Electroencephalogram monitoring system for neurology patients

Abstract

The invention relates to an electroencephalogram monitoring system for neurology patients. The system comprises a signal acquisition module, a visual capture module and a signal processing module, the signal acquisition module is used for acquiring spontaneous biopotential of the head of a user so as to output an original signal to the signal processing module; the visual capture module is used for collecting eye action information of a user, the eye action information comprises blink action information of the user, and the visual capture module outputs the eye action information to the signal processing module; and the signal processing module is used for receiving the eye action information and the original signal, segmenting the original signal according to the blink action information in the eye action information, and carrying out blink wake removal processing on a part of original signal segments containing the blink action information after segmentation.

Description

Technical Field

[0001] This application relates to the technical field of medical instruments, and particularly to an electroencephalogram (EEG) monitoring system for neurology patients. Background Art

[0002] Electroencephalogram (EEG) signals are signals that record the electrical activities of neurons in the cerebral cortex, and are widely used in clinical diagnosis, neuroscience research, and brain-computer interface (BCI) systems. However, the complexity of EEG signals and their susceptibility to external interference make precise signal processing and feature extraction key issues.

[0003] The utility model patent with the publication number CN222640492U discloses a system for EEG signal acquisition, which includes: a signal acquisition module, a power supply acquisition module, a preprocessing module, a filtering module, and a data analysis module; the signal acquisition module includes an EEG cap and EEG acquisition electrodes, the EEG cap is used to fix the EEG acquisition electrodes, and the EEG acquisition electrodes are electrically connected to the preprocessing module for acquiring the EEG signals of the human body and sending them to the preprocessing module; the preprocessing module includes a signal amplification unit and a data processing unit, the signal amplification unit is electrically connected to the data processing unit for amplifying the EEG signals and sending them to the data processing unit, the data processing unit is communicatively connected to the filtering module for preprocessing the amplified EEG signals to eliminate their DC components and baseline drift; the power supply acquisition module is communicatively connected to the filtering module for acquiring the mains frequency and mains voltage of the external power supply; the filtering module includes a notch filtering unit and a frequency adjustment unit, the notch filtering unit is electrically connected to the data analysis module for filtering the preprocessed EEG signals and sending the filtered EEG signals to the data analysis module for analysis, the frequency adjustment unit is communicatively connected to the power supply acquisition module for adjusting the center frequency of the notch filtering unit according to the mains frequency; the data analysis module includes a feature extraction unit and a signal analysis unit, the feature extraction unit is used for extracting features from the filtered EEG signals, and the signal analysis unit is used for performing signal analysis on the extracted features.

[0004] However, most common EEG signal preprocessing methods rely on simple filters to remove noise, such as removing electromyogram artifacts and eye movement artifacts. However, these methods often have difficulty in balancing noise removal and signal integrity, and may result in the loss of some valid signals. Summary of the Invention

[0005] Based on this, it is necessary to provide an EEG monitoring system for neurology patients that can reduce the loss of valid signals in view of the above technical problems.

[0006] The present application provides an electroencephalogram monitoring system for neurology patients, including a signal acquisition module, a visual capture module, and a signal processing module, where: The signal acquisition module is used to acquire the spontaneous bioelectric potential of the user's head and output the original signal to the signal processing module; The visual capture module is used to acquire the eye movement information of the user. The eye movement information includes the blink movement information of the user. The visual capture module outputs the eye movement information to the signal processing module; The signal processing module is used to receive the eye movement information and the original signal, segment the original signal according to the blink movement information in the eye movement information, and perform blink artifact removal processing on the segmented original signal segments containing the blink movement information.

[0007] In one embodiment, the signal processing module includes a notch filter unit; the notch filter unit is connected to the output end of the signal acquisition module and is used to perform notch filtering on the power frequency noise signal contained in the original signal to output a de-power-frequency original signal.

