Method and system for long-term monitoring and lateralization of seizure activity in epilepsy patients

Abstract

A system for lateralization of seizure activity includes an implantable electrode array having a reference electrode, a plurality of sensing electrodes, and a processing unit. The plurality of sensing electrodes, collectively, measure EEG signals of first and second hemispheres of a patient brain. The processing unit includes a memory and a processor communicatively coupled to the electrodes and receiving the EEG signals from the first and second pairs of electrodes. The processor stores to the memory data of the received EEG signals. A transceiver coupled to the processor transmits / receives data to / from an external computing device. The processing unit includes one or more computer-executable routines executing on the external computing device and processing the data of the received EEG signals and determining from the data of the received EEG signals for each detected epileptic event, whether the epileptic event has a focal point in the first or second hemisphere of the patient brain.

Description

METHOD AND SYSTEM FOR LONG-TERM MONITORING AND LATERALIZATION OF SEIZURE ACTIVITY IN EPILEPSY PATIENTSTECHNICAL FIELD

[0001] The present disclosure relates to systems and methods for long-term monitoring of electroencephalographical activity in a subject and, in particular, to determining in which hemisphere of the brain each observed epileptic seizure originates (lateralization), and recommending one or more treatment options according to the lateralization of the observed seizures.BACKGROUND OF THE DISCLOSURE

[0002] Epilepsy is considered the world’s most common serious brain disorder, with an estimated 50 million sufferers worldwide and 2.4 million new cases occurring each year. Epilepsy is a condition of the brain characterized by epileptic seizures that vary from brief and barely detectable seizures to more conspicuous seizures in which a sufferer vigorously shakes. Epileptic seizures are unprovoked, recurrent, and due to unexplained causes.

[0003] Epilepsy and seizures can be classified in a variety of ways, but generally can be broken down into two categories: generalized onset epilepsy and focal onset epilepsy. In generalized epilepsy, seizures begin with a widespread, excessive electrical discharge involving both hemispheres (sides) of the brain. By contrast, in focal epilepsy, an abnormal electrical discharge occurs in one small part of the brain. Focal seizures are frequently caused by an underlying structural abnormality in the brain, though the abnormality is not always visible to clinicians using standard imaging techniques. Other causes, such as head trauma, stroke, infection, tumors, can also cause focal epilepsy.

[0004] Many epilepsy patients, both those with generalized epilepsy and those with focal epilepsy, respond well to pharmacological treatments. However, for those that do not, surgical intervention is typically the next type of treatment that is considered. Surgical intervention is inappropriate, however, if the foci of the seizures change over time.

[0005] Unfortunately, characterizing the type of epilepsy a patient has typically requires detailed study of both clinical observations and electrical and / or other signals in the patient’s brain and / or body. Particularly with respect to studying electrical activity in the patient’s brain (e.g., using electroencephalography to produce an electroencephalogram (EEG)), such study usually requires the patient to be monitored for some period of time. The monitoring of electrical activity in the brain requires the patient to have a number of electrodes placed on the scalp, each of which electrodes is typically connected to a data acquisition unit that samples the signals continuously (i.e., at a rate exceeding 200 Hz) to record the signals for later analysis. Medical personnel monitor the patient to watch for outward signs of epileptic or other events,and review the recorded electrical activity signals to determine whether an event occurred, whether the event was epileptic in nature and, in some cases, the type of epilepsy and / or region(s) of the brain associated with the event. Because the electrodes are wired to the data acquisition unit, and because medical personnel must monitor the patient for outward clinical signs of epileptic or other events, the patient is typically confined to a small area (e.g., a hospital or clinical monitoring room) during the period of monitoring, which can last anywhere from several hours to several days. Moreover, where the number of electrodes placed on or under the patient’s scalp is significant, the size of the corresponding wire bundle coupling the sensors to the data acquisition unit may be significant, which may generally require the patient to remain generally inactive during the period of monitoring, and may prevent the patient from undertaking normal activities that may be related to the onset of symptoms.

[0006] While ambulatory encephalograms (aEEGs) allow for longer-term monitoring of a patient outside of a clinical setting, aEEGs are typically less reliable than EEGs taken in the clinical setting, because clinical staff do not constantly monitor the patient for outward signs of epileptic events or check if the electrodes remain fixated to the scalp and, as a result, are less reliable when it comes to determining the difference between epileptic and non-epileptic events.

[0007] The use of EEG in the determination of whether an individual has epilepsy, the type of epilepsy, and its location (or foci) in the brain is fundamental in the diagnostic pathway of individuals suspected of epilepsy. Unfortunately, while the EEG offers a rich source of information relating to the disease, the EEG signal can suffer from a poor signal to noise ratio, is, for the most part, manually reviewed by trained clinical personnel, and the review is limited to a short period of monitoring, either in-patient, as described above, or ambulatory recordings, each being no more than seven days in duration. Additionally, detection of independent bilateral seizure onset can take up to or in excess of four weeks to detect, with approximately one third of all patients requiring four or more weeks of monitoring to detect bilateral seizure onset. Further still, EEG patterns relating to bilateral onsets frequently change overtime, are patient specific, and the foci of seizure onset can alter the EEG patterns (onset, timing between channels, morphology, etc.). As a result of these limitations, the current diagnosis paradigm suffers from several deficiencies, among which is the limited recording window (up to 7 days), which may be inadequate to capture the clinically relevant events in the EEG due to the infrequency of the epileptic events. As a result, clinicians cannot easily and reliably determine whether seizures are focal or generalized in nature, or whether the foci of seizures are moving over time or with different medication protocols. Thus, while seizures that occur within the limited window of time over which ambulatory recordings can occur may be focused in a particular area of the brain, acting on that information, especially when it comes to surgical intervention, may be inappropriate if the foci of the seizures change overtime or with medication.

[0008] It is desirable to have a safe, reliable and comfortable method of determining, over an extended period of time whether seizures are focused in a single hemisphere of the brain or in both hemispheres of the brain and / or if the foci of the seizures are shifting within or between hemispheres.

[0009] Any discussion of documents, acts, materials, devices, articles or the like which has been included in the present background is not to be taken as an admission that any or all of these matters form part of the prior art base or were common general knowledge in the field relevant to the present disclosure as it existed before the priority date of each claim of this application.SUMMARY OF THE DISCLOSURE

[0010] A system for lateralization of seizure activity in a patient includes an implantable electrode array. The array includes a reference electrode, a plurality of sensing electrodes spaced linearly along a lead of the electrode array and configured, collectively, to measure electroencephalogram (EEG) signals of a first hemisphere of a patient brain and of a second hemisphere of the patient brain, and a processing unit, itself including a memory device and a processor communicatively coupled to the electrodes and receiving the EEG signals from the first and second pairs of electrodes. The processor is configured to store to the memory device data of the received EEG signals. The array also includes a transceiver coupled to the processor and configured to transmit data to, and receive data from, an external computing device. The system includes one or more computer-executable routines executing one the external computing device and configured to process the data of the received EEG signals and to determine from the data of the received EEG signals, for each of a plurality of detected epileptic events, whether the epileptic event has a focus in the first hemisphere of the patient brain or in the second hemisphere of the patient brain.

[0011] A method for lateralization of seizure activity in a patient includes implanting in a patient an implantable electrode array configured to measure electroencephalogram (EEG) signals of a first hemisphere of the patient’s brain and of a second hemisphere of the patient’s brain. The method includes monitoring over time the EEG signals of the first hemisphere and of the second hemisphere, and identifying, using a computer-implemented routine analyzing the monitored EEG signals of the first hemisphere and of the second hemisphere, a plurality of epileptic events indicated by the EEG signals. The method further includes determining from the EEG signals, using the computer implemented routine, for each of the plurality of epileptic events, whether the identified epileptic event has a focus in the first hemisphere or the in the second hemisphere.

[0012] A further method includes implanting in a patient an implantable electrode array configured to measure electroencephalogram (EEG) signals of a first hemisphere of thepatient’s brain and of a second hemisphere of the patient’s brain. The method monitors over a first period of time EEG signals the first hemisphere and of the second hemisphere, and identifies, using a computer-implemented routine analyzing the monitored EEG signals of the first hemisphere and of the second hemisphere, a plurality of epileptic events indicated by the EEG signals during the first period of time. The method further includes determining from the EEG signals, using the computer implemented routine, whether each of the identified plurality of epileptic events during the first period of time has a focus in the first hemisphere or the in the second hemisphere. A treatment is administered, and the method monitors over a second period of time EEG signals the first hemisphere and of the second hemisphere, and identifies, using a computer-implemented routine analyzing the monitored EEG signals of the first hemisphere and of the second hemisphere, a plurality of epileptic events indicated by the EEG signals during the second period of time. The method then determines from the EEG signals, using the computer implemented routine, whether each of the identified plurality of epileptic events during the second period of time has a focus in the first hemisphere or in the second hemisphere.

[0013] Yet another method includes receiving first sets of data, each of the first sets of data from a respective one of a plurality of first processor devices and comprising respective first electroencephalogram (EEG) data generated from respective first signals detected by a respective array of EEG electrodes disposed on a respective patient, the respective array communicatively coupled to the respective first processor device. The method then generates a first artificial intelligence (Al) training set based on the first sets of data, receives as feature values a selection of one or more attributes of the first Al training set, and receives one or more keys or labels for the first Al training set. The method further includes training a first iteration of a trained model with the first Al training set, using the feature values and the one or more keys or labels defined for the first Al training set, the first iteration of the trained model operable to provide a first set of classification results having corresponding first error rates relative to a target patient, the first set of classification results relating to a lateralization of a first plurality of epileptic events. The method goes on to receive, from a second processor device, a second set of data, the second set of data comprising second EEG data generated from second signals detected by an array of EEG electrodes disposed on a target patient. A second Al training set is generated based on the second set of data. Still further, the method includes receiving as feature values a selection of one or more attributes of the second Al training set, receiving one or more keys or labels for the second Al training set, and training a second iteration of a trained model with the second Al training set, using the feature values and the one or more keys or labels defined for the second Al training set, the second iteration of the trained model operable to provide a second set of classification results having corresponding second error rates relative to the target patient, the second set of classification results relating to a lateralization of asecond plurality of epileptic events. The second error rates have a reduced overall error rate compared to an overall error rate of the first error rates.

[0014] Still another method includes receiving a first set of Al training data, the first set of labeled training data comprising labeled features values for respective first electroencephalogram (EEG) data generated from respective first signals detected by a respective array of EEG electrodes disposed on respective patients, the respective array communicatively coupled to the respective first processor device. The method includes training a first iteration of a trained model with the first set of Al training data, using the labeled feature values, the first iteration of the trained model operable to provide a first set of classification results having corresponding first error rates, the first set of classification results relating to a lateralization of a first plurality of epileptic events. The method includes receiving a second set of Al training data, the second set of Al training data comprising second EEG data generated from second signals detected by a two-channel array of EEG electrodes disposed on a target patient, generating a second Al training set based on the second set of data, and receiving as feature values a selection of one or more attributes of the second Al training set. Still further, the method includes receiving one or more keys or labels for the second Al training set, and training a second iteration of a trained model with the second Al training set, using the feature values and the one or more keys or labels defined for the second Al training set, the second iteration of the trained model operable to provide a second set of classification results having corresponding second error rates, the second set of classification results relating to a lateralization of a second plurality of epileptic events. The second error rates have a reduced overall error rate compared to an overall error rate of the first error rates.BRIEF DESCRIPTION OF THE FIGURES

[0015] Fig. 1 is a block diagram of an example system according to the described embodiments.

[0016] Figs. 2A and 2B show side and top views, respectively, of another example electrode device.

[0017] Figs. 2C through 2E show cross-sectional views of portions of the electrode device of Figs. 3A and 3B.

[0018] Figs. 2F and 2G show top and side views, respectively, of a distal end portion of the electrode device of Figs. 2A and 2B.

[0019] Fig. 2H illustrates an example implantation location of electrodes of an electrode device.

[0020] Fig. 2I illustrates an example implantation location of an electrode device.

[0021] Fig. 3 is a block diagram of an example sensor array including a plurality of electrodes and a local processing device.

[0022] Fig. 4A is a block diagram depicting in greater detail an embodiment of an example system implementing a static model.

[0023] Fig. 4B is a block diagram depicting in greater detail an embodiment of an example system implementing a trained Al model.

[0024] Fig. 5 is a block diagram depicting another example embodiment, in which detection and classification of events take place on an external device.

[0025] Fig. 6A is a block diagram of a first part of an example system for use in creating a trained Al model.

[0026] Fig. 6B is a block diagram of a second part of the example system for use in creating a trained Al model.

[0027] Fig. 7A is a block diagram depicting a first Al training set of data according to described embodiments.

[0028] Fig. 7B is a block diagram depicting a second Al training set of data according to described embodiments.

[0029] Fig. 8A is a block diagram depicting an embodiment of a set of classification results.

[0030] Fig. 9 is a flow chart depicting a method for training an Al model to detect and classify events

[0031] Fig. 10 is a flow chart depicting a method for detecting and classifying events.

[0032] Fig. 11 depicts sample data for a focal seizure with an onset focus in the right hemisphere as detected by a sparse sensor array.

[0033] Fig. 12 is a block diagram of an example embodiment of a processor device coupled to a separate sensor array according to the description.

[0034] Fig. 13 is a block diagram of another example embodiment of a processor device coupled to a combined unit including the sensor array according to the description.

[0035] Fig. 14A illustrates an example communication scheme between the sensor array and the processor device.

[0036] Fig. 14B illustrates an alternate example of a communication scheme between the sensor array and the processor device.

[0037] Fig. 15A illustrates an example communication scheme between the processor device and external equipment.

[0038] Fig. 15B illustrates an alternate example of a communication scheme between the processor device and external equipment.

[0039] Fig. 15C illustrates yet another example of a communication scheme between the processor device and external equipment.

[0040] Fig. 16 depicts example EEG waveform data including an epileptiform.

[0041] Fig. 17 is an image depicting a time-frequency transformation of the EEG waveform data depicted in Fig. 16.

[0042] Fig. 18 depicts a method of training an initial model.

[0043] Fig. 19 depicts a method of refining the initial model using EEG data obtained via a sparse sensor array.

[0044] Fig. 20 is a graph depicting the rates of true positive and false positive results for a variety of EEG waveforms.

[0045] Fig. 21 shows data demonstrating improved performance of a refined model over an initial model.

[0046] Fig. 22 illustrates the output of a refined model demonstrating accurate classification of EEG waveforms of seizure and muscle movement.

[0047] Fig. 23 illustrates the output of a refined model demonstrating accurate classification of EEG waveform characteristics.DETAILED DESCRIPTION

[0048] Embodiments of the present disclosure relate to the monitoring and classification of seizure activity in a patient using an array of sensors disposed beneath the patient’s scalp, in cooperation with computer algorithms programmed to detect and classify events of interest and, in particular, to detect and classify at least the hemisphere or hemispheres of seizure foci - that is, whether seizure onset is unilateral or bilateral and, more specifically, whether seizures occurring in the patient have foci in the right hemisphere, in the left hemisphere, or both, and whether the foci are changing over time. Certain embodiments relate, for example, to electrode arrays implanted in a head of a subject to monitor epileptic brain activity, and coupled to processor devices configured to monitor and classify the brain activity to determine when events occur and, for each event, the focus or, at a minimum, which hemisphere the focus is in. (As used herein, the term lateralization refers to the determination of a hemisphere associated each of one or more seizure foci.) In turn, by tracking which side or sides of the head the seizures are originating from, the presently described systems and methods can perform and / or can facilitate tracking seizure cycles and forecasting algorithms for each hemisphere of the brain, determining and / or tracking changes in abnormal EEG activity due to drug or neurostimulation treatmentchanges, and / or training the seizure detection algorithm to learn new patterns associated with temporal changes in the origin of seizure activity across the hemispheres.

[0049] Embodiments of the present disclosure include models that can analyze data received from the implanted electrode array and determine the hemisphere in which each event has its onset focus and, in some cases, may be operable to determine the onset focus more specifically than merely the hemisphere (e.g., by looking at specific EEG signals correlated with onset foci in specific areas of the brain and / or considering patient-reported or observed symptoms). In some embodiments, the models are static, while in others the models may be Al models that are trained once or iteratively. In further embodiments, the models, whether static or Al, may determine the nature, type, severity, intensity, or other characteristics of the seizures and / or whether they are changing overtime. In various embodiments, one model may perform multiple determinations / analyses, while in other embodiments, multiple models may each perform different determinations / analyses. The various models may perform prediction of seizure events, detection of seizure events, adjustment of analog front-end settings (e.g., gain, filtering, electrode montage, etc.), may detect and / or predict seizures in respective hemispheres of the brain, may each be responsible for detecting / predicting seizures associated with different types of seizures, and / or may model treatment (e.g., pharmacological or neurostimulatory) titration for various seizure foci and / or types.

