Infant electroencephalogram detection method and detection system

Abstract

The invention discloses an infant electroencephalogram detection method and an infant electroencephalogram detection system, relates to the technical field of electroencephalogram detection, solves the problem that a corresponding judgment result is influenced due to the fact that related waveforms are not removed, and aims at a brain wave oscillogram subjected to preliminary optimization. The method comprises the following steps of: acquiring an oscillogram, confirming an abnormal frequency in the oscillogram, carrying out related confirmation on a data set based on a set preset value, readjusting the range of the data set based on the data density of the corresponding data set, and determining the data density of a critical state based on a specific process of real-time adjustment, so as to lock a standard set and the abnormal frequency. Waveform segments related to the abnormal frequency are removed from the brain wave oscillogram, a standard oscillogram is obtained, the brain wave oscillogram is optimized again, and a better waveform display effect can be achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of electroencephalogram (EEG) detection, and in particular to an infant electroencephalogram (EEG) detection method and detection system. Background Art

[0002] An electroencephalogram (EEG) records the spontaneous, rhythmic electrical activity of brain neurons by placing electrodes on the surface of the scalp. When brain neurons are active, they generate weak electric currents, which can be conducted through the electrodes and amplified and recorded by the EEG instrument. For infants and young children, although their brain electrical activity patterns are different from those of adults, the basic principles are the same, and the functional state of the brain is reflected by detecting electrical signals in different brain regions.

[0003] The application with publication number CN218899477U discloses a wireless EEG monitoring cap for infants and young children, which relates to the field of medical device technology. The key points of its technical solution are: it includes a cap body, a strap and a plurality of disc electrodes, and the cap body is provided with through holes corresponding to the disc electrodes one by one; the electrode sheets of the disc electrodes pass through the through holes, and the top surfaces of the electrode sheets of the disc electrodes are bonded and fixed to the inner wall of the cap body; a strap is provided at the open end of the cap body; the first end of the strap is fixedly connected to the outer surface of the cap body, and a male Velcro patch is provided on one side surface of the second end of the strap, and a female Velcro patch is provided on the other side surface of the strap; there is no need to manually place the electrodes, and the electrodes can be prevented from falling off and re-fixed, so as to monitor the EEG activities of the children in different states in real time and dynamically, improve the work efficiency of clinical medical staff and reduce the work intensity.

[0004] During the EEG detection process of infants and young children, the brain waves of infants and young children are generally monitored based on the corresponding monitoring instruments, and relevant monitoring waveforms are generated in real time for display. However, the displayed EEG waveforms contain a large number of related waveforms with higher frequencies, which are often caused by the limb movements of infants and young children during sleep. Since such related waveforms are not eliminated, the corresponding judgment results will be affected. The electric wave waveforms generated by the original monitoring need to be optimized. Summary of the invention

[0005] In view of the deficiencies in the prior art, the present invention provides an infant EEG detection method and detection system, which solves the problem that the corresponding judgment results are affected due to the failure to eliminate such related waveforms.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: a method for detecting electroencephalogram of infants and young children, comprising the following steps:

[0007] Step 1: Use a dedicated EEG monitoring instrument to monitor the EEG waveform of the infants and young children participating in the test, and generate an EEG waveform graph associated with this monitoring process, and then confirm the frequency of the generated EEG waveform graph to confirm the frequency sequence belonging to this EEG waveform graph. The specific sub-steps are:

[0008] S11. Marking a plurality of peak points generated in the brain wave waveform in sequence, wherein the line segments before and after the peak points have opposite trends, the waveform line segment before the peak point climbs upward, and the waveform line segment after the peak point drops downward;

[0009] S12. Identify the time interval T between adjacent peak points from the EEG waveform. i , where i represents the waveform segment between different adjacent peak points, using F i =1÷T i Confirm the waveform frequency F of the waveform segment between its adjacent peak points i , and according to the waveform frequency F of the waveform segment from front to back i The confirmation process generates the frequency sequence belonging to this EEG waveform diagram;

