CN117224151B - Early warning method and system for electroencephalogram abnormal signals - Google Patents
Early warning method and system for electroencephalogram abnormal signals Download PDFInfo
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Abstract
The invention relates to the technical field of brain waves, and particularly discloses an early warning method and system for brain electrical anomaly signals. According to the invention, by acquiring the brain electricity monitoring data of the user, analyzing the brain electricity monitoring data and judging whether an brain electricity abnormal signal exists or not; performing environment monitoring, acquiring environment monitoring data, performing environment analysis, and judging whether environment abnormality exists or not; when the environment is abnormal, comparing and early-warning the electroencephalogram monitoring data based on preset standard early-warning data; and when no environment abnormality exists, performing direct early warning processing. The method and the device avoid early warning and alarming of the normal electroencephalogram abnormality, avoid inconvenience in life and improve the use experience of users.
Description
Technical Field
The invention belongs to the technical field of brain waves, and particularly relates to an early warning method and system for brain electrical anomaly signals.
Background
The electroencephalogram abnormality refers to that the electroencephalogram deviates from the normal range, and clinically common abnormal electroencephalograms comprise diffuse slow waves, focal slow waves, triphase waves, epileptic-like discharge, periodic spike waves and the like. The diseases causing brain wave abnormality are too many, such as fever, vomiting, electrolyte disorder and the like, and serious encephalitis, meningitis, epilepsy and the like are caused. An abnormal electroencephalogram only illustrates one brain functional state, and has definite diagnostic significance only after clinical signs of a patient before and after examination are compared and observed in combination with clinic.
In the prior art, for the early warning of the brain electrical abnormal signal, the early warning and alarming are directly carried out when the brain electrical signal is abnormal. However, in practical application, the situations of the electroencephalogram abnormality are various, some electroencephalogram abnormalities are generated under normal conditions, and the normally generated electroencephalogram abnormality also carries out early warning and alarming, so that the use experience of a user can be influenced, and the inconvenience of life is caused.
Disclosure of Invention
The embodiment of the invention aims to provide an early warning method and system for an electroencephalogram abnormal signal, and aims to solve the problems in the background technology.
In order to achieve the above object, the embodiment of the present invention provides the following technical solutions:
an early warning method of an electroencephalogram abnormal signal specifically comprises the following steps:
acquiring brain electricity monitoring data of a user, analyzing the brain electricity monitoring data, and judging whether an brain electricity abnormal signal exists or not;
when the brain electrical abnormality signal exists, environment monitoring is carried out, environment monitoring data are obtained, environment analysis is carried out, and whether environment abnormality exists or not is judged;
when the environment is abnormal, comparing and early warning the electroencephalogram monitoring data based on preset standard early warning data;
and when no environment abnormality exists, performing direct early warning processing.
As a further limitation of the technical solution of the embodiment of the present invention, the acquiring the electroencephalogram monitoring data of the user, analyzing the electroencephalogram monitoring data, and judging whether there is an electroencephalogram abnormal signal specifically includes the following steps:
performing electroencephalogram connection monitoring on a user to obtain connection monitoring data;
analyzing the connection monitoring data to judge whether the brain electricity is stably connected;
acquiring brain electricity monitoring data when brain electricity is stably connected;
and analyzing the electroencephalogram monitoring data to judge whether an electroencephalogram abnormal signal exists or not.
As a further limitation of the technical solution of the embodiment of the present invention, the analyzing the electroencephalogram monitoring data to determine whether there is an electroencephalogram abnormal signal specifically includes the following steps:
constructing an electroencephalogram change waveform according to the electroencephalogram monitoring data;
detecting the frequency, amplitude and waveform of the electroencephalogram change waveform to generate an electroencephalogram detection result;
and judging whether an electroencephalogram abnormal signal exists or not according to the electroencephalogram detection result.
As a further limitation of the technical solution of the embodiment of the present invention, when an electroencephalogram abnormal signal is provided, performing environmental monitoring, obtaining environmental monitoring data, performing environmental analysis, and determining whether an environmental abnormality exists specifically includes the following steps:
when the brain electrical abnormality signal exists, performing environment monitoring on heart rate, blood pressure and blood oxygen to acquire environment monitoring data;
comparing the environment monitoring data with preset environment standard data;
when the environment monitoring data are not in the range of the environment standard data, judging that the environment is abnormal;
and when the environment monitoring data are in the range of the environment standard data, judging that no environment abnormality exists.
As a further limitation of the technical solution of the embodiment of the present invention, when an environmental abnormality exists, based on preset standard early warning data, the comparing early warning processing for the electroencephalogram monitoring data specifically includes the following steps:
when the environment is abnormal, importing preset standard early warning data;
comparing the electroencephalogram monitoring data with the standard early warning data, and judging whether the electroencephalogram monitoring data is in an abnormal early warning state or not;
generating a comparison early warning signal when the device is in an abnormal early warning state;
and comparing, early warning and alarming according to the comparison and early warning signals.
As a further limitation of the technical solution of the embodiment of the present invention, when no environmental abnormality exists, the performing the direct early warning process specifically includes the following steps:
generating a direct early warning signal when no environment abnormality exists;
and carrying out direct early warning and alarming according to the direct early warning signal.