[0050] Throughout the present disclosure, embodiments are described in which various elements are optional - present in some, but not all, embodiments of the system. Where such elements are depicted in the accompanying figures and, specifically, in figures depicting block diagrams, the optional elements are generally depicted in dotted lines to denote their optional nature.

[0051] Fig. 1 depicts, in its simplest form, a block diagram of a contemplated system 100 directed to detection and classification of neurological events. The system 100 includes an EEG sensor array 102, a processor device 104, and a user interface 106. The sensor array 102 generally provides EEG data, in the form of data representing detected electrical signals, to the processor device 104, which receives the signals and uses the signals to detect and classify seizure events using the electrical signal data. The user interface 106 may facilitate selfreporting by the patient of any of various data including events perceived by the patient, as well as other data (e.g., medication types, doses, dose times, patient mood, potentially relevant environmental data, and the like). The user interface 106 may also facilitate output of classification results, programming of the unit for a particular patient, calibration of the sensor array 102, tuning of various parameters such a gain, filtering, montage settings, etc. Additional sensors 108 may also be present in the system 100.

[0052] Generally, an EEG biomarker is a specific pattern, signal, or measurement obtained from an electroencephalogram (EEG) that provides information about a certain condition, disease, or physiological state. EEG biomarkers are often used to aid in the diagnosis, monitoring, and understanding of various neurological disorders, including epilepsy. These biomarkers can be specific waveforms, frequencies, amplitudes, or other characteristics of the EEG that are associated with particular neurological states. One class of EEG biomarkers is an epileptiform event (or epileptiform). An epileptiform event refers to a transient abnormal electrical discharge in the brain that resembles epileptic activity. These events can manifest as spikes, sharp waves, or other distinctive patterns on an EEG recording. Epileptiform events are often used as markers of potential epilepsy or seizure activity. They indicate abnormal electrical activity in the brain, which may or may not lead to actual seizures, and typically are used by clinicians as potential signals of an ictal event, indicative of the onset, duration, and offset of a seizure event. The shorter duration biomarkers within an epileptiform may include (without limitation) “spikes,” having durations between 30 and 80 ms, and “sharps,” having durations between 70 and 200 ms, although these biomarkers may also occur between seizures. The various biomarkers associated with ictal activity may be indicative of the types of seizures occurring and / or the foci of seizure onset for various seizure events. For example, absence seizures are frequently associated with generalized “spike” activity, though spike activity is not exclusive to absence seizures. Features of epileptiforms may signal additional biomarkers, and interictal (between seizure), pre-ictal, and post-ictal EEG data may provide additional biomarker information related to detection and / or prediction of seizures. In embodiments, photoplethysmography (PPG) data may be collected using one or more additional sensors, and may include PPG biomarker data related to interictal, pre-ictal, post-ictal (and ictal) state of the patient. For instance, oxygen desaturation is known to occur in a significant portion of focal seizures, including those without convulsive activity, before, during, or after a seizure. Similarly, changes in blood pressure, heart rate, or heart rate variability - all detectable within PPG data - can occur before, during, or after a seizure event. By observing EEG data and PPG data concurrently, over periods of time, additional relationships between biomarkers in EEG data and PPG data can reveal relationships and patterns that facilitate the detection and, perhaps more importantly, prediction of ictal events, and, in some embodiments establish biomarkers relating to drug side-effects and quality of life metrics that may relate to the long-term use of the applied therapeutic treatment(s). Other biomarkers may, in embodiments, be detected from microphone and / or accelerometer data. Together, all of the biomarkers collected may, in some embodiments, be analyzed to determine the types, foci, and other characteristics of seizures.

[0053] SENSOR ARRAY

[0054] Figs. 2A through 21 illustrate an embodiment of a sensor array 102, such as that described in U.S. Patent Application No. 16 / 797,315, entitled “Electrode Device for Monitoringand / or Stimulating Activity in a Subject,” the entirety of which is hereby incorporated by reference herein. With reference to Figs. 2A and 2B, in one embodiment an electrode device 157 is provided comprising an elongate, implantable body 158 and a plurality of electrodes 160 positioned along the implantable body 158 in the length direction of the implantable body 158. At a proximal end of the implantable body 158, a processing unit 144 is provided for processing electrical signals that can be sent to and / or received from the electrodes 160. Though not required, in some embodiments, an electrical amplifier 163 (e.g., a pre-amp) is positioned in the implantable body 158 between the electrodes 160 and the processing unit 144. In an alternative embodiment, the electrical amplifier 163 may be integrated into the processing unit 144 of the electrode device 157, instead of being positioned in the implantable body 158. A reference electrode 160R may also be included and, in particular, may be integrated into or on the surface of a housing of the processing unit 144. The reference electrode 160R may serve as a voltage reference to facilitate elimination of noise and / or better signal-to-noise ratio.

[0055] With reference to Fig. 2C, which shows a cross-section of a portion of the electrode device 157 adjacent one of the electrodes 160, the electrodes 160 are electrically connected, e.g., to the amplifier 163 and processing unit 144, by an electrical connection 167 that extends through the implantable body 158. A reinforcement device 168 is also provided in the electrode device 157, which reinforcement device 168 extends through the implantable body 158 and limits the degree by which the length of the implantable body 158 can extend under tension.

[0056] In this embodiment, referring to Figs. 2A and 2B, four electrodes 160 are provided that are spaced along the implantable body 158 between the amplifier 163 and a distal tip 159 of the implantable body 158. The distal tip 159 of the implantable body 158 is tapered. The four electrodes 160 are configured into two electrical pairs 161 , 162 of electrodes, the two most distal electrodes 160 providing a first pair of electrodes 161 and the two most proximal electrodes 160 providing a second pair of electrodes 162. In this embodiment, the electrodes 160 of the first pair 160 are spaced from each other at a distance x of about 40 to 60mm, e.g., about 50 mm (measured from center-to-center of the electrodes 160) and the electrodes 160 of the second pair 122 are also spaced from each other at a distance x of about 40 to 60mm, e.g., about 50 mm (measured from center-to-center of the electrodes 160). The first and second electrode pairs 161 , 162 are spaced from each other at a distance y of about 30 to 50 mm, e.g., about 40 mm (measured from center-to-center of the electrodes of the two pairs that are adjacent each other).

[0057] With reference to Figs. 2D and 2E, which provide cross-sectional views along lines B- B and C--C in Fig. 2C, respectively, the implantable body 158 has a round, e.g., substantially circular or ovate, cross-sectional profile. Similarly, each of the electrodes 160 has a round, e.g., substantially circular or ovate, cross-sectional profile. Each of the electrodes 160 extend circumferentially, completely around a portion of the implantable body 158. By configuring theimplantable body 158 and electrodes 160 in this manner, the exact orientation of the implantable body 158 and electrodes 160, when implanted in a subject, is less critical. For example, the electrodes 160 may interact electrically with tissue in substantially any direction. In this regard, the electrodes 160 may be considered to have a 360-degree functionality. The round cross- sectional configuration can also provide for easier insertion of the implantable portions of the electrode device 157 to the target location and with less risk of damaging body tissue. For example, the implantable body 158 can be used with insertion cannulas or sleeves and may have no sharp edges that might otherwise cause trauma to tissue.

[0058] In this embodiment, the implantable body 158 is formed of an elastomeric material such as medical grade silicone. Each electrode 160 comprises an annular portion of conductive material that extends circumferentially around a portion of the implantable body 158. More specifically, each electrode 160 comprises a hollow cylinder of conductive material that extends circumferentially around a portion of the implantable body 158 and, in particular, a portion of the elastomeric material of the implantable body 158. The electrodes 160 may be considered ‘ring’ electrodes.

[0059] Referring back to the embodiment of Figs. 2A and 2B, and with further reference to Figs. 2F and 2G, to strengthen the engagement between the electrodes 160 and the implantable body 158, straps 165 are provided in this embodiment that extend across an outer surface of each electrode 160. In this embodiment, two straps 165 are located on substantially opposite sides of each electrode 160 in a direction perpendicular to the direction of elongation of the implantable body 158. The straps 165 are connected between sections 166a, 166b of the implantable body 158 that are located on opposite sides of the electrodes 160 in the direction of elongation of implantable body, which sections 166a, 166b are referred to hereinafter as side sections. The straps 165 can prevent the side sections 166a, 166b from pulling or breaking away from the electrodes 160 when the implantable body 158 is placed under tension and / or is bent. In this embodiment, the straps 165 are formed of the same elastomeric material as the side sections 166a, 166b. The straps 165 are integrally formed with the side sections 166a, 166b. From their connection points with the side sections 166a, 166b, the straps 165 decrease in width towards a central part of the each electrode 160, minimizing the degree to which the straps 165 cover the surfaces of the electrodes 160 and ensuring that there remains a relatively large amount of electrode surface that is exposed around the circumference of the electrodes 160 to make electrical contact with adjacent body tissue. With reference to Fig. 2D, around a circumference of each electrode 160, at least 75% of the outer electrode surface, at least 80%, at least 85% or at least 90% of the outer electrode surface may be exposed for electrical contact with tissue, for example.

[0060] In alternative embodiments, a different number of straps 165 may be employed, e.g., one, three, four or more straps 165. Where a greater number of straps 165 is employed, thewidth of each strap 165 may be reduced. The straps 165 may be distributed evenly around the circumference of each electrode 160 or distributed in an uneven manner. Nevertheless, in some embodiments, the straps 165 may be omitted, ensuring that all of the outer electrode surface is exposed for electrical contact with tissue, around a circumference of the electrode 160.

[0061] As indicated above, the implantable body 158 is formed of an elastomeric material such as silicone. The elastomeric material allows the implantable body 158 to bend, flex and stretch such that the implantable body 158 can readily contort as it is routed to a target implantation position and can readily conform to the shape of the body tissue at the target implantation position. The use of elastomeric material also ensures that any risk of trauma to the subject is reduced during implantation or during subsequent use.

[0062] In embodiments of the present disclosure the electrical connection 167 to the electrodes 160 comprises relatively fragile platinum wire conductive elements. With reference to Figs. 2C to 2E, for example, to reduce the likelihood that the platinum wires will break or snap during bending, flexing and / or stretching of the implantable body 158, the electrical connection167 is provided with wave-like shape and, more specifically, a helical shape in this embodiment, although other non-linear shapes may be used. The helical shape, for example, of the electrical connection 167 enables the electrical connection 167 to stretch, flex and bend in conjunction with the implantable body 158. Bending, flexing and / or stretching of the implantable body 158 typically occurs during implantation of the implantable body 158 in a subject and upon any removal of the implantable body 158 from the subject after use.

[0063] As indicated above, a reinforcement device 168 is also provided in the electrode device 157, which reinforcement device 168 extends through the implantable body 158 and is provided to limit the degree by which the length of the implantable body 158 can extend under tension. The reinforcement device 168 can take the bulk of the strain placed on the electrode device 157 when the electrode device 157 is placed under tension. The reinforcement device168 is provided in this embodiment by a fiber (e.g., strand, filament, cord or string) of material that is flexible and which has a high tensile strength. In particular, a fiber of ultra-high- molecular-weight polyethylene (UHMwPE), e.g., Dyneema™, is provided as the reinforcement device 168 in the present embodiment. The reinforcement device 168 extends through the implantable body 158 in the length direction of the implantable body 158 and is generally directly encased by the elastomeric material of the implantable body 158.

[0064] The reinforcement device 168 may comprise a variety of different materials in addition to or as an alternative to UHMwPE. The reinforcement device may comprise other plastics and / or non-conductive material such as a poly-paraphenylene terephthalamide, e.g., Kevlar™. In some embodiments, a metal fiber or surgical steel may be used.

[0065] Similar to the electrical connection 167, the reinforcement device 168 also has a wavelike shape and, more specifically, a helical shape in this embodiment, although other non-linearshapes may be used. The helical shape of the reinforcement device 168 is different from the helical shape of the electrical connection 167. For example, as evident from Figs. 2C to 2E, the helical shape of the reinforcement device 168 has a smaller diameter than the helical shape of the electrical connection 167. Moreover, the helical shape of the reinforcement device 168 has a greater pitch than the helical shape of the electrical connection 167.

[0066] When the implantable body 168 is placed under tension, the elastomeric material of the implantable body will stretch, which in turns causes straightening of the helical shapes of both the electrical connection 167 and the reinforcement device 168. As the electrical connection 167 and the reinforcement device straighten 168, their lengths can be considered to increase in the direction of elongation of the implantable body 158. Thus, the lengths of each of the electrical connection 167 and the reinforcement device 168, in the direction of elongation of the implantable body 158, are extendible when the implantable body 158 is placed under tension.

[0067] For each of the electrical connection 167 and the reinforcement device 168, a theoretical maximum length of extension in the direction of elongation of the implantable body 158 is reached when its helical shape (or any other non-linear shape that may be employed) is substantially completely straightened. However, due to the differences in the helical shapes of the electrical connection 167 and the reinforcement device 168, the maximum length of extension of the reinforcement device 168 is shorter than the maximum length of extension of the electrical connection 167. Therefore, when the implantable body 158 is placed under tension, the reinforcement device 168 will reach its maximum length of extension before the electrical connection 167 reaches its maximum length of extension. Indeed, the reinforcement device 168 can make it substantially impossible for the electrical connection 167 to reach its maximum length of extension. Since the electrical connection 167 can be relatively fragile and prone to breaking, particularly when placed under tension, and particularly when it reaches a maximum length of extension, the reinforcement device 168 can reduce the likelihood that the electrical connection 167 will be damaged when the implantable body 158 is placed under tension. In contrast to the electrical connection 167, when the reinforcement device 168 reaches its maximum length of extension, its high tensile strength allows it to bear a significant amount of strain placed on the electrode device 157, preventing damage to the electrical connection 167 and other components of the electrode device 157.

[0068] In consideration of other components of the electrode device 157 that are protected from damage by the reinforcement device 168, it is notable that the implantable body 158 can be prone to damage or breakage when it is placed under tension. The elastomeric material of the implantable body 158 has a theoretical maximum length of extension in its direction of elongation when placed under tension, the maximum length of extension being the point at which the elastomeric material reaches its elastic limit. In this embodiment, the maximum lengthof extension of the reinforcement device 168 is also shorter than the maximum length of extension of the implantable body 158. Thus, when the implantable body 158 is placed under tension, the reinforcement device 168 will reach its maximum length of extension before the implantable body 158 reaches its maximum length of extension. Indeed, the reinforcement device 168 can make it substantially impossible for the implantable body 158 to reach its maximum length of extension. Since elastomeric material of the implantable body 158 can be relatively fragile and prone to breaking, particularly when placed under tension, and particularly when it reaches its elastic limit, the reinforcement device 168 can reduce the likelihood that the implantable body 158 will be damaged when it is placed under tension.

[0069] In this embodiment, the helical shapes of the reinforcement device 168 and the electrical connection 158 are provided in a concentric arrangement. Due to its smaller diameter, the reinforcement device 168 can locate radially inside of the electrical connection 167. In view of this positioning, the reinforcement device 168 provides a form of strengthening core to the implantable body 158. The concentric arrangement can provide for increased strength and robustness while offering optimal surgical handling properties, with relatively low distortion of the implantable body 158 when placed under tension.

[0070] As indicated, the reinforcement device 168 is directly encased by the elastomeric material of the implantable body 158. The helically-shaped reinforcement device 168 therefore avoids contact with material other than the elastomeric material in this embodiment. The helically shaped reinforcement device is not entwined or intertwined with other strands or fibers, for example (e.g., as opposed to strands of a rope), ensuring that there is a substantial amount of give possible in relation to its helical shape. The helical shape can move to a straightened configuration under tension as a result, for example.

[0071] The arrangement of the reinforcement device 168 is such that, when the implantable body 158 is placed under tension, the length of the reinforcement device 168 is extendible by about 20% of its length when the implantable body 158 is not under tension. Nevertheless, in embodiments of the present disclosure, a reinforcement device 168 may be used that is extendible by at least 5%, at least 10%, at least 15%, at least 20% or at least 25% or otherwise, of the length of the reinforcement device when the implantable body 158 is not under tension. The maximum length of extension of the reinforcement device 168 in the direction of elongation of the implantable body 158 may be about 5%, about 10%, about 15%, about 20% or about 25% or otherwise of its length when the implantable body 158 is not under tension.

[0072] As represented in Fig. 2C, the reinforcement device 168 has a relatively uniform helical configuration along its length. However, in some embodiments, the shape of the reinforcement device 168 can be varied along its length. For example, the reinforcement device 168 can be straighter (e.g., by having a helical shape with smaller radius and / or greater pitch)adjacent the electrodes 160 in comparison to at other portions of the implantable body 158. By providing this variation in the shape of the reinforcement device 168, stretching of the implantable body 158 may be reduced adjacent the electrodes 160, where there could otherwise be a greater risk of the electrodes 160 dislocating from the implantable body 158. This enhanced strain relief adjacent the electrodes 160 can be provided while still maintaining the ability of the reinforcement device 168, and therefore implantable body 158, to stretch to a desirable degree at other portions of the implantable body 158.