[0010] Step 2: Perform numerical analysis on the confirmed frequency sequence, divide the frequency sequence into multiple different frequency data segments with the same characteristic, and based on the change difference of adjacent waveform frequencies, evaluate whether the next group of waveform frequencies conform to the frequency change trend to confirm the frequency data segments with the same characteristic. The specific method is as follows:

[0011] Based on the confirmed frequency sequence {F 1 、F 2 ,……,F n}, where i = 1, 2, ..., n, from the first set of waveform frequencies F in the comment sequence 1 Start, confirm F 2 With F 1 The frequency difference C 2-1 =F 2 -F 1 , identify the third set of waveform frequencies F 3 Satisfied: F 3 ∈[F 2 +C 2-1 -Y1, F 2 +C 2-1 +Y1], where Y1 is the preset floating value. If it is satisfied, F 1 、F 2 and F 3 Divide into data segments with the same characteristic frequency, and continue to analyze the fourth group of waveform frequency F 4 Carry out relevant confirmation until the confirmed waveform frequency does not meet the evaluation conditions. If it does not meet the conditions, F 1 、F2 Divide into data segments with the same characteristic frequency, and from the third group of waveform frequency F 3 Start, confirm F 4 With F 3 The frequency difference C 4-3 =F 4 -F 3 , and identify the fifth set of waveform frequencies F 5 Whether the corresponding evaluation conditions are met, the evaluation conditions are F 5 ∈[F 4 +C 4-3 -Y1, F 4 +C 4-3 +Y1], and so on, the subsequent data segments with the same characteristic frequency are confirmed in sequence;

[0012] Step 3: Identify abnormal data segments from the divided data segments with the same characteristic frequency, identify and remove abnormal data segments based on the number of waveform frequencies and related mean values ​​corresponding to the data segments with the same characteristic frequency, and perform preliminary optimization on the EEG waveform diagram. The specific method is as follows:

[0013] S31, sequentially confirm the number G of waveform frequencies associated with the same characteristic frequency data segment k , where k represents different data segments with the same characteristic frequency, G k The data segments with the same characteristic frequency as Y2 are marked as the data segments to be processed, where Y2 is the preset value. k The data segments with the same characteristic frequency ≥Y2 are not calibrated;

[0014] S32. Based on the calibrated data segment to be processed, confirm the mean value associated with the data segment to be processed: perform mean processing on several waveform frequencies in the data segment to be processed, and confirm the associated mean value Jz. If Jz>6.5Hz, the data segment to be processed is calibrated as an abnormal data segment, and the waveform frequency associated with the abnormal data segment is directly calibrated as an abnormal frequency, and the waveform segment associated with the abnormal frequency is removed from the EEG waveform diagram. If Jz≤6.5Hz, no calibration is performed.

[0015] Step 4: based on the remaining multiple data segments with the same characteristic frequency after the abnormal data segments are removed, the waveform frequencies in the multiple data segments with the same characteristic frequency are confirmed, and then the confirmed waveform frequencies are uniformly processed, and the data density is determined by adjusting the data range. Based on the determined data density, the abnormal frequency is locked, and the waveform segment associated with the abnormal frequency is removed from the EEG waveform diagram, and the optimized EEG waveform diagram is optimized again. The specific sub-steps are:

[0016] S41, confirm the waveform frequency in the same characteristic frequency data segment and mark it as P q, where q represents different waveform frequencies, and the range is confirmed based on the preset value H:

[0017] S411, based on the preset value H, from a plurality of P q Select the two groups P closest to H q Get the first data set, and determine the data range of this data set. Based on this data range, determine its data length L. Use L÷G=M to get the data density M of this data set, where G represents the number of waveform frequencies contained in this data set:

[0018] If M<Bm, then the data set is expanded to the front and back range until the data density associated with the expanded range is M≥Bm. If M=Bm, then the currently determined data set is formulated as the standard set. If M>Bm, then the range of the data set is reduced to M≤Bm, and the reduced data set is formulated as the standard set:

[0019] If M ≥ Bm, directly generate the frequency analysis error signal;

[0020] S412, P that does not belong to the standard set q Marked as abnormal frequency;

[0021] Based on the determined abnormal frequency, the waveform segment associated with this abnormal frequency is identified and removed from the EEG waveform diagram that has completed the preliminary optimization, so as to obtain a re-optimized EEG waveform diagram, which is defined as a standard waveform diagram and displayed on an external display terminal.

