CN105046111A - Amplitude integrated electroencephalogram result automatic identifying system and method - Google Patents
Amplitude integrated electroencephalogram result automatic identifying system and method Download PDFInfo
- Publication number
- CN105046111A CN105046111A CN201510573753.6A CN201510573753A CN105046111A CN 105046111 A CN105046111 A CN 105046111A CN 201510573753 A CN201510573753 A CN 201510573753A CN 105046111 A CN105046111 A CN 105046111A
- Authority
- CN
- China
- Prior art keywords
- database
- waveform
- electroencephalogram
- amplitude
- amplitude integrated
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Landscapes
- Measurement And Recording Of Electrical Phenomena And Electrical Characteristics Of The Living Body (AREA)
Abstract
The invention discloses an amplitude integrated electroencephalogram result automatic identifying system and method. The amplitude integrated electroencephalogram result automatic identifying system comprises a customizing module for defining present typical diseases, a data storage module storing related disease files, an input module inputting to-be-diagnosed electroencephalogram files, a data acquiring module acquiring the files input by the input module, and an analyzing comparing module diagnosing the patient files. The amplitude integrated electroencephalogram is diagnosed to be matched with which disease; if the amplitude integrated electroencephalogram is not matched with any database, a waveform abnormality diagnosis report is output and a result is output to an output unit; and the amplitude integrated electroencephalogram can be automatically identified and a diagnosis result is given out, so clinicist work amount can be reduced and diagnosis efficiency can be improved.
Description
Technical field
The present invention relates to a kind of Amplitude integrated electroencephalogram result automatic recognition system and method.
Background technology
The other Amplitude integrated electroencephalogram (writing a Chinese character in simplified form aEEG) of bed is the continuous recording reduced form of electroencephalogram, represent the Output rusults of function of brain cell monitoring, at present in clinical position, for reflecting the situation of brain function, now become the focus of attention of neural electrophysiology gradually, become the objective standard of the doctors such as vast neonate department, Neurology for assessment of function of brain cell situation simultaneously.Compared with Routine Eeg (EEG), aEEG for the early diagnosis of brain damage and Index for diagnosis susceptibility higher, simultaneously without wound, direct, responsive, easy to operate, amplitude is high, can non-volatile recording, graph direct, do not delay children with serious disease rescue in bedside operation, not easily by biology or abiotic artifacts, be more convenient for clinical practice and analysis.
Because writing time is long, waveform is many, therefore result reads length consuming time, this work is at present completed by clinician, while busy clinical position, must work overtime and submit the physician of patient after result being read, this has obviously increased the weight of the burden of its work, carve up time and time of having a rest that he can observe the infant state of an illness, be unfavorable for very much the work of clinician.
Summary of the invention
Object of the present invention is exactly to solve the problem, a kind of Amplitude integrated electroencephalogram result automatic recognition system and method are provided, it can identify Amplitude integrated electroencephalogram voluntarily and provide diagnostic result, decreases the task of clinician, improves diagnosis and treatment efficiency.
To achieve these goals, the present invention adopts following technical scheme:
A kind of Amplitude integrated electroencephalogram result automatic recognition system, comprising:
Custom block, integrates the up-and-down boundary value of electroencephalogram waveform for self-defined input normal amplitude, and the Amplitude integrated electroencephalogram oscillogram of typical patient;
Data memory module, comprise normal boundary database, neonatal seizure waveform database, hypoxic ischemic encephalopathy of newborn database, infantile spasms database and large rural area syndrome database, these databases call for analyses and comparison module; Normal boundary database integrates the up-and-down boundary value of electroencephalogram waveform for the normal amplitude storing custom block input;
Load module, for inputting the Amplitude integrated electroencephalogram waveform of patient;
Data acquisition module, for receiving the Amplitude integrated electroencephalogram waveform of the patient of input, and flows to analyses and comparison module;
Analyses and comparison module, first extracts the up-and-down boundary of the Amplitude integrated electroencephalogram waveform of patient and compares with the border in normal boundary database, if do not exceed this boundary value, generates normal diagnosis report; If exceed this boundary value, identify one by one with other databases during data memory module stores, diagnose out matching of patient's Amplitude integrated electroencephalogram and which kind of disease, if do not mated with all databases, so diagnosis report of an output waveform exception, and result is flowed to output unit;
Output unit, for receiving and analyzing comparing module diagnostic result and shown by display device.