[0073] As indicated, the electrical connection 167 in this embodiment comprises relatively fragile platinum wire conductive elements. At least 4 platinum wires are provided in the electrical connection 167 to each connect to a respective one of the four electrodes 160. The wires are twisted together and electrically insulated from each other. Connection of a platinum wire of the electrical connection 167 to the most distal of the electrodes 160 is illustrated in Fig. 2C. As can be seen, the wire is connected to an inner surface 172 of the electrode 160, adjacent a distal end of the electrode 160, albeit other connection arrangements can be used, including connection arrangements that allow for the montage of electrodes to be adjusted (i.e., to allow different pairings of electrodes) in order to, for example, improve signal reliability.

[0074] The reinforcement device 168 extends through the hollow center of each of the electrodes 160. The reinforcement device 168 extends at least from the distal most electrode 160, and optionally from a region adjacent the distal tip 159 of the implantable body 158, to a position adjacent the amplifier 163. In some embodiments, the reinforcement device 168 may also extend between the amplifier 163 and the processing unit 144. In some embodiments, the reinforcement device 168 may extend from the distal tip 159 and / or the distal most electrode 160 of the implantable body 158 to the processing unit 144.

[0075] To prevent the reinforcement device 168 from slipping within or tearing from the elastomeric material of the implantable body 158, a series of knots 169 are formed in the reinforcement device 168 along the length of the reinforcement device 168. For example, with reference to Fig. 2F, a knot 169a can be formed at least at the distal end of the reinforcement device 168, adjacent the distal tip 159 of the implantable body 158, and / or knots 169 can be formed adjacent one or both sides of each electrode 160. The knots may alone provide resistance to movement of the reinforcement device 168 relative to the elastic material of the implantable body 158 and / or may be used to fix (tie) the reinforcement device 168 to other features of the device 157.

[0076] In the present embodiment for example, as illustrated in Fig. 2C, the reinforcement device 168 is fixed, via a knot 169b, to each electrode 160. To enable the reinforcement device 168 to be fixed to the electrode 160, the electrode 160 comprises an extension portion 173 around which knots 169 of the reinforcement device 168 can be tied. As shown in Fig. 2C, theextension portion 173 can include a loop or arm of material that extends across an open end of the hollow cylinder forming the electrode 160.

[0077] With reference to Figs. 2A, 2B, 2F, and 2G, the electrode device 158 comprises at least one anchor 164, and in this embodiment of plurality of anchors 164. The plurality of anchors 164 are positioned along a length of the implantable body 158, each adjacent a respective one of the electrodes 160. Each anchor 164 is configured to project radially outwardly from the implantable body 158 and specifically, in this embodiment, at an angle towards a proximal end of the implantable body 158. Each anchor 164 is in the form of a flattened appendage or fin with a rounded tip 170. The anchors 164 are designed to provide stabilization to the electrode device 157 when it is in the implantation position. When implanted, a tissue capsule can form around each anchor 164, securing the anchor 164 and therefore the implantable body 158 into place. In this embodiment, the anchors 164 are between about 0.5 mm and 2 mm in length, e.g., about 1 mm or 1 .5 mm in length.

[0078] So that the anchors 164 do not impede implantation of the electrode device 157, or removal of the electrode device 157 after use, each anchor 164 is compressible. The anchors 164 are compressible (e.g., foldable) to reduce the degree by which the anchors 164 projects radially outwardly from the implantable body 158. To further reduce the degree by which the anchors 164 project radially outwardly from the implantable body 158 when compressed, a recess 171 is provided in a surface of the implantable body 158 adjacent each anchor 164. The anchor 164 is compressible into the recess 171. In this embodiment, the anchors 164 project from a bottom surface of the respective recess 171 and the recess 171 extends on both proximal and distal sides of the anchor 164. Accordingly, the anchors 164 can be compressed into the respective recesses in either a proximal or distal direction. This has the advantage of allowing the anchors 164 to automatically move into a storage position in the recess 171 when pulled across a tissue surface or a surface of a implantation tool such as delivery device, in either of a proximal and a distal direction.

[0079] The electrode device 157 of the present embodiment is configured for use in monitoring electrical activity in the brain and particularly for monitoring electrical activity relating to epileptic events in the brain. The electrode device 157 is configured to be implanted at least partially in a subgaleal space between the scalp and the cranium. At least the electrodes 160 and adjacent portions of the implantable body 158 are located in the subgaleal space.

[0080] An illustration of the implantation location of the electrodes 160 is provided in Fig. 2H. As can be seen, the electrodes 160 locate in particular in a pocket between the galea aponeurotica 206 and the pericranium 203. When implanted, the first and second electrode pairs 161 , 162 are located on respective sides of the midline of the head of the subject in a substantially symmetrical arrangement. The first and second electrode pairs 161 , 162 thereforelocate over the right and left hemispheres of the brain, respectively. For example, the first electrode pair 161 can be used to monitor electrical activity at right hemisphere of the brain and the second electrode pair 162 can be used to monitor electrical activity at the left hemisphere of the brain, or vice-versa. Independent electrical activity data may be recorded for each of the right and left hemispheres, e.g., for diagnostic purposes. To position the electrode pairs 161 , 162 over the right and left hemispheres of the brain, the implantable body 158 of the electrode device 157 is implanted in a medial-lateral direction over the cranium of the subject’s head. The electrode pairs 161 , 162 are positioned away from the subject’s eyes and chewing muscles to avoid introduction of signal artifacts from these locations. The electrode device 157 implanted under the scalp in a position generally as illustrated in Fig. 2I.

[0081] The local processing device 144 of the sensor array 102 may be implanted under skin tissue. With reference to Fig. 3, the local processing device 144 can include an electrical amplifier 146, a battery 148, battery charging circuitry 149, a transceiver 150, an analogue to digital converter (ADC) 152, and a processor 154 to process electrical signals received from or transmitted to the electrodes 160. The local processing device 144 can include a memory 156 to store signal processing data. The local processing device 144 may be similar to a processing device of a type commonly used with cochlear implants, although other configurations are possible. A reference electrode 160R may also be electrically coupled to the local processing device 144, in embodiments, and specifically may be physically coupled to or part of the local processing device 144. In specific embodiments, the reference electrode 160R may be part of a housing of the local processing device 144.

[0082] The data processed and stored by the local processing device 144 may be raw EEG data or partially processed (e.g., partially or fully compressed) EEG data, for example. The EEG data may be transmitted from the local processing device 144 wirelessly, or via a wired connection, to the processor device 104 for further processing and analyzing of the data. The processor device 104 may analyze EEG signals (or other electrical signals) to determine if a target event has occurred. Data regarding the event may be generated by the processor device 104 on the basis of the analysis, as described further herein. In one example, the processor device 104 may analyze brain activity signals to determine if an epileptic event has occurred and data regarding the epileptic event (e.g., classification of the event, which hemisphere the event was focused in, what area of the brain the event was focused in, etc.) may be generated by the processor device 104 on the basis of the analysis.

[0083] By carrying out data analysis externally to the sensor array 102, using the processor device 104, for example, there may be a reduction in power consumption within the sensor array 102, enabling the sensor array 102 to retain a smaller geometrical form. Moreover, the processor device 104 may have significantly higher processing power than would be possiblewith any processor included in the sensor array 102. The processor device 104 may run software that continuously records EEG data received from the sensor array 102.

[0084] THE PROCESSOR DEVICE

[0085] Turning now to Fig. 4A, the system 100 is presented as a block diagram in greater detail. As depicted in Fig. 4A, the system 100 includes, in embodiments, the sensor array 102, the processor device 104, and the user interface 106. The sensor array 102 may sense or collect EEG data and communicate the EEG data to the processor device 104. As should be understood at this point, in embodiments, the sensor array 102 may include an array of electrode devices 110 that provide electrical signal data and, in particular, provide electrical signal data indicative of brain activity of the patient (e.g., EEG signal data). As should also be understood in view of the description above, the sensor array 102 may be disposed beneath the scalp of the patient - on and extending into the cranium 204 - so as to facilitate accurate sensing of brain activity.

[0086] The optional PPG sensor (among the optional sensors 108) detects, using a photodetector circuit, light that is transmitted through or reflected from the patient after the light interacts with the blood just beneath the surface of the patient’s skin. The PPG sensor 108 may be any type of PPG sensor suitable for disposal on the patient and, in particular, suitable for operation from a portable power source such as a battery. The PPG sensor 108 may be disposed at any of a variety of positions on the patient including, but not limited to, the patient’s finger, toe, forehead, earlobes, nasal septum, wrist, ankle, arm, torso, leg, hand, or neck. In some embodiments, the PPG sensor 108 may be integrated with the sensor array 102 and placed on or beneath the scalp of the patient with the sensor array 102, while in others the PPG sensor 108 may be integrated with the processor device 104, and still in others the PPG sensor 108 may be distinct from both the sensor array 102 and the processor device 104. Of course, while depicted in the accompanying figures as a single PPG sensor, the PPG sensor 108 may be one or more PPG sensors, disposed as connected or distinct units on a variety of positions on the patient (so-called multi-site photoplethysmography). In embodiments implementing multiple PPG sensors, the multiple PPG sensors may be of the same type, or may be different, depending on the location of each on the patient, the environment in which each is disposed, the location of each in the hardware (e.g., separate from other devices or integrated with the processor device 104, for example), etc.

[0087] Optional other sensors, such as microphones, detect sound related to the patient and the patient’s environment. Such a microphone may be any type of microphone suitable for disposal on the patient and suitable for operation from a portable power source such as a battery. In particular, the microphone may be a piezoelectric microphone, a MEMS microphone, or a fiber optic microphone. In embodiments, an accelerometer device may be adapted tomeasure vibrations and, accordingly, to detect sound, rendering the accelerometer device suitable for use as a microphone. The microphone may be disposed at any of a variety of positions on the patient including, but not limited to, the patient’s head, arm, torso, leg, hand, or neck. In some embodiments, the microphone may be integrated with the sensor array 102 and placed on or beneath the scalp of the patient with the sensor array 102, while in others the microphone may be integrated with the processor device 104, and still in others the microphone may be distinct from both the sensor array 102 and the processor device 104. In embodiments, especially those in which the patient’s voice is the primary sensing target for the microphone, the microphone senses sound via bone conduction. In some embodiments, the microphone may be integrated with a hearing or vestibular prosthesis. The microphone may be one or more microphones, disposed as an array in a particular position on the patient, or as distinct units on a variety of positions on the patient. In embodiments implementing multiple microphones, the multiple microphones may be of the same type, or may be different, depending on the location of each on the patient, the environment in which each is disposed (e.g., sub-scalp vs. not), the location of each in the hardware (e.g., separate from other devices or integrated within the processor device 104, for example), etc. Each may have the same or different directionality and / or sensitivity characteristics as the others, depending on the placement of the microphone and the noises or vibrations the microphone is intended to detect.

[0088] The microphone may detect the patient’s voice, in embodiments, with the goal of determining one or more of: pauses in vocalization; stutters; periods of extended silence; abnormal vocalization; and / or other vocal abnormalities that, individually or in combination with data from the sensor array 102, optional accelerometer(s), and / or self-reported data received via the user interface 106, may assist algorithms executing within the processor device 104 in determining whether the patient has experienced an event of interest and, if so, classifying the event as described herein. In embodiments, the microphone may also detect other noises in the patient’s environment that may be indicative that the patient experienced an event of interest. For example, the microphone may detect the sound of glass breaking, which may indicate that the patient has dropped a glass. Such an indication, in conjunction with electrical signals detected by the sensor array 102, may provide corroboration that the patient has, in fact, experienced an event of interest.

[0089] The optional accelerometer detects movement and / or orientation of the patient. The accelerometer may be any type of accelerometer suitable for disposal on the patient and suitable for operation from a portable power source such as a battery. In particular, and by way of example, the accelerometer may be a chip-type accelerometer employing MEMS technology, and may include accelerometers employing capacitive, piezoelectric resistive, or magnetic induction technologies. Like the optional microphone, the optional accelerometer may be in any of a variety of positions on the patient including, but not limited to, the patient’s head, arm, torso,leg, hand, or neck. In some embodiments, there may be multiple accelerometers, to detect motions in different parts of the body. In some embodiments, an accelerometer may be integrated with the sensor array 102 and placed on or beneath the scalp of the patient with the sensor array 102, while in others an accelerometer may be integrated with the processor device 104 and still in others the accelerometer may be distinct from both the sensor array 102 and the processor device 104. The accelerometer may be one or more accelerometers, disposed as an array in a particular position on the patient, or as distinct units on a variety of positions on the patient. In embodiments implementing multiple accelerometers, the multiple accelerometers may be of the same type, or may be different, depending on the location of each on the patient, the environment in which each is disposed (e.g., sub-scalp vs. not), the location of each in the hardware (e.g., separate from other devices or integrated within the processor device 104, for example), etc. Each may have the same or different sensitivity characteristics and / or number of detectable axes as the others, depending on the placement of the accelerometer and the motions and / or vibrations the accelerometer is intended to detect.

[0090] The optional accelerometer may detect tremors, pauses in movement, gross motor movement (e.g., during a tonic-clonic seizure), falls (e.g., during an atonic or drop seizure or a tonic seizure), repeated movements (e.g., during clonic seizures), twitches (e.g., during myoclonic seizures), and other motions or movements that, in combination with data from the sensor array 102, the optional microphone, and / or self-reported data received via the user interface 106, may assist algorithms executing with the processor device 104 in determining whether the patient has experienced an invent of interest and, if so, classifying the event. In embodiments, the accelerometer may act as an additional microphone 20 or may act as the only microphone.

[0091] Like the optional microphone, the optional accelerometer may be in any of a variety of positions on the patient including, but not limited to, the patient’s head, arm, torso, leg, hand, or neck. In some embodiments, there may be multiple accelerometers, to detect motions in different parts of the body. In some embodiments, an accelerometer may be integrated with the sensor array 102 and placed on or beneath the scalp of the patient with the sensor array 102, while in others an accelerometer may be integrated with the processor device 104.

[0092] Together, the sensor array 102 and, if present, the PPG sensor(s), microphone(s) and / or accelerometer(s) may provide data from which biomarker data related to the patient(s) may be extracted. The system 100 may be configured to determine a variety of biomarkers depending on the inclusion and / or placement of the various sensor devices (i.e., the sensor array 102 and, if present, the PPG sensor(s), microphone(s), and / or accelerometers)).

[0093] The processor device 104 receives data from the sensor array 102, the optional PPG and other sensors 108, and the user interface 106 and, using the received data, may detect andclassify events of interest. The processor device 104 includes communication circuitry 256, a microprocessor 258, and a memory device 260. The microprocessor 258 may be any known microprocessor configurable to execute the routines necessary for detecting and classifying events of interest, including, by way of example and not limitation, general purpose microprocessors (GPUs), RISC microprocessors, digital signal processors (DSPs), application specific integrated circuits (ASICs), and field-programmable gate arrays (FPGAs).

[0094] The communication circuitry 256 may be any transceiver and / or receiver / transmitter pair that facilitates communication with the various devices from which the processor device 104 receives data and / or transmits data. The communication circuitry 256 is communicatively coupled, in a wired or wireless manner, to each of the sensor array 102, the optional sensors 108, and the user interface 106. Additionally, the communication circuitry 256 is coupled to the microprocessor 258, which, in addition to executing various routines and instructions for performing analysis, may also facilitate storage in the memory 260 of data received, via the communication circuity 256, from the sensor array 102, the optional sensors 108, and the user interface 106.

[0095] The memory 260 may include both volatile memory (e.g., random access memory (RAM)) and non-volatile memory, in the form of either or both of magnetic and solid-state media. In addition to an operating system (not shown), the memory 260 may store sensor array data 262 received from the sensor array 102, data received from the optional sensors 108, and user report data 268 received from the user (and / or other person such as a caregiver) via the user interface 106. In particular, the user report data 268 may include reports from the user, received via the user interface 106, of various types of symptoms and / or other data. By way of nonlimiting examples, the information reported via the user interface 106 may include: perceived seizures / epileptic events; characteristics or features of perceived seizures / epileptic events such as severity and / or duration, perceived effects on memory, or other effects on the individual’s wellbeing (such as their ability to hold a cup or operate a vehicle); other types of physiological symptoms (e.g., headaches, impaired or altered vision, involuntary movements, disorientation, falling to the ground, repeated jaw movements or lip smacking, etc.); characteristics or features of other symptoms (e.g., severity and / or duration); medication ingestion information (e.g., medication types, dosages, and / or frequencies / timing); neurostimulation treatment application information (e.g., types, dosages, target locations, and / or freq uency / timing), perceived medication side-effects; characteristics or features of medication side-effects (e.g., severity and / or duration), and other user reported information (e.g., food and / or drink ingested, activities performed (e.g., showering, exercising, working, brushing hair, etc.), tiredness, stress levels, etc.), as well as the timing of each. These user reports and, in particular, reports related to symptoms, and to medication or neurostimulation, may facilitate use of the software modelsdescribed herein to determine and / or track changes (e.g., changes in location of seizure focus) in abnormal activity in the EEG due to drug or treatment changes.

