Guiding health based on sensed brain activity

Abstract

Methods, systems and devices for providing health information to an individual. Methods may include predicting a subject's glucose sensitivity risk based on recorded sleep brain activity signals, and optionally providing an output to the subject based on the predicted glucose sensitivity risk. Methods may include providing personalized proactive behavioral guidance for a subject for an awake period following a sleep period, the proactive behavior guidance based on at least one of predicted glucose sensitivity risk or quality of sleep.

Description

GUIDING HEALTH BASED ON SENSED BRAIN ACTIVITYINCORPORATION BY REFERENCE

[0001] This application claims priority to U.S. Provisional Patent Application Nos.63 / 691, 127 and 63 / 691,271, both filed September 5, 2024, and which are incorporated by reference herein in their entireties for all purposes.

[0002] All publications and patent applications mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication or patent application was specifically and individually indicated to be incorporated by reference.

[0003] This application incorporates by reference herein the following publications in their entireties and for all purposes: WO / 2024 / 182777A2; WO 2025 / 171400 Al; US20240293066; US20240293048;US20240293083; and US20240315611.BACKGROUND

[0004] Metabolic health of an individual can be considered one of the underlying indicators of overall health of an individual. Part of understanding metabolic health is understanding information about an individual’s blood glucose levels, or more generally one or more aspects of their glycemic profde. Blood glucose is one of the most important blood parameters to understand and measure, whether on a personal basis or more generalized to a larger segment of a population. Technology exists to estimate blood glucose levels in near real-time. For example only, continuous glucose monitors (“CGM”) include a small sensor inserted under the skin, such as the abdomen or arm. The sensor measures the interstitial glucose (“ISG”) level, which is believed to be indicative of blood glucose levels. The sensor may sample ISG every few minutes (e.g. five minutes). A transmitter can wirelessly send the information to a monitor, which may optionally be part of an insulin pump, or which may be a separate device, such as a smartphone or tablet. CGMs are essentially estimating existing or current interstitial fluid glucose levels, and because ISG levels follow or lag blood glucose levels by several minutes (e.g., 3-12 mins.), the estimated blood glucose levels (based on the ISG) provided by CGMs are estimates of past blood glucose levels.

[0005] Hyperglycemia is a condition when blood glucose levels are higher than the standard range, often considered above 180 milligrams per deciliter (mg / dL). Hyperglycemia, which is commonly linked to diabetes, occurs when the body has too little insulin (the hormone that transports glucose into cells), or if the body can't respond to or use insulin properly. Hyperglycemia can, however, be associated with any non-diabetes medical states or conditions and / or environments (e.g., healthy individuals, ICU patients). If left untreated for long periods of time, hyperglycemia, or a subject that spends relatively more time over the course of their life with elevated blood glucose levels, can cause damage to nerves, blood vessels, tissues and organs.- 1 -SG Docket No.: 14837-710.600

[0006] Additionally, subjects who are pre -diabetic may want to be able to monitor (and optionally takes steps to manage) their glucose levels in an attempt to avoid becoming diabetic.

[0007] Additionally still, a subject who may be considered healthy may simply want more information about their body, their health, and their overall well-being, including an understanding of their glucose levels and / or other factors indicative of metabolic health.

[0008] Additionally still, subjects who are unaware of their blood glucose variations, including in response to certain behaviors, may want to be informed of undesired or sub-optimal existing metabolic function (even if they are otherwise considered healthy) and make appropriate lifestyle changes. For example, some individuals who consider themselves healthy may have suboptimal metabolic function and may be heading toward prediabetes or diabetes without knowing it.

[0009] Solutions are needed that can provide subjects with more actionable information about their overall health.SUMMARY OF THE DISCLOSURE

[0010] The disclosure herein is related to health, including overall well-being. The disclosure is related to providing individuals with more information about one or more aspects of their health, optionally related to glucose regulation and / or metabolic function. In several aspects, the disclosure provides information to the individual that includes proactive behavioral guidance for the individual to utilize to improve one or more aspects of their health.

[0011] One aspect of the disclosure is a method of predicting a subject’s glucose sensitivity (“GS”) risk, optionally a waking GS risk, based on recorded sleep brain activity signals.

[0012] This aspect optionally includes recording or receiving sleep brain activity signals or information indicative thereof from a subject with one or more wearable scalp sensors during a sleep period of the subject.

[0013] This aspect optionally includes predicting a subject’s glucose sensitivity risk at least based on one or more sleep features that are in or based on the recorded sleep brain activity signals.

[0014] This aspect optionally includes providing an output to the subject on a display of a personal device based on the waking glucose sensitivity risk, optionally wherein the output includes personalized proactive behavioral guidance for the subject for an awake period that follows the sleep period, the proactive behavior guidance based on the predicted glucose sensitivity risk.

[0015] In this aspect, a glucose sensitivity risk is optionally indicative of a risk of blood glucose levels deviating from a desired range during the awake period.

[0016] In this aspect, a glucose sensitivity risk is optionally indicative of a risk of one or more hyperglycemic events during the awake period.

[0017] In this aspect, a glucose sensitivity risk is optionally indicative of a risk of one or more blood glucose level spikes during the awake period.- 2 -SG Docket No.: 14837-710.600

[0018] In this aspect, a glucose sensitivity risk is optionally indicative of predicted glucose regulation of the subject’s body during the awake period following the sleep period.

[0019] In this aspect, a glucose sensitivity risk is optionally indicative of predicted changes in blood glucose during the awake period following the sleep period.

[0020] In this aspect, a glucose sensitivity risk is optionally indicative of predicted blood glucose levels outside of a desired range during the awake period following the sleep period.

[0021] In this aspect, a glucose sensitivity risk is optionally indicative of a quality of sleep during the sleep period for the subject.

[0022] In this aspect, one or more sleep features are optionally indicative of a quality of sleep during the sleep period for the subject.

[0023] In this aspect, one or more sleep features optionally comprise macro sleep features that are optionally one or more of a number of sleep cycles completed during the sleep period, a number of sleep stages completed during the sleep period, a duration of one or more sleep cycles during the sleep period, a duration of one or more sleep stages during the sleep period, total sleep time during the sleep period, or a number of waking interruptions during the sleep period.

[0024] In this aspect, the method may further comprise recording awake brain activity signals from the subject prior to and following the sleep period, the method optionally further comprising automatically detecting when the subject falls asleep and / or wakes up based on one or more detected changes between awake brain activity signals and sleep brain activity signals.

[0025] In this aspect, predicting a subject’s glucose sensitivity risk optionally excludes features in awake brain signal data prior to the sleep period.

[0026] In this aspect, predicting a subject’s glucose sensitivity risk is optionally further based on one or more features in awake brain signal data prior to the sleep period.

[0027] In this aspect, an output optionally comprises a qualitative output indicative of the glucose sensitivity risk, the qualitative output optionally selected from one of a plurality of predefined qualitative outputs. A plurality of predefined qualitative outputs optionally comprises outputs that are respectively indicative of a low risk, a medium risk, and a high risk.

[0028] In this aspect, an output optionally comprises a quantitative output indicative of a glucose sensitivity risk.

[0029] In this aspect, personalized proactive behavioral guidance optionally comprises one or more recommended actions for the subject for the awake period based on the waking glucose sensitivity risk. One or more recommended actions are optionally related to at least one of recommended sleep activity during the awake period, recommended exercise during the awake period, or recommended consumption activity during the awake period.

[0030] In this aspect, an output optionally includes an indicator of a quality of sleep during the sleep period for the subject.

[0031] In this aspect, an output is optionally accessible on a personal device upon waking from the sleep period. The method optionally further includes automatically detecting when the subject wakes- 3 -SG Docket No.: 14837-710.600from the sleep period based on one or more detected changes between awake brain activity signals and sleep brain activity signals, and wherein the output is accessible on the personal device subsequent to automatically detecting when the subject wakes from the sleep period.

[0032] In this aspect, an output is optionally accessible on the personal device within 30 minutes of waking from the sleep period, optionally within 15 minutes of waking, and optionally within 5 minutes of waking from the sleep period.

[0033] In this aspect, an output is optionally accessible on the personal device immediately or substantially immediately upon waking from the sleep period.

[0034] In this aspect, recording sleep brain activity signals optionally comprises recording sleep brain activity signals during an entirety of the sleep period. The method optionally includes automatically initiating the recording of the sleep brain activity signals subsequent to detecting when the subject has entered the sleep period from an awake period.

[0035] In this aspect, the method optionally further comprises automatically detecting when the subject has entered a sleep period from an awake period based on one or more detected changes between awake brain activity signals and the sleep brain activity signals, and wherein predicting the subject’s glucose sensitivity risk relies more heavily on one or more sleep features than on features from the awake period. Predicting a subject’s glucose sensitivity risk optionally does not rely on any features from awake brain activity signals.

[0036] In this aspect, recording sleep brain activity signals optionally comprises recording sleep brain activity signals during only a portion of time between an initiation of sleep and a waking time.

[0037] In this aspect, predicting a glucose sensitivity risk optionally comprises inputting sleep brain activity signals and / or one or more sleep features into a trained model that has been trained on brain activity signals and / or one or more sleep features and glucose information, optionally glucose levels measured with a CGM. A trained model optionally has been trained on brain activity signals and / or one or more features and an awake time maximum blood glucose level for an awake period following the sleep period.

[0038] In this aspect, recording sleep brain activity signals during a sleep period optionally comprises recording sleep brain activity signals with at least one wearable sensor, each of the at least one wearable sensor optionally positioned at a behind an ear location or on a forehead.

[0039] In this aspect, the method further optionally comprises receiving as input an indication from the subject that the sleep period is over, wherein the predicting step occurs subsequent to receiving as input the indication that the sleep period is over.

[0040] In this aspect, the method optionally further comprises recording awake brain activity signals of the subject during an awake period following the sleep period, wherein the glucose sensitivity risk is a first glucose sensitivity risk, the method further comprising predicting a second glucose sensitivity risk during the awake period that is based on the first glucose sensitivity risk and the awake brain activity signals, and providing a second output to the subject on the display of the personal device- 4 -SG Docket No.: 14837-710.600based on the second glucose sensitivity risk. An optional second glucose sensitivity risk optionally includes any one or more features of the any of the glucose sensitivity risks in this aspect.

[0041] In this aspect, the method may include any other step or feature of any other aspect in this disclosure.

[0042] One aspect of this disclosure is method of improving metabolic health by providing proactive behavioral guidance based on a subject’s quality of sleep.

[0043] In this aspect, the method optionally includes recording or receiving sleep brain activity signals from a subject with one or more wearable scalp sensors during a sleep period of the subject.

[0044] In this aspect, the method optionally includes predicting or determining a subject’s quality of sleep based on one or more sleep features that are in or based on the recorded sleep brain activity signals.

[0045] In this aspect, the method optionally includes providing an output to the subject on a display of a personal device based on the quality of sleep, optionally wherein the output includes personalized proactive behavioral guidance for the subject for an awake period that follows the sleep period, the proactive behavior guidance based on the predicted or determined quality of sleep.

[0046] In this aspect, the one or more sleep features optionally comprise one or more macro sleep features optionally including at least one of a number of sleep cycles completed during the sleep period, a number of sleep stages completed during the sleep period, a duration of one or more sleep cycles during the sleep period, a duration of one or more sleep stages during the sleep period, total sleep time during the sleep period, or a number of waking interruptions during the sleep period.

[0047] In this aspect, the method may include any other step or feature of any other aspect in this disclosure, such as automatically determining when the subject has fallen asleep and / or woken up based on detected changes between awake brain activity signals and asleep brain activity signals.

[0048] One aspect of the disclosure is a method of monitoring an indicator of metabolic health over time.

[0049] In this aspect, the method optionally includes recording a subject’s brain activity signals with one or more wearable scalp sensors.

[0050] In this aspect, the method optionally includes predicting a plurality of maximum awake time glucose levels, each of the plurality of maximum awake time glucose levels associated with one of a plurality of separate days.

[0051] In this aspect, the method optionally includes providing an output to a display of a personal device, the output indicative of the plurality of maximum awake time glucose levels overtime.

[0052] In this aspect, an output optionally illustrates the maximum awake time glucose levels for each of the plurality of separate days.

[0053] In this aspect, an output optionally comprises a graph showing the plurality of maximum awake time glucose levels.

[0054] In this aspect, an output optionally comprises an average of at least a portion of the maximum awake time glucose levels over a period of time.- 5 -SG Docket No.: 14837-710.600

[0055] One aspect of this disclosure is a method of monitoring fasting glucose states based on brain activity signals.

[0056] In this aspect, the method optionally includes recording a subject’s brain activity signals with one or more wearable scalp sensors while the subject is fasting.

[0057] In this aspect, the method optionally includes predicting a real-time subject fasting glucose state based on the recorded brain activity signals while fasting using a model trained with recorded brain activity signals and glucose levels.

[0058] In this aspect, predicting optionally includes the real-time subject fasting glucose state occurring during an awake period immediately following a sleep period.

[0059] In this aspect, the method optionally further comprises automatically detecting when the subject wakes from a sleep period based on the recorded brain activity signals, and wherein the predicting occurs automatically at a time after the automatically detected waking from the sleep period.

[0060] In this aspect, the method optionally further comprises predicting a plurality of the subject’s fasting glucose states overtime, recording the plurality of fasting glucose states over time, and providing an output to the subject on a display of a personal device indicative of the recorded plurality of fasting glucose states over time as a way to track overall metabolic health over time.

[0061] In any of the aspects herein, a method is optionally a computer executable method stored in a non-transitory memory, wherein the computer executable method is executable by one or more processors. A computer executable method is optionally performed on a personal device and / or a remote server.

[0062] It is understood that any aspect of this Summary may be combined with any other aspect in this Summary.BRIEF DESCRIPTION OF THE FIGURES

[0063] FIG. 1 represents an exemplary method of predicting glucose states.

[0064] FIG. 2 represents an exemplary system adapted to predict glucose states.

[0065] FIG. 3 represents an exemplary system adapted to predict glucose states.

[0066] FIG. 4 illustrates exemplary behind the ear locations for one or more wearable sensor.

[0067] FIG. 5 is an exemplary method of training a glucose state prediction model.

[0068] FIG. 6 is an exemplary method of training a glucose state prediction model.

[0069] FIG. 7 illustrates an exemplary system and / or method for training and / or prediction.

[0070] FIGS. 8, 9, 10, 11, 12, 13A, 13B, 14, 15, 16A, 16B, 17A, 17B, 18A, 18B, 19A, 19B and 20 illustrate data, plots and other information from Example 1 herein.

[0071] FIGS. 21A and 2 IB illustrate exemplary systems herein, online optional online portion or care-team device.

[0072] FIG. 22A illustrates a common EEG setup and general head locations and letter-number designation for a plurality of electrodes.- 6 -SG Docket No.: 14837-710.600

[0073] FIG. 22B illustrates the location of additional electrodes A2, M2 (and similarly Al, and Ml), all of which are in behind the ear location, in Example 2 herein.

[0074] FIG. 23A illustrates a table from Example 2 herein show significant correlations between electrode location, frequency band, and whether it was positive or negative correlation.

[0075] FIG. 23B shows the high negative correlation between glucose and EEG behind the ear and high positive correlation between glucose and EEG occipitally.

[0076] FIGS. 24 A and 24B illustrate data from Example 2 in which correlations between brain signals and glucose levels were examined at a plurality of time ranges before the glucose values.

[0077] FIGS. 25A and 25B show data from an Ml electrode in the 9-12 Hz frequency at a time range of - 11 minutes to -6 mm.

