment provides an application scenario of a method for processing brain physiological data, and it should be noted that the brain physiological data may include: electroencephalogram signals, cerebral blood flow, intracranial pressure and the like, wherein the electroencephalogram signals refer to electric signals obtained when brain activities are recorded; cerebral blood flow is the flow of blood through a certain cross-sectional area of the cerebral vessels in a unit time; intracranial pressure refers to the pressure of the cerebrospinal fluid in the cranial cavity. For convenience of explanation, an electroencephalogram signal is taken as an example of brain physiological data. Specifically, the execution main body of the processing method includes: terminal equipment 101 and processing means 102.
The terminal device 101 may be any computing device with certain computing capabilities. The basic structure of the terminal apparatus 101 may include: at least one processor. The number of processors depends on the configuration and type of terminal device 101. Terminal device 101 may also include Memory, which may be volatile, such as RAM, non-volatile, such as Read-Only Memory (ROM), flash Memory, etc., or both. The memory typically stores an Operating System (OS), one or more application programs, and may also store program data and the like. In addition to the processing unit and the memory, the terminal device 101 also includes some basic configurations, such as a network card chip, an IO bus, a display component, and some peripheral devices. Alternatively, some peripheral devices may include, for example, a keyboard, a mouse, a stylus, a printer, and the like. Other peripheral devices are well known in the art and will not be described in detail herein. Alternatively, the terminal apparatus 101 may be a pc (personal computer) terminal or the like.
The processing device 102 refers to a device that can provide a computing processing service in a network virtual environment, and generally refers to a server that performs information planning using a network. In physical implementation, the processing device 102 may be any device capable of providing computing services, responding to service requests, and performing processing, and may be, for example, a conventional server, a cloud host, a virtual center, and the like. The processing device 102 mainly includes a processor, a hard disk, a memory, a system bus, and the like, and is similar to a general computer architecture.
In the present example, the terminal device 101 is configured to generate a data processing request for an electroencephalogram signal and transmit the data processing request to the processing apparatus 102; the processing device 102 is configured to receive a data processing request, and perform corresponding data processing operation on the electroencephalogram signal based on the data processing request, and specifically includes: dividing the electroencephalogram signal into a plurality of data segments; then, identifying the data type corresponding to each of the plurality of data segments, determining at least two continuous first type data in the plurality of data segments, where the first type data is non-artifact data, and processing the at least two continuous first type data, for example: the brain disease diagnosis system can perform data abnormality detection processing, disease prediction processing, abnormal discharge signal detection processing and the like, so that the processing results of the brain signal such as data abnormality detection result, disease prediction result, abnormal discharge signal detection result and the like can be obtained, and the brain disease prevention, early diagnosis and early intervention based on the brain signal are effectively realized based on the obtained processing results.
In some examples, the processing device 102 may acquire brain electrical signals of a human body through the brain electrical acquisition apparatus 100. Specifically, as shown in fig. 5, the electroencephalogram acquisition device 100 is configured to acquire an electroencephalogram signal of a human body 104, and transmit the acquired electroencephalogram signal to the processing device 102, so that the processing device 102 can acquire the electroencephalogram signal, and then the processing device 102 can analyze and process the electroencephalogram signal based on a data processing request sent by the terminal device 101.
In some examples, the terminal device 101 may transmit the data processing request together with the brain electrical signal to the processing apparatus 102, at which time the terminal device 101 may acquire the brain electrical signal of the human body through the brain electrical acquisition device 100. Specifically, as shown in fig. 6, the electroencephalogram acquisition device 100 is configured to acquire an electroencephalogram signal of a human body 104, and transmit the acquired electroencephalogram signal to the terminal device 101, so that the terminal device 101 can acquire the electroencephalogram signal, generate a data processing request based on the electroencephalogram signal, and transmit the electroencephalogram signal and the data processing request to the processing device 102, so that the processing device can analyze and process the electroencephalogram signal according to the data processing request.
Specifically, the electroencephalogram acquisition device 100 may be connected with a plurality of detection electrodes (for example, the plurality of detection electrodes include a detection electrode 103a, a detection electrode 103b, and a detection electrode 103c), and the detection electrodes may be dry electrodes or wet electrodes; and the electrode distribution of the detection electrode conforms to the electrode placement method of the international 10-20 system. During the acquisition, the detecting electrodes 103a, 103b, 103c may be fixed at various positions on the head of the human body 104. After the interface 105 of the electroencephalogram acquisition device 100 is connected with the detection electrode 103a, the detection electrode 103b, and the detection electrode 103c, the electroencephalogram acquisition device 100 can acquire an electroencephalogram signal of the human body 104. In addition, the electroencephalogram acquisition device 100 can be further connected with a display device 106, and after the electroencephalogram signals of the human body 104 are acquired, the electroencephalogram signals can be displayed in real time through the display device 106. Of course, the specific shape and structure of the brain wave acquiring apparatus 100 are only exemplary, and may further include various other devices such as a helmet, a hat, bedding, a packaged portable detecting electrode, and the like, and the application is not limited herein.
In some examples, a multi-lead brain electrical signal of the human body 104 may be acquired with the brain electrical acquisition device 100. The Electroencephalogram acquisition device 100 may include an instrument device capable of acquiring electroencephalograms (EEG), and the number of leads of the Electroencephalogram acquisition device 100 provided by different manufacturers may be different, for example: the lead interfaces may include a plurality of specifications such as 8-lead, 24-lead, 64-lead, etc., and the application is not limited thereto.
In some examples, when the processing device 102 processes at least two consecutive first type data to obtain a processing result corresponding to the data processing request, the processing device 102 may: acquiring first proportion information of data segments of each preset data type from all at least two continuous first type data; determining a first operating frequency corresponding to all of the at least two consecutive data of the first type; and processing the first proportion information and the first working frequency to obtain a first processing result of the electroencephalogram signal.
In some examples, the preset type data includes at least one of: alpha wave data, fast wave data, slow wave data.
In some examples, when the processing device 102 obtains the first proportion information of the data segments of the respective preset data types in all of the at least two consecutive data of the first type, the processing device 102 may: acquiring frequency spectrum information of all at least two continuous first type data; and calculating first proportion information of the data segments of the preset type data based on the frequency spectrum information.
In some examples, when the processing device 102 obtains spectral information for all of the at least two consecutive data of the first type, the processing device 102 may: acquiring frequency domain data corresponding to all of the at least two consecutive first type data; and performing power spectral density calculation on the frequency domain data to obtain spectral information of all at least two continuous first type data.
In some examples, when the processing device 102 calculates the first proportion information of the data segments of the respective preset types of data based on the spectrum information, the processing device 102 may: determining second proportion information of data segments of each preset type of data in each at least two continuous first types of data based on the frequency spectrum information; and determining the average value of all second proportion information of each preset type of data as the first proportion information of the data segment of each preset type of data aiming at all at least two continuous first type of data.
In some examples, when the processing device 102 determines the first operating frequency corresponding to all of the at least two consecutive first type data, the processing device 102 may: acquiring a second operating frequency corresponding to each of at least two consecutive data of the first type; based on all of the second operating frequencies, first operating frequencies corresponding to all of the at least two consecutive data of the first type are calculated.
In some examples, when the processing device 102 obtains the second operating frequency corresponding to each of the at least two consecutive first type data, the processing device 102 may: and acquiring a second working frequency corresponding to each at least two continuous first type data based on the frequency spectrum information of each at least two continuous first type data.
In some examples, when the processing device 102 calculates the first operating frequency corresponding to all of the at least two consecutive first type data based on all of the second operating frequencies, the processing device 102 may: an average of all the second operating frequencies is determined as the first operating frequency corresponding to all of the at least two consecutive data of the first type.
In some examples, after obtaining spectral information for all of the at least two consecutive data of the first type, the processing device 102 may further: a brain map corresponding to the brain electrical signal is generated based on the spectral information of all of the at least two consecutive data of the first type.
In some examples, after determining at least two consecutive first type data, the processing device 102 may further: based on at least two consecutive data of the first type, a brain topographic map corresponding to the at least two consecutive data of the first type is generated.
In some examples, after determining at least two consecutive first type data, the processing device 102 may further: acquiring time domain characteristics, frequency domain characteristics and lead connection characteristics of all at least two continuous first types of data; and analyzing and processing based on all the time domain characteristics, the frequency domain characteristics and the lead connection characteristics to obtain disease prediction information corresponding to the electroencephalogram signals.
In some examples, when the processing device 102 performs the analysis processing based on all the time domain features, the frequency domain features, and the lead connection features to obtain the disease prediction information corresponding to the brain electrical signal, the processing device 102 may: acquiring first prediction information corresponding to all of the at least two continuous first type data based on all of the time domain features, the frequency domain features and the lead connection features; disease prediction information corresponding to the electroencephalogram signal is determined based on all of the first prediction information.
In some examples, after obtaining disease prediction information corresponding to the brain electrical signal, the processing device 102 may: and processing the disease prediction information, the first proportion information and the first working frequency corresponding to the electroencephalogram signal to obtain a second processing result corresponding to the electroencephalogram signal.
In some examples, after acquiring the brain electrical signal, the processing device 102 may: identifying whether the electroencephalogram signals include abnormal discharge signals or not; and when the electroencephalogram signals comprise abnormal discharge signals, determining the position information of the abnormal discharge signals. Wherein the abnormal discharge signal comprises at least one of the following signals: spike, spike slow, multiple spike slow, spike rhythm, abnormal slow, sphenoid abnormal wave.
In some examples, after determining the location information of the abnormal discharge signal, the processing device 102 may: and processing the position information, the first proportion information and the first working frequency of the abnormal discharge signal to obtain a third processing result of the electroencephalogram signal.
In some examples, after determining the location information of the abnormal discharge signal, processing device 102 may: and processing the position information of the abnormal discharge signal, disease prediction information corresponding to the electroencephalogram signal, the first proportion information and the first working frequency to obtain a fourth processing result of the electroencephalogram signal.
In some examples, when the processing device 102 acquires the brain electrical signal, the processing device 102 may: acquiring data signals of all channels acquired by electroencephalogram acquisition equipment; filtering the data signal of each channel to obtain a filtered data signal; and acquiring an electroencephalogram signal based on the filtered data signal.
In some examples, prior to obtaining the brain electrical signal based on the filtered data signal, the processing device 102 may: identifying a data quality of the filtered data signal; and when the data quality meets the preset requirement, generating an electroencephalogram signal based on the filtered data signal.
In some examples, when the processing device 102 generates a brain electrical signal based on the filtered data signal, the processing device 102 may: acquiring data quantity information of all filtered data signals; and when the data volume information is greater than or equal to a preset threshold value, generating an electroencephalogram signal based on the filtered data signal.
In some examples, the processing device 102 may: and when the data volume information is greater than or equal to the preset threshold value, stopping acquiring the data signals of all channels acquired by the electroencephalogram acquisition equipment.
In some examples, before the data amount information is greater than or equal to the preset threshold, the processing device 102 may: acquiring the acquisition time of a data signal; and when the acquisition time is greater than or equal to the preset time threshold, generating prompt information, wherein the prompt information is used for identifying the operation abnormity of the acquired data signal.
In some examples, when the data quality meets a preset requirement, the processing device 102 may: and displaying the filtered data signal.
In some embodiments, a network connection or a communication connection is performed between the terminal device 101 and the processing apparatus 102, between the terminal device 101 and the brain wave acquisition device 100, and between the brain wave acquisition device 100 and the processing apparatus 102, and the network connection may be a wireless or wired network connection. The network format of the communication connection may be any one of 2G (gsm), 2.5G (gprs), 3G (WCDMA, TD-SCDMA, CDMA2000, UTMS), 4G (LTE), 4G + (LTE +), WiMax, and the like.
