CN109567797A - Epilepsy method for early warning, device and computer readable storage medium - Google Patents
Epilepsy method for early warning, device and computer readable storage medium Download PDFInfo
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- CN109567797A CN109567797A CN201910094742.8A CN201910094742A CN109567797A CN 109567797 A CN109567797 A CN 109567797A CN 201910094742 A CN201910094742 A CN 201910094742A CN 109567797 A CN109567797 A CN 109567797A
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
- A61B5/316—Modalities, i.e. specific diagnostic methods
- A61B5/369—Electroencephalography [EEG]
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/40—Detecting, measuring or recording for evaluating the nervous system
- A61B5/4076—Diagnosing or monitoring particular conditions of the nervous system
- A61B5/4094—Diagnosing or monitoring seizure diseases, e.g. epilepsy
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7235—Details of waveform analysis
- A61B5/7253—Details of waveform analysis characterised by using transforms
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7271—Specific aspects of physiological measurement analysis
- A61B5/7275—Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/74—Details of notification to user or communication with user or patient ; user input means
- A61B5/746—Alarms related to a physiological condition, e.g. details of setting alarm thresholds or avoiding false alarms
Abstract
The invention discloses a kind of epilepsy method for early warning, comprising: obtains the first EEG signals of the user of brain wave acquisition device acquisition in real time, and the first EEG signals that will acquire are stored to default storage region；Based on the first EEG signals stored in the default storage region, in the preset duration before timing acquisition current time, the first eeg data of the brain wave acquisition device acquisition；First eeg data and default eeg data are compared into operation, epileptic attack hidden danger whether there is with the determination user；Determining the user, there are when epileptic attack hidden danger, send prompt messages to the corresponding default terminal of the user.The invention also discloses a kind of epilepsy early warning device and computer readable storage mediums.The present invention can determine whether that there are epileptic attack hidden danger according to the eeg data of the user, and then realize the accurate early warning before epileptic attack, improve user experience.
Description
Technical field
The present invention relates to brain electro-technical field more particularly to a kind of epilepsy method for early warning, device and computer-readable storage
Medium.
Background technique
Epilepsy is a kind of common, multiple chronic neurological disorders.It is the second largest persistent ailment for being only second to cerebrovascular disease,
Epileptic attack has the characteristics that sudden, temporary and repeatability three is big, brings huge pain to patient body.Therefore, insane
Before epilepsy breaking-out, measure is acquired in time and inhibits or alleviate epileptic attack bring pain, becomes the ardent phase of numerous epileptics
It hopes, and everything is accurate epileptic attack early warning at all.
Above content is only used to facilitate the understanding of the technical scheme, and is not represented and is recognized that above content is existing skill
Art.
Summary of the invention
The main purpose of the present invention is to provide a kind of epilepsy method for early warning, device and computer readable storage medium, purports
The technical issues of being difficult to early warning before solving current patient's epileptic attack.
To achieve the above object, the present invention provides a kind of epilepsy method for early warning, and the epilepsy method for early warning includes following step
It is rapid:
The first EEG signals of the user of brain wave acquisition device acquisition, and the first EEG signals that will acquire are obtained in real time
It stores to default storage region；
Based on the first EEG signals stored in the default storage region, when default before timing acquisition current time
In length, the first eeg data of the brain wave acquisition device acquisition；
First eeg data and default eeg data are compared into operation, with the determination user with the presence or absence of insane
Epilepsy breaking-out hidden danger, wherein the eeg data before the default eeg data is user breaking-out in preset duration；
Determining that the user is corresponding default to the user there are prompt messages when epileptic attack hidden danger, are sent
Terminal.
In one embodiment, described that first eeg data and default eeg data are compared into operation, with determination
The user whether there is epileptic attack hidden danger the step of include:
First eeg data is sampled based on preset time window, to obtain the first sampled signal；
The first Wavelet Entropy in the preset time window is calculated according to first sampled signal；
First Wavelet Entropy default Wavelet Entropy corresponding with the default eeg data is compared into operation, with determination
The user whether there is epileptic attack hidden danger.
In one embodiment, it is described by first Wavelet Entropy default Wavelet Entropy corresponding with the default eeg data into
Row contrast operation, with the determination user whether there is epileptic attack hidden danger the step of include:
Calculate the difference between first Wavelet Entropy and the default Wavelet Entropy；
Determine whether the difference is less than preset difference value, wherein when the difference is less than preset difference value, determine the use
There are epileptic attack hidden danger at family.
In one embodiment, the step of relatives' corresponding default terminal for sending prompt messages to the user
Later, the epilepsy method for early warning further include:
When receiving epileptic attack confirmation message, the default eeg data is updated based on first eeg data.
In one embodiment, described the step of updating the default eeg data based on first eeg data, includes:
Obtain corresponding first weighted value of first Wavelet Entropy and corresponding second weight of the default Wavelet Entropy
Value；
It is calculated based on first weighted value, first Wavelet Entropy, the second weighted value and the default Wavelet Entropy
The default Wavelet Entropy is set as to target wavelet entropy, and by the target wavelet entropy.
In one embodiment, the acquisition corresponding first weighted value of the first Wavelet Entropy and the default small echo
The step of entropy corresponding second weighted value includes:
It obtains the corresponding epileptic attack number of the default Wavelet Entropy, and calculates described the based on the epileptic attack number
One weighted value and second weighted value.
In one embodiment, the EEG signals of the user for obtaining the acquisition of brain wave acquisition device, and the brain that will acquire
Before the step of electric signal is stored to default storage region, the epilepsy method for early warning further include:
The second EEG signals of the user of brain wave acquisition device acquisition, and the second EEG signals that will acquire are obtained in real time
It stores to default storage region；
When receiving epileptic attack information, the epileptic attack information corresponding epileptic attack moment is obtained；
It is pre- before obtaining the epileptic attack moment based on the second EEG signals stored in the default storage region
If in duration, the second eeg data of the brain wave acquisition device acquisition；
The default eeg data is determined based on second eeg data.
