CN113951885A - Magnetoencephalogram source positioning method, storage medium and equipment - Google Patents

Magnetoencephalogram source positioning method, storage medium and equipment Download PDF

Info

Publication number
CN113951885A
CN113951885A CN202111402038.8A CN202111402038A CN113951885A CN 113951885 A CN113951885 A CN 113951885A CN 202111402038 A CN202111402038 A CN 202111402038A CN 113951885 A CN113951885 A CN 113951885A
Authority
CN
China
Prior art keywords
magnetoencephalogram
source
time window
tensor
signal
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
Application number
CN202111402038.8A
Other languages
Chinese (zh)
Other versions
CN113951885B (en
Inventor
张冀聪
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Beihang University
Original Assignee
Beihang University
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Beihang University filed Critical Beihang University
Priority to CN202111402038.8A priority Critical patent/CN113951885B/en
Publication of CN113951885A publication Critical patent/CN113951885A/en
Application granted granted Critical
Publication of CN113951885B publication Critical patent/CN113951885B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/242—Detecting biomagnetic fields, e.g. magnetic fields produced by bioelectric currents
    • A61B5/245—Detecting biomagnetic fields, e.g. magnetic fields produced by bioelectric currents specially adapted for magnetoencephalographic [MEG] signals
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/40—Detecting, measuring or recording for evaluating the nervous system
    • A61B5/4076—Diagnosing or monitoring particular conditions of the nervous system
    • A61B5/4094—Diagnosing or monitoring seizure diseases, e.g. epilepsy
    • 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
    • A61B5/7203—Signal processing specially adapted for physiological signals or for diagnostic purposes for noise prevention, reduction or removal
    • 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
    • A61B5/7225—Details of analog processing, e.g. isolation amplifier, gain or sensitivity adjustment, filtering, baseline or drift compensation
    • 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

Abstract

Provided are a magnetoencephalography source localization method, a storage medium and a device, the method including: acquiring a first magnetoencephalogram signal of a user through a magnetoencephalogram sensor; detecting a ripple time window in the first magnetoencephalogram signal as a source-positioned time window by a root mean square method, and obtaining the first magnetoencephalogram signal in the ripple time window as a second magnetoencephalogram signal; performing a Tucker decomposition on an original tensor of the second magnetoencephalogram signal to calculate an estimated value of the original tensor; and calculating a covariance matrix according to the estimated value, and calculating a source position corresponding to the second magnetoencephalogram signal by an LCMV inverse problem solving method in beam forming. The method eliminates the influence of noise signals, reduces the complexity of calculation, ensures the consistency of each calculation result and improves the accuracy of positioning the seizure area.

