Patent Description:
Seizures occur commonly in patients with a wide range of medical issues. Seizures afflict more than fifty million persons worldwide. In some cases, seizures may be benign but in an extreme form, a seizure can be life threatening. Accordingly, it is important to detect and respond to seizures. The earlier seizures are detected and treated, the better the outcome for the patient. However, detecting a seizure may be challenging since there may be no visible signs that a seizure is occurring in a patient. In particular, it may be difficult to visually detect that a patient in intensive care or a young patient (a child or infant) are experiencing a seizure.

Accordingly, often, a record of EEG data may be collected for such a patient for analysis by an epileptologist, in some cases, up to twenty-four hours of continuous EEG data recording may be necessary for manual analysis by the epileptologist. Manual analysis of such a large amount of data may be cumbersome, time consuming, and expensive. Some EEG analytics algorithms for seizure detection exist, however, these algorithms have low performance in young children. For example, some seizure detection algorithms may reach detection rates of <NUM>% in adults but only attain detection rates of <NUM>-<NUM>% in young children. Furthermore, such algorithms may also have a large number of false positive rates, in some cases, more than <NUM> false positives per day for a single patient when the patient is a young child. This number of false positives requires manual review for all records of a child and as the analysis algorithms do not appropriately reduce the amount of manual EEG data review required. Part of the failure of such EEG detection algorithms in children is due to the high variability in the nature of the abnormal EEG waveforms recorded for children.

Examples of seizure detection systems are described in <CIT> and <CIT>.

A seizure detection system including 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 and identify candidate seizures with a statistical analysis that identifies the candidate seizures based on changes in non-linear features of the EEG signal. The one or more circuits are configured to cause a display device to display the candidate seizures identified by the statistical analysis, train an artificial intelligence model to classify the candidate seizures based on training data comprising the EEG signal and the candidate seizures identified by the statistical analysis, determine to switch from displaying the candidate seizures identified with the statistical analysis to displaying the candidate seizures identified with the artificial intelligence model responsive to a determination that a performance of the artificial intelligence model to classify the candidate seizures satisfies a threshold, and switch from displaying the candidate seizures identified with the statistical analysis to displaying the candidate seizures identified based on the artificial intelligence model with the EEG signal.

In some embodiments, the one or more circuits are configured to determine a first performance level of the statistical analysis and determine a second performance level of the artificial intelligence model. In some embodiments, the one or more circuits are configured to determine to switch from identifying the candidate seizures with the statistical analysis to the artificial intelligence model in response to determining that the second performance level is greater than the first performance level.

In some embodiments, the one or more circuits are configured to determine to switch from identifying the candidate seizures with the statistical analysis to the artificial intelligence model by determining that at least a predefined amount of detections of the candidate seizures by the statistical analysis are also detected by the artificial intelligence model.

In some embodiments, the one or more circuits are configured to identify the candidate seizures with the statistical analysis by determining a plurality of metrics based on the EEG signal, the plurality of metrics indicating the non-linear features of the EEG signal and determining that the EEG signal indicates the candidate seizures by determining, based at least in part on the plurality of metrics, changes in the non-linear features of the EEG signal over time, wherein the changes in the non-linear features indicates physiological forces that give rise to the candidate seizures.

In some embodiments, determining, based at least in part on the plurality of metrics, the changes in the non-linear features of the EEG signal comprises determining an increase in the non-linear features over time.

In some embodiments, the of metrics include at least one of dimensionality, synchrony, Lyapunov exponents, one or more forms of entropy, one or more forms of eigenvalues, global non-linearity, distance differences between recurrence trajectories, higher order spectra, loss of complexity, a surrogate test, or self-similarity.

In some embodiments, the one or more circuits are configured to determine that the EEG signal indicates the candidate seizures by determining, based at least in part on the plurality of metrics, the changes in the non-linear features of the EEG signal over time by performing a preliminary analysis with one of the plurality of metrics, wherein the preliminary analysis indicates that the EEG signal indicates a candidate seizure or that the EEG signal includes noise and performing a secondary analysis with one or more metrics of the plurality of metrics to determine whether the EEG signal indicates the candidate seizure or that the EEG signal includes the noise.

In some embodiments, the one or more circuits are configured to determine probabilities of a trajectory of each of the plurality of metrics at a plurality of points in time and determine whether the trajectory of each of the plurality of metrics is significant based on the probabilities. In some embodiments, determining, based at least in part on the plurality of metrics, the changes in the non-linear features of the EEG signal comprises mapping significant metrics of the plurality of metrics to a category candidate seizure.

In some embodiments, the one or more circuits are configured to operate in a first operating phase where the one or more circuits identify the candidate seizures with the statistical analysis and operate in a second operating phase where the one or more circuits identify the candidate seizures with the artificial intelligence model.

In some embodiments, the one or more circuits are configured to operate in the first operating phase by generating seizure alerts indicating that the EEG signal indicates the candidate seizures identified by the statistical analysis and causing a user interface to display the seizure alerts.

In some embodiments, the one or more circuits are configured to operate in the first operating phase by training the artificial intelligence model based on training data while the candidate seizures are identified by the statistical analysis.

In some embodiments, the one or more circuits are configured to operate in the first operating phase by training the artificial intelligence model based on the EEG signal and the candidate seizures identified by the statistical analysis.

In some embodiments, the one or more circuits are configured to operate in the first operating phase by generating seizure alerts indicating that the EEG signal indicates the candidate seizures identified by the statistical analysis, causing a user interface to display the seizure alerts, receiving labels of the candidate seizures or the EEG signal from a user via the user interface, and training the artificial intelligence model based on the labels and the EEG signal.

Another implementation of the present disclosure is a method including receiving, by a processing circuit, from a database or other memory device storing electroencephalogram (EEG) data, an EEG signal generated based on electrical brain activity of a patient. The method includes identifying, by the processing circuit, candidate seizures with a statistical analysis that identifies the candidate seizures based on changes in non-linear features of the EEG signal, causing, by the processing circuit, a display device to display the candidate seizures identified by the statistical analysis, training, by the processing circuit, an artificial intelligence model to classify the candidate seizures based on training data comprising the EEG signal and the candidate seizures identified by the statistical analysis, determining, by the processing circuit, to switch from displaying the candidate seizures identified with the statistical analysis to displaying the candidate seizures identified with the artificial intelligence model responsive to a determination that a performance of the artificial intelligence model to classify the candidate seizures satisfies a threshold, and switching, by the processing circuit, from displaying the candidate seizures identified with the statistical analysis to displaying the candidate seizures identified based on the artificial intelligence model with the EEG signal.

In some embodiments, the method includes determining, by the processing circuit, a first performance level of the statistical analysis and determining, by the processing circuit, a second performance level of the artificial intelligence model. In some embodiments, the method includes determining, by the processing circuit, to switch from identifying the candidate seizures with the statistical analysis to the artificial intelligence model in response to determining that the second performance level is greater than the first performance level.

In some embodiments, the method includes determining, by the processing circuit, to switch from identifying the candidate seizures with the statistical analysis to the artificial intelligence model by determining that at least a predefined amount of detections of the candidate seizures by the statistical analysis are also detected by the artificial intelligence model.

In some embodiments, identifying, by the processing circuit, the candidate seizures with the statistical analysis by determining a plurality of metrics based on the EEG signal, the plurality of metrics indicating the non-linear features of the EEG signal and determining that the EEG signal indicates the candidate seizures by determining, based at least in part on the plurality of metrics, changes in the non-linear features of the EEG signal over time, wherein the changes in the non-linear features indicates physiological forces that give rise to the candidate seizures.

In some embodiments, the method includes operating, by the processing circuit, in a first operating phase by identifying the candidate seizures with the statistical analysis and operating, by the processing circuit, in a second operating phase by identifying the candidate seizures with the artificial intelligence model.

In some embodiments, the method includes operating, by the processing circuit, in the first operating phase by generating seizure alerts indicating that the EEG signal indicates the candidate seizures identified by the statistical analysis and causing a user interface to display the seizure alerts.

In some embodiments, the method includes operating, by the processing circuit, in the first operating phase includes training the artificial intelligence model based on training data while the candidate seizures are identified by the statistical analysis.

In some embodiments, the method includes operating, by the processing circuit, in the first operating phase comprises training the artificial intelligence model based on the EEG signal and the candidate seizures identified by the statistical analysis.

In some embodiments, the method includes operating, by the processing circuit, in the first operating phase by generating seizure alerts indicating that the EEG signal indicates the candidate seizures identified by the statistical analysis, causing a user interface to display the seizure alerts, receiving labels of the candidate seizures or the EEG signal from a user via the user interface, and training the artificial intelligence model based on the labels and the EEG signal.

Another implementation of the present disclosure is a seizure detection system including one or more electrodes connected to a patient, the one or more electrodes configured to generate an electroencephalogram (EEG) signal based on electrical brain activity of the patient. The system includes a processing circuit configured to receive the EEG signal from the one or more electrodes, identify candidate seizures with a statistical analysis that identifies the candidate seizures based on changes in non-linear features of the EEG signal, determine to switch from identifying the candidate seizures with the statistical analysis to an artificial intelligence model, and switch from identifying the candidate seizures with the statistical analysis to identifying the candidate seizures based on the artificial intelligence model with the EEG signal.

In some embodiments, the artificial intelligence model receives output from the statistical analysis and identifies the candidate seizures based at least in part on the output received from the statistical analysis.

In some embodiments, the artificial intelligence model includes an input and a weight applied to the input. In some embodiments, the processing circuit is configured to provide a candidate seizure determination made by the statistical analysis to the input of the artificial intelligence model and provide a confidence level of the candidate seizure determination made by the statistical analysis as the weight of the artificial intelligence model.