In one embodiment, described the step of determining the default eeg data based on second eeg data, includes:
Second eeg data is sampled based on preset time window, to obtain the second sampled signal；
The second Wavelet Entropy in the preset time window is calculated according to second sampled signal, and by second small echo
Entropy is set as the default Wavelet Entropy.
In addition, to achieve the above object, the present invention also provides a kind of epilepsy early warning device, the epilepsy early warning device packet
It includes: memory, processor and the epilepsy early warning program that is stored on the memory and can run on the processor, it is described
The step of described in any item epilepsy method for early warning among the above are realized when epilepsy early warning program is executed by the processor.
In addition, to achieve the above object, it is described computer-readable the present invention also provides a kind of computer readable storage medium
Epilepsy early warning program is stored on storage medium, the epilepsy early warning program realizes any one of above-mentioned institute when being executed by processor
The step of epilepsy method for early warning stated.
The present invention by obtaining the first EEG signals of the user of brain wave acquisition device acquisition, and will acquire the in real time
One EEG signals are stored to default storage region, fixed then based on the first EEG signals stored in the default storage region
When obtain in preset duration before current time, the first eeg data of brain wave acquisition device acquisition then will be described
First eeg data and default eeg data compare operation, whether there is epileptic attack hidden danger with the determination user,
In, then the eeg data before the default eeg data is user breaking-out in preset duration is determining the user
There are when epileptic attack hidden danger, transmission prompt messages, can be according to the user's to the corresponding default terminal of the user
Eeg data determines whether that there are epileptic attack hidden danger, and sends prompt messages when there are epileptic attack hidden danger,
And then realize the accurate early warning before epileptic attack, so that the relatives and medical staff of the epileptic know that the epilepsy is suffered from time
There is currently the hidden danger of epileptic attack by person, and then be convenient for subsequent treatment etc., improve user experience.
Detailed description of the invention
Fig. 1 is the structural schematic diagram of epilepsy early warning device in hardware running environment that the embodiment of the present invention is related to；
Fig. 2 is the flow diagram of epilepsy method for early warning first embodiment of the present invention.
The embodiments will be further described with reference to the accompanying drawings for the realization, the function and the advantages of the object of the present invention.
Specific embodiment
It should be appreciated that the specific embodiments described herein are merely illustrative of the present invention, it is not intended to limit the present invention.
As shown in Figure 1, Fig. 1 is the structure of epilepsy early warning device in hardware running environment that the embodiment of the present invention is related to
Schematic diagram.
Epilepsy early warning device of the embodiment of the present invention can be PC, be also possible to smart phone, tablet computer, e-book reading
Device, MP3 (Moving Picture Experts Group Audio Layer III, dynamic image expert's compression standard audio
Level 3) player, MP4 (Moving Picture Experts Group Audio Layer IV, dynamic image expert compression
Standard audio level 4) the packaged type terminal device having a display function such as player, portable computer.
As shown in Figure 1, the epilepsy early warning device may include: processor 1001, such as CPU, network interface 1004, user
Interface 1003, memory 1005, communication bus 1002.Wherein, communication bus 1002 is for realizing the connection between these components
Communication.User interface 1003 may include display screen (Display), input unit such as keyboard (Keyboard), optional user
Interface 1003 can also include standard wireline interface and wireless interface.Network interface 1004 optionally may include having for standard
Line interface, wireless interface (such as WI-FI interface).Memory 1005 can be high speed RAM memory, be also possible to stable storage
Device (non-volatile memory), such as magnetic disk storage.Memory 1005 optionally can also be independently of aforementioned processing
The storage device of device 1001.
Optionally, epilepsy early warning device can also include camera, RF (Radio Frequency, radio frequency) circuit, sensing
Device, voicefrequency circuit, WiFi module etc..Wherein, sensor such as optical sensor, motion sensor and other sensors.When
So, epilepsy early warning device can also configure the other sensors such as gyroscope, barometer, hygrometer, thermometer, infrared sensor,
Details are not described herein.
It will be understood by those skilled in the art that epilepsy early warning device structure shown in Fig. 1 is not constituted to epilepsy early warning
The restriction of device may include perhaps combining certain components or different component cloth than illustrating more or fewer components
It sets.
As shown in Figure 1, as may include that operating system, network are logical in a kind of memory 1005 of computer storage medium
Believe module, Subscriber Interface Module SIM and epilepsy early warning program.
In epilepsy early warning device shown in Fig. 1, network interface 1004 is mainly used for connecting background server, takes with backstage
Business device carries out data communication；User interface 1003 is mainly used for connecting client (user terminal), carries out data communication with client；
And processor 1001 can be used for calling the epilepsy early warning program stored in memory 1005.
In the present embodiment, epilepsy early warning device includes: memory 1005, processor 1001 and is stored in the memory
On 1005 and the epilepsy early warning program that can be run on the processor 1001, wherein processor 1001 calls memory 1005
When the epilepsy early warning program of middle storage, and execute following operation:
The first EEG signals of the user of brain wave acquisition device acquisition, and the first EEG signals that will acquire are obtained in real time
It stores to default storage region；
Based on the first EEG signals stored in the default storage region, when default before timing acquisition current time
In length, the first eeg data of the brain wave acquisition device acquisition；
First eeg data and default eeg data are compared into operation, with the determination user with the presence or absence of insane
Epilepsy breaking-out hidden danger, wherein the eeg data before the default eeg data is user breaking-out in preset duration；
Determining that the user is corresponding default to the user there are prompt messages when epileptic attack hidden danger, are sent
Terminal.
Further, processor 1001 can call the epilepsy early warning program stored in memory 1005, also execute following
Operation:
First eeg data is sampled based on preset time window, to obtain the first sampled signal；
The first Wavelet Entropy in the preset time window is calculated according to first sampled signal；
First Wavelet Entropy default Wavelet Entropy corresponding with the default eeg data is compared into operation, with determination
The user whether there is epileptic attack hidden danger.
Further, processor 1001 can call the epilepsy early warning program stored in memory 1005, also execute following
Operation:
Calculate the difference between first Wavelet Entropy and the default Wavelet Entropy；
Determine whether the difference is less than preset difference value, wherein when the difference is less than preset difference value, determine the use
There are epileptic attack hidden danger at family.