Described Wave anomaly comprises the unusual waveforms because intensive care unit wave interference, electrode delamination or malposition of electrode factor cause, and the Amplitude integrated electroencephalogram except neonatal seizure, hypoxic ischemic encephalopathy of newborn, infantile spasms and large rural area these diseases of syndrome.
Described load module comprises scanner and self-defined load module, described scanner is used for the Amplitude integrated electroencephalogram oscillogram of scan patients, scanning result is flowed to analyses and comparison module by data memory module, the characteristic of the Amplitude integrated electroencephalogram oscillogram of patient is inputted by doctor by described self-defined load module, and also flows to analyses and comparison module by data memory module.
Described analyses and comparison module, respectively two-stage one-dimensional wavelet transform is carried out to all oscillograms in neonatal seizure waveform database, hypoxic ischemic encephalopathy of newborn database, infantile spasms database and large rural area syndrome database, reduce the resolution of Image Sub-Band after decomposing, utilize PCA method to obtain the eigenwert of image;
Using the input layer of the eigenwert of acquisition as limited Boltzmann machine, utilize multiple oscillograms in neonatal seizure waveform database, hypoxic ischemic encephalopathy of newborn database, infantile spasms database, large rural area syndrome database and unusual waveforms database to train limited Boltzmann machine, obtain the neonatal seizure waveform feature data after training, hypoxic ischemic encephalopathy of newborn characteristic, infantile spasms characteristic, large rural area syndrome characteristic and unusual waveforms characteristic;
Test is carried out for the Amplitude integrated electroencephalogram oscillogram input-bound Boltzmann machine exceeding normal amplitude and integrate the patient of the up-and-down boundary value of electroencephalogram waveform and obtains test result, unusual waveforms is diagnosed as the Amplitude integrated electroencephalogram that can not identify.
A kind of Amplitude integrated electroencephalogram result automatic identifying method, comprising:
Self-defined input normal amplitude integrates the up-and-down boundary value of electroencephalogram waveform, and the Amplitude integrated electroencephalogram oscillogram of typical patient;
Data or waveform are stored into accordingly normal boundary database, neonatal seizure waveform database, hypoxic ischemic encephalopathy of newborn database, infantile spasms database and large rural area syndrome database, these databases call for next step analysis; The up-and-down boundary value of the normal amplitude integration electroencephalogram waveform of self-defined input is stored into normal boundary database;
The Amplitude integrated electroencephalogram waveform of input patient;
Receive the Amplitude integrated electroencephalogram waveform of the patient of input;
First extract the up-and-down boundary of the Amplitude integrated electroencephalogram waveform of patient and compare with the border in normal boundary database, if do not exceed this boundary value, generating normal diagnosis report; If exceed this boundary value, identify one by one with other databases, diagnose out matching of patient's Amplitude integrated electroencephalogram and which kind of disease, if do not mated with all databases, so diagnosis report of an output waveform exception, and diagnosis report is exported;
Receive diagnosis report and shown by display device.
When the up-and-down boundary of the Amplitude integrated electroencephalogram waveform of patient exceeds this boundary value and other databases know method for distinguishing one by one and be: respectively two-stage one-dimensional wavelet transform is carried out to oscillograms all in neonatal seizure waveform database, hypoxic ischemic encephalopathy of newborn database, infantile spasms database and large rural area syndrome database, reduce the resolution of Image Sub-Band after decomposing, utilize PCA method to obtain the eigenwert of image;
Using the input layer of the eigenwert of acquisition as limited Boltzmann machine, utilize multiple oscillograms in neonatal seizure waveform database, hypoxic ischemic encephalopathy of newborn database, infantile spasms database, large rural area syndrome database and unusual waveforms database to train limited Boltzmann machine, obtain the neonatal seizure waveform feature data after training, hypoxic ischemic encephalopathy of newborn characteristic, infantile spasms characteristic, large rural area syndrome characteristic and unusual waveforms characteristic;
Test is carried out for the Amplitude integrated electroencephalogram oscillogram input-bound Boltzmann machine exceeding normal amplitude and integrate the patient of the up-and-down boundary value of electroencephalogram waveform and obtains test result, unusual waveforms is diagnosed as the Amplitude integrated electroencephalogram that can not identify.
