Neuromodulation for sleep management

Abstract

A neuromodulation system comprising a stimulation arrangement configured to deliver two or more stimulation modes; a memory storing machine-readable instructions; and a control system. The control system includes one or more processors configured to execute the machine-readable instructions to control the stimulation arrangement to deliver stimulation in one of the stimulation modes as a first primary mode to support the user's sleep onset, and to deliver stimulation in another one of the stimulation modes as a second primary mode to maintain the user's sleep after sleep onset.

Description

NEUROMODULATION FOR SLEEP MANAGEMENTTECHNICAL FIELD

[0001] The present disclosure relates generally to systems and methods for treating sleep disorders, including insomnia, using neurostimulation therapy. The present disclosure relates more specifically, but not exclusively, to systems and methods for treating medical disorders using transcutaneous stimulation therapy.BACKGROUND

[0002] Whether a user is asleep or awake can be considered a sleep state. Once asleep, sleep can be characterized by four distinct sleep stages that change throughout the night during typically five to six sleep cycles. A user, and particularly a healthy user, moves between the sleep stages, usually in an order, a number of times during sleep within sleep cycles. The sleep stages include Nl, N2, and N3, known together as non-REM (rapid eye movement) stages, and REM.

[0003] Stage Nl (also referred as simply Nl) is the lightest sleep stage and is characterized by the appearance of some low amplitude waves at multiple frequencies interspersed with the alpha waves for greater than 50% of an epoch. Stage N2 (also referred as simply N2) is a slightly deeper sleep stage (although still considered light sleep) and is marked by the appearance of sleep spindles and K-complexes, on a background of mixed frequency signals. Stage N3 (also referred as simply N3) is the deepest sleep stage and is characterized by the appearance of slow waves (e.g., 1-2 Hz frequency) for at least 20% of an epoch. Stage REM is rapid eye movement sleep and is apparent through the presence of distinct activity in the EOG signal. The EEG signals recorded are typically quite similar to Stage Nl or even awake.

[0004] Sleep disorders such as apnea and therapies used to treat apnea may also affect a person’s sleep pattern. For example, it is known that correctly used, and correctly titrated, positive airway pressure (PAP) therapy can significantly improve sleep architecture, based on before and after testing in a sleep lab. It is also known that there can be a “rebound” effect when a person starts using PAP therapy for the first time, or starts again after a period of nonuse, based on before and after testing in a sleep lab.

[0005] Rebound is where a person starved of deep (e.g., N3) and REM sleep goes on therapy with a correctly configured respiratory therapy system, but the person “binges” on these deepand REM stages of sleep (particularly deep sleep), before settling back to a more normal sleep pattern (also referred to as sleep architecture) that would be expected of a healthy (e.g., nonobstructive sleep apnea (OSA)) person. Rebound can be more severe as the Apnea-Hypopnea Index (AHI) severity increases. I.e., the worse the sleep architecture due to a high AHI, the more the body will try to recover from chronic deep and REM deprivation, and potentially over-correct for a period of time.

[0006] A person with untreated sleep-disordered breathing (SDB) tends to have a preponderance of light sleep (e.g., N1 or N2), as the apneas / hypopneas and associated hypoxia leads to arousals and awakenings. Although the person may technically be in bed asleep for a long period of time, the person can still be starved of N3 deep sleep, REM dreaming sleep, and have more light N1 or N2 sleep. An untreated person may also have many more significant arousals than an equivalent healthy person, leading to increased sleep fragmentation, and be slower to enter REM sleep. This sleep pattern can contribute to insomnia.

[0007] A treated person may take several weeks of correctly configured treatment in order to have a sleep pattern (also referred to as sleep architecture) that looks more like a healthy person with good sleep hygiene.

[0008] A person suffering from insomnia, a sleep disorder in which a person has trouble falling or staying asleep, would thus be expected to have an unhealthy or suboptimal sleep architecture. There are challenges to devising therapies for insomnia, due to the fragmented nature of the cause and presentation of insomnia across the population. For example, different patients may be affected by different types of insomnia. There are also variations in the underlying cause, duration, or severity, in insomnia in different patients. Insomnia further is a multifaceted disorder that involves both physiological and psychological components. The diversity in both the cause and effects of insomnia makes it difficult to develop a one-size-fits- all solution, as everyone with insomnia can be unique.

[0009] Some solutions for insomnia include pharmacological treatments, which provides short term relief but comes with risks of tolerance, side effects and dependency. Cognitive behavioral therapy for insomnia (CBT-I) has been shown to be effective for many individuals however it requires time, commitment and trained therapists, making accessibility and adherence difficult for some users. There is further a lack of personalization and adaptation within the techniques that make it complex to target all fragments of insomnia. Wearable sleep technologies have been used to monitor sleep patterns and provide feedback. While they are capable of tracking sleep data, they often lack the ability to intervene and treat the underlying cause of insomnia. Other interventions include light therapy, mindfulness andrelaxation techniques. While these interventions have shown to be effective for specific types of insomnia, their overall effectiveness is limited, especially for chronic or comorbid insomnia cases.

[0010] Also, current solutions for insomnia such as sleep medication, or sleep hygiene practices often provide temporary relief but do not target the root cause of insomnia. Additionally, sleep medications often come with side effects such as dependency and cognitive impairment making them less than ideal for long-term use. Another impediment is the lack of adaptability in current treatments.

[0011] It is to be understood that, if any prior art is referred to herein, such reference does not constitute an admission that the prior art forms a part of the common general knowledge in the art anywhere.SUMMARY

[0012] As insomnia is highly individualized with varying triggers, it is desirable to provide a solution to address insomnia which is personalized and capable of adjusting to an individual’s specific needs, in real-time. The present disclosures relate to systems and methods for promoting a desired sleep stage of a user in an effort to optimize the sleep architecture and thus the sleep of the user. This addresses the fragmented nature of insomnia across different individuals. The present disclosure is directed to ameliorating these problems and addressing other needs.

[0013] In a first aspect, the present disclosure relates to a neuromodulation system, comprising: a stimulation arrangement configured to deliver two or more stimulation modes; a memory storing machine-readable instructions; and a control system including one or more processors. The control system is configured to execute the machine-readable instructions to control the stimulation arrangement to deliver stimulation in one of the stimulation modes as a first primary mode to support an onset of sleep or relaxation for the user, and to deliver stimulation in another one of the stimulation modes as a second primary mode to maintain the user’s sleep or relaxation after the onset.

[0014] In some forms, the control system is further configured to execute the machine-readable instructions to: monitor physiological data from one or more sensors sensing physiological data from the user, including physiological data correlated with sleep or relaxation states of the user; and apply a feedback control to the stimulation delivered, by processing the physiological data to determine a responsive stimulation profile of stimulations to be delivered by the stimulationarrangement, and adjusting the stimulation being delivered based on the responsive stimulation profile.