In some embodiments, one or more hidden layers of the artificial intelligence model include non-linear functions.

In some embodiments, the processing circuit is configured to receive one or more indications of false positives indicating candidate seizure identifications incorrectly made by at least one of the statistical analysis or the artificial intelligence model and train the artificial intelligence model based on the one or more indications of the false positives.

Various objects, aspects, features, and advantages of the disclosure will become more apparent and better understood by referring to the detailed description taken in conjunction with the accompanying drawings, in which like reference characters identify corresponding elements throughout.

Referring generally to the FIGURES, systems and methods for seizure detection based on a statistical analysis and an artificial intelligence (AI) analysis are shown, according to various exemplary embodiments. In some embodiments, a system can receive an electroencephalogram (EEG) signal and identify candidate seizures based on the EEG data. The system can apply a statistical analysis that analyzes the EEG data to identify candidate seizures by identifying changes in non-linearities in the EEG data. Furthermore, the system can apply an AI analysis that applies one or more models to the EEG data to classify the EEG data as a candidate seizure.

In some cases, the AI analysis may outperform the statistical analysis. However, the AI analysis may only outperform the statistical analysis after the AI has collected a significant amount of data and/or has trained for a significant amount of time. Although the AI may perform well after a significant amount of time has passed, and may operate in real-time, it may be unacceptable for the patient to go through undetected and unaddressed seizures while the AI trains.

In some embodiments, the system can apply the statistical analysis to the EEG data to detect candidate seizures. The candidate seizures can be provided by the system to a user via a user interface. The user may have the ability to review and label each candidate seizure. For example, the user may be a doctor, nurse, or medical technician that can provide a label to the EEG data indicating whether the candidate seizure is a true seizure or a false positive. Based on the labeling provided by the user, the system can train an AI model to make seizure detections based on the EEG data, e.g., classify segments of EEG data as candidate seizures.

The system can be configured to continue performing the statistical analysis to detect seizure candidates which are surfaced to the user and used for training the AI model which makes additional candidate seizure detections which are not surfaced to the user. The system can compare the performance of the AI analysis to the performance of the statistical analysis.

For example, the system can identify a percentage of candidate seizures identified by the statistical analysis that the AI analysis also detects. If the percentage is greater than a particular amount, the system can switch from surfacing candidate seizures detected by the statistical analysis to the AI analysis. In some embodiments, the system determines a performance score of the AI analysis and/or a performance score of the statistical analysis by comparing the detections of the AI analysis and/or the candidate seizures to user labels. If the score of the AI analysis is greater than the score of the statistical analysis, the system can switch from surfacing candidate seizures from the statistical analysis to the AI analysis. This application is related to <CIT>, <CIT>, and <CIT>.

In some embodiments, other technical fields can used the techniques discussed herein for transitioning from a statistical analysis to an AI analysis. For example, a system could implement a statistical analysis that transitions to an AI analysis for manufacturing and/or supply chain management, e.g., for predicting the numbers of parts to manufacture and/or order. Similarly, a system could implement a statistical analysis that transitions to an AI analysis for financial transaction predictions. These various technical fields can benefit from the real-time performance offered by the statistical analysis and high performance of the AI analysis.

Referring now to <FIG>, a system <NUM> including an EEG system <NUM> including a seizure detector <NUM> for candidate seizure detection based on trends of non-linear features is shown, according to an exemplary embodiment. In system <NUM>, signal processing firmware and/or software are integrated into an EEG data acquisition system with and/or without additional signal processing boards to form the EEG system <NUM>. The EEG system <NUM> is configured to collect EEG data from a patient <NUM> and further detect a candidate seizure (e.g., detect a potential candidate seizure for review by a user) based on the EEG data with the seizure detector <NUM>, in some embodiments. The seizure detector <NUM> is a fully integrated parallel processor, in some embodiments.

A patient <NUM> is shown in <FIG> with multiple electrodes applied to the head of the patient <NUM>. The electrodes sense electrical brain activity in the patient <NUM>. The patient <NUM> may be a human, e.g., an adult, a teenager, a child, an infant, etc. Furthermore, the patient <NUM> may be an animal, e.g., a cat, a dog, a horse, a cow, etc. The number of electrodes applied to the patient <NUM> for collection of the EEG data for analysis by the seizure detector <NUM> may be determined by the desired precision of localization (when the focus is detection, accuracy of localization is less critical), the dimensions of the driving function determined by the seizure detector <NUM>, the physical limits of the skull size, the spatial distribution of the electrodes, the spatial extent of the source and the correlation structure between the electrodes, etc..

The electrodes are connected to an electrode interface <NUM> included by the EEG system <NUM>, in some embodiments. The electrode interface <NUM> can include one or more preliminary hardware circuits for generating the EEG data for analysis by the seizure detector <NUM>. The hardware circuits may include amplifier circuits (e.g., differential amplifier circuits), filters (e.g., high-pass, low-pass, band-pass), analog to digital converters (ADCs), etc..

In some embodiments, the seizure detector <NUM> can be configured to analyze signals generated by a full set of electrodes applied to the patient <NUM> and/or analyze a subset of electrodes applied to the patient <NUM>. In some embodiments, because dimensionality of a seizure does not generally exceed a value of four, approximately ten or less electrodes can be analyzed by the seizure detector <NUM> to detect a seizure. In this regard, even if a technologist applies a full set of electrodes, the seizure detector <NUM> can select the appropriate number of electrodes required (e.g., select ten electrodes).

In some embodiments, the seizure detector <NUM> is configured to determine a Lyapunov spectra which generally varies from about two to nine, with most seizures showing decreasing dimensions with seizure onset. During seizures it is unusual to see dimensions above four. Using multichannel EEG methods a trajectory can be characterized with 2d + <NUM> electrodes where d is the estimated dimensionality of the underlying function with the Lyapunov spectra. The seizure detector <NUM> can, during operation, determine the dimensionality of the underlying function and cause user interface device <NUM> to recommend a particular number of electrodes for the patient <NUM>. In this regard, the patient <NUM> may start with a predefined number of electrodes but, according to the analysis of the seizure detector <NUM>, a technician may add additional electrodes to the patient <NUM> based on the determined dimensionality.

The EEG data may be representative of one or multiple EEG signals for brain activity of the patient <NUM>. The seizure detector <NUM> can receive the EEG data and perform a non-linear analysis of the EEG data to detect whether the EEG data is indicative of a candidate seizure that has, will, or is occurring in the patient <NUM>. The candidate seizure detections detected by the seizure detector <NUM> can be provided to the user interface device <NUM> for visual and/or audio notification for a user, e.g., a doctor, a nurse, a family member of the patient <NUM>, an epileptologist, a technician, etc. Furthermore, via the user interface device <NUM>, a user may provide configuration data. The configuration data may indicate the age of the patient <NUM>, the weight of the patient <NUM>, historical EEG data of the patient <NUM>, medical conditions of the patient, etc. The non-linear analysis that the seizure detector <NUM> is configured to perform may be based, at least in part, on the configuration data.

The user interface device <NUM> may be a system or device configured to receive input from a user and/or provide output to the user. The user interface device <NUM> can be a monitor, e.g., a display screen. The display screen may be a light emitting diode (LED) screen, a Cathode ray tube display (CRT), a liquid crystal display (LCD) and/or any other type of display screen. The user interface device <NUM> may further include input devices, a mouse, a keyboard, a touch-screen, etc. Furthermore, the user interface device <NUM> may include a speaker for audio output, a microphone for audio input, etc. In some embodiments, the user interface device <NUM> is a computer, a smart phone, a tablet etc. in communication with the EEG system <NUM> and/or the seizure detector <NUM>.

In principle, if a specific chain of events that leads to the emergence of a seizure are known, a system is configured to search for the specific chain of events, in some embodiments. For example, much is known about the abnormal electrical behavior of single neurons in the causative anatomic regions of seizures. For example, particularly temporal lobe seizures can be recognized in adults. However, seizures are caused by malfunctioning networks or assemblies of brain cells. Therefore, the seizure detector <NUM> can analyze a population of behaviors in search for driving forces behind seizure onset in the patient <NUM> instead of searching for a known morphological pattern (e.g., activity in particular areas of the brain, sharp spikes in activity, etc.). The forces behind the physiology of a seizure are not random. In fact, the forces are deterministic and can be detected by the seizure detector <NUM> by applying non-linear dynamic systems tools.

The seizure detector <NUM> is configured to apply seizure detection to any range of ages and can be performed in real-time, in some embodiments. Furthermore, the accuracy of the seizure detector <NUM> may be greater than <NUM>% and less than double digit false positives in EEG data collected for the patient <NUM> over a <NUM> hour period. The seizure detector <NUM> can be implemented locally (as illustrated in <FIG>) and/or can be implemented remotely (as illustrated in <FIG>).

In some embodiments, the seizure detector <NUM> is configured to select parameter values for detecting a seizure and/or categorizing an event performed by the seizure detector <NUM> based on user input (instead of, or in addition to, using default values programmed into the seizure detector <NUM>). In some embodiments, the parameter values can be selected manually by a user, where the user provides user input via the user interface device <NUM> associated with the seizure detector <NUM>. The parameter values may be trajectory statistical significance level(s) and/or metric parameter values between component metrics when multiple metrics are simultaneously applied to a dataset. By selecting the parameter values based on the user input, false alarms generated by the seizure detector <NUM> can be reduced or a hit rate by the seizure detector <NUM> can be increased. Furthermore, by allowing a user to select the parameter values, the appropriate tradeoffs between false positives and true positives can be achieved by the seizure detector <NUM>.