Further, processor 1001 can call the epilepsy early warning program stored in memory 1005, also execute following
Operation:
When receiving epileptic attack confirmation message, the default eeg data is updated based on first eeg data.
Further, processor 1001 can call the epilepsy early warning program stored in memory 1005, also execute following
Operation:
Obtain corresponding first weighted value of first Wavelet Entropy and corresponding second weight of the default Wavelet Entropy
Value；
It is calculated based on first weighted value, first Wavelet Entropy, the second weighted value and the default Wavelet Entropy
The default Wavelet Entropy is set as to target wavelet entropy, and by the target wavelet entropy.
Further, processor 1001 can call the epilepsy early warning program stored in memory 1005, also execute following
Operation:
It obtains the corresponding epileptic attack number of the default Wavelet Entropy, and calculates described the based on the epileptic attack number
One weighted value and second weighted value.
Further, processor 1001 can call the epilepsy early warning program stored in memory 1005, also execute following
Operation:
The second EEG signals of the user of brain wave acquisition device acquisition, and the second EEG signals that will acquire are obtained in real time
It stores to default storage region；
When receiving epileptic attack information, the epileptic attack information corresponding epileptic attack moment is obtained；
It is pre- before obtaining the epileptic attack moment based on the second EEG signals stored in the default storage region
If in duration, the second eeg data of the brain wave acquisition device acquisition；
The default eeg data is determined based on second eeg data.
Further, processor 1001 can call the epilepsy early warning program stored in memory 1005, also execute following
Operation:
Second eeg data is sampled based on preset time window, to obtain the second sampled signal；
The second Wavelet Entropy in the preset time window is calculated according to second sampled signal, and by second small echo
Entropy is set as the default Wavelet Entropy.
The present invention also provides a kind of epilepsy method for early warning, are epilepsy method for early warning first of the present invention implementation referring to Fig. 2, Fig. 2
The flow diagram of example.
In the present embodiment, which includes:
Step S100 obtains the first EEG signals of the user of brain wave acquisition device acquisition, and will acquire the in real time
One EEG signals are stored to default storage region；
In the present embodiment, when epileptic wears the brain wave acquisition device, the acquisition of brain wave acquisition device is obtained in real time
User the first EEG signals, and the first EEG signals that will acquire are stored to default storage region.
Wherein, the settable acquisition instructions trigger button/key of the brain wave acquisition device is worn in nursing staff or epileptic
It when wearing the brain wave acquisition device, can be instructed by the buttons/keys triggering collection, when receiving acquisition instructions, be obtained in real time
The first EEG signals of the user of brain wave acquisition device acquisition.
Step S200, based on the first EEG signals stored in the default storage region, timing acquisition current time it
In preceding preset duration, the first eeg data of the brain wave acquisition device acquisition；
In the present embodiment, based on the first EEG signals stored in the default storage region, when timing acquisition is current
In preset duration before quarter, the first eeg data of the brain wave acquisition device acquisition stores in default storage region
The first EEG signals in, the EEG signals in preset duration before timing acquisition current time, and will acquire brain electricity
Signal is as the first eeg data.
Wherein, the time interval of timing acquisition can be rationally arranged, and when the time interval is preset more than or equal to this
Long, which can be rationally arranged.It specifically, can be in the first brain for the user for obtaining the acquisition of brain wave acquisition device in real time
The preset duration when duration of electric signal is greater than preset duration, before periodically obtaining current time in default storage region
It is interior, the first eeg data of the brain wave acquisition device acquisition.
First eeg data and default eeg data are compared operation, with the determination user by step S300
With the presence or absence of epileptic attack hidden danger, wherein the brain electricity before the default eeg data is user breaking-out in preset duration
Data；
In the present embodiment, when getting the first eeg data, by first eeg data and default eeg data into
Row contrast operation whether there is epileptic attack hidden danger with the determination user, specifically, can carry out to first EEG signals small
Wave conversion processing, obtains the corresponding Wavelet Entropy of the first eeg data, by the corresponding Wavelet Entropy of the first eeg data and default brain
The corresponding default Wavelet Entropy of electric data is compared, and whether there is epileptic attack hidden danger with the determination user.
Step S400, determining the user, there are when epileptic attack hidden danger, send prompt messages to the user
The corresponding default terminal of relatives.
In the present embodiment, determining the user, there are when epileptic attack hidden danger, send prompt messages to the use
The corresponding default terminal in family, specifically, the default terminal include the shifting of the mobile terminal of medical staff and the relatives of the user
Dynamic terminal, so that the relatives and medical staff of the epileptic know the epileptic in time, there is currently the hidden of epileptic attack
Suffer from, and then be convenient for subsequent treatment etc..
The epilepsy method for early warning that the present embodiment proposes, the first brain of the user by obtaining the acquisition of brain wave acquisition device in real time
Electric signal, and the first EEG signals that will acquire are stored to default storage region, then based in the default storage region
First EEG signals of storage, in the preset duration before timing acquisition current time, the of the brain wave acquisition device acquisition
First eeg data and default eeg data are then compared operation by one eeg data, and with determination, the user is
No there are epileptic attack hidden danger, wherein the brain electricity number before the default eeg data is user breaking-out in preset duration
According to then determining the user, there are when epileptic attack hidden danger, send prompt messages to preset to the user is corresponding
Terminal can determine whether that there are epileptic attack hidden danger according to the eeg data of the user, and that there are epileptic attacks is hidden
Prompt messages are sent when suffering from, and then realize the accurate early warning before epileptic attack, so that the relatives and doctor of the epileptic
Shield personnel know the epileptic in time, and there is currently the hidden danger of epileptic attack, and then be convenient for subsequent treatment etc., it improves
User experience.
Based on first embodiment, the second embodiment of epilepsy method for early warning of the present invention, in the present embodiment, step are proposed
S300 includes:
Step S310 samples first eeg data based on preset time window, to obtain the first sampled signal；
Step S320 calculates the first Wavelet Entropy in the preset time window according to first sampled signal；
First Wavelet Entropy default Wavelet Entropy corresponding with the default eeg data is compared behaviour by step S330
Make, epileptic attack hidden danger whether there is with the determination user.