[0096] As will be described in greater detail below, the memory 260 may also store one or more models 270 for detecting and classifying events of interest according to a set of feature values 272 extracted from the sensor array data 262, the optional sensor data, and the user report data 268. (Referred to interchangeably as a model 270 or models 270, it should be understood that one or more models may be implemented to make the determinations described herein, and that references to a single model, unless specifically stating that the model is unitary, are intended to mean “one or more models.”) Classification results 274 (and, by extension, detected events and other characteristics as described below) output by the model 270 may be stored in the memory 260. A data pre-processing routine 271 may provide preprocessing of the sensor array data 262, the user report data 268 and, if present, the data from the optional sensor(s). As will be understood (and, in part, described below), the data preprocessing routine 271 may provide a range of pre-processing steps including, for example, filtering and extraction from the data of the feature values 272. Lastly, in embodiments, the memory 260 may store an image generation routine 276 configured to receive the sensor array data 262 and generate from the sensor array data 262 one or more images. For example, the image generation routine 276 may generate EEG waveforms from the raw EEG data recorded by the sensor array 102. The EEG waveforms may, in turn, be analyzed as images, rather than as raw EEG data by the various models and routines disclosed herein.

[0097] Of course, it should be understood that wherever a routine, model, or other element stored in memory is referred to as receiving an input, producing or storing an output, or executing, the routine, model, or other element is, in fact, executing as instructions on the microprocessor 258. Further, those of skill in the art will appreciate that the model or routine or other instructions would be stored in the memory 260 as executable instructions, which instructions the microprocessor 258 would retrieve from the memory 260 and execute. Further, the microprocessor 258 should be understood to retrieve from the memory 260 any data necessary to perform the executed instructions (e.g., data required as an input to the routine or model), and to store in the memory 260 the intermediate results and / or output of any executed instructions.

[0098] In embodiments, the data pre-processing routine 271 may also extract from the sensor array data 262, and the optional other sensor data, one or more biomarkers. The one or more biomarkers may be included among the feature values that are provided as inputs to the model 270, in embodiments, in order for the model 270 to output detected and / or classified events to the classification results 274.

[0099] The data stored in the sensor array data 262, the optional sensor data, and the user report data 268 are stored with corresponding time stamps such that the data may be correlated between data types. For example, each value in the sensor array data 262 should have a corresponding time stamp such that the data from optional sensors and the user report data 268 for the same time (and / or different times) can be compared, allowing the various types of data to be lined up and analyzed for any given time period. With respect to the user report data 268, there may be multiple time stamps for any particular user report, including, for example, the time that the user filled out the user report and the time of the event, symptom, or other information that the user was reporting (as reported by the user).

[0100] Events need not be contemporaneous to be relevant or related, or to be feature values input into the model. Put another way, the model 270 may consider temporal relationships between non-contemporaneous events in detecting, predicting, and / or classifying an event. By way of example and not limitation, an electrical activity event (e.g., EEG signals) indicating a seizure may be classified as a particular type of event if preceded by the ingestion of medication, and as a different type of event if not preceded by the ingestion of the medication. Other examples of non-contemporaneous events preceding a seizure that are precursors are patient subjective reports of auras or optical lights, shortness of breath or increased cardiac pulse rate, and acoustic biomarkers suggesting the alteration of speech patterns. Additionally, the system 100 and, in particular, the model 270, may identify pre- and / or post-seizure events, such as unsteady balance, falls, slurred speech, or brain activity patterns that are indicative of a pre- and / or post-seizure event.

[0101] Of course, contemporaneous events may also be relevant. For example, accelerometer data indicative of a generalized tonic-clonic (i.e., grand mal) seizure may be classified as such if it is accompanied by contemporaneous electrical activity indicative of such a seizure.

[0102] It is considered that while some objectives of the system 100 may be achieved using the model 270 (a static model) according to known data about the patient and / or the condition, other objectives of the system 100 may implement a trained artificial intelligence (Al) model to achieve maximum benefit.

[0103] Fig. 4B is a block diagram of an example system 300 similar to the system 100 of Fig. 4A, but which includes a trained artificial intelligence (Al) model 302 instead of the model 270 based on a static algorithm. That is, Fig. 4B corresponds generally to Fig. 4A, with the only difference between the system 100 and the system 300 in the respective figures being the inclusion of the trained Al model 302 rather than the model 270 based on a static algorithm. The system 300, as depicted in Fig. 4B is the same in all respects as in Fig. 4A, above, except that the trained Al model 302 is created using Al algorithms to search for and identify patterns intraining data and, upon implementation in the processor device 104, to receive the sensor array data 262 (and, optionally, other data) and to determine from those data feature values 272 from which the trained Al model 302 may perform the evaluative functions to determine the classification results 274 and, in particular, to determine a lateralization output for each detected epileptic event. Like the static model 270, the trained Al model 302 may consider temporal relationships between non-contemporaneous events and / or biomarkers in performing the evaluative functions.

[0104] In embodiments, the model(s) may use long-term EEG monitoring across both hemispheres of the brain to detect, track, and train a system that can aid a physician in treating individuals with epilepsy. The model(s) may detect foci movement, in embodiments, by, for example, detecting new patterns in the EEG or between the signals recorded by the respective EEG recording channels. For instance, gross changes might include a pattern starting on one measurement channel and then changing from one hemisphere to the other. (After a small time-delay, the other recording channel would then detect an anomalous pattern in the EEG data.) In embodiments, the model(s) may also detect changes in foci within one hemisphere of the brain. While embodiments employing sparse (i.e., two-channel) electrode arrays cannot localize the foci (beyond to a particular hemisphere) based on solely the EEG data, the model(s) may implement algorithms that identify new anomalous patterns in the EEG that would signify a move to another part of the brain. (Known EEG patterns would be associated only with the previously established foci.) Such models could also incorporate clinical feedback from the patient, for instance from the user report data 268, including the symptoms experienced during each seizure event. New reported events and symptoms could be employed to seek and / or detect new patterns in the EEG data that represent a move of the seizure foci from one region to another. (Moving of seizure foci or “foci movement” as used herein can mean either or both of generation of new foci, while preserving the original foci, or movement of the foci in the brain that does not preserve the original foci, and may include either / both of movement within a same hemisphere - in embodiments configured to detect movement within a hemisphere - or movement to the other hemisphere - in embodiments configured to detect only movement between hemispheres.) Additionally or alternatively, when a new EEG seizure event is determined to have a focus in a hemisphere different from previous seizure events, the model(s) may look at the user report data 268 to find the event as reported by the patient and may automatically associate the symptom to the event. (Such data may be presented to the treating physician to inform the physician of movement of the foci and new symptoms specific to the change.) Conversely, the model(s) may identify new clinical symptoms / events reported by the patient and may identify new patterns in the EEG in the hemisphere in which newly detected events are identified. Either way, in embodiments, the model(s) may provide as output feedback to the physician regarding the number of epileptic events from each hemisphere (reportedindependently), and / or may provide a report of symptoms segregated based on associates with each hemisphere. These data may be used by the clinician to, for example, treat the foci either together or independently - prescribing a new drug for the new foci or an adjusted treatment dosage (or delivery timing) based on detected seizure cycles.

[0105] The model(s) may also provide feedback to the physician regarding whether drug and / or neurostimulation treatments are efficacious for old foci, new foci, or both, by monitoring the number of epileptiform EEG events in the brain for each of the foci, rather than looking only at the sum of the detected or reported seizures. That is, in embodiments in which the model(s) are operative to track and associate specific epileptiform activity with specific symptoms and / or foci, the models(s), having associated specific epileptiform features with those foci, can track seizures based on foci, symptoms, or both, and can determine whether a particular treatment is affecting a specific class of seizures and, if so, which class of seizures are targeted using the treatment in question.

[0106] Moreover, as the model(s) become adapted (through feedback and / or retraining) to detect, track, and / or predict seizures associated with multiple foci in a patient, the model(s) may track, detect, and predict seizure cycles of each focus and / or type of seizure independently, allowing, for example, predictions of when to expect the symptoms of a specific seizure associated with one focus or type of seizure versus another focus or type.

[0107] Returning to Fig. 4B, the trained Al model 302 may be created by an adaptive learning component configured to “train” an Al model (e.g., create the trained Al model 302) to detect and classify events of interest (i.e., perform the evaluative functions) using as inputs raw or pre- processed (e.g., by the data pre-processing routine 271) data from the sensor array data 262 (as such, or converted into EEG waveform images) and, optionally, other data such as medication and / or neurostimulation treatment information. As described herein, the adaptive learning component may use a supervised or unsupervised machine learning program or algorithm. The machine learning program or algorithm may employ a neural network, which may be a convolutional neural network (CNN), a deep learning neural network, a residual neural network, or a combined learning module or program that learns in two or more features or feature datasets in a particular area of interest. The machine learning programs or algorithms may also include natural language processing, image recognition, multi-class output, semantic analysis, automatic reasoning, regression analysis, support vector machine (SVM) analysis, decision tree analysis, random forest analysis, K-Nearest neighbor analysis, naive Bayes analysis, clustering, reinforcement learning, and / or other machine learning algorithms and / or techniques. Machine learning may involve identifying and recognizing patterns in existing data (i.e., training data) such as temporal correlations between biomarkers in the EEG data 262, in order to facilitate making predictions for subsequent data and / or evaluating confidence levels ofclassification outputs (e.g., determined foci / lateralizations, likelihood of future changes in foci region / hemisphere).

[0108] The trained Al model 302 may be created and trained based upon example (e.g., “training data”) inputs or data (which may be termed “features” and “labels”) in order to make valid and reliable predictions for new inputs, such as testing level or production level data or inputs. In supervised machine learning, a machine learning program operating on a server, computing device, or other processor(s), may be provided with example inputs (e.g., “features”) and their associated, or observed, outputs (e.g., “labels”) in order for the machine learning program or algorithm to determine or discover rules, relationships, or other machine learning “models” that map such inputs (e.g., “features”) to the outputs (e.g., “labels”), for example, by determining and / or assigning weights or other metrics to the model across its various feature categories. Such rules, relationships, or other models may then be provided subsequent inputs in order for the model, executing on the server, computing device, or other processors), to predict an expected output based on the discovered rules, relationships, or model.

[0109] In unsupervised learning, the server, computing device, or other processor(s), may be required to find its own structure in unlabeled example inputs, where, for example, multiple training iterations are executed by the server, computing device, or other processor(s) to train multiple generations of models until a satisfactory model (e.g., a model that provides sufficient prediction accuracy when given test level or production level data or inputs) is generated. The disclosures herein may use one or both of such supervised or unsupervised machine learning techniques.

[0110] Throughout this specification, the phrase “evaluative functions” is used to refer to the collective potential outputs of the system including at least: detecting and / or classifying events that are occurring, according to foci, to foci hemisphere, to epileptiform, and / or to seizure symptoms; detecting and / or classifying events that have occurred according to foci, to foci hemisphere, to epileptiform, and / or to seizure symptoms; predicting and / or classifying events that are about to occur according to foci, to foci hemisphere, to epileptiform, and / or to seizure symptoms; detecting and / or classifying a lateralization of a detected or predicted event (e.g., a hemisphere in which an event is or is predicted to be focused, a focus of a predicted event, an epileptiform type of a predicted event, symptoms of a predicted event); detecting and / or predicting a focus of a detected or predicted event (e.g., a brain region in which onset occurred or is predicted to occur); detecting, tracking, and / or predicting individual cycles of seizure activity associated with a particular focus, with a particular hemisphere, with a particular eplileptiform, and / or with a particular seizure type; detecting and / or predicting how a medication or neurostimulation treatment will affect seizure activity and / or focus of one or more types of seizures and / or of seizures associated with one or more foci; and / or detecting new patterns associated with temporal changes in the origin of seizure activity across hemispheres.

[0111] Fig. 5 is a block diagram depicting another example embodiment, in which the evaluative functions take place on a device other than the processor device 104 and, specifically, on an external device 273. In the embodiments depicted in Fig. 5, it is contemplated that the models performing the evaluative functions may be either the static model 270 or the trained Al model 302 and, as a result, Fig. 5 illustrates an alternate embodiment of Figs. 4A and 4B. In the embodiments contemplated within Fig. 5, the processor device 104 generally collects the data from the sensor array 102 (and, optionally, the user interface 106 and / or other sensors 108). These data are stored in the memory 260 of the processor device 104 as the sensor array data 262, the user report data 268, etc., respectively. While the processor device 104 may be equipped to perform the modeling - that is may have stored in the memory 260 the model 270 or 302 and the data pre-processing routine(s) 271 , and be configured to perform the evaluative functions to output feature values 272 and classification results 274 - in the embodiments contemplated by Fig. 5, this functionality is optional. Instead, the microprocessor 258 may be configured to communicate with the external device 273 such that the external device 273 may perform the evaluative functions. Because Fig. 5 depicts embodiments in which either the processor device 104 or the external device 273 (or both) may use the model(s) 270 or 302 to perform the evaluative functions, the model(s) 270, 302 in Fig. 5 are depicted as optional in each of the devices 104, 273. It should be understood, however, that the model(s) 270 or 302 must be present on at least one or the other of the devices 104, 273 in each embodiment.

[0112] The external device 273 may be a workstation, a server, a cloud computing platform, or the like, configured to receive data from one or more processor devices 104 associated with one or more respective patients. The external device 273 may include communication circuitry 275, coupled to a microprocessor 277 that, in turn, is coupled to a memory 279. The microprocessor 277 may be any known microprocessor configurable to execute the routines necessary for producing the evaluative results, including, by way of example and not limitation, general purpose microprocessors (GPUs), RISC microprocessors, digital signal processors (DSPs), application specific integrated circuits (ASICs), and field-programmable gate arrays (FPGAs).

[0113] The communication circuitry 275 may be any transceiver and / or receiver / transmitter pair that facilitates communication with the various devices from or to which the processor device 273 receives data and / or transmits data. The communication circuitry 256 is coupled to the microprocessor 277, which, in addition to executing various routines and instructions for performing analysis, may also facilitate storage in the memory 279 of data received, via the communication circuity 275, from the processor devices 104 of the one or more patients.

[0114] The memory 279 may include both volatile memory (e.g., random access memory (RAM)) and non-volatile memory, in the form of either or both of magnetic or solid state media.In addition to an operating system (not shown), the memory 279 may store received data 281 received from the processor devices 104, including the sensor array data 262 received from the sensor array 102, the data received from the optional other sensors 108, and / or user report data 268 received from the user via the user interface 106.

[0115] Like the processor device 104, the external device 273 may have, stored in its memory 279, the static model 270 or the trained Al model 302, as well as data pre-processing routines 271 . The microprocessor 277 may execute the data pre-processing routines 271 to refine, filter, extract biomarkers from, etc. the received data 281 and to output feature values 272 (which, in embodiments, include biomarkers or relationships between biomarkers). The microprocessor 277 may also execute the model 270, 302, receiving as inputs the feature values 272 and outputting classification results 274. One or more reporting routines 283 stored on the memory 279, when executed by the microprocessor 277, may facilitate outputting reports for use by the patient(s) or by medical personnel, such as physicians, to review the data and or treat the patient(s).

[0116] The embodiments depicted in Fig. 5 also contemplate that, even in embodiments in which the processor device 104 executes the model 270 or 302 to produce classification results, the processor device 104 may communicate the classification results 274, as well as the data 262, 264, 266, 268 upon which the classification results are based, to the external device 273. The external device 273 may receive such data for one or more patients, and may store the data forthose patients for later viewing or analysis by the patient(s), physicians, or others, as necessary. In embodiments in which the external device 273 performs analysis for multiple patients, or for which the external device 273 receives from multiple processor devices 104 data of multiple patients, the external device 273 may store the received data 281 , the classification results 274, and the feature values 272 for each patient separately in the memory.

[0117] MODEL TRAINING

[0118] Fig. 6A is a block diagram of an example system 310 for use in creating a trained Al model (e.g., the trained Al model 302). The system 310 includes one or more sets 312AI-312ANof data collection hardware that, in embodiments, are similar to the system 100 of Fig. 6. That is, each set of data collection hardware 312AI-312ANincludes a corresponding sensor array 102 (including electrode devices 110) and, optionally, one or more other sensors 108 and / or a user interface 106. The sensor array used in the data collection hardware from which training data are generated may include the sensor array described above with respect to Figs. 2A-2I in some embodiments, but in other embodiments may additionally or alternatively include traditional multi- or many-channel EEG sensor arrays that are not implanted and which are tethered to recording equipment. In embodiments, each of the sets 312AI-312ANof data collection hardware also includes a respective processor device 104, including communication circuitry256, a microprocessor 258, and a memory 260. Of course, some portion of the training data may originate from data collection hardware that is different from that depicted in Fig. 6A, including data collection hardware that does not include on-board processing devices. As in the systems 100, the memory 260 of each set 312AI-312ANof data collection hardware stores at least the sensor array data 262. Each of the sets 312AI-312ANof data collection hardware is associated with a corresponding patient A1-ANand, accordingly, each of the sets 312AI-312ANof data collection hardware collects data for a corresponding patient.