[0015] In another aspect, the present disclosure relates to a neuromodulation system, comprising: a stimulation arrangement configured to deliver two or more stimulation modes; a memory storing machine-readable instructions; and a control system including one or more processors. The control system is configured to execute the machine-readable instructions to: control the stimulation applied in at least one of the one or more stimulation modes, in accordance with a control protocol the control protocol defining control parameters used to control stimulation applied in one or more of the stimulation modes; monitor physiological data from one or more sensors sensing physiological data from the user, including physiological data correlated with sleep states of the user; and apply a feedback control to the stimulation arrangement, by processing the physiological data to determine a responsive stimulation profile of stimulations to be delivered, and adjusting the stimulation being delivered based on the responsive stimulation profile

[0016] In some forms, the first and second primary modes are of different stimulation modalities; or the first and second primary modes are of the same stimulation modality, and have different stimulation targets and / or different stimulation profiles.

[0017] In some forms of any of the aspects, the stimulation arrangement being configured to deliver stimulation in a further one of the stimulation modes as a secondary stimulation mode, in combination with or in place of the first or second primary mode.

[0018] In some forms of one or more of the aspects, the secondary stimulation mode is delivered responsive to feedback from the physiological data indicating stimulations delivered in the first primary mode are not causing an expected sleep or relaxation onset, or indicating stimulations delivered in the second primary mode are not maintaining sleep or relaxation stability.

[0019] In some forms of one or more of the aspects, the stimulation applied in at least one of the one or more stimulation modes is in accordance with a control protocol, defining control parameters used to control stimulation applied in one or more of the stimulation modes. The control protocol may be a pre-set protocol. The control system may be configured to execute the machine-readable instructions to: apply an adaptive control to tune the control protocol based on historical data, wherein the historical data comprises one or more of: historical physiological data correlated with sleep states of the user measured over a historical time frame, historical stimulation parameters of stimulations applied during the historical time frame,physiological responses to changes in stimulation parameters of stimulations applied during the historical time frame, self-reported information in relation to the user during the historical time frame.

[0020] In another aspect, the present disclosure relates to a neuromodulation system, comprising: a stimulation arrangement configured to deliver two or more stimulation modes; a memory storing machine-readable instructions; and a control system including one or more processors. The control system is configured to execute the machine-readable instructions to control the stimulation applied in at least one of the one or more stimulation modes is in accordance with a control protocol, defining control parameters used to control stimulation applied in one or more of the stimulation modes; and apply an adaptive control to tune the control protocol based on historical data, wherein the historical data comprises one or more of: historical physiological data correlated with sleep states of the user measured over a historical time frame, historical stimulation parameters of stimulations applied during the historical time frame, physiological responses to changes in stimulation parameters of stimulations applied during the historical time frame, self-reported information in relation to the user during the historical time frame.

[0021] In some forms of one or more of the aspects, the neuromodulation system has long term memory configured to record stimulation data comprising stimulation profiles of the stimulations applied, and physiological data acquired, over one or more sleep sessions.

[0022] In some forms of one or more of the aspects, the control system is configured to process the recorded stimulation data and physiological data to determine one or more patterns between the stimulation data and the physiological data.

[0023] In some forms of one or more of the aspects, applying the adaptive control comprises modifying the control protocol for adjusting one or more parameters of at least one of stimulation modes, based on at least one of the determined one or more patterns.

[0024] In some forms of one or more of the aspects, at least one of the stimulation modes is provided via neurostimulation, and stimulation current parameters for neurostimulation include one or more of: number of stimulation currents, frequency of each stimulation current, frequency difference between two stimulation currents, amplitude of each stimulation current, waveform shape of each stimulation current and waveform width of each stimulation current.

[0025] In some forms of one or more of the aspects, the stimulation arrangement comprises transcranial stimulating elements. The stimulation arrangement may be configurable to provide transcranial stimulation to target the dorsolateral prefrontal cortex (DLPFC) of the user. The stimulation arrangement may comprise transcranial stimulating elements which areconfigurable to provide both tDCS and tACs, and are adapted to be controlled to provide either tDCS or tACS by the control system.

[0026] In some forms of one or more of the aspects, the control system is configured to apply an auditory stimulation during a sleep onset period and / or a sleep maintenance period for the user.

[0027] In some forms of one or more of the aspects, the feedback control is configured to adjust the auditory stimulation in real time to enhance a physiological effect of the transcranial stimulation.

[0028] In some forms of one or more of the aspects, the control system is configured to activate the secondary mode and turn off the first primary mode, when the first primary mode does not result in a reduction of a beta activity measured from the user.

[0029] In some forms of one or more of the aspects, the control system is configured to apply the secondary mode integrated with the second primary mode during a sleep maintenance period for the user where the user is in an asleep state.

[0030] In some forms of one or more of the aspects, the feedback control is configured to adjust the secondary mode in real time to enhance a physical effect of the second primary mode responsive to the feedback control.

[0031] In some forms of one or more of the aspects, the stimulation profile of the second primary mode is adjusted based on the user’s delta brain wave activity from the physiological data signal.

[0032] In some forms of one or more of the aspects, the control system is configured to reduce or stop the second primary mode near an end of a sleep session for the user.

[0033] In some forms of one or more of the aspects, the neuromodulation system comprises auditory stimulation elements, configured conduct stimulation by air conduction or by bone conduction.

[0034] In some forms of one or more of the aspects, the neuromodulation system comprises a positioning structure, configured to hold at least part of the stimulation arrangement in close contact with the user’s skin.

[0035] In some forms of one or more of the aspects, the positioning structure is configured to be wearable during a sleep session.

[0036] In some forms of one or more of the aspects, the positioning structure is a headband or a sleep mask.

[0037] In some forms one or more, the positioning structure is configured to house the transcranial stimulating elements and the stimulating elements to be applied to user’s ears.

[0038] In some forms of one or more of the aspects, the positioning structure also houses one or more sensors configured to acquire an electroencephalogram (EEG) of the user .

[0039] In another aspect, the present disclosure relates to a neuromodulation system, comprising: a stimulation arrangement configured to deliver two or more stimulation modes; a memory storing machine-readable instructions; and a control system including one or more processors. The control system is configured to execute the machine-readable instructions to: control the stimulation applied in at least one of the one or more stimulation modes to apply stimulation over a plurality of time frames, wherein stimulation applied during the time frames is increased in stimulation level over the time frames, up to a target stimulation level.

[0040] In some forms of one or more of the aspects, the control system is further configured to execute the machine-readable instructions to: monitor physiological data from one or more sensors sensing physiological data from the user, including physiological data correlated with sleep states of the user; and apply a feedback control to the stimulation delivered, by processing the physiological data to determine a responsive stimulation profile of stimulations to be delivered under the control protocol, and adjusting the stimulation being delivered based on the responsive stimulation profile, wherein the target stimulation level is set based on the responsive stimulation profile determined in the initial session.

[0041] In some forms of one or more of the aspects, the control system is further configured to execute the machine-readable instructions to: monitor the physiological data over an initial one or more of the plurality of time frames, during which the target stimulation level is set to a preset target; determine an updated target from the responsive stimulation profiles determined during the initial one or more of the plurality of time frames; and apply stimulation over remaining one or ones of the plurality of time frames where the target stimulation level is set to the adjusted target.