Beneficial effect of the present invention:
Can after the other Amplitude integrated electroencephalogram of bed is finished automatic analysis print patient report immediately, doctor can be liberated like this, the labour saved out can go outpatient service or emergency treatment to see patient, thus solve the contradiction about 3 minutes consultation times that the masses give prominence to reflection to a certain extent, doctor is many, the patient that each doctor sees can lack relatively, so each patient will obtain relatively longer Waiting time, the satisfaction of patient can obtain lifting to a certain extent, final alleviation conflict between doctors and patients, cure reducing wound the occurrence frequency killing the severe event of doctor for a long time in the past.
Trained by the Amplitude integrated electroencephalogram of limited Boltzmann machine to common encephalopathic, utilize limited Boltzmann machine to carry out test identifying and diagnosing to the Amplitude integrated electroencephalogram of patient and go out common disease, the Amplitude integrated electroencephalogram that can not identify is diagnosed as unusual waveforms, doctor only carefully reads these unusual waveforms, greatly reduces the task amount of doctor.
Accompanying drawing explanation
Fig. 1 is structural representation of the present invention.
Embodiment
Below in conjunction with accompanying drawing and embodiment, the invention will be further described.
As shown in Figure 1, a kind of Amplitude integrated electroencephalogram result automatic recognition system, comprising:
Custom block, integrates the up-and-down boundary value of electroencephalogram waveform for self-defined input normal amplitude, and the Amplitude integrated electroencephalogram oscillogram of typical patient; Typical oscillogram also can be inputted by scanner by the Amplitude integrated electroencephalogram oscillogram of typical patient;
Data memory module, comprise normal boundary database, neonatal seizure waveform database, hypoxic ischemic encephalopathy of newborn database, infantile spasms database and large rural area syndrome database, these databases call for analyses and comparison module; Normal boundary database integrates the up-and-down boundary value of electroencephalogram waveform for the normal amplitude storing custom block input;
Load module, for inputting the Amplitude integrated electroencephalogram waveform of patient;
Data acquisition module, for receiving the Amplitude integrated electroencephalogram waveform of the patient of input, and flows to analyses and comparison module;
Analyses and comparison module, first extracts the up-and-down boundary of the Amplitude integrated electroencephalogram waveform of patient and compares with the border in normal boundary database, if do not exceed this boundary value, generates normal diagnosis report; If exceed this boundary value, identify one by one with other databases during data memory module stores, diagnose out matching of patient's Amplitude integrated electroencephalogram and which kind of disease, if do not mated with all databases, so diagnosis report of an output waveform exception, and result is flowed to output unit;
Output unit, for receiving and analyzing comparing module diagnostic result and shown by display device.
Described Wave anomaly comprises the unusual waveforms because the serious wave interference in intensive care unit, electrode delamination or malposition of electrode factor cause, and the Amplitude integrated electroencephalogram except neonatal seizure, hypoxic ischemic encephalopathy of newborn, infantile spasms and large rural area these diseases of syndrome.
Described load module comprises scanner and self-defined load module, described scanner is used for the Amplitude integrated electroencephalogram oscillogram of scan patients, scanning result is flowed to analyses and comparison module by data memory module, the characteristic of the Amplitude integrated electroencephalogram oscillogram of patient is inputted by doctor by described self-defined load module, and also flows to analyses and comparison module by data memory module.
Described analyses and comparison module, respectively two-stage one-dimensional wavelet transform is carried out to all oscillograms in neonatal seizure waveform database, hypoxic ischemic encephalopathy of newborn database, infantile spasms database and large rural area syndrome database, reduce the resolution of Image Sub-Band after decomposing, utilize PCA method to obtain the eigenwert of image;
Using the input layer of the eigenwert of acquisition as limited Boltzmann machine, utilize multiple oscillograms in neonatal seizure waveform database, hypoxic ischemic encephalopathy of newborn database, infantile spasms database, large rural area syndrome database and unusual waveforms database to train limited Boltzmann machine, obtain the neonatal seizure waveform feature data after training, hypoxic ischemic encephalopathy of newborn characteristic, infantile spasms characteristic, large rural area syndrome characteristic and unusual waveforms characteristic;
Test is carried out for the Amplitude integrated electroencephalogram oscillogram input-bound Boltzmann machine exceeding normal amplitude and integrate the patient of the up-and-down boundary value of electroencephalogram waveform and obtains test result, this test result comprises and is diagnosed as neonatal seizure, hypoxic ischemic encephalopathy of newborn, infantile spasms or large rural area syndrome, is diagnosed as unusual waveforms for the Amplitude integrated electroencephalogram that can not identify.