Fig. 7 is a scene schematic diagram of a brain electrical signal processing method according to another exemplary embodiment of the present application. As shown in fig. 7, the present embodiment provides another scenario of a processing method of an electroencephalogram signal, in which an execution subject of the processing method includes: a brain wave acquisition device 401 and a server 402.
In this application example, the electroencephalogram acquisition device 401 is configured to acquire an electroencephalogram signal, specifically, as shown in fig. 8, a plurality of detection electrodes connected to the electroencephalogram acquisition device 401 may acquire a multichannel electroencephalogram signal, and transmit the electroencephalogram signal to the amplifier 407, where the amplifier 407 may be independent of the electroencephalogram acquisition device 401, or may also be integrated in the electroencephalogram acquisition device 401, so as to form an integrated device. After the electroencephalogram signal is acquired by the amplifier 407, the electroencephalogram signal can be amplified, the amplified electroencephalogram signal is input to the computing device 406 in a wireless or wired mode, the computing device 406 can perform data display and data storage on the electroencephalogram signal after amplification, meanwhile, the data quality of the input electroencephalogram signal can be detected by using an automatic data quality detection algorithm, and whether the electroencephalogram signal meets preset requirements or not is judged based on the data quality. In some instances, the computing device 406 may be an embedded device or a personal computer. In some examples, the computing device 406 and the amplifier 407 are integrated on the brain electrical acquisition device 401, forming an integrated device.
After electroencephalogram acquisition equipment 401 acquires electroencephalogram signals, the electroencephalogram signals can be transmitted to a server 402, at the moment, the server 402 can receive the electroencephalogram signals, then a data processing request of a user for inputting the electroencephalogram signals is acquired, and corresponding data processing operation is carried out on the electroencephalogram signals based on the data processing request, and the method specifically comprises the following steps: dividing the electroencephalogram signal into a plurality of data segments; then, identifying the data type corresponding to each of the plurality of data fragments, determining at least two continuous first type data in the plurality of data fragments, wherein the first type data is non-artifact data, and processing the at least two continuous first type data, for example: signal abnormality detection processing, disease prediction processing, and the like, so that a signal abnormality detection result, a disease prediction result, and the like of the electroencephalogram signal can be acquired.
In some examples, when the server 402 processes at least two consecutive first type data to obtain a processing result corresponding to the data processing request, the server 402 may: acquiring first proportion information of data segments of each preset data type from all at least two continuous first type data; determining a first operating frequency corresponding to all of the at least two consecutive data of the first type; and processing the first proportion information and the first working frequency to obtain a first processing result of the electroencephalogram signal.
In some examples, the preset type data includes at least one of: alpha wave data, fast wave data, slow wave data.
In some examples, when the server 402 obtains the first proportion information of the data segments of each preset data type in all of the at least two consecutive data of the first type, the server 402 may: acquiring frequency spectrum information of all at least two continuous first type data; and calculating first proportion information of the data segments of the preset type data based on the frequency spectrum information.
In some examples, when the server 402 obtains spectrum information for all of the at least two consecutive data of the first type, the server 402 may: acquiring frequency domain data corresponding to all of the at least two consecutive first type data; and performing power spectral density calculation on the frequency domain data to obtain spectral information of all at least two continuous first type data.
In some examples, when the server 402 calculates the first proportion information of the data segments of the respective preset types of data based on the spectrum information, the server 402 may: determining second proportion information of data segments of each preset type of data in each at least two continuous first types of data based on the frequency spectrum information; and determining the average value of all second proportion information of each preset type of data as the first proportion information of the data segment of each preset type of data aiming at all at least two continuous first type of data.
In some examples, when the server 402 determines the first operating frequency corresponding to all of the at least two consecutive first type data, the server 402 may: acquiring a second operating frequency corresponding to each of at least two consecutive data of the first type; based on all of the second operating frequencies, first operating frequencies corresponding to all of the at least two consecutive data of the first type are calculated.
In some examples, when the server 402 obtains the second operating frequency corresponding to each of the at least two consecutive first type data, the server 402 may: and acquiring a second working frequency corresponding to each at least two continuous first type data based on the frequency spectrum information of each at least two continuous first type data.
In some examples, when the server 402 calculates the first operating frequency corresponding to all of the at least two consecutive first type data based on all of the second operating frequencies, the server 402 may: an average of all the second operating frequencies is determined as the first operating frequency corresponding to all of the at least two consecutive data of the first type.
In some examples, after obtaining spectral information for all of the at least two consecutive data of the first type, the server 402 may further: a brain map corresponding to the brain electrical signal is generated based on the spectral information of all of the at least two consecutive data of the first type.
In some instances, after determining at least two consecutive first type data, the server 402 may further: based on at least two consecutive data of the first type, a brain topographic map corresponding to the at least two consecutive data of the first type is generated.
In some instances, after determining at least two consecutive first type data, the server 402 may further: acquiring time domain characteristics, frequency domain characteristics and lead connection characteristics of all at least two continuous first types of data; and analyzing and processing based on all the time domain characteristics, the frequency domain characteristics and the lead connection characteristics to obtain disease prediction information corresponding to the electroencephalogram signals.
In some examples, when the server 402 performs the analysis processing based on all the time domain features, the frequency domain features, and the lead connection features to obtain the disease prediction information corresponding to the brain electrical signal, the server 402 may: acquiring first prediction information corresponding to all of the at least two continuous first type data based on all of the time domain features, the frequency domain features and the lead connection features; disease prediction information corresponding to the electroencephalogram signal is determined based on all of the first prediction information.
In some examples, after obtaining disease prediction information corresponding to the brain electrical signal, the server 402 may: and processing the disease prediction information, the first proportion information and the first working frequency corresponding to the electroencephalogram signal to obtain a second processing result corresponding to the electroencephalogram signal.
In some examples, after acquiring the brain electrical signal, the server 402 may: identifying whether the electroencephalogram signals include abnormal discharge signals or not; and when the electroencephalogram signals comprise abnormal discharge signals, determining the position information of the abnormal discharge signals. Wherein the abnormal discharge signal comprises at least one of the following signals: spike, spike slow, multiple spike slow, spike rhythm, abnormal slow, sphenoid abnormal wave.
In some instances, after determining the location information of the abnormal discharge signal, the server 402 may: and processing the position information, the first proportion information and the first working frequency of the abnormal discharge signal to obtain a third processing result of the electroencephalogram signal.
In some examples, after determining the location information of the abnormal discharge signal, the server 402 may: and processing the position information of the abnormal discharge signal, disease prediction information corresponding to the electroencephalogram signal, the first proportion information and the first working frequency to obtain a fourth processing result of the electroencephalogram signal.
In some examples, when the server 402 acquires the brain electrical signal, the processing device 102 may: acquiring data signals of all channels acquired by electroencephalogram acquisition equipment; filtering the data signal of each channel to obtain a filtered data signal; and acquiring an electroencephalogram signal based on the filtered data signal.
In some examples, prior to obtaining the brain electrical signal based on the filtered data signal, the server 402 may: identifying a data quality of the filtered data signal; and when the data quality meets the preset requirement, generating an electroencephalogram signal based on the filtered data signal.
In some examples, when the server 402 generates the brain electrical signal based on the filtered data signal, the server 402 may: acquiring data quantity information of all filtered data signals; and when the data volume information is greater than or equal to a preset threshold value, generating an electroencephalogram signal based on the filtered data signal.
In some instances, the server 402 may: and when the data volume information is greater than or equal to the preset threshold value, stopping acquiring the data signals of all channels acquired by the electroencephalogram acquisition equipment.
In some examples, before the data volume information is greater than or equal to the preset threshold, the server 402 may: acquiring the acquisition time of a data signal; and when the acquisition time is greater than or equal to the preset time threshold, generating prompt information, wherein the prompt information is used for identifying the operation abnormity of the acquired data signal.
In some examples, when the data quality meets a preset requirement, the server 402 may: and displaying the filtered data signal.
In some embodiments, the brain electrical acquisition device 401 and the server 402 are connected via a network or communication connection, which may be a wireless or wired network connection. The network format of the communication connection may be any one of 2G (gsm), 2.5G (gprs), 3G (WCDMA, TD-SCDMA, CDMA2000, UTMS), 4G (LTE), 4G + (LTE +), WiMax, and the like.
Fig. 9 is a flowchart illustrating a method for processing brain physiological data according to an exemplary embodiment of the present application. Referring to fig. 9, the present embodiment provides a method for processing brain physiological data, which may be executed by a processing device or a server, and the server is taken as an example for explanation. Specifically, the method may comprise the steps of:
s601: a data processing request for brain physiological data is obtained.
S602: the brain physiological data is divided into a plurality of data segments.
S603: a data type corresponding to each of the plurality of data segments is identified.
S604: in the plurality of data segments, at least two consecutive data of a first type are determined, the data of the first type being non-spurious data.
S605: at least two successive first type data are processed to obtain a processed result of the brain physiological data.
The following is detailed for the above steps:
s601: a data processing request for brain physiological data is obtained.
The data processing request may be sent by the terminal device to the server, or may also be generated based on an execution operation of the user, where the data processing request is used to request a data processing operation on the brain physiological data, and the data processing operation may include: signal anomaly detection processing, disease prediction processing, and the like. It should be noted that the above-mentioned brain physiological data may include: electroencephalogram signals, cerebral blood flow, intracranial pressure and the like, wherein the electroencephalogram signals refer to electric signals obtained when brain activities are recorded; cerebral blood flow is the flow of blood through a certain cross-sectional area of the cerebral vessels in a unit time; intracranial pressure refers to the pressure of the cerebrospinal fluid in the cranial cavity. For convenience of explanation, an electroencephalogram signal is taken as an example of brain physiological data.
In addition, the electroencephalogram signal may be data acquired by an electroencephalogram acquisition device, and the electroencephalogram signal may be a real-time digital signal data stream, such as: 01001011010, 1011100011, etc. When the electroencephalogram acquisition equipment is used for acquiring electroencephalogram signals, the signal sampling rate and the lead number of the electroencephalogram acquisition equipment are not limited, for example: the number of leads of the electroencephalogram acquisition equipment can comprise various quantity specifications such as 8 leads, 24 leads, 64 leads and the like; also, the format of the acquired brain electrical signals includes, but is not limited to: an Electroencephalogram (EEG) Format, a Fractal Image Format (FIF) Format, a European Data Format (EDF) Format, and the like.
In some examples, the electroencephalogram signal may be data after being filtered, that is, after the electroencephalogram acquisition device acquires the electroencephalogram signals of a plurality of channels through a plurality of detection electrodes, can carry out filtering processing on the EEG signals of all channels, particularly, can input the EEG signal of each channel into a filter, the EEG signal of each channel is subjected to independent band-pass filtering processing through a filter to filter certain high-frequency signals and/or certain low-frequency signals included in the EEG signal, for example, the frequency of the band-pass filter is 0.5-40Hz, at the moment, after the electroencephalogram signals are filtered by the band-pass filter, low-frequency signals lower than 0.5Hz and high-frequency signals higher than 40Hz in the electroencephalogram signals can be filtered, and therefore the electroencephalogram signals after filtering can be obtained. It is understood that the frequency of the band-pass filter is not limited to the above examples, and those skilled in the art can set the frequency according to specific application scenarios, such as: in one example, the frequency of the band pass filter may be set to 0.2-40 Hz.