[0078] FIGS. 26A and 26B show data from an M2 electrode in the 9-12 Hz frequency at a time range of - 11 minutes to -6 mm.

[0079] FIGS. 27A and 27B show data from an 01 electrode in the 1-12 Hz frequency at a time range of - 11 minutes to -6 mm.

[0080] FIGS. 28A and 28B show data from an 02 electrode in the 5-8 Hz frequency at a time range of - 11 minutes to -6 mm.

[0081] FIG. 29 illustrates an example of a visual representation of glucose state information.

[0082] FIG. 30 illustrates an example of a visual representation of glucose state information on an exemplary device.

[0083] FIG. 31 illustrates an example of a visual representation of glucose state information, including exemplary recommendation.

[0084] FIG. 32 shows an exemplary comparison between glucose levels with a traditional CGM and with some optional examples herein which are adapted with management steps to main glucose within a certain range.

[0085] FIG. 33 is an exemplary method of training a glucose state prediction model to predict a prandial event.

[0086] FIGS. 34, 35, 36, 37, 38, 39, and 40 show data from Example 3 herein.

[0087] FIG. 41 illustrates an exemplary wearable sensor.

[0088] FIGS. 42 and 43 illustrates an exemplary wearable sensor.

[0089] FIGS. 44 and 45 illustrates exemplary wearable sensor locations, with FIG. 45 showing a behind the ear location.

[0090] FIG. 46 illustrates exemplary steps during optional manufacturing methods of some wearable sensors herein.

[0091] FIGS. 47A, 47B, 47C and 47D illustrate an exemplary wearable sensor.

[0092] FIG. 48 is a merely exemplary block diagram of an exemplary data processing system, any part or all of which may be used with any embodiments of any of the inventions or embodiments set forth herein.

[0093] FIG. 49 illustrates an exemplary method of future glucose state estimation using a CGM.- 7 -SG Docket No.: 14837-710.600

[0094] FIG. 50 illustrates an exemplary method of managing glucose states based on values sensed from a CGM

[0095] FIG. 51 illustrates an exemplary method of creating an association between ISG data and EEG data.

[0096] FIG. 52 illustrates an exemplary method of calibrating a glucose monitor using EEG data.

[0097] FIG. 53 illustrates an exemplary method of calibrating a blood glucose prediction method.

[0098] FIG. 54 illustrates an exemplary method of using ISG data and EEG data to perform at least one calibration process.

[0099] FIG. 55 represent illustrative glucose sensitivity risks based on sleep brain activity signals.

[0100] FIG. 56 illustrates exemplary methods that can be used with the disclosure herein to predict a glucose sensitivity score or risk for an awake period that follows a period of sleep during which electrical biosignals are sensed from the subject.

[0101] FIG. 57 illustrates a plurality of exemplary outputs based glucose sensitivity riski.

[0102] FIG. 58 is an example of an output comprising a waking GS risk indicating a high glucose sensitivity, including proactive behavioral guidance for the individual based on the determined GS risk.

[0103] FIG. 59 is an example of an output displayed on a screen of a personal device based on a determined GS score, which in this case is a high glucose susceptibility.

[0104] FIG. 60 is an example of an output comprising a waking GS risk indicating a medium glucose sensitivity, including proactive behavioral guidance for the individual based on the determined GS risk.

[0105] FIG. 61 is an example of an output displayed on a screen of a personal device based on a determined GS score, which in this case is a medium glucose susceptibility.

[0106] FIG. 62 is an example of an output comprising a waking GS risk indicating a low glucose sensitivity, including proactive behavioral guidance for the individual based on the determined GS risk.

[0107] FIG. 63 is an example of an output displayed on a screen of a personal device based on a determined GS score, which in this case is a low glucose susceptibility.

[0108] FIG. 64 illustrates an exemplary method and system adapted for predicting one or more GS scores for an individual.

[0109] FIGS. 65A and 65B illustrate an exemplary personal device showing an App with multiple features or modes.

[0110] FIG. 66A represents aspects of a first study described herein.

[0111] FIG. 66B represents aspects of a second study described herein.

[0112] FIG. 67 graphs a validation showing predicted CGM levels based on recorded EEG data, as well as measured CGM levels.

[0113] FIG. 68 represents an exemplary graph of glucose levels over time and a general approach to determining glucose sensitivity risks.- 8 -SG Docket No.: 14837-710.600

[0114] FIG. 69 is a merely exemplary illustration of training and testing a trained model.

[0115] FIG. 70 shows results of a post-prandial example described herein.

[0116] FIG. 71 illustrates a correlation between high levels (e.g., peaks) of glucose levels measured with a CGM and predicted glucose levels from the trained model.

[0117] FIG. 72 represents predicted next-day maximum glucose levels based on depth of sleep.

[0118] FIG. 73 shows a merely representative and exemplary sleep feature vs a maximum glucose level (measured using a CGM) during the following awake period.

[0119] FIG. 74 shows on the y-axis, predicted maximum glucose values (predicted with a trained ML model) for the awake period following sleep based on sleep brain activity signals vs, on the x-axis, maximum glucose levels during the awake time measured with a CGM.

[0120] FIG. 75 illustrates a plurality of exemplary sleep features in or based on brain activity signals.

[0121] FIG. 76 is an exemplary method of determining a waking glucose sensitivity risk.

[0122] FIG. 77 is an example of a method of monitoring quality of sleep.

[0123] FIG. 78 illustrates an exemplary method that can be part of monitoring an indicator of metabolic health over time.

[0124] FIG. 79 illustrates an exemplary method of monitoring fasting glucose states (e.g., levels) over time.DETAILED DESCRIPTION

[0125] The disclosure herein is related to health, including overall well-being.

[0126] In some aspects, the disclosure is related to sensing brain activity signals of an individual and providing the individual with information related to their overall metabolic health, which may be information related to or indicative of one or more aspects of their glycemic regulation, glycemic profde, and / or glucose states. In some aspects, the disclosure is related to sensing brain activity signals of an individual and providing proactive behavioral guidance to an individual to guide one more aspects of their lifestyle, optionally comprising one or more of (but not limited to) sleep behavior, exercise / mobility behavior, eating behavior, or mindfulness behavior.

[0127] Aspects of the disclosure herein, including any methods, may be performed at least partially on a personal “App” that an individual can access on a personal device (e.g., smartphone, watch) and that can provide information, optionally providing information related to their metabolic health and / or proactive behavioral guidance to the user to improve their overall metabolic health. Aspects of the disclosure herein, including any methods, may also be performed at least partially on or using a remote server.

[0128] An exemplary aspect of the disclosure is recording brain signals from an individual and providing information to the individual related to their metabolic health in the form of a “score” or “risk” level that is based on the recorded brain signals, optionally via an App. The terms “score” and “risk” herein refer to an indicator that is understood by the user to its meaning based on their- 9 -SG Docket No.: 14837-710.600interaction with the App. The scores and risks herein may be quantitative and / or qualitative. Any instance of the term score herein can be replaced with the term risk.

[0129] The metabolic risks herein (e.g., glucose sensitivity risks) herein are generally at least partially based on recorded scalp EEG brain activity information, and may be determined or calculated using software / algorithms, and / or may be predicted using any type of prediction model (e.g., supervised learning, unsupervised learning; self-supervised learning; reinforcement learning; neural networks (e.g., deep learning)).

[0130] Any of the methods herein (e.g., Apps) may be adapted to determine or predict a score or risk, optionally upon waking, that is based at least partially on one or more sleep features in recorded sleep-time brain activity signals recorded from the individual prior to waking. The scores described herein that are provided upon waking may be referred to as morning scores (if provided in the morning), waking scores, or awake scores (or similar phrases). For example, any of the wearable sensors herein (e.g., behind the ear or forehead sensors) can be noninvasively worn, sense or record at least brain activity signals of the individual while sleeping, and communicate the signals or information indicative of the signals to a user or personal device for determining or predicting the waking glucose sensitivity risk. The method can include, at a time of waking or very near in time to waking (e.g., 1-10 minutes after waking), or when prompted by the user via the App, providing the risk to the individual, such as with one or more visual indicators on a screen or display of a personal device (any of the “personal devices” herein may also be referred to herein as a “subject device,” and vice versa). Providing the risk to the user on a screen or display of a device is an example of providing one or more outputs to the user based on the determined / predicted waking glucose sensitivity risk. The glucose sensitivity risks herein generally refer to a predicted degree, amount, and / or sufficiency of glucose regulation following a period of sleep (or other similar ways to characterize glucose regulation). The glucose sensitivity risks herein can generally refer to a prediction of the degree to which the glucose will be regulated. For example only, a low glucose sensitivity risk can be a prediction of a relatively more desirable or preferred glucose regulation by the body during an awake period following the sleep period. The glucose sensitivity risks herein can also generally refer to sensitivity to glucose changes the next day. For example, a high sensitivity risk can refer to higher sensitivity to larger glucose fluctuations the next day (e.g., higher sensitivity to glucose spikes or more episodes of being outside of a desired range). Additionally, glucose sensitivity risks herein can refer to one or more of predicted reduced insulin sensitivity, elevated fasting glucose, or higher postmeal blood sugar spikes during the awake period following the sleep period.

[0131] A sleep or awake period herein may also be referred to herein as a sleep or awake time, and vice versa.

[0132] As a non-limiting example, Apps herein, based on sleep period recorded brain activity signals using one or more wearable scalp sensors, may provide a waking glucose sensitivity risk indicative of a “glucose sensitivity” (“GS”) for the subject for an awake period that follows the sleep period during which the signals were recorded. In this aspect, an initial waking GS risk may be determined upon- 10 -SG Docket No.: 14837-710.600waking from sleep or shortly thereafter and may be based on one or more sleep features recorded in the sleep-time brain activity signals with one or more wearable scalp sensors, and optionally wherein the one or more sleep features are indicative of or representative of the quality of sleep during the sleep period. Quality of sleep can affect glucose regulation during the awake period following the sleep period. Some aspects of the disclosure are related to methods of sensing sleep brain activity signals and providing the individual about information related to predicted next day glucose regulation based on the sleep brain activity. The GS score or other output information indicative of the GS can be provided to a subject, such as by an App on a display of a personal device, to inform the subject about their determined or predicted general sensitivity to glucose changes throughout the upcoming day. The App can optionally provide proactive behavior guidance to the subject for the subject’s day based on the GS score, such as recommendations related to exercise, food consumption and / or sleep during the day to improve metabolic health (e.g., avoid undesired glucose levels). In this aspect of the disclosure, an App may further be adapted and configured to update the GS throughout the awake-time (e.g., throughout the day) based on at least one of recorded awake-time EEG scalp data, the initial GS score determined at or near in time to waking, and other subject behavior during while awake, such as consumption, activity or lack thereof, medication, or other patient sensed metrics.

[0133] In some aspects, a waking GS risk is optionally indicative of at least one of: a risk of blood glucose levels deviating from a desired range during the awake period; a risk of one or more hyperglycemic events during the awake period; a risk of one or more blood glucose level spikes during the awake period; predicted glucose regulation of the subject’s body during the awake period following the sleep period; predicted changes in blood glucose during the awake period following the sleep period; predicted blood glucose levels outside of a desired range during the awake period following the sleep period; or higher risk of post-meal blood sugar spikes.

[0134] Figure 55 illustrates a merely exemplary example of determined or predicted risk (in this example a glucose sensitivity or susceptibility (GS) risk), which may be performed by an App and / or remote server, and which is optionally unknown or invisible to the user, and which is based on one or more sleep features in recorded scalp brain activity signals. In this non-limiting example, the determined score / risk is one of a plurality of predefined and discrete qualitative scores, such as (for example only) high sensitivity, medium sensitivity, or low sensitivity. Other types of determined scores can provide the information, such as quantitative scores from 1-10, for example only. In this example, a determined high risk indicates that a relatively less desirable quality of sleep occurred, the medium risk indicates that a relatively medium quality (e.g., decent or acceptable) of sleep occurred, and a low risk indicates that a relatively more desirable quality of sleep occurred. The quality of sleep, monitored using one or more wearable brain activity scalp sensors, can be related to predicted next- day glucose regulation.

[0135] Methods herein that determine or predict a waking score may be based on one or more features in recorded scalp EEG information during sleep. The one or more sleep features may indicate- 11 -SG Docket No.: 14837-710.600or predict at least one of quality of sleep, glucose regulation, glycemic profile, glucose state, or other indicator of metabolic function of the individual.

[0136] Figure 56 illustrates exemplary methods that can be used with the disclosure herein to predict a glucose sensitivity score or risk for an awake period that follows a period of sleep during which electrical biosignals are sensed from the subject. The upper path in figure 56 illustrates inputting a plurality of sleep features into a trained model to predict the glucose sensitivity score, as is described herein. The features may include or more features from brain activity signals (which can be a wide variety of features) recorded during sleep. Alternatively, as shown in the lower path, raw electrical biosignals may be input into a deep neural network (DNN) to predict the glucose sensitivity / risk.

[0137] Figure 57 provides mere examples of one or more outputs provided to the subject on a display (e.g., via an App) based on the determined or predicted risk of the subject. In this example, each possible output from a set of predetermined outputs comprises a visual indicator of one of a plurality of “states” the user may be in throughout the day, and are optionally based on one or more of the waking score (e.g., sensitivity score), a current awake glucose state, and one or more behaviors throughout the day (e.g., consumption, exercise, sleep activity (napping). In this example, the output includes a qualitative visual indicator of state and is one of three color coded indicators and / or proactive behavior guidance in the form of textual information, as shown, while other visual indicators may be used. The one or more outputs in Figure 57 comprise proactive behavioral guidance to improve metabolic health throughout the day, which may be part of a larger goal of improving metabolic health over time, which the App can optionally be adapted to track and provide to the user. For example, the Apps herein may be adapted to display a time history of waking scores or states as a chart or graph over the course of some period of time, such as a week, month, several months, and which may be visually presented as a way to track an aspect of metabolic health over time. An exemplary benefit of this approach is that personalized behavior guidance is provided based on the individual’s metabolic score (e.g., glucose sensitivity score) obtained after waking. The method (e.g., a method executed by one or more processors) can also be configured to update the state (which may also be considered a score as described herein) at one or more times throughout the awake time to reflect an updated (e.g., more accurate) state for the subject. The update may occur constantly in realtime, or it may occur at regular intervals throughout the day (e.g., hourly). The update may alternatively or additionally optionally occur in response to a subject request for an update to the state (i.e., on-demand). The App may be configured so the update occurs just before, during, or after certain type of preset activities, such as consumption of a meal, exercise (or a certain type of exercise), a sleep related activity (e.g. nap), etc., and which may be requested by the subject upon indicating to the App of the preset activity.

[0138] Figure 58 is a mere example of an output comprising a waking GS score indicating a “high glucose sensitivity,” as well as textual proactive behavioral guidance for the individual based on the determined GS score.- 12 -SG Docket No.: 14837-710.600

[0139] Figure 59 is a mere example of an output displayed on a screen of a personal device based on a determined GS score, which in this case is a high glucose susceptibility. The output may, as shown, include a graph of glucose levels throughout the awake time, including optional visual markers (e.g., dots as shown) for relatively high and low values (peaks and valleys), wherein red may indicate out of range high values (hyperglycemia), and yellow indicating out or range of risk of low values (hypoglycemia). The visual markers (e.g., dots) may optionally be shown on the display in real time.

[0140] Figure 60 is a mere example of an output comprising a waking metabolic score indicating a medium glucose sensitivity and personalized proactive behavioral guidance for the individual based on the determined metabolic score.