The result diagnosed out is all shown by display device, doctor can analyze in more detail for unusual waveforms after seeing diagnostic result, and for being diagnosed as neonatal seizure, hypoxic ischemic encephalopathy of newborn, infantile spasms or large rural area is syndromic can check the whether correct of diagnosis again.
A kind of Amplitude integrated electroencephalogram result automatic identifying method, comprising:
Self-defined input normal amplitude integrates the up-and-down boundary value of electroencephalogram waveform, and the Amplitude integrated electroencephalogram oscillogram of typical patient;
Data or waveform are stored into accordingly normal boundary database, neonatal seizure waveform database, hypoxic ischemic encephalopathy of newborn database, infantile spasms database and large rural area syndrome database, these databases call for next step analysis; The up-and-down boundary value of the normal amplitude integration electroencephalogram waveform of self-defined input is stored into normal boundary database;
The Amplitude integrated electroencephalogram waveform of input patient;
Receive the Amplitude integrated electroencephalogram waveform of the patient of input;
First extract the up-and-down boundary of the Amplitude integrated electroencephalogram waveform of patient and compare with the border in normal boundary database, if do not exceed this boundary value, generating normal diagnosis report; If exceed this boundary value, identify one by one with other databases, diagnose out matching of patient's Amplitude integrated electroencephalogram and which kind of disease, if do not mated with all databases, so diagnosis report of an output waveform exception, and diagnosis report is exported;
Receive diagnosis report and shown by display device.
When the up-and-down boundary of the Amplitude integrated electroencephalogram waveform of patient exceeds this boundary value and other databases know method for distinguishing one by one and be: respectively two-stage one-dimensional wavelet transform is carried out to oscillograms all in neonatal seizure waveform database, hypoxic ischemic encephalopathy of newborn database, infantile spasms database and large rural area syndrome database, reduce the resolution of Image Sub-Band after decomposing, utilize PCA method to obtain the eigenwert of image;
Using the input layer of the eigenwert of acquisition as limited Boltzmann machine, utilize multiple oscillograms in neonatal seizure waveform database, hypoxic ischemic encephalopathy of newborn database, infantile spasms database, large rural area syndrome database and unusual waveforms database to train limited Boltzmann machine, obtain the neonatal seizure waveform feature data after training, hypoxic ischemic encephalopathy of newborn characteristic, infantile spasms characteristic, large rural area syndrome characteristic and unusual waveforms characteristic;
Test is carried out for the Amplitude integrated electroencephalogram oscillogram input-bound Boltzmann machine exceeding normal amplitude and integrate the patient of the up-and-down boundary value of electroencephalogram waveform and obtains test result, unusual waveforms is diagnosed as the Amplitude integrated electroencephalogram that can not identify.
By reference to the accompanying drawings the specific embodiment of the present invention is described although above-mentioned; but not limiting the scope of the invention; one of ordinary skill in the art should be understood that; on the basis of technical scheme of the present invention, those skilled in the art do not need to pay various amendment or distortion that creative work can make still within protection scope of the present invention.
Claims (6)
1. an Amplitude integrated electroencephalogram result automatic recognition system, is characterized in that, comprising:
Custom block, integrates the up-and-down boundary value of electroencephalogram waveform for self-defined input normal amplitude, and the Amplitude integrated electroencephalogram oscillogram of typical patient;
Data memory module, comprise normal boundary database, neonatal seizure waveform database, hypoxic ischemic encephalopathy of newborn database, infantile spasms database and large rural area syndrome database, these databases call for analyses and comparison module; Normal boundary database integrates the up-and-down boundary value of electroencephalogram waveform for the normal amplitude storing custom block input;
Load module, for inputting the Amplitude integrated electroencephalogram waveform of patient;
Data acquisition module, for receiving the Amplitude integrated electroencephalogram waveform of the patient of input, and flows to analyses and comparison module;
Analyses and comparison module, first extracts the up-and-down boundary of the Amplitude integrated electroencephalogram waveform of patient and compares with the border in normal boundary database, if do not exceed this boundary value, generates normal diagnosis report; If exceed this boundary value, identify one by one with other databases during data memory module stores, diagnose out matching of patient's Amplitude integrated electroencephalogram and which kind of disease, if do not mated with all databases, so diagnosis report of an output waveform exception, and result is flowed to output unit;
Output unit, for receiving and analyzing comparing module diagnostic result and shown by display device.