[0022] Preferably, an infant electroencephalogram detection system comprises:

[0023] The waveform monitoring end is a specific brain wave monitoring instrument, which monitors the brain wave of the infants and young children participating in the test and generates a brain wave waveform diagram associated with the monitoring process;

[0024] The frequency confirmation terminal performs frequency confirmation on the generated brain wave waveform, determines the waveform frequency of the corresponding waveform segment based on the time interval between adjacent peak points, and confirms the frequency sequence belonging to this brain wave waveform;

[0025] The same frequency band confirmation end performs numerical analysis on the confirmed frequency sequence, divides the frequency sequence into multiple different frequency data segments with the same characteristic, and based on the change difference of adjacent waveform frequencies, evaluates whether the next group of waveform frequencies conforms to the frequency change trend to confirm the frequency data segments with the same characteristic;

[0026] At the preliminary optimization end, abnormal data segments are identified from the divided data segments with the same characteristic frequency, and based on the number of waveform frequencies corresponding to the data segments with the same characteristic frequency and the related mean, the abnormal data segments are identified and eliminated, and the EEG waveform diagram is preliminarily optimized;

[0027] At the secondary optimization end, based on the multiple data segments with the same characteristic frequency remaining after the abnormal data segments are eliminated, the waveform frequencies within the multiple data segments with the same characteristic frequency are confirmed, and then the confirmed waveform frequencies are uniformly processed, and the data density is determined by adjusting the data range. Based on the determined data density, the abnormal frequency is locked, and the waveform segments associated with the abnormal frequency are eliminated from the EEG waveform graph, and the optimized EEG waveform graph is optimized again.

[0028] The present invention provides an infant electroencephalogram detection method and detection system. Compared with the prior art, it has the following beneficial effects:

[0029] The present invention monitors the brain waves of infants and young children, confirms the corresponding brain wave waveform, and then identifies the abnormal associated waveform segments according to the frequency change in the brain wave waveform. The identification process is based on the specific change trend of the previous set of parameters and the set associated floating value, and then the abnormal associated waveform segments are removed from the brain wave waveform to complete the preliminary optimization of the brain wave waveform.

[0030] For the EEG waveform graph after the preliminary optimization, the abnormal frequency in the waveform graph is confirmed again, and the relevant confirmation of the data set is performed based on the set preset value. Then, based on the data density of the corresponding data set, the range of the data set is readjusted, and the data density of the critical state is determined based on the specific process of real-time adjustment, so as to lock the standard set and the abnormal frequency, and then remove the waveform segments related to the abnormal frequency from the EEG waveform graph to obtain the standard waveform graph. The EEG waveform graph is optimized again to achieve a better waveform display effect, thereby obtaining a better optimized waveform graph. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 It is a schematic diagram of the process of the present invention;

[0032] Figure 2 It is a schematic diagram of the principle framework of the present invention. DETAILED DESCRIPTION

[0033] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0034] First embodiment

[0035] See also Figure 1 The present application provides a method for detecting the electroencephalogram (EEG) of an infant. The method generally detects the electroencephalogram (EEG) of an infant during sleep, so as to obtain the brain wave waveform of the infant, including the following steps:

[0036] Step 1: Use a dedicated EEG monitoring instrument to monitor the EEG waveform of the infants and young children participating in the test, and generate an EEG waveform diagram associated with this monitoring process (the monitoring process generally lasts for 20 minutes, and the infants and young children must be in a sleeping state). The EEG waveform diagram is specifically as follows: by placing electrodes on the surface of the scalp, the spontaneous and rhythmic electrical activities of brain neurons are recorded; when brain neurons are active, weak currents can be generated, which can be conducted through the electrodes and amplified and recorded by the EEG instrument, and then the frequency of the generated EEG waveform diagram is confirmed to confirm the frequency sequence belonging to this EEG waveform diagram, wherein the specific sub-steps for confirming the frequency sequence are:

[0037] S11. Marking a plurality of peak points generated in the brain wave waveform in sequence, wherein the line segments before and after the peak points have opposite trends, the waveform line segment before the peak point climbs upward, and the waveform line segment after the peak point drops downward;

[0038] S12. Identify the time interval T between adjacent peak points from the EEG waveform. i , where i represents the waveform segment between different adjacent peak points, using F i =1÷T i Confirm the waveform frequency F of the waveform segment between its adjacent peak points i , and according to the waveform frequency F of the waveform segment from front to back i The confirmation process generates a frequency sequence belonging to the brain wave waveform diagram, that is, the waveform frequencies confirmed in sequence are sorted to determine the frequency sequence; specifically, the relevant brain wave waveform is composed of a plurality of different waveform segments, that is, each waveform segment has a corresponding waveform frequency, and the frequency of the corresponding waveform is related to the time interval between the waveforms, so the corresponding waveform frequency can be directly determined based on the corresponding waveform time interval;

[0039] Step 2: Perform numerical analysis on the confirmed frequency sequence, divide the frequency sequence into multiple different frequency data segments with the same characteristic, and evaluate the frequency of the next group of waveforms based on the difference in the change of the frequencies of adjacent waveforms to confirm the frequency data segments with the same characteristic. Specifically, the frequency changes uniformly when changing before and after, because the waveform associated with the waveform generated by the corresponding infant in the sleep stage is also a mean fluctuation. When the infant moves or cries during the sleep stage, the corresponding frequency will change significantly, which will cause the frequency of the corresponding waveform to shift seriously. In order to achieve a better display effect of the monitored brain wave waveform, it is necessary to perform relevant processing on the frequency sequence, thereby performing relevant processing on the brain wave waveform. The specific sub-steps for evaluating whether the frequency of the next group of waveforms meets the standard are as follows:

[0040] Based on the confirmed frequency sequence {F 1 、F 2 ,……,F n}, where i = 1, 2, ..., n, from the first set of waveform frequencies F in the comment sequence 1 Start, confirm F 2 With F 1 The frequency difference C 2-1 =F 2 -F 1 , identify the third set of waveform frequencies F 3 Satisfied: F 3 ∈[F 2 +C 2-1 -Y1, F 2 +C 2-1 +Y1], where Y1 is a preset floating value, which is determined by the operator based on experience. If it is satisfied, F 1 、F 2 and F 3 Divide into data segments with the same characteristic frequency, and continue to analyze the fourth group of waveform frequency F 4 Make relevant confirmation (the evaluation conditions of the fourth group of waveform frequencies are: F 4 ∈[F 3 +C 3-2 -Y1, F 3 +C 3-2 +Y1],C 3-2 F 3 With F 2 The frequency difference between the two groups is calculated by analogy. The waveform frequencies that need to be confirmed later are evaluated comprehensively based on the changing trends of the first two groups of waveform frequencies until the confirmed waveform frequency does not meet the evaluation conditions. If it does not meet the conditions, F 1 、F 2 Divide into data segments with the same characteristic frequency, and from the third group of waveform frequency F 3Start, confirm F 4 With F 3 The frequency difference C 4-3 =F 4 -F 3 , and identify the fifth set of waveform frequencies F 5 Whether the corresponding evaluation conditions are met, the evaluation conditions are F 5 ∈[F 4 +C 4-3 -Y1, F 4 +C 4-3 +Y1], and so on, the subsequent frequency data segments with the same characteristics are confirmed in turn. Specifically, the frequency sequence to be confirmed is {4, 4.2, 4.1, 7, 6.5, 8, 4.1, 4.2, 4.3}, and the Y1 to be confirmed is 2. Based on the corresponding processing process, the difference between the first group of frequencies and the second group of frequencies is 0.2, so the interval to which the third group of frequencies belongs should be [2.4, 6.4], so 4.1 meets the requirements and is up to standard, but the subsequent frequency 7 does not meet the interval confirmed by 4.1 and 4.2, so the first group of frequency data segments with the same characteristics is {4, 4.2, 4.1}, and then starting from frequency 7, identify whether frequency 8 belongs to the confirmed associated interval between 7 and 6.5. After identification, it is found that it belongs, so the second group of frequency data segments with the same characteristics is determined to be {7, 6.5, 8}, and so on, the last group of frequency data segments with the same characteristics is {4.1, 4.2, 4.3};