After the electroencephalogram signals after filtering processing are acquired, the electroencephalogram acquisition equipment can send the electroencephalogram signals to the server, and at the moment, the server can receive the electroencephalogram signals after filtering processing.
In some examples, the electroencephalogram signal can also be data which is not subjected to wave filtering processing, namely, after the electroencephalogram signal of a plurality of channels is acquired by the electroencephalogram acquisition equipment through a plurality of detection electrodes, the electroencephalogram signal of the plurality of channels is directly sent to the server, at the moment, the server can acquire the electroencephalogram signal of all the channels acquired by the electroencephalogram acquisition equipment, then, the electroencephalogram signal of each channel can be subjected to filtering processing, specifically, the electroencephalogram signal of each channel can be input to the filter, the electroencephalogram signal of each channel can be subjected to independent band-pass filtering processing through the filter, certain high-frequency signals and/or certain low-frequency signals included in the electroencephalogram signal can be filtered, and then, the electroencephalogram signal can be acquired based on the filtered data signal.
In some examples, in order to ensure the data quality of the electroencephalogram signal, before obtaining the electroencephalogram signal based on the filtered data signal, the server may further identify the data quality of the filtered data signal, specifically, the filtered data signal may be analyzed and processed by using a preset quality analysis model, so that the data quality of the filtered data signal may be obtained, where the quality analysis model is trained to identify the quality of the data. Then, the data quality of the filtered data signal can be analyzed and processed, and when the data quality does not meet the preset requirement, the electroencephalogram signal is forbidden to be generated based on the filtered data signal; when the data quality meets the preset requirement, the electroencephalogram signal can be generated based on the filtered data signal. In some examples, when the data quality meets the preset requirement, the method in this embodiment further includes: and displaying the filtered data signal.
For example, in the conventional filtered data signal a and the filtered data signal b, the quality analysis model is used to analyze and identify the signal a and the signal b, so that the data quality of the signal a is 80 minutes and the data quality of the signal b is 70 minutes, and at this time, if the preset requirement includes a quality threshold of 75 minutes. Then, if the data quality 80 of the signal a is greater than the quality threshold 75, it indicates that the data quality of the signal a meets the preset requirement, and if the data quality 70 of the signal b is less than the quality threshold 75, it indicates that the data quality of the signal b does not meet the preset requirement. In summary, since the filtered data signal a satisfies the preset requirement, the electroencephalogram signal can be generated by using the filtered data signal a.
In some examples, generating a brain electrical signal based on the filtered data signal includes: acquiring data quantity information of all filtered data signals; and when the data volume information is greater than or equal to a preset threshold value, generating an electroencephalogram signal based on the filtered data signal.
The filtered data signals can include one or more than one, and when the data quality of one or more than one filtered data signals meets preset requirements, electroencephalogram signals can be generated based on all the filtered data signals so as to analyze and process the electroencephalogram signals. In specific application, accurate analysis of the electroencephalogram signal can be realized after the electroencephalogram signal meets the preset data volume, so that when the electroencephalogram signal is generated, in order to reduce the waste of data acquisition resources, the data volume information of all filtered data signals can be acquired; when the data volume information is larger than or equal to the preset threshold value, the electroencephalogram signal is generated based on the filtered data signal, and therefore the accuracy and reliability of acquiring the electroencephalogram signal data are effectively guaranteed. In some examples, the method in this embodiment further comprises: and when the data volume information is greater than or equal to the preset threshold value, stopping acquiring the data signals of all channels acquired by the electroencephalogram acquisition equipment.
For example, assume that the preset threshold P for the data amount information is 2M; the data quality of the signal a, the signal b, the signal d and the signal e meets the preset requirement, so that the signal a, the signal b, the signal d and the signal e can generate an electroencephalogram signal. Specifically, the data volume P1 of the signal a, the signal b, the signal d, and the signal e is obtained to be 2.3M, and at this time, the data volume P1> P can generate an electroencephalogram signal based on the signal a, the signal b, the signal d, and the signal e, and stop the acquisition operation of the electroencephalogram signal. If the data volume P2 of the signal a, the signal b, the signal d, and the signal e is 1.9M, and P2< P, the electroencephalogram signal can be continuously acquired to acquire a filtered data signal f with data quality meeting preset requirements, after the filtered data signal f is acquired, the electroencephalogram signal can be generated based on the signal a, the signal b, the signal d, the signal e, and the signal f, and the acquisition operation of the electroencephalogram signal is stopped.
Specifically, when the data volume information for acquiring the data volume information of all the filtered data signals is greater than or equal to the preset threshold, the electroencephalogram signal is generated based on the filtered data signals, and the electroencephalogram signal can be accurately analyzed through the generated electroencephalogram signal, so that the acquisition of the data signals of all the channels acquired by the electroencephalogram acquisition equipment can be stopped, and the waste of data acquisition resources is effectively avoided.
In some examples, before the data amount information is greater than or equal to the preset threshold, the method further includes: acquiring the acquisition time of a data signal; and when the acquisition time is greater than or equal to the preset time threshold, generating prompt information, wherein the prompt information is used for identifying the operation abnormity of the acquired data signal.
Specifically, in the process of acquiring the electroencephalogram signals, in order to ensure the acquisition efficiency of the electroencephalogram signals, the acquisition time of the data signals can be acquired, then the acquisition time is analyzed and compared with a preset time threshold, when the acquisition time is less than the preset time threshold, the acquisition time of the electroencephalogram signals is short, and the acquisition operation of the electroencephalogram signals can be continued; when the acquisition time is greater than or equal to the preset time threshold, the acquisition time of the electroencephalogram signal is longer, and the electroencephalogram signal meeting the preset requirement is still not acquired. In some examples, the prompt message is displayed through the display device, so that the user can view the prompt message in time, and perform corresponding adjustment and checking operations based on the prompt message, for example: checking or adjusting the connection relation of the detection electrodes of the electroencephalogram acquisition equipment, adjusting the impedance information of the electroencephalogram acquisition equipment, and the like. In some examples, the prompt message can be sent to the client to remind the user to check and adjust the acquisition operation of the electroencephalogram acquisition device in time.
S602: the electroencephalogram signal is divided into a plurality of data segments.
After acquiring the electroencephalogram signal, the electroencephalogram signal can be divided into a plurality of data segments according to a preset rule, the specific number of the data segments is not limited in this embodiment, and a person skilled in the art can set the data segments according to specific application requirements and design requirements, for example: the number of data segments may be 8, 10 or 12, etc.
In addition, the embodiment does not limit the specific implementation manner of dividing the electroencephalogram signal into a plurality of data segments, and those skilled in the art may perform any setting according to a specific application scenario, for example: the segment start positions corresponding to the plurality of data segments can be configured, and the electroencephalogram signal is divided into the plurality of data segments through the segment start positions corresponding to the plurality of data segments.
In some examples, dividing the brain electrical signal into a plurality of data segments may include: acquiring the length of a sliding window and the window overlapping rate between adjacent windows; based on the length of the sliding window and the overlapping rate of the window, the electroencephalogram signal is divided into a plurality of data segments by using a sliding window algorithm.
The sliding window length and the window overlapping rate between adjacent windows can be configured according to specific application scenes and application requirements, and in some examples, the sliding window length and the window overlapping rate can be determined through a convolutional neural network model, wherein the convolutional neural network model is used for identifying electroencephalogram signals of various preset data types; specifically, the sliding window initial value and the step length information may be set based on a convolutional neural network model, for example: the initial value of the sliding window is 0.5s, the step length information is 0.1, then a convolutional neural network model can be used for analyzing and identifying the data segments obtained based on the initial value of the sliding window, and the accuracy rate of analysis and identification is obtained; when the accuracy is smaller than the preset threshold, the initial value of the sliding window can be adjusted based on the step length information, the convolutional neural network model is used for analyzing and identifying the data segment obtained based on the adjusted length of the sliding window, the accuracy of analysis and identification is obtained, the operation is repeated, and then the length of the sliding window with higher identification accuracy can be determined as the length of the target sliding window, so that the specific implementation process of the length of the sliding window is realized.
In addition, the specific implementation process of determining the window overlapping rate through the convolutional neural network model is similar to the specific implementation process of determining the sliding window length, and is not described herein again.
In some examples, the length of the brain electrical signal may also be integrated to configure the sliding window length and window overlap ratio, for example: when the length of the electroencephalogram signal is 10s, the length of the sliding window can be 2s, the window overlapping rate can be 50%, and at the moment, the adjacent windows are overlapped for 1 s. When the length of the electroencephalogram signal is 20s, the length of the sliding window can be 3s, the window overlapping rate can be 50%, and at the moment, the adjacent windows are overlapped for 1.5 s. It should be noted that the window overlap is less than 100%.
After the sliding window length and the window overlap ratio are obtained, the electroencephalogram signal can be divided into a plurality of data segments by using a sliding window algorithm, for example: when the length of the electroencephalogram signal is 10s, the length of the sliding window is 2s, and the window overlapping rate is 50%, at the moment, the electroencephalogram signal can be divided into 9 data segments by using a sliding window algorithm, and the length of each data segment is equal, so that the electroencephalogram signal is divided into a plurality of data segments with fixed lengths.
S603: a data type corresponding to each of the plurality of data segments is identified.
After the plurality of data segments are obtained, the plurality of data segments may be analyzed to identify data types corresponding to the plurality of data segments, where one data segment may correspond to one data type, and the data types corresponding to the plurality of data segments may include: alpha wave data, pseudo-differential wave data, other types of wave data.
In some examples, identifying the data type to which each of the plurality of data segments corresponds may include: identifying the data types corresponding to the plurality of data fragments according to the convolutional neural network model; the convolutional neural network model is trained to recognize electroencephalographic signals of multiple preset data types. Specifically, the plurality of preset data types include at least one of the following: alpha wave data, pseudo-differential wave data, other types of wave data.
The convolutional neural network model is a model which is trained in advance and used for identifying electroencephalogram signals of various preset data types. In training the convolutional neural network model, the training may include: acquiring a plurality of electroencephalogram signals, wherein the plurality of electroencephalogram signals correspond to a plurality of preset data types; at this time, the plurality of preset data types include: alpha wave data, pseudo-differential wave data, other types of wave data. Then, the plurality of electroencephalogram signals are subjected to supervised training, and a convolutional neural network model for identifying the data types of the electroencephalogram signals can be obtained.
Specifically, when a convolutional neural network is used for identifying the data type corresponding to each of a plurality of data fragments, each data fragment can correspond to probability information of preset type data; and then determining the data type corresponding to the data fragment according to the preset type information with the maximum probability information. For example: the preset type data comprises alpha wave data, pseudo-difference wave data and other type wave data, and for one data segment, the probability L1 of the alpha wave data is 70%, the probability L2 of the pseudo-difference wave data is 10%, and the probability L3 of the other type wave data is 20%, wherein L1+ L2+ L3 is 1; by comparison, the relationship between the three probabilities is L1> L3> L2, and therefore, the data type of the data segment is determined to be alpha wave data.
In some examples, after identifying the data type to which each of the plurality of data segments corresponds, the plurality of data segments may be added with type tagging information based on the data type to which each of the plurality of data segments corresponds. For example: the length of the electroencephalogram signal is 10s, the length of the sliding window is 2s, the window overlapping rate is 50%, namely overlapping is 1s, at the moment, the whole electroencephalogram signal can be divided into 9 data segments, and after the convolutional neural network model identifies the respective corresponding data types of the data segments, the assumption is made that: the alpha wave data is marked as 0, the pseudo-difference wave data is marked as 1, and the other type wave data is marked as 2, at this time, it can be obtained that the type mark sequences corresponding to the 9 data fragments are 1, 2, 0, 0, 0, 1, 1, 1, 2.