Description

Magnetoencephalogram source positioning method, storage medium and equipment
The invention is a divisional application with Chinese patent application No. ZL202011526796.6 entitled "magnetoencephalogram source positioning method and device based on Tucker decomposition and ripple time window", the contents of which are incorporated herein by reference in their entirety.
Technical Field
The invention relates to a magnetoencephalography source positioning method, a storage medium and equipment, and belongs to the field of biomedicine.
Background
Clinically, up to one third of epileptic patients are refractory epilepsy. While drug therapy with antiepileptic drugs is difficult to control effectively. Epileptic foci can affect cortical excitability, resulting in abnormal discharges, not only directly affecting focal or surrounding tissues, but also affecting distant brain regions. Therefore, these patients need to surgically excise epileptogenic foci (epileptogenic zones) to achieve seizure-free, and whether or not an epileptogenic focus (epileptogenic focus) is postoperatively dependent on the localization of the epileptogenic focus. However, it is difficult to determine the area due to the lack of a tool to directly measure the area.
The Magnetoencephalogram (MEG) is a method for monitoring brain functions in a non-invasive and real-time manner, and in the past decades, several brain source localization methods based on electroencephalogram or electroencephalogram, such as spike-based dipole-fitting methods, have been proposed, but the localization accuracy of these methods still cannot meet the requirement of preoperative accurate localization, the calculation complexity is high, the localization result is susceptible to noise signals, and for the same magnetoencephalogram signal, the results of multiple localizations have deviation and poor consistency.
In view of the above, the present invention aims to provide a magnetoencephalography source localization method, a storage medium and a device to solve one or more of the above technical problems.
Disclosure of Invention
In order to solve one or more technical problems in the prior art, the applicant finds, through long-term research, that accuracy of location of an epileptogenic focus (epileptogenic region) can be improved by extracting a ripple time window of an original magnetoencephalogram signal and combining with Tucker decomposition, and reference and basis can be provided for subsequent operations. Based on the research result, according to an aspect of the present invention, there is provided a magnetoencephalography source positioning device based on a Tucker decomposition and a ripple time window, comprising:
the magnetoencephalogram sensor is used for acquiring a first magnetoencephalogram signal of a user;
a ripple detection unit, configured to detect a ripple time window in the first magnetoencephalogram signal as a source-located time window by a root mean square method, and obtain the first magnetoencephalogram signal in the ripple time window as a second magnetoencephalogram signal, where at least four continuous oscillation signals having a frequency of 80-250Hz and an amplitude higher than a background signal are defined as ripple;
a Tucker decomposition unit based on high-order orthogonal iteration, configured to perform Tucker decomposition on an original tensor of the second magnetoencephalogram signal to calculate an estimated value of the original tensor, where the estimated value calculated by the Tucker decomposition is constrained by the high-order orthogonal iteration, so that the estimated value remains unique, and a noise factor tensor in the original tensor is removed from the estimated value; and
and the source positioning unit is used for calculating a covariance matrix for the estimated value and calculating a source position corresponding to the second magnetoencephalogram signal by an LCMV inverse problem solving method in beam forming.
According to another aspect of the invention, the calculation process of the higher order orthogonal iteration is as follows:
1) calculating factor matrix U of original tensor X by using high-order singular value decomposition algorithm(n)And a core tensor G; let k equal to 0;
2) let k be k +1 and N be 1,2, … N, the following operations are performed:
B(k)←X×1U(1)T…×n-1U(n-1)T×NUNT；
and performs the core tensor B(k)Determining the number Rn of main singular values of the singular value decomposition of the n-mode expansion, wherein B(k)For the core tensor obtained for the kth iteration, then the operations are performed:
U(N)←U(：，1：Rn)；
3) calculating a core tensor B obtained by the k iteration(k)Meter G(k)＝B(k)And judging whether convergence occurs by the following formula:
||G(k)-G(k-1)||F< E; wherein E is a minimum value;
if the convergence condition is met, executing the next step, otherwise, returning to the step 2) to continue iteration until convergence;
4) output factor matrix U(N)And the core tensor G(k)(ii) a According to the factor matrix U(N)And the core tensor G(k)Reconstructing the estimate of the original tensor.
According to another aspect of the present invention, the magnetoencephalogram source positioning device based on the Tucker decomposition and ripple time window further comprises a source display unit for displaying the source position.
According to yet another aspect of the invention, the source location is a seizure region of the brain.
According to another aspect of the present invention, the apparatus for source localization of magnetoencephalogram based on Tucker decomposition and ripple time window further includes a preprocessing unit, configured to perform filtering, linear trend elimination and independent component analysis on the first magnetoencephalogram signal to obtain a preprocessed first magnetoencephalogram signal, and the preprocessed first magnetogram signal is input to the ripple detecting unit for subsequent processing.
According to another aspect of the present invention, the ripple time window is a plurality of windows, and for the second magnetoencephalogram signal corresponding to each ripple time window, the source localization unit calculates a plurality of corresponding source locations, and concentrates a corresponding region according to the plurality of source locations as a localization result.
According to another aspect of the present invention, there is also provided a method for positioning a magnetoencephalogram source based on a Tucker decomposition and a ripple time window, comprising the steps of:
acquiring a first magnetoencephalogram signal of a user through a magnetoencephalogram sensor;
detecting a ripple time window in the first magnetoencephalogram signal as a source-positioned time window through a root mean square method, and obtaining the first magnetoencephalogram signal in the ripple time window as a second magnetoencephalogram signal, wherein at least four continuous oscillation signals with the frequency of 80-250Hz and the amplitude higher than that of a background signal are defined as ripple;