[0041] Step 3: confirm the abnormal data segments from the divided data segments with the same characteristic frequency, confirm and eliminate the abnormal data segments based on the number of waveform frequencies and related mean values ​​corresponding to the data segments with the same characteristic frequency, and perform preliminary optimization on the EEG waveform diagram. The specific method of confirmation and elimination is as follows:

[0042] S31, sequentially confirm the number G of waveform frequencies associated with the same characteristic frequency data segment k , where k represents different data segments with the same characteristic frequency, G k The data segments with the same characteristic frequency of G<Y2 are marked as the data segments to be processed, where Y2 is a preset value, and its specific value is determined by the operator based on experience, generally 2. k The data segments with the same characteristic frequency ≥Y2 are not calibrated;

[0043] S32. Based on the calibrated data segment to be processed, confirm the mean associated with the data segment to be processed: perform mean processing on several waveform frequencies in the data segment to be processed, and confirm the associated mean Jz. If Jz>6.5Hz (under normal circumstances, the waveform frequency of infants and young children during sleep is generally 4Hz. Selecting a larger value here can better confirm the abnormality), then this data segment to be processed is calibrated as an abnormal data segment, and the waveform frequency associated with this abnormal data segment is directly calibrated as an abnormal frequency, and the waveform segment associated with the abnormal frequency is removed from the EEG waveform diagram (this type of frequency does not belong to the frequency generated during the sleep stage, and is most likely generated by muscle movements or crying during sleep. Therefore, the waveform associated with this type of frequency will only affect the final judgment result in the EEG waveform diagram, so this type of abnormal waveform is directly removed). If Jz≤6.5Hz, no calibration is performed.

[0044] Second embodiment

[0045] Compared with the first embodiment, this embodiment further optimizes the implementation process;

[0046] Step 4: based on the remaining multiple data segments with the same characteristic frequency after the abnormal data segments are removed, the waveform frequencies in the multiple data segments with the same characteristic frequency are confirmed, and then the confirmed waveform frequencies are uniformly processed, and the data density is determined by adjusting the data range, and then based on the determined data density, the abnormal frequency is locked, and the waveform segment associated with the abnormal frequency is removed from the EEG waveform diagram, and the optimized EEG waveform diagram is optimized again. Specifically, because the corresponding frequency value selected in step 3 is 6.5 Hz during optimization, there is still a numerical difference compared with the original standard frequency of 4 Hz, then the corresponding waveform segment only removes the obviously abnormal bands, but does not remove the waveform segments in the change process from 4 Hz to 6.5 Hz, so the original EEG waveform diagram is not optimized thoroughly enough. Therefore, in order to achieve a better optimization effect, it is necessary to make an abnormal judgment based on the change of the corresponding frequency to determine the abnormal difference;

[0047] The specific sub-steps of locking the abnormal frequency are as follows:

[0048] S41, confirm the waveform frequency in the same characteristic frequency data segment and mark it as P q , where q represents different waveform frequencies, and the range is confirmed based on the preset value H, which is set in advance by the operator based on experience, and is generally 3Hz:

[0049] S411, based on the preset value H, from a plurality of P q Select the two groups P closest to H qGet the first data set, and determine the data range of this data set (that is, the interval between the maximum and minimum values), determine its data length L (that is, the difference between the maximum and minimum values) based on this data range, and use L÷G=M to get the data density M about this data set, where G represents the number of waveform frequencies contained in this data set:

[0050] If M < Bm, then the data set is expanded to the front and back ranges, where Bm is a preset value, which is set by the operator based on experience and is the standard density. The process stops when the data density M associated with the expanded range is greater than or equal to Bm:

[0051] If M = Bm, the currently determined data set is proposed as the standard set;

[0052] If M>Bm, the range of the data set is reduced to when M≤Bm, and the reduced data set is formulated as the standard set:

[0053] If M ≥ Bm, a frequency analysis error signal is directly generated. This situation rarely occurs unless the monitored EEG waveform is not the waveform obtained when the infant is sleeping.

[0054] S412, P that does not belong to the standard set q Marked as abnormal frequency;

[0055] S42, based on the determined abnormal frequency, identifying the waveform segment associated with the abnormal frequency, and removing it from the electroencephalogram waveform diagram that has completed the preliminary optimization, obtaining a re-optimized electroencephalogram waveform diagram, defining the electroencephalogram waveform diagram as a standard waveform diagram, and displaying the standard waveform diagram on an external display terminal;

[0056] After the above series of processing, the irrelevant frequency waveforms in the EEG waveform diagram are confirmed in turn and eliminated accordingly, so the corresponding EEG waveform diagram is fully optimized, which can achieve better data display effect and facilitate relevant personnel to view.

[0057] Combination Figure 2 , an infant electroencephalogram detection system, comprising:

[0058] The waveform monitoring end is a specific brain wave monitoring instrument, which monitors the brain wave of the infants and young children participating in the test and generates a brain wave waveform diagram associated with the monitoring process;

[0059] The frequency confirmation terminal performs frequency confirmation on the generated brain wave waveform, determines the waveform frequency of the corresponding waveform segment based on the time interval between adjacent peak points, and confirms the frequency sequence belonging to this brain wave waveform;

[0060] The same frequency band confirmation end performs numerical analysis on the confirmed frequency sequence, divides the frequency sequence into multiple different frequency data segments with the same characteristic, and based on the change difference of adjacent waveform frequencies, evaluates whether the next group of waveform frequencies conforms to the frequency change trend to confirm the frequency data segments with the same characteristic;

[0061] At the preliminary optimization end, abnormal data segments are identified from the divided data segments with the same characteristic frequency, and based on the number of waveform frequencies corresponding to the data segments with the same characteristic frequency and the related mean, the abnormal data segments are identified and eliminated, and the EEG waveform diagram is preliminarily optimized;

[0062] At the secondary optimization end, based on the multiple data segments with the same characteristic frequency remaining after the abnormal data segments are eliminated, the waveform frequencies within the multiple data segments with the same characteristic frequency are confirmed, and then the confirmed waveform frequencies are uniformly processed, and the data density is determined by adjusting the data range. Based on the determined data density, the abnormal frequency is locked, and the waveform segments associated with the abnormal frequency are eliminated from the EEG waveform graph, and the optimized EEG waveform graph is optimized again.

[0063] Third embodiment

[0064] When implemented specifically, this embodiment includes the entire implementation process of the above two groups of embodiments.

[0065] Some of the data in the above formulas are dimensionless and numerically calculated. Meanwhile, the contents not described in detail in this specification belong to the prior art known to those skilled in the art.