In the present embodiment, first eeg data is sampled according to preset time window, to obtain the first sampling
Signal calculates the first Wavelet Entropy in preset time window using existing calculation based on the first sampled signal, and according to small
Wave entropy is compared with default first Wavelet Entropy, whether there is epileptic attack hidden danger with the determination user, and then accurate determining
The user whether there is epileptic attack hidden danger.
Wherein, which can be rationally arranged, for example, being existed before carrying out early warning operation by the user
EEG signals before epileptic attack are handled to obtain the default Wavelet Entropy, and preset time window can be equal to the preset duration, or
Person's preset time window is less than preset duration, or, preset duration is the integral multiple of preset time window.
Further, in one embodiment, after step S400, the epilepsy method for early warning further include:
Step S500 is updated described default when receiving epileptic attack confirmation message based on first eeg data
Eeg data.
In the present embodiment, if after sending prompt messages, the epileptic attack of the user, then medical staff or parent
Category can feed back epileptic attack confirmation message, when receiving epileptic attack confirmation message, be updated based on first eeg data
The default eeg data, specifically, update the default Wavelet Entropy according to first Wavelet Entropy, i.e., according to first Wavelet Entropy with
And target wavelet entropy is calculated in default Wavelet Entropy, and using the target wavelet entropy as default Wavelet Entropy.
The epilepsy method for early warning that the present embodiment proposes, by being adopted based on preset time window to first eeg data
Sample calculates the first small echo in the preset time window then according to first sampled signal to obtain the first sampled signal
First Wavelet Entropy default Wavelet Entropy corresponding with the default eeg data is then compared operation, with determination by entropy
The user whether there is epileptic attack hidden danger, can accurately determine that the current user whether there is epilepsy according to the first Wavelet Entropy
Breaking-out hidden danger, and then the accuracy of early warning before epileptic attack is improved, further improve user experience.
Based on second embodiment, the 3rd embodiment of epilepsy method for early warning of the present invention, in the present embodiment, step are proposed
S330 includes:
Step S331 calculates the difference between first Wavelet Entropy and the default Wavelet Entropy；
Step S332, determines whether the difference is less than preset difference value, wherein when the difference is less than preset difference value,
Determining the user, there are epileptic attack hidden danger.
In the present embodiment, when obtaining the first Wavelet Entropy, the difference of first Wavelet Entropy and default Wavelet Entropy is calculated,
In, which is the absolute value of the difference of the first Wavelet Entropy and default Wavelet Entropy, and determines whether the difference is less than preset difference value, if
It is, it is determined that there are epileptic attack hidden danger by the user, and then can be according between the first Wavelet Entropy and the default Wavelet Entropy
Difference determines that the current user whether there is epileptic attack hidden danger, improves the accuracy of early warning before epileptic attack, further mentions
High user experience.
Wherein, preset difference value can be rationally arranged.
The epilepsy method for early warning that the present embodiment proposes, by calculating between first Wavelet Entropy and the default Wavelet Entropy
Difference, then determine whether the difference is less than preset difference value, wherein the difference be less than preset difference value when, determine institute
Stating user, there are epileptic attack hidden danger, can be determined according to the difference between the first Wavelet Entropy and the default Wavelet Entropy current
The user whether there is epileptic attack hidden danger, improves the accuracy of early warning before epileptic attack, further improves user experience.
Based on second embodiment, the fourth embodiment of epilepsy method for early warning of the present invention, in the present embodiment, step are proposed
S500 includes:
Step S510, obtains corresponding first weighted value of the first Wavelet Entropy and the default Wavelet Entropy is corresponding
Second weighted value；
Step S520 is based on first weighted value, first Wavelet Entropy, the second weighted value and the default small echo
Target wavelet entropy is calculated in entropy, and sets the default Wavelet Entropy for the target wavelet entropy.
In the present embodiment, if after sending prompt messages, the epileptic attack of the user, then medical staff or parent
Category can feed back epileptic attack confirmation message, and when receiving epileptic attack confirmation message, it is corresponding to obtain first Wavelet Entropy
First weighted value and corresponding second weighted value of the default Wavelet Entropy, wherein the first weighted value and the second weighted value can
To be rationally arranged, for example, the first weighted value and the second weighted value may be configured as 0.5, alternatively, the first weighted value and
Second weighted value can be calculated according to the corresponding attack times of default Wavelet Entropy.
When getting the first weighted value and the second weighted value, based on first weighted value, first Wavelet Entropy,
Target wavelet entropy is calculated in second weighted value and the default Wavelet Entropy, and sets described pre- for the target wavelet entropy
If Wavelet Entropy, specifically, target wavelet entropy=first the first Wavelet Entropy of weighted value * the+the second weighted value * presets Wavelet Entropy.
By resetting default Wavelet Entropy according to the first Wavelet Entropy, the accuracy of default Wavelet Entropy can be improved, in turn
Improve the accuracy of subsequent epilepsy early warning.
Further, in one embodiment, step S510 includes: to obtain the corresponding epileptic attack of the default Wavelet Entropy
Number, and first weighted value and second weighted value are calculated based on the epileptic attack number.
Specifically, the first weighted value=1/ (epileptic attack number+1), the second weighted value=epileptic attack number/(epilepsy
Attack times+1), and then the first weighted value and the second weighted value can be rationally set according to epileptic attack number, according to
One weighted value and the second weighted value recalculate default Wavelet Entropy, improve the accuracy of default Wavelet Entropy, and then improve subsequent
The accuracy of epilepsy early warning, further improves user experience.
The epilepsy method for early warning that the present embodiment proposes, by obtaining corresponding first weighted value of first Wavelet Entropy, with
And corresponding second weighted value of the default Wavelet Entropy, then based on first weighted value, first Wavelet Entropy, the second power
Target wavelet entropy is calculated in weight values and the default Wavelet Entropy, and sets the default small echo for the target wavelet entropy
Entropy realizes and resets default Wavelet Entropy by the first Wavelet Entropy, can be improved the accuracy of default Wavelet Entropy, and then improve subsequent
The accuracy of epilepsy early warning, further improves user experience.