performing Tucker decomposition on an original tensor of the second magnetoencephalogram signal to calculate an estimated value of the original tensor, wherein the estimated value calculated by the Tucker decomposition is constrained by high-order orthogonal iteration to keep the estimated value unique, and a noise factor tensor in the original tensor is removed from the estimated value;
and calculating a covariance matrix for the estimated value, and calculating a source position corresponding to the second magnetoencephalogram signal by an LCMV inverse problem solving method.
According to still another aspect of the present invention, the source location is displayed by a source display unit.
According to yet another aspect of the invention, the source location is taken as a seizure region of the brain.
According to another aspect of the invention, the preprocessing unit filters, trends linearly and analyzes independent components of the first magnetoencephalogram signal to obtain a preprocessed first magnetoencephalogram signal, and the preprocessed first magnetogram signal is used for subsequent ripple time window detection.
According to another aspect of the present invention, the ripple time window is a plurality of windows, for each second magnetoencephalogram signal corresponding to the ripple time window, a plurality of corresponding source positions are calculated, and a region having the first concentration corresponding to the plurality of source positions is used as a positioning result.
According to yet another aspect of the present invention, there is also provided a computer-readable storage medium, characterized in that a computer program is stored in the computer-readable storage medium, the computer program being adapted to be loaded and executed by a processor, so as to cause a computer device having the processor to perform the aforementioned method.
According to another aspect of the present invention, there is also provided a computer apparatus, comprising: a processor and a memory;
the processor is connected to a memory, wherein the memory is used for storing a computer program, and the processor is used for calling the computer program to make the computer device execute the method.
Compared with the prior art, the invention has one or more of the following technical effects:
(1) in the magnetoencephalogram source positioning, high-frequency noise and low-frequency noise in magnetoencephalogram signals are effectively removed through a Tucker decomposition method, the influence of noise signals on the positioning result of epileptogenic foci (epileptogenic regions) is reduced, and meanwhile, the complexity of calculation is reduced.
(2) The result of the Tucker decomposition is subjected to uniqueness constraint by using a high-order orthogonal iteration (HOOI) method, and the consistency of the calculation result every time is ensured.
(3) The accuracy of the location of the epileptic focus (epileptogenic region) is further improved by taking ripple as a time window for source location selection and combining with Tucker decomposition, and reference and basis are provided for subsequent operations.
(4) By detecting a plurality of ripple time windows and obtaining a plurality of corresponding source positions, the final positioning result is determined according to the corresponding areas in the source position sets, and the positioning precision is further improved; combining multiple source localization results, taking the brain region with the first concentration (45% -85%) of more than 10 brain regions (source localization regions) as the localization result can further reduce the workload of doctors and eliminate potential interference.
Drawings
So that the manner in which the above recited features of the present invention can be understood in detail, a more particular description of the invention, briefly summarized above, may be had by reference to embodiments. The drawings relate to preferred embodiments of the invention and are described below:
FIG. 1 is a schematic diagram of a magnetoencephalography source positioning device based on a Tucker decomposition and ripple time window according to a preferred embodiment of the present invention;
FIG. 2 is a diagram illustrating the detection result of a ripple time window according to a preferred embodiment of the present invention;
FIG. 3 is a schematic diagram showing the comparison between the positioning accuracy of the tracker decomposition based on the ripple time window and the positioning accuracy of the dipole-fitting based on the spike.
DETAILED DESCRIPTION OF EMBODIMENT (S) OF INVENTION
Reference will now be made in detail to the various embodiments, one or more examples of which are illustrated in each figure. The examples are provided by way of explanation and are not meant as limitations. For example, features illustrated or described as part of one embodiment can be used on or in conjunction with any other embodiment to yield yet a further embodiment. It is intended that the present invention encompass such modifications and variations.
In the following description of the drawings, like reference numerals designate identical or similar structures. Generally, only the differences between the individual embodiments will be described. Descriptions of parts or aspects in one embodiment can also be applied to corresponding parts or aspects in another embodiment, unless explicitly stated otherwise.
Example 1
Referring to fig. 1-3, fig. 1 is a schematic diagram of a positioning apparatus for a magnetoencephalogram source based on a Tucker decomposition and ripple time window according to a preferred embodiment of the present invention; FIG. 2 is a diagram illustrating the detection result of a ripple time window according to a preferred embodiment of the present invention; FIG. 3 is a schematic diagram showing the comparison between the positioning accuracy of the tracker decomposition based on the ripple time window and the positioning accuracy of the dipole-fitting based on the spike. In accordance with a preferred embodiment of the present invention, referring to fig. 1-2, there is provided a magnetoencephalography source localization apparatus based on Tucker decomposition and ripple time window, comprising:
the magnetoencephalogram sensor is used for acquiring a first magnetoencephalogram signal of a user;
a ripple detection unit, configured to detect a ripple time window in the first magnetoencephalogram signal as a source-located time window by a root mean square method, and obtain the first magnetoencephalogram signal in the ripple time window as a second magnetoencephalogram signal, where at least four continuous oscillation signals having a frequency of 80-250Hz and an amplitude higher than a background signal are defined as ripple;
