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JP-7856233-B2 - Systems, methods, and apparatus for neurological activity data analysis

JP7856233B2JP 7856233 B2JP7856233 B2JP 7856233B2JP-7856233-B2

Inventors

  • ジン ヒュン リー
  • チョンナン ファン

Assignees

  • エルビス・コーポレイション

Dates

Publication Date
20260511
Application Date
20220302
Priority Date
20210303

Claims (18)

  1. A system, wherein the system is At least one processor, The system comprises memory accessible to at least one processor, the memory being encoded with computer-readable instructions, and the computer-readable instructions, when executed, cause the system to calculate at least one metric comprising node visit frequency from neurological activity data . The aforementioned neurological activity data includes a three-dimensional (3D) source localization comprising multiple nodes, where each of the multiple nodes corresponds to a part of the brain. Calculating the node visit frequency for one of the aforementioned multiple nodes is: The number of times that one of the aforementioned nodes is labeled as a local maximum within the analysis time window, The number of times is divided by the duration of the analysis time window, Equipped with , system.
  2. The system according to claim 1, wherein the neurological activity data includes electroencephalogram (EEG) data.
  3. The system according to claim 1, further comprising a display communicatively coupled to the at least one processor, wherein the computer-readable instruction, when executed, causes the system to provide a graphic representation of the at least one metric on the display.
  4. The system according to claim 3, wherein the graphic representation of at least one metric comprises a node for three-dimensional source localization that is overlaid on the brain image.
  5. The system according to claim 1, further comprising a display communicatively coupled to the at least one processor, wherein, when a computer-readable instruction is executed, the system further causes the system to provide a report on the display, the report comprising a plurality of contents comprising one or more graphic representations of the at least one metric, and further comprising a timeline of the neurological activity data, one or more statistics, one or more additional parameters, or a combination thereof.
  6. The system according to claim 5, further comprising an input device configured to receive user input, wherein the user input indicates which of the plurality of contents is included in the report.
  7. Receiving neurological activity data, From the aforementioned neurological activity data , calculate at least one metric that includes node visit frequency , Equipped with , The aforementioned neurological activity data includes a three-dimensional (3D) source localization comprising multiple nodes, where each of the multiple nodes corresponds to a part of the brain. Calculating the node visit frequency for one of the aforementioned multiple nodes is: The number of times that one of the aforementioned nodes is labeled as a local maximum within the analysis time window, The number of times is divided by the duration of the analysis time window, Equipped with , method.
  8. The at least one metric further includes node transition frequency, Calculating the node transition frequency means The number of local maximal transitions between the first node and the second node of the plurality of nodes within the analysis time window, The number of times is divided by the duration of the analysis time window, Equipped with, The method according to claim 7 .
  9. The at least one metric further includes node transition polarity, Calculating the node transition polarity for one node of multiple nodes is, The first number of times a local maximum occurs when transitioning from one or more of the aforementioned nodes to the aforementioned node, The number of times the local maximum occurs when transitioning from one of the multiple nodes to one or more of the multiple nodes is counted. Taking the difference between the first number of times and the second number of times, Equipped with, The method according to claim 7 .
  10. The method according to claim 9, wherein the node among the plurality of nodes has inward polarity when the first number of times is greater than the second number of times, and the node among the plurality of nodes has outward polarity when the second number of times is greater than the first number of times.
  11. The aforementioned method, By comparing the values of the neurological activity data with a threshold, Based on comparing, discarding, or ignoring one or more of the values of the neurological activity data , By calculating at least one local maxima from the remaining values of the aforementioned neurological activity data, The method according to claim 7, further comprising preprocessing the neurological activity data.
  12. The method according to claim 7, further comprising providing a graphic representation of the at least one metric on a display.
  13. The method according to claim 12 , wherein the graphic representation of the at least one metric comprises a node of three-dimensional (3D) source localization that is overlaid on the image of the brain.
  14. The method according to claim 13 , wherein the size of the node is proportional to the value of the node visit frequency for the node.
  15. The method according to claim 13 , wherein the shading, color, or combination thereof of the node indicates a value for the node visit frequency .
  16. The method according to claim 13, wherein the graphic representation further comprises a second node of the 3D source localization and an arrow between the node and the second node, the direction of the arrow indicating a local maximum transition between the node and the second node.
  17. The method according to claim 16 , wherein the shading, color, or combination thereof of the arrows indicates the frequency of the transitions over an analysis time window.
  18. The method according to claim 7, further comprising providing a report on a display, wherein the report comprises a plurality of contents comprising one or more graphic representations of the at least one metric, and further comprising a timeline of the neurological activity data, one or more statistics, one or more additional parameters, or a combination thereof.

