CN104173046A - Method for extracting color marked amplitude-integrated electroencephalogram - Google Patents
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Abstract
The invention discloses a method for extracting a color marked amplitude-integrated electroencephalogram. The method includes extracting information to be marked from an electroencephalogram, marking the information to be marked with colors to obtain a color variation diagram, and integrating the color variation diagram and an amplitude-integrated electroencephalogram to obtain the color marked amplitude-integrated electroencephalogram, wherein the amplitude-integrated electroencephalogram is extracted by vibrating the electroencephalogram. According to the method for extracting the color marked amplitude-integrated electroencephalogram, through extraction of time variable information except amplitude in the electroencephalogram and use of colors for marking, the ultimately obtained amplitude-integrated electroencephalogram not only comprises amplitude information, but also displays other time variable information at the same time; due to use of colors for marking, the electroencephalogram is easy to observe and read, originally collected information in the electroencephalogram can be well reflected and applied, and a use ratio of the electroencephalogram is increased.
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 by accurate electronic machine, the figure that the spontaneous bioelectric potential of brain is amplified to record from scalp and obtain, the brain cell group's who records by electrode spontaneity, rhythmicity electrical activity, simultaneously EEG is also the comprehensive embodiment on scalp of the electrical activity of cerebral cortex nerve.EEG (electrocardiogram) examination is a kind of effective ways that carry out noninvasive test that brain function is changed conventional in current medical examination.EEG checks the abnormal conditions that can help discovery cerebral nerve electrical activity, helps clinical neurodevelopment to be judged and prognosis evaluation.
Amplitude integrated electroencephalogram (amplitude-integrated EEG is called for short aEEG) is the form after a kind of simplification of continuous eeg recording (continuous EEG is called for short cEEG).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, in prior art, from eeg data, extract shown in schematic diagram Fig. 1 of digitizing solution of aEEG waveform, concrete process is as follows:
1) filtering: the signal component comprising in aEEG is only generally 2 Hz～15 Hz, therefore first will carry out bandpass filtering before extracting aEEG.Meanwhile, for the transmission attenuation of the non-rhythm and pace of moving things composition in eeg data compensates, utilize Chebyshev's approximation theory, design high performance finite impulse response digital filter, realize the Amplitude Compensation of 12dB/ ten octaves in passband.
2) end points extracts: eeg data is divided into nonoverlapping fragment in short-term (L=6 second), extract maximum, the minima of the EEG signals peak-to-peak value in each section according to open country point ratio Δ=10%, and set it 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% is that maximum, minima are occupied identical ratio 10% in maximum of points with minimum point sum.
3) amplitude compression: in order to show aEEG wavestrip lower limb information, also consequently the form of condensation wave band is shown simultaneously, wave-shape amplitude is less than to 6 μ V parts not to be compressed, compress according to the mode of logarithmic compression and be greater than 6 μ V parts, advantage is that the aEEG waveform extracting has narrower dynamic range, can be shown as narrow wavestrip.
4) Time Compression: make the corresponding 6 seconds long original electroencephalograms of each aEEG vertical line, thereby compress EEG waveform on time shaft, be convenient to whole observation.
Utilize existing Amplitude integrated electroencephalogram machine can show aEEG waveform, but due to aEEG waveform high compression on time shaft, the amplitude information that is 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 fails to embody, and needs clinician to judge, will lean on visual observations for the information spinner comprising in aEEG waveform.The result causing is like this to lack objective criterion on the one hand, easily causes and misreads, judges by accident; Also the ability to medical personnel's visual observations and experience have proposed higher requirement on the other hand.If adopt the mode of the mutual reference of many figure, cause extra burden, the efficiency of impact judgement can to clinician's diagnosis.Out of Memory in visible existing conductive pattern except amplitude information, owing to being not easy observation and reading, fails to receive rational application, and causes utilization rate lower.
Summary of the invention
Fail the rationally technical problem of application in order to solve the information except amplitude information in electroencephalogram, the invention provides a kind of extracting method of color indicia Amplitude integrated electroencephalogram, comprising:
Extract information to be marked from electroencephalogram, and described information to be marked is carried out to labelling with color, 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 to carry out according to described electroencephalogram the figure that amplitude extraction obtains.