The user input can indicate a balance level (e.g., a weight) between decreasing false positives and increasing hit rates. This can be accomplished either by use of the suggested default values or by adjustment of the values, where the adjustment can be made based on personal preference or to best suit a particular patient situation. The balance level can be a value in a range and can correspond to lower or higher statistical significance levels (e.g., a balance level that favors decreasing false positives may be associated with lower probability values of a trajectory of a metric changing in a particular direction (increasing or decreasing) while a balance level that favors increasing hit rates may be associated with a higher probability value for the trajectory of the metric changing).

The seizure detector <NUM> is configured to detect shifting patterns of forces which produce the state transition from non-seizure to seizures without attempting to detect target waveform morphologies, in some embodiments. These abnormal physiological forces produce waveform trajectories that the seizure detector <NUM> is configured to quantify, in some embodiments. The seizure detector <NUM> can utilize the trajectories to detect multiple state changes, including seizure state changes, i.e., a change from a normal state in the patient <NUM> into a seizure state. More specifically, the seizure detector <NUM> is configured to determine one or multiple non-linear metrics based on EEG data which reflect the emergence of these trajectories, in some embodiments. The seizure detector <NUM> is configured to apply non-linear dynamic system tools to detect the emergence of these abnormal trajectories, in some embodiments.

The seizure detector <NUM> is configured to search for a seizure, in an EEG time series, by searching for a specific category of state change, in some embodiments. More particularly, the seizure detector <NUM> is configured to search for a change, i.e., an alteration to the structural non-linearities in the EEG data, in some embodiments. The seizure detector <NUM> is configured to apply non-linear methods to detecting the state changes in a mathematical state space, i.e., a starting point for the reconstruction of the systems dynamics (the dynamics of the brain activity of the patient <NUM>).

The seizure detector <NUM> is configured to detect candidate seizures where there is a gradual and/or an abrupt transitions into the seizure state in the patient <NUM>, in some embodiments. More particularly, the seizure detector <NUM> can apply dynamic systems analysis to detect both the abrupt changes, (e.g., bifurcations), along with many forms of gradual change. The search for a seizure is not a search for a specific isolated event nor a specific single value of a feature, instead, the seizure detector <NUM> can determine multiple non-linear metrics and track the non-linear metrics overtime to detect diagnostic shifts and patterns of changes in non-linear features of the EEG data. For example, the seizure detector <NUM> can determine whether a statistically significant increasing or decreasing trajectory of the non-linear features is occurring. For example, for a metric indicating non-linearity, seven sequential increasing values for the metric may be statistically significant to indicate that the trajectory is increasing. However, five sequential increases in the value of the metric may not be statistically significant to indicate an increasing trajectory. Similarly, the number of sequential decreasing values can be associated with a probability of occurring, e.g., five sequential decreasing values of the metric may not be significant while seven sequential decreasing values of the metric may be statistically significant.

For example, the probability that five sequential increasing values of a metric may be <NUM> (which can be determined by the seizure detector <NUM> from the five sequential increasing values and/or historical trajectory data). The probability that seven sequential increasing values of a metric may be <NUM>. Because the probability that seven sequential increasing values is less than the probability that five sequential increasing values, seven sequential increasing values may be a greater statistical significance than five sequential increasing values (a lower probability level). A probability threshold could be applied by the seizure detector <NUM> to determine whether the increase or decrease of a metric is statistically significant, e.g., is the probability of the occurrence less than the probability threshold. Furthermore, the seizure detector <NUM> could apply a change number threshold, i.e., is the number of sequential increasing or sequential decreasing values greater than or equal to the change number threshold, i.e., if the threshold is seven, seven sequential increasing values of a metric are statistically significant while five sequential increasing values of the metric are not statistically significant.

The threshold for determining statistical significance can define the amount of false positives and missed seizure detections. For example, a threshold that requires a higher number of sequential increasing or sequential decreasing values may have less false positives but miss a high number of seizures. However, a lower value of the threshold may result in more false positives but miss less seizures. An optimization can be performed by the seizure detector <NUM> to properly set the thresholds for determining statistical significance. The optimization may attempt to minimize missing seizures and minimize false positives. The optimization can be based on user input, e.g., user feedback that identifies certain periods of a historical EEG signal as corresponding to a seizure or other periods of the EEG signal pertaining to a false positive.

The seizure detector <NUM> is configured to detect a change in the pattern of non-linear dynamics of the EEG data since the pattern of change is a constant aspect of the seizure state transition, in some embodiments. Often, the state changes in non-linearities precede, in time, the appearance of spikes, sharp waves or other visual signs in the EEG data of an electrographic or clinical seizure. Hence, the seizure detector <NUM> is configured to first determine a non-specific detector of changes in non-linearities from the EEG, i.e., eigenvalues, in some embodiments. If the non-specific detector indicates a candidate seizure, the seizure detector <NUM> can apply subsequent metric calculation and/or analysis. This allows the seizure detector <NUM> to save computational resources by applying low computational requirement calculation, e.g., eigenvalues, followed by higher computational requirement calculations, e.g., dimensionality.

Because of the potential instability of multiple non-linear measures, at small sample sizes, the seizure detector <NUM> is configured to apply a moving window for calculations of the metrics, in some embodiments. The particular values of the moving window duration and percent overlap within the window, may be predefined based on the specific metric, i.e., each metric may be associated with its own window duration and percent overlap. The greater the dependence of the particular metric upon sample size, to ensure stability of estimates, the seizure detector <NUM> is configured to determine the metric with a longer window duration, in some embodiments.

The seizure detector <NUM> is configured to analyze changes in eigenvalues to detect a seizure, in some embodiments. However, changes in eigenvalues can arise from either quantitative changes in the ratio of linear to non-linear activity of the EEG data, or the presence of noise within the EEG data. Hence seizure detector <NUM> can determine and analyze multiple non-linear metrics together to detect a candidate seizure. For example, the seizure detector <NUM> is configured to determine entropy (e.g., permutation entropy, wavelet entropy, etc.) along with the eigenvalues (e.g., special eigenvalue, temporal eigenvalue, Fiedler eigenvalue, etc.) to help make this distinction between a seizure and noise, in some embodiments. Noise often increases entropy, when the noise is not rhythmic, while most seizures decrease entropy.

The seizure detector <NUM> is configured to determine and analyze many other non-linear metrics, in some embodiments. The metrics that the seizure detector <NUM> is configured to analyze may be based on the configuration data, i.e., a clinical picture or syndrome of the patient <NUM> (e.g., drop attacks, infantile spasms, Lennox Gastaut Syndrome, post hypoxic encephalopathy, age, weight, etc.) and a baseline EEG pattern associated with the patient <NUM>. The seizure detector <NUM> is configured to analyze the particular configuration data and determine and/or analyze the metrics appropriate for the patient <NUM>, in some embodiments.

The clinical syndromes and the baseline EEG pattern (e.g., EEG patterns of normal brain activity, seizure patterns, etc.), the age of the patient, the weight of the patient, etc. can be included in the configuration data and can be utilized by the seizure detector <NUM> in the selection of the composition of the mixture (or a weighting of the mixture) of non-linear metrics in the second phase (and/or the preliminary phase). Candidate metrics include but are not limited to dimensionality, synchrony, Lyapunov exponents, various forms of entropy, global nonlinearity (via surrogate testing), distance differences between the recurrence trajectories in phase space, self-similarity, etc..

The output of the analysis performed by the seizure detector <NUM> may be a panel of non-linear values that change over time. Some of these patterns may be indicative of candidate seizures while other patterns reflect sleep onset and others, artifacts. Accordingly, the seizure detector <NUM> can map the panel of non-linear values to particular categories, e.g., seizure, noise, sleep, etc. The number of metrics in the panel may be set by the seizure detector <NUM> based on by the signal processing power of the hardware and/or firmware architecture of the EEG system. The selection of the metrics may change based on whether the seizure detector <NUM> is operating in a real-time mode where EEG data is being analyzed in real-time or in a historical analysis mode where previously recorded EEG data is analyzed.

Referring now to <FIG>, a system <NUM> including an EEG acquisition system <NUM> for collecting EEG data and an analysis system <NUM> including the seizure detector <NUM> for analyzing the EEG data to detect a candidate seizure is shown, according to an exemplary embodiment. In the system <NUM>, the signal processing hardware, firmware, and/or software of the seizure detector <NUM> is fully integrated into a stand-alone local computer separate from the EEG acquisition system <NUM>, i.e., in the analysis system <NUM>.

The analysis system <NUM> is configured to operate with the EEG acquisition system <NUM> using the output of the EEG acquisition system, i.e., the EEG data acquired by the EEG acquisition system <NUM>, in some embodiments. The system <NUM> can be implemented in multiple embodiments, e.g., the analysis system <NUM> can be a screening device with a simplified head-box for the EEG acquisition system <NUM> and limited signal processing capabilities. The head-box could be structured to sit on top of an enclosure of the EEG acquisition system <NUM>. The system <NUM> may be appropriate for warning and/or screening at a hospital or within a home of a patient. In some embodiments, the analysis system <NUM> is a plugin card (e.g., a circuit board configured with a connection port that can connect to a connection port of the EEG acquisition system <NUM>). A user can insert the plugin card into the EEG acquisition system <NUM> to give the EEG acquisition system <NUM> all of the operational abilities of the analysis system <NUM>. For example, the plug-in card can include a graphics or digital signal processing circuit and memory comprising instructions for implementing the operations described herein.