[0042] In another aspect, the present disclosure relates to a neuromodulation method for providing a sleep therapy to a user, comprising: controlling a stimulation arrangement to deliver stimulation in a first primary mode during a sleep or relaxation onset period to aid sleep or relaxation onset, and controlling the stimulation arrangement to deliver stimulation in a second primary mode during a sleep or relaxation maintenance period to aid sleep or relaxation stability.

[0043] In some forms, the method comprises monitoring physiological data from one or more sensors collecting physiological data from the user, including physiological data correlated with sleep states of the user, wherein the controlling of the stimulation arrangement is on the basis of the monitored physiological data. The controlling of the stimulation arrangement maycomprise applying a feedback control to the first and / or second primary modes on the basis of physiological data from the one or more sensors during delivery of the stimulation, by determining a responsive stimulation profile for the stimulation delivered by the stimulation arrangement, and controlling the stimulation arrangement to deliver the determined responsive stimulation profile.

[0044] The above summary is not intended to represent each implementation or every aspect of the present disclosure. Additional features and benefits of the present disclosure are apparent from the detailed description and figures set forth below.BRIEF DESCRIPTION OF THE DRAWINGS

[0045] FIG. 1 is a schematic overview of a neuromodulation system according to an embodiment of the present disclosure;

[0046] FIG. 2 is a conceptual depiction of a positioning structure including a plurality of stimulating elements;

[0047] FIGs. 3 and 4 are conceptual depictions of embodiments of a neuromodulation system;

[0048] FIG. 5 depicts an operational flow during an initial period for initializing personalized tDCS parameters, in accordance with an embodiment of the present disclosure;

[0049] FIG. 6-1 depicts an operational flow during operation of a personalized combined tDCS and closed-loop auditory stimulation mode; and

[0050] FIG. 6-2 depicts an operational flow during operation of a personalized combined tACS and closed-loop auditory stimulation mode.

[0051] While the present disclosure is susceptible to various modifications and alternative forms, specific implementations and embodiments thereof have been shown by way of example in the drawings and will herein be described in detail. It should be understood, however, that it is not intended to limit the present disclosure to the particular forms disclosed, but on the contrary, the present disclosure is to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the present disclosure as defined by the appended claims.DETAILED DESCRIPTION

[0052] The present disclosure concerns the interaction between sleep stages of a user and the physiological and / or environmental factors in relation to the user. Some of the physiological and / or environmental data may be obtained from one or more devices in an environment of the user, or attached to or worn by the user, or a respiratory therapy system in use by the user. Thepresent disclosure concerns an interaction between the sleep stages of a user and the control of a device to promote a desired sleep stage of the user. Promoting the desired sleep stage of the user can minimize a likelihood of the user experiencing an undesired sleep stage, or mitigating the effects thereof by e.g. minimizing a duration of the undesired sleep stage, while the user sleeps. This maximizes the chances of the user experiencing a desired sleep architecture during a sleep session.

[0053] Systems and methods of the present disclosure can use current sleep stage information, alone or in combination with user parameters and / or information from one or more previous sleep sessions, and feedback data concerning a current state or environment of the user, to determine a sleep stage or predict a likelihood that a sleep stage of a user will vary from a desired sleep stage. In response, the systems and methods can adjust control parameters of a therapy system to promote a desired sleep architecture for the user. Alternatively, or in addition, in response, the systems and methods can adjust control parameters of one or more devices within an environment of the user to promote the desired sleep architecture for the user.

[0054] The current sleep stage of the user can be one of light sleep or deep sleep. Alternatively, the current sleep stage can be light sleep, deep sleep, or REM. Alternatively, the current sleep stage can be Nl, N2, N3 or REM. A graphical representation of sleep stages is referred to as a hypnogram (sometimes called ‘sleep architecture’ as the outline looks like the silhouette of a city skyline).

[0055] The user can have an optimized sleep architecture. Such an optimized sleep architecture can be a set number (or range) of sleep cycles, and a set number (or range), order and duration of sleep stages within the sleep cycles, during a sleep session, which can maximize the quality of sleep for the user. The desired sleep stage can be a current or future sleep stage that fits the desired sleep architecture of the user. For example, if the user is a length of time or a number of sleep cycles into a sleep session or a desired sleep architecture, the desired sleep stage can be the sleep stage corresponding to the sleep stage which should occur at that point in the sleep session or in the desired sleep architecture.

[0056] The current sleep stage information can be the current sleep stage of the user. This can include the actual sleep stage, such as Nl, N2, N3, or REM, and also the sleep cycle of the sleep stage. As discussed above, although generally referred to throughout as having four distinct stages, the sleep stages can alternatively be considered to be two distinct stages, such as light sleep and deep sleep; or three distinct stages, such as light sleep, deep sleep, and REM sleep. The desired sleep stage may be any sleep stage other than the future sleep stage. Forexample, the desired sleep stage is N3 or REM when the future sleep stage is N1 or N2, and vice versa.

[0057] In one or more implementations, sleep stages can be considered as discrete stages that are determined / updated every 30 seconds. They may also be described by a “continuously” varying value at a much higher sampling rate to reflect the actual physiological changes which are gradual or sudden. Usually a sleeper ascends from deep sleep briefly to light sleep before going into REM. These stages may be more fully understood based on the following specific information:

[0058] Stage 1 (“Nl”):• Transition between being awake and being asleep.• Loss of awareness of surroundings (a feeling of drowsiness when not completely awake), and can be easily woken from this stage.• May experience generalized or localized muscle contraction associated with vivid visual imagery.• Sleep onset usually lasts 5-10 minutes.

[0059] Stage 2 (“N2”):• Sleeping, but not particularly deeply (easy to wake from this stage).• Usually lasts 10-25 minutes at a time.• Typically, about half the night sleeping is spent in this stage.• Heart rate, breathing, and brain activity slows down in this sleep stage and the body completely relaxes.

[0060] Stages 3 (“N3”) — SWS, formerly known as stages 3&4:• Deep, slow wave sleep (SWS). This is believed to be the time where the body renews and repairs itself• After falling asleep it might take up to half an hour to reach this deepest part of sleep. Far more effort is taken to wake up from this stage.• Breathing becomes more regular, blood pressure falls, and pulse rate slows.• The amount of deep sleep varies with age.• There is a decrease in deep sleep (and increase in lighter sleep) as one gets older.• Sleep duration typically decreases with age. Therefore, one is more likely to wake up during the night as one ages (i.e., one is in light sleep for longer, from which one can more easily be disturbed by noise, movement of a bed partner, discomfort etc.). This is normal, and most older adults continue to enjoy their sleep.

[0061] Rapid Eye Movement (REM):• Eyes move beneath closed lids, and most dreams occur during REM. The mind races, while the body is virtually paralyzed.• It is believed this stage facilitates learning and memory.• If woken from this stage, there is a tendency to remember dreams. This can happen particularly as REM is followed by light sleep (i.e., starting a new cycle).• The first period of REM may only last 5 minutes or so, but progressively lasts longer over the course of a night, with the last period being up to 30 minutes long.• REM sleep dominates in the final third of the night.• There are more changes in breathing pattern in REM as compared to slow wave sleep.