An early warning system of brain electricity abnormal signal, the system includes brain electricity monitoring analysis module, environment abnormality judgement module, comparison early warning processing module and direct early warning processing module, wherein:
the electroencephalogram monitoring and analyzing module is used for acquiring electroencephalogram monitoring data of a user, analyzing the electroencephalogram monitoring data and judging whether an electroencephalogram abnormal signal exists or not;
the environment abnormality judging module is used for carrying out environment monitoring when the electroencephalogram abnormality signal is provided, acquiring environment monitoring data, carrying out environment analysis and judging whether environment abnormality exists or not;
the comparison early warning processing module is used for comparing and early warning the electroencephalogram monitoring data based on preset standard early warning data when the environment is abnormal;
and the direct early warning processing module is used for carrying out direct early warning processing when no environment abnormality exists.
As a further limitation of the technical solution of the embodiment of the present invention, the electroencephalogram monitoring and analyzing module specifically includes:
the connection monitoring unit is used for carrying out electroencephalogram connection monitoring on a user and acquiring connection monitoring data;
the connection judging unit is used for analyzing the connection monitoring data and judging whether the brain electricity is stably connected or not;
the data acquisition unit is used for acquiring brain electricity monitoring data when brain electricity is stably connected;
and the anomaly judging unit is used for analyzing the electroencephalogram monitoring data and judging whether an electroencephalogram anomaly signal exists or not.
As further defined by the technical solution of the embodiment of the present invention, the environmental anomaly determination module specifically includes:
the environment monitoring unit is used for carrying out environment monitoring on heart rate, blood pressure and blood oxygen when the brain electrical abnormality signal exists, and acquiring environment monitoring data;
the environment comparison unit is used for comparing the environment monitoring data with preset environment standard data;
an environment judging unit for judging that an environment abnormality exists when the environment monitoring data is not in the range of the environment standard data; and when the environment monitoring data are in the range of the environment standard data, judging that no environment abnormality exists.
As a further limitation of the technical solution of the embodiment of the present invention, the comparison early-warning processing module specifically includes:
the standard importing unit is used for importing preset standard early warning data when the environment is abnormal;
the state judging unit is used for comparing the electroencephalogram monitoring data with the standard early warning data and judging whether the electroencephalogram monitoring data is in an abnormal early warning state or not;
the signal generating unit is used for generating a comparison early warning signal when the signal generating unit is in an abnormal early warning state;
and the comparison early warning unit is used for carrying out comparison early warning according to the comparison early warning signal.
Compared with the prior art, the invention has the beneficial effects that:
according to the embodiment of the invention, the electroencephalogram monitoring data of the user is obtained, and is analyzed to judge whether an electroencephalogram abnormal signal exists or not; performing environment monitoring, acquiring environment monitoring data, performing environment analysis, and judging whether environment abnormality exists or not; when the environment is abnormal, comparing and early-warning the electroencephalogram monitoring data based on preset standard early-warning data; and when no environment abnormality exists, performing direct early warning processing. The electroencephalogram monitoring data can be analyzed, when an electroencephalogram abnormal signal exists, environment analysis is carried out, when the environment abnormality exists, comparison early warning processing is carried out on the electroencephalogram monitoring data, when the environment abnormality does not exist, direct early warning processing is carried out, early warning and warning are stopped on the electroencephalogram abnormality which is normally generated, inconvenience in life is avoided, and the use experience of a user is improved.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following description will briefly introduce the drawings that are needed in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are only some embodiments of the present invention.
Fig. 1 shows a flowchart of a method provided by an embodiment of the present invention.
Fig. 2 shows a flowchart of electroencephalogram monitoring data acquisition and analysis in the method according to the embodiment of the invention.
Fig. 3 shows a flowchart of determining an electroencephalogram abnormal signal in the method according to the embodiment of the present invention.
Fig. 4 shows a flowchart of environment analysis anomaly determination in the method according to the embodiment of the present invention.
Fig. 5 shows a flowchart of a comparison early warning process in the method provided by the embodiment of the invention.
Fig. 6 shows a flowchart of a direct early warning process in the method provided by the embodiment of the invention.
Fig. 7 shows an application architecture diagram of a system provided by an embodiment of the present invention.
Fig. 8 shows a block diagram of a system electroencephalogram monitoring and analyzing module according to an embodiment of the present invention.
Fig. 9 is a block diagram showing a configuration of an environmental anomaly determination module in the system according to an embodiment of the present invention.
Fig. 10 shows a block diagram of a comparison early warning processing module in the system according to the embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
It can be understood that in the prior art, for the early warning of the brain electrical abnormal signal, the early warning and alarming are directly carried out when the brain electrical signal is abnormal. However, in practical application, the situations of the electroencephalogram abnormality are various, some electroencephalogram abnormalities are generated under normal conditions, and the normally generated electroencephalogram abnormality also carries out early warning and alarming, so that the use experience of a user can be influenced, and the inconvenience of life is caused.
In order to solve the problems, the embodiment of the invention analyzes the brain electrical monitoring data by acquiring the brain electrical monitoring data of the user and judges whether the brain electrical monitoring data has brain electrical abnormal signals or not; performing environment monitoring, acquiring environment monitoring data, performing environment analysis, and judging whether environment abnormality exists or not; when the environment is abnormal, comparing and early-warning the electroencephalogram monitoring data based on preset standard early-warning data; and when no environment abnormality exists, performing direct early warning processing. The invention can analyze the brain electrical monitoring data, analyze the environment when the brain electrical abnormal signal exists, compare the brain electrical monitoring data with the early warning when the environment abnormality exists, and directly early warning when the environment abnormality does not exist, thereby avoiding the early warning and alarming of the brain electrical abnormality which is normally generated, avoiding the inconvenience of life and improving the use experience of users.