The EEG acquisition system <NUM> may include an acquisition manager <NUM>. The acquisition manager <NUM> is configured to collect the EEG data and maintain a historical record of the EEG data. Furthermore, the acquisition manager <NUM> can provide the EEG data to the analysis system <NUM> for analysis and seizure detection. Upon receiving a request from the analysis system <NUM>, the acquisition manager <NUM> can provide the analysis system <NUM> requested historical EEG data that the acquisition manager <NUM> stores.

Referring now to <FIG>, a system <NUM>, a cloud-based implementation of the seizure detector <NUM> is shown, according to an exemplary embodiment. In the system <NUM>, the seizure detection and associated signal processing is performed at a remote site, i.e., by a cloud platform <NUM>. The cloud platform <NUM> may be one or more remote servers and/or local servers within a hospital, can be a cloud analysis system such as MICROSOFT AZURE, AMAZON WEB SERVICES, etc..

The system <NUM> includes a network interface <NUM> which communicates the EEG data and/or the configuration data to the cloud platform <NUM> for analysis by the seizure detector <NUM> via a network <NUM>. The network <NUM> can act as a pipeline between the system <NUM> and the cloud platform <NUM> where the feature extraction and/or analysis is performed by the seizure detector <NUM>. Results of the analysis performed by the analysis system <NUM> can be transmitted back to the system <NUM> for display via the user interface device <NUM> and decision making by a user.

The network <NUM> can include one or multiple different wired and/or wireless networks. The networks may be a local area network (LAN) or a wide area network (WAN). The networks may be wired and include Ethernet wires, cables, and/or fiber optic connections and/or may be wireless and be Wi-Fi and/or cellular based networks. The network interface <NUM> can include one or more receivers, transmitters, transceivers, wireless radios, signal processing circuitry, etc. that the network interface <NUM> is configured to operate to communicate via the network <NUM>, in some embodiments.

Referring now to <FIG>, a system <NUM> including the seizure detector <NUM> is shown, according to an exemplary embodiment. The seizure detector <NUM> is shown to receive the configuration data and the EEG data. Furthermore, the seizure detector <NUM> is shown to output a user interface causing the user interface device <NUM> to display the user interface. The user interface may include indications of the presence of a candidate seizure and/or calculated metrics that the seizure detector <NUM> determines from the EEG data.

The seizure detector <NUM> includes an analysis circuit <NUM>. The analysis circuit <NUM> can include one or more processing circuits for digital signal processing. The analysis circuit <NUM> can include field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), one or more central processing units (CPUs), one or more digital signal processing (DSP) units, one or more graphics processing units (GPUs), etc. There may be high processing requirements of the seizure detector <NUM> and the seizure detector <NUM> can apply shared computing across multiple processing units (e.g., separate processing cards, graphics cards, remote servers, cloud-based systems, etc.).

Furthermore, the analysis circuit <NUM> can include one or more memory devices. The memory devices can store instructions and/or computed data for execution on one or more processors. The memory devices can include random access memory (RAM), solid state drives (SSDs), hard disk drives (HDDs), FLASH memory, electrically erasable programmable read-only memory (EEPROM), and/or any other type of memory, either transitory or non-transitory.

The seizure detector <NUM> is configured to detect candidate seizures with an analysis of historical data and/or in a real-time analysis, in some embodiments. The seizure detector <NUM> is configured to detect a candidate seizure with less than fifteen seconds of delay between seizure onset and detection, in some embodiments. The seizure detector <NUM> is configured to detect a wide range of electrographic patterns (i.e., a mapping between types of seizures and an optimal detection algorithm performed by the seizure detector <NUM> for that type of seizure), in some embodiments. Furthermore, the seizure detector <NUM> is configured to separate seizure state transitions from artifacts and noise, in some embodiments. Furthermore, the seizure detector <NUM> is configured to detect several seizure types within the same patient, arising from several locations, again, within the same patient (multifocality), in some embodiments. The seizure detector <NUM> may have a true positive rate of more than <NUM>% and a false positive rate of less than <NUM> false detections per day for a single patient.

A constant quantitative feature of the transition from the non-seizure to seizure state is a change in the contribution of non-linearities to the energy level of the signal. Many, and in most adults, transitions to the seizure state result in increased rhythmicity or increased synchronization between cellular groups. This is reflected in decreased eigenvalues (decreased contribution of linearities), decreased entropy, decreased dimensionality, and increased global nonlinearity, as revealed by surrogate testing. This pattern is not universal, however. The exceptions to this pattern are particularly notable in children and infants where existing algorithms fail. For example, in many patients with drop attacks the electrographic correlate is an initial brief burst of high energy slowing, followed by low voltage desynchronized activity. The temporal pattern of quantitative metrics would be more complex and show a period of increased entropy, increased eigenvalues and decreased global non-linearities. For this reason, the seizure detector <NUM> focuses on change in metrics rather than absolute values and utilizes multiple forms of change, to detect candidate seizures. This captures a wide range of ictal electrographic morphologies.

A challenge arises when the baseline EEG activity is poorly organized, has excessive slow wave activity, and is punctuated by high voltage sharp waves or spikes (e.g. Lennox-Gastaut Syndrome). In this case the baseline eigenvalue that the seizure detector <NUM> is configured to detect could be so low that the emergence of seizures may not be reflected in a drop of eigenvalues (e.g., a floor effect). In this instance the analysis with multiple metrics performed by the seizure detector <NUM> increases the likelihood of avoiding floor and/or ceiling effects. All of these changes can be distinguished from the intrusion of increased noise or artifacts (noise generally decreases eigenvalues and increases entropy).

The seizure detector <NUM> is configured to apply quantitative temporal change analysis to multiple metrics identifying a pattern of change across metrics which leads to the categorization of an event as a candidate seizure (e.g., a review alert), signal noise (no alert), or an uncertain classification (potential seizure alert occurs), in some embodiments. The seizure detector <NUM> can be adjusted to alter the trade-offs between true detections, false alarms, and misses by adjusting the significance levels of the probabilities required to be recognized as a significant change. In some embodiments, the temporal pattern of the metric trajectories can be subjected to post-processing (e.g., smoothing to remove transients) to decrease the variability in the application of the statistical criteria.

The analysis circuit <NUM> can apply a pipeline of analysis stages and can include a component configured to apply each stage. The components may be software modules, circuits, etc. The analysis circuit <NUM> includes a channel selector <NUM>, a filtering stage <NUM>, a preliminary analyzer <NUM>, a secondary analyzer <NUM>, and an interface generator <NUM>. The EEG data received by the seizure detector <NUM> may first pass through the channel selector <NUM>. The channel selector <NUM> may control which channels of the EEG data the seizure detector <NUM> performs analysis on. For example, where multiple electrodes are presents, one or more sets of electrodes may be appropriate for analysis by the analysis circuit <NUM>. Accordingly, the channel selector <NUM> can select the appropriate EEG signal channels and provide the EEG signals of the selected channels to the filtering stage <NUM>.

The filtering stage <NUM> can filter the EEG data with one or multiple low pass, high pass, and/or band-pass filters. The filters may be digital and/or hardware filters, for example, infinite impulse response (IIR) and/or finite impulse response (FIR) filters. The bandwidth appropriate for the signal analyzed by the analysis circuit <NUM> may be specific to the age of the patient <NUM>. Accordingly, the filtering stage <NUM> may receive configuration data indicating a characteristic of the patient (e.g., age) and is configured to perform filtering based on the configuration data, in some embodiments.

The bandwidth that the filtering stage <NUM> passes may depend not only on the age of the patient but the metrics determined by the seizure detector <NUM> for detection of the candidate seizure. In some embodiments, the filtering stage <NUM> passes frequencies between <NUM> and <NUM>. In some embodiments, the band of frequencies passed by the filtering stage <NUM> may be a range between <NUM> to <NUM>.

The analysis circuit <NUM> is configured to determine multiple non-linear metrics and combine patterns of evolution of the multiple non-linear metrics together to detect a candidate seizure via the preliminary analyzer <NUM> and the secondary analyzer <NUM>, in some embodiments. The preliminary analyzer <NUM> and the secondary analyzer <NUM> is configured to concatenate the application of several non-linear metric algorithms in a sequence and/or in parallel, in some embodiments. The seizure detector <NUM> can detect the presence of a candidate seizure through a first screening for non-specific global non-linear transformations, i.e., the metric <NUM> which may be eigenvalues. Furthermore, the secondary analyzer <NUM> is configured to process more computational intense metrics which focus on more specific types of non-linearities via the secondary analyzer <NUM>, in some embodiments.

The preliminary analyzer <NUM> is configured to perform a screening stage by determining a moving window implementation of eigenvalues, e.g., the metric <NUM>, in some embodiments. The eigenvalues decrease with the emergence of nonlinear interactions (e.g., seizures) or the appearance of noise. The eigenvalues increase when the EEG data becomes less rhythmic or periodic. Furthermore, the preliminary analyzer <NUM> is configured to determine whether the changes in the eigenvalues are statistically significant (e.g., have a significance value greater than a predefined amount or an probability of error less than a predefined amount) by determining the statistical significance <NUM> of the trend of the metric <NUM> such that only statistically significant changes in the eigenvalues are analyzed by the preliminary analyzer <NUM> to determine a candidate seizure, in some embodiments.

In some embodiments, the preliminary analyzer <NUM> determines the statistical significance with a moving window, i.e., determines trends of the metric <NUM> with a moving window similar to, or the same as, the moving window based trend analysis described with reference to the secondary analyzer <NUM>. Over a particular window of samples of the metric <NUM>, the preliminary analyzer <NUM> can determine whether the metric <NUM> is increasing or decreasing. With multiple windows, the preliminary analyzer <NUM> can determine a trend of the metric <NUM> and determine a probability level (the statistical significance <NUM>) of the trend based on previous windows to increase or decrease over future windows.

