CN104173046B - A kind of extracting method of color indicia Amplitude integrated electroencephalogram - Google Patents
A kind of extracting method of color indicia Amplitude integrated electroencephalogram Download PDFInfo
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Abstract
The invention discloses a kind of extracting method of color indicia Amplitude integrated electroencephalogram, comprising: extract information to be marked from electroencephalogram, and labelling is carried out to described information color to be marked, obtain color change figure; Described color change figure and Amplitude integrated electroencephalogram are integrated, obtain color indicia Amplitude integrated electroencephalogram, wherein said Amplitude integrated electroencephalogram is carry out according to described electroencephalogram the figure that amplitude extraction obtains.Said method by electroencephalogram in addition to the amplitude and be that other information time dependent are extracted, and utilize color to carry out labelling, make not only to comprise amplitude information in the Amplitude integrated electroencephalogram finally obtained, the time dependent out of Memory of synchronization can also be shown simultaneously.Owing to being utilize color to carry out labelling, be therefore easy to observe and read, make acquired original to electroencephalogram in information more embodied and apply, raising electroencephalogram utilization rate.
Description
Technical field
The present invention relates to field of biomedicine technology, particularly a kind of extracting method of color indicia Amplitude integrated electroencephalogram.
Background technology
Electroencephalogram (Electroencephalogram, be called for short EEG) be electronic machine by precision, the spontaneous bioelectric potential of brain is amplified record and the figure that obtains from scalp, the spontaneity of the brain cell group recorded by electrode, rhythmicity electrical activity, EEG is also the comprehensive embodiment of electrical activity on scalp of brain cortex neural simultaneously.EEG (electrocardiogram) examination is a kind of effective ways brain function change being carried out to noninvasive test conventional in current medical examination.EEG checks the abnormal conditions that can help discovery cerebral nerve electrical activity, helps clinically to judge and prognosis evaluation neurodevelopment.
Amplitude integrated electroencephalogram (amplitude-integratedEEG is called for short aEEG) is the form after continuous eeg recording (continuousEEG is called for short cEEG) a kind of simplification.The extraction of aEEG is mainly by the rectification of electronic devices and components, level and smooth, EEG is converted into the fine and close wavestrip along high compression on time shaft, shown in the schematic diagram Fig. 1 extracting the digitizing solution of aEEG waveform in prior art from eeg data, concrete process is as follows:
1) filtering: the signal component comprised in aEEG is generally only 2Hz ~ 15Hz, therefore first will bandpass filtering be carried out before extraction aEEG.Meanwhile, in order to compensate the transmission attenuation of the non-rhythm and pace of moving things composition in eeg data, utilize peakedness coefficient theoretical, designing high performance finite impulse response digital filter, realize the Amplitude Compensation of 12dB/ ten octave in passband.
2) end points extracts: eeg data is divided into nonoverlapping fragment in short-term (L=6 second), maximum, the minima of the EEG signals peak-to-peak value in each section is extracted according to open country point ratio Δ=10%, and it can be used as upper extreme point and the lower extreme point of the corresponding vertical line of every bit in aEEG waveform, wherein the implication of wild some ratio Δ=10% be maximum, minima maximum of points with occupy identical ratio 10% in minimum point sum.
3) amplitude compression: in order to aEEG wavestrip lower limb information can better be shown, simultaneously also can so that the form of condensation wave band show, be less than 6 μ V parts to wave-shape amplitude not compress, and be greater than 6 μ V part compress according to the mode of logarithmic compression, advantage is that the aEEG waveform extracted has narrower dynamic range, can be shown as narrow wavestrip.
4) Time Compression: make the original electroencephalogram that corresponding 6 seconds of each aEEG vertical line is long, thus compress EEG waveform on a timeline, be convenient to whole observation.
Utilize existing Amplitude integrated electroencephalogram machine can show aEEG waveform, but due to aEEG waveform high compression on a timeline, the amplitude information being mainly EEG signals of displaying.In fact in aEEG figure, quantity of information is very large, and amplitude information wherein easily judges, the information except amplitude information then fails to embody, and needs clinician to judge, will lean on visual observations for the information spinner comprised in aEEG waveform.The result caused so on the one hand lacks objective criterion, easily causes to misread, judge by accident; On the other hand also higher requirement is proposed to the ability of medical personnel's visual observations and experience.If adopt the mode of the mutual reference of many figure, then cause extra burden, the efficiency that impact judges can to the diagnosis of clinician.Out of Memory in visible existing conductive pattern except amplitude information, owing to being not easy to observe and read, being failed to receive rational application, is caused utilization rate lower.