Optionally, described information to be marked is except amplitude, time dependent information in described eeg data.
Optionally, described information to be marked is classified information or power-law distribution information.
Optionally, in the time that described information to be marked is classified information, describedly from electroencephalogram, extracts information to be marked and comprise:
Described electroencephalogram is carried out to amplitude extraction and obtain Amplitude integrated electroencephalogram;
Described Amplitude integrated electroencephalogram is fainted from fear successively and detected and background waveforms detection, obtain classified information.
Optionally, described convulsions detects and comprises:
Described Amplitude integrated electroencephalogram is extracted to lower limb;
All have corresponding reference edge for the each time point on described lower limb, in described reference edge, the amplitude of each time point is got 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 amplitude of current point in time is greater than reference value with the difference of time point corresponding in described reference edge on described lower limb, current point in time is judged to be to faint from fear a little.
Optionally, if all time points of first 6 minutes are convulsions point, 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, described normal point is further carried out to background waveforms detection, the result of described background waveforms detection and the time point that is judged to be to faint from fear are integrated.
Optionally, in the time that described information to be marked is power-law distribution information, describedly from electroencephalogram, extracts information to be marked and comprise:
Primary signal in described electroencephalogram is carried out to segmentation, and each section of primary signal carried out to spectrum transformation, obtain spectrogram;
From described spectrogram, the spectrum signal of selecting frequency between 4Hz～25Hz got double-log, and the line linearity matching of going forward side by side obtains linear fit coefficient and time dependent time coefficient.
Optionally, described when each section of primary signal carried out to spectrum transformation, getting time length is that 6 seconds time windows carry out smoothing processing.
Method provided by the invention by electroencephalogram except amplitude and be that time dependent other information are extracted, and utilize color to carry out labelling, in the Amplitude integrated electroencephalogram that makes finally to obtain, not only comprise amplitude information, can also show the time dependent out of Memory of synchronization simultaneously.Because being utilizes 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.
Brief description of the drawings
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 mono-;
Fig. 5 be in embodiment mono-step S102 faint from fear detect flow chart of steps;
Fig. 6 is the flow chart that carries out classification and Detection in embodiment mono-;
Fig. 7 is the schematic diagram of the extracting method processing procedure of the color indicia Amplitude integrated electroencephalogram that provides in embodiment mono-;
Fig. 8 is the flow chart of steps of step S10 in embodiment bis-;
Fig. 9 extracts and obtains EEG original signal waveform figure in embodiment bis-;
Figure 10 is that the power-law distribution eigenvalue that in embodiment bis-, extraction obtains is 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 that provides in embodiment bis-.
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 are used for illustrating 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 are integrated, obtain color indicia Amplitude integrated electroencephalogram, wherein Amplitude integrated electroencephalogram is to carry out according to electroencephalogram the figure that amplitude extraction obtains.
Wherein electroencephalogram (EEG) is the figure that utilizes eeg collection system to collect brain signal, electroencephalogram (EEG) is further carried out to amplitude extraction and obtain Amplitude integrated electroencephalogram (aEEG), therefrom extract time dependent information for EEG or aEEG, as information to be marked simultaneously.Treat afterwards label information and utilize color to carry out labelling, obtain color change figure, and this color change figure is also time dependent.Finally the aEEG obtaining and color change figure are integrated, just obtain color indicia Amplitude integrated electroencephalogram (being Colored aEEG), in displaying aEEG, in amplitude information, can also show the out of Memory of synchronization simultaneously.It is exactly temporal evolution that these information have a characteristic, 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 except amplitude, time dependent information in eeg data.
Optionally, information to be marked is classified information or power-law distribution information.
First, be with the classification to it and be evaluated as basis for the application of aEEG, when therefore it classification, need to divide according to certain standard, the conventional criteria for classifying has several as follows at present:
1) classify according to amplitude: Al Naqeeb etc. has created a categorizing system, taking the overall amplitude of aEEG as basis, 14 healthy newborns are matched group, and aEEG to be divided and matched group aEEG compare, 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 normally continuously, normally discontinuous, break out-suppress, continuously low-voltage, smooth ripple etc., as table 1.