In response to the preliminary analyzer <NUM> determining a decrease in the metric <NUM> by a statistically significant amount (e.g., the probability of an increase or decrease being less than a particular probability), the secondary analyzer <NUM> can calculate and analyze other metrics, i.e., the metrics <NUM>-<NUM>. For example, the metrics <NUM>-<NUM> may include Renyi permutation entropy. The Renyi permutation may be determined by the secondary analyzer <NUM> on only the samples of the EEG data that the preliminary analyzer <NUM> detects statistically significant decreases in eigenvalues on. Permutation entropies may the computationally simplest and robust to noise and artifact. The secondary analyzer <NUM> is configured to further determine statistical significance of the metrics <NUM>-<NUM>, i.e., determine the statistical significances <NUM>-<NUM> for the metrics <NUM>-<NUM>, in some embodiments. The metrics <NUM>-<NUM> can include permutation entropy, wavelet entropy, special eigenvalue, temporal eigenvalue, Fiedler eigenvalue, higher order spectra, loss of complexity, and/or surrogate test.

More specifically, each of the metrics <NUM>-<NUM>, Mi, calculated by the secondary analyzer <NUM> may be time series of data. Based on the time series of the metrics <NUM>-<NUM>, the secondary analyzer <NUM> is configured to determine statistical significances <NUM>-<NUM>, P(Mi) that indicate the probability for a pattern of shifts of a trajectory of the metric under the null hypothesis, in some embodiments. Similarly, the statistical significances <NUM>-<NUM> can be time series. The trend analyzer <NUM> is configured to analyze a pattern of significant and non-significant values of the metrics <NUM>-<NUM> based on the statistical significances <NUM>-<NUM> across time, in some embodiments. A current set of significant metrics can be analyzed by the trend analyzer <NUM> as a group or panel of results. Each panel can be mapped to a particular category, e.g., a clinical category such as a candidate seizure event, no seizure, an indeterminate state, etc. Furthermore, the panels can map to other types of spurious events (non-seizures).

The metrics <NUM>-<NUM> may be many and varied, for example, there may be more than a dozen non-linear metric types described with many variants of each of these metric types. For example there are at least fourteen different forms of, or calculation methods for, entropy. The metrics <NUM>-<NUM> can include a loss of complexity metric. Each entropy metric may have performance advantages and disadvantages in specific settings (e.g., sample entropy performs better than most in detecting voltage suppression, Kolmogorov entropy is more vulnerable than multiple forms of permutation entropy which also have low computational complexity, etc.). Fuzzy entropy has an appeal in that class membership is graded so that the user has better control of the class boundaries. The frequency of the target events (seizures) can be included in the parameter values for some forms of entropy, for example tsalli entropy. Renyi entropy may be a better selection in instances in which state changes are frequent or profound (e.g., anesthesia). Information regarding the frequency of seizures, whether or not anesthesia is present, etc. can be included in the configuration data and thus the secondary analyzer <NUM> can determine and analyze an appropriate mixture of non-linear metrics. Examples of methodologies for calculating entropy can be found in <NPL>, the entirety of which is incorporated by reference herein.

As described, the metrics <NUM>-<NUM> can be based upon and therefore derived from the EEG signal. One important aspect of the metrics <NUM>-<NUM> may be a trajectory over time of each of the metrics <NUM>-<NUM>. The absolute values of the metrics <NUM>-<NUM> may vary enormously, as a function of patient age, state, syndrome, concomitant medications, etc. Therefore, the trend analyzer <NUM> is configured to analyze the trajectory of metrics <NUM>-<NUM>, and not necessarily the absolute values of the metrics <NUM>-<NUM>, to detect and/or classify candidate seizures. The direction of change in the metrics <NUM>-<NUM> over time caused by a candidate seizure (increase versus decrease) can vary based on patient age and/or the type of candidate seizure. For this reason, the secondary analyzer <NUM> is configured to determine the trajectories of the metrics <NUM>-<NUM> such that the trend analyzer <NUM> can determine, based on the trajectories, whether any segment of the EEG signal is indicative of a candidate seizure and/or should be surfaced for visual evaluation by an electroencephalographer.

The metrics <NUM>-<NUM> themselves also vary in terms of their stability and reliability, according to sample size. Sample size can be increased by increasing sample duration. However, an increased sample duration may risk missing a seizure event if the seizure event is shorter than the requisite sample duration. In some embodiments, the secondary analyzer <NUM> is configured to determine the direction of change of the metrics <NUM>-<NUM> by using moving windows.

For example, at a sampling rate of <NUM>, a five second window that the secondary analyzer <NUM> can be configured to apply contains <NUM>,<NUM> samples. The step size and overlap for each of the windows applied to the metrics <NUM>-<NUM> by the secondary analyzer <NUM> can be user defined via the user interface device <NUM> and/or predefined. Typical values might be one second step sizes with four out of five samples overlapping between windows (i.e., four out of five samples being the same between two window positions for a window as the window moves).

Each window, when analyzed by the secondary analyzer <NUM>, may indicate an increase or a decrease of the value of one of the metrics <NUM>-<NUM> and constitute the trajectory of the metric over time. The trend analyzer <NUM> may have statistical criteria for reviewing and/or analyzing a segment defined by one of the window positions of a window of one of the metrics <NUM>-<NUM>. For example, assuming each sample is independent and behaves randomly, the probability of n consecutive changes in the same direction would be <NUM>/<NUM> to the nth power. In some embodiments, the secondary analyzer <NUM> is configured to determine the probabilities for the patterns (increasing or decreasing) of the metrics <NUM>-<NUM>. The probabilities may be probabilities that a predefined amount of changes will occur in one of the metrics <NUM>-<NUM> in a particular direction (e.g., a predefined amount of windows into the future will indicate increasing or decreasing values of the metrics based on the trajectories of previous windows). The trend analyzer <NUM> can apply threshold values which, if the probabilities rise above or fall below the threshold values, indicates that a particular one of the metrics <NUM>-<NUM> is increasing or decreasing at a statistically significant level. The trend analyzer <NUM> can apply one or more user defied and/or predefined thresholds to determine the statistically significant metrics <NUM>-<NUM> and/or map the statistically significant metrics <NUM>-<NUM> to a category, e.g., a seizure, noise, etc..

The selection of particular methods of calculating metrics performed by the secondary analyzer <NUM> may be dependent upon, the frequency of events, their spatial extent, the sample size, the dimension, the state of the patient <NUM>, the seizure syndrome of the patient <NUM>, the signal to noise ratio of the time epoch, all of which can be indicated through the configuration data or extracted by the secondary analyzer <NUM> from the EEG signal (e.g., signal to noise ratio). The calculation and mapping of metrics performed by the secondary analyzer <NUM> can take into signal and subject factors into account as well as the intrinsic computational complexity to determine which features should receive prioritization. This same process applies to the calculation of dimensionality, complexity (or loss of complexity), Lyapunov exponents, etc..

The metrics <NUM>-<NUM> and their statistical significances <NUM>-<NUM> can be passed into the trend analyzer <NUM> which can detect which trends in statistically significant metrics indicate a candidate seizure, noise, etc. For example, when the trend analyzer <NUM> detect that the Renyi permutation entropy increases determined by the secondary analyzer <NUM> along with the eigenvalues decreasing, the EEG data is indicative of noise or a burst suppression pattern of seizures in which case additional metrics should be analyzed. For example one of the metrics <NUM>-<NUM> may be sample entropy that the secondary analyzer <NUM>, via phase space analyzer <NUM>, determines in phase space. The sample entropy may be calculated by the secondary analyzer <NUM> after the calculation and analysis of the Renyi permutation entropy and/or may be calculated in parallel with the eigenvalues and/or Renyi permutation entropy. Calculation of the sample entropy may be less than a second delay.

Sample entropy may be more sensitive than permutation entropies to burst suppression. The trend analyzer <NUM> can determine whether the sample entropy is positive or negative and can classify the EEG data associated with the decreasing eigenvalues as noise if the sample entropy is positive. These results of the metrics <NUM>-<NUM> can be combined by the trend analyzer <NUM> to categorize the event. When both Renyi permutation entropy and eigenvalues decrease, the trend analyzer <NUM> can determine that the EEG data is indicative of a candidate seizure and the secondary analyzer <NUM> may not determine the Sample Entropy.

The phase space analyzer <NUM> is configured to perform a phase space analysis to determine metrics such as dimensionality, in some embodiments. The phase space analyzer <NUM> is configured to generate a phase space plot for the EEG signal, in some embodiments. Dynamical systems can be represented by a series of differential equations whose solutions may not exist in closed form. However, the phase space analyzer <NUM> can identify candidate seizure behavior by generating a trajectory in phase space. At each instance in a time series of the EEG signal, the phase space analyzer <NUM> is configured to generate a single point in phase space and a sequence of these points form a trajectory whose pattern provides insight into the nature of the driving function, i.e., insight into the presence or absence of a seizure, in some embodiments. The trajectories can occupy the entirety of the phase space or can converge to a lower dimensional region, called an attractor. The phase space trajectory of noise never converges. When adjacent points begin close to one another and then diverge, a strange attractor is said to exist and suggests the presence of chaotic behavior.

The phase space analyzer <NUM> is configured to perform the Takens method of time shift to generate a phase space plot based on empirical data of the EEG time series, in some embodiments. The Takens method is described in greater detail in <NPL>. The EEG signal may be represented as the time-series, <MAT>.