Summary of the invention
In order to the information solved in electroencephalogram except amplitude information fails the technical problem of rationally application, the invention provides a kind of extracting method of color indicia Amplitude integrated electroencephalogram, comprising:
Extract information to be marked from electroencephalogram, and labelling is carried out to described information color to be marked, obtain color change figure;
Described color change figure and Amplitude integrated electroencephalogram are integrated, obtain color indicia Amplitude integrated electroencephalogram, wherein said Amplitude integrated electroencephalogram is carry out according to described electroencephalogram the figure that amplitude extraction obtains.
Optionally, described information to be marked is in addition to the amplitude, time dependent information in described eeg data.
Optionally, described information to be marked is classified information or power-law distribution information.
Optionally, when described information to be marked is classified information, describedly from electroencephalogram, extracts information to be marked comprise:
Amplitude extraction is carried out to described electroencephalogram and obtains Amplitude integrated electroencephalogram;
Carry out convulsions successively to described Amplitude integrated electroencephalogram to detect and background waveforms detection, obtain classified information.
Optionally, described convulsions detection comprises:
Lower limb is extracted to described Amplitude integrated electroencephalogram;
All have corresponding reference edge for each time point on described lower limb, in described reference edge, the amplitude of each time point gets the median of the corresponding amplitude of current point in time of described lower limb and the amplitude of current point in time all normal point of first 6 minutes;
If the difference of the time point that the amplitude of current point in time is corresponding with in described reference edge is greater than reference value on described lower limb, then current point in time is judged to be convulsions point.
Optionally, if all time points of first 6 minutes are convulsions point, then on described lower limb, the reference edge of current point in time equals the reference edge of previous time point.
Optionally, if be judged as convulsions point when the persistent period of high voltage electrical activity is greater than 12 seconds in described reference edge, if be judged as normal point when the persistent period of high voltage electrical activity is not more than 12 seconds in described reference edge.
Optionally, further background waveforms detection is carried out to described normal point, by the result of described background waveforms detection be judged to be that the time point of fainting from fear is integrated.
Optionally, when described information to be marked is power-law distribution information, describedly from electroencephalogram, extracts information to be marked comprise:
Segmentation is carried out to primary signal in described electroencephalogram, and spectrum transformation is carried out to each section of primary signal, obtain spectrogram;
From described spectrogram, the spectrum signal of selecting frequency between 4Hz ~ 25Hz gets double-log, line linearity matching of going forward side by side, and obtains linear fit coefficient and time dependent time coefficient.
Optionally, described when carrying out spectrum transformation to each section of primary signal, getting time length is the smoothing process of time window in 6 seconds.
Method provided by the invention by electroencephalogram in addition to the amplitude and be that other information time dependent are extracted, and utilize color to carry out labelling, make not only to comprise amplitude information in the Amplitude integrated electroencephalogram finally obtained, the time dependent out of Memory of synchronization can also be shown simultaneously.Owing to being utilize color to carry out labelling, be therefore easy to observe and read, make acquired original to electroencephalogram in information more embodied and apply, raising electroencephalogram utilization rate.
Accompanying drawing explanation
Fig. 1 is for providing the schematic diagram of the digitizing solution of aEEG waveform in prior art;
Fig. 2 is the flow chart of steps of the extracting method of a kind of color indicia Amplitude integrated electroencephalogram provided by the invention;
Fig. 3 is the schematic diagram of the extracting method processing procedure of a kind of color indicia Amplitude integrated electroencephalogram provided by the invention;
Fig. 4 is the flow chart of steps of step S10 in embodiment one;
Fig. 5 is that in embodiment one, step S102 carries out the flow chart of steps detected of fainting from fear;
Fig. 6 is the flow chart carrying out classification and Detection in embodiment one;
Fig. 7 is the schematic diagram of the extracting method processing procedure of the color indicia Amplitude integrated electroencephalogram provided in embodiment one;
Fig. 8 is the flow chart of steps of step S10 in embodiment two;
Fig. 9 extracts in embodiment two to obtain EEG original signal waveform figure;
Figure 10 is that to extract the power-law distribution eigenvalue obtained in embodiment two be time dependent PL value and linear fit coefficient r;
Figure 11 is the schematic diagram of the extracting method processing procedure of the color indicia Amplitude integrated electroencephalogram provided in embodiment two.
