An electroencephalogram monitoring system for neurology patients

Abstract

This application relates to an electroencephalogram monitoring system for neurology patients. The system includes a signal acquisition module, a visual capture module, and a signal processing module, where: the signal acquisition module is used to collect spontaneous bioelectrical potentials of the user's head and output the original signal to the signal processing module; the visual capture module is used to collect the user's eye movement information, and the eye movement information includes the user's blink 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, segment the original signal according to the blink movement information in the eye movement information, and perform blink artifact removal processing on the partial original signal segments containing the blink movement 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 an 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 struggle to balance noise removal and signal integrity and may result in the loss of some valid signals. Summary of the Invention

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

[0006] This application provides an electroencephalogram monitoring system for neurology patients, including a signal acquisition module, a visual capture module, and a signal processing module, where:

[0007] 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;

[0008] 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;

[0009] 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 blink artifact removal on the segmented original signal segments containing the blinking movement information.

[0010] 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.

[0011] In one embodiment, the blinking 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;

[0012] 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 paragraph signals and output at least some of the paragraph signals to the time-frequency conversion unit;

[0013] The time-frequency conversion unit is used to perform time-frequency conversion on the paragraph signal to obtain a frequency-domain signal and feedback it to the signal analysis unit.

[0014] In one embodiment, the signal acquisition module includes electrodes, and the electrodes are used to collect the spontaneous bioelectric potential of the user's head.

[0015] 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:

[0016] Obtain the blinking movement information and the de-power frequency original signal;

[0017] Segment the de-power frequency original signal according to the closed-eye timestamp information and the open-eye timestamp information contained in the blinking movement information to obtain a plurality of paragraph signals and output them to the time-frequency conversion unit;

[0018] Wait 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 blinking movement;

[0019] Output the de-power-frequency original signal to the notch filter unit to control the notch filter unit to filter out the signal in the characteristic frequency band of the de-power-frequency original signal.

[0020] In one embodiment, the specific steps of segmenting the de-power-frequency original signal according to the closed-eye timestamp information and the open-eye timestamp information included in the blink action information to obtain a plurality of segment signals and outputting them to the time-frequency conversion unit include:

[0021] Linearly arrange the closed-eye timestamp information and the open-eye timestamp in chronological order;

[0022] Calculate the duration between each closed-eye timestamp information and the next open-eye timestamp information as the half-course duration, and then calculate subtracting the corresponding half-course duration before each closed-eye timestamp information to obtain the estimated start time;

[0023] Take the estimated start time as the blink start time, take the open-eye 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.

[0024] In one embodiment, the specific steps of segmenting the de-power-frequency original signal according to the closed-eye timestamp information and the open-eye 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:

[0025] Take the de-power-frequency original signal segment within a preset time period before the estimated start time as the reference segment;

[0026] Combine the blink segment and the reference segment obtained according to the same estimated start time as a reference group;

[0027] Output the obtained plurality of reference groups to the time-frequency conversion unit.

[0028] In one embodiment, the specific steps of waiting and obtaining 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 include:

[0029] Wait and obtain the frequency-domain signal of the reference group;

[0030] Compare the frequency-domain signal of the blink segment and the frequency-domain signal of the reference segment in the same reference group, and take the different frequency bands as the difference frequency bands.

[0031] In one embodiment, the specific steps of waiting and obtaining 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 include:

[0032] Summarize the differential frequency bands of all reference groups and record the frequency of occurrence of each differential frequency band;

[0033] Filter 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 in the differential frequency bands as characteristic frequency bands and output them.

[0034] 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 the signal of the characteristic frequency band in the de-power-frequency original signal include:

[0035] 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;

[0036] Output the de-power-frequency original signal to the notch filter unit to obtain the de-blink-artifact signal;

[0037] 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.

[0038] 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 only filter out the waveforms related to the blinking action in the original waveform, achieving the purpose of reducing the loss of effective signals. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 It is a system structure diagram of an electroencephalogram monitoring system for neurology patients in one embodiment;

[0040] Figure 2 It is a schematic flowchart of a preset electroencephalogram signal monitoring method in one embodiment;

[0041] Figure 3 It is a schematic flowchart of step S200 in one embodiment;

[0042] Figure 4 It is a schematic flowchart of step S300 in one embodiment;

[0043] Figure 5 It is a schematic flowchart of step S400 in one embodiment. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0044] 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 accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0045] This application provides an electroencephalogram monitoring system for neurology patients, including a signal acquisition module, a visual capture module, and a signal processing module, where:

[0046] 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;

[0047] The visual capture module is used to collect 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;

[0048] 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.

