IP Library › Granted Patent US 12,697,058
Granted Patent B2
US 12,697,058 · App. 18/673,573 · Granted Aug 4, 2026

Systems and methods for seizure detection based on changes in electroencephalogram (EEG) non-linearities

Inventor: Kurt E. Hecox (New Berlin, WI)
Assignee: Advanced Global Clinical Solutions Inc.
A61B5/4094A61B5/0006A61B5/002A61B5/0022A61B5/31A61B5/372A61B5/7203A61B5/742A61B5/746G16H50/20A61B2503/045
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Quick Facts
Patent No.
US 12,697,058
App. No.
18/673,573
Granted
Aug 4, 2026
Kind
B2
Abstract

A seizure detection system including one or more circuits, the one or more circuits configured to receive an electroencephalogram (EEG) signal generated based on electrical brain activity of a patient and determine different types of metrics based on the EEG signal, the different types of metrics indicating non-linear features of the EEG signal. The one or more circuits are configured to determine whether one or more of the different types of metrics exhibit changes over time that meet a predefined level of statistical significance, generate a user interface, the user interface including a real-time trend of the EEG signal and the one or more of the different types of metrics, and cause a user interface device to display the user interface.

Claims (73)

1 . A seizure detection system comprising one or more circuits, the one or more circuits configured to:

receive an electroencephalogram (EEG) signal generated based on electrical brain activity of a patient;

determine a plurality of different types of metrics based on the EEG signal, the plurality of different types of metrics indicating non-linear features of the EEG signal, the plurality of different types of metrics including eigenvalues;

generate a user interface, the user interface comprising:

a real-time trend of the EEG signal; and

a real-time trend of the eigenvalues;

cause a user interface device to display the user interface;

determine an occurrence of a pattern of a trajectory of a metric indicating a non-linear feature of the non-linear features;

determine that a probability value of the pattern of the trajectory occurring is less than a probability level;

detect a candidate seizure responsive to a determination that the probability value of the pattern of the trajectory occurring is less than the probability level; and

update an appearance of the metric in the user interface in response to a determination that the probability value of the pattern of the trajectory occurring is less than the probability level.

2 . The seizure detection system of claim 1 , wherein the seizure detection system is a cloud-based system, wherein the one or more circuits are configured to receive the EEG signal from a local EEG acquisition system via a network and provide result data to the local EEG acquisition system via the network.

3 . The seizure detection system of claim 1 , wherein the seizure detection system is a local system, wherein the local system is:

integrated with a local EEG system; or

connected locally to an EEG acquisition system.

4 . The seizure detection system of claim 1 , wherein the plurality of different types of metrics comprise at least one of dimensionality, synchrony, Lyapunov exponents, entropy, global non-linearity, distance differences between recurrence trajectories, or self-similarity.

5 . The seizure detection system of claim 1 , wherein the one or more circuits are configured to:

perform a preliminary analysis with the eigenvalues, wherein the preliminary analysis indicates whether the EEG signal is insignificant.

6 . The seizure detection system of claim 1 , wherein the one or more circuits are configured to:

determine probabilities of occurrences of patterns of trajectories of each of the plurality of different types of metrics at a plurality of points in time;

determine whether the probabilities of the patterns of the occurrences of the trajectories of each of the plurality of different types of metrics meet a predefined level of statistical significance based on the probabilities; and

map metrics that meet the predefined level of statistical significance of the plurality of different types of metrics to a category, wherein the category is a seizure category.

7 . The seizure detection system of claim 1 , wherein each value of a plurality of values of the metric of the non-linear feature decreases with respect to a previous value of the plurality of values;

wherein the one or more circuits are configured to determine the probability value of the pattern of the trajectory occurring based on a number of the plurality of values each decreasing with respect to the previous value of the plurality of values.

8 . The seizure detection system of claim 1 , wherein the one or more circuits are configured to:

receive user input via the user interface device; and

set the probability level to a value selected by a user via the user input.

9 . The seizure detection system of claim 1 , wherein the one or more circuits are configured to:

retrieve a default value from a memory device of the seizure detection system; and

set the probability level to the default value retrieved from the memory device.

10 . The seizure detection system of claim 1 , wherein the one or more circuits are configured to update the appearance of the real-time trend of the eigenvalues in the user interface by:

highlighting a section of the real-time trend of the eigenvalues in the user interface.

