CN112401907A - Method for reliably dividing brain low-frequency fluctuation sub-region based on Fourier synchronous compression transformation - Google Patents
Method for reliably dividing brain low-frequency fluctuation sub-region based on Fourier synchronous compression transformation Download PDFInfo
- Publication number
- CN112401907A CN112401907A CN202011297563.3A CN202011297563A CN112401907A CN 112401907 A CN112401907 A CN 112401907A CN 202011297563 A CN202011297563 A CN 202011297563A CN 112401907 A CN112401907 A CN 112401907A
- Authority
- CN
- China
- Prior art keywords
- frequency
- brain
- time
- low
- fourier
- 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.)
- Pending
Links
- 210000004556 brain Anatomy 0.000 title claims abstract description 56
- 238000000034 method Methods 0.000 title claims abstract description 26
- 230000001360 synchronised effect Effects 0.000 title claims abstract description 13
- 230000009466 transformation Effects 0.000 title claims abstract description 13
- 230000006835 compression Effects 0.000 title claims abstract description 6
- 238000007906 compression Methods 0.000 title claims abstract description 6
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 22
- 238000004364 calculation method Methods 0.000 claims abstract description 11
- 238000005481 NMR spectroscopy Methods 0.000 claims description 17
- 238000011160 research Methods 0.000 claims description 7
- 238000001228 spectrum Methods 0.000 claims description 4
- 238000012795 verification Methods 0.000 claims description 4
- 238000007635 classification algorithm Methods 0.000 claims description 3
- 239000000203 mixture Substances 0.000 claims description 3
- 230000003044 adaptive effect Effects 0.000 claims description 2
- 238000013507 mapping Methods 0.000 claims description 2
- 230000010355 oscillation Effects 0.000 description 5
- 238000004458 analytical method Methods 0.000 description 4
- 230000007177 brain activity Effects 0.000 description 3
- 230000009286 beneficial effect Effects 0.000 description 2
- 230000003925 brain function Effects 0.000 description 2
- 238000012937 correction Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 230000006870 function Effects 0.000 description 2
- 238000012360 testing method Methods 0.000 description 2
- 208000014644 Brain disease Diseases 0.000 description 1
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 description 1
- 230000015572 biosynthetic process Effects 0.000 description 1
- 239000008280 blood Substances 0.000 description 1
- 210000004369 blood Anatomy 0.000 description 1
- 210000000746 body region Anatomy 0.000 description 1
- 238000012512 characterization method Methods 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 230000001054 cortical effect Effects 0.000 description 1
- 230000001419 dependent effect Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000005284 excitation Effects 0.000 description 1
- 230000003340 mental effect Effects 0.000 description 1
- 230000002503 metabolic effect Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000001537 neural effect Effects 0.000 description 1
- 210000002569 neuron Anatomy 0.000 description 1
- 229910052760 oxygen Inorganic materials 0.000 description 1
- 239000001301 oxygen Substances 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 208000020016 psychiatric disease Diseases 0.000 description 1
- 230000000284 resting effect Effects 0.000 description 1
- 230000002269 spontaneous effect Effects 0.000 description 1
Images
Classifications
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7235—Details of waveform analysis
- A61B5/7253—Details of waveform analysis characterised by using transforms
- A61B5/7257—Details of waveform analysis characterised by using transforms using Fourier transforms
Landscapes
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- Biophysics (AREA)
- Heart & Thoracic Surgery (AREA)
- Physiology (AREA)
- Psychiatry (AREA)
- Signal Processing (AREA)
- Artificial Intelligence (AREA)
- Mathematical Physics (AREA)
- Pathology (AREA)
- Biomedical Technology (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Medical Informatics (AREA)
- Molecular Biology (AREA)
- Surgery (AREA)
- Animal Behavior & Ethology (AREA)
- General Health & Medical Sciences (AREA)
- Public Health (AREA)
- Veterinary Medicine (AREA)
- Magnetic Resonance Imaging Apparatus (AREA)
Abstract
The invention discloses a method for reliably dividing brain low-frequency fluctuation sub-regions based on Fourier synchronous compression transformation. Due to the particularity of time-frequency signals, numerous voxels of brain signals and huge data sets, an automatic target generation process algorithm is introduced to adapt to a Kmeans algorithm under FSST data, an initial label of a class is found by a space projection method, distance calculation is carried out again, and the heart-like calculation is carried out again until iteration is completed to find the optimal heart-like. The distance calculation selects the correlation coefficient and the region selects the union of the low frequency regions in the data set. And finally, restoring the data to a space, and observing the spatial relation among different classifications from the brain space atlas.