Detailed description of the invention
Below in conjunction with drawings and Examples, the specific embodiment of the present invention is described in further detail.Following examples for illustration of the present invention, but are not used for limiting the scope of the invention.
The invention provides a kind of extracting method of color indicia Amplitude integrated electroencephalogram, flow chart of steps as shown in Figure 2, comprises the following steps:
Step S10, extract information to be marked from electroencephalogram, and treat label information color and carry out labelling, obtain color change figure.
Step S20, color change figure and Amplitude integrated electroencephalogram to be integrated, obtain color indicia Amplitude integrated electroencephalogram, wherein Amplitude integrated electroencephalogram is carry out according to electroencephalogram the figure that amplitude extraction obtains.
Wherein electroencephalogram (EEG) is the figure utilizing eeg collection system to collect brain signal, amplitude extraction is carried out further to electroencephalogram (EEG) and obtains Amplitude integrated electroencephalogram (aEEG), time dependent information is therefrom extracted for EEG or aEEG, as information to be marked simultaneously.Treating label information afterwards utilizes color to carry out labelling, obtains color change figure, and this color change figure is also time dependent.Finally the aEEG obtained and color change figure is integrated, just obtain color indicia Amplitude integrated electroencephalogram (i.e. ColoredaEEG), in displaying aEEG while amplitude information, the out of Memory of synchronization can also be shown simultaneously.These information have a characteristic to be exactly change in time, and the schematic diagram of above-mentioned processing procedure as shown in Figure 3.
Therefore the information to be marked in the present invention is in addition to the amplitude, time dependent information in eeg data.
Optionally, information to be marked is classified information or power-law distribution information.
First, the application for aEEG is with to its classification be evaluated as basis, and need when therefore classify to it to divide according to certain standard, the criteria for classifying commonly used at present has several as follows:
1) classify according to amplitude: AlNaqeeb etc. create a categorizing system, based on the overall amplitude of aEEG, 14 healthy newborns are matched group, and aEEG to be divided and matched group aEEG compares, and obtain following three kinds of situations:
Wavestrip top edge range value >10 μ V, lower limb range value >5 μ V is normal amplitude;
Wavestrip top edge range value >10 μ V, lower limb range value≤5 μ V is mile abnormality amplitude;
Wavestrip top edge range value <10 μ V, lower limb range value <5 μ V is severe anomalous amplitude.
2) according to background waveform separation: according to the pattern of aEEG background waveform, can be divided into normal continuously, normally discontinuous, break out-suppress, continuous low-voltage, smooth ripple etc., as table 1.
The classification of table 1aEEG background activity
3) according to classification of fainting from fear: namely have the degree that it's too late faints from fear to divide according to fainting from fear, can judge owing to fainting from fear according to the change of relevant information in electroencephalogram, general minimum length in time of fainting from fear is about 10 seconds.
Above three kinds of mode classifications are except amplitude classification, and it is all time dependent for remaining two kinds of mode classifications, therefore can extract classified information, are finally integrated into again and originally only comprise in the aEEG of amplitude information.
Secondly, power-law distribution (Power-law) is a kind of common distributed model, and in real world, the Connected degree distribution of many complex networks is all rendered as the form of certain power-law distribution function.If represent node degree with k, p (k) degree of a representation is the probability density of k, then power-law distribution is p (k) ~ k
-α, wherein k is greater than certain normal number, and power law coefficient α is greater than 1 (ensure normally to count to infinite integration to probability density from certain and can have astringency).
Because cerebral nerve network is one of known network the most complicated, cannot describes accurately it with simple network model and add up.Although concrete definition cannot be provided to its characteristic at present, the power-law distribution characteristic of existing many research and probe brain cortex neural network at present.Freeman etc. once proposed, and the frequency domain characteristic of Cortical ECoG meets power-law distribution; FMRI and ECoG combines and the power-law distribution characteristic of cortex cerebration and other complex networks of occurring in nature is contrasted by He etc.; Frasson etc. draw after carrying out com-parison and analysis to adult and neonatal brain electricity, and neonatal cerebration meets the power-law distribution of uncalibrated visual servo, and the brain electrical acti of neonate and adult is had any different in power-law distribution characteristic.And due to power-law distribution information be also time dependent information, method provided by the invention therefore can be utilized to extract power-law distribution information, be finally integrated into again and originally only comprise in the aEEG of amplitude information.