11 . The seizure detection system of claim 1 , wherein the one or more circuits are configured to update the appearance of the real-time trend of the eigenvalues in the user interface by:

changing a color of at least a portion of the real-time trend of the eigenvalues in the user interface.

12 . A method of seizure detection, comprising:

receiving, by a processing circuit, an electroencephalogram (EEG) signal generated based on electrical brain activity of a patient;

determining, by the processing circuit, a plurality of different types of metrics based on the EEG signal, the plurality of different types of metrics indicating non-linear features of the EEG signal, the plurality of different types of metrics including eigenvalues;

generating, by the processing circuit, a user interface, the user interface comprising:

a real-time trend of the EEG signal; and

a real-time trend of the eigenvalues;

causing, by the processing circuit, a user interface device to display the user interface;

determining, by the processing circuit, an occurrence of a pattern of a trajectory of a metric of a non-linear feature of the non-linear features;

determining, by the processing circuit, that a probability value of the pattern of the trajectory occurring is less than a probability level;

detecting, by the processing circuit, a candidate seizure responsive to a determination that the probability value of the pattern of the trajectory occurring is less than the probability level; and

updating, by the processing circuit, an appearance of the metric in the user interface in response to a determination that the probability value of the pattern of the trajectory occurring is less than the probability level.

13 . The method of claim 12 , wherein each value of a plurality of values of the metric of the non-linear feature decreases with respect to a previous value of the plurality of values;

wherein the method further comprises determining, by the processing circuit, the probability value of the pattern of the trajectory occurring based on a number of the plurality of values each decreasing with respect to the previous value of the plurality of values.

14 . The method of claim 12 , further comprising:

receiving, by the processing circuit, user input via the user interface device; and

setting, by the processing circuit, the probability level to a value selected by a user via the user input.

15 . The method of claim 12 , further comprising:

retrieving, by the processing circuit, a default value from a memory device of a seizure detection system; and

setting, by the processing circuit, the probability level to the default value retrieved from the memory device.

16 . The method of claim 12 , further comprising updating, by the processing circuit, the appearance of the real-time trend of the eigenvalues in the user interface by:

highlighting a section of the real-time trend of the eigenvalues in the user interface.

17 . The method of claim 12 , further comprising updating, by the processing circuit, the appearance of the real-time trend of the eigenvalues in the user interface by:

changing a color of at least a portion of the real-time trend of the eigenvalues in the user interface.

18 . One or more non-transitory storage media storing instructions thereon that, when executed by one or more processors, cause the one or more processors to perform operations, comprising:

receiving an electroencephalogram (EEG) signal generated based on electrical brain activity of a patient;

determining a plurality of different types of metrics based on the EEG signal, the plurality of different types of metrics indicating non-linear features of the EEG signal, the plurality of different types of metrics including eigenvalues;

generating a user interface, the user interface comprising:

a real-time trend of the EEG signal; and

a real-time trend of the eigenvalues;

causing a user interface device to display the user interface;

determining an occurrence of a pattern of a trajectory of a metric of a non-linear feature of the non-linear features;

determining that a probability value of the pattern of the trajectory occurring is less than a probability level;

detecting a candidate seizure responsive to a determination that the probability value of the pattern of the trajectory occurring is less than the probability level; and

updating an appearance of the metric in the user interface in response to a determination that the probability value of the pattern of the trajectory occurring is less than the probability level.

19 . The one or more non-transitory storage media of claim 18 , wherein the instructions cause the one or more processors to perform operations, comprising:

monitoring a moving window of the eigenvalues of the EEG signal; and

determining that the eigenvalues are decreasing.

20 . The one or more non-transitory storage media of claim 18 , wherein the instructions cause the one or more processors to perform operations, comprising:

determining a dimensionality of the EEG signal by performing a phase space analysis by increasing a value of the dimensionality until a number of false neighbors reaches zero, wherein a starting value of the dimensionality is based on an age of the patient.

Continuity (7)
Continuation 17366854 · Jul 2, 2021
Continuation 16938501 · Jul 24, 2020
Continuation 16938541 · Jul 24, 2020
Continuation PCTUS2020025136 · Mar 27, 2020
Continuation PCTUS2020025136 · Mar 27, 2020
Provisional Application 62890497 · Aug 22, 2019
Related Publication 20240306979A1 · Sep 19, 2024
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