Description
Technical Field
The invention relates to the field of brain low-frequency fluctuation region subdivision, in particular to a method for reliably dividing a brain low-frequency fluctuation sub-region based on Fourier synchronous compression transformation.
Background
Blood oxygen level dependent effects of the brain can be used to characterize metabolic conditions of neurons in the brain, thereby indirectly reflecting neuronal activity. Researches show that the human brain in a resting state has a spontaneous low-frequency fluctuation brain function network, the frequency band of the network is usually between 0.01HZ and 0.08HZ, and the oscillation wave of the frequency band reflects the excitation degree of cortical local activity and information exchange among brain areas. Low frequency amplitude is an important indicator of decoded brain activity. The analysis of the low-frequency amplitude sub-regions can find out the correlation between the regions, and can study the functional connectivity of the brain region from another angle, thereby providing a new starting point for the brain connectivity study. Based on the above research background, the present invention combines a latest research result: the FSST algorithm is more suitable for analyzing signals with faster frequency conversion, and partial signals in the brain wave signals have more frequent oscillation phenomena, so that the FSST algorithm is more suitable for exploring areas with obvious oscillation of the brain wave signals. In addition, because FSST algorithm data has a large amount of redundancy and large voxels, the invention also provides a classification algorithm for adapting data: ATGP-Kmeans is used for dividing the time frequency data more reliably and improving the value of the brain time frequency signal clustering result.
Disclosure of Invention
The invention aims to study whether dynamic frequency correlation exists between sub-areas in a low-frequency area under time-frequency fluctuation or not from the time-frequency space angle and whether the space connectivity of a brain can be analyzed from the time-frequency angle or not. And then, by remapping the brain image, the correlation of brain voxels in a spatial region is observed so as to verify the idea proposed by the background technology.
In order to achieve the purpose, the invention provides the following technical scheme: the method for reliably dividing the brain low-frequency fluctuation subarea based on Fourier synchronous compression transformation comprises the following steps:
step 1: dividing a low-frequency area, searching areas with low-frequency amplitude extracted from the same tested state at different moments, selecting the areas by adopting a traditional ALFF method, removing some noise areas and selecting the areas with strict verification;
step 2: performing time-frequency reconstruction on the preprocessed functional nuclear magnetic resonance signals, converting the preprocessed functional nuclear magnetic resonance signals into time-frequency domains, and acquiring related time-frequency-power spectrograms to form a time-frequency data set of the nuclear magnetic resonance signals;
and step 3: based on the time-frequency dynamic correlation angle, the ATGP-Kmeans is adopted to adapt to FSST data by utilizing the dynamic synchronicity of different voxels in time-frequency, correlation coefficient calculation is carried out, clustering is completed, the clustering is mapped into a spatial brain map, and the correlation of different brain areas in the space is observed.
As a preferred technical solution of the present invention, in the step 1, a low frequency region is used as a research region for selecting the classification region;
as a preferred technical scheme of the invention, in the step 2 and the step 3, through time-frequency reconstruction of data, an adaptive classification algorithm is introduced, the data are clustered and then mapped into a space, and time-frequency relation of different brain areas embodied on the space is researched.
The selection of the region in the step 1 adopts a traditional ALFF method formula as follows:
ak(fk)，bk(fk) Real and imaginary parts, respectively.