[0141] Figure 61 is a mere example of an output displayed on a screen of a personal device based on a determined metabolic score, which in this case is a medium glucose susceptibility. The output may, as shown, include a graph of predicted glucose levels throughout the awake time, including optional visual markers for relatively high and low values (peaks and valleys), wherein red may indicate out of range high values (hyperglycemia), yellow indicating out or range of risk of low values (hypoglycemia), and green may indicates times of in-range levels. The visual markers (e.g., dots) may optionally be shown on the display in real time.

[0142] Figure 62 is a mere example of an output comprising a morning metabolic score indicating a low glucose sensitivity and proactive behavioral guidance based on the determined metabolic score.

[0143] Figure 63 is a mere example of an output displayed on a screen of a personal device based on a determined metabolic score, which in this case is a low glucose susceptibility. The output may, as shown, include a graph of glucose levels throughout the awake time, including optional visual markers for relatively high and low values (peaks and valleys), wherein yellow indicating predicted at risk times for peaks (hyperglycemia) and green indicating times of in-range levels. The visual markers (e.g., dots) may optionally be shown on the display in real time.

[0144] Figure 64 illustrates an exemplary method and system adapted for predicting one or more GS scores for an individual, which may include any other features of the disclosure herein. Figure 64 illustrates an individual wearing a sensor 51 on the scalp at a behind the ear location, examples of which are described herein, the disclosure of which is fully incorporated by reference into this example, although the wearable sensor may be positioned at other locations such as the forehead. Illustrative recorded brain activity signals are shown. Recorded scalp signals may be input into a trained model (“AF7ML”) 53, which is adapted to predict one or more subject parameters 57 (e.g., one or more aspects of sleep; a glucose state, etc.), and cause one or more outputs to be displayed on a display of a personal device 59 (such as a smart phone as shown), wherein the App may be adapted to provide one or more outputs (e.g., visual outputs) indicative of the one or more predicted parameters. For example, an App stored on device 59 may be adapted to provide an output to the subject indicative or based on a predicted metabolic score, wherein the one or more outputs may comprise one or more of the score, a “state” based on the score, and / or proactive behavioral guidance to improve metabolic health, examples of which are described herein.- 13 -SG Docket No.: 14837-710.600

[0145] Figures 65A and 65B illustrate an exemplary personal device showing an App with multiple features or modes (figures 65A and 65B), which can be displayed separately on the same display. For example, one mode may be adapted to display a current predicted glucose state (e.g. value) 61, and a time history of values 63 on a chart or graph. Other health metrics may also be shown, such as heart rate, blood pressure, and / or glucose sensitivity score.

[0146] MSMs herein may be adapted to, additionally or alternatively to providing a predicted quantitative blood glucose state (e.g., 100), provide a general indicator of metabolic score for the subject for an awake period. It is of course understood that any MSMs herein may also be adapted to provide one or more actual predicted glucose levels. Providing the subject with information related to their metabolic score at or near the start of their awake-time and based at least partially on sleep EEG data can inform them of their score (optionally susceptibility to glucose levels rising or lowering to undesirable levels throughout the day), and provide proactive behavioral guidance to help them plan their daily behavior to improve metabolic health, optionally reducing the likelihood of their blood glucose rising or lowering to undesired levels. This is a technological advancement that arms the subject with proactive actionable personal health information about the daily glucose sensitivity or susceptibility and guidance to improve metabolic health. This can help improve metabolic health, including helping maintain a subject’s glucose levels at more desired levels over the course of their life (or during shorter time periods), providing myriad benefits. The MSMs can also provide information and insight into sleep activities that may have led to suboptimal sleep quality (e.g., less time in deep sleep), and that if improved upon, can help the subject have greater percentage of days with lower glucose sensitivity and overall better metabolic health.

[0147] Example

[0148] A first example study illustrated in figure 66A included obtaining CGM data and scalp EEG data, using a sensor worn at a behind the ear location, which is described elsewhere herein. Sixteen subjects wore a conventional CGM and a scalp sensor at a behind the year location for several days, and glucose data was obtained from the CGM and brain activity information obtained using the scalp sensor. The recordings were made from healthy controls and pre-diabetes individuals in a hospital setting, with normal meals and sleep.

[0149] CGM and scalp brain activity data from all but one of the subjects was used to train a machine learning model, and EEG data from the final test subject was used to validate the model by predicting CGM levels with the EEG data as input to the model. Figure 67 graphs the validation showing predicted CGM levels in orange based on EEG data (predicted with the trained model from the other subjects) and actual CGM levels for the final test subject in blue.

[0150] In this particular example, each CGM prediction (in this example made every five minutes) was made based on the preceding four hours of EEG input data into the prediction model and is an example of predictions made incrementally every 5 minutes on a rolling basis based on four hours of previous EEG data. The predictive graph shown in figure 67 is similar and representative of the graphs shown in figures 59, 61 and 63.- 14 -SG Docket No.: 14837-710.600

[0151] In any of the methods and with any of the devices herein, the awake-time and real-time metabolic scores (e.g., GS) provided throughout the day (referred to as updated GS) optionally takes as input the initial wake-time metabolic score (e.g., waking GS) as well as the subject recorded EEG data on a four hour rolling basis to determine an updated metabolic score (e.g., updated GS). It is understood that other time periods may be used on a rolling basis (e.g., use an hour or data, two hours of data, three hours of data, etc.).

[0152] Updated scores (e.g., updated GSs) may change for the better during the day, even if the initial score (e.g., GS) is high or medium, for example. A subject may engage in behavior on those days, which may be in response to the proactive behavioral guidance from methods herein, that reduces the risk throughout the day and moves them to a lower score level, or more generally simply improves metabolic health. A subject with an initial low risk score may alternatively engage in behavior during the day that raises them into a medium or high-risk score, for example (e.g., sitting all day, eating poorly all day, etc.).

[0153] Figure 68 illustrates an exemplary graph of glucose levels over time and a general approach to determining glucose sensitivity risks (or metabolic scores generally), or risk levels. The arrow to the left represents the subject waking, and as shown sleep-time glucose levels generally dropping during sleep. During the sleep-time, as described herein, the methods can record scalp EEG data and use that in determining or predicting the waking metabolic score. The App can then provide one or more outputs, such as proactive behavioral guidance or an indicator of the personalized state of the subject. After the subject is awake, an App can provide updated score information (e.g., GS) using the “daytime EEG analyzer,” which can use both awake-time EEG recordings and the waking score.

[0154] An aspect of this disclosure is related to determining an aspect of fasting glucose based on recorded scalp EEG data during sleep time, and optionally using that to track and / or improve health changes over time. Rather than having to rely on relatively infrequent blood draws (e.g., once a year) for fasting glucose information, the MSM herein can provide at least one predicted daily fasting glucose output when the subject wakes up, which can be tracked overtime as frequently as desired (e.g., daily, weekly, etc.). Upon waking, the subject will have presumably fasted for a certain period of time, so recorded EEG data can be used to predict either glucose levels and / or a qualitative indicator of fasting glucose, such as any of the scores herein. Prediction may include inputting sleep time EEG data into a trained model that is trained to predict fasting glucose states (e.g., trained with both EEG information and CGM glucose levels). The fasting glucose states can be predicted during sleep time, upon waking, or after waking such as after glucose value have started to rise, as is typical upon waking. It may even be conceivable that unlike wake time blood draws that typically occur in a medical office some-time after waking but before consumption (e.g., an hour or two), fasting glucose states just before waking or shortly after waking (e.g.,), when tracked over time using sensed scalp EEG data, may provide a better indicator of metabolic function and may be more effective in tracking overall health.- 15 -SG Docket No.: 14837-710.600

[0155] As an example, blood glucose data (e.g., from CGMs) and EEG data can be used to train a fasting glucose monitor model, and thereafter the model can take in as input subsequent EEG data during sleep time and / or shortly after waking to predict a fasting glucose state for the individual (e.g., number and / or qualitative metric).

[0156] Any of the Apps herein may be adapted to track the fasting glucose information to help the subject toward a fasting glucose goal, which is an example of overall metabolic health.

[0157] One of the benefits of the systems and methods herein is that, if they are used to track fasting glucose levels over time, the systems can be adapted to automatically determine when the subject has woken up by analyzing the scalp EEG data, which changes in predictable ways when a subject transitions from sleep to awake. The system is optionally adapted to automatically determine when the subject has woken up and can be used to facilitate tracking of fasting glucose, including distinguishing between sleep time fasting glucose and awake time fasting glucose.

[0158] Figure 79 illustrates an exemplary method of monitoring fasting glucose states (e.g., levels) over time, including recording or receiving brain activity signals during a fasting period, and predicting a fasting glucose state based on the recorded brain activity signals, optionally using a trained model trained on at least recording brain activity signals and glucose levels (e.g., measured with a glucose monitor).

[0159] Training any of the models herein can be performed using a wide variety of machine learning techniques and approaches. Without being limited in any way, training models herein can include assessing a plurality of EEG permutation entropy features, and which may occur over a certain period of time (e.g., 2 hours, 3 hours, 4 hours, or less or more). Figure 69 includes a merely exemplary illustration of training and testing a trained model, which may be referred generally as a k-fold cross- validation method.

[0160] Post-prandial Example

[0161] One example herein, the general approach to which is shown in figure 66B, included training a model to predict a post prandial event. Similar to the example above (and other examples herein related to post-prandial event prediction), the example included recording CGM and scalp EEG data from seventeen subjects after administering glucose to the subjects (e.g., drinking a sugary drink), training a machine learning model with the CGM data and the EEG scalp data for all but one subject, and testing the trained model on the other subject. Figure 70 represents some of the results, with four of the training subject data on the right (P001, P004, P008, and P014), and the test set on the left. The blue line shows CGM recorded data and the orange shows predicted CGM data based on the EEG data.

[0162] Figure 71 illustrates a moderately high correlation between high levels (e.g., peaks) of glucose levels measured with a CGM and predicted glucose levels (from the trained model). This illustrates that methods and systems may optionally include (or even be limited to) predicting glucose levels related to peaks, while also being able to provide more qualitative metabolic scores at one or more other times. This may prove beneficial in that information about glucose peaks provide valuable- 16 -SG Docket No.: 14837-710.600insights to the subject, including trying to avoid high peaks so they stay in more desirable glucose states throughout the day and their lives.

[0163] As such, any of the Apps herein may optionally be adapted and configured to be able to predict one or more parameters of a high glucose peak / spike (e.g., amplitude or glucose level, amplitude or glucose level based on particular food, duration of the peak, time when the peak occurs, time when peak occurs after a meal, area under the glucose level curve (“AUC”)) while also being adapted to provide a metabolic score, such as GS (e.g., any of the scores and / or guidance herein) throughout the day.

[0164] As stated above, methods herein may include determining a subject’s awake-time glucose sensitivity (“GS”) for the subject based at least on one or more sleep features detected in the recorded sleep-time brain activity signals. The one or more sleep features can be indicative of the quality of sleep during the sleep-time. The sleep features herein optionally include macro sleep features and / or micro sleep features, with exemplary micro sleep features shown in figure 75. Examples of macro sleep features include a number of sleep cycles completed, a number of sleep stages completed, a duration of one or more sleep cycles, a duration of one or more sleep stages, total sleep time during the sleep-time, and a number of waking interruptions during the sleep-time. In some examples, electrical biosignals sensed using one or more wearable scalp sensors (e.g., any of the behind the ear sensors herein) may be used to derive / predict sleep stages (including any information related to one or more sleep stages), wherein the sleep stages or information indicative thereof may optionally be input into any of the glucose state or score prediction algorithms as a feature to improve glucose state prediction and / or metabolic regulation information (e.g., waking fasting blood glucose levels; personalized daily recommendations). Illustrative and non-limiting macro and / or micro sleep features that may be measured and used to predict a waking glucose sensitivity risk can be found in Valat, R., Shah, V., Walker, M., Coordinated human sleeping brainwaves map peripheral body glucose homeostas s, Cell Reports Medicine, Volume 4, Issue 7, 101100 (2023), which is fully incorporated by reference herein for all purposes.

[0165] Any feature of any of the aspects herein may, if suitably combinable, be integrated with or into the aspect above related to glucose sensitivity.

[0166] One aspect of this disclosure is related to methods of predicting a glucose state of a subject, which may be integrated with any of the above aspects and which may provide to the subject a predicted glucose state (e.g., actual quantitative levels or qualitative states of general categories). This may occur throughout the day, and may optionally occur at regular intervals. This may optionally additionally or alternatively occur in response to a subject request for the App to provide information about the glucose state (on-demand).

[0167] One aspect of the disclosure is a method and / or system that is adapted to provide a periodic (either regular intervals, irregular, and / or on-demand), such as weekly or monthly, metabolic health score, either in addition to or alternatively to a discrete metabolic score (e.g., a discrete morning “glucose sensitivity score” that occurs at a certain time on a given day). A periodic (e.g., monthly)- 17 -SG Docket No.: 14837-710.600metabolic health score may optionally be the average maximum glucose level (as derived from EEG) for the time period, such as for a month. A beneficial use of this App feature (or other similar feature) is that it allows a user (or health care provider) to track a metabolic parameter of the individual over time to be more informed about their personalized metabolic health, including tracking the parameter over time in combination with proactive behavior guidance from the App. Maximum glucose levels (e.g., max per day) is an indicator of overall metabolic health, information many presumably metabolically healthy people and prediabetic people are completely unaware of. By periodically tracking average maximum glucose levels, for example, the Apps can provide the user with this particular indicator of metabolic health, and either the user and / or the App (e.g., automatically) can track the max level over time to understand their progress in improving their health, which can be based on the proactive behavioral guidance provided by the App, described herein. The Apps herein can thus both guide the user to better health based on recording brain signals, and periodically track at least one parameter or indicator of metabolic health over time to track the improvements in overall health. As an individual gets metabolically healthier, their body gets more efficient at processing glucose and the glucose spikes are not as high (as measured by max glucose level, described herein). This may be an even better measure of metabolic health than currently measured A1C levels. While this example described an App that is adapted to monitor daily max glucose levels over time, other metabolic health indicators may also be monitored and similarly used to assess progress in improving overall health. For example only, average area under a glucose curve (“AUC”) following a meal may be monitored periodically (e.g., monthly, weekly) and used to monitor overall metabolic health.

[0168] Figure 78 illustrates an exemplary method that can be part of monitoring an indicator of metabolic health over time (in this example maximum glucose levels), comprising recording or receiving brain activity signals, predicting a plurality of awake maximum glucose levels for different awake times (e.g. on different days) based on the brain activity signals, and providing an output indicative of the plurality of maximum glucose levels. The method also optionally records the plurality of maximum glucose levels over time and optionally stores them in a memory at least temporarily.