The classification of table 1 aEEG background activity
3) according to the classification of fainting from fear: have the degree that it's too late faints from fear to divide according to convulsions, can judge according to the variation of relevant information in electroencephalogram owing to fainting from fear, 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 remaining two kinds of mode classifications, therefore can extract classified information, are finally integrated into 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 connection degree of many complex networks distributes and is all rendered as the form of certain power-law distribution function.If represent node degree with k, the probability density that p (k) degree of a representation is k, 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 that probability density is normally counted to infinite integration from certain and can have astringency).
Because cerebral nerve network is one of known the most complicated network, cannot it be described accurately and be added up with simple network model.Although cannot provide concrete definition to its characteristic at present, the power-law distribution characteristic of existing many research and probe cerebral cortex neutral nets at present.Freeman etc. once proposed, and the frequency domain characteristic of cortex brain electricity meets power-law distribution; He etc. contrast fMRI and ECoG combination by the power-law distribution characteristic of cortex cerebration and other complex networks of occurring in nature; Frasson etc. are drawing after analyzing adult and neonatal brain electricity, and neonatal cerebration meets the power-law distribution without scale, and neonate and adult's brain electrical acti is had any different in power-law distribution characteristic.And because power-law distribution information is also time dependent information, therefore can utilize method provided by the invention to extract power-law distribution information, be finally integrated into again and originally only comprise in the aEEG of amplitude information.
Apply method provided by the invention as information to be marked respectively using above-mentioned classified information and power-law distribution information and process, specifically referring to following examples one and embodiment bis-.
Embodiment mono-
A kind of extracting method of color indicia Amplitude integrated electroencephalogram is provided in the present embodiment, comprises above-mentioned steps S10 and S20, in the time that information to be marked is classified information, the detailed process that step S10 extracts information to be marked from electroencephalogram comprises:
Step S101, electroencephalogram is carried out to amplitude extraction obtain Amplitude integrated electroencephalogram.
Step S102, to Amplitude integrated electroencephalogram faint from fear successively detect and background waveforms detection, obtain classified information, namely obtain information to be marked.
Above-mentioned steps flow process as shown in Figure 4.
The detection of wherein first fainting from fear in step S102, steps flow chart as shown in Figure 5, comprises the following steps:
Step S1021, to Amplitude integrated electroencephalogram extract lower limb.Because convulsions shows as the high voltage electrical activity of burst, persistence, sometimes go up lower limb and rise simultaneously, sometimes only top edge rises.Therefore, in the present embodiment, choose lower limb as detecting the characteristic quantity of fainting from fear.
Step S1022, all have corresponding reference edge for the each time point on lower limb, in reference edge, the amplitude of each time point is taken 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, 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 again median (get median but not average is the interference causing for fear of abnormal data).If the convulsions that has a little all been judged as of first 6 minutes, the some set of not getting median, on lower limb, the reference edge of this point equals the reference edge of previous point.
If the amplitude of current point in time is greater than reference value with the difference of time point corresponding in reference edge on step S1023 lower limb, current point in time is judged to be to faint from fear a little.
Minimum length in time that it is generally acknowledged convulsions is 10 seconds, in the present embodiment in conjunction with the time span of the upper each some representative of aEEG, the minimum length in time threshold value of 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.
From above-mentioned convulsions detection algorithm, can find out, the detection order of accuarcy of this algorithm is relevant with the selection of reference value, and reference value is generally the empirical value obtaining according to test of many times.By to data with existing experiment Analysis, it is reference value that the present embodiment is finally got 1.43 μ V.
In the present embodiment, first detect fainting from fear, for the part that is not classified as convulsions, carried out again the classification and Detection of background waveform, after detection obtains 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
For the flow chart that carries out classification and Detection in the present embodiment as shown in Figure 6, wherein normal point is further carried out to background waveforms detection, the result of background waveforms detection and the time point that is judged to be to faint from fear are integrated, before integration, also need to do smoothing processing.