[0119] The data collection hardware may not be necessary to collect training data in some embodiments. For example, in embodiments, one or more models are trained using at least some data that are collected, classified, and labeled and are archived and available for use and / or research generally. These publicly available data may be combined with data obtained using the sensor array 102 to further train the model(s) 302 such that the model(s) 302 may accurately perform the evaluative functions based solely data from the sensor array 102.

[0120] In any event, unlike the systems 100, 300 depicted in Figs. 4A and 4B, however, the sets 312AI-312ANof data collection hardware in the system 310 need not necessarily include the models 270, 302 stored in the memory 260, and the memory 260 need not necessarily store feature values 272 or classification results 274. That is, the sets 312AI-312ANof data collection hardware in the system 310 need not necessarily be capable of performing the evaluative functions, but may, in embodiments, merely act as collectors of, and conduits for, information to be used as “training data” for to create the trained Al model 302.

[0121] The data collected by the sets 312AI-312ANof data collection hardware may be communicated to a modeling processor device 314. The modeling processor device 314 may be any computer workstation, laptop computer, mobile computing device, server, cloud computing environment, etc. that is configured to receive the data from the sets 312AI-312ANof data collection hardware and to use the data from the sets 312AI-312ANof data collection hardware to create the trained Al model 302. The modeling processor device 314 may receive the data from the sets 312AI-312AN of data collection hardware via wired connection (e.g., Ethernet, serial connection, etc.) or wireless connection (e.g., mobile telephony, IEEE 802.11 protocol, etc.), directly (e.g., a connection with no intervening devices) or indirectly (e.g., a connection through one or more intermediary switches, access points, and / or the Internet), between the communication circuitry 256 of the processor device 104 and communication circuitry 316 of the modeling processor device 314. Additionally, though not depicted in Fig. 6A, the data may be communicated from one or more of the sets 312AI-312ANof data collection hardware to the modeling processor device 314 via storage media, rather than by respective communication circuitry. The storage media may include any known storage memory type including, by way of example and not limitation, magnetic storage media, solid state storage media, secure digital (SD) memory cards, USB drives, and the like.

[0122] The modeling processor device 314 includes the communication circuitry 316, in embodiments in which it is necessary, a microprocessor 318, and a memory device 320. Though it should be understood, the microprocessor 318 may be one or more stand-alone microprocessors, one or more shared computing resources or processor arrays (e.g., a bank of processors in a cloud computing device), one or more multi-core processors, one or more DSPs, one or more FPGAs, etc. Similarly, the memory device 320 may be volatile or nonvolatile memory, and may be memory dedicated solely to the modeling processor device 314 or shared among a variety of users, such as in a cloud computing environment.

[0123] The memory 320 of the modeling processor device 314 may store as a first Al training set 322 (depicted in Fig. 7A) the received data 262 (sensor data and optional other data) received from each of the sets 312AI-312ANof data collection hardware and, optionally, from publicly available training data 263. As depicted in Fig. 7A, the user report data 268 may include perceived events (e.g., epileptic / seizure events) 350; characteristics or features of perceived events 352 such as severity and / or duration, perceived effects on memory, or other effects on the individual’s well-being (such as their ability to hold a cup or operate a vehicle, etc.); other types of physiological symptoms (e.g., headaches, impaired or altered vision, involuntary movements, disorientation, falling to the ground, repeated jaw movements or lip smacking, etc.) 354; characteristics or features of other symptoms 356 (e.g., severity and / or duration); treatment (e.g., medication ingestion, neurostimulation application) information 358 (e.g., medication types, dosages, and / or frequencies / timing); perceived medication side-effects 360; characteristics or features of medication side effects 362 (e.g., severity and / or duration), and other user reported information 364 (e.g., food and / or drink ingested, activities performed (e.g., showering, exercising, working, brushing hair, etc.), tiredness, stress levels, etc.), as well as the timing of each. An adaptive learning component 324 may comprise instructions that are executable by the microprocessor 318 to implement a supervised or unsupervised machine learning program or algorithm, as described above. One or more data pre-processing routines 326, when executed by the microprocessor 318, may retrieve the data in the first Al training set 322, which may be raw recorded data, and may perform various pre-processing algorithms on the data in preparation for use of the data as training data by the adaptive learning component 324. The pre-processing routines 326 may include routines for removing noisy data, cleaning data, reducing or removing irrelevant and / or redundant data, normalization, transformation, and extraction of biomarkers and other features. The pre-processing routines 326 may also include routines for detection of muscle activity in the electrical activity data and particularly in the EEG data by analyzing the spectral content of the signal and / or routines for selection of the channel or channels of the electrical activity data that have the best (or at least better, relatively) signal to noise ratios. The output of the pre-processing routines 326 and, optionally, in embodimentsusing image recognition of EEG waveform image data, of the image generation routine 276, is a final training set stored in the memory 320 as a set 328 of feature values.

[0124] In embodiments in which the adaptive learning component 324 implements unsupervised learning algorithms, the adaptive learning component 324, executed by the microprocessor 318, finds its own structure in the unlabeled feature values 328 and, therefrom, generates a first trained Al model 330.

[0125] In embodiments in which the adaptive learning component 324 implements supervised learning algorithms, the memory 320 may also store one or more classification routines 332 that facilitate the labeling of the feature values (e.g., by an expert, such as a neurologist, reviewing the feature values 328 and / or the first Al training set 322) to create a set of key or label attributes 334, and / or feature values 328 provided for the publicly available training data 263. In embodiments in which EEG data are represented by waveform images, the classification routines 332 may facilitate selection of portions of EEG waveform images, for example. The adaptive learning component 324, executed by the microprocessor 318, may use both the feature values 328 and the key or label attributes 334 to discover rules, relationships, or other “models” that map the features to the labels by, for example, determining and / or assigning weights or other metrics. The adaptive learning component 324 may output the set of rules, relationships, or other models as a first trained Al model 330.

[0126] Regardless of the manner in which the adaptive learning component 324 creates the first trained Al model 330, the microprocessor 318 may use the first trained Al model 330 with the first Al training set 322 and / or the feature values 328 extracted therefrom, or on a portion of the first Al training set 322 and / or a portion of the feature values 328 extracted therefrom that were reserved for validating the first trained Al model 330, in order to provide classification results 336 for comparison and / or analysis by a trained professional in order to validate the output of the model.

[0127] As should be apparent, the first Al training set 322 may include data from one or more of the sets 312AI-312ANof data collection hardware and, as a result, from one or more patients. Thus, the adaptive learning component 324 may use data from a single patient, from multiple patients, or from a multiplicity of patients when creating the first trained Al model 330. The population from which the patient or patients are selected may be tailored according to particular demographic (e.g., a particular type of epilepsy, a particular age group, etc.), in some instances, or may be non-selective. In embodiments, at least some of the patients associated with the sets 312AI-312AN of data collection hardware from which the first Al training set 322 is created may be patients without any symptoms of the condition(s) in question and, as such, may serve to provide additional control data to the first Al training set 322.

[0128] In embodiments, the first trained Al model 330 may be transmitted to (or otherwise received - e.g., via portable storage media) to another set of data collection hardware (e.g., the system 300 depicted in of Fig. 4B). The set of data collection hardware may implement the first trained Al model 330 to provide classification results 274 based on data that was not part of the first Al training set 322 collected by the sets 312AI-312ANof data collection hardware or, alternatively, may simply collect additional data for use by the modeling processor device 314 to iterate the first trained Al model 330.

[0129] Fig. 6B depicts such an embodiment. In Fig. 6B, a system 340 includes a set 342 of data collection hardware for a patient. Like hardware previously described, the set of 342 of data collection hardware includes the sensor array 102, optional other sensors 108 and / or the user interface 106, and the processor device 104. The processor device 104 includes the communication circuitry 256, the microprocessor 258, and the memory device 260. The memory device 260 has stored thereon at least the sensor array data 262. However, the memory 260 of the processor device 104 in the set 342 of data collection hardware optionally has stored thereon the first trained Al model 330. In such embodiments, the processor device 104 of the set 342 of data collection hardware may implement the data pre-processing routine 271 to extract feature values 272 and may provide associated classification results 274.

[0130] Any or all of the data stored in the memory device 260 of the set 342 of data collection hardware may be communicated from the set 342 of data collection hardware to the modeling processor device 314. As above, the modeling processor device 314 may receive the data from the set 342 of data collection hardware via wired connection (e.g., Ethernet, serial connection, etc.) or wireless connection (e.g., mobile telephony, IEEE 802.11 protocol, etc.), directly (e.g., a connection with no intervening devices) or indirectly (e.g., a connection through one or more intermediary switches, access points, and / or the Internet), between the communication circuitry 256 of the processor device 104 and the communication circuitry 316 of the modeling processor device 314. Additionally, though not depicted in Fig. 6B, the data may be communicated from the set 342 of data collection hardware to the modeling processor device 314 via storage media, rather than by respective communication circuitry. The storage media may include any known storage memory type including, by way of example and not limitation, magnetic storage media, solid state storage media, secure digital (SD) memory cards, USB drives, and the like.

[0131] The received data may be stored in the memory 320 as a second Al training set 344 (depicted in Fig. 7B). The second Al training set 344 may include the sensor array data 262, user report data 268, and the other data 264 received from the set 342 of data collection hardware. As depicted in Fig. 7A, the user report data 268 may include perceived events (e.g., epileptic / seizure events) 350; characteristics or features of perceived events 352 such as severity and / or duration, perceived effects on memory, or other effects on the individual’s wellbeing (such as their ability to hold a cup or operate a vehicle, etc.); other types of physiologicalsymptoms (e.g., headaches, impaired or altered vision, involuntary movements, disorientation, falling to the ground, repeated jaw movements or lip smacking, etc.) 354; characteristics or features of other symptoms 356 (e.g., severity and / or duration); treatment (e.g., medication ingestion, neurostimulation application) information 358 (e.g., medication types, dosages, and / or frequencies / timing); perceived medication side-effects 360; characteristics or features of medication side effects 362 (e.g., severity and / or duration), and other user reported information 364 (e.g., food and / or drink ingested, activities performed (e.g., showering, exercising, working, brushing hair, etc.), tiredness, stress levels, etc.), as well as the timing of each. The adaptive learning component 324 may comprise instructions that are executable by the microprocessor 318 to implement a supervised or unsupervised machine learning program or algorithm, as described above, for iterating the first trained Al model 330, which may have a first error rate associated with its classification results 336 (e.g., the results of the evaluative functions), to create a second trained Al model 346, which may have a second error rate, reduced from the first error rate, associated its classification results 348 (e.g., the results of the evaluative functions). The data pre-processing routines 326, when executed by the microprocessor 318, may retrieve the data in the second Al training set 344, which may be raw recorded data, and may perform various pre-processing algorithms on the data in preparation for use of the data as training data by the adaptive learning component 324. The pre-processing routines 326 may include routines for removing noisy data, cleaning data, reducing or removing irrelevant and / or redundant data, normalization, transformation, and extraction of biomarkers and other features. The pre-processing routines 326 may also include routines for detection of muscle activity in the electrical activity data and particularly in the EEG data by analyzing the spectral content of the signal and / or routines for selection of the channel or channels of the electrical activity data that have the best (or at least better, relatively) signal to noise ratios. The output of the preprocessing routines 326 is a final training set stored in the memory 320 as a set 328 of feature values.

[0132] In embodiments in which the adaptive learning component 324 implements unsupervised learning algorithms, the adaptive learning component 324, executed by the microprocessor 318, finds its own structure in the unlabeled feature values 328 and, therefrom, generates a second trained Al model 346.

[0133] In embodiments in which the adaptive learning component 324 implements supervised learning algorithms, the memory 320 may also store one or more classification routines 332 that facilitate the labeling of the feature values (e.g., by an expert, such as a neurologist, reviewing the feature values 328 and / or the second Al training set 344) to create a set of key or label attributes 334. The adaptive learning component 324, executed by the microprocessor 318, may use both the feature values 328 and the key or label attributes 334 to discover rules, relationships, or other “models” that map the features to the labels by, for example, determiningand / or assigning weights or other metrics. The adaptive learning component 324 may output an updated set of rules, relationships, or other models as a second trained Al model 346.

[0134] Regardless of the manner in which the adaptive learning component 324 iterates and / or updates the first trained Al model 330 to be the second trained Al model 346, the microprocessor 318 may use the second trained Al model 346 with the second Al training set 344 and / or the feature values 328 extracted therefrom, or on a portion of the second Al training set 344 and / or a portion of the feature values 328 extracted therefrom that were reserved for validating the second trained Al model 346, in order to provide classification results 348 for comparison and / or analysis by a trained professional in order to validate the output of the model. An error rate of the classification results 348 output by the second trained Al model 346 will be reduced relative to an error rate of the classification results 336 output by the first trained Al model 330. The second trained Al model 346 may be programmed into or communicated to the system depicted, for example, in Fig. 4B, for use performing evaluative functions for patients.

[0135] The static model 270, and the traisned Al model 302, may each be programmed to perform the evaluative functions by detecting within the received data (e.g., the sensor array data 262 and / or other data such as the user report data 268 and the other sensor data 264) relevant biomarkers for the condition(s) of interest (e.g., epilepsy / seizure activity) and performing the evaluative functions based on the presence, absence, and / or temporal relationships between the relevant biomarkers. In various embodiments, the models 270 and 302 may be programmed or trained to perform one or more of the following evaluative functions:

[0136] (1) detecting a seizure;

[0137] (2) classifying a seizure as epileptic or cardiac in origin;

[0138] (3) classifying a seizure as ictal hypoxemic or not;

[0139] (4) predicting a seizure event;

[0140] (5) classifying a severity of a seizure event;

[0141] (6) determining a pre- or post-ictal impact of a seizure event on patient well-being;

[0142] (7) predicting a pre- or post-ictal impact of a seizure event on patient well-being (severity of the event, ictal cardiac changes; types of ictal respiratory changes);

[0143] (8) predicting a recovery time from post-ictal impacts of a seizure event on patient well-being;

[0144] (9) detecting a hemisphere in which a detected seizure event originated;

[0145] (10) predicting a hemisphere in which a predicted seizure event will originate;

[0146] (11) determining that the foci of seizure activity are moving within a hemisphere or between hemispheres;

[0147] (12) determining, tracking, and / or predicting changes in seizure focus and / or EEG patterns as a result of medication and / or neurostimulation application.

[0148] Fig. 8 depicts the first set of classification results 336 and the second set of classification results 348, resulting respectively from the output of the first trained Al model 330 and the second trained Al model 346, when the first and second trained Al models 330 and 346 are trained to perform evaluative functions related to epilepsy events. (The classification results 274 output by the static model 270 may be similar.) The classification results 336 and 348 may each include a set 370 of data related to detected seizure events. The set 370 of data may include data 371 related to detected seizure events and, optionally, data 372 related to predicted seizure events. The classification results 336 and 348 may, in various embodiments, include any number of combinations of lateralization determinations with other information depicted in Fig. 8 and, accordingly, all of the data, except the lateralization determinations, are depicted in Fig. 8 as optional. However, it should be understood that certain data would be pre-requisite to other data - for example, if the data 370 do not include the data 371 of detected events, then other data for detected events such as severity, duration, etc. would not be included either. In any case, the data 371 related to detected seizure events may include data for each detected event including lateralization 373 of the event, duration 374 of the event, and severity 375 of the event. The data 371 related to detected events may also include, in embodiments, a confidence level 377 indicating the degree of certainty that the lateralization (left hemisphere or right hemisphere) of the event is accurate. Likewise, the data 372 related to predicted seizure events may include data for each predicted event including predicted lateralization 373A of the event, predicted duration 374A of the event, and predicted severity 375A of the event. The data 372 related to predicted events may also include, in embodiments, a confidence level 377A indicating the degree of certainty that the lateralization (left hemisphere or right hemisphere) of the predicted event is accurate, a prediction as to whether the seizure onset foci are likely to change in the future, and / or a confidence level that the prediction as to whether seizure onset foci are likely to change is accurate.

[0149] In view of the description above, it will also be appreciated that the classification results may also include additional data not depicted in Fig. 8. By way of example and not limitation, depending on the embodiment, the classification results may include any of the data indicated as potential outputs described herein including, but not limited to: data associating specific symptoms to specific foci; data associating specific symptoms to specific epileptiforms; data associating specific epileptiforms to specific hemispheres; data associating specific symptoms with events occurring in specific hemispheres; data associating specific epileptiforms to specific foci; data associating and / or characterizing specific treatments (pharmacologicaland / or neurostimulatory) with specific foci; data associating and / or characterizing specific treatments (pharmacological and / or neurostimulatory) with specific hemispheres; data associating and / or characterizing specific treatments (pharmacological and / or neurostimulatory) with specific epileptiforms; multi-cycle seizure forecasting data (i.e., prediction data for different types - foci, hemispheres, epileptiforms - of seizures); and / or historical data relating to any of the previously mentioned data.