[0008] In one embodiment, the blink movement information includes closed-eye timestamp information and open-eye timestamp information; the signal processing module includes a time-frequency conversion unit and a signal analysis unit; The signal analysis unit is used to segment the de-power-frequency original signal according to the closed-eye timestamp information and the open-eye timestamp information to obtain several paragraph signals, and output at least part of the paragraph signals to the time-frequency conversion unit; The time-frequency conversion unit is used to perform time-frequency conversion on the paragraph signals to obtain frequency domain signals and feedback them to the signal analysis unit.

[0009] In one embodiment, the signal acquisition module includes electrodes, and the electrodes are used to acquire the spontaneous bioelectric potential of the user's head.

[0010] In one embodiment, the signal analysis unit is further used to execute a preset electroencephalogram signal monitoring method, and the preset electroencephalogram signal monitoring method includes: Obtain the blink movement information and the de-power-frequency original signal; Segment the de-power-frequency original signal according to the closed-eye timestamp information and the open-eye timestamp information contained in the blink movement information to obtain several paragraph signals, and output them to the time-frequency conversion unit; Wait and obtain the frequency domain signals fed back by the time-frequency conversion unit, compare the frequency domain signals to obtain the characteristic frequency band representing the blink movement; Output the de-power-frequency original signal to the notch filter unit to control the notch filter unit to filter out the signals in the characteristic frequency band of the de-power-frequency original signal.

[0011] In one embodiment, the specific steps of segmenting the de-power-frequency original signal according to the eye-closure timestamp information and eye-opening timestamp information included in the blink action information to obtain a plurality of segment signals and outputting them to the time-frequency conversion unit are as follows: Linearly arrange the eye-closure timestamp information and eye-opening timestamp in chronological order; Calculate the duration between each eye-closure timestamp information and the next eye-opening timestamp information as the half-duration, and then calculate the corresponding half-duration subtracted from each eye-closure timestamp information to obtain the estimated start time; Use the estimated start time as the blink start time and the eye-opening timestamp information as the blink end time, and obtain the signal segment from the start time to the end time in the de-power-frequency original signal as the blink segment.

[0012] In one embodiment, the specific steps of segmenting the de-power-frequency original signal according to the eye-closure timestamp information and eye-opening timestamp information included in the blink action information to obtain a plurality of segment signals and outputting them to the time-frequency conversion unit further include: Use the de-power-frequency original signal segment within a preset time period before the estimated start time as the reference segment; Combine the blink segment and the reference segment obtained according to the same estimated start time as a reference group; Output the obtained plurality of reference groups to the time-frequency conversion unit.

[0013] In one embodiment, the specific steps of waiting to obtain the frequency-domain signal fed back by the time-frequency conversion unit and comparing the frequency-domain signals to obtain the characteristic frequency band representing the blink action are as follows: Wait to obtain the frequency-domain signal of the reference group; Compare the frequency-domain signals of the blink segment and the reference segment in the same reference group, and use the different frequency bands as the differential frequency bands.

[0014] In one embodiment, the specific steps of waiting to obtain the frequency-domain signal fed back by the time-frequency conversion unit and comparing the frequency-domain signals to obtain the characteristic frequency band representing the blink action are as follows: Summarize the differential frequency bands of all reference groups and record the frequency of occurrence of each differential frequency band; Screen the differential frequency bands according to a preset frequency threshold, and use the differential frequency bands with a frequency of occurrence higher than the preset frequency threshold as the characteristic frequency bands and output them.

[0015] In one embodiment, the specific steps of outputting the de-power-frequency original signal to the notch filter unit to control the notch filter unit to filter out the signal of the characteristic frequency band in the de-power-frequency original signal are as follows: Use the characteristic frequency band as the filtering frequency band and output it to the notch filter unit to set the filtering frequency of the notch filter; Output the original signal without power frequency to the notch filter unit to obtain the signal without blink artifacts; Use the power frequency as the filtering frequency band and output it to the notch filter unit to reset the filtering frequency of the notch filter.