S604: in the plurality of data segments, at least two consecutive data of a first type are determined, the data of the first type being non-spurious data.
After the data types corresponding to the multiple data segments are obtained, target data corresponding to the electroencephalogram signal can be determined based on the data types corresponding to the multiple data segments, where the target data are at least two continuous first-type data, and the data type of the first-type data is non-artifact data, specifically, the two continuous data segments refer to two adjacent data segments, and the non-artifact data may include alpha-wave type data and other types of wave data.
For example, if the alpha wave data is identified as 0, the pseudo-difference wave data is identified as 1, and the other types of wave data are identified as 2, the electroencephalogram signal is divided into a plurality of data segments, which are segment a, segment b, segment c, segment d, segment e, and segment f, as shown in fig. 10, if the data types corresponding to the data segments are: 0.1, 2, 0, 1 and 0, and determining the target data to be two continuous segments c and d according to the data types corresponding to the data segments. As shown in fig. 11, if the data types corresponding to the data fragments are: 1. 0, 2, 0, 1, and 2, the target data can be determined to be three consecutive segments b, c, and d according to the data types corresponding to the data segments.
S605: and processing at least two continuous first type data to obtain a processing result of the electroencephalogram signal.
After at least two consecutive first type data (also referred to as key segment data) are determined, the determined key segment data may be analyzed, and it is understood that the number of the key segment data may be one or more.
In some examples, processing at least two consecutive data of the first type to obtain a processing result corresponding to the data processing request includes: acquiring first proportion information of data segments of each preset data type from all at least two continuous first type data; determining a first operating frequency corresponding to all of the at least two consecutive data of the first type; and processing the first proportion information and the first working frequency to obtain a first processing result of the electroencephalogram signal.
Wherein the preset type data comprises at least one of the following: alpha wave data, fast wave data, slow wave data. Specifically, the frequency range of the alpha wave data may be 8-13Hz, and the fast wave data refers to data with a frequency higher than the highest frequency of the alpha wave data, for example: the fast wave data may include beta wave data having a frequency in the range of 13-30 Hz. Slow wave data refers to data having a frequency lower than the lowest frequency of alpha wave data, for example: the slow wave data may include delta wave data having a frequency range of 1-4Hz, theta wave data having a frequency range of 4-8Hz, sawtooth data having a frequency range of 2-6Hz, and the like.
Since the number of the key segment data (at least two consecutive data of the first type) may be one or more, all the key segment data need to be analyzed in order to be able to analyze the entire electroencephalogram signal. Specifically, first proportion information of data segments of each preset data type can be acquired from all key segment data; it can be understood that the number of the first proportion information corresponds to the preset data type, that is, one preset data type corresponds to one first proportion information.
In some embodiments, obtaining the first proportion information of the data segment of each preset data type in all of the at least two consecutive data of the first type includes: acquiring frequency spectrum information of all at least two continuous first type data; and then calculating first proportion information of the data segments of each preset type of data based on the frequency spectrum information.
In some embodiments, obtaining spectral information for all of the at least two consecutive data of the first type comprises: acquiring frequency domain data corresponding to all of the at least two consecutive first type data; and performing power spectral density calculation on the frequency domain data to obtain spectral information of all at least two continuous first type data.
In some embodiments, calculating first proportion information of the data segments of the respective preset types of data based on the spectrum information includes: determining second proportion information of data segments of each preset type of data in each at least two continuous first types of data based on the frequency spectrum information; and determining the average value of all second proportion information of each preset type of data as the first proportion information of the data segment of each preset type of data aiming at all at least two continuous first type of data.
For example, assume that a data segment of alpha wave data is identified as 1, a data segment of fast wave data is identified as 2, and a data segment of slow wave data is identified as 0; the determined at least two consecutive first type data comprise: data a, data B, data C, and data D; after the data is acquired, fast fourier transform processing may be performed on each data, so that frequency domain data a corresponding to data a, frequency domain data B corresponding to data B, frequency domain data C corresponding to data C, and frequency domain data D corresponding to data D may be acquired. The above frequency domain data is then subjected to power spectral density calculation to obtain spectral information f1 corresponding to the frequency domain data a, spectral information f2 corresponding to the frequency domain data b, spectral information f3 corresponding to the frequency domain data c, and spectral information f4 corresponding to the frequency domain data d.
After the spectrum information is acquired, second proportion information of the data segments of the preset type data in the data a, the data B, the data C and the data D may be determined based on the frequency characteristics corresponding to the preset type data. Assume that data a includes: 1 and 2, data B comprises 1 and 2, data C comprises 0, 1 and 2, and data D comprises 1 and 0. By analyzing and identifying the frequency spectrum information f1, in the data a, the second proportion information corresponding to 1 is 65%, and the second proportion information corresponding to 2 is 35%; similarly, the following data may be obtained: in the data B, the second proportion information corresponding to 1 is 45%, and the second proportion information corresponding to 2 is 55%; in the data C, the second proportion information corresponding to 0 is 25%, the second proportion information corresponding to 1 is 55%, and the second proportion information corresponding to 2 is 20%; in the data D, the second proportion information corresponding to 0 is 32%, and the second proportion information corresponding to 1 is 68%.
After the second proportion information is acquired, an average value of all the second proportion information of each preset type of data may be determined as the first proportion information of the data segment of each preset type of data. Specific data are shown in the following table:
similarly, in some embodiments, determining a first operating frequency corresponding to all of the at least two consecutive data of the first type comprises: acquiring a second operating frequency corresponding to each of at least two consecutive data of the first type; based on all of the second operating frequencies, first operating frequencies corresponding to all of the at least two consecutive data of the first type are calculated.
In some embodiments, obtaining a second operating frequency corresponding to each of the at least two consecutive data of the first type comprises: and acquiring a second working frequency corresponding to each at least two continuous first type data based on the frequency spectrum information of each at least two continuous first type data.
In some embodiments, calculating the first operating frequency corresponding to all of the at least two consecutive data of the first type based on all of the second operating frequencies comprises: an average of all the second operating frequencies is determined as the first operating frequency corresponding to all of the at least two consecutive data of the first type.
For example, assume that a data segment of alpha wave data is identified as 1, a data segment of fast wave data is identified as 2, and a data segment of slow wave data is identified as 0; the determined at least two consecutive first type data comprise: data a, data B, data C, and data D; after the above data is acquired, fast fourier transform processing and power spectral density processing may be performed on each data, so that it is possible to acquire spectral information f1 corresponding to frequency domain data a, spectral information f2 corresponding to frequency domain data B, spectral information f3 corresponding to frequency domain data C, and spectral information f4 corresponding to frequency domain data D.
After the spectrum information is acquired, the second operating frequency in the data a, the data B, the data C, and the data D may be determined based on the frequency characteristics corresponding to the preset type of data. Assume that the second operating frequency FA of the data A is 14Hz, the second operating frequency FB of the data B is 16Hz, the second operating frequency FC of the data C is 10Hz, and the second operating frequency FD of the data D is 6 Hz. After the second operating frequencies are acquired, an average value of all the second operating frequencies may be determined as the first operating frequency corresponding to all of the at least two consecutive data of the first type. Specific data are shown in the following table:
after the first proportion information and the first working frequency are obtained, the first proportion information and the first working frequency can be processed, specifically, the first proportion information and preset proportion information can be analyzed and compared, when the first proportion information is larger than the preset proportion information, a first processing result can be obtained, the first processing result is used for being identified in the electroencephalogram signal, and the proportion of data segments of the preset data type is higher; when the first occupation ratio information is smaller than the preset occupation ratio information, a second processing result can be obtained, wherein the second processing result is used for identifying that the occupation ratio of the data segments of the preset data type in the electroencephalogram signal is lower. Similarly, the first working frequency and the preset working frequency can be analyzed and compared, when the first working frequency is greater than the preset working frequency, a third processing result can be obtained, and the third processing result is used for identifying that the working frequency of the electroencephalogram signal is higher; when the first working frequency is lower than the preset working frequency, a fourth processing result can be obtained, and the fourth processing result is used for marking that the working frequency of the electroencephalogram signal is lower, so that the processing result of the electroencephalogram signal can be obtained. The brain disease prevention, early diagnosis and early intervention based on the brain electrical signals can be realized through the acquired brain electrical signal processing result.
The method for processing an electroencephalogram signal provided by this embodiment divides the electroencephalogram signal into a plurality of data segments by obtaining a data processing request for the electroencephalogram signal, identifies respective data types corresponding to the plurality of data segments, at least two consecutive data of the first type are then determined among the plurality of data segments based on the data type to which each of the plurality of data segments corresponds, thereby effectively realizing the extraction operation of normal and useful brain electrical signals, then processing at least two continuous first type data, specifically, disease risk prediction processing, signal abnormity detection processing and the like, thereby obtaining the disease risk prediction result of the brain electrical signal, the signal abnormity detection result and the like, therefore, the prevention, early diagnosis and early intervention on brain diseases based on the electroencephalogram signals are effectively realized, and the practicability of the method is further improved.
In some examples, after obtaining spectral information for all of the at least two consecutive data of the first type, the method further comprises: and generating a brain topographic map corresponding to the electroencephalogram signal based on the frequency spectrum information of all at least two continuous first types of data, wherein the brain topographic map at the moment corresponds to the whole electroencephalogram signal. In some examples, after obtaining the brain map, the method further comprises: and storing the brain map to a preset area.
In some examples, after determining at least two consecutive first type data, the method further comprises: based on at least two consecutive first type data, a brain topographic map corresponding to the at least two consecutive first type data is generated, where the brain topographic map corresponds to the at least two consecutive first type data. In some examples, after obtaining the brain map, the method further comprises: and storing the brain map to a preset area.
Fig. 12 is a flowchart illustrating a method for processing brain physiological data according to another exemplary embodiment of the present application. On the basis of any one of the above embodiments, referring to fig. 12, the method for processing brain physiological data according to this embodiment can implement disease risk prediction operation based on the brain physiological data, and specifically, when the user has a need for disease risk prediction, the method can perform processing such as senile dementia evaluation, depression evaluation, stroke risk evaluation, sleep quality evaluation, epilepsia abnormal wave examination, and the like. Specifically, after determining at least two consecutive first type data, the method in this embodiment further includes:
s901: time domain features, frequency domain features, and lead connection features of all at least two consecutive data of the first type are obtained.
S902: and analyzing and processing based on all the time domain characteristics, the frequency domain characteristics and the lead connection characteristics to obtain disease prediction information corresponding to the brain physiological data.
The following is detailed for the above steps:
s901: time domain features, frequency domain features, and lead connection features of all at least two consecutive data of the first type are obtained.
After all at least two continuous first type data are acquired, all at least two continuous first type data can be input into a feature extractor, and time domain features, frequency domain features and lead connection features of all at least two continuous first type data can be acquired through the feature extractor, wherein the time domain features comprise amplitude statistical information, proportion information of preset frequency and the like; the lead connection characteristics include the positional relationship between any two leads, and the like. In specific application, the feature dimension obtained by the feature extractor is about 2000 dimensions, and it can be understood that the obtained feature dimension is not limited to the above 2000 dimensions, and those skilled in the art can perform any setting according to a specific application scenario, which is not described herein again.