a Tucker decomposition unit based on high-order orthogonal iteration, configured to perform Tucker decomposition on an original tensor of the second magnetoencephalogram signal to calculate an estimated value of the original tensor, where the estimated value calculated by the Tucker decomposition is constrained by the high-order orthogonal iteration, so that the estimated value remains unique, and a noise factor tensor in the original tensor is removed from the estimated value; and
and the source positioning unit is used for calculating a covariance matrix for the estimated value and calculating a source position corresponding to the second magnetoencephalogram signal by an LCMV (linear constrained minimum variance) inverse problem solving method in a beam forming method.
Advantageously, the method improves the detection precision of the source position by the Tucker decomposition based on the high-order orthogonal iteration (HOOI) and taking the ripple time period of the MEG signal as the positioning time window, can be widely applied to deep source and shallow source positioning, and improves the reference value of clinical brain source positioning in preoperative evaluation.
According to another preferred embodiment of the present invention, the calculation process of the higher order orthogonal iteration is as follows:
1) calculating factor matrix U of original tensor X by using high-order singular value decomposition algorithm(n)And a core tensor G; let k equal to 0;
2) let k be k +1 and N be 1,2, … N, the following operations are performed:
B(k)←X×1U(1)T…×n-1U(n-1)T×NUNT；
and performs the core tensor B(k)Determining the number Rn of main singular values of the singular value decomposition of the n-mode expansion, wherein B(k)For the core tensor obtained for the kth iteration, then the operations are performed:
U(N)←U(：，1：Rn)；
3) calculating a core tensor B obtained by the k iteration(k)Meter G(k)＝B(k)And judging whether convergence occurs by the following formula:
||G(k)-G(k-1)||F< E; wherein E is a minimum value;
if the convergence condition is met, executing the next step, otherwise, returning to the step 2) to continue iteration until convergence;
4) output factor matrix U(N)And the core tensor G(k)(ii) a According to the factor matrix U(N)And the core tensor G(k)Reconstructing the estimate of the original tensor.
According to another preferred embodiment of the present invention, the apparatus for source localization of magnetoencephalogram based on Tucker decomposition and ripple time window further comprises a source display unit for displaying the source location.
According to yet another preferred embodiment of the invention, the source location is an epileptogenic zone (epileptogenic zone) of the brain.
According to another preferred embodiment of the present invention, the apparatus for source localization of magnetoencephalogram based on Tucker decomposition and ripple time window further comprises a preprocessing unit, configured to perform filtering, linear trend elimination and Independent Component Analysis (ICA) on the first magnetoencephalogram signal to obtain a preprocessed first magnetoencephalogram signal, and the preprocessed first magnetogram signal is input to the ripple detecting unit for subsequent processing.
According to another preferred embodiment of the present invention, the ripple time window is a plurality of windows, and the source localization unit calculates a plurality of corresponding source locations for the second magnetoencephalogram signal corresponding to each ripple time window, and concentrates a corresponding region as a localization result according to the plurality of source locations. Specifically, for example, a region corresponding to at least Y iterations of the Z source positions is output as a final result, and Y is preferably equal to or greater than 3. According to experimental verification, the repeatedly corresponding (indicating) areas for more than 3 times are basically close to the real epileptogenic areas, so that the positioning accuracy is further improved, and reliable reference is provided for later operations of doctors.
Preferably, the ripple time windows are Z, and for the second magnetoencephalogram signal corresponding to each ripple time window, the source localization unit calculates Z source locations corresponding to each ripple time window, determines Z brain regions corresponding to the Z source locations respectively, and takes the brain region with the first concentration in the Z brain regions as a localization result. Research shows that the Z is more than or equal to 10, the first concentration ratio is 45% -85%, accurate positioning of a real seizure area can be achieved, and compared with single source positioning, the method can further reduce workload of doctors and eliminate potential interference.
According to another preferred embodiment of the present invention, there is also provided a method for positioning a magnetoencephalogram source based on a Tucker decomposition and a ripple time window, comprising the steps of:
acquiring a first magnetoencephalogram signal of a user through a magnetoencephalogram sensor;
detecting a ripple time window in the first magnetoencephalogram signal as a source-positioned time window through a root mean square method, and obtaining the first magnetoencephalogram signal in the ripple time window as a second magnetoencephalogram signal, wherein at least four continuous oscillation signals with the frequency of 80-250Hz and the amplitude higher than that of a background signal are defined as ripple;
performing Tucker decomposition on an original tensor of the second magnetoencephalogram signal to calculate an estimated value of the original tensor, wherein the estimated value calculated by the Tucker decomposition is constrained by high-order orthogonal iteration to keep the estimated value unique, and a noise factor tensor in the original tensor is removed from the estimated value;
and calculating a covariance matrix according to the estimated value, and calculating a source position corresponding to the second magnetoencephalogram signal by an LCMV inverse problem solving method in beam forming.
According to still another preferred embodiment of the present invention, the source position is displayed by a source display unit.
According to a further preferred embodiment of the invention, the source location is a seizure region of the brain.
According to another preferred embodiment of the present invention, the pre-processing unit filters, trends linearly, and analyzes the independent components of the first magnetoencephalogram signal to obtain a pre-processed first magnetoencephalogram signal, which is used for subsequent ripple time window detection.
According to another preferred embodiment of the present invention, the ripple time window is a plurality of windows, and for the second magnetoencephalogram signal corresponding to each ripple time window, a plurality of corresponding source positions are calculated, and a corresponding region in the plurality of source positions is determined as a positioning result.