Process as Information application to be marked method provided by the invention, specifically see following examples one and embodiment two using above-mentioned classified information and power-law distribution information.
Embodiment one
There is provided a kind of extracting method of color indicia Amplitude integrated electroencephalogram in the present embodiment, comprise above-mentioned steps S10 and S20, when information to be marked is classified information, the detailed process that step S10 extracts information to be marked from electroencephalogram comprises:
Step S101, amplitude extraction is carried out to electroencephalogram obtain Amplitude integrated electroencephalogram.
Step S102, Amplitude integrated electroencephalogram carried out successively to convulsions and detect and background waveforms detection, obtain classified information, namely obtain information to be marked.
Above-mentioned steps flow process as shown in Figure 4.
Wherein first carry out convulsions in step S102 to detect, steps flow chart as shown in Figure 5, comprises the following steps:
Step S1021, to Amplitude integrated electroencephalogram extract lower limb.Due to the high voltage electrical activity showing as burst, persistence of fainting from fear, sometimes lower edges rises simultaneously, and sometimes only top edge rises.Therefore, lower limb is chosen in the present embodiment as detecting the characteristic quantity of fainting from fear.
Step S1022, all have corresponding reference edge for each time point on lower limb, in reference edge, the amplitude of each time point takes off the median of the corresponding amplitude of current point in time in edge and the amplitude of current point in time all normal point of first 6 minutes.If all time points of first 6 minutes are convulsions point, then on lower limb, the reference edge of current point in time equals the reference edge of previous time point.Namely give first 6 minutes add a little one 0 or 1 weight after get median (get median but not average is interference in order to avoid abnormal data causes) again.If the convulsions that has a little all been judged as of first 6 minutes, namely do not get the some set of median, then on lower limb, the reference edge of this point equals the reference edge of previous point.
If the difference of the time point that the amplitude of current point in time is corresponding with in reference edge is greater than reference value on step S1023 lower limb, then current point in time is judged to be convulsions point.
It is generally acknowledged that the minimum length in time of convulsions is 10 seconds, in conjunction with the time span representated by point each on aEEG in the present embodiment, the minimum length in time threshold value choosing convulsions is 12 seconds, if be judged as convulsions point when the persistent period of high voltage electrical activity is greater than 12 seconds in reference edge, if be judged as normal point when the persistent period of high voltage electrical activity is not more than 12 seconds in reference edge.
As can be seen from above-mentioned convulsions detection algorithm, the detection order of accuarcy of this algorithm is relevant with the selection of reference value, and reference value is generally the empirical value obtained according to test of many times.By to data with existing experiment Analysis, it is reference value that the present embodiment finally gets 1.43 μ V.
In the present embodiment, first convulsions is detected, for the part not being classified as faint from fear, carried out the classification and Detection of background waveform again, detect after obtaining the classification of each point, then the point of the same category of closing on is integrated into event, its specific category and criteria for classification are as table 2.
Table 2 background waveform separation and criteria for classification
Carry out the flow chart of classification and Detection as shown in Figure 6 in the present embodiment, wherein further background waveforms detection carried out to normal point, by the result of background waveforms detection be judged to be that the time point of fainting from fear is integrated, also need before integration to do smoothing processing.
Further, color-code is used to the classified information obtained, obtain time dependent classification color change figure, then the colouring information in this classification color change figure and existing aEEG are integrated, namely obtain the color indicia Amplitude integrated electroencephalogram with classified information, processing procedure schematic diagram as shown in Figure 7.
By the method that the present embodiment provides, utilize color to carry out labelling to classified information, and integrate with aEEG, in the Amplitude integrated electroencephalogram finally obtained, not only comprise amplitude information, the time dependent classified information of synchronization can also be shown simultaneously.Owing to being utilize color to carry out labelling, be therefore easy to observe and read, make acquired original to electroencephalogram in amplitude information and classified information all can be embodied, improve the utilization rate to electroencephalogram.