As a preferred technical scheme of the invention, in the step 1, some noise regions are removed, and selected
Checking a stricter area;
In a preferred embodiment of the present invention, the step 2 is performed on the preprocessed functional nuclear magnetic resonance signalThe time-frequency reconstruction formula is as follows:
converting the time spectrum into a time-frequency domain to obtain a related time-frequency-power spectrogram; the signal f (t) is a plurality of fk(t) composition, with the STFT predominant ridge at (t, φ'k(t)), can be approximated by
Substitute phi'k(t)。
As a preferred technical solution of the present invention, in the step 3, based on the time-frequency dynamic correlation angle, the dynamic synchronicity of different voxels in time-frequency and the local correlation principle of the brain are utilized, and the low-frequency voxel signals are processed by means of clustering, and due to the redundancy and the complex nature of the signals, an ATGP algorithm is introduced: t is t1＝arg{maxr[rTr]R are all voxels to be observed, and U isTThe pseudo-inverse of U is set to,
as an initial centroid selection algorithm of Kmeans, ATGP-Kmeans is adopted to adapt FSST data, and a loss function is minimized:
as a preferred technical solution of the present invention, in the step 3, the correlation coefficient calculation is performed to complete the clustering, wherein the correlation coefficient calculation is selected:
reselecting a class center:
and finishing clustering until the maximum distance is reached, mapping the clustering to a spatial brain map, and observing the correlation of different brain areas in the space.
The invention has the beneficial effects that: the method starts with the selection of a low-frequency region, selects the low-frequency region which is subjected to strict T test correction, performs time-frequency reconstruction and clustering algorithm and remaps a spatial map; specifically, time-frequency analysis is carried out on a low-frequency region through an FSST algorithm, a time-frequency signal is obtained and used as representation information of a voxel, an ATGP algorithm is introduced to adapt to a Kmeans algorithm for clustering, and finally a brain map is remapped to obtain a spatial correlation diagram.
Drawings
FIG. 1 is a schematic flow diagram of the present invention;
FIG. 2 is a flow chart of the present invention.
Detailed Description
The following detailed description of the preferred embodiments of the present invention, taken in conjunction with the accompanying drawings, will make the advantages and features of the invention more readily understood by those skilled in the art, and thus will more clearly and distinctly define the scope of the invention.
Example (b): referring to fig. 1-2, the present invention provides a technical solution: the method comprises the following steps:
step 1: dividing a low-frequency area, searching areas with low-frequency amplitude extracted from the same tested state at different moments, selecting the areas by adopting a traditional ALFF method, removing some noise areas and selecting the areas with strict verification;
step 2: performing time-frequency reconstruction on the preprocessed functional nuclear magnetic resonance signals, converting the preprocessed functional nuclear magnetic resonance signals into time-frequency domains, and acquiring related time-frequency-power spectrograms to form a time-frequency data set of the nuclear magnetic resonance signals;
and step 3: based on the time-frequency dynamic correlation angle, the ATGP-Kmeans is adopted to adapt to FSST data by utilizing the dynamic synchronicity of different voxels in time-frequency, correlation coefficient calculation is carried out, clustering is completed, the clustering is mapped into a spatial brain map, and the correlation of different brain areas in the space is observed.
The invention aims to study whether dynamic frequency correlation exists between sub-areas in a low-frequency area under time-frequency fluctuation or not from the time-frequency space angle and whether the space connectivity of the brain can be analyzed from the time-frequency angle or not. Based on data redundancy under the FSST algorithm and a huge voxel group of a brain region, firstly, an ATGP algorithm is introduced to replace a traditional random sample selection algorithm to serve as an initial class center of Kmeans, and therefore the classification efficiency and reliability are improved. And then the brain voxels are observed to be associated in the space region through remapping of the brain map.
For convenience of description, terms specific to the present invention are first defined as follows:
brain low frequency fluctuation subregion:
the brain hypo-frequency fluctuation sub-region is referred to in the present invention as: based on the low-frequency oscillation attribute of human brain activity, when decoding a low-frequency oscillation signal, a main body region of the low-frequency signal is taken as an analysis key point to study whether dynamic association on time frequency exists in sub-regions in the region.
Secondly, the method comprises the following specific steps:
step 1: selection of low-frequency region of brain function nuclear magnetic resonance signal: and dividing a low-frequency area, and searching areas of the low-frequency amplitude of the union set extracted from the same tested state at different times. The traditional ALFF method is adopted for selecting the area, meanwhile, some noise areas are removed, and the area with stricter verification is selected.
Step 1.1, dividing a low-frequency area, and searching areas of union low-frequency amplitude extracted from the same tested state at different times; the selection of the regions uses the conventional ALFF method:
ak(fk)，bk(fk) Real and imaginary parts, respectively.