Based on first embodiment, the 5th embodiment of epilepsy method for early warning of the present invention, in the present embodiment, step are proposed
Before S100, which includes:
Step S600 obtains the second EEG signals of the user of brain wave acquisition device acquisition, and will acquire the in real time
Two EEG signals are stored to default storage region；
Step S700 obtains the corresponding epileptic attack of the epileptic attack information when receiving epileptic attack information
It carves；
Step S800 obtains the epileptic attack based on the second EEG signals stored in the default storage region
In preset duration before quarter, the second eeg data of the brain wave acquisition device acquisition；
Step S900 determines the default eeg data based on second eeg data.
In the present embodiment, when epileptic wears the brain wave acquisition device, such as epileptic wears the brain for the first time
Electric acquisition device, obtains the second EEG signals of the user of brain wave acquisition device acquisition in real time, and the second brain electricity that will acquire
Signal is stored to default storage region.
In epileptic's epileptic attack, the corresponding relatives of the epileptic or medical staff can input or feed back
Current epileptic attack information, which may include the epileptic attack moment.When receiving epileptic attack information, obtain
The epileptic attack information corresponding epileptic attack moment, then based on the second brain telecommunications stored in the default storage region
Number, it obtains in the preset duration before the epileptic attack moment, the second eeg data of the brain wave acquisition device acquisition, tool
Body, the second EEG signals stored in default storage region obtain in the preset duration before epileptic attack moment
EEG signals and as the second eeg data.When getting the second eeg data, institute is determined according to second eeg data
State default eeg data.
The epilepsy method for early warning that the present embodiment proposes, the second brain of the user by obtaining the acquisition of brain wave acquisition device in real time
Electric signal, and the second EEG signals that will acquire are stored to default storage region, then when receiving epileptic attack information,
The epileptic attack information corresponding epileptic attack moment is obtained, then based on the second brain stored in the default storage region
Electric signal obtains in the preset duration before the epileptic attack moment, the second brain electricity number of the brain wave acquisition device acquisition
According to, be then based on second eeg data and determine the default eeg data, and then realize according to user's epileptic attack it
Default eeg data is arranged in preceding eeg data, and then can obtain accurately presetting eeg data, and passes through the subsequent epilepsy
Eeg data before the epileptic attack of patient is compared with default eeg data, accurately determines the user with the presence or absence of epilepsy
Breaking-out hidden danger, further increases user experience.
Based on the 5th embodiment, the sixth embodiment of epilepsy method for early warning of the present invention, in the present embodiment, step are proposed
S900 includes:
Step S100 samples second eeg data based on preset time window, to obtain the second sampled signal；
Step S200 calculates the second Wavelet Entropy in the preset time window according to second sampled signal, and by institute
It states the second Wavelet Entropy and is set as the default Wavelet Entropy.
In the present embodiment, presetting eeg data includes default Wavelet Entropy, when getting the second eeg data, according to pre-
If time window samples second eeg data, to obtain the second sampled signal, based on the second sampled signal using existing
Some calculations calculate the second Wavelet Entropy in preset time window, and set the default small echo for second Wavelet Entropy
Entropy, and then default eeg data and default Wavelet Entropy can be accurately set, with the epileptic attack by the subsequent epileptic it
Preceding eeg data is compared with default Wavelet Entropy, accurately determines that the user with the presence or absence of epileptic attack hidden danger, further mentions
High user experience.
The epilepsy method for early warning that the present embodiment proposes, by being adopted based on preset time window to second eeg data
Sample calculates the second small echo in the preset time window then according to second sampled signal to obtain the second sampled signal
Entropy, and the default Wavelet Entropy is set by second Wavelet Entropy, default eeg data and default small echo can be accurately set
Eeg data before entropy, with the epileptic attack by the subsequent epileptic is compared with default Wavelet Entropy, accurate to determine
The user whether there is epileptic attack hidden danger, further increase user experience.
In addition, the embodiment of the present invention also proposes a kind of computer readable storage medium, the computer readable storage medium
On be stored with epilepsy early warning program, following operation is realized when the epilepsy early warning program is executed by processor:
The first EEG signals of the user of brain wave acquisition device acquisition, and the first EEG signals that will acquire are obtained in real time
It stores to default storage region；
Based on the first EEG signals stored in the default storage region, when default before timing acquisition current time
In length, the first eeg data of the brain wave acquisition device acquisition；
First eeg data and default eeg data are compared into operation, with the determination user with the presence or absence of insane
Epilepsy breaking-out hidden danger, wherein the eeg data before the default eeg data is user breaking-out in preset duration；
Determining that the user is corresponding default to the user there are prompt messages when epileptic attack hidden danger, are sent
Terminal.
Further, following operation is also realized when the epilepsy early warning program is executed by processor:
First eeg data is sampled based on preset time window, to obtain the first sampled signal；
The first Wavelet Entropy in the preset time window is calculated according to first sampled signal；
First Wavelet Entropy default Wavelet Entropy corresponding with the default eeg data is compared into operation, with determination
The user whether there is epileptic attack hidden danger.
Further, following operation is also realized when the epilepsy early warning program is executed by processor:
Calculate the difference between first Wavelet Entropy and the default Wavelet Entropy；
Determine whether the difference is less than preset difference value, wherein when the difference is less than preset difference value, determine the use
There are epileptic attack hidden danger at family.
Further, following operation is also realized when the epilepsy early warning program is executed by processor:
When receiving epileptic attack confirmation message, the default eeg data is updated based on first eeg data.
Further, following operation is also realized when the epilepsy early warning program is executed by processor:
Obtain corresponding first weighted value of first Wavelet Entropy and corresponding second weight of the default Wavelet Entropy
Value；
It is calculated based on first weighted value, first Wavelet Entropy, the second weighted value and the default Wavelet Entropy
The default Wavelet Entropy is set as to target wavelet entropy, and by the target wavelet entropy.
Further, following operation is also realized when the epilepsy early warning program is executed by processor:
It obtains the corresponding epileptic attack number of the default Wavelet Entropy, and calculates described the based on the epileptic attack number
One weighted value and second weighted value.