[0150] Of course, in Fig. 8, the detected and / or predicted events may be associated with a time at which the detected event was detected to have occurred or a time at which the predicted event is predicted to occur.

[0151] It should be understood that the system and, in particular, the adaptive learning component 324 (whether implemented in the separate modeling processor device 314), the data collection hardware 342, or even in the processor device 104 alongside the trained Al model 302, may be programmed to analyze the predicted event data (e.g., predicted seizure event data 372) relative to detected event data (e.g., detected seizure event data 371) to determine the accuracy of the predictions made by the trained Al model 302. The results of the analysis may be used by the adaptive learning component 324 to further refine the trained Al model 302.

[0152] Fig. 9 is a flow chart depicting a method 410 for training an Al model (e.g., the trained Al model 302) to detect, classify, and / or predict events. The method 410 may include receiving, at a modeling processor device 314, from a first processor device 104 a first set of data (block 412). The first set of data may include sensor array data 262 from one or more first sensor arrays 102 disposed on respective first patients and, in embodiments, publicly available training data 263. In embodiments, the first set of data may include image data of EEG waveforms representing the sensor array data 262. The method 410 may also include generating a first Al training set 322 based on the first set of data and, optionally, on corresponding user reported data (block 414), including (again, optionally) the user reports 268 also received from the first processor device 104. Generating the first Al training set 322 may also include, in embodiments, converting raw EEG data into waveform data, deriving a time-frequency representation of the data, and / or deriving an image representative of the waveform data and / or the time-frequency representation of the data. The method also includes receiving a selection of one or more attributes of the first Al training set 322 as feature values 328 (block 416) and receiving one or more keys or labels 334 for the first Al training set 322 (block 418). The feature values 328 and the keys or labels 334 may be received via the classification routine 332. The modeling processor device 314 then trains a first iteration of a trained model 330, using the feature values and the one or more keys or labels for the first Al training set 322 (block 420).

[0153] As will be understood, the feature values may include EEG snippets (short sets of EEG data or images of the waveforms, or derivatives of the waveforms such as time-frequencyrepresentations of the waveforms, resulting from the data), for example, that are indictive of events of interest, while the keys or labels may include information identifying the EEG snippets as indicative of a focus, a hemisphere in which a focus is located, or other characteristics for which the model is being trained. The Al model 302, and the adaptive learning component 324, may operate based on raw EEG data, on image data representing waveforms depicting the EEG data, or some combination of the two.

[0154] The method 410 may also include receiving, at the modeling processor device 314, from a second processor device 104, a second set of data (block 422). The second set of data may include sensor array data 262 from one or more first sensor arrays 102 disposed on a second patient. The method 410 may also include generating a second Al training set 344 based on the second set of data and, optionally, on corresponding user reported data (block 424), including (again, optionally) the user reports 268 also received from the second processor device 104. The method may also include receiving a selection of one or more attributes of the second Al training set 344 as feature values 328 (block 426) and receiving one or more keys or labels 334 for the second Al training set 344 (block 428). The feature values 328 and the keys or labels 334 may be received via the classification routine 332. The modeling processor device 314 then trains a second iteration of a trained model 346, using the feature values and the one or more keys or labels for the second Al training set 344 (block 430).

[0155] Fig. 10 is a flow chart depicting a method 440 for detecting and classifying events. The method 440 includes receiving, at a processor device 104, a set data (block 442). The set of data may include sensor array data 262 from a sensor array 102 disposed on a patient (or EEG waveform images generated therefrom) and communicatively coupled, directly or indirectly, to the processor device 104. The method 440 also includes extracting from the set of data a plurality of feature values 272 (block 444), the plurality of feature values 272 including each of one or more feature values of the sensor array data 262 and one or more feature values of the PPG data 264. The method 440 then includes inputting into a trained model 302 executing on the processor device 104, the plurality of feature values 272 (block 446). The trained model 302 is configured according to an Al algorithm based on a previous plurality of feature values, and the trained model 302 is configured to provide one or more classification results 274 (block 448) based on the plurality of feature values 272, the one or more classification results 274 corresponding to one or more events captured in the biomarker data.

[0156] The classification results 274 may then optionally be used to perform one or more actions. For example, the classification results 274 may be reviewed by a clinician to determine whether the foci of the detected seizures are static (i.e., occurring in the same hemisphere and / or the same region of the brain) or moving (i.e., occurring in different hemispheres and / or regions of the brain) and, from these determinations, may determine that surgical intervention is or is not likely to resolve the epileptic episodes and, in what region(s) of the brain such .Alternatively or additionally, the classification results 274 may be reviewed by a clinician to determine one or more non-surgical (e.g., pharmacological) interventions.

[0157] In view of the present specification, it should be understood that a trained model 270, 302 may, at a minimum, classify seizure events as having foci in the left or right hemispheres, based on EEG data collected by a sparse (e.g., two-channel) sensor array such as the sensor array 102. Fig. 11 depicts an image representing EEG data collected by a sparse sensor array. A first channel 278A represents EEG data collected for the left hemisphere, and a second channel 278B represents EEG data collected for the right hemisphere. In Fig. 11 , seizure activity can be observed in the second channel 278B and, at a later time, as the amplitude of the epileptiform increases in the second channel 278B, that epileptiform activity appears in the first channel 278A, indicating that the seizure activity is spreading to the left hemisphere.

[0158] As may by now be understood, the presently disclosed method and system are amenable to a variety of embodiments, many of which have already been explicitly described, with reference to Figs. 12-150.

[0159] As described throughout the specification, the system 100 includes an EEG sensor array 102. In various embodiments, the EEG sensor array 102 may be separate from, but communicatively coupled to, the processor device 104, as depicted in Fig. 12. Fig. 13 depicts an embodiment in which the processor device 104 is integrated with the EEG sensor 102. The local processor device 104 may be communicatively coupled to external equipment 472, which may be one or more of the modeling processor device 314, the external device 273, etc.

[0160] Various communication schemes are contemplated, as well. Figs. 14A and 14B illustrate possible communication schemes between the sensor array 102 and the processor device 104 and, in particular, Fig. 14A illustrates a wireless connection 482 between the sensor array 102 and the processor device 104 (i.e., between the communication circuitry 150 of the sensor array 102 and the communication circuitry 256 of the processor device 104). The wireless connection 482 may be any known type of wireless connection, including a Bluetooth® connection (e.g., low-energy Bluetooth), a wireless internet connect (e.g., IEEE 802.11 , known as “WiFi”), a near-field communication connection, or similar. Fig. 14B illustrates a wired connection 484 between the sensor array 102 and the processor device 104. The wired connection 474 may be a serial connection, for example.

[0161] The sensor array 102 may communicate data to the processor device 104 as the data are acquired or periodically. For example, the sensor array 102 may store, in the memory 156 of the local processing unit 144, data as it is acquired from the electrode devices 110 that are part of the sensor array 102 and may periodically (e.g., every second, every minute, every half hour, every hour, every day, when the memory 156 is full, etc.) transmit the data to the processor device 104. In other embodiments, the sensor array 102 may store data until theprocessor device 104 is coupled to the respective device (e.g., via wireless or wired connection). The sensor array 102 may also store the data until the processor device 104 requests the transmission of the data from the respective deice to the processor device 104. In these manners, the sensor array 102 may be optimized, for example, to preserve battery life, etc.

[0162] Figs. 15A-15C illustrate possible communication schemes between the processor device 104 and external equipment or servers 472, regardless of whether or not the processor device 104 is integrated with the sensor array 102 (e.g., as in Fig. 12). In Fig. 15A, for example, the processor device 104 may be coupled by a wireless communication connection to a mobile device 486, such as a mobile telephony device, which may, in turn, be coupled to the external equipment 472 by, for example, the Internet. In Fig. 15B, the processor device 104 is coupled to one or more intermediary devices 488 (e.g., a mobile telephony base station, a wireless router, etc.), which in turn provides connectivity to the external equipment 472 via the Internet. In Fig. 15C, the processor device 104 is itself a mobile device, such as a mobile telephony device, which may be coupled by one or more intermediary devices 488 to the external equipment 472 by way of the Internet.

[0163] EXAMPLE

[0164] Figs. 16-23 depict aspects of a system implemented according to the principles described herein. Fig. 16 depicts an image 500 of a montage of 20 scalp EEG signals 502 (as waveform data derived from raw EEG readings) over a 30-second time window 504 that includes an apparent epileptiform 506 (feature) labeled as such. The EEG signals 502 was fed through a Power Spectral Analysis (PSA) to derive a time-frequency representation of the data, which, in turn, was converted into an image 508, depicted in Fig. 17. In the image 508, the x- axis represents time, the y-axis represents frequency, and the z-axis (represented by the grey scale) represents image amplitude. The image 508 was analyzed using a multi-classifier segmentation model (an image recognition model).

[0165] A number of these labeled features were analyzed to create a seizure detection algorithm. The data used in this portion of the process was selected from data of 7-day ambulatory EEG recordings from patients with epilepsy, which EEG recordings were curated by trained EEG reviewers and warehoused in the Seer Medical data archive. The EEG recordings were curated and labeled by experts (e.g., clinicians, neurologists, etc.). The curated scalp EEG data were split into three sets - one each fortraining, validation, and testing of the Al model. When training the Al model only pairs of electrodes directly above / near the locations of the corresponding electrodes in the sparse (e.g., two-channel) sensor array 102 were selected from the 20-electrodes available. That is, each labeled feature was selected to have a montage that approximated the electrode configuration of the sparse sensor array 102 (creating, essentially, virtual electrodes roughly matching the electrode configuration of the sensor array 102).

[0166] Fig. 18 depicts this process. A series of labeled training features from the scalp-based EEG recordings, each represented as an image depicting a time-frequency representation of EEG data derived from the selected sets electrodes (to create the virtual electrodes) was input into the multi-classifier segmentation model (i.e., the adaptive learning component) to train and tune the model. A set of labeled validation features, also selected from the scalp-based EEG recordings were used to validate the resulting trained model, which was then tested using feature data from patients monitored using the four-electrode (two-channel) sensor array 102. The output of the trained model according to the test data exhibited a first error rate and other characteristics (see Fig. 21). In particular, the trained model accurately detected 156 events (true positives), and falsely detected “events” (false positives) as occurring in 6167 instances, for a precision rate of roughly 2%. The adjudication of the trained model output required approximately 105 person-hours of time.

[0167] The trained model was next iterated / refined, using as a second set of training data a set of labeled feature data collected from epilepsy patients using the sparse sensor array 102, as illustrated in Fig. 19. The second set of training data, like the first, included a set of EEG recordings, each represented as an image depicting a time-frequency representation of the corresponding EEG data recorded by the sensor array 102. The second set of training data included labeled features representing seizure events, but also included labeled events representing patient muscle activity (e.g., chewing), and non-biological noise (e.g., mechanical artifacts in the EEG signals caused by movement of the skin / tissue around the electrode lead). The second set of training data, like the first set, was input into the multi-classifier segmentation model to further train and refine the Al model. The refined model was therefore trained based on both “virtual electrode” EEG data recorded by scalp-based EEG monitoring and EEG data recorded by the sub-scalp sparse sensor array 102. A set of labeled validation features, also selected from the EEG data recorded by the sub-scalp sparse sensor array 102, were used to validate the resulting refined model, which was then tested using additional feature data from patients monitored using the sensor array 102. The output of the refined model according to the test data exhibited a second error rate less than that of the trained model (see Fig. 21). In particular, the refined model accurately detected 107 events (true positives), and falsely detected “events” (false positives) as occurring in 125 instances, for a precision rate of roughly 43%. This represents a 20x improvement in precision, and a 50x improvement in false positive rate. The adjudication of the refined model output required approximately 4 person-hours of time, representing a 25x reduction in time required to validate the model results.

[0168] Fig. 20 demonstrates that, in one set of tests, for the sub-scalp data recorded by the sensor array 102, the refined model exhibited the highest accuracy for sleep events, closely followed by inter-ictal (short and long) and seizure events.

[0169] Fig. 22 illustrates example events identified by the refined model. Specifically, the refined model correctly identified a first event as muscle movement. Confidence levels, indicated by the two waveforms in the lower portion of the figure, indicate that the refined model showed a high confidence level that the detected event was muscle movement. The refined model also correctly identified a second event as a seizure event, as indicated by the confidence level indication. In one test, the model was trained to output more refined classification of EEG wave types, rather than classifying merely as seizure or muscle movement. The output of the model in that case is depicted in Fig. 23, and demonstrates that the model, trained on appropriate data, was able to use the data from the sparse sensor array 102 to accurately detect various event biomarkers including sharp waveforms, polyspike waveforms, sharp and wave waveforms, and vertex sharp waveforms.

[0170] The following list of aspects reflects a variety of the embodiments explicitly contemplated by the present disclosure. Those of ordinary skill in the art will readily appreciate that the aspects below are neither limiting of the embodiments disclosed herein, nor exhaustive of all of the embodiments conceivable from the disclosure above, but are instead meant to be exemplary in nature.

[0171] ASPECT SET 1

[0172] 1 . A system for lateralization of seizure activity in a patient, the system comprising: an implantable electrode array comprising: a reference electrode; a plurality of sensing electrodes spaced linearly along a lead of the electrode array and configured, collectively, to measure electroencephalogram (EEG) signals of a first hemisphere of a patient brain and of a second hemisphere of the patient brain; a processing unit, the processing unit comprising: a memory device; a processor communicatively coupled to the electrodes and receiving the EEG signals from the first and second pairs of electrodes, the processor configured to store to the memory device data of the received EEG signals; a transceiver coupled to the processor and configured to transmit data to, and receive data from, an external computing device; one or more computer-executable routines executing one the external computing device and configured to process the data of the received EEG signals and to determine from the data of the received EEG signals, for each of a plurality of detected epileptic events, whether the epileptic event has a focus in the first hemisphere of the patient brain or in the second hemisphere of the patient brain.

[0173] 2. A system according to aspect 1 , wherein the plurality of sensing electrodes spaced linearly along a lead of the electrode comprises two pairs of electrodes, disposed along the lead of the electrode array such that a first pair of the two pairs of electrodes is configured to be disposed adjacent to the first hemisphere of the patient brain and such that a second pair ofthe two pairs of electrodes is configured to be disposed adjacent to the second hemisphere of the patient brain.

[0174] 3. A system according to aspect 1 or aspect 2, where the one or more computerexecutable routines comprise a trained Al model.

[0175] 4. A system according to aspect 3, wherein the trained Al model receives as input EEG signal data and user report data.

[0176] 5. A system according to aspect 3 or aspect 4, wherein the Al model receives as input EEG signal data in the form of images of EEG waveforms.

[0177] 6. A system according to any one of aspects 3 to 5, wherein the Al model is trained using EEG data from scalp-based EEG sensors and EEG data from a plurality of sensing electrodes spaced linearly along a lead of the electrode array and configured, collectively, to measure electroencephalogram (EEG) signals of a first hemisphere of a patient brain and of a second hemisphere of the patient brain.

[0178] 7. A system according to any one of aspects 1 to 6, wherein the one or more computer-executable routines are further configured to determine, for the plurality of detected epileptic events, whether or not the foci of the detected epileptic events are the same.

[0179] 8. A system according to aspect 7, wherein determining whether or not the foci of the detected epileptic events are the same comprises determining whether the foci are moving (i) within the determined first hemisphere or second hemisphere or (ii) between the first and second hemispheres of the brain.

[0180] 9. A system according to any one of aspects 1 to 8, wherein the one or more computer-executable routines are further configured to determine a seizure type of reach of the plurality of detected epileptic events.

[0181] 10. A system according to any one of aspects 1 to 9, wherein the one or more computer-executable routines are further configured to determine whether the plurality of detected epileptic events have focal points in a same one of the first hemisphere and the second hemisphere or in different ones of the first hemisphere and the second hemisphere.

[0182] 11. A system according to any one of aspects 1 to 10, wherein the one or more computer-executable routines are further configured to determine whether the plurality of detected epileptic events are of a same type or of different types.

[0183] 12. A system according to any one of aspects 1 to 11 , wherein the one or more computer-executable routines are further configured to: classify as different event types detected epileptic events according to: (i) user reported symptoms of the detected epileptic events, based on user reports of events and / or epileptiform data received by the one or morecomputer-implemented routines; (ii) determined foci of the detected epileptic events based on the user reports and / or based on epileptiform data associated with the detected epileptic events; (iii) the determination of the detected epileptic events as having a focus in the first hemisphere of the patient brain or in the second hemisphere of the patient brain; and / or (iv) determined type of epileptic event based on the user reports and / or based on epileptiform data associated with the detected epileptic events.