[0169] One aspect of the disclosure, which is illustrated in part in figure 72, is predicting one or more aspects of awake period glucose regulation (e.g., glucose sensitivity risk) for a period of awake-time that follows a period of sleep-time using information from signals that are sensed from the subject during the sleep-time. In this exemplary illustration, the one or more predicted aspects of glucose regulation during the awake time comprise a predicted daily maximum glucose level, as indicative by star icon 208. Sleep-time periods 200 and awake-time periods 202 are shown. Additionally shown in figure 72 in an indicator of quality of sleep 206 during successive sleep-periods, ranging from deep (relatively better sleep) to shallow (relatively worse sleep). The sleep quality in this example comprises at least depth of sleep, on a scale ranging from deep to shallow. In the first period of sleep 200 on the left, the sleep is generally shallow compared to the second period of sleep 200 on right,- 18 -SG Docket No.: 14837-710.600which is overall deeper (higher quality). The max glucose levels 208 during the first awake time following the first period of sleep is higher (less desirable) than the max glucose level 208 during the second awake time following the second period of sleep, as shown. A predicted maximum glucose level during an awake time that follows a sleep time during which brain activity signals are recorded can be used to determine or predict a sensitivity score upon waking. For example, one or more sleep features measured during sleep that are indicative of quality of sleep may be used to predict a maximum glucose level during the awake time. The predicted maximum glucose level for the awake time following sleep may be at least partially used to determine an awake time sensitivity risk upon waking. For example, predicted maximum next day glucose levels following sleep may be categorized into a plurality of discrete ranges, such as in either high-risk range, a medium-risk range, or a low-risk range, and which can be presented to the subject as output from the App upon waking, as described herein.

[0100] Figure 73 represents a merely representative and exemplary sleep feature (e.g., “feature 1 from figure 56) vs the maximum glucose level (measured using a CGM) during the following awake- time period, obtained from a data set of 16 patients with 39 associations between the sleep feature on the x-axis and max glucose level in the following awake-time period on the y-axis. As for the sleep feature, values closer to 1.0 represent better quality sleep, and values of 3.0 represent less quality sleep. As shown, better quality sleep (x-axis values closer to 1.0) was associated with lower max glucose levels during the subsequent awake-time period, and poorer quality sleep (x-axis values closer to 3.0) was associated with higher max glucose levels during the subsequent awake-time period, as shown.

[0101] Figure 74 illustrates, on the y-axis, predicted maximum glucose values (predicted with a trained ML model) for the awake-time period following sleep using sleep EEG vs maximum glucose levels during the awake time measured with a CGM (x-axis). The data shown in figure 74 was obtained from a LASSO regression with 8 sleep features, and shows a mean absolute relative difference (MARD) of 12.1%.

[0102] Any of the wearable sensors herein, may also include a PPG sensor, which is described elsewhere herein. One or more PPG sensors may be used to obtain SpO2 (oxygen saturation data), which may be used to determine any aspect of sleep described herein, such as determining an awake score. For example, if oxygen saturation is low, this may be an indicator of poor sleep, which can be input data to help determine a waking sensitivity score. Additionally, PPG sensors incorporated into the wearable scalp sensors herein may be used at least partially to detect sleep and / or sleep stages.

[0103] Figure 76 is an example of a method of determining a waking glucose sensitivity risk, including recording or receiving sleep brain activity signals during a sleep period while the subject is sleeping, predicting a waking risk based on sleep brain activity signals, providing an output based on the waking risk, and optionally wherein the output includes proactive behavioral guidance based on the risk.- 19 -SG Docket No.: 14837-710.600

[0104] Figure 77 is an example of a method of monitoring quality of sleep, including recording or receiving sleep brain activity signals during a sleep period while the subject is sleeping, predicting or determining a quality of sleep, providing an output based on the quality of sleep, and optionally wherein the output includes proactive behavioral guidance based on the quality of sleep. Clinical and / or research settings have used EEG to record brain waves and associate brainwave types with sleep stages and sleep quality. These and other attempts to characterize quality of sleep are incorporated by reference into the methods herein that can provide actionable, proactive behavioral guidance to improve overall metabolic health.

[0170] Appendix and Additional Related Disclosure

[0171] The disclosure that follows is related to predicting future and / or real-time glucose states, any portion of which may be integrated and incorporated with any of the methods, devices and systems above and vice versa. It is understood, however, that any of the disclosure that follows may include features that need not or cannot be included with the disclosure above, and vice versa. The disclosure that follows may be included in US publication US20240293083, which is incorporated by reference in its entirety for all purposes. Any specific “Examples” below (such as Example 1, Example 2, and Example 3 below) are understood to relate to the description of that example, even though additional Examples are described above.

[0172] The predicted health information and / or benefits herein optionally include one or more glucose states of the subject, such as a real-time glucose state or a future glucose state subsequent in time to when the one or more biosignals were sensed from the subject. Predicted glucose states may be based on one or more types of sensed biosignals obtained and / or sensed from the subject. Alternative approaches are needed that can provide information about a glucose state of a subject without relying solely on a glucose sensor, and optionally without requiring a glucose sensor at all.

[0173] An exemplary and non-limiting aspect of this disclosure is related to predicting a predicted glucose state of a subject, wherein the predicted glucose state is based at least partially on one or more brain activity signals or data sensed from the subject. Aspects of the disclosure herein provide approaches that facilitate minimally invasive or non-invasive devices and systems that can predict glucose states without requiring an implantable device and that do not rely solely on devices that provide information with a lag time as do glucose monitors that are adapted to measure interstitial glucose.

[0174] As used herein, a “glucose state” may refer to a variety of aspects of the subject’s condition. Glucose state may optionally refer to predicted and / or calculated blood glucose values (e.g., 140 mg / dL (or 7.8 mmol / L)), a general characterization of blood glucose values such as a designation between “high,” “normal” or “low,” a characterization whether the subject is “in range” or “out of range,” and / or may be related to blood glucose values and / or interstitial fluid glucose values.

[0175] Benefits of some of the approaches herein include being able to predict glucose states without accessing intracranial locations of the subject, and not relying solely on glucose sensors (and optionally do not rely on glucose sensors at all). Aspects of the disclosure include wearable devices- 20 -SG Docket No.: 14837-710.600that can be worn in an ambulatory manner and are optionally sized and configured to be worn discretely on the scalp of a subject. Additional exemplary benefits of the approaches herein are that the predicted glucose states may optionally be predicted before the glucose state or event occurs and / or the predicted glucose states may be more accurate and reliable than existing glucose monitors. The approaches herein may provide a large number of people with beneficial health information that can enhance their overall health care and / or well-being, including providing access to their glucose state information that people previously did not have access to. While portions of this disclosure may describe predictions for diabetic or prediabetic subjects, the benefits may be realized by the entire population. For example, as is set forth herein, forecasting (future predictions) and / or predicting realtime glucose levels using sensed EEG data may have benefits for a wide variety of individuals, including use by healthy individuals who want more control over their personalized nutrition and health. In fact, some attention has been recently given to using CGM outside of diabetes, and the benefits of forecasting and real-time glucose monitoring using EEG (as described herein) can similarly have benefits outside of diabetes in a wide variety of applications. The following exemplary list provides examples of applications of the innovations herein: athletics; overall health and wellness; gestational diabetes; obesity; metabolic syndromes; personalized glycemic profile (how their body clears and otherwise adapts to glucose); disorders such as stress or anxiety; stress of an infection (sepsis); personalized nutrition; personalized post-meal glycemic profiles.

[0176] Using EEG data to predict glucose levels may be more accurate than existing monitoring approaches, such as sensing ISG as is done with existing CGMs. For example, predicting blood glucose states in the future can arm the subject with a predictive outlook on their future blood glucose levels, whether primarily for informational purposes or providing them with actionable information to allow them to improve their health. Additionally for example only, and as described in more details below, methods herein that use EEG data to predict a glucose state (e.g. one or more predicted blood glucose values) during a sleep-state (asleep) may be more accurate information than existing CGMs, including predicting glucose states before they occur, which may be beneficial since some subject’s may be more likely to become hypoglycemic while sleeping.

[0177] FIG. 1 represents an exemplary and general method of predicting a glucose state of a subject 10. Method 10 includes, at step 11, sensing brain activity signals or data from a subject. Method 10 includes, at step 12, predicting a predicted glucose state of the subject based at least partially on the sensed brain activity signals of the subject. Method 10 optionally includes, at optional step 13, communicating information indicative of the predicted glucose state (e.g., providing information on a display related to glucose state; providing a warning that ranges are trending out of range; communication information to a care-team). Method 10 optionally includes, at optional step 14, managing one or more aspects of the subject’s glucose state (e.g., automatically initiating a management step such as delivering insulin; or providing a recommendation to the subject such as recommendation on a time to exercise and / or eat, optionally via a display of a personal subject device).- 21 -SG Docket No.: 14837-710.600

[0178] Sensing at step 11 may comprise, and may preferably comprise, non-invasively sensing brain activity signals. Non-invasive sensing using wearable sensing devices herein can provide a large number of people access to personal health information and the benefits thereof compared with more intrusive approaches (e.g., solely intracranial and / or implantable).

[0179] Sensing at step 11 may comprise, and may preferably comprise, non-invasively sensing brain activity signals at one or more specific locations on the head of a subject, and optionally the scalp. The one or more specific locations are, at least in most beneficial uses herein, fewer than all the locations that are typically used during a typical electroencephalogram (EEG) where electrodes are worn at many locations about the head. One of the benefits of approaches herein is that brain activity signals can be sensed and utilized to predict glucose states in an ambulatory manner that is not available with typical full-head EEG recordings in clinical settings, providing myriad individuals access to health information heretofore inaccessible and even unknown. As used herein, “EEG” refers generally to recordings of brain activity electrical signals sensed from a subject at one or more particular locations, but in other uses relative to traditional full head EEG, it may refer to clinical recording of brain activity signals in which signals are obtained with many electrodes placed about the subject’s head in a clinical setting.

[0180] As described below, Examples 1-3 herein provide data that blood glucose levels may be predicted using brain data recorded from wearable sensing devices that may be placed at one or more behind-the-ear locations (e.g. M1 / M2 / A1 / A2 as shown herein) and / or at one or more occipital locations (e.g., O1 / O2 using 10-20 EEG set nomenclature).

[0181] FIG. 2 illustrates an exemplary and general system 20 adapted for predicting a predicted glucose state of subject 21. System 20 includes at least one wearable sensing device 22 (wearable sensor) that is configured to sense brain activity signals from a subject when worn on the head of the subject. System 20 also includes a computer executable method with instructions that cause the performance of predicting a predicted glucose state. The computer executed method may be stored in the wearable sensing device, in a different device (e.g., a personal device) or location (cloud).

[0182] FIG. 3 illustrates an exemplary system 30 that is adapted to predict a glucose state of subject 31. System 30 includes at least one wearable sensing device 32 (wearable sensor) that is configured to sense brain activity signals from subject 31 when worn on the scalp of subject 31. System 30 also includes a device 33 in communication with sensing device 32, wherein the device 33 is adapted to receive information from sensor 32 and output instructions to initiate a communication indicative of the predicted glucose state based on and in response to the predicted glucose state. Device 33 may be a device accessible by subject 31 (e.g., a smartphone or smartwatch) and / or device 33 may be accessible by a care-team for subject 31, optionally one not accessible by subject 31.

[0183] Prediction models herein may reside or be stored on one or more wearable sensing devices and / or other device in wireless communication with the wearable sensing device. Depending on processing power, it may be beneficial to store the prediction models on the personal device or careteam device.- 22 -SG Docket No.: 14837-710.600

[0184] An important aspect of some of the examples herein is related to wearable sensors that can be worn on the head of a subject and in a location that can reliably and non-invasively detect brain activity signals that can be used to predict predicted glucose states of the subject. FIG. 4 illustrates an exemplary behind-the-ear location 40, which is not meant to refer to a strictly defined region on the subject’s head. Location 40 refers to a region on the head that includes surfaces behind (posterior) to the ear as well as surface in the Cephalad direction relative to the ear. Location 40 may also extend to some extend inferior (or Caudal) to the ear. Behind the ear locations as used herein include the locations labeled M1 / M2 / A1 / A2.

[0185] Behind-the-ear location 40, including locations proximate the mastoid process, may be beneficial for non-invasively recording brain activity signals because there is a region of skin on which the sensing device may be positioned and maintained during sensing (and without requiring a full-head EEG set-up). FIG. 4 illustrates merely illustrative outer profiles of wearable sensors 42a, 42b and 42c positioned in a behind the ear location. A wearable device need not be placed entirely within a predefined region to be considered to be “behind-the-ear,” such as exemplary location 42c. All locations 42a-42c are considered behind-the-ear locations as described herein.

[0186] One aspect of this disclosure is related to methods of training machine learning algorithms or models to create a trained glucose state prediction model. Training predictive glucose state models herein may include one or more of supervised or unsupervised training techniques.

[0187] Methods of training models herein may include providing sensed brain activity signals (raw or processed to some extent) and a history of glucose values, whereby the model may select one or more features of the sensed brain activity signals most correlated with one or more aspects of the glucose values. For example, models may select one or more frequencies, frequency ranges, and / or frequency bands most correlated with glucose values. Additionally, for example, models may select one or more most correlated time periods between brain signal sensing and sensed glucose values. FIG. 5 illustrates an exemplary method 50 of training a predictive model. Method 50 includes, at step 51, providing sensed brain activity signals from a subject. Method 50 includes at step 52, providing glucose values sensed from the subject, and which have a temporal relationship with the brain activity signals sensed from the subject. Glucose values may be sensed or measured from one or more of a CGM; an implantable sensor; optical and non-invasive sensors; or from finger prick measurements. Method 50 includes, at step 53, selecting one or more features of the brain activity signals more correlated with glucose values than other brain signals features (e.g., one or more frequencies, frequency ranges, and / or medically established frequency bands). Once trained, at step 54, the model is adapted to receive or generate as input one or more features of subsequently sensed brain activity signals (e.g., brain signals within one or more frequency bands), and predict a predicted glucose state based at least partially on the brain activity signals.

[0188] Methods of training models herein may include providing a model with one or more features of sensed brain activity signals (e.g., one or more frequencies, frequency ranges, and / or medically established frequency bands) as inputs with glucose values as targets. Once trained, the model can- 23 -SG Docket No.: 14837-710.600predict a predicted glucose state based at least partially on subsequently received brain activity signals that include the one or more features. FIG. 6 provides an illustrative method 60 of training predictive models herein. Method 60 includes, at step 61, providing one or more features of sensed brain activity signals from a subject. Method 60 includes, at step 62, providing glucose values sensed from the subject that have a temporal relationship with the brain activity signals sensed from the subject (e.g., sensed from one or more of a CGM; an implantable sensor; optically and non-invasively; or from finger prick measurements). Method 60 includes, at step 63, the learning algorithm finding or determining a relationship (functions / parameters) between the one or more features of the sensed brain activity signals and one or more glucose values. Method 60, at step 64, includes, once trained, the prediction model is adapted to receive as input one or more features of subsequently received brain activity signals, and predict a predicted glucose state.

[0189] The glucose state prediction models or methods herein may comprise computer executable instructions executable by one or more processors, and may be stored in any device, such as a wearable sensor, a subject device, a care-team computing device, and / or a cloud device that is adapted to be in communication with any of the devices herein.

[0190] Features of brain activity signals that may be used in training and / or during the prediction step may include a variety of features of brain activity signals. For example, features may include powerband activity in certain frequencies, such as one or more frequencies between 0 - 50Hz. For example only, features may include powerband activity in one or more of the delta band (.5 - 4 Hz), theta (4 -7 Hz), alpha (8-13 Hz), beta (13-30 Hz), gamma (e.g., 30-60 Hz).

[0191] A mere example of a machine learning method can be adapted to identify a subset of the most important features from non-invasively sensed brain activity signals from one or more single channel locations, and then subsequently fit a linear regression model using the reduced set of features, implemented examples of which are described below.

[0192] FIG. 7 illustrates a merely exemplary system and method of training models and using the trained models to predict predicted glucose states, any aspect of which may be incorporated into any training and / or prediction method herein. In exemplary FIG. 7, brain activity signals and glucose values sensed from a CGM are shown to be used in the training process, and once trained, the model is adapted to predict glucose states based on subsequently recorded brain activity signals / data.