[0062] Sleep can be considered in terms of discrete states, or a continuous value that varies from full wakefulness, actively trying to fall asleep, asleep (NREM Nl, N2, N3, and REM), and awakening — e.g. to consider in terms of a smooth curve varying like a float parameter capturing small changes (a continuously varying value), rather than an integer referencing individual stages.1. Neuromodulation system

[0063] One of more of the disorders described herein may be treated using neuromodulation techniques to improve the user’s sleep architecture. The neuromodulation may be provided using neurostimulation. For example, stimulating elements can provide electrical, auditory and / or magnetic stimulation to the user to aid in managing at least one or more of: the transition into a sleep stage, the transition out of a sleep stage, the maintenance of a sleep stage. The electrical stimulation may be able to modulate the neurological activities of a user which promote a desired sleep stage.

[0064] FIG. 1 conceptually depicts a system overview for a sleep management system 10 comprising the neuromodulation system 100 described herein. Physiological data is acquired from the user 20. Although not depicted, further data may be acquired from the user 20 using an interface platform (not shown) such as via a mobile application configured to provide data to be used by the neuromodulation system. The acquisition or measurement of this data may be performed prior to the start of the neuromodulation therapy in order to set a baseline, in addition to being performed during therapy. This data may be saved locally and / or remotely, e.g., in a cloud. Some or all of the data, including some or all of the data acquired during therapy, will be processed so that it provides biofeedback which will be used to control theparameters of the stimulation applied by the neuromodulation system, such as but not necessarily limited to, the amplitude and frequency of stimulation. The control may be provided in real time or close to real time. The stimulation parameters are provided to the stimulation device 11 in the neuromodulation system. In this manner, the neuromodulation system involves a closed-loop dynamic feedback. Furthermore, some or all of the data, along with the param eter(s) used during the session, may be saved for the purpose of longitudinal monitoring, to help to further personalize the sleep management for the user, by providing adaptive feedback over time. This may be used to tune the therapy parameters or tune how the neuromodulation system adjusts the therapy parameters in response to real time biofeedback data. Other personalization approaches may instead or also be implemented using the adaptive feedback.

[0065] The sleep management system 10 may also perform further longitudinal monitoring. For example, the longitudinal monitoring may be used to determine how particular lifestyle parameters may affect the sleep characteristics of the user. The longitudinal monitoring may be used to determine how the user responds to the stimulations provided by the sleep management system over time. Here the sleep characteristics may refer to sleep stage duration, the numbers of particular sleep stages in a sleep session, length of sleep session, number of awake periods in a sleep session, sleep latency (time taken to fall asleep), sleep fragmentations, sleep onset and wake time consistency, heart rate variability, body movements and any instances of restlessness or sleep disturbances etc.

[0066] It will be appreciated that the closed-loop control and / or adaptive control may be generally applicable for stimulation of neurological targets, such as the hypoglossal nerve which has application in apnea treatment, the vagus nerve to enhance a parasympathetic response, etc.1.1 Functional modes and multi-modal neuromodulation

[0067] As is described herein and illustrated in FIGs 2, 3, and 4, the present disclosure provides a neuromodulation system 100, configured to support multiple functional modes, to provide within the one system treatments to address the initiation and maintenance of sleep. Preferably, the functional modes can be provided using hardware in the same device unit.

[0068] The neuromodulation system 100 may provide the stimulations in the supported modes, in accordance with control parameters used to control the stimulation applied in one or more of the supported stimulation modes. The control parameters may be set in accordance with acontrol protocol. This may encompass different specific protocols for different types of stimulations. The control protocol may define parameter protocols. A parameter protocol defines how the stimulation parameters are controlled. The control protocol may also define how to select the parameter protocols if different parameter protocols are available. For example, the selection may be on the basis of demographic data such as the user’s age, gender, weight, or on the basis of a baseline monitoring of the user’ s physiological response to electrical or other stimuli. The selected protocol may be used as a pre-set control protocol, so that when the neuromodulation system 100 initiates its operation, the parameters of an applied stimulation will be set in accordance with the pre-set control protocol. Once the system is in use, the stimulation parameters may be subject to further personalization via biofeedback and / or adaptation, as will be described.

[0069] The neuromodulation system 100 may provide multiple functional modes. The multiple functional modes may include at least two or more modes configured to support different parts of the sleep cycle, so that the system as a whole is able to support the overall sleep architecture for the user. For example, a first primary mode may be provided to support sleep onset, and a second primary mode may be provided to support sleep maintenance. A secondary mode may be used in conjunction with the first or second primary mode to enhance or supplement the sleep onset or sleep maintenance support. The secondary mode may be used as a replacement for the first primary mode or the second primary mode when the data obtained from monitoring the user indicates that the first or second primary modes do not have the expected efficaciousness. There may be more than one secondary modes.

[0070] The operation parameters and the onset of the functional modes may be personalized for each user, and adaptively adjusted during the sleep session. The selection of the functional modes for supporting different stages of the user’s sleep may also be personalized, based on the observed efficaciousness of each functional mode in supporting the particular stage(s) of the user’s sleep. For example, if based on uses over one or more sleep sessions and the physiological data and feedback data obtained over the session(s), it is determined that a secondary functional mode is more effective than a preset first primary mode in supporting sleep onset, then the secondary mode may be set as the first primary mode. As another example, it may be determined that a user only needs the neuromodulation support during a particular stage or phase in his or her sleep, and thus a single functional mode may be used as a primary mode to support that sleep stage or phase (and if required a secondary mode may be switched on to support or replace the primary mode).

[0071] Different functional modes included in the neuromodulation system 100 may deliver the same type of modulation, but target different nerves or neurological regions. Two or more of the different functional modes included in the neuromodulation system 100 may be delivered by different modulation modalities.

[0072] The control for turning on each functional mode or switching between functional modes may be based on physiological data acquired from the user. For the switching on of the functional mode to support the sleep onset, this may also be subject to control by the user. For example, the user may turn on the first primary mode. Or the user may switch the first primary mode to standby and then the neuromodulation system 100 will start to monitor the user and turn on the first primary mode when the user’s physiological data indicates the user may need support for sleep onset.

[0073] In some embodiments, the multiple functional modes may include transcranial direct current stimulation (tDCS), transcranial alternating current stimulation (tACS), closed-loop auditory stimulation (CL AS), and Transcutaneous Electrical Nerve Stimulation (TENS). tDCS may be applied to modulate cortical excitability. One or more modes may be utilized in a sleep session to deliver personalized therapy.