Embodiment two
Also provide a kind of extracting method of color indicia Amplitude integrated electroencephalogram in the present embodiment, comprise above-mentioned steps S10 and S20, when information to be marked is power-law distribution information, step S10 extracts information to be marked and comprises the following steps from electroencephalogram:
Step S111, segmentation is carried out to primary signal in electroencephalogram, and spectrum transformation is carried out to each section of primary signal, obtain spectrogram.When wherein carrying out spectrum transformation to each section of primary signal, getting time length is the smoothing process of time window in 6 seconds.Therefore this step extracts raw EEG signal, it is divided in turn duration 60 seconds, has 30 seconds overlapping segments between adjacent two sections, afterwards Fourier transform is carried out to the every a bit of EEG signal obtained and obtain frequency spectrum, getting time window when calculating frequency spectrum is 6 seconds, to the process of its smoothingization, finally obtain comparatively smooth spectrogram.
Step S112, from spectrogram, the spectrum signal of selecting frequency between 4Hz ~ 25Hz gets double-log, and line linearity matching of going forward side by side, obtains linear fit coefficient and time dependent time coefficient.
Above-mentioned steps flow process as shown in Figure 8, because low frequency signal is comparatively sparse after double-log process, may have interference to fitting result, selects 4Hz to be passband lower limit after observing; And eeg signal acquisition system itself has filtering to high frequency, 25Hz is selected to be upper cut-off frequency by inquiry initial parameter.The coefficient that matching obtains should be-α, and hereinafter unification represents with PL, i.e. PL=-α.Meanwhile, extract correlation coefficient (Pearson product-moment correlation coefficient) r of linear fit, in the present embodiment, be called linear fit coefficient r.Computing formula wherein for the linear fit coefficient r of the variable X and Y (variable X is spectrum signal, and variable Y is the linear function that matching obtains) of carrying out matching is herein:
Finally obtain power-law distribution and meet p (k) ~ k
-αif wherein extract the EEG original signal waveform figure that obtains as shown in Figure 9, therefrom extract the power-law distribution eigenvalue obtained be time dependent PL value and linear fit coefficient r as shown in Figure 10, wherein PL value and r two kinds of line styles represent.
Further, color-code is used to the power law information obtained, obtain time dependent power-law distribution color change figure, then the colouring information of this power-law distribution color change figure and existing aEEG are integrated, obtain the color indicia Amplitude integrated electroencephalogram with power law information, processing procedure schematic diagram as shown in figure 11.
By the method that the present embodiment provides, utilize color to carry out labelling to power-law distribution information, and integrate with aEEG, in the Amplitude integrated electroencephalogram finally obtained, not only comprise amplitude information, the time dependent classified information of synchronization can also be shown simultaneously.Owing to being utilize color to carry out labelling, be therefore easy to observe and read, make acquired original to electroencephalogram in amplitude information and power-law distribution information all can be embodied, improve the utilization rate to electroencephalogram.
The method provided for embodiment one and embodiment two can be applied to newborn baby function monitoring aspect, can analyze fully the information in Electroencephalogramin in Neonates and utilize, for clinical definite and diagnosis and treatment provide foundation.
Also it should be noted that, the information to be marked in the present embodiment, except above-mentioned classified information and power-law distribution information, can also be the Power Spectral Entropy of sleep-waking cycle classification or sleep cerebral electricity.Wherein the classification of sleep-waking cycle (sleep-wakecycling, SWC) mainly refers to the mechanical periodicity of lower boundary.The general broadband phase represents QS, and the arrowband phase represents AS.If background activity changes without sinusoidal sample, be called without SWC, have unconspicuous cyclically-varying to be called immature SWC have obvious discernible sinusoidal sample change and cycle duration is greater than 20 minutes is called ripe SWC.
Sleep is very important psychological need for the mankind, and sleep insuffience or sleep quality are not high, there will be the situations such as irritability is fidgety, behavior disorder, hypomnesis, mobility reduction.Therefore, be an emerging cross-section subject in edge to the research of sleep, and the signal obtained by electroencephalogram and EEG signals (EEG) are the important channels of research sleep.EEG signals can react the four-stage of sleep intuitively, comprises lucid interval (WAKE), rapid eye movement phase (REM) and nonrapid eye movements (NREM) phase (NREM comprises I, II, III, IV phase).The quality of sleep quality depends mainly on the length of nonrapid eye movements (NREM) interim III, IV phase deep sleep time, but judge that the sleep quality reacted in electroencephalogram is impossible by artificial method, adopt the Power Spectral Entropy based on shannon entropy concept to carry out the EEG signals of Analysis of Complex herein.Power Spectral Entropy is as the index of a kind of brain electricity complexity analyzing, and its spectrum entropy rule shows as in signal has obvious oscillatory rhythms, and the spectrum peak namely existed in regular, the complexity hour EEG power spectrum of signal waveform is narrower, and spectrum entropy is less; Otherwise power spectrum is more smooth when signal waveform is irregular stochastic signal, spectrum entropy is larger.Therefore method provided by the invention all can be adopted to carry out color indicia for other time dependent information, and be incorporated into existing and comprise in the aEEG of amplitude information.