Fig. 1 shows a flowchart of a method provided by an embodiment of the present invention.
Specifically, the method for early warning of an electroencephalogram abnormal signal specifically comprises the following steps:
step S101, acquiring brain electricity monitoring data of a user, analyzing the brain electricity monitoring data, and judging whether an brain electricity abnormal signal exists or not.
In the embodiment of the invention, the connection monitoring data is acquired by carrying out electroencephalogram connection monitoring on the user, the connection monitoring data is analyzed, whether the electroencephalogram is stably connected is judged, and when the correct connection of the electroencephalogram acquisition is completed and a feedback connection signal can be received, the electroencephalogram stable connection is judged. At this time, an electroencephalogram monitoring instruction is sent, electroencephalogram monitoring acquisition is carried out, electroencephalogram monitoring data are obtained, the electroencephalogram monitoring data are processed, an electroencephalogram variation waveform reflecting the electroencephalogram variation of a user is constructed, an electroencephalogram detection result is generated by detecting the frequency, the amplitude, the waveform and the like of the electroencephalogram variation waveform, and whether an electroencephalogram abnormal signal exists or not is judged according to the electroencephalogram detection result.
Specifically, fig. 2 shows a flowchart of electroencephalogram monitoring data acquisition and analysis in the method provided by the embodiment of the invention.
In a preferred embodiment of the present invention, the acquiring the electroencephalogram monitoring data of the user, analyzing the electroencephalogram monitoring data, and judging whether there is an electroencephalogram abnormal signal specifically includes the following steps:
step S1011, performing brain electrical connection monitoring on a user to obtain connection monitoring data;
step S1012, analyzing the connection monitoring data to judge whether the brain electricity is stably connected;
the method for analyzing the connection monitoring data and judging whether the brain electricity is stably connected comprises the following substeps:
step S1012a, obtaining the connection monitoring data in a preset test time period, and analyzing the connection monitoring data to obtain a monitoring data waveform image;
step S1012b, judging whether there is a break in the monitored data waveform image;
step S1012c, if not, obtaining a signal-to-noise ratio from the monitoring data waveform image, and judging whether the signal-to-noise ratio is within a preset signal-to-noise ratio range;
step S1012d, if yes, determining the corresponding current brain wave type according to the waveform of the monitoring data waveform image;
step S1012e, obtaining frequency data according to the monitoring data waveform image, wherein the frequency data at least comprises a minimum frequency value and a maximum frequency value;
step S1012f, judging whether the current brain wave type is in the frequency range according to the minimum frequency value and the maximum frequency value; and step S1012g, if yes, determining stable connection of the brain electricity.
Step S1013, acquiring brain electricity monitoring data when brain electricity is stably connected;
step S1014, analyzing the electroencephalogram monitoring data to determine whether an electroencephalogram abnormality signal exists.
Specifically, fig. 3 shows a flowchart of determining an electroencephalogram abnormal signal in the method according to the embodiment of the present invention.
In a preferred embodiment of the present invention, the analyzing the electroencephalogram monitoring data to determine whether there is an electroencephalogram abnormal signal specifically includes the following steps:
step S10141, constructing an electroencephalogram change waveform according to the electroencephalogram monitoring data;
step S10142, detecting the frequency, amplitude and waveform of the electroencephalogram variation waveform to generate an electroencephalogram detection result;
in step S10142, the method for detecting the frequency, amplitude and waveform of the electroencephalogram variation waveform and generating the electroencephalogram detection result includes the following sub-steps:
step S10142a, comparing the electroencephalogram variation waveform with various waveforms in a preset waveform database to obtain a corresponding waveform similarity value, and judging whether the waveform similarity value is larger than a preset waveform similarity value or not;
step S10142b, if yes, confirming the current brain wave type corresponding to the brain wave change waveform;
step S10142c, acquiring a plurality of frequency values and a plurality of wave amplitudes based on the electroencephalogram variation waveform, and confirming corresponding reference frequency values and reference wave amplitudes according to the current electroencephalogram type;
step S10142d, calculating a plurality of frequency difference values according to the plurality of frequency values and the reference frequency value, and confirming to obtain the maximum frequency difference value;
step S10142e, calculating a plurality of amplitude differences according to the plurality of amplitude values and the reference amplitude value, and confirming to obtain the maximum amplitude difference;
step S10142f, calculating to obtain an abnormal signal index according to the waveform similarity value, the maximum frequency difference value and the maximum amplitude difference value;
in the present embodiment, the calculation formula of the abnormal signal index is expressed as:
；
wherein,indicating an abnormal signal index,/-, and>reference outlier, < ++representing waveform similarity term>Calibration factor representing waveform similarity term, +.>Representing waveform similarity value, ++>Reference value representing waveform similarity, ++>Reference outlier representing frequency term, +.>Calibration factor representing frequency term, +.>Representing the maximum frequency difference,/>Mean value of the frequency difference>Reference outlier representing amplitude term, +.>Calibration factor representing amplitude term, +.>Indicating the maximum amplitude difference value of the wave,a reference value representing the amplitude difference.
Step S10142g, when judging that the abnormal signal index is larger than a preset abnormal index, judging that the brain electrical abnormal signal exists. As described above, the abnormal signal index is calculatedThereafter, the abnormality signal index ++>Comparing with the preset abnormality index, and judging that the brain electrical abnormality signal exists if the abnormality signal index is larger than the preset abnormality index. Step S10143, according to the electroencephalogramAnd judging whether an electroencephalogram abnormal signal exists or not according to the detection result.