S902: and analyzing and processing based on all the time domain characteristics, the frequency domain characteristics and the lead connection characteristics to obtain disease prediction information corresponding to the brain physiological data.
Specifically, all the acquired time domain features, frequency domain features and lead connection features may be input into a machine learning model trained in advance, and the machine learning model is trained to implement preset assessment of diseases; after the machine learning model obtains all the feature information, the disease prediction information corresponding to each type of disease may be obtained, for example: a predicted risk value corresponding to depression of 0.3, a predicted risk value corresponding to senile dementia of 0.4, a predicted risk value corresponding to stroke risk of 0.5, and the like. After the disease prediction information is acquired, the disease prediction information can be stored in a preset area so as to be convenient for calling and checking the disease prediction information.
In some examples, performing analysis processing based on all time domain features, frequency domain features, and lead connection features to obtain disease prediction information corresponding to brain physiological data, including: acquiring first prediction information corresponding to all of the at least two continuous first type data based on all of the time domain features, the frequency domain features and the lead connection features; disease prediction information corresponding to the brain physiological data is determined based on all of the first prediction information.
For example, the determined at least two consecutive first type data comprise: the system comprises data A, data B, data C and data D, wherein a characteristic set a is formed by time domain characteristics, frequency domain characteristics and lead connection characteristics corresponding to the data A, a characteristic set B is formed by the time domain characteristics, the frequency domain characteristics and the lead connection characteristics corresponding to the data B, a characteristic set C is formed by the time domain characteristics, the frequency domain characteristics and the lead connection characteristics corresponding to the data C, and a characteristic set D is formed by the time domain characteristics, the frequency domain characteristics and the lead connection characteristics corresponding to the data D.
Then, the feature set a, the feature set b, the feature set c, and the feature set d may be respectively input to the machine learning model, and after the machine learning model obtains the feature sets, first prediction information corresponding to each feature set may be obtained, for example: the feature set a corresponds to the first prediction information Y1, the feature set b corresponds to the first prediction information Y2, the feature set c corresponds to the first prediction information Y3, and the feature set d corresponds to the first prediction information Y4, and when the first preset information is numerical information, the disease prediction information corresponding to the electroencephalogram signal can be determined by averaging all the first prediction information.
In some examples, after obtaining the disease prediction information corresponding to the electroencephalogram signal, the method in this embodiment may further include: and processing the disease prediction information, the first proportion information and the first working frequency corresponding to the electroencephalogram signal to obtain a second processing result corresponding to the electroencephalogram signal.
In some examples, a data report corresponding to the electroencephalogram signal may be generated based on the second processing result, and the data report may be summarized with corresponding image information, so that the second processing result may be conveniently viewed by a user.
After the disease prediction information corresponding to the electroencephalogram signal is acquired, the first proportion information and the first working frequency acquired by the embodiment can be combined for comprehensive analysis and processing, so that a second processing result corresponding to the electroencephalogram signal can be acquired, and the accuracy and reliability of analysis and processing of the electroencephalogram signal are further improved.
Fig. 13 is a flowchart illustrating a method for processing brain physiological data according to another exemplary embodiment of the present application. On the basis of any one of the above embodiments, referring to fig. 13, the method for processing brain physiological data according to this embodiment can implement detection of an abnormal discharge signal based on the brain physiological data, and specifically, when there is a need for epilepsy detection for a user, the method can implement detection operation of the abnormal discharge signal, where the abnormal discharge signal includes at least one of the following signals: spike, spike slow, multiple spike slow, spike rhythm, abnormal slow, sphenoid abnormal wave. Specifically, after acquiring the brain physiological data, the method in this embodiment may include:
s1001: brain physiological data is acquired.
S1002: whether an abnormal discharge signal is included in the brain physiological data is identified.
S1003: when the abnormal discharge signal is included in the brain physiological data, the position information of the abnormal discharge signal is determined.
Specifically, after the electroencephalogram signals are acquired, the electroencephalogram signals can be input into the abnormal signal detection model component, and abnormal discharge signals in the multi-lead electroencephalogram signals and signal types of the abnormal discharge signals are output through the abnormal signal detection model component, wherein the abnormal signal detection model component is obtained by training a plurality of electroencephalogram sample signals, and the electroencephalogram sample signals are marked with abnormal discharge signals of different signal types. And when the electroencephalogram signals comprise abnormal discharge signals, determining the position information of the abnormal discharge signals in the whole electroencephalogram signals.
In some examples, the anomaly signal detection model component can include a model component trained using machine learning. The machine learning mode can also comprise a K nearest neighbor algorithm, a perception machine algorithm, a decision tree, a support vector machine, a logistic background regression, a maximum entropy and the like, and correspondingly, the generated model components such as naive Bayes, hidden Markov and the like. Of course, in other embodiments, the machine learning manner may further include a deep learning manner, a reinforcement learning manner, and the like, and the generated model component may include a Convolutional Neural Network model Component (CNN), a Recurrent Neural Network model component (RNN), LeNet, ResNet, a Long-Short Term Memory Network model component (LSTM), a bidirectional Long-Short Term Memory Network model component (Bi-LSTM), and the like, which is not limited herein.
In some examples, after determining the position information of the abnormal discharge signal, the method in this embodiment further includes: and processing the position information, the first proportion information and the first working frequency of the abnormal discharge signal to obtain a third processing result of the electroencephalogram signal.
In some examples, a data report corresponding to the electroencephalogram signal may be generated based on the third processing result, and the data report may be summarized with corresponding image information, so that the user may conveniently view the third processing result.
In other examples, after determining the position information of the abnormal discharge signal, the method in this embodiment further includes: and processing the position information of the abnormal discharge signal, the disease prediction information corresponding to the electroencephalogram signal, the first proportion information and the first working frequency to obtain a fourth processing result of the electroencephalogram signal.
After the position information of the abnormal discharge signal is acquired, the first proportion information and the first working frequency acquired by the embodiment can be combined for comprehensive analysis and processing, so that a third processing result corresponding to the electroencephalogram signal can be acquired, and the accuracy and reliability of analysis and processing of the electroencephalogram signal are further improved.
Fig. 14 is a flowchart illustrating a method for acquiring physiological data of a brain according to another exemplary embodiment of the present application. Referring to fig. 14, the present embodiment provides a method for acquiring brain physiological data, wherein the brain physiological data may include: electroencephalogram signals, cerebral blood flow, intracranial pressure and the like, wherein the electroencephalogram signals refer to electric signals obtained when brain activities are recorded; cerebral blood flow is the flow of blood through a certain cross-sectional area of the cerebral vessels in a unit time; intracranial pressure refers to the pressure of the cerebrospinal fluid in the cranial cavity. Specifically, the method may be executed by a brain electrical acquisition device, and specifically, the method may include the following steps:
s1101: brain physiological data of a human body is acquired.
S1102: and filtering the brain physiological data to obtain a filtered data signal.
S1103: the data quality of the filtered data signal is identified.
S1104: and when the data quality meets the preset requirement, generating the brain physiological data by using the filtered data signal.
In some examples, generating brain physiological data using the filtered data signal includes: acquiring data quantity information of all filtered data signals; and when the data volume information is greater than or equal to a preset threshold value, generating the brain physiological data based on the filtered data signal.
In some examples, the method in this embodiment further comprises: and stopping acquiring the brain physiological data when the data volume information is greater than or equal to a preset threshold value.
In some examples, before the data amount information is greater than or equal to the preset threshold, the method further includes: acquiring the acquisition time of a data signal; and when the acquisition time is greater than or equal to the preset time threshold, generating prompt information, wherein the prompt information is used for identifying the operation abnormity of the acquired data signal.
In some examples, when the data quality meets a preset requirement, the method further comprises: and displaying the filtered data signal.
For example, when the brain physiological data is an electroencephalogram signal, assuming that the length of the multi-lead electroencephalogram signal is 6 seconds, the number of leads is 32 leads or 16 leads, and the sampling rate is 256, in this case, for the electroencephalogram acquisition device, the input signal is a 1536 × 32 matrix, and the range of a single value is-32768-32767. Specifically, during signal acquisition, a multi-lead electroencephalogram signal can be acquired every 6 seconds, and then the acquired multi-lead electroencephalogram signal can be input into a filter for filtering to obtain a filtered data signal. And then, performing type recognition on the filtered data signals by using a trained neural network algorithm, judging whether the data signals are artifact or sleep waves, and continuing to acquire and perform acquisition operation of the multi-lead brain electrical signals if the data signals are artifact or sleep waves. If the data signal is not artifact or sleep wave, the quality of the filtered data signal can be identified, specifically, the filtered data signal is input into a trained signal quality scoring neural network to obtain a data quality score, and if the data quality score is higher than a preset threshold, the signal quality of the filtered data signal is better; if the data quality score is lower than the preset threshold value, the signal quality of the filtered data signal is poor.
After the data quality scores are obtained, the signals with better signal quality and poorer signal quality can be input into a register for storage, and meanwhile, the signals with better quality are displayed in a display. With the continuous collection of the electroencephalogram signals, the number of segments with good signal quality in the register is more and more, and if the data volume of the filtered data signals with good signal quality is greater than or equal to the preset number threshold, for example: and identifying the number of the acquired fragments of the filtered data signals, and when the number of the fragments is greater than or equal to 10 preset fragments, generating electroencephalogram signals based on all the filtered data signals, and displaying the electroencephalogram signals in a display. After the electroencephalogram signal is generated, prompt information can be generated and displayed in the display to prompt an operator that the current electroencephalogram signal acquisition process is finished and the acquisition can be stopped.
Specifically, in the process of acquiring the electroencephalogram signals, in order to ensure the acquisition efficiency of the electroencephalogram signals, the acquisition time of the data signals can be acquired in real time, then the acquisition time is analyzed and compared with a preset time threshold, when the acquisition time is less than the preset time threshold, the acquisition time of the electroencephalogram signals is short, and the acquisition operation of the electroencephalogram signals can be continued; when the acquisition time is greater than or equal to the preset time threshold, the acquisition time of the electroencephalogram signal is longer, and the electroencephalogram signal meeting the preset requirement is still not acquired. In some examples, the prompt information may also be displayed through the display device, so that the user may view the prompt information in time, and perform corresponding adjustment and checking operations based on the prompt information, for example: checking or adjusting the connection relation of the detection electrodes of the electroencephalogram acquisition equipment, adjusting the impedance information of the electroencephalogram acquisition equipment, and the like. In some examples, the prompt message may also be sent to the client to prompt the user to perform corresponding adjustment and inspection operations in time.
According to the method for acquiring the brain physiological data, the brain physiological data of the human body is acquired, filtering processing is carried out on the brain physiological data, the filtered data signal is acquired, the data quality of the filtered data signal is identified, the quality of the signal is effectively automatically evaluated, and when the data quality meets the preset requirement, the brain physiological data is generated by using the filtered data signal, so that the quality of acquiring the brain physiological data is further ensured, and the accuracy and reliability of analyzing and identifying the electroencephalogram data are improved.
Fig. 15 is a flowchart illustrating a data processing method according to an exemplary embodiment of the present application. Referring to fig. 15, the present embodiment provides a data processing method that can be executed by a server that acquires data to be processed. Specifically, the method may include:
s1201: acquiring a data processing request aiming at data to be processed;
s1202: dividing data to be processed into a plurality of data fragments;
s1203: identifying data types corresponding to the plurality of data fragments respectively;
s1204: determining at least two continuous first type data in a plurality of data segments, wherein the first type data are non-artifact data;
s1205: and processing at least two continuous first type data to obtain a processing result of the data to be processed.