Step 1.2, removing some noise areas and selecting
Checking a stricter area;
Step 2: time-frequency reconstruction and voxel characterization signal formation: performing time-frequency reconstruction on the preprocessed functional nuclear magnetic resonance signals, converting the preprocessed functional nuclear magnetic resonance signals into time-frequency domains, and acquiring related time-frequency-power spectrograms to form a time-frequency data set of the nuclear magnetic resonance signals;
step 2.1, performing time-frequency reconstruction on the preprocessed functional nuclear magnetic resonance signals: the signal f (t) is a plurality of fk(t) composition, with the STFT predominant ridge at (t, φ'k(t)), can be approximated by
Substitute phi'k(t), the time-frequency reconstruction formula is as follows:
converting the time spectrum into a time-frequency domain to obtain a related time-frequency-power spectrogram;
step 2.2, changing the time amplitude signal of each voxel into a time-frequency-power spectrum signal to form a time-frequency data set of nuclear magnetic resonance signals;
and step 3: remapping of voxel clustering and space: based on the time-frequency dynamic correlation angle, the ATGP-Kmeans is adopted to adapt to FSST data by utilizing the dynamic synchronicity of different voxels in time-frequency, correlation coefficient calculation is carried out, clustering is completed, the clustering is mapped into a spatial brain map, and the correlation of different brain areas in the space is observed.
And 3.1, processing the low-frequency voxel signals by adopting a clustering means based on a time-frequency dynamic correlation angle and by utilizing the dynamic synchronism of different voxels in time-frequency and the local correlation principle of the brain. Due to the redundant and cumbersome nature of these signals, the ATGP algorithm was introduced: t is t1＝arg{maxr[rTr]R are all voxels to be observed, and U isTThe pseudo-inverse of U is set to,
as an initial centroid selection algorithm for Kmeans, ATGP-Kmeans is adopted to adapt FSST data. Minimization of the loss function:
and 3.2, calculating the distance by adopting a correlation coefficient:
reselecting a class center:
clustering is completed until the maximum distance is reached. And then mapped into a spatial brain map to observe the association of different brain regions in the space.
The method starts with the selection of a low-frequency region, selects the low-frequency region which is subjected to strict T test correction, performs time-frequency reconstruction and clustering algorithm and remaps a spatial map; specifically, time-frequency analysis is carried out on a low-frequency region through an FSST algorithm, a time-frequency signal is obtained and used as representation information of a voxel, an ATGP algorithm is introduced to adapt to a Kmeans algorithm for clustering, and finally a brain graph is remapped to obtain a spatial correlation graph. The invention is beneficial to the research of the relevance between the low-frequency brain activity areas of the human brain and is applied to the research of mental and mental diseases, brain diseases, occupational plasticity and the like.
The above examples only show some embodiments of the present invention, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention.
Claims (8)
1. The method for reliably dividing the brain low-frequency fluctuation subarea based on Fourier synchronous compression transformation is characterized by comprising the following steps of:
step 1: dividing a low-frequency area, searching areas with low-frequency amplitude extracted from the same tested state at different moments, selecting the areas by adopting a traditional ALFF method, removing some noise areas and selecting the areas with strict verification;
step 2: performing time-frequency reconstruction on the preprocessed functional nuclear magnetic resonance signals, converting the preprocessed functional nuclear magnetic resonance signals into time-frequency domains, and acquiring related time-frequency-power spectrograms to form a time-frequency data set of the nuclear magnetic resonance signals;
and step 3: based on the time-frequency dynamic correlation angle, the ATGP-Kmeans is adopted to adapt to FSST data by utilizing the dynamic synchronicity of different voxels in time-frequency, correlation coefficient calculation is carried out, clustering is completed, the clustering is mapped into a spatial brain map, and the correlation of different brain areas in the space is observed.
2. The method for reliably dividing the brain low-frequency fluctuation subarea based on the Fourier synchronous compressive transformation as claimed in claim 1, wherein: and selecting a classification region in the step 1, wherein a low-frequency region is used as a research region.