According to another preferred embodiment of the present invention, there is also provided a novel source localization apparatus based on Tucker decomposition and ripple time window, including a ripple detection unit, a tensor decomposition unit, a source localization unit, and a verification unit. Preferably, the Ripple detection unit applies a root mean square method. The MEG (magnetoencephalography) signal adopts Tucker decomposition to calculate the estimation value of an original tensor, the Tucker decomposition result is kept unique based on the mode constraint of high-order orthogonal iteration, and the factor tensor of which the main information is noise is removed. And the source positioning unit calculates a covariance matrix for the estimated value of the tensor decomposed by the Tucker, so that the source position of the epileptic brain magnetic signal is calculated by an LCMV method. The validation unit includes validation on top of the real magnetoencephalography data, wherein the real data-based validation compares the ripple-based Tucker decomposition with spike-based clinical dipole methods. As shown in fig. 3, which shows the comparison result, wherein the present invention greatly improves the positioning accuracy of the seizure-causing region of the brain with respect to the spike-based clinical dipole method.
Preferably, the ripple detection unit defines at least four continuous oscillations of 80-250Hz and amplitude above the background signal as ripple. Preferably, at least four oscillations of 80-250Hz and amplitudes above the first threshold of the background signal are defined as ripple, further improving the positioning accuracy. Preferably, in the present invention, a Root Mean Square (RMS) method is used for detecting ripples in MEG, and the conditions thereof can be specifically selected as follows: the waveform power value should be 3-9 times the background signal and the duration of the waveform should last at least 15ms (corresponding to at least four oscillations). Preferably, false positive ripples can be eliminated manually.
According to another preferred embodiment of the present invention, in the orthogonal iteration process, the original tensor X is input; outputting the factor matrix U after iteration is finished(n)And a core tensor G.
The calculation process of the orthogonal iteration is preferably as follows:
1) calculating a factor matrix U of an original tensor X by using a high-order singular value decomposition (HOSVD) algorithm(n)And a core tensor G. Let k equal to 0;
2) let k be k +1 and N be 1,2, … N, the following operations are performed:
B(k)←X×1U(1)T…×n-1U(n-1)T×NUNT(ii) a Wherein, X is multiplied by nU(n)TRepresenting tensor X and matrix U(n)TN-modulo product of;
and performs the core tensor B(k)Determining the number Rn of main singular values of the SVD, and then executing the operation:
U(N)←U(：，1：Rn)
3) computing the core tensor B for the kth iteration(k)Meter G(k)＝B(k)And pass throughThe convergence formula of the surfaces determines whether to converge:
||G(k)-G(k-1)||F＜E
e is a minimum value.
If the convergence condition is met, executing the next step, otherwise returning to the step 2 to continue iteration until convergence.
4) Output factor matrix U(N)And the core tensor G(k)(ii) a According to the factor matrix U(N)And the core tensor G(k)Reconstructing the estimate of the original tensor.
It is understood that, among other things, the core tensor G is initialized(0)Is the zero tensor (all elements are 0). I G(k)-G(k-1)||F<E denotes determination of G(k)-G(k-1)Whether a convergence formula of a convergence condition is met.
Preferably, for the estimated value, an inverse problem is solved by using a beamforming (beamforming) analysis method to calculate the covariance matrix and the position of the estimated source, and the principle of beamforming is as follows: the signal is filtered through time domain information and frequency domain information to obtain information in a given direction while attenuating noise interference from other directions.
There is further provided, in accordance with yet another preferred embodiment of the present invention, a computer-readable storage medium, characterized in that a computer program is stored therein, the computer program being adapted to be loaded and executed by a processor to cause a computer device having the processor to perform the aforementioned method.
There is also provided, in accordance with yet another preferred embodiment of the present invention, computer apparatus, including: a processor and a memory;
the processor is connected to a memory, wherein the memory is used for storing a computer program, and the processor is used for calling the computer program to make the computer device execute the method.
Compared with the prior art, the invention has one or more of the following technical effects:
(1) in the magnetoencephalogram source positioning, high-frequency noise and low-frequency noise in magnetoencephalogram signals are effectively removed through a Tucker decomposition method, the influence of noise signals on the positioning result of epileptogenic foci (epileptogenic regions) is reduced, and meanwhile, the complexity of calculation is reduced.
(2) The result of the Tucker decomposition is subjected to uniqueness constraint by using a high-order orthogonal iteration (HOOI) method, and the consistency of the calculation result every time is ensured.
(3) The accuracy of the location of the epileptic focus (epileptogenic region) is further improved by taking ripple as a time window for source location selection and combining with Tucker decomposition, and reference and basis are provided for subsequent operations.
(4) By detecting a plurality of ripple time windows and obtaining a plurality of corresponding source positions, the final positioning result is determined according to the corresponding areas in the source position sets, and the positioning precision is further improved; combining multiple source localization results, taking the brain region with the first concentration (45% -85%) of more than 10 brain regions (source localization regions) as the localization result can further reduce the workload of doctors and eliminate potential interference.
While the foregoing is directed to embodiments of the present invention, other and further embodiments of the invention may be devised without departing from the basic scope thereof, and the scope thereof is determined by the claims that follow.
The above embodiments are merely preferred embodiments of the present invention, which are not intended to limit the present invention, and the features of the embodiments that do not violate each other may be combined with each other. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (7)