In some embodiments, from this time-series, the phase space analyzer <NUM> is configured to determine a phase space representation of the EEG signal with a time delay, td and an embedding dimension, m.

The shape of the trajectory in phase space can be strongly influenced by the choice of the time lag, utilized by the phase space analyzer <NUM> to generate the phase space plot. In some embodiments, the time lag is the first zero in an autocorrelation function and is determined and then used by the phase space analyzer <NUM> to embed the signal in phase space. The phase space analyzer <NUM> is configured to apply one or multiple different methods for estimating the time lag. In some embodiments, the phase space analyzer <NUM> may determine the lag based on a non-linear metric that the phase space analyzer <NUM> is attempting to determine. In some embodiments, the estimators used by the phase space analyzer <NUM> to determine the lag are linear and/or non-linear.

The second value which is selected by the phase space analyzer <NUM> is the embedding dimension. If the dimension of the attractor is k, then the embedding theorem of Witney states that the embedding dimension must be <NUM>k + <NUM>. Accordingly, the phase space analyzer <NUM> is configured to select the embedding dimension based on a known or determined dimension of the attractor, in some embodiments.

The phase space analyzer <NUM> is configured to estimate the dimension for phase space with the Cao method, in some embodiments. The dimension estimated by the phase space analyzer <NUM> may be a dimension of an attractor within the phase space. The phase space analyzer <NUM> is configured to start with a low dimension and successively increase the dimension until the number of false neighbors reaches zero, in some embodiments. The dimension reached by the phase space analyzer <NUM> can be linked to the presence or absence of a candidate seizure. For example, the trend analyzer <NUM> can determine, whether there is a candidate seizure based on the metrics <NUM>-<NUM> and/or based on the dimensionality determined by the phase space analyzer <NUM>.

In some cases, the value of the dimension may be as low as one during a seizure. Furthermore, the dimension is usually below eight interictally. From a practical perspective, with this ascending method performed by the phase space analyzer <NUM>, i.e., starting from a low dimension and increasing the dimension value, there can be a real-time compromise of performance based upon the computational burden of the ascending method. To overcome this computational burden, the phase space analyzer <NUM> can receive the configuration data which indicates the age of the patient. The phase space analyzer <NUM> is configured to select a starting dimension value based on the age of the patient to reduce the number of steps where the phase space analyzer <NUM> increments the dimensional value and determines when the number of false neighbors reaches zero, in some embodiments.

The starting dimensional value utilized by the phase space analyzer <NUM> in the ascending method may be lower for young children and greater in older children. This may be because the younger the age the lower the dimensionality, whether ictal or interictal. The selection of a starting dimension value may only be applied for young children, e.g., when the configuration data indicates the patient <NUM> is less than ten years old. There may be no clear difference in dimensionality between awake versus sleep in neonates and dimensionality age adjustments may be insignificant in older children and adults. The trend analyzer <NUM> may analyze trends in the dimensionality, not necessarily the absolute value of the dimensionality. For example, if the dimensionality falls over time, the trend analyzer <NUM> can classify the EEG signal as indicating a candidate seizure. In this regard, the consequences of minor errors in the estimates of absolute values of dimensionality are partially decreased because classification of events is based upon changes in metrics, rather than absolute values.

The interface generator <NUM> is configured to generate an interface for display on the user interface device <NUM> based on the metrics determined by the preliminary analyzer <NUM> and/or the secondary analyzer <NUM>, in some embodiments. Furthermore, the interface generator <NUM> is configured to generate the interface based on the presence of a candidate seizure as determined by the trend analyzer <NUM>, in some embodiments. Furthermore, the user interface generated by the interface generator <NUM> may be based on user input, e.g., a request to display particular metrics, display historical EEG data, etc..

In some embodiments, the interface includes a trend of the EEG data in real-time. In some embodiments, the trend of the EEG data is displayed constantly. Furthermore, the interface generated by the interface generator <NUM> may include a superimposed graph of a trend of the eigenvalues determined by the preliminary analyzer <NUM> over the trend of the EEG. There may be a <NUM> millisecond delay between the eigenvalue and the EEG waveform. Every <NUM> milliseconds, the secondary analyzer <NUM> is configured to determine a new value of each of the metrics <NUM>-<NUM>, in some embodiments. These values together form a trajectory for each of the metrics <NUM>-<NUM>. Assuming that each value can only go up or down compared to the preceding value, the secondary analyzer <NUM> is configured to calculate the probability of n consecutive changes in the same direction, in some embodiments. If the secondary analyzer <NUM> detects eight consecutive changes in the same direction, this may indicate a sufficient probability of change in a particular direction. The secondary analyzer <NUM> may use six seconds of time to determine the probability of an increase or a decrease of the metrics <NUM>-<NUM> since each metric is determined over a <NUM> millisecond period and eight values may be determined in total to detect the increase or decrease. The preliminary analyzer <NUM> can be configured to perform the same processing for the metric <NUM>.

When the changes in the eigenvalues become significant, the interface generator <NUM> is configured to cause the superimposed eigenvalue waveform to change color, in some embodiments. The interface generator <NUM> is configured to store the EEG trend time linked with the eigenvalue trend, in some embodiments. This allows a user to request, via the user interface device <NUM>, a particular portion of historical EEG data. In response to the request, the interface generator <NUM> can cause the interface to display the requested portion of EEG data and the corresponding eigenvalue trend for that requested portion. In some embodiments, any section of EEG data, and the corresponding metrics determined for the section of EEG data, that is classified as a candidate seizure, is highlighted in the user interface generated by the interface generator <NUM>. This can allow a trained clinician to review particular sections of EEG data that is possibly a candidate seizure and make a final determination regarding whether the section of data is indicative of a seizure.

In some embodiments, the interface generator <NUM> causes the interface to include a panel of the non-linear metrics determined by the secondary analyzer <NUM> that are statistically significant. The interface generator <NUM> can receive a user specified significance level via the user interface device <NUM> and cause the interface to include a particular non-linear metric in response to the statistical significance of the non-linear metric being greater than the user specified significance level for that metric.

In some embodiments, as a metric transitions from being non-significant to significant based on a threshold significance level and the statistical significance of each metric, the interface generator <NUM> causes the metric displayed in the interface to change from a first color to a second color, e.g., from black and white to yellow. When a pattern of a metric changes at a higher significance level (which can be defined based on a user setting or predefined parameters), the interface generator <NUM> can cause the metrics to become a third color, e.g., become blue. In some embodiments, the interface generator <NUM> displays a split screen of EEG data such that the EEG data is shown in a first window in real time and a period of historical EEG data that has been categorized as a candidate seizure is also displayed.

When there are no significant changes in the EEG trajectory, there may be no significance EEG data for review and the interface generator <NUM> can cause the interface to include an indication of no seizure. In some embodiments, the patterns and significance levels of the metrics may be user defined. In some embodiments, the patterns and/or significance levels may be predefined.

Referring now to <FIG>, a process <NUM> of detecting a candidate seizure by determining changes of non-linear features of an EEG signal is shown, according to an exemplary embodiment. The seizure detector <NUM> is configured to perform the process <NUM>, in some embodiments. In particular, the channel selector <NUM>, the filtering stage <NUM>, the preliminary analyzer <NUM>, and/or the secondary analyzer <NUM> of the seizure detector <NUM> are configured to perform some and/or all of the process <NUM>, in some embodiments. Furthermore, any computing system or device as described herein can be configured to perform the process <NUM>.

In step <NUM>, the channel selector <NUM> receives EEG data from an array of EEG electrodes configured to sense brain activity of the patient <NUM>. In some embodiments, the channel selector <NUM> receives the EEG data directly from the electrodes in real-time, i.e., as the data is collected. In the invention, the channel selector <NUM> receives the data after the data has been collected, i.e., from a database or other memory device storing the EEG data.

In step <NUM>, the channel selector <NUM> can select an EEG signal of a particular channel or from multiple channels of the EEG data receive din the step <NUM>. Furthermore, the filtering stage <NUM> can perform filtering on the EEG signal. In some embodiments, the selection of the channel includes selecting an EEG signal of particular electrode or group of electrodes from other EEG signals of other electrodes. In some embodiments, the selection performed by the channel selector <NUM> is predefined, i.e., the same channel is always selected. In some embodiments, the channel selection is selected based on configuration data, i.e., data indicating characteristics of the patient <NUM>, e.g., age, height, medical syndromes, etc. The filtering may allow a particular range of frequencies to be passed. In some embodiments, the range of frequencies passed is predetermined. In some embodiments, the ranges of frequencies passed is also based on the configuration data.

In step <NUM>, the preliminary analyzer <NUM> performs a preliminary analysis with a generalized metrics suited to detecting a shift in the ratio of non-linear versus linear contributors with a moving window. The preliminary analyzer <NUM> can detect trends in the metric. For example, the preliminary analyzer <NUM> may determine eigenvalues with a moving window and determine whether a trajectory of the eigenvalues is increasing or decreasing. The preliminary analyzer <NUM> may calculate probability values for the trajectory based on the values of the eigenvalues. If the probability values become small, i.e., less than a predefined amount, a statistical significance that the eigenvalue is increasing or decreasing can be identified by the preliminary analyzer <NUM> (e.g., the increase or decrease is statistically significant because the probability of the increase or decrease occurring is low).

A decrease in the eigenvalues may indicate a shift in the ratio of non-linear and linear contributors, i.e., an increase in the non-linear features of the EEG signal. In response to a detection of an increase in the non-linear features of the EEG signal in the step <NUM>, the step <NUM> of the process <NUM> may be performed. If the non-linear features of the EEG signal are not increasing, the step <NUM> may be skipped so that computational resources are not utilized inefficiently.