2. a kind of Amplitude integrated electroencephalogram result automatic recognition system as claimed in claim 1, it is characterized in that, described Wave anomaly comprises the unusual waveforms because intensive care unit wave interference, electrode delamination or malposition of electrode factor cause, and the Amplitude integrated electroencephalogram except neonatal seizure, hypoxic ischemic encephalopathy of newborn, infantile spasms and large rural area these diseases of syndrome.
3. a kind of Amplitude integrated electroencephalogram result automatic recognition system as claimed in claim 1, it is characterized in that, described load module comprises scanner and self-defined load module, described scanner is used for the Amplitude integrated electroencephalogram oscillogram of scan patients, scanning result is flowed to analyses and comparison module by data memory module, the characteristic of the Amplitude integrated electroencephalogram oscillogram of patient is inputted by doctor by described self-defined load module, and also flows to analyses and comparison module by data memory module.
4. a kind of Amplitude integrated electroencephalogram result automatic recognition system as claimed in claim 1, it is characterized in that, described analyses and comparison module, respectively two-stage one-dimensional wavelet transform is carried out to all oscillograms in neonatal seizure waveform database, hypoxic ischemic encephalopathy of newborn database, infantile spasms database and large rural area syndrome database, reduce the resolution of Image Sub-Band after decomposing, utilize PCA method to obtain the eigenwert of image;
Using the input layer of the eigenwert of acquisition as limited Boltzmann machine, utilize multiple oscillograms in neonatal seizure waveform database, hypoxic ischemic encephalopathy of newborn database, infantile spasms database, large rural area syndrome database and unusual waveforms database to train limited Boltzmann machine, obtain the neonatal seizure waveform feature data after training, hypoxic ischemic encephalopathy of newborn characteristic, infantile spasms characteristic, large rural area syndrome characteristic and unusual waveforms characteristic;
Test is carried out for the Amplitude integrated electroencephalogram oscillogram input-bound Boltzmann machine exceeding normal amplitude and integrate the patient of the up-and-down boundary value of electroencephalogram waveform and obtains test result, unusual waveforms is diagnosed as the Amplitude integrated electroencephalogram that can not identify.
5. an Amplitude integrated electroencephalogram result automatic identifying method, is characterized in that, comprising:
Self-defined input normal amplitude integrates the up-and-down boundary value of electroencephalogram waveform, and the Amplitude integrated electroencephalogram oscillogram of typical patient;
Data or waveform are stored into accordingly normal boundary database, neonatal seizure waveform database, hypoxic ischemic encephalopathy of newborn database, infantile spasms database and large rural area syndrome database, these databases call for next step analysis; The up-and-down boundary value of the normal amplitude integration electroencephalogram waveform of self-defined input is stored into normal boundary database;
The Amplitude integrated electroencephalogram waveform of input patient;
Receive the Amplitude integrated electroencephalogram waveform of the patient of input;
First extract the up-and-down boundary of the Amplitude integrated electroencephalogram waveform of patient and compare with the border in normal boundary database, if do not exceed this boundary value, generating normal diagnosis report; If exceed this boundary value, identify one by one with other databases, diagnose out matching of patient's Amplitude integrated electroencephalogram and which kind of disease, if do not mated with all databases, so diagnosis report of an output waveform exception, and diagnosis report is exported;
Receive diagnosis report and shown by display device.