[0049] By capturing the eye movements of the patient through the visual capture module, the blink time of the patient is recorded. When performing waveform processing, the waveform during the patient's blink is differentially processed with the waveform during eye opening, so as to filter out only the waveforms related to the blink movement in the original waveform, achieving the purpose of reducing the loss of effective signals.

[0050] In this embodiment, the visual capture module collects the eye movements of the patient through an image acquisition device, identifies the action of the patient closing the eyes through a pattern recognition algorithm, and outputs the blink movement information. The blink movement information includes the closed-eye timestamp information and the open-eye timestamp information.

[0051] The signal acquisition module includes electrodes, which 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, which 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 the de-power-frequency original signal.

[0052] In the embodiment of this 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.

[0053] Furthermore, the signal processing module further includes a time-frequency conversion unit and a signal analysis unit; where:

[0054] 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 some of the paragraph signals to the time-frequency conversion unit;

[0055] The time-frequency conversion unit is used to perform a fast Fourier transform on the paragraph signal to achieve time-frequency conversion, so as to obtain the frequency-domain signal and feedback it to the signal analysis unit.

[0056] 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. Each of the above modules can be embedded in the processor of the computer device in hardware form or independent of it, or stored in the memory of the computer device in software form, so that the processor can call and execute the operations corresponding to each of the above modules.

[0057] In one embodiment, the signal analysis unit is further configured to execute a preset electroencephalogram signal monitoring method, and the preset electroencephalogram signal monitoring method includes the following steps:

[0058] Step S100: Obtain the blink action information and the original signal without power frequency;

[0059] Step S200: Segment the original signal without power frequency according to the closed-eye timestamp information and the open-eye timestamp information included in the blink action information to obtain a number of paragraph signals, and output them to the time-frequency conversion unit;

[0060] 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;

[0061] 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 of the original signal without power frequency.

[0062] 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 closed-eye timestamp information used to describe the complete closure of the patient's eyes, and the open-eye timestamp information used to describe the first opening of the patient's eyes after closing.

[0063] In one embodiment, the specific steps of step S200 include:

[0064] Step S210: Linearly arrange the closed-eye timestamp information and the open-eye timestamp in chronological order.

[0065] In this step, the specific way of linearly arranging in chronological order is to arrange the closed-eye timestamp information and the open-eye timestamp information in the order of their occurrence time from the earliest to the latest.

[0066] Step S220: Calculate the duration between each closed-eye timestamp information and the next open-eye timestamp information as the half-duration, and then calculate the corresponding half-duration subtracted before each closed-eye timestamp information to obtain the estimated start time.

[0067] In this step, since the closed-eye timestamp information represents the time when the patient's eyes are fully 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 of the time. Therefore, the closed-eye timestamp information is subtracted by the calculated half-duration to obtain a time before the closed-eye timestamp information as the estimated start time. The period from the estimated start time to the closed-eye timestamp information is a relatively complete closed-eye action segment.

[0068] Step S230: Use the estimated start time as the blink start time and the open-eye timestamp information as the blink end time, and obtain the signal segment from the start time to the end time from the de-power-frequency raw signal as the blink segment.

[0069] By performing steps S210 - S230, the segment with eye movement artifacts can be intercepted from the de-power-frequency raw signal. By filtering the waveforms of some frequency bands in this segment, the waveform after removing the artifacts can be obtained.

[0070] In one embodiment, the steps of step S200 further include:

[0071] Step S240: Use the de-power-frequency raw signal segment within a preset time period before the estimated start time as the reference segment.

[0072] In this step, the preset time period is set to be within 0.5 seconds before the estimated start time, and the de-power-frequency raw signal segment within 3 seconds before the estimated start time is used as the reference segment.

[0073] Step S250: Combine the blink segment and the reference segment obtained based on the same estimated start time as a reference group.

[0074] Among them, a reference group includes the blink segment and the reference segment corresponding to the same estimated start time; since the waveform before the blink segment appears is relatively stable and contains fewer wave components related to eye movement artifacts, using the waveform of this part as the reference waveform is more conducive to eliminating eye movement artifacts.

[0075] Step S260: Output the obtained several reference groups to the time-frequency conversion unit.

[0076] After outputting the reference group to the time-frequency conversion unit through step S260, the time-frequency conversion unit will convert the time-frequency waveforms of the blink segment and the reference segment in the reference group into frequency-domain signals and output them.