[0184] 13. A system according to aspect 12, wherein the one or more computer-executable routines are further configured to track the classified detected epileptic events such that frequencies of occurrence of the different classifications can be tracked overtime.

[0185] 14. A system according to any one of aspects 1 to 13, wherein the one or more computer-executable routines are further configured to output, for the patient, a recommendation regarding whether the patient is a candidate for surgical intervention.

[0186] 15. A system according to any one of aspects 1 to 14, wherein the one or more computer-executable routines are further configured to output one or more confidence levels associated with each of the plurality of detected epileptic events.

[0187] 16. A system according to aspect 15, wherein the one or more confidence levels indicate the likelihood that the determination that the detected epileptic event is accurate.

[0188] 17. A system according to aspect 15 or aspect 16, wherein the one or more confidence levels indicate the likelihood that the determination of whether the detected epileptic event has a focus in the first hemisphere of the patient brain or in the second hemisphere of the patient brain is accurate.

[0189] 18. A system according to any one of aspects 15 to 17, wherein the one or more confidence levels indicate the likelihood that future epileptic events will occur in a different one of the first and second hemispheres.

[0190] 19. A system according to any one of aspects 15 to 18, wherein the one or more confidence levels indicate the likelihood that future epileptic events will have different foci than the plurality of detected epileptic events.

[0191] 20. A system according to any one of aspects 1 to 19, wherein the one or more computer-executable routines are further configured to track event types, the event types having different foci, different epileptiform characteristics, and / or different patient- re ported symptoms.

[0192] 21 . A system according to aspect 20, wherein the one or more computer-executable routines are further configured to: receive information about one or more treatments applied to the patient; monitor the event types according to the different foci, the different epileptiform characteristics, and / or the different patient-reported symptoms; and determine respective effects of each of the one or more treatments on each of the event types.

[0193] 22. A system according to any one of aspects 1 to 21 , wherein the one or more computer-executable routines comprise a trained Al model.

[0194] ASPECT SET 2

[0195] 1 . A method for lateralization of seizure activity in a patient, the method comprising: implanting in a patient an implantable electrode array configured to measure electroencephalogram (EEG) signals of a first hemisphere of the patient’s brain and of a second hemisphere of the patient’s brain; monitoring over time the EEG signals of the first hemisphere and of the second hemisphere; identifying, using a computer-implemented routine analyzing the monitored EEG signals of the first hemisphere and of the second hemisphere, a plurality of epileptic events indicated by the EEG signals; and determining from the EEG signals, using the computer implemented routine, for each of the plurality of epileptic events, whether the identified epileptic event has a focus in the first hemisphere or the in the second hemisphere.

[0196] 2. A method according to aspect 1 , wherein implanting the implantable electrode array comprises implanting an electrode array comprising a reference electrode, first and second pairs of sensing electrodes spaced linearly along a lead of the electrode array and configured, respectively, as first and second monitoring channels, a memory device, a transceiver, and a processor communicatively coupled to the electrodes and configured to receive the EEG signals from the first and second pairs of electrodes and store the received EEG signals to the memory device.

[0197] 3. A method according to aspect 1 or aspect 2, wherein monitoring over time EEG signals of the first hemisphere and of the second hemisphere comprises monitoring the EEG signals over a period exceeding 2 weeks.

[0198] 4. A method according to aspect 1 or aspect 2, wherein monitoring over time EEG signals of the first hemisphere and of the second hemisphere comprises monitoring the EEG signals over a period exceeding 1 month.

[0199] 5. A method according to aspect 1 or aspect 2, wherein monitoring over time EEG signals of the first hemisphere and of the second hemisphere comprises monitoring the EEG signals over a period exceeding 3 months.

[0200] 6. A method according to aspect 1 or aspect 2, wherein monitoring over time EEG signals of the first hemisphere and of the second hemisphere comprises monitoring the EEG signals over a period exceeding 6 months.

[0201] 7. A method according to any one of aspects 1 to 6, further comprising: determining that all of the identified plurality of epileptic events have a focus in the first hemisphere, or that all of the identified plurality of epileptic events have a focus in the second hemisphere; and providing an indication that the patient is a candidate for surgical intervention.

[0202] 8. A method according to any one of aspects 1 to 6, further comprising: determining that some of the identified plurality of epileptic events have a focus in the first hemisphere, and that some of the identified plurality of epileptic events have a focus in the second hemisphere; and providing an indication that the patient is a not a candidate for surgical intervention.

[0203] 9. A method according to any one of aspects 1 to 6, further comprising: determining that all of the identified plurality of epileptic events have a focus in the first hemisphere, or that all of the identified plurality of epileptic events have a focus in the second hemisphere; and performing surgery on the patient to reduce the seizure activity in the patient.

[0204] 10. A method according to any one of aspects 1 to 6, further comprising: determining that some of the identified plurality of epileptic events have a focus in the first hemisphere, and that some of the identified plurality of epileptic events have a focus in the second hemisphere; and changing a treatment regimen for the patient.

[0205] 11. A method according to any one of aspects 1 to 10, further comprising classifying epileptic events according to: (i) user reported symptoms of the identified epileptic events, based on user reports of events and / or epileptiform data associated with the identified epileptic events;(ii) determined foci of the identified epileptic events based on the user reports and / or based on the epileptiform data associated with the identified epileptic events; (iii) the determination of the identified epileptic events as having a focus in the first hemisphere of the patient brain or in the second hemisphere of the patient brain; and / or (iv) determined type of epileptic event based on the user reports and / or based on epileptiform data associated with the identified epileptic events.

[0206] 12. A method according to aspect 11 further comprising tracking the classified epileptic events such that frequencies of occurrence of the different classifications can be tracked overtime.

[0207] 13. A method according to any one of aspects 1 to 12, further comprising generating, for each identified epileptic event, one or more confidence levels associated with the respective identified epileptic event.

[0208] 14. A method according to aspect 13, wherein the one or more confidence levels indicate the likelihood that the determination that the identified epileptic event is accurate.

[0209] 15. A method according to aspect 13 or aspect 14, wherein the one or more confidence levels indicate the likelihood that the determination of whether the identified epileptic event has a focus in the first hemisphere of the patient brain or in the second hemisphere of the patient brain is accurate.

[0210] 16. A method according to any one of aspects 13 to 15, wherein the one or more confidence levels indicate the likelihood that future epileptic events will occur in a different one of the first and second hemispheres.

[0211] 17. A method according to any one of aspects 13 to 16, wherein the one or more confidence levels indicate the likelihood that future epileptic events will have different foci than the plurality of detected epileptic events.

[0212] 18. A method according to any one of aspects 1 to 17, further comprising determining, for each of the plurality of detected epileptic events, a focus more specific than a one of the first hemisphere or the second hemisphere.

[0213] 19. A method according to any one of aspects 1 to 18, further comprising tracking event types, the event types having different foci, different epileptiform characteristics, and / or different patient-reported symptoms.

[0214] 20. A method according to aspect 19, further comprising: receiving information about one or more treatments applied to the patient; monitoring the event types according to the different foci, the different epileptiform characteristics, and / or the different patient-reported symptoms; and determining respective effects of each of the one or more treatments on each of the event types.

[0215] 21. A method according to any one of aspects 1 to 20, wherein the computer- implemented routine comprises implementing a trained Al model.

[0216] ASPECT SET 3

[0217] 1 . A method comprising: implanting in a patient an implantable electrode array configured to measure electroencephalogram (EEG) signals of a first hemisphere of the patient’s brain and of a second hemisphere of the patient’s brain; monitoring over a first period of time EEG signals the first hemisphere and of the second hemisphere; identifying, using a computer-implemented routine analyzing the monitored EEG signals of the first hemisphere and of the second hemisphere, a plurality of epileptic events indicated by the EEG signals during the first period of time; and determining from the EEG signals, using the computer implemented routine, whether each of the identified plurality of epileptic events during the first period of time has a focus in the first hemisphere or the in the second hemisphere; administering a treatment; monitoring over a second period of time EEG signals the first hemisphere and of the second hemisphere; identifying, using a computer-implemented routine analyzing the monitored EEG signals of the first hemisphere and of the second hemisphere, a plurality of epileptic events indicated by the EEG signals during the second period of time; and determining from the EEG signals, using the computer implemented routine, whether each of the identified plurality ofepileptic events during the second period of time has a focus in the first hemisphere or in the second hemisphere.

[0218] 2. A method according to aspect 1 , wherein administering a treatment comprises administering a pharmacological agent.

[0219] 3. A method according to aspect 1 or aspect 2, wherein administering a treatment comprises administering neurostimulation.

[0220] 4. A method according to any one of aspects 1 to 3, wherein each of the first period of time and the second period of time exceed 2 weeks.

[0221] 5. A method according to any one of aspects 1 to 3, wherein each of the first period of time and the second period of time exceed 1 month.

[0222] 6. A method according to any one of aspects 1 to 3, wherein, taken together, the first period of time and the second period of time exceed 3 months.

[0223] 7. A method according to any one of aspects 1 to 3, wherein, taken together, the first period of time and the second period of time exceed 6 months.

[0224] 8. A method according to any one of aspects 1 to 7, further comprising: determining that all of the identified plurality of epileptic events during the first period of time and during the second period of time have a focus in the first hemisphere, or that all of the identified plurality of epileptic events during the first period of time and during the second period of time have a focus in the second hemisphere; and performing surgery on the patient to reduce the seizure activity in the patient.

[0225] 9. A method according to any one of aspects 1 to 7, further comprising: determining that all of the identified plurality of epileptic events during the first period of time have a focus in the first hemisphere, and that at least some of the plurality of epileptic events during the second period of time have a focus in the second hemisphere; and administering a second treatment that is different from the first treatment.

[0226] 10. A method according to any one of aspects 1 to 9, further comprising: classifying epileptic events according to: (i) user reported symptoms of the identified epileptic events, based on user reports of events and / or epileptiform data associated with the identified epileptic events; (ii) determined foci of the identified epileptic events based on the user reports and / or based on the epileptiform data associated with the identified epileptic events; (iii) the determination of the identified epileptic events as having a focus in the first hemisphere of the patient brain or in the second hemisphere of the patient brain; and / or (iv) determined type of epileptic event based on the user reports and / or based on epileptiform data associated with the identified epileptic events.

[0227] 11. A method according to aspect 10 further comprising tracking the classified epileptic events such that frequencies of occurrence of the different classifications can be tracked overtime.

[0228] 12. A method according to any one of aspects 1 to 11 , further comprising generating, for each identified epileptic event, one or more confidence levels associated with the respective identified epileptic event.

[0229] 13. A method according to aspect 12, wherein the one or more confidence levels indicate the likelihood that the determination that the identified epileptic event is accurate.

[0230] 14. A method according to aspect 12 or aspect 13, wherein the one or more confidence levels indicate the likelihood that the determination of whether the identified epileptic event has a focus in the first hemisphere of the patient brain or in the second hemisphere of the patient brain is accurate.

[0231] 15. A method according to any one of aspects 12 to 14, wherein the one or more confidence levels indicate the likelihood that future epileptic events will occur in a different one of the first and second hemispheres.

[0232] 16. A method according to any one of aspects 12 to 15, wherein the one or more confidence levels indicate the likelihood that future epileptic events will have different foci than the plurality of detected epileptic events.

[0233] 17. A method according to any one of aspects 1 to 16, further comprising determining, for each of the plurality of detected epileptic events, a focus more specific than a one of the first hemisphere or the second hemisphere.

[0234] 18. A method according to any one of aspects 1 to 17, further comprising tracking event types, the event types having different foci, different epileptiform characteristics, and / or different patient-reported symptoms.

[0235] 19. A method according to aspect 18, further comprising: receiving information about one or more treatments applied to the patient; monitoring the event types according to the different foci, the different epileptiform characteristics, and / or the different patient-reported symptoms; and determining respective effects of each of the one or more treatments on each of the event types.

[0236] 20. A method according to any one of aspects 1 to 19, where the computer- implemented routine comprises a trained Al model.

[0237] ASPECT SET 4

[0238] 1 . A method comprising: receiving first sets of data, each of the first sets of data from a respective one of a plurality of first processor devices and comprising respective firstelectroencephalogram (EEG) data generated from respective first signals detected by a respective array of EEG electrodes disposed on a respective patient, the respective array communicatively coupled to the respective first processor device; generating a first artificial intelligence (Al) training set based on the first sets of data; receiving as feature values a selection of one or more attributes of the first Al training set; receiving one or more keys or labels for the first Al training set; training a first iteration of a trained model with the first Al training set, using the feature values and the one or more keys or labels defined for the first Al training set, the first iteration of the trained model operable to provide a first set of classification results having corresponding first error rates relative to a target patient, the first set of classification results relating to a lateralization of a first plurality of epileptic events; receiving, from a second processor device, a second set of data, the second set of data comprising second EEG data generated from second signals detected by an array of EEG electrodes disposed on a target patient; generating a second Al training set based on the second set of data; receiving as feature values a selection of one or more attributes of the second Al training set; receiving one or more keys or labels for the second Al training set; training a second iteration of a trained model with the second Al training set, using the feature values and the one or more keys or labels defined for the second Al training set, the second iteration of the trained model operable to provide a second set of classification results having corresponding second error rates relative to the target patient, the second set of classification results relating to a lateralization of a second plurality of epileptic events; wherein the second error rates have a reduced overall error rate compared to an overall error rate of the first error rates.

[0239] 2. A method according to aspect 1 , wherein: generating the first artificial intelligence (Al) training set based on the first sets of data comprises generating the first Al training set based on corresponding first reports from the patients, caregivers of the patients, clinicians, and / or other observers; and generating the second Al training set based on the second set of data comprises generating the second Al training set based on corresponding second reports from the target patient, a caregiver of the target patient, a clinician of the target patient, and / or another observer of the target patient.

[0240] ASPECT SET 5

[0241] 1 . A method comprising: receiving a first set of Al training data, the first set of labeled training data comprising labeled features values for respective first electroencephalogram (EEG) data generated from respective first signals detected by a respective array of EEG electrodes disposed on respective patients, the respective array communicatively coupled to the respective first processor device; training a first iteration of a trained model with the first set of Al training data, using the labeled feature values, the first iteration of the trained model operable to provide a first set of classification results having corresponding first error rates, the first set of classification results relating to a lateralization of a first plurality of epileptic events; receiving asecond set of Al training data, the second set of Al training data comprising second EEG data generated from second signals detected by a two-channel array of EEG electrodes disposed on a target patient; generating a second Al training set based on the second set of data; receiving as feature values a selection of one or more attributes of the second Al training set; receiving one or more keys or labels for the second Al training set; training a second iteration of a trained model with the second Al training set, using the feature values and the one or more keys or labels defined for the second Al training set, the second iteration of the trained model operable to provide a second set of classification results having corresponding second error rates, the second set of classification results relating to a lateralization of a second plurality of epileptic events; wherein the second error rates have a reduced overall error rate compared to an overall error rate of the first error rates.

[0242] 2. A method according to aspect 1 , wherein receiving a first set of Al training data comprises: receiving first sets of data, each of the first sets of data from a respective one of a plurality of first processor devices and comprising respective first electroencephalogram (EEG) data generated from respective first signals detected by a respective array of EEG electrodes disposed on a respective patient, the respective array communicatively coupled to the respective first processor device; generating a first artificial intelligence (Al) training set based on the first sets of data, the training set having a montage of EEG signals selected to approximate a two-channel array of EEG electrodes having a first channel recording EEG signals of a first hemisphere and a second channel recording EEG signals of a second hemisphere; receiving as feature values a selection of one or more attributes of the first Al training set; receiving one or more keys or labels for the first Al training set.

Claims

CLAIMS1 . A system for lateralization of seizure activity in a patient, the system comprising: an implantable electrode array comprising: a reference electrode; a plurality of sensing electrodes spaced linearly along a lead of the electrode array and configured, collectively, to measure electroencephalogram (EEG) signals of a first hemisphere of a patient brain and of a second hemisphere of the patient brain; a processing unit, the processing unit comprising: a memory device; a processor communicatively coupled to the electrodes and receiving the EEG signals from the first and second pairs of electrodes, the processor configured to store to the memory device data of the received EEG signals; a transceiver coupled to the processor and configured to transmit data to, and receive data from, an external computing device; one or more computer-executable routines executing one the external computing device and configured to process the data of the received EEG signals and to determine from the data of the received EEG signals, for each of a plurality of detected epileptic events, whether the epileptic event has a focus in the first hemisphere of the patient brain or in the second hemisphere of the patient brain.

2. A system according to claim 1 , wherein the plurality of sensing electrodes spaced linearly along a lead of the electrode comprises two pairs of electrodes, disposed along the lead of the electrode array such that a first pair of the two pairs of electrodes is configured to be disposed adjacent to the first hemisphere of the patient brain and such that a second pair of the two pairs of electrodes is configured to be disposed adjacent to the second hemisphere of the patient brain.