1. A magnetoencephalography source positioning method is characterized by comprising the following steps:
acquiring a first magnetoencephalogram signal of a user through a magnetoencephalogram sensor;
detecting a ripple time window in the first magnetoencephalogram signal as a source-positioned time window through a root mean square method, and obtaining the first magnetoencephalogram signal in the ripple time window as a second magnetoencephalogram signal, wherein at least four continuous oscillation signals with the frequency of 80-250Hz and the amplitude higher than that of a background signal are defined as ripple;
performing Tucker decomposition on an original tensor of the second magnetoencephalogram signal to calculate an estimated value of the original tensor, wherein the estimated value calculated by the Tucker decomposition is constrained by high-order orthogonal iteration to keep the estimated value unique, and a noise factor tensor in the original tensor is removed from the estimated value;
calculating a covariance matrix of the estimated value, and calculating a source position corresponding to the second magnetoencephalogram signal by an LCMV inverse problem solving method in beam forming;
wherein, the calculation process of the high-order orthogonal iteration is as follows:
1) calculating factor matrix U of original tensor X by using high-order singular value decomposition algorithm(n)And a core tensor G; let k equal to 0;
2) let k be k +1 and N be 1,2, … N, the following operations are performed:
B(k)←X×1U(1)T…×n-1U(n-1)T×NUNT；
and performs the core tensor B(k)Determining the number Rn of main singular values of the singular value decomposition of the n-mode expansion, wherein B(k)For the core tensor obtained for the kth iteration, then the operations are performed:
U(N)←U(:,1:Rn)；
3) calculating a core tensor B obtained by the k iteration(k)Meter G(k)＝B(k)And judging whether convergence occurs by the following formula:
||G(k)-G(k-1)||F<e; wherein E is a minimum value;
if the convergence condition is met, executing the next step, otherwise, returning to the step 2) to continue iteration until convergence;
4) output factor matrix U(N)And the core tensor G(k)(ii) a According to the factor matrix U(N)And the core tensor G(k)Reconstructing the estimate of the original tensor.
2. The method of claim 1, wherein the source location is displayed by a source display unit.
3. The method of claim 2, wherein said source location is a seizure region of the brain.
4. A method according to any of claims 1-3, characterized in that the preprocessed first magnetoencephalogram signal for subsequent ripple time window detection is obtained by filtering, linear trending and independent component analysis of the first magnetoencephalogram signal by a preprocessing unit.
5. The method according to any one of claims 1-3, wherein the ripple time window is plural, and for the second magnetoencephalogram signal corresponding to each ripple time window, a corresponding plurality of source positions are calculated, and a corresponding region is concentrated according to the plurality of source positions as a positioning result.
6. A computer-readable storage medium, in which a computer program is stored which is adapted to be loaded and executed by a processor to cause a computer device having said processor to carry out the method of any one of claims 1 to 5.
7. A computer device, comprising: a processor and a memory;
the processor is coupled to a memory, wherein the memory is configured to store a computer program, and the processor is configured to invoke the computer program to cause the computer device to perform the method of any of claims 1-5.
CN202111402038.8A 2020-12-22 2020-12-22 Magnetoencephalogram source positioning method, storage medium and equipment Active CN113951885B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202111402038.8A CN113951885B (en) 2020-12-22 2020-12-22 Magnetoencephalogram source positioning method, storage medium and equipment