[0016] The above-mentioned electroencephalogram monitoring system for neurology patients captures the eye movements of the patient through the visual capture module, thereby recording the blinking time of the patient. When performing waveform processing, the waveforms during the patient's blinking are differentially processed with the waveforms during eye opening, so as to filter out only the waveforms related to the blinking action in the original waveform, achieving the purpose of reducing the loss of effective signals. Description of the Drawings

[0017] Figure 1 It is the system structure diagram of an electroencephalogram monitoring system for neurology patients in an embodiment; Figure 2 It is the flow schematic diagram of the preset electroencephalogram signal monitoring method in an embodiment; Figure 3 It is the flow schematic diagram of step S200 in an embodiment; Figure 4 It is the flow schematic diagram of step S300 in an embodiment; Figure 5 It is the flow schematic diagram of step S400 in an embodiment. Detailed Embodiments

[0018] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present application and are not used to limit the present application.

[0019] The present application provides an electroencephalogram monitoring system for neurology patients, including a signal acquisition module, a visual capture module, and a signal processing module, where: The signal acquisition module is used to collect the spontaneous bioelectric potential of the user's head and output the original signal to the signal processing module; The visual capture module is used to collect the eye movement information of the user. The eye movement information includes the blinking movement information of the user. The visual capture module outputs the eye movement information to the signal processing module; The signal processing module is used to receive the eye movement information and the original signal, segment the original signal according to the blinking movement information in the eye movement information, and perform de-blinking tail trace processing on the segmented original signal segments containing the blinking movement information.

[0020] The eye movement of the patient is captured by the visual capture module, so as to record the patient's blinking time. When performing waveform processing, the waveform during the patient's blinking is differentially processed with the waveform during eye opening, so as to filter out only the waveform related to the blinking action in the original waveform, achieving the purpose of reducing the loss of effective signals.

[0021] In this embodiment, the visual capture module collects the eye movement of the patient through an image acquisition device, and identifies the action of the patient closing the eyes through a pattern recognition algorithm, and outputs blinking action information, where the blinking action information includes closed-eye timestamp information and open-eye timestamp information.

[0022] The signal acquisition module includes electrodes, and the electrodes are used to collect the spontaneous bioelectric potential of the user's head. The signal acquisition module obtains the bioelectricity of the patient's head through the electrodes and then outputs the original waveform to the signal processing module; the signal processing module includes a notch filter unit, and the notch filter unit is connected to the output end of the signal acquisition module, and is used to perform notch filtering on the power frequency noise signal contained in the original signal to output a de-power frequency original signal.

[0023] In the embodiment of the present application, the notch filter unit is a tunable band-stop filter, which is used to filter the waveforms of the power frequency and / or other frequencies and frequency bands according to the control instruction.

[0024] Furthermore, the signal processing module further includes a time-frequency conversion unit and a signal analysis unit; where: The signal analysis unit is used to segment the de-power frequency original signal according to the closed-eye timestamp information and the open-eye timestamp information to obtain a plurality of segment signals, and output at least some of the segment signals to the time-frequency conversion unit; The time-frequency conversion unit is used to perform a fast Fourier transform on the segment signal to achieve time-frequency conversion, so as to obtain a frequency-domain signal and feedback it to the signal analysis unit.

[0025] Each module in the above-mentioned electroencephalogram monitoring system for neurology patients can be implemented in whole or in part by software, hardware and their combinations. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above-mentioned modules.

[0026] In one of the embodiments, the signal analysis unit is further used to execute a preset electroencephalogram signal monitoring method, and the preset electroencephalogram signal monitoring method includes the following steps: Step S100: Obtain blinking action information and de-power frequency original signal; Step S200: Segment the original signal without power frequency according to the eye - closing timestamp information and eye - opening timestamp information included in the blink action information to obtain a number of segment signals, and output them to the time - frequency conversion unit; Step S300: Wait for and obtain the frequency - domain signal fed back by the time - frequency conversion unit, and compare the frequency - domain signals to obtain the characteristic frequency band representing the blink action; Step S400: Output the original signal without power frequency to the notch filter unit to control the notch filter unit to filter out the signals in the characteristic frequency band from the original signal without power frequency.