As described above, the electroencephalogram detection result is the calculated abnormal signal index, and whether the electroencephalogram abnormal signal exists or not can be determined by comparing the abnormal signal index with the preset abnormal index.
Further, the early warning method of the brain electrical anomaly signal further comprises the following steps:
and step S102, when the brain electrical abnormality signal is provided, performing environment monitoring, acquiring environment monitoring data, performing environment analysis, and judging whether the environment abnormality exists.
In the embodiment of the invention, when the brain electrical abnormality signal exists, the environment monitoring is carried out. The environment monitoring is to monitor heart rate, blood pressure, blood oxygen and the like of a user, acquire environment monitoring data, acquire preset environment standard data, compare the environment monitoring data with the preset environment standard data, and judge that the environment is abnormal when the environment monitoring data is not in the range of the environment standard data and the user is in an abnormal state environment (such as running, rope skipping, tension and the like); when the environment monitoring data is in the range of the environment standard data, the user is in a normal state environment, and at the moment, it is judged that no environment abnormality exists.
Specifically, fig. 4 shows a flowchart of environment analysis anomaly determination in the method provided by the embodiment of the present invention.
In the preferred embodiment of the present invention, when the electroencephalogram abnormality signal is present, the method performs environmental monitoring, obtains environmental monitoring data, performs environmental analysis, and determines whether an environmental abnormality exists, including the following steps:
step S1021, when an electroencephalogram abnormal signal is provided, performing environment monitoring of heart rate, blood pressure and blood oxygen to obtain environment monitoring data;
step S1022, comparing the environment monitoring data with preset environment standard data;
step S1023, judging that the environment is abnormal when the environment monitoring data is not in the range of the environment standard data;
in this step, if it is determined that an abnormality exists in one of the heart rate, the blood pressure, and the blood oxygen, it may be determined that an environmental abnormality exists.
And step S1024, when the environment monitoring data is in the range of the environment standard data, judging that no environment abnormality exists.
In the invention, in order to more intuitively embody the abnormality degree of the environment monitoring data, the evaluation is performed by an environment abnormality index. Specifically, the calculation formula of the environmental abnormality index is expressed as:
；
wherein,index of environmental abnormality, ->Abnormality index reference value representing heart rate term, +.>Calibration factor representing heart rate term, < ->Representing the current heart rate value in the environmental monitoring data, < >>Represents heart rate reference value, ++>Abnormality index reference value indicating blood pressure item, +.>Calibration factor representing blood pressure term,/->Representing the current blood pressure value in the environmental monitoring data, < >>Indicates the blood pressure reference value->Abnormality index reference value indicating blood oxygen term, +.>A calibration factor representing the blood oxygen term,representing the current blood oxygen value in the environmental monitoring data, < >>Represents the blood oxygen reference value.
In the process of calculating and obtaining the environment abnormality indexThereafter, environmental abnormality index->Comparing with preset environmental abnormality index threshold, and judging if environmental abnormality index +.>If the environmental abnormality index is larger than the preset environmental abnormality index threshold, the existence of environmental abnormality can be judged.
Further, the early warning method of the brain electrical anomaly signal further comprises the following steps:
and step S103, comparing and early-warning the electroencephalogram monitoring data based on preset standard early-warning data when the environment is abnormal.
In the embodiment of the invention, when the environment is abnormal, a comparison early warning instruction is generated, preset standard early warning data is imported according to the comparison early warning instruction, then the electroencephalogram monitoring data is compared with the standard early warning data, when the electroencephalogram monitoring data is not in a numerical range corresponding to the standard early warning data, the electroencephalogram monitoring data is judged to be in an abnormal early warning state, a comparison early warning signal is generated at the moment, and the comparison early warning is carried out according to the comparison early warning signal; and when the electroencephalogram monitoring data is in a numerical range corresponding to the standard early warning data, judging that the electroencephalogram monitoring data is not in an abnormal early warning state at the moment, and not comparing and early warning.
Specifically, fig. 5 shows a flowchart of comparison early warning processing in the method provided by the embodiment of the invention.
In the preferred embodiment of the present invention, when an environmental abnormality exists, the comparing and early-warning processing for the electroencephalogram monitoring data based on the preset standard early-warning data specifically includes the following steps:
step S1031, when the environment is abnormal, importing preset standard early warning data;
when the environment is abnormal, the method for importing the preset standard early warning data comprises the following steps:
step S1031a, when the environment abnormality exists, acquiring the calculated environment abnormality index, and searching and confirming the environment abnormality level in a preset environment abnormality level table according to the environment abnormality index;
step S1031b, searching and confirming preset standard early warning data of the corresponding grade in a preset standard early warning database according to the environment abnormality grade;
step S1031c, importing preset standard early warning data of corresponding grade.
Step S1032, comparing the electroencephalogram monitoring data with the standard early warning data, and judging whether the electroencephalogram monitoring data is in an abnormal early warning state or not;
step S1032 includes the following sub-steps:
step S1032a, obtaining an abnormal signal index in the electroencephalogram monitoring data, and confirming and obtaining a corresponding abnormal grade of the electroencephalogram monitoring data according to the abnormal signal index;
step S1032b, judging whether the abnormal grade of the electroencephalogram monitoring data is consistent with the grade of the currently-imported preset standard early-warning data;
step S1032c, if not, confirming that the abnormal early warning state exists. Step S1033, when in an abnormal early warning state, generating a comparison early warning signal;
step S1034, according to the comparison early warning signal, comparison early warning is carried out.