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN202111402038.8A CN113951885B (en) 2020-12-22 2020-12-22 Magnetoencephalogram source positioning method, storage medium and equipment
CN202011526796.6A CN112674773B (en) 2020-12-22 2020-12-22 Magnetoencephalogram source positioning method and device based on Tucker decomposition and ripple time window

Related Parent Applications (1)

Application Number Title Priority Date Filing Date
CN202011526796.6A Division CN112674773B (en) 2020-12-22 2020-12-22 Magnetoencephalogram source positioning method and device based on Tucker decomposition and ripple time window

Publications (2)

Publication Number Publication Date
CN113951885A true CN113951885A (en) 2022-01-21
CN113951885B CN113951885B (en) 2022-07-05

Family

ID=75450400

Family Applications (2)

Application Number Title Priority Date Filing Date
CN202011526796.6A Active CN112674773B (en) 2020-12-22 2020-12-22 Magnetoencephalogram source positioning method and device based on Tucker decomposition and ripple time window
CN202111402038.8A Active CN113951885B (en) 2020-12-22 2020-12-22 Magnetoencephalogram source positioning method, storage medium and equipment

Family Applications Before (1)

Application Number Title Priority Date Filing Date
CN202011526796.6A Active CN112674773B (en) 2020-12-22 2020-12-22 Magnetoencephalogram source positioning method and device based on Tucker decomposition and ripple time window

Country Status (2)

Country Link
CN (2) CN112674773B (en)
WO (1) WO2022135448A1 (en)

Families Citing this family (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112674773B (en) * 2020-12-22 2021-12-24 北京航空航天大学 Magnetoencephalogram source positioning method and device based on Tucker decomposition and ripple time window
CN113951887B (en) * 2021-12-22 2022-03-25 北京航空航天大学杭州创新研究院 Frequency spectrum matching independent component analysis method and system
CN114065825B (en) * 2022-01-17 2022-04-19 北京航空航天大学杭州创新研究院 Brain magnetic MEG source positioning method based on structural similarity

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2012133185A1 (en) * 2011-03-31 2012-10-04 独立行政法人理化学研究所 Brain wave analysis apparatus, brain wave analysis method, program and recording medium
US20160022168A1 (en) * 2014-07-24 2016-01-28 University Of Lethbridge Brain state dependent therapy for improved neural training and rehabilitation
CN105854193A (en) * 2009-11-04 2016-08-17 代理并代表亚利桑那州立大学的亚利桑那董事会 Devices and methods for modulating brain activity
CN107518898A (en) * 2017-08-08 2017-12-29 北京航空航天大学 Magneticencephalogram source positioner with beam forming is decomposed based on sensor array

Family Cites Families (13)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7461045B1 (en) * 2003-08-18 2008-12-02 University Of Florida Research Foundation, Inc. Optimization of spatio-temporal pattern processing for seizure warning and prediction
US20140378815A1 (en) * 2011-05-24 2014-12-25 The Regents Of The University Of California Magnetoencephalography source imaging
US20130144153A1 (en) * 2011-12-01 2013-06-06 The Regents Of The University Of California Functional magnetic resonance imaging apparatus and methods
CN105147288B (en) * 2015-07-23 2018-06-08 中国科学院苏州生物医学工程技术研究所 Brain source strength localization method
CN105212895B (en) * 2015-08-24 2019-01-15 中国科学院苏州生物医学工程技术研究所 Dynamic brain source localization method
CN105615877B (en) * 2016-02-22 2018-11-27 广州视源电子科技股份有限公司 The positioning system of epileptic EEG Signal feature
EP3634211A4 (en) * 2017-06-07 2021-03-17 Covidien LP Systems and methods for detecting strokes
JP7428386B2 (en) * 2017-11-10 2024-02-06 エルビス・コーポレイション Efficacy and/or treatment parameter recommendation using individual patient data and treatment brain network maps
US11331028B2 (en) * 2019-03-14 2022-05-17 Ricoh Company, Ltd. Multi-sphere head model for dipole localization
CN111096745B (en) * 2020-01-02 2021-03-30 苏州大学 Steady-state evoked response brain source positioning method based on sparse Bayesian learning
CN111209974A (en) * 2020-01-14 2020-05-29 华中科技大学鄂州工业技术研究院 Tensor decomposition-based heterogeneous big data core feature extraction method and system
CN111340142B (en) * 2020-05-14 2020-08-14 南京慧脑云计算有限公司 Epilepsia magnetoencephalogram spike automatic detection method and tracing positioning system
CN112674773B (en) * 2020-12-22 2021-12-24 北京航空航天大学 Magnetoencephalogram source positioning method and device based on Tucker decomposition and ripple time window