3. The method for reliably dividing the brain low-frequency fluctuation subarea based on the Fourier synchronous compressive transformation as claimed in claim 1, wherein: in the step 2 and the step 3, through time-frequency reconstruction of data, an adaptive classification algorithm is introduced, the data are clustered and then mapped into a space, and time-frequency relation of different brain areas embodied on the space is researched.
4. The method for reliably dividing the brain low-frequency fluctuation subarea based on the Fourier synchronous compressive transformation as claimed in claim 1, wherein: the selection of the region in the step 1 adopts a traditional ALFF method formula as follows:
ak(fk)，bk(fk) Are respectively asReal and imaginary parts.
5. The method for reliably dividing the brain low-frequency fluctuation subarea based on the Fourier synchronous compressive transformation as claimed in claim 1, wherein: removing some noise regions in the step 1, and selecting
Checking a stricter area;
6. The method for reliably dividing the brain low-frequency fluctuation subarea based on the Fourier synchronous compressive transformation as claimed in claim 1, wherein: the formula for performing time-frequency reconstruction on the preprocessed functional nuclear magnetic resonance signals in the step 2 is as follows:
converting the time spectrum into a time-frequency domain to obtain a related time-frequency-power spectrogram; the signal f (t) is a plurality of fk(t) composition, with the STFT predominant ridge at (t, φ'k(t)), can be approximated by
Substitute phi'k(t)。
7. The method for reliably dividing the brain low-frequency fluctuation subarea based on the Fourier synchronous compressive transformation as claimed in claim 1, wherein: in the step 3, different voxels are utilized in time based on the time-frequency dynamic correlation angleInter-frequency dynamic synchronism and brain local correlation principle, the low-frequency voxel signals are processed by adopting a clustering means, and due to the redundancy and complexity of the signals, an ATGP algorithm is introduced: t is t1＝arg{maxr[rTr]R are all voxels to be observed, and U isTThe pseudo-inverse of U is set to,
as an initial centroid selection algorithm of Kmeans, ATGP-Kmeans is adopted to adapt FSST data, and a loss function is minimized:
8. the method for reliably dividing the brain low-frequency fluctuation subarea based on the Fourier synchronous compressive transformation as claimed in claim 1, wherein: and 3, performing correlation coefficient calculation to complete clustering, wherein the correlation coefficient calculation is selected:
reselecting a class center:
and finishing clustering until the maximum distance is reached, mapping the clustering to a spatial brain map, and observing the correlation of different brain areas in the space.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011297563.3A CN112401907A (en) | 2020-11-18 | 2020-11-18 | Method for reliably dividing brain low-frequency fluctuation sub-region based on Fourier synchronous compression transformation |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011297563.3A CN112401907A (en) | 2020-11-18 | 2020-11-18 | Method for reliably dividing brain low-frequency fluctuation sub-region based on Fourier synchronous compression transformation |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112401907A true CN112401907A (en) | 2021-02-26 |
Family
ID=74774022
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011297563.3A Pending CN112401907A (en) | 2020-11-18 | 2020-11-18 | Method for reliably dividing brain low-frequency fluctuation sub-region based on Fourier synchronous compression transformation |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112401907A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113197583A (en) * | 2021-05-11 | 2021-08-03 | 广元市中心医院 | Electrocardiogram waveform segmentation method based on time-frequency analysis and recurrent neural network |
| CN116058851A (en) * | 2023-02-20 | 2023-05-05 | 之江实验室 | Electroencephalogram data processing method, electroencephalogram data processing device, electroencephalogram data analysis system, electronic device and electroencephalogram data processing medium |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106037741A (en) * | 2016-07-04 | 2016-10-26 | 电子科技大学 | Data method for detecting dynamic brain spontaneous activity based on fMRI |
| US20170128025A1 (en) * | 2015-11-10 | 2017-05-11 | Baycrest Centre | Quantitative mapping of cerebrovascular reactivity using resting-state functional magnetic resonance imaging |