[0027] In step S100, the blink action information is the information used to describe the blink action of the patient's eyes. Specifically, the blink action information includes the eye - closing timestamp information used to describe the complete closing of the patient's eyes, and the eye - opening timestamp information used to describe the first eye - opening after the eyes are closed.

[0028] In one embodiment, the specific steps of step S200 include: Step S210: Linearly arrange the eye - closing timestamp information and the eye - opening timestamp in chronological order.

[0029] In this step, the specific way of linearly arranging in chronological order is to arrange the eye - closing timestamp information and the eye - opening timestamp information in the order of their occurrence from the earliest to the latest.

[0030] Step S220: Calculate the duration between each eye - closing timestamp information and the next eye - opening timestamp information as the half - course duration, and then subtract the corresponding half - course duration from each eye - closing timestamp information to obtain the estimated start time.

[0031] In this step, since the eye - closing timestamp information is the time when the patient's eyes are completely closed, but the time to complete this action is the second half of the actual eye movement artifact, it is necessary to supplement the first - half time. Therefore, subtract the calculated half - course duration from the eye - closing timestamp information to obtain a time before the eye - closing timestamp information as the estimated start time. The period from the estimated start time to the eye - closing timestamp information is a relatively complete eye - closing action segment.

[0032] Step S230: Take the estimated start time as the blink start time and the eye - opening timestamp information as the blink end time, and obtain the signal segment from the start time to the end time from the original signal without power frequency as the blink segment.

[0033] By executing steps S210 - S230, the segment where the blink artifact exists can be intercepted from the original signal without power frequency. By filtering the waveforms of some frequency bands existing in this segment, the waveform after removing the artifact can be obtained.

[0034] In one embodiment, step S200 further includes: Step S240: taking the common frequency-removed original signal segment within a preset time period before the estimation start time as a reference segment.

[0035] In this step, the preset time period is set to be within 0.5 seconds before the estimation start time, and the common frequency-removed original signal segment within 3 seconds before the estimation start time is used as the reference segment.

[0036] Step S250: combining the blink segments and the reference segments acquired according to the same estimated start time into a reference group.

[0037] Among them, a reference group includes a blink segment and a reference segment corresponding to the same estimated start time; since the waveform before the blink segment appears is relatively stable, it contains fewer waveform components related to the blink artifact, so using this part of the waveform as a reference waveform is more conducive to eliminating the blink artifact.

[0038] Step S260: outputting the obtained reference groups to the time-frequency conversion unit.

[0039] After the reference group output is transmitted to the time-frequency conversion unit in step S260, the time-frequency conversion unit converts the time-frequency waveforms of the blink segment in the reference group and the reference segment into frequency domain signals and outputs them.

[0040] For the frequency domain signal output by the time-frequency conversion unit, the signal analysis unit will continue to execute steps S300 and S400, so as to further process the original signal without common frequency.

[0041] In one embodiment, the specific steps of step S300 include: Step S310: Waiting and acquiring the frequency domain signal of the reference group.

[0042] In this step, the frequency domain signal is the frequency domain distribution data of the reference segment and the blink segment obtained by the time-frequency conversion unit through fast Fourier transform.

[0043] Step S320: comparing the frequency domain signals of the blink segments in the same reference group and the frequency domain signals of the reference segments, and taking the different frequency bands as difference frequency bands.

[0044] In this step, the difference segments included in the blink segment but not included in the reference segment can be obtained by analyzing the frequency domain distribution data obtained in step S310.

[0045] Step S330: Summarize the difference frequency bands of all reference groups and record the frequency of occurrence of each difference frequency band.

[0046] Step S340: Screen the differential frequency bands according to a preset frequency threshold, and take the differential frequency bands with frequencies higher than the preset frequency threshold in the differential frequency bands as characteristic frequency bands and output them.