6. a kind of Amplitude integrated electroencephalogram result automatic identifying method as claimed in claim 5, it is characterized in that, when the up-and-down boundary of the Amplitude integrated electroencephalogram waveform of patient exceeds this boundary value and other databases know method for distinguishing one by one and be: respectively two-stage one-dimensional wavelet transform is carried out to oscillograms all in neonatal seizure waveform database, hypoxic ischemic encephalopathy of newborn database, infantile spasms database and large rural area syndrome database, reduce the resolution of Image Sub-Band after decomposing, utilize PCA method to obtain the eigenwert of image;
Using the input layer of the eigenwert of acquisition as limited Boltzmann machine, utilize multiple oscillograms in neonatal seizure waveform database, hypoxic ischemic encephalopathy of newborn database, infantile spasms database, large rural area syndrome database and unusual waveforms database to train limited Boltzmann machine, obtain the neonatal seizure waveform feature data after training, hypoxic ischemic encephalopathy of newborn characteristic, infantile spasms characteristic, large rural area syndrome characteristic and unusual waveforms characteristic;
Test is carried out for the Amplitude integrated electroencephalogram oscillogram input-bound Boltzmann machine exceeding normal amplitude and integrate the patient of the up-and-down boundary value of electroencephalogram waveform and obtains test result, unusual waveforms is diagnosed as the Amplitude integrated electroencephalogram that can not identify.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510573753.6A CN105046111B (en) | 2015-09-10 | 2015-09-10 | A kind of Amplitude integrated electroencephalogram result automatic recognition system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510573753.6A CN105046111B (en) | 2015-09-10 | 2015-09-10 | A kind of Amplitude integrated electroencephalogram result automatic recognition system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN105046111A true CN105046111A (en) | 2015-11-11 |
| CN105046111B CN105046111B (en) | 2018-09-25 |
Family
ID=54452651
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201510573753.6A Expired - Fee Related CN105046111B (en) | 2015-09-10 | 2015-09-10 | A kind of Amplitude integrated electroencephalogram result automatic recognition system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN105046111B (en) |
Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107038337A (en) * | 2017-03-21 | 2017-08-11 | 广州华康基因医学科技有限公司 | A kind of neonate's Inherited Metabolic Disorders screening method |
| CN108491074A (en) * | 2018-03-09 | 2018-09-04 | 广东欧珀移动通信有限公司 | Electronic device, exercising support method and Related product |
| CN108542384A (en) * | 2018-03-09 | 2018-09-18 | 王永新 | A kind of brain wave intelligent monitor system and its method |
| CN109009102A (en) * | 2018-08-10 | 2018-12-18 | 中南大学 | A kind of aided diagnosis method and system based on electroencephalogram deep learning |
| CN109069081A (en) * | 2015-12-04 | 2018-12-21 | 爱荷华大学研究基金会 | For predicting, screening and monitoring encephalopathy/delirium equipment, system and method |
| CN109300544A (en) * | 2018-12-10 | 2019-02-01 | 南京伟思医疗科技股份有限公司 | A kind of seven step analysis method of newborn baby function standardizing |
| CN112545535A (en) * | 2020-12-07 | 2021-03-26 | 杭州沃维医疗科技有限公司 | Sleep-wake cycle analysis method based on amplitude integrated electroencephalogram |
| CN113362934A (en) * | 2021-06-03 | 2021-09-07 | 深圳市妇幼保健院 | System for simulating disease attack characterization based on electroencephalogram of children |
| CN113436728A (en) * | 2021-07-05 | 2021-09-24 | 复旦大学附属儿科医院 | Method and equipment for automatically analyzing electroencephalogram of newborn clinical video |
| CN113476059A (en) * | 2021-06-02 | 2021-10-08 | 南京伟思医疗科技股份有限公司 | Method for judging left-right brain symmetry based on amplitude integrated electroencephalogram |
| CN116250847B (en) * | 2023-03-22 | 2023-08-01 | 中国人民解放军东部战区总医院 | Brain-computer interaction intelligent epileptic early warning control method suitable for craniocerebral trauma patient |
Citations (10)
| 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 | Multi-parameter infant brain functional monitoring and analyzing instrument |