[0074] A therapy mode in which tDCS is delivered may be utilized to reduce hyperarousal states, thereby helping to induce sleep in individuals with insomnia or high-stress levels. Thus, tDCS may be provided as a first primary mode in some embodiments. The tDCS therapy mode may be used to modulate cortical excitability, to target the sleep onset, in order to induce sleep in the user. tACS, on the other hand, is typically frequency-dependent and may be used to modulate natural brain waves, making it suitable for aiding sleep by synchronizing with the brain’s natural rhythms to maintain slow-wave sleep (SWS). tACS may therefore be used during system operation as the second primary mode to maintain the user’s sleep in a particular sleep stage and enhance sleep stability. In embodiments where the system 100 is capable of delivering both tDCS and tACS, a hybrid or “dual” mode may be provided where the system 100 first operates to induce sleep and then changes to tACS to maintain sleep through modulation of brain activity. It will be understood the dual mode operation is not necessarily limited to tDCS and tACS operations. The dual mode operation may be performed throughout the sleep session, where the transition from sleep onset to sleep maintenance is based on realtime EEG feedback. In embodiments where the TENS mode is included, in particular but not necessarily for patients with OSA, the system may activate the TENS mode.

[0075] The CLAS may be provided as a secondary functional mode via air or bone conduction to promote relaxation and / or to aid sleep onset. A TENS mode may be provided as analternative or additional secondary functional mode. The secondary functional mode may be provided in a standalone therapy mode or may be integrated with the primary functional mode in real time. For example, in an integrated mode (also referred to as the “combined mode”) combining CL AS and tDCS, the auditory stimulation in CL AS may be synchronized with neuromodulation patterns to enhance relaxation and facilitate sleep onset. Integration of the auditory stimulation may be performed to enhance the effects of the neurostimulation during either or both of the sleep onset and sleep maintenance for the user. The auditory component of the stimulation may be adaptive, i.e., dynamically changing in response to real-time physiological feedback, such as electroencephalogram (EEG) feedback, in order to maintain optimal sleep conditions throughout the sleep session. In particular, the system may adjust the auditory feedback to maintain the SWS. Integration between the CLAS and neuromodulation may be used to enhance or replace tDCS or tACS, with CLAS, particularly if the feedback data show that tDCS or tACS has not resulted in the expected efficaciousness. The triggers for the system to supplement or replace tDCS / tACS with CLAS may include physiological triggers. Non-limiting examples include heart rate variability (HRV) and sleep stage transitions. Therefore, the control for the tDCS or tACS, and the control for the CLAS, can be said to be correlated with each other. The auditory component may be at least partially controlled by the user, by allowing the user to control their auditory experience through a user interface, such as an interface provided by a mobile application, configured to be operable by the user and to receive user input. For example the user may be enabled to select preferred sounds or tones to promote relaxation and sleep. Parameters of the selected audio may be changed in response to physiological feedback.

[0076] In some embodiments, the neuromodulation system 100 may operate in two main electrical stimulation modes, being the tDCS and tACS modes, with the CLAS mode being used in combination with the tDCS or tACS modes as set by the user, or as triggered by the control system 110. The tDCS and tACS functional modes can be personalized through realtime adjustment and also adaptive feedback, which are discussed later in this document. The tDCS is triggered before the sleep process, to reduce hyperarousal by modulating brain activity. A constant direct current is applied with the anode electrode typically placed on the forehead corresponding to the dorsolateral prefrontal cortex (DLPFC) and the cathode electrode placed over a contralateral area, enabling targeted modulation of the DLPFC. The effect is ultimately modulating cortical neurons involved in emotional regulation (i.e., hyperarousal). Example parameters for a tDCS may include an amplitude between 1 to 2 milli Amps (mA), for a duration of 10 to 30 minutes. These may be adjusted based on real-time biofeedback, such as real-time EEG feedback. It will be understood that these parameters are provided as examples only, and do not limit the scope of the invention.

[0077] The application of auditory stimulation (CLAS) whilst the tDCS functional mode is operating, may work synergistically with the tDCS to promote relaxation, for an easier transition to sleep. In some embodiments, the CLAS may be interchangeable with the tDCS mode, for the interchanging between the modes, and parameters of the CLAS mode, to be adjustable in real-time based on biofeedback, e.g., EEG feedback, to optimize the reduction of hyperarousal.

[0078] The trigeminal nerve may also be targeted for tDCS (TN-DCS). That is, the TN-DCS may also be integrated with the tDCS, or may be stand alone. In this mode, the trigeminal nerve is stimulated using cathodal tDCS, to modulate the sensory pathways involved in the autonomic responses which correlate to hyperarousal. The modulation of the sensory pathways may also promote relaxation.

[0079] In tACS, the DLPFC and / or the trigeminal nerve may be triggered as in the case for tDCS, to help maintain stable brainwave patterns throughout the sleep cycle. The alternating currents may have a sinusoidal waveform. Other waveforms may be used, such as rectangular, pulsed, triangular waveforms, or other waveforms suitable for transcranial stimulation. The frequency of the AC waveform may be personalised to the user’s unique brain wave activity. This may be ascertained based on the EEG data. Example parameters for the tACS may be a frequency range of 0.75 to 7 Hertz (Hz), an amplitude of 1-2 mA, and a duration between 5 to 10 minutes. In operation it is expected that the parameters at which the tACS is applied will be adjusted in real-time based on biofeedback.

[0080] The CLAS may be provided in tandem with tACS, or may be interchangeably provided with tACS, to maintain and enhance sleep quality, which may be defined in terms of sleep onset latency, waking after sleep onset, sleep efficiency, number of awakenings, sleep architecture, subjective sleep satisfaction, daytime altemess and functioning, respiratory metrics, body movement, etc. The sleep quality may be monitored by monitoring the slow- wave activity from the EEG, where the CLAS may be used to reinforce the slow-wave brainwaves, particularly during deep sleep stages. The system may adjust the tDCS and tACS (&CLAS) parameters, based on real-time EEG data and other physiological sensors, responding to any disturbances detected during this sleep state.

[0081] It will be appreciated that other nerves or cortical areas may be selected as stimulation targets, as the efficacy of modulating the neural activities in these areas in relation to sleep onset or sleep maintenance become known. Other neurological regions may also be targeteddepending on the specific application. For example, nerves or regions in the parasympathetic nervous system, such as the vagus nerve, may be stimulation targets for applications promoting relaxation.1.2 Neuromodulation system components

[0082] To support the delivery of one or more stimulation modes, the neuromodulation system 100 comprises a stimulation arrangement 130. The stimulation arrangement 130 may comprise a plurality of stimulating elements. In the case of transcranial stimulation, the stimulating electrodes used are each considered to be a stimulating element. The stimulation arrangement 130 is configured to deliver the functional modes which are supported by the neuromodulation system. The neuromodulation system 100 may include safety mechanisms to ensure operation safety of the device. For example, the neuromodulation system 100 may be adapted to measure the skin-electrode impedance in real-time, to ensure stimulations are either changing or stopping based on impedance measurements (open circuit, short circuit readings, or high impedance readings).

[0083] The neuromodulation system 100 comprises a sensor arrangement 150 having one or more sensors (FIG 4) which will be used to acquire feedback data so as to provide a closed- loop control of therapy modes. The one or more sensors may include physiological sensors to measure one or more of brainwave activity (EEG), heart rate variability (HRV), SpO2 (oxygen saturation), accelerometer (to measure body movements), and sensors to measure respiration. Other types of sensors may be provided. Further examples of other sensors which may be included are provided in this document. By monitoring the physiological feedback during operation and adapting the therapy control parameters accordingly, embodiments of the neuromodulation system 100 are configured to provide adaptive closed-loop sleep modulation.