[0047] Among them, the preset frequency threshold is 0.8 times the maximum frequency of all differential frequency bands. Through the above steps S330 - 340, summarize and count the differential frequency bands, obtain the occurrence frequency of each differential frequency band, and obtain the frequency quantity of the differential frequency band with the highest occurrence frequency. Multiply this frequency quantity by 0.3 to obtain the maximum frequency. When the maximum frequency is not an integer, round up. For example, if the maximum frequency is 7, 7 * 0.3 = 2.1, and at this time the preset frequency threshold is 3. Subsequently, eliminate the differential frequency bands with frequencies less than the frequency threshold, and retain the differential frequency bands with frequencies greater than the frequency threshold as characteristic frequency bands.

[0048] In one embodiment, the specific steps of step S400 include: Step S410: Take the characteristic frequency band as the filtering frequency band and output it to the notch filter unit to set the filtering frequency of the notch filter; Step S420: Output the original signal without power frequency to the notch filter unit to obtain the signal without blink artifacts; Step S430: Take the power frequency as the filtering frequency band and output it to the notch filter unit to reset the filtering frequency of the notch filter.

[0049] Through steps S410 - S430, the parameters of the notch filter unit can be set, so as to meet the requirement of further band-stop (notch) filtering of the original waveform after removing the power frequency, so as to remove the blink artifact frequency band in the original waveform after removing the power frequency, and thus obtain the signal without blink artifacts.

[0050] It should be understood that although the steps in the flowcharts involved in the above-described embodiments are shown in sequence according to the arrows, these steps do not necessarily execute in the order indicated by the arrows. Unless there is a clear indication in this article, the execution of these steps has no strict order limit, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-described embodiments may include multiple steps or multiple stages. These steps or stages do not necessarily execute at the same moment, but can execute at different moments. The execution order of these steps or stages is not necessarily sequential, but can be executed alternately or alternately with at least a part of other steps or steps or stages in other steps.

[0051] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, database, or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memories. Non-volatile memories can include read-only memory (ROM), magnetic tapes, floppy disks, flash memories, optical memories, high-density embedded non-volatile memories, resistive random access memories (ReRAM), magnetoresistive random access memories (MRAM), ferroelectric random access memories (FRAM), phase change memories (PCM), graphene memories, etc. Volatile memories can include random access memory (RAM) or external cache memories, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The databases involved in the embodiments provided in the present application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., without limitation. The processors involved in the embodiments provided in the present application can be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logics, data processing logics based on quantum computing, etc., without limitation.

[0052] The technical features of the above embodiments can be combined arbitrarily. For the sake of concise description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope described in this specification.

[0053] The above-described embodiments only represent several implementation manners of the present application. The description is relatively specific and detailed, but it should not be construed as a limitation on the patent scope of the present application. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the appended claims.

Claims

1. An electroencephalogram monitoring system for neurology patients, characterized in that: It includes a signal acquisition module, a visual capture module and a signal processing module, among which: The signal acquisition module is used to collect the spontaneous biopotential of the user's head to output the original signal to the signal processing module; The visual capture module is used to collect the user's eye movement information, the eye movement information includes the user's blinking movement information, and the visual capture module outputs the eye movement information to the signal processing module; The signal processing module is used to receive the eye movement information and the original signal, and to segment the original signal according to the blinking movement information in the eye movement information, and to remove blink trails on the original signal segments containing the blinking movement information after segmentation.

2. The EEG monitoring system for neurology patients according to claim 1, characterized in that: The signal processing module comprises a notch filter unit; the notch filter unit is connected to the output end of the signal acquisition module and is used for performing notch filtering on the common frequency noise signal contained in the original signal to output the original signal without the common frequency.

3. The EEG monitoring system for neurology patients according to claim 2, characterized in that: The blink action information includes eye closing timestamp information and eye opening timestamp information; the signal processing module includes a time-frequency conversion unit and a signal analysis unit; The signal analysis unit is used to segment the original signal without common frequency according to the eye-closing timestamp information and the eye-opening timestamp information to obtain a plurality of segment signals, and output at least part of the segment signals to the time-frequency conversion unit; The time-frequency conversion unit is used to perform time-frequency conversion on the paragraph signal to obtain a frequency domain signal and feed it back to the signal analysis unit.