[0193] Any of methods / algorithms herein may optionally be trained on one or more of normal / healthy individuals, hyperglycemia (e.g., diabetes; hyperglycemia ICU, sepsis, traumatic brain energy; diabetic ketoacidosis) or hypoglycemia states. Any of methods / algorithms herein may optionally be trained on data from the general population, some subset of the population, or personally trained based solely on data from an individual.

[0194] Training methods herein may include training on real-time glucose state values (i.e., predicting real-time blood glucose values), or training on future glucose states (predicting a glucose state at one or more times that are in the future relative to when the non-invasively sensed brain activity signals were sensed by a wearable sensor.- 24 -SG Docket No.: 14837-710.600

[0195] As mentioned above, brain activity signals may be trained on glucose state data regardless of the manner in which the glucose state data is derived. For example only, brain activity signals can be trained on blood glucose values / levels sensed from existing CGMs, from measured finger prick blood glucose values (measured with a meter), blood glucose values from systems that are configured to optically detect glucose values (e.g., watches, rings), or implantable sensors that sense glucose at a location more invasive than CGMs (e.g., from within a blood vessel). Trained models herein are adapted to predict a glucose state (e.g. values), and the “state” may be based on the manner in which the values were initially detected or sensed. It is thus understood that the glucose state predictions as described herein may be predictive based on the blood glucose values and the manner in which they were obtained or sensed. Regardless of the manner in which the glucose values are sensed, prediction models based on that data can still provide a predicted state, which provides the subject the predicted values before they occur.

[0196] While there may be instances where general features may be used to predict a glucose state for at least parts of a population, there may be a need or preference to determine one or more features of sensed brain data for a particular subject (patient specific) that provides more accurate blood glucose prediction (e.g., due to a higher correlation between the one or more features and glucose levels). The phrase features as used herein in this context can refer to a variety of patient parameters, such as, without any limitation, location of one or more wearable sensors (e.g., using one or more of: one or more behind the ear location or one or more of 01 / 02 locations); one or more frequency bands (e.g. alpha, beta, gamma, theta); one or more frequency ranges (e.g., 0-50 Hz, or any subrange within 0-50Hz, and not necessarily the discrete “bands”), whether the range spans multiple bands or not; one or more particular and discrete frequencies; features related to brain signal sensing (e.g., AUC, Power, Phase synchrony or coherence between sensors); predictive temporal lag with higher accuracy-better correlation (examples of which are described below), etc.

[0197] The example that follows is an illustrative, non-limiting, example of a process or method of training a model or method that includes determining or selecting one or more features in non- invasively sensed brain activity signals from a wearable sensor adapted for single channel sensing (or at least operable in bipolar sensing even if optionally also operable for monopolar sensing), as is shown in FIG. 5. Once trained, the model is adapted to predict future blood glucose levels based on subsequently received single channel EEG data. Alternatively, one or more features can be established as inputs and used to train a predictive model with glucose data as targets (FIG. 6).

[0198] Example 1

[0199] In this non-limiting example, data from 1 subject with type 1 diabetes and 3 non-diabetic subjects was sensed over the course of several days to about 45 days. The data that was sensed included brain activity signals or data sensed while wearing a wearable brain activity sensor, worn unilaterally at a location behind the ear on the scalp (skin), examples of which are shown in FIG. 4 (optionally one of an M1 / M2 / A1 / A2 location). Additionally, interstitial glucose levels were also contemporaneously sensed using a commercially available CGM traditionally worn and used.- 25 -SG Docket No.: 14837-710.600

[0200] While a variety of software tools may be used (e.g. Python), the example herein utilized MATLAB® software.

[0201] While the following example includes several steps, it is understood that this is meant to be illustrative and enabling, but that it is an exemplary method and in other methods not all of these steps are necessarily performed or in this order. After sensing raw brain activity signals, the raw brain activity signal data may undergo data conversion, such as converting it into a particular toolbox, depending on the software used, for processing (e.g., convert the brain activity signal data into fieldtrip format for processing). The raw converted data is shown in the plot in FIG. 8.

[0202] The converted data was filtered with a low pass filter (50Hz) and high pass filter (5 Hz), but other filters may be used (e.g., high pass 1 Hz, low pass 70 Hz). After filtering, the data was plotted, as shown in FIG. 9. In this example, 5-50 Hz signals were processed, but in other examples other ranges may be processed such as .5-50 Hz. Signals were epoched into 5-minute segments, and single time points were rejected if they fell outside of 3.5 median average deviations from the median signal amplitude. Further, entire epochs were rejected if their power x frequency curve for that time bin did not follow the physiologically-defined 1 / f curve.

[0203] A power analysis was then performed for the range 5-50 Hz, in 1 Hz intervals, and the power fast fourier transform (FFT) was plotted, as shown in FIG. 10. An optional artifact rejection step was performed, which included binning the data into 5-minute periods. Spikes were removed that exceeded a certain median average deviation (MAD; 3.5 MAD), and the data without spikes was then reshaped and plotted, as shown in FIG. 11. A power analysis was then performed again after the spikes were removed. The FFT was plotted, as shown in FIG. 12.

[0204] Training predictive methods that are trained to predict a subject’s blood glucose state (realtime blood glucose or future blood glucose) may comprise receiving as inputs one or more aspects of processed brain activity signals (e.g., from a wearable sensor adapted to record a single channel (e.g. with a single pair of sensing electrodes), but wherein the wearable sensor may also be adapted to record in monopolar mode with a third referential electrode, examples of which are provided herein), and sensed glucose data (e.g., glucose values). In this particular example, this was performed individually for each subject, and is illustrative of the optional personalized training approach that may be used (or which may be necessary for accuracy) to train predictive models herein.

[0205] Next, the glucose monitor data was overlapped with or aligned in time with the brain activity data. In this example, one day of data was overlapped / aligned at a time, but other time periods can be used. The process further included matching the sampling rate of the brain activity signals and the glucose data. The process further included correlating all glucose data and brain activity data, running cross correlations (and plotting, an example of which is shown in FIG. 14A), averaging individual frequencies into bands, and running cross correlations per band (and plotting, an example of which is shown in FIG. 14B). The process further included running correlations for sleep and awake time periods, optionally extracting indices that fall during standard sleep and wake time periods. The process further included, for each of sleep and awake time periods, extracting brain activity signals- 26 -SG Docket No.: 14837-710.600and glucose data, correlating frequency data, and averaging data into bands and correlating the bands. The correlations can be plotted or otherwise analyzed.

[0206] The process further included developing lag indices of, for example, 10-minute intervals, but other intervals can be used, such as, for example without limitation, from a 1 mm lag to a 20-minute lag, including any specific time interval therein (e.g., 5 min lag, 8 min lag, 15 min lag). For each lag, for each frequency band, linear mixed-effects (LME) model analysis was run with predicted blood glucose and fixed variable EEG. Residuals between the model and actual values of blood glucose were plotted, which are shown in FIGS. 14 and 15 for the asleep and awake correlations.

[0207] Least absolute shrinkage and selection operator (LASSO) was performed, the results plotted and saved (plots shown in FIGS. 16A, 16B, 17A, and 17B, for awake and sleep, respectively). Predicted blood glucose values were extracted from the model training on single -channel EEG and CGM values, from a single lag. FIG. 16A shows all power estimates in 1:50 Hz intervals, while FIG. 16B is an average of those into four frequency bands (alpha, theta, beta, and gamma).

[0208] FIG. 20 plots % accuracy of predicting blood glucose levels vs EEG temporal prediction (hours) from the two subjects in this example on the trained model. As can be appreciated, the accuracy of predicting glucose values on the time scale up to 12.7 hours was 90% or greater from zero to 12.7 hours in advance, demonstrating significant accuracy. From this plot, and as part of a process of developing / creating / training a glucose state predicting algorithm / model, an exemplary approach may include selecting or determining that one or more particular times or one or more particular time ranges with higher correlations is preferred as more accurate. For example only, an approach may include selecting or determining that during time period 2102, as labeled, the accuracy is at least 94%, and that time range of about 8.3 hours to 12.7 in advance, or one or more epochs within that range, is a preferred time range to input sensed brain data into the model and predict one or more glucose values forthat particular subject. Temporal time lags may change for each subject over the course of the subject’s daily routine, such as changing from day to day. Optional systems and methods herein may thus benefit from periodically sensing not just brain activity data but also estimate blood glucose values with a glucose monitor, and adjust the temporal lag as may be desired or needed. Time range 2112 is a mere example of a temporal lag range for subject 2 that might be selected or determined due to the relatively high % accuracy of prediction. In some applications, a specific time point may be selected, such as time point 2104 or 2114, as a temporal lag for the relative subj ects, due to a relatively high % accuracy of prediction. The temporal time lags in this context, if they are discussed in the context of a personalized lag with relatively high accuracy for that particular subject, are referred to herein as a personal time lag or other similar term.

[0209] Additionally, any of the Apps herein may be adapted to provide the functionality to allow the subject or care-team to select a time or time range in the future for which they would like predictions. For example only, any of the Apps herein (“Apps” described below) may be configured to visually present on a display or screen of the device a selectable user input to choose or select a time for which they would like predictions. For example only, a subject may prefer predictions 2-15 minutes in- 27 -SG Docket No.: 14837-710.600advance (for more near-term predictions), or, for example, 1 hour in advance, or 30 minutes in advance, for example. Any of the Apps herein can be configured to operate and predict with the selected or chosen time period in advance, and make glucose state predictions that are predicting that subject’s glucose levels that time in the future (relative to when the brain activity was sensed).

[0210] Training methods herein may utilize one or more features of the sensed brain activity signals (including one or more patient specific features that may lead to greater accuracy for the subject) on glucose states (however sensed / measured), and once trained, the model is adapted to receive subsequently sensed brain activity signals and predict glucose states (e.g., values), wherein the trained model may be specifically trained on the one or more features of the sensed brain activity signals. That is, the trained models herein (which may be stored on any of the subject devices herein, such as part of any of the Apps herein) may beneficially not need to receive or utilize all of the sensed brain activity signals. For example, only, trained models or other algorithms may utilize or receive a subset of the single-channel sensed brain activity signals that have a more significant correlation with glucose values and more accurately predict glucose values, and which may be a more significant correlation between power of one or more bands and glucose values, described above.

[0211] Example 1 as described above illustrates that at least a portion of single-channel EEG, from a behind-the-ear location on the skin of a subject, is predictive of blood glucose values, and thus can be used to predict glucose values based on single channel EEG. It also showed that there may be, at least following the processes above in Example 1, some patient-to-patient variability in the % accuracy of prediction, such as when single channel signals may be most predictive. In fact, it may optionally be desirable or even necessary to train prediction methods on data from the intended subject for a personalized prediction App, which is referred to herein as personal or personalized prediction. That is, each subject may have a personalized App that is trained to process EEG data in a way that will be the most predictive for that individual, which may also be considered to be an individual calibration step or process prior to predictive use of the system. Additionally, a subject may benefit from periodically recalibrating their App to ensure it continues to process brain signals in a way that is most predictive for that subject. Personalized temporal lag, as described herein, is a mere example of a patient feature in or related to single channel, non-invasively sensed, EEG signals that can be used in the prediction methods herein.

[0212] Features of brain activity data upon which the models may be trained may be personalized to an individual feature, or they may be features that can be utilized across larger populations of people, or even all subjects using a predictive method.

[0213] As can be seen in FIGS. 17B and 19B, the extracranially sensed EEG from a single channel was highly correlated with current or real-time blood glucose values sensed using a CGM during sleep time, as indicated by the zero “0” lag. It is of note that following the process above, the extracranially- sensed single channel EEG data was generally not as predictive of future glucose values during sleep time as during awake time. Optionally, methods and systems herein may use a future prediction approach that relies more heavily on brain activity signals sensed during the awake time than during - 28 -SG Docket No.: 14837-710.600asleep time, which again may provide a benefit of processing less information, thus requiring less power, while obtaining more accurate predictions. Relying more on brain activity signals sensed during the awake time than during the asleep time may optionally comprise applying a weighted factor to awake time signals that is greater than a weighted factor for asleep time signals, for example only. Relying more on brain activity signals sensed during the awake time than during the asleep time may optionally comprise not relying at all on sleep time signals for future glucose state predictions. Any of the methods and systems herein may include this feature of relying more heavily on awake time EEG data for predicting future glucose levels.

[0214] FIG. 21A is an additional merely exemplary glucose prediction system (GPS), optionally continuously sensing (CGPS) that can be adapted and configured for training models and / or predicting blood states. System 2100 includes optional portal device 2108 accessible by a care team member, such as a physician. The GPS may be adapted to predict blood glucose values by itself (e.g., without input from other devices like CGM 2104), but optionally it may be adapted to also predict using or based on one or more inputs from one or more devices (e.g., a CGM 2104 as shown, or other devices that are Bluetooth enabled), which may optionally communicate with the GPS via Bluetooth, as shown. System 2100 includes a wearable sensor 2102 (optionally a second on the other side of the head behind the other ear) and subject device 2106, features of which are described elsewhere herein. It is understood that the optional CGM 2104 shown in FIG. 21A may be replaced with a different type of glucose monitor or sensor, such as an invasive monitor (e.g., implanted in an arm or abdomen) or a non-invasive monitor such as an optical monitor (e.g., integrated into a watch). FIG. 2 IB illustrates the system 2100 in FIG. 21 A, but without the optional glucose monitor. All reference numbers and descriptions from FIG. 21A apply to FIG. 2 IB.

[0215] Example 2

[0216] A second herein example used a similar process to Example 1, but additionally utilized more EEG data from atypical EEG setup. In the second example, both glucose data and EEG data were contemporaneously sensed / monitored from a subject for at least 2 days. The subject was wearing an existing continuous glucose monitor (CGM) sensing glucose data, while undergoing a common EEG recording with a setup shown in FIG. 22A, and with the addition of Al (left) and A2 (right) electrodes in exemplary behind the ear locations, as shown in FIG. 22B. Ml and M2 electrodes are also shown in behind the ear locations.

[0217] The preprocessing of data included: 1) only selected “awake” period, and epoched to 10 sec window; 2) aligned the timepoints in CGM and EEG signals; 3) selected 27 EEG channels and common average re-referenced; 4) band-passed [1,99] and notch-filtered

[5565] ; 5. eye blink artifact removed by ICA; and 6) bad trial rejected based on MAD,

[0218] The steps below illustrate exemplary power extraction and CGM matching: 1) scalp EEG power is extracted using fast fourier transform in FieldTrip toolbox for each 5-s epochs; 2) The trials of power from 1 min before and after to CGM timepoint were selected to align the CGM value; 3)- 29 -SG Docket No.: 14837-710.600The trials and each frequency band were averaged; 4) linear regression model was used to obtain the correlation between frequency band and the glucose (similar to in Example 1).

[0219] FIG. 23 A represents a Summary of brain region and bands for which there was a significant correlation between the power and glucose level, and whether it was a positive or negative correlation. Positive correlation refers to higher the power in that particular band corresponding to higher the glucose levels; negative correlation refers to less power being associated with higher glucose values. FIG. 23B shows the high negative correlation between glucose and EEG behind the ear and the high positive correlation between glucose and EEG occipitally.

[0220] This Example also assessed a variety of time windows prior to the sensed glucose values, as shown in FIGS. 24A and 24B, with the table shown in FIG. 24B providing channels / locations with significant correlation with glucose values in each of the time windows. The significant correlation is related to the table in FIG. 23 A for the particular brain region and band of significance.