Further, the early warning method of the brain electrical anomaly signal further comprises the following steps:
step S104, when no environment abnormality exists, direct early warning processing is carried out.
In the embodiment of the invention, when no environment abnormality exists, the condition that the brain waves of the user have sudden abnormalities is indicated, and a direct early warning signal is generated at the moment, so that the direct early warning and alarming are carried out according to the direct early warning signal.
Specifically, fig. 6 shows a flowchart of a direct early warning process in the method provided by the embodiment of the present invention.
In the preferred embodiment of the present invention, when no environmental abnormality exists, the performing the direct early warning process specifically includes the following steps:
step S1041, when no environment abnormality exists, generating a direct early warning signal;
step S1042, performing direct early warning according to the direct early warning signal.
Further, fig. 7 shows an application architecture diagram of the system provided by the embodiment of the present invention.
In another preferred embodiment of the present invention, an early warning system for an electroencephalogram abnormal signal includes:
the electroencephalogram monitoring and analyzing module 101 is used for acquiring electroencephalogram monitoring data of a user, analyzing the electroencephalogram monitoring data and judging whether an electroencephalogram abnormal signal exists or not.
In the embodiment of the invention, the electroencephalogram monitoring and analyzing module 101 performs electroencephalogram connection monitoring on a user to obtain connection monitoring data, analyzes the connection monitoring data to determine whether electroencephalogram is stably connected, determines whether electroencephalogram is stably connected when correct connection of electroencephalogram acquisition is completed and a feedback connection signal can be received, sends an electroencephalogram monitoring instruction at this time to perform electroencephalogram monitoring acquisition to obtain electroencephalogram monitoring data, processes the electroencephalogram monitoring data to construct an electroencephalogram change waveform reflecting the change of the electroencephalogram of the user, detects frequency, amplitude, waveform and the like on the electroencephalogram change waveform to generate an electroencephalogram detection result, and determines whether an electroencephalogram abnormal signal exists according to the electroencephalogram detection result.
Specifically, fig. 8 shows a block diagram of a system electroencephalogram monitoring and analyzing module 101 according to an embodiment of the present invention.
In a preferred embodiment of the present invention, the electroencephalogram monitoring and analyzing module 101 specifically includes:
the connection monitoring unit 1011 is used for performing electroencephalogram connection monitoring on a user and acquiring connection monitoring data;
a connection judging unit 1012, configured to analyze the connection monitoring data and judge whether the electroencephalogram is connected stably;
a data acquisition unit 1013 configured to acquire electroencephalogram monitoring data when electroencephalogram is stably connected;
an anomaly determination unit 1014 is configured to analyze the electroencephalogram monitoring data and determine whether an electroencephalogram anomaly signal is present.
Further, the early warning system of the brain electrical anomaly signal further comprises:
the environment anomaly determination module 102 is configured to perform environment monitoring when the electroencephalogram anomaly signal is present, obtain environment monitoring data, perform environment analysis, and determine whether an environment anomaly exists.
In the embodiment of the present invention, when an electroencephalogram abnormality signal is provided, the environment abnormality determination module 102 performs environment monitoring, where the environment monitoring is monitoring on heart rate, blood pressure, blood oxygen, etc. of a user, obtains environment monitoring data, obtains preset environment standard data, compares the environment monitoring data with the preset environment standard data, and determines that an environment abnormality exists when the environment monitoring data is not within the range of the environment standard data and the user is in an abnormal state environment (for example, running, rope skipping, tension, etc.); when the environment monitoring data is in the range of the environment standard data, the user is in a normal state environment, and at the moment, it is judged that no environment abnormality exists.
Specifically, fig. 9 shows a block diagram of the environment anomaly determination module 102 in the system according to an embodiment of the present invention.
In a preferred embodiment of the present invention, the environmental anomaly determination module 102 specifically includes:
an environment monitoring unit 1021 for performing environment monitoring of heart rate, blood pressure and blood oxygen when an electroencephalogram abnormality signal is provided, and acquiring environment monitoring data;
an environment comparing unit 1022 for comparing the environment monitoring data with preset environment standard data;
an environment judging unit 1023 for judging that an environment abnormality exists when the environment monitoring data is not in the range of the environment standard data; and when the environment monitoring data are in the range of the environment standard data, judging that no environment abnormality exists.
Further, the early warning system of the brain electrical anomaly signal further comprises:
and the comparison early-warning processing module 103 is used for comparing and early-warning processing the electroencephalogram monitoring data based on preset standard early-warning data when the environment is abnormal.
In the embodiment of the invention, when the environment is abnormal, the comparison early-warning processing module 103 generates a comparison early-warning instruction, and according to the comparison early-warning instruction, the preset standard early-warning data is imported, then the electroencephalogram monitoring data is compared with the standard early-warning data, and when the electroencephalogram monitoring data is not in the numerical range corresponding to the standard early-warning data, the electroencephalogram monitoring data is judged to be in an abnormal early-warning state, and at the moment, a comparison early-warning signal is generated, and according to the comparison early-warning signal, the comparison early-warning is carried out; and when the electroencephalogram monitoring data is in a numerical range corresponding to the standard early warning data, judging that the electroencephalogram monitoring data is not in an abnormal early warning state at the moment, and not comparing and early warning.