4. The EEG monitoring system for neurology patients according to any one of claims 1 to 3, characterized in that: The signal acquisition module includes electrodes, and the electrodes are used to collect spontaneous biopotentials of the user's head.

5. The EEG monitoring system for neurology patients according to claim 4, characterized in that: The signal analysis unit is further used to execute a preset EEG signal monitoring method, which includes: Acquire the blink action information and the original signal with the common frequency removed; Segmenting the de-common-frequency original signal according to the eye-closing timestamp information and the eye-opening timestamp information contained in the blinking action information to obtain a plurality of segment signals, and outputting the signals to a time-frequency conversion unit; Waiting for and acquiring the frequency domain signal fed back by the time-frequency conversion unit, and comparing the frequency domain signals to acquire a characteristic frequency band representing a blinking action; The original signal without the common frequency is output to the notch filter unit to control the notch filter unit to filter out the signal of the characteristic frequency band in the original signal without the common frequency.

6. The EEG monitoring system for neurology patients according to claim 5, characterized in that: The specific steps of segmenting the de-common-frequency original signal according to the eye-closing timestamp information and the eye-opening timestamp information contained in the blinking action information to obtain a plurality of segment signals and outputting them to the time-frequency conversion unit include: Arrange the eye-closing timestamp information and the eye-opening timestamp information linearly in chronological order; Calculate the time between each eye-closing timestamp information and the next eye-opening timestamp information as the half-time length, and then subtract the corresponding half-time length from each eye-closing timestamp information to obtain the estimated start time; The estimated start time is used as the blink start time, the eye opening timestamp information is used as the blink end time, and a signal segment from the start time to the end time is obtained from the common frequency-removed original signal as the blink segment.

7. The EEG monitoring system for neurology patients according to claim 6, characterized in that: The specific step of segmenting the de-common-frequency original signal according to the eye-closing timestamp information and the eye-opening timestamp information included in the blinking action information to obtain a plurality of segment signals and outputting them to the time-frequency conversion unit also includes: Using the common frequency-removed original signal segment within a preset time period before the estimation start time as a reference segment; combining the blink segment and the reference segment obtained according to the same estimated start time to form a reference group; The obtained reference groups are output to a time-frequency conversion unit.

8. The EEG monitoring system for neurology patients according to claim 7, characterized in that: The specific steps of waiting for and acquiring the frequency domain signal fed back by the time-frequency conversion unit, and comparing the frequency domain signal to acquire the characteristic frequency band representing the blinking action include: Waiting for and acquiring the frequency domain signal of the reference group; The frequency domain signals of the blink segments and the frequency domain signals of the reference segments in the same reference group are compared, and the different frequency bands are taken as difference frequency bands.

9. The EEG monitoring system for neurology patients according to claim 8, characterized in that: The specific steps of waiting for and acquiring the frequency domain signal fed back by the time-frequency conversion unit, and comparing the frequency domain signal to acquire the characteristic frequency band representing the blinking action include: Summarize the difference frequency bands of all the reference groups, and record the frequency of occurrence of each difference frequency band; The difference frequency bands are screened according to a preset frequency threshold, and the difference frequency bands whose occurrence frequency in the difference frequency bands is higher than the preset frequency threshold are taken as feature frequency bands and outputted.

10. The EEG monitoring system for neurology patients according to claim 8, characterized in that: The specific steps of waiting for and acquiring the frequency domain signal fed back by the time-frequency conversion unit, and comparing the frequency domain signal to acquire the characteristic frequency band representing the blinking action include: Summarize the difference frequency bands of all the reference groups, and record the frequency of occurrence of each difference frequency band; The difference frequency bands are screened according to a preset frequency threshold, and the difference frequency bands whose occurrence frequency in the difference frequency bands is higher than the preset frequency threshold are taken as feature frequency bands and outputted.

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