In step <NUM>, the secondary analyzer <NUM> performs a second analysis including determining metrics which will more precisely categorize the form of the non-linear change (e.g. changing dimensionality, entropy, degree of separation of the recurrence loops in phase space, Lyapunov exponents, etc.). The metrics analyzed the preliminary analysis (step <NUM>) may be computationally efficient while the metrics analyzed in the second phase (step <NUM>) may require greater computing resources, accordingly, processing the metrics in separate stages allows for use of computational resources only when necessary, i.e., only after the preliminary analysis indicates the possibility of a seizure. In the step <NUM>, the secondary analyzer <NUM> can identify trends in the second metrics and, based on a particular pattern of changes in the second metrics, determine whether the EEG data indicates a candidate seizure or no seizure.

Referring now to <FIG>, a process <NUM> of detecting a candidate seizure by determining changes of non-linear features of an EEG signal with eigenvalues, Renyi permutation entropy, and sample entropy is shown, according to an exemplary embodiment. The process <NUM> provides an exemplary metric analysis that the seizure detector <NUM> is configured to perform, in some embodiments. The decisions of the process <NUM> are exemplary, there may be many combinations of metrics and/or analysis rules that can be applied by the seizure detector <NUM> to detect a candidate seizure. The seizure detector <NUM> is configured to analyze various different patterns with various different non-linear metrics in addition to, or instead of, eigenvalues, Renyi permutation entropy, and/or sample entropy, in some embodiments. The seizure detector <NUM> is configured to perform the process <NUM>, in some embodiments. In particular, the preliminary analyzer <NUM> and/or the secondary analyzer <NUM> of the seizure detector <NUM> are configured to perform some and/or all of the process <NUM>, in some embodiments. Furthermore, any computing system or device as described herein can be configured to perform the process <NUM>.

In step <NUM>, the preliminary analyzer <NUM> can receive an EEG signal. The EEG signal may be a signal generated based on electrical brain activity of the patient <NUM>. Furthermore, the EEG signal may be processed by the channel selector <NUM> and/or the filtering stage <NUM> before being received by the preliminary analyzer <NUM>. The EEG signal may be a time series of data samples.

In step <NUM>, the preliminary analyzer <NUM> can determine eigenvalues based on the EEG signal. In some embodiments, the preliminary analyzer <NUM> determines the eigenvalues with a moving window of eigenvalues. For example, the preliminary analyzer <NUM> can apply a window with a predefined length and a predefined overlap with a previous location of the window to generate an eigenvalue based on samples of the EEG signal falling within the window.

In step <NUM>, the preliminary analyzer <NUM> can analyze a trend of the eigenvalues determined in the step <NUM> to determine whether the eigenvalue is increasing or decreasing over time. In some embodiments, the preliminary analyzer <NUM> determines a probability of a trajectory, i.e., an overall increase or decrease in the eigenvalues. If the probability of the trajectory to increase is greater than a predefined amount, the preliminary analyzer <NUM> performs the step <NUM>. Similarly, if the probability of the trajectory to decrease is greater than a predefined amount, the preliminary analyzer <NUM> performs the step <NUM>. If the eigenvalues do not demonstrate a significant increase or decrease, the preliminary analyzer <NUM> can classify the EEG data as insignificant.

If the eigenvalues are increasing, the preliminary analyzer <NUM> can classify the EEG data (particular samples of the EEG signal) indicating the increase as insignificant in step <NUM>. However, if the preliminary analyzer <NUM> determines that the eigenvalues are decreasing, the data of the EEG signal can be classified by the preliminary analyzer <NUM> as significant and potentially indicating a candidate seizure, in step <NUM>.

In step <NUM>, the secondary analyzer <NUM> can determine Renyi permutation entropy values based on the EEG signal. In some embodiments, the secondary analyzer <NUM> determines the Renyi permutation entropy values for only segments of EEG data that the preliminary analyzer <NUM> has classified as significant. This may allow the secondary analyzer <NUM> to only perform calculations and utilize computational resources efficiently.

In step <NUM>, the secondary analyzer <NUM> can analyze a trend of the Renyi permutation entropy determined in the step <NUM> to determine whether the Renyi permutation entropy is increasing or decreasing over time. In some embodiments, the secondary analyzer <NUM> determines a probability of a trajectory, i.e., an overall increase or decrease in the Renyi permutation entropy. If the probability of the trajectory is to increase is greater than a predefined amount, the secondary analyzer <NUM> performs the step <NUM>. Similarly, if the probability of the trajectory is to decrease is greater than a predefined amount, the secondary analyzer <NUM> performs the step <NUM>. If the Renyi permutation entropy does not demonstrate a significant increase or decrease, the secondary analyzer <NUM> can perform the step <NUM>.

In step <NUM>, the secondary analyzer <NUM> can determine sample entropy values based on the EEG signal. In some embodiments, the secondary analyzer <NUM> determines the sample permutation entropy values for only segments of EEG data that the preliminary analyzer <NUM> has classified as significant. This may allow the secondary analyzer <NUM> to only perform calculations, and utilize computational resources, when necessary. The sample permutation entropy may be determined by the secondary analyzer <NUM> with a moving window, in some embodiments.

In step <NUM>, the secondary analyzer <NUM> determines whether the sample entropy is positive or negative. Based on the polarity of the sample entropy, the secondary analyzer <NUM> classifies the data as a candidate seizure, the step <NUM> when the sample entropy is negative, or as noise, the step <NUM> when the sample entropy is positive.

Referring now to <FIG>, the seizure detector <NUM> implementing a statistical analyzer <NUM> and an artificial intelligence (AI) analyzer <NUM> are shown, according to an exemplary embodiment. The seizure detector <NUM> includes the analysis circuit <NUM> which includes the statistical analyzer <NUM> and the AI analyzer <NUM>. Furthermore, the analysis circuit <NUM> includes a switching manager <NUM> and the interface generator <NUM>.

The interface generator <NUM> can be configured to surface candidate seizures via a user interface to a user via the user interface device <NUM>. The interface generator <NUM> can be configured to surface candidate seizure detections, e.g., generate and display notifications made by the statistical analyzer <NUM> until the switching manager <NUM> indicates that the seizure detections are being made by the AI analyzer <NUM>. Once the switch has been made to the AI analyzer <NUM>, the interface generator <NUM> can surface candidate seizure detections made by the AI analyzer <NUM> to a user via the user interface device <NUM>.

The statistical analyzer <NUM> can be configured to perform a statistical analysis to identify changes in non-linearities in an EEG signal received from the electrode interface <NUM>. The statistical analysis can be the analysis described with reference to <FIG>. The statistical analyzer <NUM> can be configured to generate one or multiple metrics that indicate non-linearities in the EEG signal. The statistical analyzer <NUM> can perform a trajectory analysis to map changes in one or multiple metrics to a candidate seizure or no candidate seizure.

The AI analyzer <NUM> can be configured to train one or more models based on the EEG signal, the candidate seizures determined by the statistical analyzer <NUM>, and/or user labeled seizures. In some embodiments, the AI analyzer <NUM> can be configured to implement machine learning algorithms. With the trained one or more models, the AI analyzer <NUM> can identify sections of an EEG signal as a candidate seizure or normal activity. The AI analyzer <NUM> can implement support vector machines, artificial neural networks, convolutional neural networks, Bayesian models, decision trees, naive Bayes, k- Nearest Neighbors, random forest, and/or any other type of supervised and unsupervised machine learning model. The AI analyzer <NUM> may learn to identify candidate seizures in an unsupervised manner based on the EEG signal. In some embodiments, the AI analyzer <NUM> is configured to perform a supervised learning algorithm where the models are trained based on the user labeled seizures received from the interface generator <NUM>.

The switching manager <NUM> is configured to switch between using the statistical analyzer <NUM> to determine candidate seizures for surfacing to a user via the user interface device <NUM> and using the AI analyzer <NUM> to determine candidate seizures for surfacing to the user via the user interface device <NUM>. The switching manager <NUM> can be configured to identify a time at which to switch from the statistical analyzer <NUM> to the AI analyzer <NUM>.

The switching manager <NUM> can be configured to detect a condition indicating that the AI analyzer <NUM> has performed a significant amount of training and/or has collected a significant amount of data such that the AI analyzer <NUM> can take over determining the candidate seizures from the statistical analyzer <NUM>. The switching manager <NUM> can receive an indication of performance of the statistical analyzer <NUM> and an indication of performance of the AI analyzer <NUM>. If the performance of the AI analyzer <NUM> is greater (or a particular amount greater) than the performance of the AI analyzer <NUM>, the switching manager <NUM> is configured to switch from the statistical analyzer <NUM> to the AI analyzer <NUM>.

In some embodiments, the performances of the statistical analyzer <NUM> and the AI analyzer <NUM> are numerical scores, e.g., a percentage of correct candidate seizure identifications, a percentage of missed candidate seizure identifications, etc. In some embodiments, the scores are determined based on comparisons of candidate seizure detections made by the statistical analyzer <NUM> and/or the AI analyzer <NUM> to the user labeled seizures received from the interface generator <NUM>.

In some embodiments, the switching manager <NUM> compares detections made by the AI analyzer <NUM> to the detections made by the statistical analyzer <NUM>. If the AI analyzer <NUM> determines at least a number or percentage of the determinations made by the statistical analyzer <NUM>, the switching manager <NUM> can determine to switch from the statistical analyzer <NUM> to the AI analyzer <NUM>.