| CN101627909A (en) * | 2009-05-05 | 2010-01-20 | 复旦大学附属儿科医院 | Digital amplitude-integrated cerebral function monitor |
| CN102024089A (en) * | 2009-09-15 | 2011-04-20 | 英业达股份有限公司 | Real-time diagnosis system and real-time diagnose method based on health statistics |
| CN102306250A (en) * | 2011-09-08 | 2012-01-04 | 程浩川 | Method and system for monitoring brain electrical impedance image based on cloud computing |
| CN102429657A (en) * | 2011-09-22 | 2012-05-02 | 上海师范大学 | Epilepsia electroencephalogram signal classified detection device and method |
| CN102551887A (en) * | 2010-12-09 | 2012-07-11 | 苏州生物医学工程技术研究所 | Graphic intelligent monitoring system for common diseases of neonates |
| US20120289848A1 (en) * | 2011-05-10 | 2012-11-15 | Industrial Technology Research Institute | Method and system for discriminating heart sound and cardiopathy |
| CN103431860A (en) * | 2013-08-16 | 2013-12-11 | 清华大学深圳研究生院 | Newborn brain function monitor based on DSP and ARM dual-core processor |
| CN103955610A (en) * | 2014-04-22 | 2014-07-30 | 韩燕� | Medical image computer-aided analysis method |
| CN104173046A (en) * | 2014-09-01 | 2014-12-03 | 清华大学 | Method for extracting color marked amplitude-integrated electroencephalogram |
-
2015
- 2015-09-10 CN CN201510573753.6A patent/CN105046111B/en not_active Expired - Fee Related
Patent Citations (10)
| 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 | Multi-parameter infant brain functional monitoring and analyzing instrument |
| CN101627909A (en) * | 2009-05-05 | 2010-01-20 | 复旦大学附属儿科医院 | Digital amplitude-integrated cerebral function monitor |
| CN102024089A (en) * | 2009-09-15 | 2011-04-20 | 英业达股份有限公司 | Real-time diagnosis system and real-time diagnose method based on health statistics |
| CN102551887A (en) * | 2010-12-09 | 2012-07-11 | 苏州生物医学工程技术研究所 | Graphic intelligent monitoring system for common diseases of neonates |
| US20120289848A1 (en) * | 2011-05-10 | 2012-11-15 | Industrial Technology Research Institute | Method and system for discriminating heart sound and cardiopathy |
| CN102306250A (en) * | 2011-09-08 | 2012-01-04 | 程浩川 | Method and system for monitoring brain electrical impedance image based on cloud computing |
| CN102429657A (en) * | 2011-09-22 | 2012-05-02 | 上海师范大学 | Epilepsia electroencephalogram signal classified detection device and method |
| CN103431860A (en) * | 2013-08-16 | 2013-12-11 | 清华大学深圳研究生院 | Newborn brain function monitor based on DSP and ARM dual-core processor |
| CN103955610A (en) * | 2014-04-22 | 2014-07-30 | 韩燕� | Medical image computer-aided analysis method |
| CN104173046A (en) * | 2014-09-01 | 2014-12-03 | 清华大学 | Method for extracting color marked amplitude-integrated electroencephalogram |
Non-Patent Citations (4)
| Title |
|---|
| 刘凯等: "基于遗传算法的ＲＢＭ优化设计", 《微电子学与计算机》 * |
| 吴证等: "结合主元成分分析的受限玻耳兹曼机神经网络的降维方法", 《上海交通大学学报》 * |
| 李双双等: "振幅整合脑电及在新生猪缺氧脑损伤中的应用", 《清华大学学报（自然科学版）》 * |
| 郭志梅等: "新生儿低血糖脑损伤振幅整合脑电图特征研究", 《临床儿科杂志》 * |
Cited By (17)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109069081A (en) * | 2015-12-04 | 2018-12-21 | 爱荷华大学研究基金会 | For predicting, screening and monitoring encephalopathy/delirium equipment, system and method |
| CN107038337A (en) * | 2017-03-21 | 2017-08-11 | 广州华康基因医学科技有限公司 | A kind of neonate's Inherited Metabolic Disorders screening method |
| CN108491074B (en) * | 2018-03-09 | 2021-07-09 | Oppo广东移动通信有限公司 | Electronic device, exercise assisting method and related product |
| CN108542384A (en) * | 2018-03-09 | 2018-09-18 | 王永新 | A kind of brain wave intelligent monitor system and its method |
| CN108491074A (en) * | 2018-03-09 | 2018-09-04 | 广东欧珀移动通信有限公司 | Electronic device, exercising support method and Related product |
| CN109009102B (en) * | 2018-08-10 | 2021-02-12 | 中南大学 | Electroencephalogram deep learning-based auxiliary diagnosis method and system |
| CN109009102A (en) * | 2018-08-10 | 2018-12-18 | 中南大学 | A kind of aided diagnosis method and system based on electroencephalogram deep learning |
| CN109300544A (en) * | 2018-12-10 | 2019-02-01 | 南京伟思医疗科技股份有限公司 | A kind of seven step analysis method of newborn baby function standardizing |
| CN109300544B (en) * | 2018-12-10 | 2024-04-26 | 南京伟思医疗科技股份有限公司 | Neonatal brain function standardization seven-step analysis method |