Description

Cross-reference of related applications This application claims priority to U.S. Provisional Application No. 63/156,040, filed on 3 March 2021, which is incorporated herein by reference in its entirety for any purpose. Technical Field: The examples described herein generally relate to the processing, analysis, and provision of neurological activity data. Background Data on neurological activity can be collected from subjects by various methods. For example, electroencephalography (EEG) data can be obtained by applying multiple probes (e.g., electrodes) to the subject's scalp. In some applications, the number of electrodes (e.g., channels) may be 19. However, other numbers of probes may be used in other applications. Electrical signals in the subject's brain may be measured by the probes. These electrical signals can indicate neurological activity. In some applications, these electrical signals may be measured over time. EEG data may be plotted in two dimensions (2D). For example, the electrical signals measured by each probe may be plotted over time. Each plot may be referred to as a trace. An example of a 2D EEG trace is shown in Image A of Figure 1. Reading 2D EEG traces (e.g., plots of electrical signal magnitudes over time) to recognize and characterize seizures, for example, to estimate the underlying neural activity of a seizure, requires several years of training. Even experts typically cannot pinpoint the exact location of neural activity (e.g., a seizure) from EEG data alone. This limits the diagnostic and laboratory values of EEG traces. Overview As illustrated in the examples disclosed herein, electroencephalogram (EEG) data may be analyzed to calculate various metrics (also referred to as parameters), such as maximum amplitude projection, node visit frequency, node transition frequency, and/or node transition polarity. These metrics can be displayed, stored, and/or used to diagnose a patient, determine or adjust treatment plans, and/or take other actions. According to examples of this disclosure, the system may include at least one processor and memory accessible to at least one processor, the memory being encoded with computer-readable instructions, which, when executed, cause the system to calculate from neurological activity data at least one metric comprising maximum amplitude projection, node visit frequency, node transition frequency, node transition polarity, or a combination thereof. In some examples, the neurological activity data comprises electroencephalogram (EEG) data. In some examples, the system may further include a display communicatively coupled to at least one processor, and when computer-readable instructions are executed, the system further causes the system to provide a graphic representation of at least one metric on the display. In some examples, the graphic representation of at least one metric comprises nodes of three-dimensional source localization overlaid on an image of the brain. In some examples, the system may further include a display communicatively coupled to at least one processor, and when a computer-readable instruction is executed, the system may further cause the system to provide a report on the display. Here, the report comprises multiple contents having one or more graphic representations of at least one metric, and further comprising a timeline of neurological activity data, one or more statistics, one or more additional parameters, or a combination thereof. In some examples, the system may further include an input device configured to receive user input, which indicates which of the multiple contents are included in the report. In some examples, the method may involve receiving neurological activity data and calculating from the neurological activity data at least one metric comprising maximum amplitude projection, node visit frequency, node transition frequency, node transition polarity, or a combination thereof. In some examples, the neurological activity data comprises a three-dimensional (3D) source localization with multiple nodes, where each of the multiple nodes corresponds to a part of the brain. In some examples, calculating the maximum amplitude projection of one node among several nodes may involve finding the number of times this node among the several nodes was labeled as a local maximum within the analysis time window, and determining the maximum value of the local maximum from the number of times this node among the several nodes was labeled as a local maximum. In some examples, calculating the frequency of node visits for one node among several nodes involves counting the number of times this node was labeled as a local maxima within the analysis time window, and then dividing this count by the duration of the analysis time window. In some examples, calculating node transition frequencies involves counting the number of transitions between a first local maximal node and a second local maximal node of multiple nodes within the analysis