Further, the classified information obtaining is used to color-code, obtain time dependent classification color change figure, then colouring information and existing aEEG in this classification color change figure are integrated, obtain the color indicia Amplitude integrated electroencephalogram with classified information, processing procedure schematic diagram as shown in Figure 7.
The method providing by the present embodiment, utilizes color to carry out labelling to classified information, and integrates with aEEG, not only comprises amplitude information in the Amplitude integrated electroencephalogram finally obtaining, and can also show the time dependent classified information of synchronization simultaneously.Because being utilizes 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 utilization rate to electroencephalogram.
Embodiment bis-
The extracting method that a kind of color indicia Amplitude integrated electroencephalogram is also provided in the present embodiment, comprises above-mentioned steps S10 and S20, and in the time that 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, primary signal in electroencephalogram is carried out to segmentation, and each section of primary signal carried out to spectrum transformation, obtain spectrogram.While wherein each section of primary signal being carried out to spectrum transformation, getting time length is that 6 seconds time windows carry out smoothing processing.Therefore this step is extracted original EEG signal, it is divided into duration between 60 seconds, adjacent two sections in turn and has 30 seconds overlapping segments, afterwards the every a bit of EEG signal obtaining is carried out to Fourier transform and obtain frequency spectrum, while calculating frequency spectrum, getting time window is 6 seconds, it is carried out to smoothing techniques, finally obtain comparatively smooth spectrogram.
Step S112, from spectrogram, the spectrum signal of selecting frequency between 4Hz～25Hz got double-log, and the 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 after double-log is processed is comparatively sparse, may have interference to fitting result, and after observing, selecting 4Hz is passband lower limit; And eeg signal acquisition system itself has filtering to high frequency, selecting 25Hz by inquiry initial parameter is upper cut-off frequency.Should be-α of the coefficient that matching obtains, 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.For the computing formula of carrying out the variable X of matching and the linear fit coefficient r of Y (variable X is spectrum signal, and variable Y is the linear function that matching obtains) be herein wherein:
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 obtaining and be time dependent PL value and linear fit coefficient r as shown in figure 10, wherein PL value and r represent by two kinds of line styles.
Further, the power law information obtaining is used to color-code, obtain time dependent power-law distribution color change figure, then colouring information and the existing aEEG of this power-law distribution color change figure are integrated, obtain the color indicia Amplitude integrated electroencephalogram with power law information, processing procedure schematic diagram as shown in figure 11.
The method providing by the present embodiment, utilizes color to carry out labelling to power-law distribution information, and integrates with aEEG, not only comprises amplitude information in the Amplitude integrated electroencephalogram finally obtaining, and can also show the time dependent classified information of synchronization simultaneously.Because being utilizes 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 utilization rate to electroencephalogram.
The method providing for embodiment mono-and embodiment bis-can be applied to newborn baby function monitoring aspect, can the information in Electroencephalogramin in Neonates be analyzed fully and be utilized, 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-wake cycling, SWC) mainly refers to that the cycle of lower boundary changes.The general broadband phase represents QS, and the arrowband phase represents AS.If background activity changes and is called without SWC without sinusoidal sample, there is unconspicuous cyclically-varying to be called immature SWC, there are obvious discernible sinusoidal sample variation and persistent period in cycle to be greater than 20 minutes and are 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 a cross-section subject in emerging edge to the research of sleep, and the important channel of the signal obtaining by electroencephalogram to be EEG signals (EEG) be research sleep.EEG signals can be reacted 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 interim III of nonrapid eye movements (NREM), the length of IV phase deep sleep time, but judge that the sleep quality reacting in electroencephalogram is impossible by artificial method, adopt 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 has the obvious vibration rhythm and pace of moving things in signal, and the spectrum peak existing 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 all can adopt method provided by the invention to carry out color indicia for other time dependent information, and be incorporated in existing aEEG that comprises amplitude information.
Above embodiment is only for illustrating the present invention; and be not limitation of the present invention; the those of ordinary skill in relevant technologies field; without departing from the spirit and scope of the present invention; can also make a variety of changes and modification; therefore all technical schemes that are equal to also belong to category of the present invention, and scope of patent protection of the present invention should be defined by the claims.
