# US 11,076,799 B2 · Systems and methods for seizure detection based on changes in electroencephalogram (EEG) non linearities

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- Number: US 11,076,799 B2
- Document ID: US11076799B2
- Status: Granted
- Kind: B2
- Application number: 16/938,541
- Filed: 2020-07-24
- Published: 2021-02-25
- Granted: 2021-08-03
- Earliest claimed: 2019-08-22
- Assignees: Advanced Global Clinical Solutions Inc.
- Applicants: Advanced Global Clinical Solutions Inc.
- Inventors: Kurt E. Hecox
- Web page: https://patentgrep.com/patent/US11076799B2

## CPC

- A61B 5/4094: {Diagnosing or monitoring seizure diseases, e.g. epilepsy}
- A61B 5/0006: {ECG or EEG signals}
- A61B 5/002: {Monitoring the patient using a local or closed circuit, e.g. in a room or building (A61B5/0017 takes precedence)}
- A61B 5/0022: {Monitoring a patient using a global network, e.g. telephone networks, internet}
- A61B 5/31: for electroencephalography [EEG]
- A61B 5/372: Analysis of electroencephalograms
- A61B 5/7203: {for noise prevention, reduction or removal}
- A61B 5/7235: {Details of waveform analysis (detecting specific parameters of the electrocardiograph cycle A61B5/349)}
- A61B 5/7264: {Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems}
- A61B 5/7275: {Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor}
- A61B 5/7282: {Event detection, e.g. detecting unique waveforms indicative of a medical condition (cough events A61B5/0823; seizures A61B5/4094; sleep apnoea A61B5/4818)}
- A61B 5/742: {using visual displays (displays for heart-related electrical signals, e.g. ECG, A61B5/339)}
- A61B 5/746: {Alarms related to a physiological condition, e.g. details of setting alarm thresholds or avoiding false alarms}
- G16H 40/63: for local operation
- G16H 40/67: for remote operation
- G16H 50/20: for computer-aided diagnosis, e.g. based on medical expert systems
- A61B 2503/045: Newborns, e.g. premature baby monitoring
- A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D)
- G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA

## 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. The one or more circuits are configured to determine metrics based on the EEG signal, the metrics indicating non-linear features of the EEG signal, determine that the EEG signal indicates a candidate seizure by determining, based at least in part on the metrics, a change in the non-linear features of the EEG signal over time, and generate a seizure alert indicating that the EEG signal indicates the candidate seizure. The change in the non-linear features indicates a physiological force that gives rise to the candidate seizure.

## Claims

### Claim 1

A seizure detection system comprising one or more circuits, the one or more circuits are 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, one type of metric of the plurality of different types of metrics being eigenvalues, the plurality of different types of metrics indicating one or more non-linear features of the EEG signal;
monitor a moving window of the eigenvalues of the EEG signal;
determine that the eigenvalues are decreasing;
determine that the EEG signal indicates a candidate seizure by determining that the eigenvalues are decreasing and that another one of the plurality of different types of metrics is changing in a set pattern; and
cause a display of a user interface device to display a visual indicator responsive to determining that the EEG signal indicates the candidate seizure.

### Claim 2 (depends on 1)

The seizure detection system of claim 1, wherein at least one of the moving window or the set pattern is based on a default parameter value.

### Claim 3 (depends on 1)

The seizure detection system of claim 1, wherein the one or more circuits are configured to determine, based on the plurality of different types of metrics, an increase in the one or more non-linear features over time.

### Claim 4 (depends on 1)

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.

### Claim 5 (depends on 1)

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; and
perform a secondary analysis with one or more metrics of the plurality of different types of metrics to determine whether the EEG signal indicates the candidate seizure or that the EEG signal includes noise.

### Claim 6 (depends on 1)

The seizure detection system of claim 1, wherein the one or more circuits are configured to:
determine probabilities of an occurrence of a pattern of a trajectory of each of the plurality of different types of metrics at a plurality of points in time;
determine whether the probabilities of the occurrence of a pattern of the trajectory 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.