Above embodiment is only for illustration of the present invention; and be not limitation of the present invention; the those of ordinary skill of relevant technical field; without departing from the spirit and scope of the present invention; can also make a variety of changes and modification; therefore all equivalent technical schemes also belong to category of the present invention, and scope of patent protection of the present invention should be defined by the claims.
Claims (9)
1. an extracting method for color indicia Amplitude integrated electroencephalogram, is characterized in that, comprising:
Extract information to be marked from electroencephalogram, and carry out labelling to described information color to be marked, obtain color change figure, wherein, described information to be marked is classified information or power-law distribution information;
Described color change figure and Amplitude integrated electroencephalogram are integrated, obtain color indicia Amplitude integrated electroencephalogram, wherein said Amplitude integrated electroencephalogram is carry out according to described electroencephalogram the figure that amplitude extraction obtains.
2. method according to claim 1, is characterized in that, described information to be marked is in addition to the amplitude, time dependent information in described eeg data.
3. method according to claim 1 and 2, is characterized in that, when described information to be marked is classified information, describedly from electroencephalogram, extracts information to be marked comprise:
Amplitude extraction is carried out to described electroencephalogram and obtains Amplitude integrated electroencephalogram;
Carry out convulsions successively to described Amplitude integrated electroencephalogram to detect and background waveforms detection, obtain classified information.
4. method according to claim 3, is characterized in that, described convulsions detection comprises:
Lower limb is extracted to described Amplitude integrated electroencephalogram;
All have corresponding reference edge for each time point on described lower limb, in described reference edge, the amplitude of each time point gets the median of the corresponding amplitude of current point in time of described lower limb and the amplitude of current point in time all normal point of first 6 minutes;
If the difference of the time point that the amplitude of current point in time is corresponding with in described reference edge is greater than reference value on described lower limb, then current point in time is judged to be convulsions point.
5. method according to claim 4, is characterized in that, if all time points of first 6 minutes are convulsions point, then on described lower limb, the reference edge of current point in time equals the reference edge of previous time point.
6. method according to claim 4, it is characterized in that, if be judged as convulsions point when the persistent period of high voltage electrical activity is greater than 12 seconds in described reference edge, if be judged as normal point when the persistent period of high voltage electrical activity is not more than 12 seconds in described reference edge.
7. method according to claim 4, is characterized in that, carries out background waveforms detection further to described normal point, by the result of described background waveforms detection be judged to be that the time point of fainting from fear is integrated.
8. method according to claim 1 and 2, is characterized in that, when described information to be marked is power-law distribution information, describedly from electroencephalogram, extracts information to be marked comprise:
Segmentation is carried out to primary signal in described electroencephalogram, and spectrum transformation is carried out to each section of primary signal, obtain spectrogram;
From described spectrogram, the spectrum signal of selecting frequency between 4Hz ~ 25Hz gets double-log, line linearity matching of going forward side by side, and obtains linear fit coefficient and time dependent time coefficient.
9. method according to claim 8, is characterized in that, described when carrying out spectrum transformation to each section of primary signal, getting time length is the smoothing process of time window in 6 seconds.
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| CN106562781A (en) * | 2016-05-20 | 2017-04-19 | 杨燕 | Novel intelligent therapeutic apparatus for neurology department |
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| CN108703753B (en) * | 2018-05-08 | 2020-10-27 | 南京伟思医疗科技股份有限公司 | Method for detecting sleep wake cycle of newborn based on dynamic electroencephalogram |
| CN108830865B (en) * | 2018-05-08 | 2021-06-15 | 南京伟思医疗科技股份有限公司 | Method for determining stable upper and lower boundaries of dynamic electroencephalogram image |
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| CN111513675B (en) * | 2020-04-07 | 2023-09-22 | 南京伟思医疗科技股份有限公司 | Neonate sleep wake period detection method based on amplitude integrated electroencephalogram |
| CN111938631B (en) * | 2020-08-07 | 2023-01-31 | 南京伟思医疗科技股份有限公司 | Convulsion detection method based on neonatal electroencephalogram signal |
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