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Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105046111A (en) * | 2015-09-10 | 2015-11-11 | 济南市儿童医院 | Amplitude integrated electroencephalogram result automatic identifying system and method |
| CN106562781A (en) * | 2016-05-20 | 2017-04-19 | 杨燕 | Novel intelligent therapeutic apparatus for neurology department |
| CN108703753A (en) * | 2018-05-08 | 2018-10-26 | 南京伟思医疗科技股份有限公司 | A kind of neonatal sleep wake cycle detection method based on dynamic electroencephalogram |
| CN108830865A (en) * | 2018-05-08 | 2018-11-16 | 南京伟思医疗科技股份有限公司 | A kind of determination method of the stabilization up-and-down boundary for Ambulatory EEG image |
| CN109922725A (en) * | 2016-11-04 | 2019-06-21 | 艾森蒂亚股份有限公司 | A computer-implemented method for processing ECG data |
| CN110251117A (en) * | 2019-07-04 | 2019-09-20 | 博睿康科技（常州）股份有限公司 | A kind of electroencephalogram Time-Frequency Information method for visualizing |
| CN110275455A (en) * | 2018-03-14 | 2019-09-24 | 佛山市顺德区美的电热电器制造有限公司 | A kind of control method based on EEG signals, central control equipment, Cloud Server and system |
| CN111513675A (en) * | 2020-04-07 | 2020-08-11 | 南京伟思医疗科技股份有限公司 | Amplitude-integrated electroencephalogram-based neonatal sleep-wake cycle detection method |
| CN111938631A (en) * | 2020-08-07 | 2020-11-17 | 南京伟思医疗科技股份有限公司 | Convulsion detection method based on neonatal electroencephalogram signal |
| WO2022135605A1 (en) * | 2020-12-25 | 2022-06-30 | 深圳迈瑞生物医疗电子股份有限公司 | Monitoring information display method, electroencephalogram abnormality alarm method, and monitoring system |
| CN116035595A (en) * | 2023-01-11 | 2023-05-02 | 南京大学 | An automatic diagnosis method for neonatal convulsions based on complex network features of amplitude-integrated EEG |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101015451A (en) * | 2007-02-13 | 2007-08-15 | 电子科技大学 | Music brain electricity analytical method |
| CN200957081Y (en) * | 2006-06-30 | 2007-10-10 | 复旦大学附属儿科医院 | Newborn multi-parameter brain function monitoring analyzer |
| US20090312664A1 (en) * | 2006-08-03 | 2009-12-17 | Rodriguez Villegas Esther O | Apparatus and method for obtaining eeg data |
| CN101627909A (en) * | 2009-05-05 | 2010-01-20 | 复旦大学附属儿科医院 | Digital amplitude-integrated cerebral function monitor |
| CN203524671U (en) * | 2013-09-26 | 2014-04-09 | 北京太阳电子科技有限公司 | Electroencephalogram power spectrum frequency-division color display device |
-
2014
- 2014-09-01 CN CN201410440166.5A patent/CN104173046B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN200957081Y (en) * | 2006-06-30 | 2007-10-10 | 复旦大学附属儿科医院 | Newborn multi-parameter brain function monitoring analyzer |
| US20090312664A1 (en) * | 2006-08-03 | 2009-12-17 | Rodriguez Villegas Esther O | Apparatus and method for obtaining eeg data |
| CN101015451A (en) * | 2007-02-13 | 2007-08-15 | 电子科技大学 | Music brain electricity analytical method |
| CN101627909A (en) * | 2009-05-05 | 2010-01-20 | 复旦大学附属儿科医院 | Digital amplitude-integrated cerebral function monitor |
| CN203524671U (en) * | 2013-09-26 | 2014-04-09 | 北京太阳电子科技有限公司 | Electroencephalogram power spectrum frequency-division color display device |
Non-Patent Citations (6)
| Title |
|---|
| C.P.STEWART等: "Seizurei dentification in the ICU using quantitative EEG displays", 《NEUROLOGY》 * |