In some embodiments, in response to the switching manager <NUM> detecting to switch from using the statistical analyzer <NUM> to the AI analyzer <NUM>, the interface generator <NUM> can display a request to switch to a user via the user interface device <NUM>. The user can approve the switching manager <NUM> to switch from the statistical analyzer <NUM> to the AI analyzer <NUM>. Alternatively, the user can reject the switch and the switching manager <NUM> can continue using the statistical analyzer <NUM>.

In some embodiments, after a period of time has passed and/or once the performance of the AI analyzer <NUM> has improved, the switching manager <NUM> can again recommend switching to the AI analyzer <NUM> to the user via the interface generator <NUM>. In some embodiments, a performance score and/or training information of the AI analyzer <NUM> is surfaced to the user via the interface generator <NUM> along with the recommendation to switch.

Referring now to <FIG>, a process <NUM> of switching between the statistical analyzer <NUM> and the AI analyzer <NUM> is shown, according to an exemplary embodiment. The seizure detector <NUM> is configured to perform the process <NUM>, in some embodiments. In particular, the statistical analyzer <NUM>, the switching manager <NUM>, and/or the AI analyzer <NUM> are configured to perform some and/or all of the process <NUM>, in some embodiments. Furthermore, any computing system or device as described herein can be configured to perform the process <NUM>.

The process can include a first phase and a second phase. In the first phase, the statistical analyzer <NUM> can operate to identify candidate seizures and train the AI analyzer <NUM>. In the second phase, the AI analyzer <NUM> can take over determining the candidate seizures from the statistical analyzer <NUM>. The steps <NUM>-<NUM> can be steps of the first phase. The steps <NUM>-<NUM> can be steps of the second phase.

In step <NUM>, the seizure detector <NUM> can receive the EEG signal from the electrode interface <NUM>. In step <NUM>, the statistical analyzer <NUM> identify candidate seizures based on a statistical analysis performed on the EEG signal by the statistical analyzer <NUM>. The statistical analyzer <NUM> can identify candidate seizures by identifying changes in the non-linearities of the EEG signal. The statistical analyzer <NUM>, can identify changes in metrics and map the changes in the metrics to the candidate seizure. In step <NUM>, the interface generator <NUM> can display the candidate seizure or seizures to a user via the user interface device <NUM>.

In step <NUM>, the seizure detector <NUM> can receive labels from the user interface device <NUM>. The labels can indicate one or more sections of the EEG signal. The sections can be labeled as seizures or non-seizures.

In step <NUM>, the AI analyzer <NUM> can train one or more models to classify the EEG signal as a seizure or a non-seizure. In some embodiments, the AI analyzer <NUM> can train the models based on the EEG signal, the candidate seizure detections determined by the statistical analyzer <NUM>, and the user labeled seizures received from the interface generator <NUM>.

In step <NUM>, the switching manager <NUM> can determine whether to switch from the statistical analyzer <NUM> to the AI analyzer <NUM>. The switching manager <NUM> can determine that the AI analyzer <NUM> performs better than the statistical analyzer <NUM> and/or the AI analyzer <NUM> is ready to take over from the statistical analyzer <NUM>. The switching manager <NUM> can identify, in some embodiments, that a particular amount of time has passed and that the AI analyzer <NUM> is ready to take over from the statistical analyzer <NUM>. In step <NUM>, if the AI analyzer <NUM> is not ready to take over from the statistical analyzer <NUM>, the process can return to the step <NUM>.

In some embodiments, the interface generator <NUM> displays a notification to a user via the user interface device <NUM> that the AI analyzer <NUM> is ready to take over. The user can interact with the user interface device <NUM> to cause the switching manager <NUM> to switch to the AI analyzer <NUM>. In some cases, the user can reject moving to the AI analyzer <NUM> and the process <NUM> can return to the step <NUM>.

In step <NUM>, the AI analyzer <NUM> can identify candidate seizures based on the AI model with the EEG signal. The AI analyzer <NUM> can apply the model to the EEG signal to classify the EEG signal and/or a segment of the EEG signal as a candidate seizure or a non-candidate seizure. In response to the candidate seizures being detected by the AI analyzer <NUM>, the interface generator <NUM> can display the candidate seizures to the user interface device <NUM> in step <NUM>. The user can, via the user interface device <NUM>, provide labels to the EEG signal and make updates to the training of the AI model based on the AI analyzer <NUM> in the step <NUM>.

Referring now to <FIG>, a block diagram of the AI analyzer <NUM> is shown, according to an exemplary embodiment. The AI analyzer <NUM> includes a training manager <NUM>, an inference manager <NUM>, and a model <NUM>. The training manager <NUM> can train the model <NUM> based on a training data set <NUM>. The training data set <NUM> can include the user labeled seizures, the EEG signal, and/or candidate seizures detected by the statistical analysis performed by the statistical analyzer <NUM>. The training manager <NUM> can perform various training algorithms to train the model <NUM> to detect candidate seizures based on the inference data set <NUM>. The model can be any model described in <FIG> or elsewhere herein. The model <NUM> can be any form of artificial intelligence and/or machine learning. In some embodiments, the model <NUM> includes hidden layers. In some embodiments, the hidden layers form non-linear functions and/or features. The inference manager <NUM> can be configured to apply the inference data set <NUM>, e.g., the EEG signal, to the model <NUM>. The result may be a classification of a segment of the EEG signal as a candidate seizure or a non-seizure segment.

In some embodiments, the model <NUM> includes one or multiple inputs. One of the inputs may be a seizure candidate determined by the statistical analyzer <NUM>. In this regard, the training manager <NUM> can train the model <NUM> based on the performance of the statistical analyzer <NUM> and/or the determinations made by the statistical analyzer <NUM> can be used by the inference manager <NUM> to identify a candidate seizure by the model <NUM>.

In some embodiments, the model <NUM> includes a weight on one or all of the inputs into the model <NUM>. In some embodiments, the weight is a confidence of the accuracy of the candidate seizure determination made by the statistical analyzer <NUM>. The confidence can be determined based on the statistical analyzer <NUM>. The confidence can be the statistical significance in the trajectories of the metrics used by the statistical analyzer <NUM> to identify the candidate seizure. In some embodiments, the greater the confidence of the candidate seizure determined by the statistical analyzer <NUM>, the greater the weight of the model <NUM> weighting the candidate seizure determined by the statistical analyzer.

In some embodiments, the training manager <NUM> receives false positive indications. The false positive indications can be false positives for candidate seizures identified by the AI analyzer <NUM> and/or the statistical analyzer <NUM>. In some embodiments, the false positives are identified and provided by a user via the user interface device <NUM>. In some embodiments, the training manager <NUM> can be configured to train the model <NUM> based on the false positives. By training based on the false positives, the model <NUM> can improve to reduce the number of false positives identified by the AI analyzer <NUM>.

Referring now to <FIG>, a block diagram of different types of the model <NUM> is shown, according to an exemplary embodiment. The model <NUM> can include one or multiple of the models <NUM>-<NUM>. Furthermore, any type of artificial intelligence model can be the model <NUM>. The model <NUM> can be a neural network <NUM>, a convolutional neural network <NUM>, a recurrent neural network <NUM>, a deep neural network <NUM>, a decision tree <NUM>, a support vector machine <NUM>, a regression <NUM>, and/or a Bayesian network <NUM>.

The construction and arrangement of the systems and methods as shown in the various exemplary embodiments are illustrative only. Although only a few embodiments have been described in detail in this disclosure, many modifications are possible (e.g., variations in sizes, dimensions, structures, shapes and proportions of the various elements, values of parameters, mounting arrangements, use of materials, colors, orientations, etc.). For example, the position of elements can be reversed or otherwise varied and the nature or number of discrete elements or positions can be altered or varied. Accordingly, all such modifications are intended to be included within the scope of the present disclosure. The order or sequence of any process or method steps can be varied or re-sequenced according to alternative embodiments. Other substitutions, modifications, changes, and omissions can be made in the design, operating conditions and arrangement of the exemplary embodiments without departing from the scope of the present disclosure.

The present disclosure contemplates methods, systems and program products on any machine-readable media for accomplishing various operations. The embodiments of the present disclosure can be implemented using existing computer processors, or by a special purpose computer processor for an appropriate system, incorporated for this or another purpose, or by a hardwired system. Embodiments within the scope of the present disclosure include program products comprising machine-readable media for carrying or having machine-executable instructions or data structures stored thereon. Such machine-readable media can be any available media that can be accessed by a general purpose or special purpose computer or other machine with a processor. By way of example, such machine-readable media can comprise RAM, ROM, EPROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to carry or store desired program code in the form of machine-executable instructions or data structures and which can be accessed by a general purpose or special purpose computer or other machine with a processor. Combinations of the above are also included within the scope of machine-readable media. Machine-executable instructions include, for example, instructions and data which cause a general purpose computer, special purpose computer, or special purpose processing machines to perform a certain function or group of functions.

Claim 1:
A seizure detection system comprising one or more circuits, wherein the one or more circuits are configured to:
receive an electroencephalogram (EEG) signal generated based on electrical brain activity of a patient;
identify candidate seizures with a statistical analysis that identifies the candidate seizures based on changes in non-linear features of the EEG signal;
cause a display device to display the candidate seizures identified by the statistical analysis;
train an artificial intelligence model to classify the candidate seizures based on training data comprising the EEG signal and the candidate seizures identified by the statistical analysis;
determine to switch from displaying the candidate seizures identified with the statistical analysis to displaying the candidate seizures identified with the artificial intelligence model responsive to a determination that a performance of the artificial intelligence model to classify the candidate seizures satisfies a threshold; and
switch from displaying the candidate seizures identified with the statistical analysis to displaying the candidate seizures identified based on the artificial intelligence model with the EEG signal.