### Claim 7 (depends on 1)

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

### Claim 8 (depends on 1)

The seizure detection system of claim 1, wherein the one or more circuits are configured to:
determine whether one or more of the plurality of different types of metrics exhibit changes over time that meet a predefined level of statistical significance;
generate a user interface, the user interface comprising:
a real-time trend of the EEG signal; and
the one or more of the plurality of different types of metrics; and
cause the user interface device to display the user interface.

### Claim 9 (depends on 8)

The seizure detection system of claim 8, wherein the user interface further comprises a historical window of the EEG signal, the historical window of the EEG signal associated with the candidate seizure.

### Claim 10 (depends on 1)

The seizure detection system of claim 1, wherein the one or more circuits are configured to:
determine Renyi permutation entropy values based on the EEG signal;
determine that the Renyi permutation entropy values are decreasing;
determine that the EEG signal indicates the candidate seizure in response to a determination that the eigenvalues are decreasing and a second determination that the Renyi permutation entropy values are decreasing; and
determine that the EEG signal indicates noise based at least in part on eigenvalues decreasing and the Renyi permutation entropy values decreasing.

### Claim 11 (depends on 1)

The seizure detection system of claim 1, wherein the one or more circuits are configured to:
determine Renyi permutation entropy values based on the EEG signal;
determine that the Renyi permutation entropy values are increasing;
determine sample entropy values based on the EEG signal in response to a first determination that the Renyi permutation entropy values are increasing;
determine that the EEG signal indicates the candidate seizure in response to a second determination that the sample entropy values are negative; and
determine that the EEG signal does not indicate the candidate seizure in response to a third determination that the sample entropy values are positive.

### Claim 12 (depends on 1)

The seizure detection system of claim 1, wherein at least one of a step size or an overlap of a window used in a secondary analysis with one metric of the different types of metrics is based on user input.

### Claim 13

A seizure detection system comprising:
an interface configured to receive an electroencephalogram (EEG) signal from electrodes, the EEG signal being related to electrical brain activity of a patient; and
a processor configured to:
determine a plurality of different types of metrics based on the EEG signal, one type of metric of the plurality of different types of metrics being eigenvalues, the plurality of different types of metrics indicating one or more non-linear features of the EEG signal;
determine that the eigenvalues are decreasing;
determine that the EEG signal indicates a candidate seizure by determining that the eigenvalues are decreasing and that another one of the plurality of different types of metrics is changing in a set pattern; and
cause a display of a user interface device to display a visual indicator responsive to determining that the EEG signal indicates the candidate seizure.

### Claim 14 (depends on 13)

The seizure detection system of claim 13, wherein the set pattern is based on a default parameter value or a user defined parameter value.

### Claim 15 (depends on 13)

The seizure detection system of claim 13, wherein the processor is configured to determine, based on the plurality of different types of metrics, an increase in the one or more non-linear features over time.

### Claim 16 (depends on 13)

The seizure detection system of claim 13, 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.

### Claim 17 (depends on 13)

The seizure detection system of claim 13, wherein the processor is disposed in bedside equipment coupled to the electrodes.

### Claim 18

A seizure detection system comprising one or more circuits, the one or more circuits are 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, one type of metric of the plurality of different types of metrics being eigenvalues, the plurality of different types of metrics indicating one or more non-linear features of the EEG signal;
determine whether the eigenvalues are decreasing; and
determine that the EEG signal indicates a candidate seizure by determining that the eigenvalues are decreasing and that another one of the plurality of different types of metrics is changing in a set pattern; and
cause a display of a user interface device to display a visual indicator responsive to determining that the EEG signal indicates the candidate seizure.

### Claim 19 (depends on 18)

The seizure detection system of claim 18, wherein the one or more circuits are configured to determine 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.

### Claim 20 (depends on 18)

The seizure detection system of claim 18, wherein the one or more circuits are configured to:
generate a user interface, the user interface comprising:
a real-time trend of the EEG signal; and
the one or more of the plurality of different types of metrics; and
cause the user interface device to display the user interface.