In some examples, the data to be processed includes at least one of the following types: electroencephalogram signals, electrocardiosignals and stomach electric signals. Specifically, when the data to be processed includes an electrocardiographic signal, this embodiment provides a processing method for an electrocardiographic signal, and the specific implementation process, implementation principle, and implementation effect of the processing method are similar to those of the above-mentioned processing method for an electroencephalogram signal, and reference may be specifically made to the above statements, and details are not described here again. Similarly, when the data to be processed includes the gastric electrical signal, this embodiment provides a method for processing the gastric electrical signal, and the specific implementation process, implementation principle, and implementation effect thereof are similar to the implementation process, implementation principle, and implementation effect of the method for processing the electroencephalogram signal described above, and reference may be specifically made to the above statements, and details are not described here.
It may be understood that the data to be processed in this embodiment is not limited to the data exemplified above, and may also include other types of data, and a person skilled in the art may perform any setting according to specific application requirements and design requirements, as long as the data to be processed can be removed from the artifact data included in the data to be processed, and the target data corresponding to the data to be processed is obtained, which is not described herein again.
It should be noted that the method in this embodiment may also include the method in the embodiment shown in fig. 1 to 13, and reference may be made to the related description of the embodiment shown in fig. 1 to 13 for a part of this embodiment that is not described in detail. The implementation process and technical effect of the technical solution refer to the descriptions in the embodiments shown in fig. 1 to 13, and are not described herein again.
Fig. 16 is a schematic structural diagram of an apparatus for processing brain physiological data according to an exemplary embodiment of the present application. Referring to fig. 16, the present embodiment provides a brain physiological data processing apparatus, which may include: a first obtaining module 1301, a first dividing module 1302, a first identifying module 1303, a first determining module 1304, and a first processing module 1305. In particular, the method comprises the following steps of,
a first obtaining module 1301, configured to obtain a data processing request for brain physiological data;
a first dividing module 1302, configured to divide the brain physiological data into a plurality of data segments;
the first identification module 1303 is configured to identify data types corresponding to the multiple data segments;
a first determining module 1304, configured to determine at least two consecutive data of a first type among the plurality of data segments, where the data of the first type is non-artifact data;
a first processing module 1305, configured to process at least two consecutive data of the first type to obtain a processing result of the brain physiological data.
In some examples, when the first processing module 1305 processes at least two consecutive data of the first type to obtain a processing result corresponding to the data processing request, the first processing module 1305 is configured to: acquiring first proportion information of data segments of each preset data type from all at least two continuous first type data; determining a first operating frequency corresponding to all of the at least two consecutive data of the first type; the first proportion information and the first working frequency are processed to obtain a first processing result of the brain physiological data.
In some examples, the preset type data includes at least one of: alpha wave data, fast wave data, slow wave data.
In some examples, when the first processing module 1305 obtains the first proportion information of the data segment of each preset data type in all of the at least two consecutive data of the first type, the first processing module 1305 is configured to perform: acquiring frequency spectrum information of all at least two continuous first type data; and calculating first proportion information of the data segments of the preset type data based on the frequency spectrum information.
In some examples, when the first processing module 1305 obtains the spectrum information of all of the at least two consecutive data of the first type, the first processing module 1305 is configured to perform: acquiring frequency domain data corresponding to all of the at least two consecutive first type data; and performing power spectral density calculation on the frequency domain data to obtain spectral information of all at least two continuous first type data.
In some examples, when the first processing module 1305 calculates the first proportion information of the data segments of the respective preset types of data based on the spectrum information, the first processing module 1305 is configured to perform: determining second proportion information of data segments of each preset type of data in each at least two continuous first types of data based on the frequency spectrum information; and determining the average value of all second proportion information of each preset type of data as the first proportion information of the data segment of each preset type of data aiming at all at least two continuous first type of data.
In some examples, when the first processing module 1305 determines the first operating frequency corresponding to all of the at least two consecutive data of the first type, the first processing module 1305 is configured to perform: acquiring a second operating frequency corresponding to each of at least two consecutive data of the first type; based on all of the second operating frequencies, first operating frequencies corresponding to all of the at least two consecutive data of the first type are calculated.
In some examples, when the first processing module 1305 obtains the second operating frequency corresponding to each of the at least two consecutive data of the first type, the first processing module 1305 is configured to perform: and acquiring a second working frequency corresponding to each at least two continuous first type data based on the frequency spectrum information of each at least two continuous first type data.
In some examples, when the first processing module 1305 calculates the first operating frequency corresponding to all of the at least two consecutive data of the first type based on all of the second operating frequencies, the first processing module 1305 is configured to perform: an average of all the second operating frequencies is determined as the first operating frequency corresponding to all of the at least two consecutive data of the first type.
In some examples, after obtaining the spectrum information of all of the at least two consecutive data of the first type, the first processing module 1305 in this embodiment may be further configured to: a brain topographic map corresponding to the brain physiological data is generated based on the spectral information of all of the at least two consecutive data of the first type.
In some examples, after determining at least two consecutive first type data, the first processing module 1305 in this embodiment may be further configured to: based on at least two consecutive data of the first type, a brain topographic map corresponding to the at least two consecutive data of the first type is generated.
In some examples, after determining at least two consecutive first type data, the first processing module 1305 in this embodiment may be further configured to: acquiring time domain characteristics, frequency domain characteristics and lead connection characteristics of all at least two continuous first types of data; and analyzing and processing based on all the time domain characteristics, the frequency domain characteristics and the lead connection characteristics to obtain disease prediction information corresponding to the brain physiological data.
In some examples, when the first processing module 1305 performs an analysis process based on all of the time domain features, the frequency domain features, and the lead connection features to obtain disease prediction information corresponding to the brain physiological data, the first processing module 1305 is configured to perform: acquiring first prediction information corresponding to all of the at least two continuous first type data based on all of the time domain features, the frequency domain features and the lead connection features; disease prediction information corresponding to the brain physiological data is determined based on all of the first prediction information.
In some examples, after obtaining disease prediction information corresponding to brain physiological data, the first processing module 1305 in this embodiment may be further configured to: and processing the disease prediction information, the first proportion information and the first working frequency corresponding to the brain physiological data to obtain a second processing result corresponding to the brain physiological data.
In some examples, after acquiring the brain physiological data, the first processing module 1305 in this embodiment may also be configured to: identifying whether an abnormal discharge signal is included in the brain physiological data; when the abnormal discharge signal is included in the brain physiological data, the position information of the abnormal discharge signal is determined.
In some examples, after determining the position information of the abnormal discharge signal, the first processing module 1305 in this embodiment may be further configured to: and processing the position information, the first duty ratio information and the first working frequency of the abnormal discharge signal to obtain a third processing result of the brain physiological data.
In some examples, after determining the position information of the abnormal discharge signal, the first processing module 1305 in this embodiment may be further configured to: and processing the position information of the abnormal discharge signal, disease prediction information corresponding to the brain physiological data, the first proportion information and the first working frequency to obtain a fourth processing result of the brain physiological data.
In some examples, when the first acquisition module 1301 acquires brain physiological data, the first acquisition module 1301 may be configured to perform: acquiring data signals of all channels acquired by electroencephalogram acquisition equipment; filtering the data signal of each channel to obtain a filtered data signal; based on the filtered data signal, brain physiological data is obtained.
In some examples, prior to obtaining brain physiological data based on the filtered data signal, the first obtaining module 1301 in this embodiment may be configured to perform: identifying a data quality of the filtered data signal; and when the data quality meets the preset requirement, generating the brain physiological data based on the filtered data signal.
In some examples, when the first acquisition module 1301 generates brain physiological data based on the filtered data signal, the first acquisition module 1301 may be configured to perform: acquiring data quantity information of all filtered data signals; and when the data volume information is greater than or equal to a preset threshold value, generating the brain physiological data based on the filtered data signal.
In some examples, the first processing module 1305 in this embodiment may be further configured to: and when the data volume information is greater than or equal to the preset threshold value, stopping acquiring the data signals of all channels acquired by the electroencephalogram acquisition equipment.
In some examples, before the data amount information is greater than or equal to the preset threshold, the first processing module 1305 in this embodiment may be further configured to: acquiring the acquisition time of a data signal; and when the acquisition time is greater than or equal to the preset time threshold, generating prompt information, wherein the prompt information is used for identifying the operation abnormity of the acquired data signal.
In some examples, when the data quality meets a preset requirement, the first processing module 1305 may be further configured to: and displaying the filtered data signal.
The apparatus shown in fig. 16 can perform the method of the embodiment shown in fig. 4-13, and the related descriptions of the embodiment shown in fig. 4-13 can be referred to for the part not described in detail in this embodiment. The implementation process and technical effect of the technical solution refer to the descriptions in the embodiments shown in fig. 4 to 13, and are not described herein again.
In one possible design, the structure of the brain physiological data processing apparatus shown in fig. 16 may be implemented as an electronic device, which may be a brain electrical acquisition device, a server, or other various devices. As shown in fig. 17, the electronic device may include: a processor 1401, and a memory 1402. Wherein the memory 1402 is used for storing programs of corresponding electronic devices to execute the processing methods of brain physiological data provided in the embodiments shown in fig. 4-13, and the processor 1401 is configured to execute the programs stored in the memory 1402.
The program comprises one or more computer instructions, wherein the one or more computer instructions, when executed by the processor 901, enable the following steps to be performed:
acquiring a data processing request aiming at brain physiological data;
dividing the brain physiological data into a plurality of data segments;
identifying data types corresponding to the plurality of data fragments respectively;
determining at least two continuous first type data in a plurality of data segments, wherein the first type data are non-artifact data;
at least two successive first type data are processed to obtain a processed result of the brain physiological data.
Further, the processor 1401 is also used to execute all or part of the steps in the embodiments shown in fig. 4 to 13.
The electronic device may further include a communication interface 1403 in the structure, which is used for the electronic device to communicate with other devices or a communication network.
In addition, the embodiment of the present invention provides a computer storage medium for storing computer software instructions for an electronic device, which contains a program for executing the method for processing brain physiological data in the method embodiments shown in fig. 4 to 13.
Fig. 18 is a schematic structural diagram of an apparatus for acquiring brain physiological data according to an exemplary embodiment of the present application; referring to fig. 18, the present embodiment provides an acquisition apparatus for brain physiological data, which may include: a second acquisition module 1501, a second filtering module 1502, a second identification module 1503 and a second processing module 1504. In particular, the method comprises the following steps of,
a second obtaining module 1501, configured to obtain physiological data of a brain of a human body;
a second filtering module 1502, configured to perform filtering processing on the brain physiological data to obtain a filtered data signal;
a second identifying module 1503 for identifying the data quality of the filtered data signal;
and the second processing module 1504 is configured to generate the brain physiological data by using the filtered data signal when the data quality meets a preset requirement.
In some examples, when the second processing module 1504 uses the filtered data signal to generate brain physiological data, the second processing module 1504 may be operable to perform: acquiring data quantity information of all filtered data signals; and when the data volume information is greater than or equal to a preset threshold value, generating the brain physiological data based on the filtered data signal.
In some examples, the second processing module 1504 in this embodiment may be configured to perform: and stopping acquiring the brain physiological data when the data volume information is greater than or equal to a preset threshold value.
In some examples, before the data amount information is greater than or equal to the preset threshold, the second processing module 1504 in this embodiment may be configured to perform: acquiring the acquisition time of a data signal; and when the acquisition time is greater than or equal to the preset time threshold, generating prompt information, wherein the prompt information is used for identifying the operation abnormity of the acquired data signal.