3. A system according to claim 1 or claim 2, where the one or more computerexecutable routines comprise a trained Al model.

4. A system according to claim 3, wherein the trained Al model receives as input EEG signal data and user report data.

5. A system according to claim 3 or claim 4, wherein the Al model receives as input EEG signal data in the form of images of EEG waveforms.

6. A system according to any one of claims 3 to 5, wherein the Al model is trained using EEG data from scalp-based EEG sensors and EEG data from a plurality of sensing electrodes spaced linearly along a lead of the electrode array and configured, collectively, to measure electroencephalogram (EEG) signals of a first hemisphere of a patient brain and of a second hemisphere of the patient brain.

7. A system according to any one of claims 1 to 6, wherein the one or more computer-executable routines are further configured to determine, for the plurality of detected epileptic events, whether or not the foci of the detected epileptic events are the same.

8. A system according to claim 7, wherein determining whether or not the foci of the detected epileptic events are the same comprises determining whether the foci are moving (i) within the determined first hemisphere or second hemisphere or (ii) between the first and second hemispheres of the brain.

9. A system according to any one of claims 1 to 8, wherein the one or more computer-executable routines are further configured to determine a seizure type of reach of the plurality of detected epileptic events.

10. A system according to any one of claims 1 to 9, wherein the one or more computer-executable routines are further configured to determine whether the plurality of detected epileptic events have focal points in a same one of the first hemisphere and the second hemisphere or in different ones of the first hemisphere and the second hemisphere.

11. A system according to any one of claims 1 to 10, wherein the one or more computer-executable routines are further configured to determine whether the plurality of detected epileptic events are of a same type or of different types.

12. A system according to any one of claims 1 to 11 , wherein the one or more computer-executable routines are further configured to: classify as different event types detected epileptic events according to: (i) user reported symptoms of the detected epileptic events, based on user reports of events and / or epileptiform data received by the one or more computer-implemented routines; (ii) determined foci of the detected epileptic events based on the user reports and / or based on epileptiform data associated with the detected epileptic events; (iii) the determination of the detected epileptic events as having a focus in the first hemisphere of the patient brain or in the second hemisphereof the patient brain; and / or (iv) determined type of epileptic event based on the user reports and / or based on epileptiform data associated with the detected epileptic events.

13. A system according to claim 12, wherein the one or more computer-executable routines are further configured to track the classified detected epileptic events such that frequencies of occurrence of the different classifications can be tracked overtime.

14. A system according to any one of claims 1 to 13, wherein the one or more computer-executable routines are further configured to output, for the patient, a recommendation regarding whether the patient is a candidate for surgical intervention.

15. A system according to any one of claims 1 to 14, wherein the one or more computer-executable routines are further configured to output one or more confidence levels associated with each of the plurality of detected epileptic events.

16. A system according to claim 15, wherein the one or more confidence levels indicate the likelihood that the determination that the detected epileptic event is accurate.

17. A system according to claim 15 or claim 16, wherein the one or more confidence levels indicate the likelihood that the determination of whether the detected epileptic event has a focus in the first hemisphere of the patient brain or in the second hemisphere of the patient brain is accurate.

18. A system according to any one of claims 15 to 17, wherein the one or more confidence levels indicate the likelihood that future epileptic events will occur in a different one of the first and second hemispheres.

19. A system according to any one of claims 15 to 18, wherein the one or more confidence levels indicate the likelihood that future epileptic events will have different foci than the plurality of detected epileptic events.

20. A system according to any one of claims 1 to 19, wherein the one or more computer-executable routines are further configured to track event types, the event types having different foci, different epileptiform characteristics, and / or different patient- re ported symptoms.21 . A system according to claim 20, wherein the one or more computer-executable routines are further configured to:receive information about one or more treatments applied to the patient; monitor the event types according to the different foci, the different epileptiform characteristics, and / or the different patient-reported symptoms; and determine respective effects of each of the one or more treatments on each of the event types.

22. A system according to any one of claims 1 to 21 , wherein the one or more computer-executable routines comprise a trained Al model.

23. A method for lateralization of seizure activity in a patient, the method comprising: implanting in a patient an implantable electrode array configured to measure electroencephalogram (EEG) signals of a first hemisphere of the patient’s brain and of a second hemisphere of the patient’s brain; monitoring overtime the EEG signals of the first hemisphere and of the second hemisphere; identifying, using a computer-implemented routine analyzing the monitored EEG signals of the first hemisphere and of the second hemisphere, a plurality of epileptic events indicated by the EEG signals; and determining from the EEG signals, using the computer implemented routine, for each of the plurality of epileptic events, whether the identified epileptic event has a focus in the first hemisphere or the in the second hemisphere.

24. A method according to claim 23, wherein implanting the implantable electrode array comprises implanting an electrode array comprising a reference electrode, first and second pairs of sensing electrodes spaced linearly along a lead of the electrode array and configured, respectively, as first and second monitoring channels, a memory device, a transceiver, and a processor communicatively coupled to the electrodes and configured to receive the EEG signals from the first and second pairs of electrodes and store the received EEG signals to the memory device.

25. A method according to claim 23 or claim 24, wherein monitoring over time EEG signals of the first hemisphere and of the second hemisphere comprises monitoring the EEG signals over a period exceeding 2 weeks.

26. A method according to claim 23 or claim 24, wherein monitoring over time EEG signals of the first hemisphere and of the second hemisphere comprises monitoring the EEG signals over a period exceeding 1 month.

27. A method according to claim 23 or claim 24, wherein monitoring over time EEG signals of the first hemisphere and of the second hemisphere comprises monitoring the EEG signals over a period exceeding 3 months.

28. A method according to claim 23 or claim 24, wherein monitoring over time EEG signals of the first hemisphere and of the second hemisphere comprises monitoring the EEG signals over a period exceeding 6 months.

29. A method according to any one of claims 23 to 28, further comprising: determining that all of the identified plurality of epileptic events have a focus in the first hemisphere, or that all of the identified plurality of epileptic events have a focus in the second hemisphere; and providing an indication that the patient is a candidate for surgical intervention.

30. A method according to any one of claims 23 to 28, further comprising: determining that some of the identified plurality of epileptic events have a focus in the first hemisphere, and that some of the identified plurality of epileptic events have a focus in the second hemisphere; and providing an indication that the patient is a not a candidate for surgical intervention.31 . A method according to any one of claims 23 to 28, further comprising: determining that all of the identified plurality of epileptic events have a focus in the first hemisphere, or that all of the identified plurality of epileptic events have a focus in the second hemisphere; and performing surgery on the patient to reduce the seizure activity in the patient.

32. A method according to any one of claims 23 to 28, further comprising: determining that some of the identified plurality of epileptic events have a focus in the first hemisphere, and that some of the identified plurality of epileptic events have a focus in the second hemisphere; and changing a treatment regimen for the patient.

33. A method according to any one of claims 23 to 32, further comprising classifying epileptic events according to: (i) user reported symptoms of the identified epileptic events, based on user reports of events and / or epileptiform data associated with the identified epileptic events; (ii) determined foci of the identified epileptic events based on the user reports and / or based on the epileptiform data associated with the identified epileptic events; (iii) the determination of theidentified epileptic events as having a focus in the first hemisphere of the patient brain or in the second hemisphere of the patient brain; and / or (iv) determined type of epileptic event based on the user reports and / or based on epileptiform data associated with the identified epileptic events.

34. A method according to claim 33 further comprising tracking the classified epileptic events such that frequencies of occurrence of the different classifications can be tracked over time.

35. A method according to any one of claims 23 to 34, further comprising generating, for each identified epileptic event, one or more confidence levels associated with the respective identified epileptic event.

36. A method according to claim 35, wherein the one or more confidence levels indicate the likelihood that the determination that the identified epileptic event is accurate.

37. A method according to claim 35 or claim 36, wherein the one or more confidence levels indicate the likelihood that the determination of whether the identified epileptic event has a focus in the first hemisphere of the patient brain or in the second hemisphere of the patient brain is accurate.

38. A method according to any one of claims 35 to 37, wherein the one or more confidence levels indicate the likelihood that future epileptic events will occur in a different one of the first and second hemispheres.

39. A method according to any one of claims 35 to 38, wherein the one or more confidence levels indicate the likelihood that future epileptic events will have different foci than the plurality of detected epileptic events.

40. A method according to any one of claims 23 to 39, further comprising determining, for each of the plurality of detected epileptic events, a focus more specific than a one of the first hemisphere or the second hemisphere.41 . A method according to any one of claims 23 to 40, further comprising tracking event types, the event types having different foci, different epileptiform characteristics, and / or different patient-reported symptoms.

42. A method according to claim 41 , further comprising: receiving information about one or more treatments applied to the patient; monitoring the event types according to the different foci, the different epileptiform characteristics, and / or the different patient-reported symptoms; and determining respective effects of each of the one or more treatments on each of the event types.

43. A method according to any one of claims 23 to 42, wherein the computer- implemented routine comprises implementing a trained Al model.

44. A method comprising: implanting in a patient an implantable electrode array configured to measure electroencephalogram (EEG) signals of a first hemisphere of the patient’s brain and of a second hemisphere of the patient’s brain; monitoring over a first period of time EEG signals the first hemisphere and of the second hemisphere; identifying, using a computer-implemented routine analyzing the monitored EEG signals of the first hemisphere and of the second hemisphere, a plurality of epileptic events indicated by the EEG signals during the first period of time; and determining from the EEG signals, using the computer implemented routine, whether each of the identified plurality of epileptic events during the first period of time has a focus in the first hemisphere or the in the second hemisphere; administering a treatment; monitoring over a second period of time EEG signals the first hemisphere and of the second hemisphere; identifying, using a computer-implemented routine analyzing the monitored EEG signals of the first hemisphere and of the second hemisphere, a plurality of epileptic events indicated by the EEG signals during the second period of time; and determining from the EEG signals, using the computer implemented routine, whether each of the identified plurality of epileptic events during the second period of time has a focus in the first hemisphere or in the second hemisphere.

45. A method according to claim 44, wherein administering a treatment comprises administering a pharmacological agent.

46. A method according to claim 44 or claim 45, wherein administering a treatment comprises administering neurostimulation.

47. A method according to any one of claims 44 to 46, wherein each of the first period of time and the second period of time exceed 2 weeks.

48. A method according to any one of claims 44 to 46, wherein each of the first period of time and the second period of time exceed 1 month.

49. A method according to any one of claims 44 to 46, wherein, taken together, the first period of time and the second period of time exceed 3 months.

50. A method according to any one of claims 44 to 46, wherein, taken together, the first period of time and the second period of time exceed 6 months.51 . A method according to any one of claims 44 to 50, further comprising: determining that all of the identified plurality of epileptic events during the first period of time and during the second period of time have a focus in the first hemisphere, or that all of the identified plurality of epileptic events during the first period of time and during the second period of time have a focus in the second hemisphere; and performing surgery on the patient to reduce the seizure activity in the patient.

52. A method according to any one of claims 44 to 50, further comprising: determining that all of the identified plurality of epileptic events during the first period of time have a focus in the first hemisphere, and that at least some of the plurality of epileptic events during the second period of time have a focus in the second hemisphere; and administering a second treatment that is different from the first treatment.

53. A method according to any one of claims 44 to 52, further comprising: classifying epileptic events according to: (i) user reported symptoms of the identified epileptic events, based on user reports of events and / or epileptiform data associated with the identified epileptic events; (ii) determined foci of the identified epileptic events based on the user reports and / or based on the epileptiform data associated with the identified epileptic events; (iii) the determination of the identified epileptic events as having a focus in the first hemisphere of the patient brain or in the second hemisphere of the patient brain; and / or (iv) determined type of epileptic event based on the user reports and / or based on epileptiform data associated with the identified epileptic events.

54. A method according to claim 53 further comprising tracking the classified epileptic events such that frequencies of occurrence of the different classifications can be tracked over time.

55. A method according to any one of claims 44 to 54, further comprising generating, for each identified epileptic event, one or more confidence levels associated with the respective identified epileptic event.

56. A method according to claim 55, wherein the one or more confidence levels indicate the likelihood that the determination that the identified epileptic event is accurate.

57. A method according to claim 55 or claim 56, wherein the one or more confidence levels indicate the likelihood that the determination of whether the identified epileptic event has a focus in the first hemisphere of the patient brain or in the second hemisphere of the patient brain is accurate.

58. A method according to any one of claims 55 to 57, wherein the one or more confidence levels indicate the likelihood that future epileptic events will occur in a different one of the first and second hemispheres.

59. A method according to any one of claims 55 to 58, wherein the one or more confidence levels indicate the likelihood that future epileptic events will have different foci than the plurality of detected epileptic events.

60. A method according to any one of claims 44 to 59, further comprising determining, for each of the plurality of detected epileptic events, a focus more specific than a one of the first hemisphere or the second hemisphere.61 . A method according to any one of claims 44 to 60, further comprising tracking event types, the event types having different foci, different epileptiform characteristics, and / or different patient-reported symptoms.

62. A method according to claim 61 , further comprising: receiving information about one or more treatments applied to the patient; monitoring the event types according to the different foci, the different epileptiform characteristics, and / or the different patient-reported symptoms; anddetermining respective effects of each of the one or more treatments on each of the event types.

63. A method according to any one of claims 44 to B+62, where the computer- implemented routine comprises a trained Al model.

64. A method comprising: receiving first sets of data, each of the first sets of data from a respective one of a plurality of first processor devices and comprising respective first electroencephalogram (EEG) data generated from respective first signals detected by a respective array of EEG electrodes disposed on a respective patient, the respective array communicatively coupled to the respective first processor device; generating a first artificial intelligence (Al) training set based on the first sets of data; receiving as feature values a selection of one or more attributes of the first Al training set; receiving one or more keys or labels for the first Al training set; training a first iteration of a trained model with the first Al training set, using the feature values and the one or more keys or labels defined for the first Al training set, the first iteration of the trained model operable to provide a first set of classification results having corresponding first error rates relative to a target patient, the first set of classification results relating to a lateralization of a first plurality of epileptic events; receiving, from a second processor device, a second set of data, the second set of data comprising second EEG data generated from second signals detected by an array of EEG electrodes disposed on a target patient; generating a second Al training set based on the second set of data; receiving as feature values a selection of one or more attributes of the second Al training set; receiving one or more keys or labels for the second Al training set; training a second iteration of a trained model with the second Al training set, using the feature values and the one or more keys or labels defined for the second Al training set, the second iteration of the trained model operable to provide a second set of classification results having corresponding second error rates relative to the target patient, the second set of classification results relating to a lateralization of a second plurality of epileptic events; wherein the second error rates have a reduced overall error rate compared to an overall error rate of the first error rates.

65. A method according to claim 64, wherein: generating the first artificial intelligence (Al) training set based on the first sets of data comprises generating the first Al training set based on corresponding first reports from the patients, caregivers of the patients, clinicians, and / or other observers; and generating the second Al training set based on the second set of data comprises generating the second Al training set based on corresponding second reports from the target patient, a caregiver of the target patient, a clinician of the target patient, and / or another observer of the target patient.

66. A method comprising: receiving a first set of Al training data, the first set of labeled training data comprising labeled features values for respective first electroencephalogram (EEG) data generated from respective first signals detected by a respective array of EEG electrodes disposed on respective patients, the respective array communicatively coupled to the respective first processor device; training a first iteration of a trained model with the first set of Al training data, using the labeled feature values, the first iteration of the trained model operable to provide a first set of classification results having corresponding first error rates, the first set of classification results relating to a lateralization of a first plurality of epileptic events; receiving a second set of Al training data, the second set of Al training data comprising second EEG data generated from second signals detected by a two-channel array of EEG electrodes disposed on a target patient; generating a second Al training set based on the second set of data; receiving as feature values a selection of one or more attributes of the second Al training set; receiving one or more keys or labels for the second Al training set; training a second iteration of a trained model with the second Al training set, using the feature values and the one or more keys or labels defined for the second Al training set, the second iteration of the trained model operable to provide a second set of classification results having corresponding second error rates, the second set of classification results relating to a lateralization of a second plurality of epileptic events; wherein the second error rates have a reduced overall error rate compared to an overall error rate of the first error rates.

67. A method according to claim 66, wherein receiving a first set of Al training data comprises: receiving first sets of data, each of the first sets of data from a respective one of a plurality of first processor devices and comprising respective first electroencephalogram (EEG)data generated from respective first signals detected by a respective array of EEG electrodes disposed on a respective patient, the respective array communicatively coupled to the respective first processor device; generating a first artificial intelligence (Al) training set based on the first sets of data, the training set having a montage of EEG signals selected to approximate a two-channel array of EEG electrodes having a first channel recording EEG signals of a first hemisphere and a second channel recording EEG signals of a second hemisphere; receiving as feature values a selection of one or more attributes of the first Al training set; receiving one or more keys or labels for the first Al training set.

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