Specifically, fig. 10 shows a block diagram of a comparison early-warning processing module 103 in the system according to the embodiment of the present invention.
In a preferred embodiment of the present invention, the comparison early-warning processing module 103 specifically includes:
a standard importing unit 1031, configured to import preset standard early warning data when an environmental abnormality exists;
the state judging unit 1032 is configured to compare the electroencephalogram monitoring data with the standard early-warning data and judge whether the electroencephalogram monitoring data is in an abnormal early-warning state or not;
the signal generating unit 1033 is configured to generate a comparison early warning signal when in an abnormal early warning state;
and a comparison and early warning unit 1034, configured to perform comparison and early warning according to the comparison and early warning signal.
Further, the early warning system of the brain electrical anomaly signal further comprises:
and the direct early warning processing module 104 is used for performing direct early warning processing when no environment abnormality exists.
In the embodiment of the invention, when no environment abnormality exists, it indicates that sudden abnormal conditions exist in the brain waves of the user, and at this time, the direct early warning processing module 104 generates a direct early warning signal, and then carries out direct early warning and alarming according to the direct early warning signal.
It should be understood that, although the steps in the flowcharts of the embodiments of the present invention are shown in order as indicated by the arrows, these steps are not necessarily performed in order as indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in various embodiments may include multiple sub-steps or stages that are not necessarily performed at the same time, but may be performed at different times, nor do the order in which the sub-steps or stages are performed necessarily performed in sequence, but may be performed alternately or alternately with at least a portion of the sub-steps or stages of other steps or other steps.
Those skilled in the art will appreciate that all or part of the processes in the methods of the above embodiments may be implemented by a computer program for instructing relevant hardware, where the program may be stored in a non-volatile computer readable storage medium, and where the program, when executed, may include processes in the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the various embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), memory bus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.
The technical features of the above-described embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above-described embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The foregoing examples illustrate only a few embodiments of the invention and are described in detail herein without thereby limiting the scope of the invention. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the invention, which are all within the scope of the invention. Accordingly, the scope of protection of the present invention is to be determined by the appended claims.
The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, and alternatives falling within the spirit and principles of the invention.
Claims (4)
1. The early warning method of the brain electrical anomaly signal is characterized by comprising the following steps of:
acquiring brain electricity monitoring data of a user, analyzing the brain electricity monitoring data, and judging whether an brain electricity abnormal signal exists or not;
when the brain electrical abnormality signal exists, environment monitoring is carried out, environment monitoring data are obtained, environment analysis is carried out, and whether environment abnormality exists or not is judged;
when the environment is abnormal, comparing and early warning the electroencephalogram monitoring data based on preset standard early warning data;
when no environment abnormality exists, direct early warning processing is carried out;
when the environment is abnormal, comparing and early-warning the electroencephalogram monitoring data based on preset standard early-warning data, wherein the comparing and early-warning processing specifically comprises the following steps of:
when the environment is abnormal, importing preset standard early warning data;
comparing the electroencephalogram monitoring data with the standard early warning data, and judging whether the electroencephalogram monitoring data is in an abnormal early warning state or not;
generating a comparison early warning signal when the device is in an abnormal early warning state;
according to the comparison early warning signal, comparison early warning and warning are carried out;
when the environment is abnormal, the method for importing the preset standard early warning data comprises the following steps:
when the environment abnormality exists, acquiring the calculated environment abnormality index, and searching and confirming the environment abnormality level in a preset environment abnormality level table according to the environment abnormality index;
searching preset standard early warning data for confirming the corresponding grade in a preset standard early warning database according to the environment abnormal grade;
leading in preset standard early warning data of corresponding grades;
the method for comparing the electroencephalogram monitoring data with the standard early warning data and judging whether the electroencephalogram monitoring data is in an abnormal early warning state comprises the following substeps:
acquiring an abnormal signal index in the electroencephalogram monitoring data, and confirming and obtaining a corresponding abnormal grade of the electroencephalogram monitoring data according to the abnormal signal index;
whether the abnormal grade of the electroencephalogram monitoring data is consistent with the grade of the currently-imported preset standard early warning data or not is judged;
if not, confirming that the alarm is in an abnormal early warning state;
the step of acquiring the brain electricity monitoring data of the user, analyzing the brain electricity monitoring data and judging whether an brain electricity abnormal signal exists or not specifically comprises the following steps:
performing electroencephalogram connection monitoring on a user to obtain connection monitoring data;
analyzing the connection monitoring data to judge whether the brain electricity is stably connected;
acquiring brain electricity monitoring data when brain electricity is stably connected;
analyzing the electroencephalogram monitoring data and judging whether an electroencephalogram abnormal signal exists or not;
the method for analyzing the connection monitoring data and judging whether the brain electricity is stably connected comprises the following substeps:
acquiring the connection monitoring data in a preset test time period, and analyzing the connection monitoring data to obtain a monitoring data waveform image;
judging whether the monitoring data waveform image has fracture or not;
if not, acquiring a signal-to-noise ratio from the monitoring data waveform image, and judging whether the signal-to-noise ratio is within a preset signal-to-noise ratio range or not;
if yes, determining a corresponding current brain wave type according to the waveform of the monitoring data waveform image;
acquiring frequency data according to the monitoring data waveform image, wherein the frequency data at least comprises a minimum frequency value and a maximum frequency value;
judging whether the current brain wave type belongs to the frequency range of the current brain wave type according to the minimum frequency value and the maximum frequency value;
if yes, determining stable connection of the brain electricity;
the step of analyzing the electroencephalogram monitoring data and judging whether an electroencephalogram abnormal signal exists or not specifically comprises the following steps:
constructing an electroencephalogram change waveform according to the electroencephalogram monitoring data;