Further, following operation is also realized when the epilepsy early warning program is executed by processor:
The second EEG signals of the user of brain wave acquisition device acquisition, and the second EEG signals that will acquire are obtained in real time
It stores to default storage region；
When receiving epileptic attack information, the epileptic attack information corresponding epileptic attack moment is obtained；
It is pre- before obtaining the epileptic attack moment based on the second EEG signals stored in the default storage region
If in duration, the second eeg data of the brain wave acquisition device acquisition；
The default eeg data is determined based on second eeg data.
Further, following operation is also realized when the epilepsy early warning program is executed by processor:
Second eeg data is sampled based on preset time window, to obtain the second sampled signal；
The second Wavelet Entropy in the preset time window is calculated according to second sampled signal, and by second small echo
Entropy is set as the default Wavelet Entropy.
It should be noted that, in this document, the terms "include", "comprise" or its any other variant are intended to non-row
His property includes, so that the process, method, article or the system that include a series of elements not only include those elements, and
And further include other elements that are not explicitly listed, or further include for this process, method, article or system institute it is intrinsic
Element.In the absence of more restrictions, the element limited by sentence "including a ...", it is not excluded that including being somebody's turn to do
There is also other identical elements in the process, method of element, article or system.
The serial number of the above embodiments of the invention is only for description, does not represent the advantages or disadvantages of the embodiments.
Through the above description of the embodiments, those skilled in the art can be understood that above-described embodiment side
Method can be realized by means of software and necessary general hardware platform, naturally it is also possible to by hardware, but in many cases
The former is more preferably embodiment.Based on this understanding, technical solution of the present invention substantially in other words does the prior art
The part contributed out can be embodied in the form of software products, which is stored in one as described above
In storage medium (such as ROM/RAM, magnetic disk, CD), including some instructions are used so that terminal device (it can be mobile phone,
Computer, server, air conditioner or network equipment etc.) execute method described in each embodiment of the present invention.
The above is only a preferred embodiment of the present invention, is not intended to limit the scope of the invention, all to utilize this hair
Equivalent structure or equivalent flow shift made by bright specification and accompanying drawing content is applied directly or indirectly in other relevant skills
Art field, is included within the scope of the present invention.
Claims (10)
1. a kind of epilepsy method for early warning, which is characterized in that the epilepsy method for early warning the following steps are included:
The the first EEG signals storage that obtains the first EEG signals of the user of brain wave acquisition device acquisition in real time, and will acquire
To default storage region；
Preset duration based on the first EEG signals stored in the default storage region, before timing acquisition current time
It is interior, the first eeg data of the brain wave acquisition device acquisition；
First eeg data and default eeg data are compared into operation, sent out with the determination user with the presence or absence of epilepsy
Make hidden danger, wherein the eeg data before the default eeg data is user breaking-out in preset duration；
Determining the user, there are when epileptic attack hidden danger, send prompt messages to the user corresponding default end
End.
2. epilepsy method for early warning as described in claim 1, which is characterized in that described by first eeg data and default brain
Electric data compare operation, include: with the presence or absence of the step of epileptic attack hidden danger with the determination user
First eeg data is sampled based on preset time window, to obtain the first sampled signal；
The first Wavelet Entropy in the preset time window is calculated according to first sampled signal；
First Wavelet Entropy default Wavelet Entropy corresponding with the default eeg data is compared into operation, described in determination
User whether there is epileptic attack hidden danger.
3. epilepsy method for early warning as claimed in claim 2, which is characterized in that described by first Wavelet Entropy and described default
The step of corresponding default Wavelet Entropy of eeg data compares operation, whether there is epileptic attack hidden danger with the determination user
Include:
Calculate the difference between first Wavelet Entropy and the default Wavelet Entropy；
Determine whether the difference is less than preset difference value, wherein when the difference is less than preset difference value, determine that the user deposits
In epileptic attack hidden danger.
4. epilepsy method for early warning as claimed in claim 2, which is characterized in that the transmission prompt messages to the user
Relatives' corresponding default terminal the step of after, the epilepsy method for early warning further include:
When receiving epileptic attack confirmation message, the default eeg data is updated based on first eeg data.
5. epilepsy method for early warning as claimed in claim 4, which is characterized in that described to update institute based on first eeg data
The step of stating default eeg data include:
Obtain corresponding first weighted value of first Wavelet Entropy and corresponding second weighted value of the default Wavelet Entropy；
Mesh is calculated based on first weighted value, first Wavelet Entropy, the second weighted value and the default Wavelet Entropy
Wavelet Entropy is marked, and sets the default Wavelet Entropy for the target wavelet entropy.
6. epilepsy method for early warning as claimed in claim 5, which is characterized in that described to obtain first Wavelet Entropy corresponding the
The step of one weighted value and the default Wavelet Entropy corresponding second weighted value includes:
The corresponding epileptic attack number of the default Wavelet Entropy is obtained, and first power is calculated based on the epileptic attack number
Weight values and second weighted value.
7. the epilepsy method for early warning as described in any one of claim 2 to 6, which is characterized in that the acquisition brain wave acquisition dress
It is described before the step of EEG signals setting the EEG signals of the user of acquisition, and will acquire are stored to default storage region
Epilepsy method for early warning further include:
The the second EEG signals storage that obtains the second EEG signals of the user of brain wave acquisition device acquisition in real time, and will acquire
To default storage region；
When receiving epileptic attack information, the epileptic attack information corresponding epileptic attack moment is obtained；
Based on the second EEG signals stored in the default storage region, when default before obtaining the epileptic attack moment
In length, the second eeg data of the brain wave acquisition device acquisition；
The default eeg data is determined based on second eeg data.
8. epilepsy method for early warning as claimed in claim 7, which is characterized in that described to determine institute based on second eeg data
The step of stating default eeg data include:
Second eeg data is sampled based on preset time window, to obtain the second sampled signal；
The second Wavelet Entropy in the preset time window is calculated according to second sampled signal, and second Wavelet Entropy is set
It is set to the default Wavelet Entropy.