In some examples, when the data quality meets the preset requirement, the second processing module 1504 in this embodiment may be configured to perform: and displaying the filtered data signal.
The apparatus shown in fig. 18 can execute the method of the embodiment shown in fig. 14, and reference may be made to the related description of the embodiment shown in fig. 14 for a part of this embodiment that is not described in detail. The implementation process and technical effect of this technical solution are described in the embodiment shown in fig. 14, and are not described herein again.
In one possible design, the structure of the brain physiological data acquisition apparatus shown in fig. 18 can be implemented as an electronic device, which can be a brain electrical acquisition device, a server, or other various devices. As shown in fig. 19, the electronic device may include: a processor 1601, and a memory 1602. Wherein, the memory 1602 is used for storing a program for executing the method for acquiring brain physiological data provided in the embodiment shown in fig. 14 by a corresponding electronic device, and the processor 1601 is configured to execute the program stored in the memory 1602.
The program comprises one or more computer instructions, wherein the one or more computer instructions, when executed by the processor 901, enable the following steps to be performed:
acquiring brain physiological data of a human body;
filtering the brain physiological data to obtain a filtered data signal;
identifying a data quality of the filtered data signal;
and when the data quality meets the preset requirement, generating the brain physiological data by using the filtered data signal.
Further, the processor 1601 is further configured to perform all or part of the steps in the embodiment shown in fig. 14.
The electronic device may further include a communication interface 1603 for enabling the electronic device to communicate with other devices or a communication network.
In addition, the embodiment of the present invention provides a computer storage medium for storing computer software instructions for an electronic device, which contains a program for executing the method for acquiring brain physiological data in the embodiment of the method shown in fig. 14.
Fig. 20 is a schematic structural diagram of a data processing apparatus according to an exemplary embodiment of the present application; referring to fig. 20, the present embodiment provides a data processing apparatus, which may include: a third obtaining module 1701, a third dividing module 1702, a third identifying module 1703, a third determining module 1704, and a third processing module 1705. In particular, the method comprises the following steps of,
a third obtaining module 1701 for obtaining a data processing request for data to be processed;
a third dividing module 1702, configured to divide the data to be processed into a plurality of data segments;
a third identifying module 1703, configured to identify a data type corresponding to each of the plurality of data segments;
a third determining module 1704, configured to determine at least two consecutive data of a first type in the plurality of data segments, where the data of the first type is non-artifact data;
the third processing module 1705 is configured to process at least two consecutive data of the first type to obtain a processing result of the data to be processed.
In some examples, the data to be processed includes at least one of the following types: brain physiological data, electrocardiosignals and stomach electric signals.
The apparatus shown in fig. 20 can execute the method of the embodiment shown in fig. 15, and reference may be made to the related description of the embodiment shown in fig. 15 for a part of this embodiment that is not described in detail. The implementation process and technical effect of this technical solution are described in the embodiment shown in fig. 15, and are not described herein again.
In one possible design, the structure of the data processing apparatus shown in fig. 20 may be implemented as an electronic device, which may be a mobile phone, a terminal device, a server, or other devices. As shown in fig. 21, the electronic device may include: a processor 1801 and a memory 1802. Wherein, the memory 1802 is used for storing a program for executing the processing method of the data provided in the embodiment shown in fig. 15 described above by the corresponding electronic device, and the processor 1801 is configured to execute the program stored in the memory 1802.
The program includes one or more computer instructions which, when executed by the processor 1801, enable the following steps to be performed:
acquiring a data processing request aiming at data to be processed;
dividing data to be processed into a plurality of data fragments;
identifying data types corresponding to the plurality of data fragments respectively;
determining at least two continuous first type data in a plurality of data segments, wherein the first type data are non-artifact data;
and processing at least two continuous first type data to obtain a processing result of the data to be processed.
Further, the processor 1801 is also configured to perform all or part of the steps in the foregoing embodiment shown in fig. 15.
The electronic device may further include a communication interface 1803, which is used for the electronic device to communicate with other devices or a communication network.
Furthermore, an embodiment of the present invention provides a computer storage medium for storing computer software instructions for an electronic device, which includes a program for executing the processing method of data in the method embodiment shown in fig. 15.
Fig. 22 is a schematic structural diagram of a detection cap according to an exemplary embodiment of the present application. Referring to fig. 22, the present embodiment provides a detection cap 2201, wherein the detection cap 2201 can be used to detect and transmit brain physiological data to a server 2202, and specifically, the detection cap 2201 includes:
the detection electrode 2201a is used for contacting with the brain of a human body to acquire brain physiological data;
a data transmission module 2201b for transmitting the brain physiological data to a server 2202, the server 2202 configured to: dividing the brain physiological data into a plurality of data segments; identifying data types corresponding to the plurality of data fragments respectively; determining at least two consecutive data of a first type in the plurality of data segments, the data of the first type being non-artifact data; processing the at least two consecutive first type data to obtain a processing result of the brain physiological data.
In some examples, the data transmission module 2201b is configured to receive a processing result of the brain physiological data. The detection cap 2201 further comprises: an output module 2201c, configured to output the processing result.
Specifically, after the detection electrode 2201a acquires the brain physiological data, the brain physiological data can be directly sent to the server 2202 through the data transmission module 2201b, and the server 2202 can analyze and process the brain physiological data to obtain a processing result of the brain physiological data; and the processing result of the brain physiological data can be returned to the detection cap 2201, at this time, the detection cap 2201 can receive the processing result of the brain physiological data through the data transmission module 2201b and output the processing result by the output module 2201 c.
In some examples, the data transmission module 2201b is communicatively coupled to the mobile device 2203 and configured to transmit the brain physiological data to the server 2202 via the mobile device 2203.
Specifically, after the detection electrode 2201a acquires the brain physiological data, the brain physiological data can be sent to the mobile device 2203 through the data transmission module 2201b, the mobile device 2203 can display the brain physiological data and send the brain physiological data to the server 2202, and at this time, the server 2202 can analyze and process the brain physiological data to obtain a processing result of the brain physiological data; and may return the results of the processing of the brain physiological data to the mobile device 2203, at which point the mobile device 2203 may receive and display the results of the processing of the brain physiological data.
In some examples, the data transmission module 2201b is further configured to receive a prompt message; the output module 2201c is further configured to output the prompt message.
Specifically, the server 2202 is configured to generate corresponding prompt information based on a processing result of the brain physiological data, and send the prompt information to the detection cap 2201, the detection cap 2201 receives the prompt information through the data transmission module 2201b, and can output the prompt information through the output module 2201c, where the output module 2201c may include a voice output module and/or a text output module, and when the output module 2201c includes the voice output module, the prompt information may be broadcasted by voice in a preset sound, for example: the preset sound is used for broadcasting 'no abnormity of the detection at this time'; when the output module 2201c includes a text output module, the prompt information may be displayed in a preset text form, for example: and displaying a preset text that the detection has a slight Parkinson precursor, and the patient is asked to go to a hospital for examination, and the like. In some examples, in order to facilitate prompting the user, the data transmission module 2201b in this embodiment may further send the received prompt information and the processing result to the mobile device 2203, so that the user may directly view the relevant prompt information through the mobile device 2203, thereby further improving the convenience of using the detection cap 2201.
In addition, in some of the flows described in the above embodiments and the drawings, a plurality of operations are included in a specific order, but it should be clearly understood that the operations may be executed out of the order presented herein or in parallel, and the sequence numbers of the operations, such as 601, 602, 603, etc., are merely used for distinguishing different operations, and the sequence numbers themselves do not represent any execution order. Additionally, the flows may include more or fewer operations, and the operations may be performed sequentially or in parallel. It should be noted that, the descriptions of "first", "second", etc. in this document are used for distinguishing different messages, devices, modules, etc., and do not represent a sequential order, nor limit the types of "first" and "second" to be different.
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by adding a necessary general hardware platform, and of course, can also be implemented by a combination of hardware and software. With this understanding in mind, the above-described aspects and portions of the present technology which contribute substantially or in part to the prior art may be embodied in the form of a computer program product, which may be embodied on one or more computer-usable storage media having computer-usable program code embodied therein, including without limitation disk storage, CD-ROM, optical storage, and the like.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable multimedia data computing device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable multimedia data computing device, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable multimedia data computing device to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable multimedia data computing device to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (52)
1. A system for processing brain physiological data, comprising:
the system comprises a preprocessing component, a data processing component and a data processing component, wherein the preprocessing component is used for acquiring a data processing request aiming at brain physiological data, dividing the brain physiological data into a plurality of data fragments, identifying data types corresponding to the data fragments respectively, and determining at least two continuous first type data in the data fragments, wherein the first type data are non-artifact data;
and the data segment analysis component is in communication connection with the preprocessing component and is used for acquiring the at least two continuous first-type data and processing the at least two continuous first-type data to obtain a processing result of the brain physiological data.
2. The system of claim 1, the data segment analysis component comprising:
the first obtaining subcomponent is used for obtaining first proportion information of data fragments of each preset data type in all at least two continuous first type data;
a second acquisition subcomponent for determining a first operating frequency corresponding to all of the at least two consecutive data of the first type;
a data processing subassembly, communicatively connected to the first acquisition subassembly and the second acquisition subassembly, for processing the first proportion information and the first operating frequency to obtain a first processing result of the brain physiological data.
3. The system of claim 2, wherein the pre-set type data comprises at least one of: alpha wave data, fast wave data, slow wave data.
4. The system according to claim 2, wherein when the first obtaining subcomponent obtains the first proportion information of the data segments of each preset data type in all of the at least two consecutive first type data, the first obtaining subcomponent is configured to:
acquiring frequency spectrum information of all at least two continuous first type data;
and calculating first proportion information of the data segments of the preset type data based on the frequency spectrum information.
5. The system of claim 4, wherein when the first acquisition subcomponent acquires spectral information for all of the at least two consecutive data of the first type, the first acquisition subcomponent is configured to:
acquiring frequency domain data corresponding to all of the at least two consecutive first type data;
and performing power spectral density calculation on the frequency domain data to obtain spectral information of all at least two continuous first type data.
6. The system according to claim 4, wherein when the first obtaining subcomponent calculates first proportion information of data segments of respective preset types of data based on the spectrum information, the first obtaining subcomponent is configured to:
determining second proportion information of data segments of each preset type of data in each at least two continuous first types of data based on the frequency spectrum information;
and determining the average value of all second proportion information of each preset type of data as the first proportion information of the data segment of each preset type of data aiming at all at least two continuous first type of data.
7. The system of claim 2, wherein when the second acquisition subcomponent determines a first operating frequency corresponding to all of the at least two consecutive first type data, the second acquisition subcomponent is to:
acquiring a second operating frequency corresponding to each of the at least two consecutive first type data;
based on all of the second operating frequencies, first operating frequencies corresponding to all of the at least two consecutive data of the first type are calculated.
8. The system of claim 7, wherein while the second acquisition subcomponent acquires a second operating frequency corresponding to each of the at least two consecutive first type data, the second acquisition subcomponent is configured to:
and acquiring a second working frequency corresponding to each of the at least two continuous first type data based on the frequency spectrum information of each of the at least two continuous first type data.
9. The system of claim 7, wherein when the second acquisition subcomponent calculates the first operating frequency corresponding to all of the at least two consecutive first type data based on all of the second operating frequencies, the second acquisition subcomponent is configured to:
an average of all the second operating frequencies is determined as the first operating frequency corresponding to all of the at least two consecutive data of the first type.