detecting the frequency, amplitude and waveform of the electroencephalogram change waveform to generate an electroencephalogram detection result;
judging whether an electroencephalogram abnormal signal exists or not according to the electroencephalogram detection result;
the method for detecting the frequency, amplitude and waveform of the electroencephalogram change waveform and generating an electroencephalogram detection result comprises the following sub-steps:
comparing the electroencephalogram change waveform with various waveforms in a preset waveform database to obtain a corresponding waveform similarity value, and judging whether the waveform similarity value is larger than a preset waveform similarity value or not;
if yes, confirming the current brain wave type corresponding to the brain wave change waveform;
acquiring a plurality of frequency values and a plurality of wave amplitudes based on the electroencephalogram change waveform, and confirming corresponding reference frequency values and reference wave amplitudes according to the current electroencephalogram type;
calculating a plurality of frequency difference values according to the plurality of frequency values and the reference frequency value, and confirming to obtain a maximum frequency difference value;
calculating a plurality of amplitude differences according to the plurality of wave amplitudes and the reference amplitude value, and confirming to obtain the maximum amplitude difference;
calculating to obtain an abnormal signal index according to the waveform similarity value, the maximum frequency difference value and the maximum amplitude difference value;
the calculation formula of the abnormal signal index is expressed as:
；
wherein,indicating an abnormal signal index,/-, and>reference outlier, < ++representing waveform similarity term>Calibration factor representing waveform similarity term, +.>Representing waveform similarity value, ++>Reference value representing waveform similarity, ++>Reference outlier representing frequency term, +.>Calibration factor representing frequency term, +.>Representing the maximum frequency difference,/>Representing the average value of the frequency difference values,reference outlier representing amplitude term, +.>Calibration factor representing amplitude term, +.>Representing the maximum amplitude difference,/-)>A reference value representing the amplitude difference;
and when judging that the abnormal signal index is larger than a preset abnormal index, judging that the brain electrical abnormal signal exists.
2. The method for early warning of an electroencephalogram abnormal signal according to claim 1, wherein when the electroencephalogram abnormal signal is provided, the method for early warning of the electroencephalogram abnormal signal is characterized in that when the electroencephalogram abnormal signal is provided, environmental monitoring is carried out, environmental monitoring data is obtained, environmental analysis is carried out, and whether environmental abnormality exists or not is judged specifically comprises the following steps:
when the brain electrical abnormality signal exists, performing environment monitoring on heart rate, blood pressure and blood oxygen to acquire environment monitoring data;
comparing the environment monitoring data with preset environment standard data;
when the environment monitoring data are not in the range of the environment standard data, judging that the environment is abnormal;
when the environment monitoring data are in the range of the environment standard data, judging that no environment abnormality exists;
the abnormality degree of the environmental monitoring data is evaluated through an environmental abnormality index, and a calculation formula of the environmental abnormality index is expressed as follows:
；
wherein,index of environmental abnormality, ->Abnormality index reference value representing heart rate term, +.>Calibration factor representing heart rate term, < ->Representing the current heart rate value in the environmental monitoring data, < >>Representing heart rateReference value,/>Abnormality index reference value indicating blood pressure item, +.>Calibration factor representing blood pressure term,/->Representing the current blood pressure value in the environmental monitoring data, < >>Indicates the blood pressure reference value->Abnormality index reference value indicating blood oxygen term, +.>Calibration factor representing blood oxygen term, +.>Representing the current blood oxygen value in the environmental monitoring data, < >>Represents the blood oxygen reference value.
3. The method for early warning of an electroencephalogram abnormality signal according to claim 1, wherein when no environmental abnormality exists, the direct early warning process specifically includes the following steps:
generating a direct early warning signal when no environment abnormality exists;
and carrying out direct early warning and alarming according to the direct early warning signal.
4. An electroencephalogram anomaly signal early warning system, characterized in that an electroencephalogram anomaly signal early warning method according to any one of claims 1 to 3 is executed, the system comprises an electroencephalogram monitoring analysis module, an environmental anomaly judgment module, a comparison early warning processing module and a direct early warning processing module, wherein:
the electroencephalogram monitoring and analyzing module is used for acquiring electroencephalogram monitoring data of a user, analyzing the electroencephalogram monitoring data and judging whether an electroencephalogram abnormal signal exists or not;
the environment abnormality judging module is used for carrying out environment monitoring when the electroencephalogram abnormality signal is provided, acquiring environment monitoring data, carrying out environment analysis and judging whether environment abnormality exists or not;
the comparison early warning processing module is used for comparing and early warning the electroencephalogram monitoring data based on preset standard early warning data when the environment is abnormal;
the direct early warning processing module is used for carrying out direct early warning processing when no environment abnormality exists;
the electroencephalogram monitoring and analyzing module specifically comprises:
the connection monitoring unit is used for carrying out electroencephalogram connection monitoring on a user and acquiring connection monitoring data;
the connection judging unit is used for analyzing the connection monitoring data and judging whether the brain electricity is stably connected or not;
the data acquisition unit is used for acquiring brain electricity monitoring data when brain electricity is stably connected;
the anomaly judging unit is used for analyzing the electroencephalogram monitoring data and judging whether an electroencephalogram anomaly signal exists or not;
the environment abnormality judging module specifically includes:
the environment monitoring unit is used for carrying out environment monitoring on heart rate, blood pressure and blood oxygen when the brain electrical abnormality signal exists, and acquiring environment monitoring data;
the environment comparison unit is used for comparing the environment monitoring data with preset environment standard data;
an environment judging unit for judging that an environment abnormality exists when the environment monitoring data is not in the range of the environment standard data; when the environment monitoring data are in the range of the environment standard data, judging that no environment abnormality exists;
the comparison early warning processing module specifically comprises:
the standard importing unit is used for importing preset standard early warning data when the environment is abnormal;
the state judging unit is used for comparing the electroencephalogram monitoring data with the standard early warning data and judging whether the electroencephalogram monitoring data is in an abnormal early warning state or not;
the signal generating unit is used for generating a comparison early warning signal when the signal generating unit is in an abnormal early warning state;
and the comparison early warning unit is used for carrying out comparison early warning according to the comparison early warning signal.