[0084] The neuromodulation system 100 may comprises a positioning structure 35, to position the electrodes so that they can deliver the neuromodulation to the appropriate neurological regions of the user, and where included, to position sensors to measure the physiological data such as the neurological activities of the user. In some embodiments, at least one sensor of the sensor arrangement 150 is collocated with the stimulation arrangement 130 in the positioning structure 35.

[0085] The stimulation arrangement 130 in the illustrated embodiment comprises an electrode array 132 comprising one or more electrodes. Electrode arrays may be provided bilaterally. For example, the electrode array 132 having a plurality of electrodes, may be provided as a left (orright) electrode array, and a second array 134 may be provided as a right (or left) electrode array. Multiple arrays may be provided as required, depending on the stimulation being provided and / or neurological activity sensing required. The positioning structure 35 may be provided in the form of a mask, a strap to be worn on the head, a headband or headset, etc., which can be designed so that they are comfortable for overnight wear.

[0086] The structure 35 is represented in FIG 2 in dashed lines denoting that the exact form factor is not a limiting feature and can be varied in different embodiments. The neuromodulation system 100 further comprises a control system 110 (FIG 2) that is communicatively coupled to the positioning structure 35. The control system 110, in some embodiments, may be physically located in the positioning structure 35 or may be physically connected to the positioning structure 35 via a connector.

[0087] The stimulation arrangement 130 may further include stimulating elements configured to be located on the user’s ears 133, such as auditory stimulation elements, or neurostimulation elements. The auditory stimulation elements may be configured to provide auditory stimulation via air or bone conduction.

[0088] The ear stimulation elements 133 may be located within the positioning structure 35 and thereby positioned on the user, during use. Alternatively, the ear stimulation elements 133 may be external to the positioning structure 35. In embodiments where external ear stimulation elements 133 are provides, they may be included in a speaker device such as a headset or earbuds. The speaker device may be physically coupled or otherwise attached to the positioning structure 35. Alternatively the speakers may be a separate device 184 in communication with the control system 110.

[0089] A number of other elements of the system 100 can be coupled to the positioning structure 35. By coupled to the positioning structure 35 it is meant that the element coupled to the positioning structure 35 is completely encased within the positioning structure 35, attached to an exterior surface of the positioning structure 35, partially protruding from one or more openings in the positioning structure 35, directly or indirectly attached to the positioning structure 35, or any combination thereof. For example, in some implementations, one or more of the transmitters 140 and / or one or more of the receivers 142 can be coupled to or integrated in the positioning structure 35. In such implementations, the transmitter 140 and / or receiver 142 allow the components within the positioning structure 35 to wirelessly communicate (e.g., using a Bluetooth communication protocol, a WiFi communication protocol, or any other suitable RF communication protocol) with components external to the positioning structure 35. Depending on the implementation, such components may include the control system 110,external sensors, wearables, external devices, , or any combination thereof (e.g., to transmit a signal to actuate the positioning structure 35 to deliver electrical stimulation). In other implementations, the transmitter 140 and the receiver 142 are combined as a transceiver.

[0090] The positioning structure 35 may be manufactured from a suitably lightweight and flexible material to enable the user 10 to wear the structure 35 when the user sleeps. This enables the neuromodulation system 100 to be used for improving sleep architecture, thus providing a therapy device for conditions such as insomnia. Surfaces that contact the user’s skin (such as the electrode arrays 132 and 134) may be covered with a suitable material such as a film or gel to prevent skin irritation and enhance electrical coupling.

[0091] FIG. 3 depicts one embodiment of the neuromodulation system 100. FIG. 4 depicts an embodiment of a neuromodulation system 100 with an example sensor arrangement 150. The neuromodulation system 100 includes a control system 110 and the electrodes 130 as mentioned. The system 100 may further include a memory device 114. The neuromodulation system 100 may include one or more transmitters 140 (hereinafter, transmitter 140), and one or more receivers 142 (hereinafter, receiver 142). For example, the transmitter 140 and receiver 142 may facilitate the communication coupling between the control system 110 and the stimulation device 130 and / or the one or more sensors. The neuromodulation system 100 may include an external device 180 configured to receive data signal from the transmitter 140 and send data signal to the receiver 142. The transmitter 140 and the receiver 142 can be implemented by one or more transceivers (hereinafter, transceiver), or it can be implemented by a communication module configured to be in data communication with the external device 180 via mobile data such as 3G, 4G, or 5G, Wifi, or Bluetooth®. The external device 180 may be configured to include a user interface to allow the user to operate or adjust the operation of the system 100, or include data storage and / or processing components to store and / or analyze data gathered by the system 100. It may further include communication hardware so that information such as one or more of the gathered data, analysis result, or records of therapy applied, can be transmitted to a remote computing system such as a server. The external device 180 can be a purpose-built device or it can be provided by a mobile device such as a mobile phone, tablet, or laptop, or a wearable device such as a smart watch.

[0092] Referring to FIGs. 3 and 4, the control system 110 includes one or more processors 112 (hereinafter, processor 112). The control system 110 is generally used to control the various components of the system 100 or control actuators thereof, and / or analyze data obtained and / or generated by the components of the system 100. The processor 112 can be a general or special purpose processor or microprocessor. While one processor 112 is shown, the control system110 can include any suitable number of processors (e.g., one processor, two processors, five processors, ten processors, etc.) that can be in a single housing, or located remotely from each other. The control system 110 can be coupled to and / or positioned within, for example, the positioning structure 35, a housing of the external device 180, a housing for an auditory stimulation device (if included), within a housing of a respiration monitoring device 120 (in embodiments where one is provided), or any combination thereof. The control system 110 can be centralized (within one such housing) or decentralized (within two or more of such housings, which are physically distinct). In such implementations including two or more housings containing the control system 110, such housings can be located proximately and / or remotely from each other.

[0093] The memory device 114 stores machine-readable instructions that are executable by the processor 112 of the control system 110. The memory device 114 can be any suitable computer readable storage device or media, such as, for example, a random or serial access memory device, a hard drive, a solid state drive, a flash memory device, etc. While one memory device 114 is shown in FIG. 3, the system 100 can include any suitable number of memory devices 114 (e.g., one memory device, two memory devices, five memory devices, ten memory devices, etc.). Like the control system 110, the memory device 114 can be centralized (within one such housing) or decentralized (within two or more of such housings, which are physically distinct). For example, the memory device 114 can be coupled to and / or positioned within the housing of a respiration monitoring device (if included) coupled with the control system 110, within the positioning structure 35 for the stimulation device 130, or any combination thereof.