| CN108681391A (en) * | 2018-03-19 | 2018-10-19 | 南京邮电大学 | A kind of EEG signals dummy keyboard design method based on multi-mode |
| CN111623986A (en) * | 2020-05-19 | 2020-09-04 | 安徽智寰科技有限公司 | Signal feature extraction method and system based on synchronous compression transformation and time-frequency matching |
-
2020
- 2020-11-18 CN CN202011297563.3A patent/CN112401907A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20170128025A1 (en) * | 2015-11-10 | 2017-05-11 | Baycrest Centre | Quantitative mapping of cerebrovascular reactivity using resting-state functional magnetic resonance imaging |
| CN106037741A (en) * | 2016-07-04 | 2016-10-26 | 电子科技大学 | Data method for detecting dynamic brain spontaneous activity based on fMRI |
| CN108681391A (en) * | 2018-03-19 | 2018-10-19 | 南京邮电大学 | A kind of EEG signals dummy keyboard design method based on multi-mode |
| CN111623986A (en) * | 2020-05-19 | 2020-09-04 | 安徽智寰科技有限公司 | Signal feature extraction method and system based on synchronous compression transformation and time-frequency matching |
Non-Patent Citations (2)
| Title |
|---|
| 刘扬 等: "基于小波域的fMRI 脑功能连通性检测方法", 《计算机系统应用》 * |
| 张黎明等: "用于稳态视觉诱发电位特征频率提取的同步压缩短时傅里叶变换方法", 《西安交通大学学报》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113197583A (en) * | 2021-05-11 | 2021-08-03 | 广元市中心医院 | Electrocardiogram waveform segmentation method based on time-frequency analysis and recurrent neural network |
| CN116058851A (en) * | 2023-02-20 | 2023-05-05 | 之江实验室 | Electroencephalogram data processing method, electroencephalogram data processing device, electroencephalogram data analysis system, electronic device and electroencephalogram data processing medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108714026B (en) | Fine-grained electrocardiosignal classification method based on deep convolutional neural network and online decision fusion | |
| Unser et al. | A review of wavelets in biomedical applications | |
| CN105411565B (en) | Heart rate variability tagsort method based on broad sense multi-scale wavelet entropy | |
| CN110506278A (en) | Target detection in latent space | |
| CN112401907A (en) | Method for reliably dividing brain low-frequency fluctuation sub-region based on Fourier synchronous compression transformation | |
| CN108241865A (en) | Multiple dimensioned more subgraph liver fibrosis multi-stage quantizations based on ultrasonoscopy method by stages | |
| CN114578963A (en) | Electroencephalogram identity recognition method based on feature visualization and multi-mode fusion | |
| Ma et al. | Diagnosis of thyroid nodules based on image enhancement and deep neural networks | |
| CN114492519A (en) | Lung ultrasonic special sign B-line identification and classification method based on ultrasonic echo radio frequency signals | |
| Tariq et al. | Multimodal lung disease classification using deep convolutional neural network | |
| CN105260609A (en) | Method and apparatus storing medical images | |
| Sun et al. | A practical cross-domain ecg biometric identification method | |
| WO2022006917A1 (en) | Artificial intelligence-based lung magnetic resonance image recognition apparatus and method | |
| CN106175673B (en) | A kind of method of automatic identification and spindle wave in extraction sleep cerebral electricity | |
| CN116340812A (en) | Transformer partial discharge fault mode identification method and system | |
| CN113925495B (en) | Arterial and venous fistula abnormal tremor signal identification system and method combining statistical learning and time-frequency analysis | |
| CN111563411B (en) | Electrocardiosignal classification method using AdaBoost and weak classifier | |
| Lu et al. | Pulse waveform analysis for pregnancy diagnosis based on machine learning | |
| Bhuiyan et al. | Estimating the parameters of audible clinical percussion signals by fitting exponentially damped harmonics | |
| Dawoud et al. | Best wavelet function for face recognition using multi-level decomposition | |
| Yao et al. | A study of heart sound analysis techniques for embedded-link e-health applications | |
| AU2020103785A4 (en) | Method for improving recognition rates of mri images of prostate tumors based on cad system | |
| CN116052872B (en) | Facial expression-based intelligent data evaluation method and system for Parkinson disease | |
| CN116250804A (en) | Sleepiness detection method based on electroencephalogram signals | |
| Yu et al. | Research on the Development of Localized Music Curriculum System Based on the Theory of Multiple Intelligences |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20210226 |
|
| RJ01 | Rejection of invention patent application after publication |