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Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20170026893A (en) * | 2015-08-31 | 2017-03-09 | 한양대학교 에리카산학협력단 | Home care system using brain wave |
| CN107951496A (en) * | 2017-11-27 | 2018-04-24 | 新绎健康科技有限公司 | Method and system based on multi-scale entropy analysis psychosoma relevance |
| CN109363649A (en) * | 2018-11-29 | 2019-02-22 | 浙江清华柔性电子技术研究院 | Physiological compensation effects clothes and method |
| CN109614932A (en) * | 2018-12-12 | 2019-04-12 | 西安科技大学 | Identify Environment, the mining helmet and cloud platform based on brain electric field change mechanism |
| CN110269610A (en) * | 2019-07-16 | 2019-09-24 | 河北医科大学第二医院 | A kind of prior-warning device of brain electrical anomaly signal |
| CN111671399A (en) * | 2020-06-18 | 2020-09-18 | 清华大学 | Method and device for measuring noise perception intensity and electronic equipment |
| CN111956198A (en) * | 2020-10-21 | 2020-11-20 | 北京妙医佳健康科技集团有限公司 | Equipment state determination method and device |
| CN113057655A (en) * | 2020-12-29 | 2021-07-02 | 深圳迈瑞生物医疗电子股份有限公司 | Recognition method, recognition system and detection system for electroencephalogram signal interference |
| CN114668391A (en) * | 2022-03-31 | 2022-06-28 | 浙江科技学院 | Old people falling judgment device and method |
| CN115414044A (en) * | 2022-09-27 | 2022-12-02 | 河南大学 | Human mental state management system, method and equipment based on electroencephalogram analysis |
| CN115644873A (en) * | 2022-10-26 | 2023-01-31 | 复旦大学 | Brain wave anxiety monitoring and analyzing method for noise occupational hazards |
| CN115844338A (en) * | 2022-07-20 | 2023-03-28 | 杭州格物智安科技有限公司 | Physical sign detection and early warning method, system and device fusing environment and individual data |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2013042768A (en) * | 2011-08-22 | 2013-03-04 | Sony Corp | Information processing device and method, program, and recording medium |
| US9619997B2 (en) * | 2014-12-09 | 2017-04-11 | General Electric Company | System and method for physiological monitoring |
-
2023
- 2023-11-14 CN CN202311509241.4A patent/CN117224151B/en active Active
Patent Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20170026893A (en) * | 2015-08-31 | 2017-03-09 | 한양대학교 에리카산학협력단 | Home care system using brain wave |
| CN107951496A (en) * | 2017-11-27 | 2018-04-24 | 新绎健康科技有限公司 | Method and system based on multi-scale entropy analysis psychosoma relevance |
| CN109363649A (en) * | 2018-11-29 | 2019-02-22 | 浙江清华柔性电子技术研究院 | Physiological compensation effects clothes and method |
| CN109614932A (en) * | 2018-12-12 | 2019-04-12 | 西安科技大学 | Identify Environment, the mining helmet and cloud platform based on brain electric field change mechanism |
| CN110269610A (en) * | 2019-07-16 | 2019-09-24 | 河北医科大学第二医院 | A kind of prior-warning device of brain electrical anomaly signal |
| CN111671399A (en) * | 2020-06-18 | 2020-09-18 | 清华大学 | Method and device for measuring noise perception intensity and electronic equipment |
| CN111956198A (en) * | 2020-10-21 | 2020-11-20 | 北京妙医佳健康科技集团有限公司 | Equipment state determination method and device |
| CN113057655A (en) * | 2020-12-29 | 2021-07-02 | 深圳迈瑞生物医疗电子股份有限公司 | Recognition method, recognition system and detection system for electroencephalogram signal interference |
| CN114668391A (en) * | 2022-03-31 | 2022-06-28 | 浙江科技学院 | Old people falling judgment device and method |
| CN115844338A (en) * | 2022-07-20 | 2023-03-28 | 杭州格物智安科技有限公司 | Physical sign detection and early warning method, system and device fusing environment and individual data |
| CN115414044A (en) * | 2022-09-27 | 2022-12-02 | 河南大学 | Human mental state management system, method and equipment based on electroencephalogram analysis |
| CN115644873A (en) * | 2022-10-26 | 2023-01-31 | 复旦大学 | Brain wave anxiety monitoring and analyzing method for noise occupational hazards |
Non-Patent Citations (1)
| Title |
|---|
| 基于脑电和机器学习的脑功能网络异常分析;孙兰芳;优秀硕士论文;14-49 * |
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