[0094] In some implementations, the memory device 114 (see FIGs. 3 and 4) stores a user profile associated with the user. The user profile can include one or more of, for example, demographic information associated with the user, biometric information associated with the user, medical information associated with the user, self-reported user feedback, sleep parameters associated with the user (e.g., sleep-related parameters recorded from one or more earlier sleep sessions.) The demographic information can include, for example, information indicative of an age of the user, a gender of the user, a weight of the user, a race of the user, a family history of insomnia or sleep apnea, an employment status of the user, an educational status of the user, a socioeconomic status of the user, or any combination thereof. The medical information can include, for example, information indicative of one or more medical conditions associated with the user, medication usage by the user, or both. The medical information data can further include a multiple sleep latency test (MSLT) result or score and / or a Pittsburgh Sleep Quality Index (PSQI) score or value. The self-reported user feedback can includeinformation indicative of a self-reported subjective sleep score (e.g., poor, average, excellent), a self-reported subjective stress level of the user, a self-reported subjective fatigue level of the user, a self-reported subjective health status of the user, a recent life event experienced by the user, medication information, dietary intake information such as information regarding intake of stimulants (such as caffeine) or depressants (such as alcohol), or any combination thereof. One or more of the categories of information may be used to adjust a level of stimulation applied by the stimulation device. Thus, in some embodiments, such information may be but is not necessarily stored against a profile. It may instead be retained only temporarily, e.g., for the purpose of using it as input to control or adjust the stimulation level.

[0095] While the control system 110 and the memory device 114 are described and shown in FIGs. 3 and 4 as being a separate and distinct component of the system 100, in some implementations, the control system 110 and / or the memory device 114 are integrated in the external device 180, a separate auditory stimulating device or respiration monitoring device 120 (if provided) and / or the positioning structure 35 for the stimulation device 130. Alternatively, in some implementations, the control system 110 or a portion thereof (e.g., the processor 112) can be located in a cloud (e.g., integrated in a server, integrated in an Internet of Things (loT) device, connected to the cloud, be subject to edge cloud processing, etc.), located in one or more servers (e.g., remote servers, local servers, etc., or any combination thereof. For example, controls parameters based on longitudinal data may be computed by control modules located in the cloud.

[0096] The neuromodulation system 100 of the present disclosure controls the stimulation, i.e., the currents delivered by the electrodes 132, 134 in the stimulation arrangement 130 and / or the signals delivered by the ear stimulating elements 133 in the stimulation arrangement 130. The stimulation profile may therefore be, for example, a stimulation current profile or a stimulation audio profile. Stimulation current profiles may be represented as data structures that encapsulate relevant parameters of the stimulation currents such as: the spatial locations and / or orientations of the electrodes for delivering the stimulation which depend on the target neurological region(s), temporal parameters such as the number of stimulation currents that the electrode arrays deliver, the activation status of each stimulation current, the frequency of each stimulation current if the current has a periodic waveform, waveform parameters such as but not limited to the amplitude of each stimulation current, waveform shape of each stimulation current (such as a sine-wave, square wave and the like) and waveform width of each stimulation current (namely on-off timings of the stimulation current). The stimulation profiles may be defined for one session or multiple sessions of neuromodulation.

[0097] Stimulation audio profiles may be represented as data structures that encapsulate relevant parameters of the stimulation audio signal, such as the bandwidth(s) of the audio signal, the tempo(s) of the audio signal, the amplitude(s) of the wavelength(s) included in the audio signal, the ratio between different frequency or frequency bands, etc.

[0098] The neuromodulation system 100 of the present disclosure utilizes physiological data taken from the user 20 to responsively control aspects of the stimulation that the stimulation arrangement 130 delivers. This responsive control is facilitated by way of stimulation current profiles and / or stimulation audio profiles. In this regard, the neurostimulation system 100 of the present disclosure receives physiological data (typically from sensors) that is indicative of various physiological states of the user that are relevant to whether neural and / or auditory stimulation is occurring and / or whether the transitioning or maintenance of sleep stages is being successfully achieved by the applied stimulation.

[0099] Examples of physiological data that the neurostimulation system 100 receives may include data pertaining to whether the user 10 has fallen asleep, data pertaining to whether the user 10 has woken from sleep and data pertaining to one or more of sleep state, sleep quality, upper airway condition, a phase within an inhalation-exhalation cycle, body position, heart rate, heart rate variability, impedance, tone of muscles proximate to the target nerve of the user, oxygen saturation, nasal airflow, respiratory flow, respiratory volume and neck impedance.

[0100] In some embodiments, the physiological data includes data pertaining to a characterization of the neurological area that is being targeted for stimulation (referred to hereinafter as a “stimulation characterization”). For example, a transcranial stimulation of a cortical area may be characterized by the neural activities of the cortex being modulated on the basis of the applied transcranial stimulation. In this instance, the physiological data includes data pertaining to the pattern of the neurological activity recorded, e.g., the electroencephalogram (EEG).

[0101] The neuromodulation system 100 processes the physiological data (typically by way of a suitable algorithm, lookup table or machine learning model) and computes a stimulation waveform that is responsive to the received physiological data (referred to hereinafter as a “responsive stimulation profile”). The physiological data can therefore be considered to provide biofeedback which is used in a closed-loop control to control the component s) in the neuromodulation system 100. For example, if the physiological data indicates that the desired transition into or out of a sleep stage is not occurring or is occurring more slowly than expected, suggesting the patient may be under-stimulated or overstimulated, then the neuromodulation system 100 updates the modulation profile. The update to the profile may include theneuromodulation system 100 computing a responsive stimulation profile that includes parameters (such as frequency, amplitude, pulse duration) that addresses the under or over stimulation, where it is an electrical stimulation being adjusted.

[0102] The neuromodulation system 100 computes suitable responsive stimulation profiles to respond to a wide range of phenomena that the physiological data indicates is occurring, and in doing is responsive to this biofeedback. For example, in the case where the physiological data indicates that the user 10 has fallen asleep, the neuromodulation system 100 computes a responsive stimulation profile that includes instructions to the stimulation arrangement 130 to provide sleep support by neuromodulation when required. These instructions (also referred to as “activation instructions”) typically but not necessarily also modify the status of the relevant stimulation to “active” from “inactive” (e.g., to inactivate neuromodulation for sleep onset). Similarly, in the case where the physiological data indicates that the user 10 has woken from sleep, the neurostimulation system 100 computes a responsive stimulation profile that includes deactivation instructions to the stimulation arrangement to deactivate any active stimulation.