| CN112545535B (en) * | 2020-12-07 | 2023-01-06 | 杭州沃维医疗科技有限公司 | Sleep-wake cycle analysis method based on amplitude integrated electroencephalogram |
| CN112545535A (en) * | 2020-12-07 | 2021-03-26 | 杭州沃维医疗科技有限公司 | Sleep-wake cycle analysis method based on amplitude integrated electroencephalogram |
| CN113476059A (en) * | 2021-06-02 | 2021-10-08 | 南京伟思医疗科技股份有限公司 | Method for judging left-right brain symmetry based on amplitude integrated electroencephalogram |
| CN113476059B (en) * | 2021-06-02 | 2023-06-27 | 南京伟思医疗科技股份有限公司 | Method for judging left-right brain symmetry based on amplitude integrated electroencephalogram |
| CN113362934A (en) * | 2021-06-03 | 2021-09-07 | 深圳市妇幼保健院 | System for simulating disease attack characterization based on electroencephalogram of children |
| CN113436728B (en) * | 2021-07-05 | 2022-10-28 | 复旦大学附属儿科医院 | Method and equipment for automatically analyzing electroencephalogram of clinical video of neonate |
| CN113436728A (en) * | 2021-07-05 | 2021-09-24 | 复旦大学附属儿科医院 | Method and equipment for automatically analyzing electroencephalogram of newborn clinical video |
| CN116250847B (en) * | 2023-03-22 | 2023-08-01 | 中国人民解放军东部战区总医院 | Brain-computer interaction intelligent epileptic early warning control method suitable for craniocerebral trauma patient |
Also Published As
| Publication number | Publication date |
|---|---|
| CN105046111B (en) | 2018-09-25 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN105046111A (en) | Amplitude integrated electroencephalogram result automatic identifying system and method | |
| Liang et al. | A new, short-recorded photoplethysmogram dataset for blood pressure monitoring in China | |
| US9949714B2 (en) | Method, electronic apparatus, and computer readable medium of constructing classifier for disease detection | |
| USRE46189E1 (en) | Field deployable concussion assessment device | |
| US9477813B2 (en) | Method and device for multimodal neurological evaluation | |
| RU2768581C2 (en) | Devices, systems and methods for prediction, screening and control of encephalopathy/delirium | |
| US8364254B2 (en) | Method and device for probabilistic objective assessment of brain function | |
| US20090264785A1 (en) | Method and Apparatus For Assessing Brain Function Using Diffusion Geometric Analysis | |
| Lee et al. | QRS detection method based on fully convolutional networks for capacitive electrocardiogram | |
| Perpetuini et al. | Study of memory deficit in Alzheimer’s disease by means of complexity analysis of fNIRS signal | |
| CN106901727A (en) | A kind of depression Risk Screening device based on EEG signals | |
| Qayyum et al. | High-density electroencephalography and speech signal based deep framework for clinical depression diagnosis | |
| CN112102938A (en) | Sleep staging method and device | |
| CN109480822A (en) | A kind of electrocardiographic diagnosis function detecting method and its processing method | |
| CN115691734A (en) | Syncope medical history acquisition system, terminal, computer equipment and storage medium | |
| WO2015187401A1 (en) | Method and device for multimodal neurological evaluation | |
| WO2022067071A1 (en) | Systems for recording and analyzing electroencephalogram signals for brain disorder detection | |
| Shnitzer et al. | Alternating diffusion maps for dementia severity assessment | |
| RU2445916C2 (en) | Method of prehospital examination of functional state of individual and automatic system for functional state of user (versions) | |
| Yen et al. | Design and implementation of platform for collecting physiological and psychological data for mood status inference | |
| Fangmeng et al. | Emotional changes detection for dementia people with spectrograms from physiological signals | |
| AU2021103997A4 (en) | Artificial neural network based brain disorder diagnostic system | |
| CN116616798A (en) | Brain age prediction method and system | |
| Krishnapriya | EEG-Based Identification of Schizophrenia Using Deep Learning Techniques | |
| Waili et al. | A Framework for Non-contact ECG Signal Detection Device on Smart Chair and Its Implementation |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| CF01 | Termination of patent right due to non-payment of annual fee | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20180925 Termination date: 20190910 |