| KATSUHIRO KOBAYASHI等: "Amplitude-integrated EEG colored according to spectral edge frequency", 《EPILEPSY RESEARCH》 * |
| KATSUHIRO KOBAYASHI等: "Amplitude-integrated EEG colored according to spectral edge frequency", 《EPILEPSY RESEARCH》, vol. 96, 31 December 2011 (2011-12-31), pages 276 - 282, XP028312303, DOI: doi:10.1016/j.eplepsyres.2011.06.012 * |
| 汪吉梅等: "振幅整合脑电图在新生儿脑损伤中的应用", 《中国当代儿科杂志》 * |
| 王秀霞等: "振幅整合脑电图在新生儿科应用的研究进展", 《脑与神经疾病杂志》 * |
| 陈小兰: "振幅整合脑电图在新生儿脑功能监护中的应用", 《中国医药指南》 * |
Cited By (20)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105046111A (en) * | 2015-09-10 | 2015-11-11 | 济南市儿童医院 | Amplitude integrated electroencephalogram result automatic identifying system and method |
| CN105046111B (en) * | 2015-09-10 | 2018-09-25 | 济南市儿童医院 | A kind of Amplitude integrated electroencephalogram result automatic recognition system |
| CN106562781A (en) * | 2016-05-20 | 2017-04-19 | 杨燕 | Novel intelligent therapeutic apparatus for neurology department |
| US11006884B2 (en) * | 2016-11-04 | 2021-05-18 | Icentia Inc. | Computer-implemented method of handling electrocardiogram data |
| AU2021250923B2 (en) * | 2016-11-04 | 2023-09-28 | Icentia Inc. | Computer-implemented method of handling electrocardiogram data |
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| US12178584B2 (en) | 2016-11-04 | 2024-12-31 | Icentia Inc. | Computer-implemented method of handling electrocardiogram data |
| AU2017354968B2 (en) * | 2016-11-04 | 2022-01-20 | Icentia Inc. | Computer-implemented method of handling electrocardiogram data |
| CN109922725B (en) * | 2016-11-04 | 2022-04-01 | 艾森蒂亚股份有限公司 | Computer-implemented electrocardiogram data processing method |
| CN110275455A (en) * | 2018-03-14 | 2019-09-24 | 佛山市顺德区美的电热电器制造有限公司 | A kind of control method based on EEG signals, central control equipment, Cloud Server and system |
| CN110275455B (en) * | 2018-03-14 | 2021-05-25 | 佛山市顺德区美的电热电器制造有限公司 | Control method based on electroencephalogram signals, central control equipment, cloud server and system |
| CN108830865A (en) * | 2018-05-08 | 2018-11-16 | 南京伟思医疗科技股份有限公司 | A kind of determination method of the stabilization up-and-down boundary for Ambulatory EEG image |
| CN108703753A (en) * | 2018-05-08 | 2018-10-26 | 南京伟思医疗科技股份有限公司 | A kind of neonatal sleep wake cycle detection method based on dynamic electroencephalogram |
| CN110251117A (en) * | 2019-07-04 | 2019-09-20 | 博睿康科技（常州）股份有限公司 | A kind of electroencephalogram Time-Frequency Information method for visualizing |
| CN111513675A (en) * | 2020-04-07 | 2020-08-11 | 南京伟思医疗科技股份有限公司 | Amplitude-integrated electroencephalogram-based neonatal sleep-wake cycle detection method |
| 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 |
| CN111938631A (en) * | 2020-08-07 | 2020-11-17 | 南京伟思医疗科技股份有限公司 | Convulsion detection method based on neonatal electroencephalogram signal |
| WO2022135605A1 (en) * | 2020-12-25 | 2022-06-30 | 深圳迈瑞生物医疗电子股份有限公司 | Monitoring information display method, electroencephalogram abnormality alarm method, and monitoring system |
| CN116035595A (en) * | 2023-01-11 | 2023-05-02 | 南京大学 | An automatic diagnosis method for neonatal convulsions based on complex network features of amplitude-integrated EEG |
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