[0066] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A method for detecting electroencephalogram (EEG) of infants and young children, characterized in that: The following steps are involved: Step 1: Use a dedicated EEG monitoring instrument to monitor the EEG waveform of the infants and young children participating in the test, and generate an EEG waveform graph associated with this monitoring process, and then confirm the frequency of the generated EEG waveform graph to confirm the frequency sequence belonging to this EEG waveform graph; Step 2: numerically analyze the confirmed frequency sequence, divide the frequency sequence into multiple different frequency data segments with the same characteristic, and based on the change difference of adjacent waveform frequencies, evaluate whether the next group of waveform frequencies conforms to the frequency change trend to confirm the frequency data segments with the same characteristic; Step 3: confirming abnormal data segments from the divided data segments with the same characteristic frequency, confirming and eliminating the abnormal data segments based on the number of waveform frequencies corresponding to the data segments with the same characteristic frequency and the related mean, and preliminarily optimizing the EEG waveform diagram; Step 4: based on the multiple data segments with the same characteristic frequency remaining after the abnormal data segments are eliminated, the waveform frequencies within the multiple data segments with the same characteristic frequency are confirmed, and then the confirmed waveform frequencies are processed uniformly, and the data density is determined by adjusting the data range. Based on the determined data density, the abnormal frequency is locked, and the waveform segments associated with the abnormal frequency are eliminated from the EEG waveform diagram, and the optimized EEG waveform diagram is optimized again.

2. The method for detecting electroencephalogram of infants and young children according to claim 1, characterized in that: In step 1, the specific sub-steps of confirming the frequency sequence are: S11. Marking a plurality of peak points generated in the brain wave waveform in sequence, wherein the line segments before and after the peak points have opposite trends, the waveform line segment before the peak point climbs upward, and the waveform line segment after the peak point drops downward; S12. Identify the time interval T between adjacent peak points from the EEG waveform. i , where i represents the waveform segment between different adjacent peak points, using F i =1÷T i Confirm the waveform frequency F of the waveform segment between its adjacent peak points i , and according to the waveform frequency F of the waveform segment from front to back i The confirmation process generates the frequency sequence belonging to this EEG waveform diagram.

3. The method for detecting electroencephalogram of infants according to claim 1, characterized in that: In step 2, the specific method of dividing the frequency sequence into a plurality of different frequency data segments with the same characteristic is: Based on the confirmed frequency sequence {F1, F2, ..., F n }, where i = 1, 2, ..., n, starting from the first set of waveform frequencies F1 in the review sequence, determine the frequency difference C between F2 and F1 2-1 =F2-F1, identify whether the third set of waveform frequency F3 satisfies: F3∈[F2+C 2-1 -Y1, F2+C 2-1 +Y1], where Y1 is a preset floating value. If it is satisfied, F1, F2 and F3 are divided into data segments with the same characteristic frequency, and the fourth group of waveform frequency F4 is continued to be confirmed until the confirmed waveform frequency does not meet the evaluation conditions. If it does not meet the conditions, F1 and F2 are divided into data segments with the same characteristic frequency, and starting from the third group of waveform frequency F3, the frequency difference C between F4 and F3 is determined. 4-3 =F4-F3, and identify whether the fifth group of waveform frequencies F5 meets the corresponding evaluation conditions, and so on, and confirm the subsequent data segments with the same characteristic frequencies in turn.

4. The method for detecting the electroencephalogram of infants and young children according to claim 3, characterized in that: The evaluation condition of the fifth group of waveform frequencies F5 is F5∈[F4+C 4-3 -Y1, F4+C 4-3 +Y1].

5. The method for detecting electroencephalogram of infants and young children according to claim 1, characterized in that: In step 3, the specific method of confirming and eliminating abnormal data segments is: S31, sequentially confirm the number G of waveform frequencies associated with the same characteristic frequency data segment k , where k represents different data segments with the same characteristic frequency, G k The data segments with the same characteristic frequency of <Y2 are marked as the data segments to be processed, where Y2 is the preset value; S32. Based on the calibrated data segment to be processed, confirm the mean associated with the data segment to be processed: perform mean processing on several waveform frequencies in the data segment to be processed, and confirm the associated mean Jz. If Jz>6.5Hz, mark the data segment to be processed as an abnormal data segment, and directly mark the waveform frequency associated with the abnormal data segment as an abnormal frequency, and remove the waveform segment associated with the abnormal frequency from the EEG waveform diagram.