10. The system of claim 4, wherein the data processing subcomponent is further configured to:
after acquiring the spectrum information of all of the at least two consecutive data of the first type, generating a brain topographic map corresponding to the brain physiological data based on the spectrum information of all of the at least two consecutive data of the first type.
11. The system of claim 2, wherein the data processing subcomponent is further configured to:
after determining at least two consecutive first type data, generating a brain topographic map corresponding to the at least two consecutive first type data based on the at least two consecutive first type data.
12. The system of claim 2, further comprising: a disease risk assessment component, communicatively coupled to the preprocessing component, to:
acquiring the at least two continuous data of the first type;
acquiring time domain characteristics, frequency domain characteristics and lead connection characteristics of all at least two continuous first types of data;
and analyzing and processing based on all the time domain characteristics, the frequency domain characteristics and the lead connection characteristics to obtain disease prediction information corresponding to the brain physiological data.
13. The system of claim 12, wherein when the disease risk assessment component performs analysis processing based on all of the time domain features, frequency domain features, and lead connection features to obtain disease prediction information corresponding to the brain physiological data, the disease risk assessment component is configured to:
acquiring first prediction information corresponding to all of the at least two continuous first type data based on all of the time domain features, the frequency domain features and the lead connection features;
determining disease prediction information corresponding to the brain physiological data based on all the first prediction information.
14. The system of claim 12, further comprising: an integrated analysis component, communicatively coupled to the data segment analysis component and the disease risk assessment component, to:
acquiring the first proportion information, the first working frequency and disease prediction information corresponding to the brain physiological data;
and processing the disease prediction information, the first proportion information and the first working frequency corresponding to the brain physiological data to obtain a second processing result corresponding to the brain physiological data.
15. The system according to any one of claims 2-14, further comprising: an abnormal discharge signal detection assembly for:
acquiring brain physiological data;
identifying whether an abnormal discharge signal is included in the brain physiological data;
when an abnormal discharge signal is included in the brain physiological data, determining location information of the abnormal discharge signal.
16. The system of claim 15, further comprising: the comprehensive analysis component is in communication connection with the data segment analysis component and the abnormal discharge signal detection component and is used for:
acquiring the first proportion information, the first working frequency and the position information of the abnormal discharge signal;
and processing the position information of the abnormal discharge signal, the first duty ratio information and the first working frequency to obtain a third processing result of the brain physiological data.
17. The system of claim 16, wherein the integrated analysis component is communicatively coupled to a disease risk assessment component for:
acquiring disease prediction information corresponding to the brain physiological data;
and processing the position information of the abnormal discharge signal, disease prediction information corresponding to the brain physiological data, the first proportion information and the first working frequency to obtain a fourth processing result of the brain physiological data.
18. The system of claim 15, further comprising: a data acquisition component in communicative connection with the preprocessing component for:
acquiring a brain data signal of a human body;
filtering the brain data signal to obtain a filtered data signal;
obtaining the brain physiological data based on the filtered data signal.
19. The system of claim 18, wherein the data collection component is further configured to:
identifying a data quality of the filtered data signal prior to obtaining the brain physiological data based on the filtered data signal;
and when the data quality meets a preset requirement, generating the brain physiological data based on the filtered data signal.
20. The system of claim 19, wherein the data collection component is further configured to:
acquiring data quantity information of all filtered data signals;
and when the data volume information is greater than or equal to a preset threshold value, generating the brain physiological data based on the filtered data signal.
21. The system of claim 20, wherein the data collection component is further configured to:
and when the data volume information is greater than or equal to a preset threshold value, stopping collecting the brain data signals of the human body.
22. The system of claim 20, wherein the data collection component is further configured to:
acquiring the acquisition time of the data signal before the data volume information is greater than or equal to a preset threshold;
and when the acquisition time is greater than or equal to a preset time threshold, generating prompt information, wherein the prompt information is used for identifying the operation abnormity of acquiring the data signal.
23. The system of claim 19, wherein the data collection component is further configured to: and when the data quality meets a preset requirement, displaying the filtered data signal.
24. A method for processing brain physiological data, comprising:
acquiring a data processing request aiming at brain physiological data;
dividing the brain physiological data into a plurality of data segments;
identifying data types corresponding to the plurality of data fragments respectively;
determining at least two consecutive data of a first type in the plurality of data segments, the data of the first type being non-artifact data;
processing the at least two consecutive first type data to obtain a processing result of the brain physiological data.
25. The method of claim 24, wherein processing the at least two consecutive data of the first type to obtain a processing result corresponding to the data processing request comprises:
acquiring first proportion information of data segments of each preset data type from all at least two continuous first type data;
determining a first operating frequency corresponding to all of the at least two consecutive data of the first type;
and processing the first proportion information and the first working frequency to obtain a first processing result of the brain physiological data.
26. The method of claim 25, wherein the preset type data comprises at least one of: alpha wave data, fast wave data, slow wave data.
27. The method of claim 25, wherein obtaining first proportion information of data segments of each preset data type in all of the at least two consecutive first type data comprises:
acquiring frequency spectrum information of all at least two continuous first type data;
and calculating first proportion information of the data segments of the preset type data based on the frequency spectrum information.
28. The method of claim 27, wherein obtaining spectral information for all of the at least two consecutive data of the first type comprises:
acquiring frequency domain data corresponding to all of the at least two consecutive first type data;
and performing power spectral density calculation on the frequency domain data to obtain spectral information of all at least two continuous first type data.
29. The method according to claim 27, wherein the calculating the first ratio information of the data segments of each preset type of data based on the spectrum information comprises:
determining second proportion information of data segments of each preset type of data in each at least two continuous first types of data based on the frequency spectrum information;
and determining the average value of all second proportion information of each preset type of data as the first proportion information of the data segment of each preset type of data aiming at all at least two continuous first type of data.
30. The method of claim 27, wherein determining the first operating frequency corresponding to all of the at least two consecutive data of the first type comprises:
acquiring a second operating frequency corresponding to each of the at least two consecutive first type data;
based on all of the second operating frequencies, first operating frequencies corresponding to all of the at least two consecutive data of the first type are calculated.
31. The method of claim 30, wherein said obtaining a second operating frequency corresponding to each of said at least two consecutive data of the first type comprises:
and acquiring a second working frequency corresponding to each of the at least two continuous first type data based on the frequency spectrum information of each of the at least two continuous first type data.
32. The method of claim 30, wherein calculating the first operating frequency corresponding to all of the at least two consecutive data of the first type based on all of the second operating frequencies comprises:
an average of all the second operating frequencies is determined as the first operating frequency corresponding to all of the at least two consecutive data of the first type.
33. The method of claim 27, wherein after obtaining spectral information for all of the at least two consecutive data of the first type, the method further comprises:
generating a brain topographic map corresponding to the brain physiological data based on the spectral information of all of the at least two consecutive data of the first type.
34. The method of any one of claims 25, wherein after determining at least two consecutive data of the first type, the method further comprises:
based on the at least two consecutive first type data, a brain map corresponding to the at least two consecutive first type data is generated.
35. The method of any one of claims 25, wherein after determining at least two consecutive data of the first type, the method further comprises:
acquiring time domain characteristics, frequency domain characteristics and lead connection characteristics of all at least two continuous first types of data;
and analyzing and processing based on all the time domain characteristics, the frequency domain characteristics and the lead connection characteristics to obtain disease prediction information corresponding to the brain physiological data.
36. The method of claim 35, wherein the analyzing process based on all time domain features, frequency domain features and lead connection features to obtain disease prediction information corresponding to the brain physiological data comprises:
acquiring first prediction information corresponding to all of the at least two continuous first type data based on all of the time domain features, the frequency domain features and the lead connection features;
determining disease prediction information corresponding to the brain physiological data based on all the first prediction information.
37. The method of claim 35, wherein after obtaining disease prediction information corresponding to the brain physiological data, the method further comprises:
and processing the disease prediction information, the first proportion information and the first working frequency corresponding to the brain physiological data to obtain a second processing result corresponding to the brain physiological data.
38. The method according to any one of claims 25-37, further comprising:
acquiring brain physiological data;
identifying whether an abnormal discharge signal is included in the brain physiological data;
when an abnormal discharge signal is included in the brain physiological data, determining location information of the abnormal discharge signal.
39. The method of claim 38, wherein after determining the position information of the abnormal discharge signal, the method further comprises:
and processing the position information of the abnormal discharge signal, the first duty ratio information and the first working frequency to obtain a third processing result of the brain physiological data.
40. The method of claim 38, wherein after determining the position information of the abnormal discharge signal, the method further comprises:
and processing the position information of the abnormal discharge signal, disease prediction information corresponding to the brain physiological data, the first proportion information and the first working frequency to obtain a fourth processing result of the brain physiological data.
41. The method of claim 38, wherein acquiring brain physiological data comprises:
acquiring a brain data signal of a human body;
filtering the brain data signal to obtain a filtered data signal;
obtaining the brain physiological data based on the filtered data signal.
42. The method of claim 41, wherein prior to obtaining the brain physiological data based on the filtered data signal, the method further comprises:
identifying a data quality of the filtered data signal;
and when the data quality meets a preset requirement, generating the brain physiological data based on the filtered data signal.
43. The method of claim 42, wherein generating the brain physiological data based on the filtered data signal comprises:
acquiring data quantity information of all filtered data signals;
and when the data volume information is greater than or equal to a preset threshold value, generating the brain physiological data based on the filtered data signal.
44. The method of claim 43, further comprising:
and when the data volume information is greater than or equal to a preset threshold value, stopping acquiring the data signals of all channels acquired by the electroencephalogram acquisition equipment.
45. The method of claim 43, wherein before the data amount information is greater than or equal to a preset threshold, the method further comprises:
acquiring the acquisition time of the data signal;
and when the acquisition time is greater than or equal to a preset time threshold, generating prompt information, wherein the prompt information is used for identifying the operation abnormity of acquiring the data signal.
46. The method of claim 42, wherein when the data quality meets a preset requirement, the method further comprises:
and displaying the filtered data signal.
47. A device for processing brain physiological data, comprising a memory and a processor;
the memory for storing a computer program;
the processor to execute the computer program to:
acquiring a data processing request aiming at brain physiological data;
dividing the brain physiological data into a plurality of data segments;
identifying data types corresponding to the plurality of data fragments respectively;
determining at least two consecutive data of a first type in the plurality of data segments, the data of the first type being non-artifact data;
processing the at least two consecutive first type data to obtain a processing result of the brain physiological data.
48. A computer readable storage medium having a computer program stored thereon, which, when executed by one or more processors, causes the one or more processors to carry out the steps of the method of any one of claims 24-46.
49. A detection cap, comprising:
the detection electrode is used for contacting with the human brain to acquire brain physiological data;
a data transmission module for transmitting the brain physiological data to a server, the server configured to: dividing the brain physiological data into a plurality of data segments; identifying data types corresponding to the plurality of data fragments respectively; determining at least two consecutive data of a first type in the plurality of data segments, the data of the first type being non-artifact data; processing the at least two consecutive first type data to obtain a processing result of the brain physiological data.
50. The detection cap of claim 49,
the data transmission module is in communication connection with the mobile device and is used for sending the brain physiological data to the server through the mobile device.
51. The detection cap of claim 49,
the data transmission module is used for receiving the processing result of the brain physiological data;
the detection cap further comprises:
and the output module is used for outputting the processing result.
52. The detection cap of claim 51,
the data transmission module is also used for receiving prompt information;
the output module is further used for outputting the prompt information.
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