[0221] As shown, at least one of occipital 1 and occipital 2 (01 and 02), the location of which is shown in FIG. 22A, shows significant positive correlation in all time ranges from T1 = -13 and T2 = - 5. Additionally, as shown, Ml and M2 showed significant correlation in at least some of the time ranges from T1 = -13 and T2 = -5. Additionally, as shown, F7 and F8 showed significant correlation in at least some of the time ranges from T1 = -13 and T2 = -5.

[0222] The data showing significant correlation at these locations and in these bands supports placement of wearable sensors at one or both of Ml and M2 locations (an example of behind the ear) and / or one or both 01 and 02 locations. Additionally, any of the trained models herein may be trained on any of the bands shown in FIG. 23A and / or specifically related to a particular band. For example, any of the models herein may be trained to receive and predict based on only a subset of frequencies from 0-50 Hz, such as at least some frequencies between 0-20 Hz (but not frequencies greater than 20 Hz), such as at least some frequencies between 0-15 Hz, such as at least some frequencies between 0- 12 Hz, such as from 5-12 Hz, or from 5-8 Hz, or from 9-12 Hz (alpha). In the table in FIG. 23 A, the data from 02 (positive correlation at 5-8 Hz) is an example of a feature that is within a particular band (theta), while the data from Ml and M2 (negative correlation at 9-12 Hz) is an example of a feature within a particular band (alpha). In the table in FIG. 23 A, 01 is an example of a brain region with features that span multiple bands, in this case, spanning delta, theta, and alpha. FIGS. 25B, 26B, 27B, and 28B show exemplary plots of raw power and glucose readings for particular electrode locations, at particular frequencies (as shown) and from 11 minutes before to 6 minutes prior to glucose value (T=-l 1 min and T2 = -6 min). CGM levels referenced against the left axis, time is on the bottom axis, and power for that location and frequency range refenced on the right axis. In color, CGM data is blue and power in red. FIGS. 25A, 26A, 27A, and 28A show plots of z-scored power and glucose for select electrode location and frequency range.

[0223] One aspect of the disclosure is related to, after predicting a glucose state, outputting instructions to initiate a communication indicative of the predicted glucose state. In some examples, a computing device (optionally a subject device, such as shown in FIGS. 3 and 21) is adapted to output - 30 -SG Docket No.: 14837-710.600instructions to create one or more visual communications of information related to the predicted glucose states. In some examples, the predicted glucose states (e.g., predicted values) may be visually presented on a display of the device, which may be controlled by an App on the device.

[0224] FIG. 29 is a non-limiting example of a visual representation of predicted future blood glucose levels on a display of a device, such as a smartphone or other computing device. For example, an App may comprise a computer executable method that is adapted to cause predicted future blood glucose levels to be displayed, the computer executable method stored in a non-transitory memory, the method comprising: receiving as input extracranially-sensed EEG data or information indicative of extracranially-sensed EEG from a subject; and causing a visual representation of the predicted future blood glucose levels to be displayed on a display of a device. In exemplary FIG. 29, time is on the X- axis and the predicted blood sugar levels on the Y-axis (which may be shown with any unit desired, such as mg / dL). The left side of the X-axis can be considered 3am, where predicted out or range levels are shown. The software can be adapted to display visual indicators (e.g., the red icons) that can indicate predicted peaks and valleys. The range or any of the ranges herein may be user-adjustable on the display so the user can modify any high or low level for the range that is shown as “in-range.” This is one way to provide for personalized glucose management, which is an improvement in technology.

[0225] FIG. 30 is an illustration of a computing device (e.g., smartphone) with a display, wherein glucose state information is being visually presented on the display. The information communicated in FIG. 30 can be the same or similar to information shown in FIGS. 29 and 31.

[0226] FIG. 31 is an additional non-limiting example of a visual representation of predicted blood glucose values being displayed on a display of a device, such as a smartphone or other computing device. FIG. 31 also presents a visual representation of suggested actions to be taken and when, which are correlated with high and low trends on the predicted glucose levels. The suggestions shown are merely exemplary, but provide an exemplary of personalized health management provided by the methods and systems herein.

[0227] FIG. 32 is an illustrate example of some examples herein that can predict and maintain blood glucose levels at desired levels. FIG. 32 illustrates exemplary graphs comparing traditional CGMs (top) and optional systems and methods herein that are adapted to predict and maintain blood glucose levels within (or more closely aligned with) a target range (bottom). It is noted that systems and methods herein do not need to include a management aspect, and may provide significant benefits by provide health information (e.g., predicted glucose state).

[0228] One aspect of the disclosure is related to methods and systems that allow a subject to choose the prediction window, or the time in advance they wish to receive prediction information about predicted glucose states (relative to one or both of when the brain data was sensed or relative to the current time). For example, a user may wish to only be informed of relatively near-term future predictions. For example, a subject may wish to select a prediction window of 20 minutes. In this example, the x-axis shown in FIGS. 29-31 may only include a time period of 20 minutes (e.g., every - 31 -SG Docket No.: 14837-710.600minute demarcated on the x-axis with a dash). For example only, a subject may wish to be informed mostly about sleep glucose swings (e.g., to avoid hypoglycemia), and may only wish to focus on near- term predictions. The Apps herein may include a user input feature that is adapted to allow the user to select or choose the prediction window. Choosing a prediction window may optionally be part of an initial user set-up process, wherein it may be beneficial to train a model based on the desired prediction window. Any of the prediction methods herein may thus be adapted to predict 30 minutes in the future (or any number less than 30), and optionally not more than 30 minutes in the future (or any number less than 30). An exemplary use scenario is during sleep, and a subject may receive alerts up to 30 minutes in advance if they are trending towards or in a hypoglycemic state.

[0229] One aspect of predicting glucose states herein is related to predicting prandial blood glucose events, optionally post-prandial. Exemplary post-prandial events include, but are not limited to, postmeal peaks in glucose levels or time until a post-meal peak in glucose levels. An exemplary rationale for systems and methods adapted to predicting postprandial glucose events is that elevated postprandial glucose concentrations may contribute to suboptimal glycemic control. Additionally, postprandial hyperglycemia is one of the earliest abnormalities of glucose homeostasis associated with type 2 diabetes and is markedly exaggerated in diabetic patients with fasting hyperglycemia.

[0230] As used herein, the term “meal” includes any consumption or ingestion, and may simply be a snack, a drink, etc., and is understood not to be limited to an amount or type of consumption or ingestion.

[0231] After a meal, there is a rise in blood glucose levels. Glucose monitoring data (e.g., from CGMs) has been shown in the literature to be able to identify post-meal glucose peaks. One aspect of this disclosure is a method of training an algorithm or model to predict, using as input extracranially sensed single channel brain activity signals from a subject and glucose values estimated using an existing CGM (but any other type of sensor / monitor may be used to obtain glucose data). A merely exemplary method of training an algorithm may include: obtaining an indicator of consumption by a subject (e.g., an input from a subject that food has been consumed, optionally via an App); obtaining blood glucose measurements from the subject (optionally with a CGM, an implanted glucose monitor; or a noninvasive glucose monitor such as a watch; etc.); and obtaining extracranially sensed brain activity signals from the subject. The method of training can further include temporally identifying on the glucose measurement readings (with glucose values) when consumption has occurred (such as via input from the subject about when food was consumed), and identifying (e.g., visually or automatically identifying) a post-prandial glucose peak subsequent in time to the consumption (e.g., 5 to 90 minutes, for example), such as including the timing post-consumption and / or amplitude of the peak. An algorithm / model can then be trained to identify patterns or other features of the brain activity signals (and optionally other subject measurements, such as heart rate, heart rate variability, and / or others described herein) preceding the glucose peak that are predictive of the subsequent glucose peak. After the algorithm has been trained using the EEG data and monitored glucose data, subsequent brain activity signals can be used as input to the trained model to predict the subject’s- 32 -SG Docket No.: 14837-710.600postprandial glucose peaks (and which optionally may be performed without monitoring glucose levels with a glucose monitor). Any of the aspects and description above related to training machine learning model is fully incorporated by reference into the post-prandial aspect of the disclosure herein.

[0232] FIG. 33 illustrates an exemplary method 2600 of training a model to predict a prandial event, with a post-prandial event as the example. At step 2601, the method includes providing sensed brain activity data or signals from a subject. Step 2602 includes identifying (manually or automatically) a post-prandial peak (or other prandial event) on glucose values sensed from the subject that have a temporal relationship with the brain activity signals sensed from the subject. Step 2603 includes selecting or identifying one or more features of the brain activity signals correlated with the postprandial event (or other prandial event). At step 2604, once trained, the trained prediction model is adapted to receive subsequently sensed brain activity signals and predict a post-prandial event.

[0233] Subjects may have individualized blood glucose (BG) changes after a meal. For example, one subject may have a post-meal peak 30 minutes after a meal, while a second subject consuming the same meal may have a peak 45 minutes after the meal. Additionally, some subjects may respond with a greater change in glucose levels following meal consumption than others, such as if a first subject does not experience a significant increase in BG while a second subject does in fact experience a significant increase in BG following the same meal. Personalized or individualized knowledge about a post-prandial glucose event (e.g., a BG peak) can provide valuable medical information to the subject. An exemplary but non limiting benefit of an algorithm that is adapted to predict post-prandial BG events such as BG peaks is that a subject can be informed about at least one of the timing or the amplitude of the peak, which may be beneficial to a wide variety of subjects. For example, it may provide information to a subject about when to take insulin and / or what dose of insulin to administer. Additionally, for example, a subject monitoring their overall health may simply want to know when their BG levels may be elevated, such as with a post-meal peak. Additionally, for example, an athlete may benefit from knowing when an elevated or peak BG level is likely to occur, as well as a predicted amplitude of the BG level. Additionally, for example. A subject may wish to avoid certain foods if they know some meals provoke BG levels that are quite elevated. There are many reasons why an individual may benefit from knowing about an elevated BG level, including a peak.

[0234] Any of the methods herein, upon predicting a post-prandial peak, can communicate information related to the peak to the subject, such as visually providing a predicted time of peak and / or the predicted glucose value of the peak on a display of the subject device.

[0235] One aspect of the disclosure continues to utilize input from the subject about meal consumption as a way to modify and / or improve the EEG BG prediction algorithms herein.

[0236] Example 3

[0237] FIGS. 34 - 37 illustrate aspects of a merely exemplary data set that is related to and descriptive of the disclosure herein related to post-prandial or post-meal glucose event prediction (e.g., peaks) and training methods related to the same, such as the exemplary training method in FIG. 6. In this exemplary data set, there were three individuals: one pre-diabetes, one type 1 diabetes, and 1- 33 -SG Docket No.: 14837-710.600non-prediabetes (who may otherwise be referred to as healthy). 2-3 days of EEG data and CGM data were obtained from the subjects at the same time. The healthy subject logged all meals during the time, and thus any meal information on any chart is related to the healthy individual.

[0238] FIG. 34 illustrates blood glucose readings for the healthy subject over a period of time, including time of logged meals (again, the logging of meals may not be necessary). BG was recorded with a CGM. Post-prandial glucose peaks are labelled following three logged meals, as well as suspected CGM artifact, which is known to occur to some extent with CGMs.

[0239] FIG. 35 illustrates healthy individual recorded glucose levels vs time, by date, with day and night hours labeled. FIG. 36 illustrates recorded glucose levels vs time for the prediabetic subject, by date, with day and night hours labeled. FIG. 37 illustrates recorded glucose levels vs time for the Type 1 diabetic subject, by date, with day and night hours labeled.

[0240] FIG. 38 shows a series of EEG plots created showing Frequency (Hz) on the y-axis (or left axis) vs Time (s) on the x-axis (bottom axis) vs Power (color scale on the right axis) of how EEG varies in the day vs night from 60 minutes before a known glucose peak to 60 minutes after a known glucose peak. The middle plot for each of day and night is the time of the peak, with 60 minutes before at the top and 60 minutes following the peak at the bottom.

[0241] FIG. 39 illustrates an exemplary blood glucose recording (which is also representative of any sensed glucose data herein), with consumption (in this case food) at 18:28 hours and a post-meal peak 30 minutes later at 19:03 hours.

[0242] FIG. 40, similar to FIG. 38, shows a series of EEG plots with Frequency (Hz) on the y-axis vs Time (s) on the x-axis vs Power (color scale on right), from a time period spanning 60 minutes before a peak until 60 minutes after the peak, at 15 minute increments, (as labeled) with the peak being designated as time “0 minutes” (center plot in FIG. 40). The meal is labeled at 30 minutes before the peak.

[0243] FIGS. 38 and 40 illustrate an aspect of this disclosure, which includes creating or receiving (which may occur automatically) and utilizing 2-dimensional plots relating EEG data with Frequency (Hz), Time, and Power (color scale on right) as an input in any of the training methods or trained algorithms herein. In alternatives, the order of the axes may be modified. The 15-minute time increments in FIG. 40 are exemplary and not limiting in any way. For example, the time increments may be from 5 seconds to 30 minutes, for example.

[0244] In any of the examples herein, methods of predicting include sensing EEG data and predicting postprandial peaks within 5 min - 90 mins of the predicted peaks.

[0245] Once an algorithm is trained on brain signal data and known postprandial peaks, the algorithm can optionally be trained to predict other blood glucose variations outside of the postprandial peaks.

[0246] The system shown in FIG. 7 is illustrative of exemplary systems and methods of using training algorithms and predicting post-meal glucose events.

[0247] One aspect of this disclosure is related to wearable devices and systems that are adapted to sense or record one or more different types of biosignals, and in some particular embodiments they- 34 -SG Docket No.: 14837-710.600are adapted to sense brain activity signals / data, and optionally adapted to sense additional biosignals. Any of the description that follows may be used in any of the brain activity signal / data sensing and glucose state prediction described herein (future and / or real-time), and vice versa. While the disclosure describes wearables in the context of sensing brain activity signals, it is understood that one or more concepts herein may have broader applicability outside of brain activity signal sensing (e.g., the suction ports described below) and may have applicability to devices that do not include one or more electrodes. The wearable sensing devices and systems herein may be referred to generally as wearable sensors, wearable sensing devices, sensing systems, or wearables. All of the disclosure herein related to wearable devices and systems for sensing the subject’s brain activity signals / data (EEG data) is incorporated by reference into this portion to this disclosure, and vice versa.

[0248] Wearable sensors described herein are, unless indicated the contrary, considered “all-in-one,” or self-contained wearable sensors, which allows them to be worn by the subject as they go about their daily life (e.g., without wires connected to a console) wherein the wearable sensors wirelessly communicate data (e.g., via Bluetooth) to a second device (which may be a subject device or other device such as a care-team device).

[0249] FIG. 41 illustrate a schematically illustrative wearable sensing device (i.e., wearable sensor) 3400, with optional components and features in dashed lines. Any of the features of Sensor 3400 may be incorporated with any other wearable sensor herein, and vice versa.

[0250] The wearable sensing devices herein may be configured with one or two-way wireless communication with a different device (e.g., Bluetooth), such as a subject / user device (e.g., smartphone, smartwatch). In some examples, the sensing device is adapted to continuously sense brain activity signals from the subject, for at least some continuous period or epoch of time, and continuously communicate (for at least some continuous period or epoch of time) in real or substantially real-time the raw or processed EEG data to an App on a subject device, which is described elsewhere herein.

[0251] The wearable sensing devices herein may include one or more memory units adapted to store sensed or processed brain activity signals, at least temporarily (e.g., up to 8 hours). For example, in some embodiments the data can be stored temporarily until it is communicated to a subject device, or until communication can be re-established with a user device if communication was interrupted.