9. a kind of epilepsy early warning device, which is characterized in that the epilepsy early warning device includes: memory, processor and is stored in
On the memory and the epilepsy early warning program that can run on the processor, the epilepsy early warning program is by the processor
It realizes when execution such as the step of epilepsy method for early warning described in any item of the claim 1 to 8.
10. a kind of computer readable storage medium, which is characterized in that it is pre- to be stored with epilepsy on the computer readable storage medium
Alert program, realizes such as epilepsy early warning described in any item of the claim 1 to 8 when the epilepsy early warning program is executed by processor
The step of method.
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111110218A (en) * | 2019-12-31 | 2020-05-08 | 北京品驰医疗设备有限公司 | Sudden death from epilepsy prediction method and equipment |
| CN111557813A (en) * | 2020-06-09 | 2020-08-21 | 首都医科大学宣武医院 | Fence system for monitoring children with epilepsy |
| CN111557809A (en) * | 2020-06-09 | 2020-08-21 | 首都医科大学宣武医院 | Telescopic protection warning bed shelves |
| CN112494053A (en) * | 2020-12-23 | 2021-03-16 | 深圳市德力凯医疗设备股份有限公司 | Method, system, equipment and storage medium for monitoring cerebral anoxia risk degree |
| CN112972892A (en) * | 2021-02-05 | 2021-06-18 | 杭州诺为医疗技术有限公司 | Method and device for automatically detecting epilepsy based on line length algorithm for implanted closed-loop system |
Citations (25)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20080021342A1 (en) * | 2000-10-20 | 2008-01-24 | Echauz Javier R | Unified Probabilistic Framework For Predicting And Detecting Seizure Onsets In The Brain And Multitherapeutic Device |
| US20080255469A1 (en) * | 2007-04-12 | 2008-10-16 | Yuan Ze University | Method for Monitoring the Depth of Anesthesia |
| CN101340846A (en) * | 2005-12-20 | 2009-01-07 | 皇家飞利浦电子股份有限公司 | Device for detecting and warning of a medical condition |
| US20100286747A1 (en) * | 2007-09-28 | 2010-11-11 | Arizona Board Of Regents, Acting For And On Behalf Of Arizona State University | Methods for applying brain synchronization to epilepsy and other dynamical disorders |
| CN102122133A (en) * | 2011-01-21 | 2011-07-13 | 北京工业大学 | Self-adaption wavelet neural network abnormity detection and fault diagnosis classification system and method |
| CN102429657A (en) * | 2011-09-22 | 2012-05-02 | 上海师范大学 | Epilepsia electroencephalogram signal classified detection device and method |
| CN102697493A (en) * | 2012-05-03 | 2012-10-03 | 北京工业大学 | Method for rapidly and automatically identifying and removing ocular artifacts in electroencephalogram signal |
| CN103156603A (en) * | 2012-11-20 | 2013-06-19 | 中国人民解放军第四军医大学第一附属医院 | Epileptic seizure pre-warning device |
| CN104287706A (en) * | 2014-10-29 | 2015-01-21 | 云南大学 | Human health state real-time monitoring, diagnosis and treatment recommendation system |
| WO2015040532A2 (en) * | 2013-09-19 | 2015-03-26 | Tata Consultancy Services Limited | System and method for evaluating a cognitive load on a user corresponding to a stimulus |
| CN104545901A (en) * | 2015-01-29 | 2015-04-29 | 中国科学院电子学研究所 | Electroencephalogram detecting system |
| US20160055305A1 (en) * | 2014-08-19 | 2016-02-25 | eagleyemed, Inc. | Video enhancements for live sharing of medical images |
| CN106911943A (en) * | 2017-02-21 | 2017-06-30 | 腾讯科技（深圳）有限公司 | A kind of video display method and its device |
| WO2017152059A1 (en) * | 2016-03-04 | 2017-09-08 | University Of Tennessee Research Foundation | Ambulatory seizure monitoring system and method |
| CN107203778A (en) * | 2017-05-05 | 2017-09-26 | 平安科技（深圳）有限公司 | PVR intensity grade detecting system and method |
| CN107714038A (en) * | 2017-10-12 | 2018-02-23 | 北京翼石科技有限公司 | The feature extracting method and device of a kind of EEG signals |
| CN108209872A (en) * | 2017-12-29 | 2018-06-29 | 佛山市幻云科技有限公司 | Epilepsy Children monitoring method, device, server and system |
| CN108346428A (en) * | 2017-09-13 | 2018-07-31 | 腾讯科技（深圳）有限公司 | Voice activity detection and its method for establishing model, device, equipment and storage medium |
| CN108766532A (en) * | 2018-05-11 | 2018-11-06 | 深圳市心流科技有限公司 | Improve teaching method, device and the computer readable storage medium of attention |
| CN108805259A (en) * | 2018-05-23 | 2018-11-13 | 北京达佳互联信息技术有限公司 | neural network model training method, device, storage medium and terminal device |
| CN108992066A (en) * | 2018-08-15 | 2018-12-14 | 东北大学 | Portable lower limb behavior pattern real-time identifying system and method based on electromyography signal |
| CN109035960A (en) * | 2018-06-15 | 2018-12-18 | 吉林大学 | Driver's driving mode analysis system and analysis method based on simulation driving platform |
| CN109009171A (en) * | 2018-08-01 | 2018-12-18 | 深圳市心流科技有限公司 | Attention assessment method, system and computer readable storage medium |
| CN109106365A (en) * | 2018-09-04 | 2019-01-01 | 杭州航弈生物科技有限责任公司 | Epileptic attack source of early warning based on EEG Processing |
| CN109199375A (en) * | 2018-11-30 | 2019-01-15 | 东南大学 | A kind of noninvasive Fetal ECG detection device and ecg signal data processing method |
-
2019
- 2019-01-30 CN CN201910094742.8A patent/CN109567797B/en active Active