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105854193A (en) * 2009-11-04 2016-08-17 代理并代表亚利桑那州立大学的亚利桑那董事会 Devices and methods for modulating brain activity
WO2012133185A1 (en) * 2011-03-31 2012-10-04 独立行政法人理化学研究所 Brain wave analysis apparatus, brain wave analysis method, program and recording medium
US20160022168A1 (en) * 2014-07-24 2016-01-28 University Of Lethbridge Brain state dependent therapy for improved neural training and rehabilitation
CN107518898A (en) * 2017-08-08 2017-12-29 北京航空航天大学 Magneticencephalogram source positioner with beam forming is decomposed based on sensor array

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
龚英等: "分子影像学对比剂的研究进展", 《国外医学(临床放射学分册)》 *

Also Published As

Publication number Publication date
WO2022135448A1 (en) 2022-06-30
CN112674773B (en) 2021-12-24
CN112674773A (en) 2021-04-20
CN113951885B (en) 2022-07-05

Similar Documents

Publication Publication Date Title
CN113951885B (en) Magnetoencephalogram source positioning method, storage medium and equipment
US10426411B2 (en) System and method for providing a real-time signal segmentation and fiducial points alignment framework
Roonizi et al. A signal decomposition model-based Bayesian framework for ECG components separation
van der Meer et al. Carbon-wire loop based artifact correction outperforms post-processing EEG/fMRI corrections—A validation of a real-time simultaneous EEG/fMRI correction method
US20190167137A1 (en) Double bipolar configuration for atrail fibrillation annotation
Panigrahy et al. Extraction of fetal ECG signal by an improved method using extended Kalman smoother framework from single channel abdominal ECG signal
Oster et al. Nonlinear Bayesian filtering for denoising of electrocardiograms acquired in a magnetic resonance environment
EP1872715B1 (en) Method and system for multi-channel biosignal processing
US10912470B2 (en) System and method for tracking cerebral blood flow in fMRI
CN108509823A (en) The detection method and device of QRS complex
CN108937916A (en) A kind of electrocardiograph signal detection method, device and storage medium
Zhang et al. A comparison study on multidomain EEG features for sleep stage classification
EP3679860B1 (en) Control method and system for filtering out working frequency interference signal
Slama et al. Application of statistical features and multilayer neural network to automatic diagnosis of arrhythmia by ECG signals
Arunachalam et al. Improved multiscale entropy technique with nearest-neighbor moving-average kernel for nonlinear and nonstationary short-time biomedical signal analysis
Kotas et al. Averaging of nonlinearly aligned signal cycles for noise suppression
Calisto et al. A new approach for investigating intracranial pressure signal: filtering and morphological features extraction from continuous recording
Spicher et al. ECG delineation using a piecewise Gaussian derivative model with parameters estimated from scale-dependent algebraic expressions
Raatikainen et al. Combined spatiotemporal ICA (stICA) for continuous and dynamic lag structure analysis of MREG data
Stepanov et al. Beat-to-beat cardiovascular hemodynamic parameters based on wavelet spectrogram of impedance data
Nenova et al. An automated algorithm for fast pulse wave detection
Hegde et al. A review on ECG signal processing and HRV analysis
US20160196649A1 (en) Method for determining an intracortical working state of a functional network in the brain
Fahruzi et al. Screening of non-overlapping apnea and non-apnea from single lead ECG-apnea recordings using time-frequency approach
Caiani et al. Analysis of cardiac left-ventricular volume based on time warping averaging

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
GR01 Patent grant
GR01 Patent grant