6. The method for detecting the electroencephalogram of infants and young children according to claim 5, characterized in that: In step S31, for G k No calibration is performed on the data segments with the same characteristic frequency ≥Y2.

7. The method for detecting electroencephalogram of infants according to claim 5, characterized in that: In the step S32, if Jz≤6.5Hz, no calibration is performed.

8. The method for detecting electroencephalogram of infants according to claim 5, characterized in that: In step 4, the specific sub-steps of locking the abnormal frequency are: S41, confirm the waveform frequency in the same characteristic frequency data segment and mark it as P q , where q represents different waveform frequencies, and the range is confirmed based on the preset value H: S411, based on the preset value H, from a plurality of P q Select the two groups P closest to H q Get the first data set, and determine the data range of this data set. Based on this data range, determine its data length L. Use L÷G=M to get the data density M of this data set, where G represents the number of waveform frequencies contained in this data set: If M<Bm, then the data set is expanded to the front and back ranges until the data density associated with the expanded range is M≥Bm. If the data density associated with the expanded range is M=Bm, then the currently determined data set is formulated as the standard set. S412, P that does not belong to the standard set q Marked as abnormal frequency.

9. The method for detecting the electroencephalogram of infants and young children according to claim 8, characterized in that: If the data density associated with the expanded range is M>Bm, the range of the data set is reduced until M≤Bm, and the reduced data set is formulated as the standard set.

10. The method for detecting the electroencephalogram of infants and young children according to claim 8, characterized in that: In the step S411, a frequency analysis error signal is directly generated for the data density M≥Bm associated with the first data set.

11. The method for detecting electroencephalogram of infants according to claim 8, characterized in that: In the step 4, re-optimizing the optimized EEG waveform diagram includes: Based on the determined abnormal frequency, the waveform segment associated with this abnormal frequency is identified and removed from the EEG waveform diagram that has completed the preliminary optimization, so as to obtain a re-optimized EEG waveform diagram, which is defined as a standard waveform diagram and displayed on an external display terminal.

12. An infant EEG detection system, the detection system is used to execute an infant EEG detection method according to any one of claims 1 to 11, characterized in that: include: The waveform monitoring end is a specific brain wave monitoring instrument, which monitors the brain wave of the infants and young children participating in the test and generates a brain wave waveform diagram associated with the monitoring process; The frequency confirmation terminal performs frequency confirmation on the generated brain wave waveform, determines the waveform frequency of the corresponding waveform segment based on the time interval between adjacent peak points, and confirms the frequency sequence belonging to this brain wave waveform; The same frequency band confirmation end performs numerical analysis on the confirmed frequency sequence, divides the frequency sequence into multiple different frequency data segments with the same characteristic, and based on the change difference of adjacent waveform frequencies, evaluates whether the next group of waveform frequencies conforms to the frequency change trend to confirm the frequency data segments with the same characteristic; At the preliminary optimization end, abnormal data segments are identified from the divided data segments with the same characteristic frequency, and based on the number of waveform frequencies corresponding to the data segments with the same characteristic frequency and the related mean, the abnormal data segments are identified and eliminated, and the EEG waveform diagram is preliminarily optimized; At the secondary optimization end, based on the multiple data segments with the same characteristic frequency remaining after the abnormal data segments are eliminated, the waveform frequencies within the multiple data segments with the same characteristic frequency are confirmed, and then the confirmed waveform frequencies are uniformly processed, and the data density is determined by adjusting the data range. Based on the determined data density, the abnormal frequency is locked, and the waveform segments associated with the abnormal frequency are eliminated from the EEG waveform graph, and the optimized EEG waveform graph is optimized again.

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