Patent Citations (25)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20080021342A1 (en) * | 2000-10-20 | 2008-01-24 | Echauz Javier R | Unified Probabilistic Framework For Predicting And Detecting Seizure Onsets In The Brain And Multitherapeutic Device |
| CN101340846A (en) * | 2005-12-20 | 2009-01-07 | 皇家飞利浦电子股份有限公司 | Device for detecting and warning of a medical condition |
| US20080255469A1 (en) * | 2007-04-12 | 2008-10-16 | Yuan Ze University | Method for Monitoring the Depth of Anesthesia |
| US20100286747A1 (en) * | 2007-09-28 | 2010-11-11 | Arizona Board Of Regents, Acting For And On Behalf Of Arizona State University | Methods for applying brain synchronization to epilepsy and other dynamical disorders |
| CN102122133A (en) * | 2011-01-21 | 2011-07-13 | 北京工业大学 | Self-adaption wavelet neural network abnormity detection and fault diagnosis classification system and method |
| CN102429657A (en) * | 2011-09-22 | 2012-05-02 | 上海师范大学 | Epilepsia electroencephalogram signal classified detection device and method |
| CN102697493A (en) * | 2012-05-03 | 2012-10-03 | 北京工业大学 | Method for rapidly and automatically identifying and removing ocular artifacts in electroencephalogram signal |
| CN103156603A (en) * | 2012-11-20 | 2013-06-19 | 中国人民解放军第四军医大学第一附属医院 | Epileptic seizure pre-warning device |
| WO2015040532A2 (en) * | 2013-09-19 | 2015-03-26 | Tata Consultancy Services Limited | System and method for evaluating a cognitive load on a user corresponding to a stimulus |
| US20160055305A1 (en) * | 2014-08-19 | 2016-02-25 | eagleyemed, Inc. | Video enhancements for live sharing of medical images |
| CN104287706A (en) * | 2014-10-29 | 2015-01-21 | 云南大学 | Human health state real-time monitoring, diagnosis and treatment recommendation system |
| CN104545901A (en) * | 2015-01-29 | 2015-04-29 | 中国科学院电子学研究所 | Electroencephalogram detecting system |
| WO2017152059A1 (en) * | 2016-03-04 | 2017-09-08 | University Of Tennessee Research Foundation | Ambulatory seizure monitoring system and method |
| CN106911943A (en) * | 2017-02-21 | 2017-06-30 | 腾讯科技（深圳）有限公司 | A kind of video display method and its device |
| CN107203778A (en) * | 2017-05-05 | 2017-09-26 | 平安科技（深圳）有限公司 | PVR intensity grade detecting system and method |
| CN108346428A (en) * | 2017-09-13 | 2018-07-31 | 腾讯科技（深圳）有限公司 | Voice activity detection and its method for establishing model, device, equipment and storage medium |
| CN107714038A (en) * | 2017-10-12 | 2018-02-23 | 北京翼石科技有限公司 | The feature extracting method and device of a kind of EEG signals |
| CN108209872A (en) * | 2017-12-29 | 2018-06-29 | 佛山市幻云科技有限公司 | Epilepsy Children monitoring method, device, server and system |
| CN108766532A (en) * | 2018-05-11 | 2018-11-06 | 深圳市心流科技有限公司 | Improve teaching method, device and the computer readable storage medium of attention |
| CN108805259A (en) * | 2018-05-23 | 2018-11-13 | 北京达佳互联信息技术有限公司 | neural network model training method, device, storage medium and terminal device |
| CN109035960A (en) * | 2018-06-15 | 2018-12-18 | 吉林大学 | Driver's driving mode analysis system and analysis method based on simulation driving platform |
| CN109009171A (en) * | 2018-08-01 | 2018-12-18 | 深圳市心流科技有限公司 | Attention assessment method, system and computer readable storage medium |
| CN108992066A (en) * | 2018-08-15 | 2018-12-14 | 东北大学 | Portable lower limb behavior pattern real-time identifying system and method based on electromyography signal |
| CN109106365A (en) * | 2018-09-04 | 2019-01-01 | 杭州航弈生物科技有限责任公司 | Epileptic attack source of early warning based on EEG Processing |
| CN109199375A (en) * | 2018-11-30 | 2019-01-15 | 东南大学 | A kind of noninvasive Fetal ECG detection device and ecg signal data processing method |
Non-Patent Citations (5)
| Title |
|---|
| HUSSEIN: ""Focal and Non-Focal Epilepsy Localization: A Review"", 《IEEE ACCESS》 * |
| 万婷: ""实现癫痫始发区精确定位的高频振荡节律自动检测算法"", 《中国优秀硕士学位论文全文数据库医药卫生科技辑》 * |
| 李红利: ""癫痫脑电信号的非线性分析"", 《中国博士学位论文全文数据库信息科技辑》 * |
| 王海玉: ""脑电信号处理方法的研究综述"", 《计算机时代》 * |
| 陈多: ""基于小波变换的癫痫脑电信号分析"", 《中国博士学位论文全文数据库医药卫生科技辑》 * |
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|---|---|---|---|---|
| CN111110218A (en) * | 2019-12-31 | 2020-05-08 | 北京品驰医疗设备有限公司 | Sudden death from epilepsy prediction method and equipment |
| CN111110218B (en) * | 2019-12-31 | 2024-03-08 | 北京品驰医疗设备有限公司 | Sudden epileptic death prediction method and device |
| CN111557813A (en) * | 2020-06-09 | 2020-08-21 | 首都医科大学宣武医院 | Fence system for monitoring children with epilepsy |
| CN111557809A (en) * | 2020-06-09 | 2020-08-21 | 首都医科大学宣武医院 | Telescopic protection warning bed shelves |
| CN111557809B (en) * | 2020-06-09 | 2021-11-30 | 首都医科大学宣武医院 | Telescopic protection warning bed shelves |
| CN112494053A (en) * | 2020-12-23 | 2021-03-16 | 深圳市德力凯医疗设备股份有限公司 | Method, system, equipment and storage medium for monitoring cerebral anoxia risk degree |
| CN112494053B (en) * | 2020-12-23 | 2023-10-03 | 深圳市德力凯医疗设备股份有限公司 | Method, system, equipment and storage medium for monitoring hypoxia risk degree of brain |
| CN112972892A (en) * | 2021-02-05 | 2021-06-18 | 杭州诺为医疗技术有限公司 | Method and device for automatically detecting epilepsy based on line length algorithm for implanted closed-loop system |
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