[0077] For the frequency-domain signals output by the time-frequency conversion unit, the signal analysis unit will continue to execute steps S300 and S400 to further process the de-power-frequency raw signal.

[0078] In one embodiment, the specific steps of step S300 include:

[0079] Step S310: Wait for and obtain the frequency-domain signal of the reference group.

[0080] 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.

[0081] Step S320: Compare the frequency-domain signals of the blink segments and the reference segments in the same reference group, and use the different frequency bands as the differential frequency bands.

[0082] In this step, by using the frequency-domain distribution data obtained in step S310, the differential segments included in the blink segments but not in the reference segments can be obtained.

[0083] Step S330: Summarize the differential frequency bands of all reference groups, and record the frequency of occurrence of each differential frequency band.

[0084] Step S340: 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.

[0085] Among them, the preset frequency threshold is 0.8 times the maximum frequency of all differential frequency bands. Through the above steps S330 to 340, the frequency of occurrence of each differential frequency band is obtained by summarizing and counting the differential frequency bands, and the frequency quantity of the differential frequency band with the highest frequency of occurrence is obtained. 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 number is 7, 7 * 0.3 = 2.1, and at this time the preset frequency threshold is 3. Subsequently, the differential frequency bands with a frequency less than the frequency threshold are removed, and the differential frequency bands with a frequency greater than the frequency threshold are retained as the characteristic frequency bands.

[0086] In one embodiment, the specific steps of step S400 include:

[0087] Step S410: 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;

[0088] Step S420: Output the original signal without power frequency to the notch filter unit to obtain the signal without blink artifacts;

[0089] Step S430: 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.

[0090] Through steps S410 to 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 implement the blinking artifact frequency band in the original waveform after removing the power frequency, so as to obtain the signal without blinking artifacts.

[0091] It should be understood that although the steps in the flowcharts involved in the above-described embodiments are sequentially shown in the direction of the arrows, these steps are not necessarily executed 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 restriction, 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, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and 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 the steps or stages in other steps or other steps.

[0092] Those of ordinary skill in the art can understand that all or part of the processes in the above-described embodiment methods 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 above-described method embodiments. 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 memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, 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 logic devices, data processing logics based on quantum computing, etc., without limitation.

[0093] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of 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.

[0094] 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 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 EEG monitoring system for neurology patients, characterized by: It includes signal acquisition module, visual capture module and signal processing module, among which: The signal acquisition module is used to collect the spontaneous biopotential of the user's head and output the original signal to the signal processing module; The visual capture module is used to collect user eye movement information, wherein the eye movement information includes the user's blinking movement information, and the blinking movement information includes eye closing timestamp information and eye opening timestamp information. The visual capture module outputs the eye movement information to the signal processing module; The signal processing module is configured 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 trail removal processing on the segmented original signal segments containing the blink movement information; The signal processing module includes a notch filter unit, a signal analysis unit and a time-frequency conversion 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 common frequency noise signal contained in the original signal to output the original signal without the common frequency. The signal analysis unit is used to segment the common frequency-removed original signal 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 signal analysis unit is further configured 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 blink action information to obtain a plurality of segment signals, and outputting the segment signals to the time-frequency conversion unit, wherein the time-frequency conversion unit is used to perform time-frequency conversion on the segment signals to obtain frequency domain signals and feed the frequency domain signals back to the signal analysis 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, so as to control the notch filter unit to filter out the signal of the characteristic frequency band in the original signal without the common frequency.

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

3. The EEG monitoring system for neurology patients according to claim 2, characterized in that: The specific steps of segmenting the common frequency-removed original signal according to the eye-closing timestamp information and the 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 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 duration, and then subtract the corresponding half-time duration 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.

4. The EEG monitoring system for neurology patients according to claim 3, characterized in that: The specific step of segmenting the common frequency-removed original signal according to the eye-closing timestamp information and the 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 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.

5. The EEG monitoring system for neurology patients according to claim 4, 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.

6. The EEG monitoring system for neurology patients according to claim 5, 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 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 is higher than the preset frequency threshold are taken as feature frequency bands and outputted.

7. The EEG monitoring system for neurology patients according to claim 6, characterized in that: The specific steps of outputting the original signal without the common frequency 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 include: The characteristic frequency band is used as a filtering frequency band and outputted to the notch filter unit to set the filtering frequency of the notch filter; Outputting the common frequency-removed original signal to a notch filter unit to obtain a blink artifact-removed signal; The common frequency is output as a filtering frequency band to the notch filter unit to reset the filtering frequency of the notch filter.

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