[0252] In one exemplary, non-limiting embodiment shown in FIG. 42 and FIG. 43, wearable sensing device 3510 (which may be considered wearable sensor 3510) includes a wearable sensor 3512. In this example, wearable sensor 3512 includes flexible electrode housing 3513 and a plurality of electrodes (shown) secured to the flexible electrode housing 3513. System 3510 further includes electronics member 3514 that is configured and adapted to be releasably coupled to wearable sensing device 3512 so that, when coupled, it is in communication with the plurality of electrodes. Electronics member 3514 may include connector pins as shown or other electrically conductive elements, each of which are adapted to create an electrical connection to one of the electrodes. Electronics member 3514 may be releasably secured to wearable sensor 3512 in a variety of ways, such as mechanical and / or- 35 -SG Docket No.: 14837-710.600magnetic coupling. The views of FIGS. 42 and 43 show first surfaces of each of the plurality of electrodes facing away from the flexible electrode housing 3513 so as to be positioned to be coupled to a surface of a subject, an example of which is shown in FIGS. 44 and 45 (either directly coupled or indirectly coupled to the surface of the subject). The views of FIGS. 42 and 43 also show examples of second surfaces of each of the plurality of electrodes optionally disposed within electrode wells in the flexible electrode housing 3513.

[0253] One of the plurality of electrodes may be positioned and adapted for use as a ground electrode, an example of which is shown in FIG. 42. The plurality of electrodes may also include a plurality of sensing or recording electrodes, examples of which are shown in FIG. 42 (labeled as “recording pair"). The electronics member can be adapted with a processor such that the first and second sensing electrodes can sense in either monopolar mode (each recording with the ground) or in bipolar mode (recording between the recording pair), which can provide an advantage over some devices that can only sense in bipolar mode. In monopolar mode, the third optionally larger electrode as shown is the ground, or referential electrode.

[0254] Wearable sensors herein, such as those in FIGS. 42, 43, 47A-47D, provide the ability for single or dual sensing modalities, compared with some alternative wearable devices configured for only single channel sensing.

[0255] As a mere example, first and second sensing electrodes are optionally spaced 1.0 mm - 4 mm apart. A ground electrode may be spaced from the nearest of the first and second sensing electrodes a greater distance than a spacing between first and second sensing electrodes, an example of which is shown in FIGS. 35 and 36.

[0256] Any of the plurality of electrodes herein may be rigid, flexible, or comprise both rigid and flexible elements. In some non-limiting examples, sensing electrodes herein may comprise one or more of steel, gold, carbon, or silver.

[0257] Any of the electrode housings herein (e.g., 5313 in FIGS. 42 and 43) may comprise a flexible body, wherein at least a portion of the body is flexible, optionally the entirety is flexible. In some embodiments, a flexible electrode housing comprises one or more polymeric materials. In the example of FIGS. 42 and 43, the flexible electrode housing may comprise a silicone, and may consist of a silicone.

[0258] The wearable sensor 3512 is adapted to be adhered to a subject’s body (e.g., skin, e.g., scalp), exemplary locations for of which are shown in FIG. 44 and 45, with FIG. 45 illustrating an exemplary behind the ear location.

[0259] In some additional examples, the electronics member is configured with a communication module (illustrated in FIG. 41), such as for Bluetooth capabilities to communicate data to a user device / App, such as one or more of a smartphone, a computer, or an insulin pump or other drug delivery device.

[0260] In any of the embodiments herein, the optional electronics member may optionally include one or more of a memory (which may store at a minimum of 10 hrs., for example); a signal amplifier- 36 -SG Docket No.: 14837-710.600and processor that is adapted to allow for monopolar and bipolar mode sensing; adaptation for bidirectional communication; Bluetooth capability; include one or more sensors adapted for impedance measurement to monitor adequate skin contact for recording; and / or a rechargeable power supply.

[0261] FIG. 46 illustrates exemplary molds for manufacturing exemplary wearable sensing devices, including the housings herein.

[0262] The disclosure that follows describes alternative wearable sensing devices (wearable sensors), but which may include one or more features from the aforementioned wearable sensing systems (e.g. more than two electrodes, one or which may be a ground electrode, and which may be configured for monopolar and / or bipolar mode sensing).

[0263] FIGS. 47A - 47D illustrate a merely exemplary wearable sensing system 400.

[0264] As shown and as described herein, the wearable sensor may further include a power source (e.g. a battery), optionally a rechargeable power source, embedded in the electrode housing (which is optionally flexible). The electrode housing may optionally be adapted to inductively recharge a battery, and optionally via the plurality of electrodes, which can enhance water resistance of the device (i.e. a port is not required for a charging cable).

[0265] Any of the wearable sensors herein may include a Bluetooth module disposed within a flexible electrode housing (e.g., as shown in FIGS. 47A and 47B), the system adapted to communicate information to an external device (e.g., a smartphone).

[0266] Any of the wearable sensors herein may include one or more memory structures disposed within a flexible electrode housing (e.g., within an EEG ASIC) adapted for on-board storage, at least temporary storage of sensed information or information indicative of sensed information.

[0267] Any of the wearable sensors herein may include a processor within a flexible electrode housing, such as within an EEG ASIC.

[0268] Wearable sensor 400 (or any other wearable sensors herein) may be configured for real-time (or near real-time) streaming of sensed brain activity signals to an external device, which optionally has stored thereon any of the Applications described herein.

[0269] While some parts of the disclosure herein may be related to predicting a future glucose state, such as levels or risk indicators, an aspect of the disclosure herein is optionally related to predicting real-time glucose states. The phrase “real-time” as used herein refers to actual blood levels that are occurring in the subject’s blood at that time. The systems and methods herein may thus optionally be adapted to predict real-time or future glucose states (such as levels), or information indicative thereof. The systems herein may thus also be considered to be continuous glucose monitors, similar to existing monitors.

[0270] Methods of predicting glucose states herein may optionally be performed on a personal device (or a care-team device), such as a smartphone, tablet, smartwatch, and which may include one or more processors thereon adapted to execute one or more methods / algorithms stored thereon. Any type of executable application may be referred to herein as an “App,” and may be adapted to, in response to- 37 -SG Docket No.: 14837-710.600receiving or based on raw and / or processed EEG data, including only a subset of sensed data (or information indicative of the raw data, processed data, and / or a subset thereof) that has been sensed from the subject, cause the performance of the glucose state predictions herein. In some alternatives, one or more processing steps may take place within the wearable sensing device (e.g., scalp device or sub-scalp device), which is an example that methods herein (including portions thereof) may be performed in one or more different devices.

[0271] One or more aspects of a predicted glucose levels may optionally be visually represented or presented on a display of a device, examples of which are shown in FIG. 29, FIG. 30, and FIG. 31 (e.g., smartphone, tablet, smartwatch, electronic ophthalmic device such as a contact lens, or glasses). For example only, an executable application (an “App”) may be adapted to visually present a risk indicator and / or predicted glucose levels for some time period in the future, and which may be updated (continuously or periodically) such that the forecast includes predicted levels for that particular time period in the future (e.g., 1 hour, 2 hours, 3 hours, 4 hours, etc.). Additionally, for example only, an App may be adapted to visually present or indicate a specific time at which a glycemic event is forecasted to occur, examples of which are shown in FIG. 29 with the high and low visual markers, or the App may provide a discrete time for a predicted glucose event (e.g., 4: 17 pm). Additionally, for example only, an App may be adapted to present a timer with a countdown indicating the time remaining before a predicted glycemic event.

[0272] In some examples, the methods herein may optionally be adapted to provide a relatively short-term prediction of future blood glucose values in advance (such as 1 hour in advance, 2 hours in advance, 3 hours in advance, etc.), and a longer-term “risk forecast” of blood glucose hours in advance (e.g., such as 10 hours in advance, 11 hours in advance, 12 hours in advance, etc.). Any of the methods herein (e.g., an App on a personal device) may optionally be adapted to communicate an actual and / or forecast of future glucose levels and / or risk indicators to a patient / care team, optionally to one or more different devices (an example of which is described herein as a Portal, examples of which are shown in FIG. 21A and 21B). Any of the methods herein (e.g., an App on a personal device) may optionally be adapted to provide a user / patient with a relatively longer (e.g., 10+ hours) forecast of risk indicators, and optionally may be adapted to provide suggested (e.g., optimal) times of day to perform certain activities, such as exercise, eat, take medication based on the forecast (examples of which are shown in FIG. 31). Any of the methods herein (e.g., an App on a personal device) may optionally be adapted to visually present (e.g., plot) an amount (e.g., percent) of time in optimal / preferred glucose range and time spent out of the optimal / preferred range, wherein the range may be adjustable and / or personalized, optionally wherein the method (e.g. App) is adapted to allow for personalized adjustment and setting of the range via interaction with a display of the personal device.

[0273] The methods and systems herein may optionally be adapted to continuously stream real-time EEG data to a different device, such as any of the personal devices herein (smartphone, watch, etc.). Any of the apps herein may thus be receiving continuous or near-continuous real-time EEG data that- 38 -SG Docket No.: 14837-710.600is being sensed from the subject, and using the continuously streamed real-time EEG data to make the predictions.

[0274] As described herein, systems, devices, and methods herein may be adapted to be incorporated and used to some extent with existing glucose monitors, such as CGMs, invasive glucose monitors, and non-invasive optical glucose monitors, and / or their methods of use. For example, existing CGMs may be modified and adapted to incorporate sensed EEG data (for example only, any sensing concepts / methods in U.S. Pat. No. 6,572,542 and / or US Pat. No. 8,118,741) and / or forecasting concepts herein to improve performance. For example only, CGM sensed data can be analyzed with patient EEG data, and the predictive EEG data can train the glucose data (e.g., ISG data), so that the CGM may then be adapted to use ISG readings to better predict future blood glucose states, exemplary method steps of which are shown in FIG. 49, and which may be combined with any other suitable method step herein. For example, a certain pattern of EEG-trained ISG data (readings well before an impending event) can then be used to predict a future glycemic event. It is thus understood that any existing CGM may be modified and adapted to incorporate any of the features or methods herein. For example, a CGM can be adapted to communicate with an App and make an alert that a subject should prepare to drink a sugary drink in a certain period of time, such as 1 hour in the future, or that a hypoglycemic event is likely to occur 2.5 hours in the future. Additionally, for example, a CGM could be modified to deliver insulin at a time much earlier than with previous technologies, and could deliver longer lasting insulin well in advance of a hyperglycemic event. FIG. 50 illustrates merely exemplary method steps in which sensed ISG levels can be used to manage a future glucose state of a subject.

[0275] FIG. 51 illustrates merely exemplary steps that may be included in a method of training ISG data with EEG data, which may be performed to allow a glucose monitor to sense ISG and 1) forecast information indicative of a future glucose state (e.g., FIG. 49) and / or 2) facilitate the management of a future glucose state (e.g., FIG. 50).

[0276] Additionally, and only for example, any of the EEG data and methods herein may optionally be used to help calibrate and / or recalibrate glucose monitors (e.g., CGMs) (which need recalibrating over time), which could avoid the need to use glucose meters and finger pricks to re-calibrate glucose monitors such as CGMs. FIG. 52 illustrates a merely exemplary method of calibrating or recalibrating a glucose monitor, optionally a CGM, comprising: calibrating or recalibrating a glucose monitor using at least one of EEG data sensed from the subject or information indicative of the EEG data sensed from the subject. Additionally, glucose monitors (e.g., CGMs) and / or glucose meters may similarly be used to calibrate any of the EEG forecasting methods (e.g., algorithms) herein, an example of which is shown in FIG. 53.

[0277] One aspect of the disclosure is an optional bi-directional calibration method and / or system, an example of which is shown in FIG. 54. In one example, a bi-directional calibration method may include sensing interstitial glucose of a subject with a glucose monitor, optionally a CGM; sensing EEG signals from one or more subjects, optionally with any of the wearable devices herein; and- 39 -SG Docket No.: 14837-710.600performing at least one of, and optionally both of: calibrating (or re -calibrating) the glucose monitor using the sensed EEG signals and / or information indicative of the sensed EEG signals; or calibrating a method that is adapted to determine an existing blood glucose level or forecast a future blood glucose level from the sensed EEG signals and / or information indicative of the sensed EEG signals using the sensed ISG or information indicative of the sensed ISG.

[0278] Exemplary CGMs, features and methods of use of which may be incorporated herein include those by Dexcom (e.g., G6 CGM System), Medtronic (e.g., Guardian™ Connect), Abbott (e.g., any FreeStyle Libre), and the Eversense® E3 CGM. Exemplary Glucose Meters (glucometers), features and methods of use of which may be incorporated herein: LifeScan OneTouch®, Accu-Chek®, and FreeStyle Lite by Abbott.

[0279] Even if not specifically indicated, one or more methods or techniques described in this disclosure (e.g. any of the computer executable methods) may be implemented, at least in part, in hardware, software, firmware or any combination thereof. For example, various aspects of the techniques or components may be implemented within one or more processors, including one or more microprocessors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), programmable logic circuitry, or the like, either alone or in any suitable combination. The term “processor” or “processing circuitry” may generally refer to any of the foregoing circuitry, alone or in combination with other circuitry, or any other equivalent circuitry.

[0280] Such hardware, software, or firmware may be implemented within one device or within separate devices to support the various operations and functions described in this disclosure. In addition, any of the described units, modules or components may be implemented together or separately as discrete but interoperable logic devices. Depiction of different features as modules or units is intended to highlight different functional aspects and does not necessarily imply that such modules or units must be realized by separate hardware or software components. Rather, functionality associated with one or more modules or units may be performed by separate hardware or software components, or integrated within common or separate hardware or software components.

[0281] When implemented in software, the functionality ascribed to the systems, devices and techniques described in this disclosure may be embodied as instructions on a computer-readable medium such as random access memory (RAM), read only memory (ROM), non-volatile RAM (NVRAM), electrically erasable programmable ROM (EEPROM), Flash memory, and the like. The instructions may be executed by a processor to support one or more aspects of the functionality described in this disclosure.

[0282] FIG. 48 is a merely exemplary block diagram of an exemplary data processing system, any part or all of which may be used with any embodiments of any of the inventions or embodiments set forth herein. Also note that one or more of the components shown in FIG. 48 may be optional even if it is not stated as such herein. Note that while FIG. 48 illustrates various components of a computer system, it is not intended to represent any particular architecture or manner of interconnecting the- 40 -SG Docket No.: 14837-710.600components; as such details are not germane to the present invention. It will also be appreciated that network computers, handheld computers, mobile devices, tablets, cell phones and other data processing systems which have fewer components, or perhaps more components may also be used with the present invention.

[0283] As shown in FIG. 48, exemplary computer system 4100, which is a form of a data processing system, includes a bus or interconnect 4102 which is coupled to one or more microprocessors 4103 and a ROM 4107, a volatile 41M 3505, and a non-volatile memory 4106. The microprocessor 4103 is coupled to cache memory 4104. The bus 4102 interconnects these various components together and also interconnects these components 4103, 4107, 4105, and 4106 to a display controller and display device 4108, as well as to optional input / output (I / O) devices 4110, which may be mice, keyboards, modems, network interfaces, printers, and other devices.

[0284] Typically, the input / output devices 4110 are coupled to the system through input / output controllers 4109. The volatile RAM 4105 is typically implemented as dynamic RAM (DRAM) which requires power continuously in order to refresh or maintain the data in the memory. The non-volatile memory 4106 is typically a magnetic hard drive, a magnetic optical drive, an optical drive, or a DVD RAM or other type of memory system which maintains data even after power is removed from the system. Typically, the non-volatile memory will also be a random access memory, although this is not required.

[0285] While FIG. 48 shows that the non-volatile memory is a local device coupled directly to the rest of the components in the data processing system, the present invention may utilize a non-volatile memory which is remote from the system; such as, a network storage device which is coupled to the data processing system through a network interface such as a modem or Ethernet interface. The bus 4102 may include one or more buses connected to each other through various bridges, controllers, and / or adapters, as is well-known in the art. In one embodiment, the I / O controller 4109 includes a USB (Universal Serial Bus) adapter for controlling USB peripherals. Alternatively, I / O controller 4109 may include IEEE- 1394 adapter, also known as FireWire adapter, for controlling FireWire devices, SPI (serial peripheral interface), I2C (inter-integrated circuit) or UART (universal asynchronous receiver / transmitter), or any other suitable technology. Wireless communication protocols may include Wi-Fi, Bluetooth, ZigBee, near-field, cellular and other protocols.

[0286] Some portions of the preceding detailed descriptions have been presented in terms of algorithms and symbolic representations of operations on data bits within a computer memory. These algorithmic descriptions and representations are the ways used by those skilled in the data processing arts to most effectively convey the substance of their work to others skilled in the art. An algorithm is here, and generally, conceived to be a self-consistent sequence of operations leading to a desired result. The operations are those requiring physical manipulations of physical quantities.

[0287] It should be borne in mind, however, that all of these and similar terms are to be associated with the appropriate physical quantities and are merely convenient labels applied to these quantities. Unless specifically stated otherwise as apparent from the above discussion, it is appreciated that- 41 -SG Docket No.: 14837-710.600throughout the description, discussions utilizing terms such as those set forth in the claims below, refer to the action and processes of a computer system, or similar electronic computing device, that manipulates and transforms data represented as physical (electronic) quantities within the computer system's registers and memories into other data similarly represented as physical quantities within the computer system memories or registers or other such information storage, transmission or display devices.

[0288] The techniques shown in the figures herein can be implemented using code (e.g., computer executable methods, algorithms) and data stored and executed on one or more electronic devices. Such electronic devices store and communicate (internally and / or with other electronic devices over a network) code and data using computer-readable media, such as non-transitory computer-readable storage media (e.g., magnetic disks; optical disks; random access memory; read only memory; flash memory devices; phase-change memory) and transitory computer-readable transmission media (e.g., electrical, optical, acoustical or other form of propagated signals — such as carrier waves, infrared signals, digital signals).

[0289] The processes or methods depicted in the preceding figures may be performed by processing logic that comprises hardware (e.g. circuitry, dedicated logic, etc.), firmware, software (e.g., embodied on a non-transitory computer readable medium), or a combination of both. Although the processes or methods are described above in terms of some sequential operations, it should be appreciated that some of the operations described may be performed in a different order. Moreover, some operations may be performed in parallel rather than sequentially.- 42 -SG Docket No.: 14837-710.600

Claims

CLAIMS1. A method of predicting a subject’s waking glucose sensitivity risk based on recorded sleep brain activity signals, comprising: recording sleep brain activity signals from a subject with one or more wearable scalp sensors during a sleep period of the subject; predicting a subject’s waking glucose sensitivity risk at least based on one or more sleep features that are in or based on the recorded sleep brain activity signals; and providing an output to the subject on a display of a personal device based on the waking glucose sensitivity risk, the output including personalized proactive behavioral guidance for the subject for an awake period that follows the sleep period, the proactive behavior guidance based on the predicted waking glucose sensitivity risk.

2. The method of claim 1, wherein the waking glucose sensitivity risk is indicative of a risk of blood glucose levels deviating from a desired range during the awake period.

3. The method of claim 1, wherein the waking glucose sensitivity risk is indicative of a risk of one or more hyperglycemic events during the awake period.

4. The method of claim 1, wherein the waking glucose sensitivity risk is indicative of a risk of one or more blood glucose level spikes during the awake period.

5. The method of claim 1, wherein the waking glucose sensitivity risk is indicative of a quality of sleep during the sleep period for the subject.

6. The method of claim 1, wherein the one or more sleep features are indicative of a quality of sleep during the sleep period for the subject.

7. The method of Claim 1, wherein the one or more sleep features comprise one or more of a number of sleep cycles completed during the sleep period, a number of sleep stages completed during the sleep period, a duration of one or more sleep cycles during the sleep period, a duration of one or more sleep stages during the sleep period, total sleep time during the sleep period, or a number of waking interruptions during the sleep period.- 43 -SG Docket No.: 14837-710.6008. The method of Claim 1, further comprising recording awake brain activity signals from the subject prior to and following the sleep period, the method further comprising automatically detecting when the subject falls asleep based on one or more changes between the awake brain activity signals and the sleep brain activity signals.

9. The method of Claim 1, wherein predicting the subject’s waking glucose sensitivity risk excludes features in awake brain signal data prior to the sleep time.

10. The method of Claim 1, wherein predicting the subject’s waking glucose sensitivity risk is further based on one or more features in awake brain signal data prior to the sleep time.

11. The method of Claim 1, wherein the output comprises a qualitative output indicative of the waking glucose sensitivity risk, the qualitative output selected from one of a plurality of predefined qualitative outputs.

12. The method of Claim 11, wherein the plurality of predefined qualitative outputs comprises outputs that are respectively indicative of a low risk, a medium risk, and a high risk.

13. The method of Claim 1, wherein the output comprises a quantitative output indicative of the waking glucose sensitivity risk.

14. The method of Claim 1, wherein the personalized proactive behavioral guidance comprises one or more recommended actions for the subject for the awake period based on the waking glucose sensitivity risk.

15. The method of Claim 14, wherein the one or more recommended actions are related to at least one of recommended sleep activity during the awake period, recommended exercise during the awake period, or recommended consumption activity during the awake period.

16. The method of Claim 15, wherein the output includes an indicator of a quality of sleep during the sleep period for the subject.

17. The method of Claim 1, wherein the output is accessible on the personal device upon waking from the sleep period.

18. The method of Claim 17, further comprising automatically detecting when the subject wakes from the sleep period based on one or more detected changes between awake brain activity signals and the sleep brain activity signals, and wherein the output is accessible on the- 44 -SG Docket No.: 14837-710.600personal device subsequent to automatically detecting when the subject wakes from the sleep period.

19. The method of Claim 1, wherein the output is accessible on the personal device within 30 minutes of waking from the sleep period.

20. The method of Claim 19, wherein the output is accessible on the personal device immediately or substantially immediately upon waking from the sleep period.

21. The method of Claim 1, wherein recording sleep brain activity signals comprises recording sleep brain activity signals during an entirety of the sleep period.

22. The method of Claim 21, wherein the method automatically begins recording the sleep brain activity signals upon detecting when the subject has entered the sleep period from an awake period.

23. The method of claim 1, further comprising automatically detecting when the subject has entered the sleep period from an awake period based on one or more detected changes between awake brain activity signals and the sleep brain activity signals, and wherein predicting the subject’s waking glucose sensitivity risk relies more heavily on the one or more sleep features than on features from the awake period.

24. The method of claim 23, wherein predicting the subject’s waking glucose sensitivity risk does not rely on any awake brain activity signals.

25. The method of claim 1, wherein recording sleep brain activity signals comprises recording sleep brain activity signals during only a portion of time between an initiation of sleep and a waking time.

26. The method of claim 1, wherein predicting the waking glucose sensitivity risk comprising inputting the one or more features into a trained model that has been trained on brain activity signals and glucose information.

27. The method of claim 26, wherein the trained model has been trained on brain activity signals and an awake time maximum blood glucose level for an awake period following the sleep period.- 45 -SG Docket No.: 14837-710.60028. The method of Claim 1, wherein recording sleep brain activity signals during the sleep period comprises recording sleep brain activity signals with at least one wearable sensor, each of the at least one wearable sensor positioned at a behind an ear location or on a forehead.

29. The method of Claim 1, further comprising receiving as input an indication from the subject that the sleep period is over, wherein the predicting step occurs subsequent to receiving as input the indication that the sleep period is over.

30. The method of Claim 1, further comprising recording awake brain activity signals of the subject during an awake period following the sleep period, wherein the waking glucose sensitivity risk is a first glucose sensitivity risk, the method further comprising predicting a second glucose sensitivity risk during the awake period that is based on the first glucose sensitivity risk and the awake brain activity signals, and providing a second output to the subject on the display of the personal device based on the second glucose sensitivity risk.

31. The method of claim 30, wherein the second glucose sensitivity risk includes any one or more features of the any of the waking glucose sensitivity risks included in claims 1-30.

32. The method of claim 30 or claim 31, further comprising providing a plurality of outputs subsequent to the output and the second output, the plurality of outputs each based on one of a plurality of additional glucose sensitivity risks.

33. The method of claim 1, wherein the waking glucose sensitivity risk is indicative of predicted glucose regulation of the subject’s body during the awake period following the sleep period.

34. The method of claim 1, wherein the waking glucose sensitivity risk is indicative of predicted changes in blood glucose during the awake period following the sleep period.

35. The method of claim 1, wherein the glucose sensitivity risk is indicative of predicted blood glucose levels outside of a desired range during the awake period following the sleep period.

36. A method of predicting a subject’s waking glucose sensitivity risk based on recorded sleep brain activity signals, comprising:- 46 -SG Docket No.: 14837-710.600recording sleep brain activity signals from a subject with one or more wearable scalp sensors during a sleep period of the subject; predicting a subject’s waking glucose sensitivity risk at least based on one or more sleep features in the recorded sleep brain activity signals; and providing an output to the subject on a display of a personal device that is based on the predicted waking glucose sensitivity risk.

37. The method of claim 36, further comprising any one or more steps or features from any one of claims 1-32.

38. A method of improving metabolic health by providing proactive behavioral guidance based on a subject’s quality of sleep, comprising: recording sleep brain activity signals from a subject with one or more wearable scalp sensors during a sleep period of the subject; predicting or determining a subject’s quality of sleep based on one or more sleep features that are in or based on the recorded sleep brain activity signals; and providing an output to the subject on a display of a personal device with personalized proactive behavioral guidance for the subject for an awake period that follows the sleep period, the proactive behavior guidance based on the predicted or determined quality of sleep.

39. The method of claim 38, wherein the one or more sleep features comprise one or more of a number of sleep cycles completed during the sleep period, a number of sleep stages completed during the sleep period, a duration of one or more sleep cycles during the sleep period, a duration of one or more sleep stages during the sleep period, total sleep time during the sleep period, or a number of waking interruptions during the sleep period.

40. The method of any one of claims 1-39, wherein the method is a computer executable method stored in a memory, the computer executable method executable by one or more processors.

41. The method of claim 40, wherein the method is performed on the personal device and / or a remote server.

42. A method of monitoring an indicator of metabolic health over time, comprising:- 47 -SG Docket No.: 14837-710.600recording a subject’s brain activity signals with one or more wearable scalp sensors; and predicting a plurality of maximum awake time glucose levels, each of the plurality of maximum awake time glucose levels associated with one of a plurality of separate days; and providing an output to a display of a personal device, the output indicative of the plurality of maximum awake time glucose levels over time.

43. The method of claim 42, wherein the output illustrates the maximum awake time glucose levels for each of the plurality of separate days.

44. The method of claim 42, wherein the output comprises a graph showing the plurality of maximum awake time glucose levels.

45. The method of claim 42, wherein the output comprises an average of at least a portion of the maximum awake time glucose levels over a period of time.

46. A method of monitoring fasting glucose states based on brain activity signals, comprising: recording a subject’s brain activity signals with one or more wearable scalp sensors while the subject is fasting; and predicting a real-time subject fasting glucose state based on the recorded brain activity signals using a model trained with recorded brain activity signals and glucose levels.

47. The method of Claim 46, wherein predicting the real-time subject fasting glucose state occurs during an awake period immediately following a sleep period.

48. The method of Claim 46, further comprising automatically detecting when the subject wakes from a sleep period based on the recorded brain activity signals, and wherein the predicting occurs automatically at a time after the automatically detected waking from the sleep period.

49. The method of Claim 46, further comprising predicting a plurality of the subject’s fasting glucose states over time, recording the plurality of fasting glucose states over time, and providing an output to the subject on a display of a personal device indicative of the recorded plurality of fasting glucose states over time as a way to track overall metabolic health over time.- 48 -SG Docket No.: 14837-710.60050. A system comprising: one or more processors; a memory coupled to the one or more processors, the memory storing computerprogram instructions, that, when executed by the one or more processors, perform a computer-implemented method comprising: recording or receiving sleep brain activity signals from a subject with one or more wearable scalp sensors during a sleep period of the subject; predicting a subject’s waking glucose sensitivity risk at least based on one or more sleep features that are in or based on the recorded sleep brain activity signals; and providing an output to the subject on a display of a personal device based on the waking glucose sensitivity risk, the output including personalized proactive behavioral guidance for the subject for an awake period that follows the sleep period, the proactive behavior guidance based on the predicted waking glucose sensitivity risk.

51. The system of claim 50, wherein the computer-implemented method further includes any one or more steps or features from any one of claims 1-32.

52. A system comprising: one or more processors; a memory coupled to the one or more processors, the memory storing computerprogram instructions, that, when executed by the one or more processors, perform a computer-implemented method comprising: recording or receiving sleep brain activity signals from a subject with one or more wearable scalp sensors during a sleep period of the subject; predicting a subject’s waking glucose sensitivity risk at least based on one or more sleep features that are in or based on the recorded sleep brain activity signals; and providing an output to the subject on a display of a personal device that is based on the predicted waking glucose sensitivity risk.- 49 -SG Docket No.: 14837-710.60053. The system of claim 52, wherein the computer-implemented method further includes any one or more steps or features from any one of claims 1-32.

54. A system comprising: one or more processors; a memory coupled to the one or more processors, the memory storing computerprogram instructions, that, when executed by the one or more processors, perform a computer-implemented method comprising: recording or receiving sleep brain activity signals from a subject with one or more wearable scalp sensors during a sleep period of the subject; predicting or determining a subject’s quality of sleep based on one or more sleep features that are in or based on the recorded sleep brain activity signals; and providing an output to the subject on a display of a personal device with personalized proactive behavioral guidance for the subject for an awake period that follows the sleep period, the proactive behavior guidance based on the predicted or determined quality of sleep.

55. A method of predicting a subject’s glucose sensitivity risk based on recorded sleep brain activity signals, comprising: recording sleep brain activity signals from a subject with one or more wearable scalp sensors during a sleep period of the subject; predicting a subject’s glucose sensitivity risk for an awake period following the sleep period at least based on one or more sleep features that are in or based on the recorded sleep brain activity signals; and providing an output to the subject on a display of a personal device based on the glucose sensitivity risk.

56. The method of claim 55, wherein the output includes personalized proactive behavioral guidance for the subject for the awake period that follows the sleep period, the proactive behavior guidance based on the predicted glucose sensitivity risk.- 50 -SG Docket No.: 14837-710.60057. The method of claim 55, wherein the glucose sensitivity risk is indicative of predicted glucose regulation of the subject’s body during the awake period following the sleep period.

58. The method of claim 55, wherein the glucose sensitivity risk is indicative of predicted changes in blood glucose during the awake period following the sleep period.

59. The method of claim 55, wherein the glucose sensitivity risk is indicative of predicted blood glucose levels outside of a desired range during the awake period following the sleep period.- 51 -SG Docket No.: 14837-710.600

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