[0103] This functionality allows the neuromodulation system 100 to selectively and automatically activate a part or all of the stimulation arrangement 130 to commence neuromodulation therapy. Likewise, the functionality allows the neuromodulation system 100 to selectively and automatically deactivate part or all of the stimulation arrangement 130 when the user wakes from sleep to cease the neuromodulation therapy. Moreover, during the course of neuromodulation therapy, the feedback loop of receiving and processing physiological data and computing suitable responsive modulation and / or stimulation profiles allows the neuromodulation system 100 to respond to the received data in real time and make appropriate modifications to the mode of the therapy or the therapy parameters. For example, the neuromodulation system 100, when operating to try to maintain the user’s sleep stage in a deep sleep stage, in response to receiving specified physiological data, computes a responsive stimulation current profile to stimulate the target neurological region. In another example, the neuromodulation system 100, in response to receiving physiological data from the motion sensor 154 and / or camera 156 (discussed below) indicating that the user is lying in a particular body position in which the user may be more prone to having an apnea event, computes a responsive stimulation profile to account for the sensed body position. For example the neuromodulation system 100 could increase the amplitude of stimulation to reduce snoring and maintain sleep.1.3 Personalisation via Mobile Application

[0104] As mentioned above, the neuromodulation system 100 may incorporate a coupled external device, which can provide a human-machine interface. The interface may be provided using a mobile application. It could be provided by a web-based application where any information input via the application is provided to the local memory 114 as required for the purpose of controlling the neuromodulation device. Via the interface, users can customize their therapy settings for sleep induction, maintenance, or waking cycles. The user may be enabled to operate the interface to adjust particular operation of the system, e.g., in relation to the CLAS to choose their preferred auditory experience as mentioned above. The user may be enabled to provide or update via the interface, information such their user profile information. The device adapts stimulation patterns based on real-time EEG and other physiological feedback (e.g., heart rate variability (HRV), respiratory patterns, body position). This provides the further ability for the user to personalize their therapy and experience using the mobile application.2. Adaptive Closed-loop control

[0105] The neuromodulation system 100 is adapted to receive biofeedback comprising at least the user’s physiological data for a closed loop control of the operation of the neuromodulation system 100.2.1 Biofeedback data

[0106] The physiological data may be acquired using one or more sensors. The system 100 operates as a biofeedback system, which uses real time physiological data tracking to dynamically adjust therapeutic interventions to be delivered by the stimulation modes (e.g., tDCS, tACS, CLAS, TENS) for sleep onset and sleep maintenance management. The one or more sensors can include sensor(s) suitable for generated data from which a signal indicative of the sleep stage of the user can be determined. This enables sleep tracking, i.e., sleep state monitoring. For example, the biofeedback is provided by sensors configured to acquire data which may be used to infer a sleep state of the user or a sleep disturbance. A sensor may be considered to acquire relevant biofeedback data, if it is measuring a physiological parameter known to have a correlation or be indicative of the sleep state, or if it is measuring a parameter which when considered together with one or more other parameters can be used to infer the sleep state or sleep disturbance.

[0107] The biofeedback may include physiological data acquired by an electroencephalogram (EEG) sensor. Integration of real-time EEG feedback allows the control system 110 to continuously monitor sleep patterns, and adjust neuromodulation in response to detecteddisturbances or changes in the sleep stages. The system uses adaptive algorithms to tailor the intensity and type of stimulation based on whether the user is in REM, NREM, or deep sleep, ensuring optimal modulation throughout the night. Over time, such as a period of multiple nights, the system learns from the user’s sleep patterns, refining its approach to optimize restorative sleep phases and reduce unnecessary stimulation. This provides the adaptive feedback mentioned with reference to FIG. 1.

[0108] In some implementations, the neuromodulation system 100 may be used in conjunction with an airway pressure therapy device. In these and other embodiments, the biofeedback may include data provided from a respiration monitoring device including one or more respiratory monitoring sensors. However the biofeedback may alternatively or additionally be provided by one or more other sensors not necessarily used to monitor the user’s respiration.

[0109] The biofeedback may be provided by an oxygen sensor 152, a motion sensor 154, a camera 156, an acoustic sensor 158, a radio-frequency (RF) sensor 164, a PPG sensor 170, a capacitive sensor 172, a force sensor 174, a strain gauge sensor 176, an EMG sensor 178, a temperature sensor, and an electrocardiogram (ECG) sensor 179, or any combination thereof. Data from the sensor(s) 150 can be received and stored in the memory device 114 or one or more other memory devices.

[0110] The oxygen sensor 152 outputs oxygen data indicative of an oxygen concentration of gas (e.g., in the blood of the user). The oxygen sensor 152 can be, for example, a pulse oximeter sensor an ultrasonic oxygen sensor, an electrical oxygen sensor, a chemical oxygen sensor, an optical oxygen sensor, or any combination thereof.[OHl] The motion sensor 154 outputs motion data that is indicative of movement of the user. The motion data from the motion sensor 154 can be used by the control system 110 to determine movement of the user (e.g., respiration). The camera 156 outputs image data reproducible as one or more images (e.g., still images, video images, thermal images, or a combination thereof) that can be stored in the memory device 114. The image data from the camera 156 can be used by the control system 110 to determine movement of the user (e.g., respiration).

[0112] The microphone 160 outputs sound data that can be stored in the memory device 114 and / or analyzed by the processor 112 of the control system 110. The microphone 160 can be used to record sound(s) to determine (e.g., using the control system 110), for example, a respiration signal for the user. The speaker 162 outputs sound waves that are audible to a user of the system 100. The speaker 162 can be used, for example, as an alarm clock or to play an alert or message to the user.

[0113] In some implementations, the microphone 160 and the speaker 162 can be combined into an acoustic sensor 158.

[0114] The RF transmitter 168 generates and / or emits radio waves having a predetermined frequency and / or a predetermined amplitude (e.g., within a high frequency band, within a low frequency band, long wave signals, short wave signals, etc.). The RF receiver 166 detects the reflections of the radio waves emitted from the RF transmitter 168, and this data can be analyzed by the control system 110 to determine movement of the user. While the RF receiver 166 and RF transmitter 168 are shown as being separate and distinct, in some implementations, the RF receiver 166 and RF transmitter 168 are combined as a part of an RF sensor 164. In some such implementations, the RF sensor 164 includes a control circuit. The specific format of the RF communication could be WiFi, Bluetooth, etc.

[0115] In some implementations, the RF sensor 164 is a part of a mesh system. One example of a mesh system is a WiFi mesh system, which can include mesh nodes, mesh router(s), and mesh gateway(s), each of which can be mobile / movable or fixed. In such implementations, the WiFi mesh system includes a WiFi router and / or a WiFi controller and one or more satellites (e.g., access points), each of which include an RF sensor that the is the same as, or similar to, the RF sensor 164. The WiFi router and satellites continuously communicate with one another using WiFi signals. The WiFi mesh system can be used to generate motion data based on changes in the WiFi signals (e.g., differences in received signal strength) between the router and the satellite(s) due to an object or person moving partially obstructing the signals. The motion data can be indicative of motion, breathing, heart rate, gait, falls, behavior, etc., or any combination thereof.

[0116] The PPG sensor 170 outputs physiological data associated with the user that can be used to determine, for example, a heart rate, a heart rate variability, a cardiac cycle, respiration rate, an inspiration amplitude, an expiration amplitude, an inspiration-expiration ratio, estimated blood pressure parameter(s), or any combination thereof.

[0117] The capacitive sensor 172, the force sensor 174, and the strain gauge sensor 176 output data that can be stored in the memory device 114 and used by the control system 110 to determine movement of the user (e.g., respiration). The EMG sensor 178 outputs physiological data associated with electrical activity produced by one or more muscles. The ECG sensor 179 outputs physiological data associated with electrical activity of the heart of the user. In some implementations, the ECG sensor 179 includes one or more electrodes that are positioned on or around a portion of the user.

[0118] While shown separately in FIG. 4, any combination of the one or more sensors 150 can be integrated in and / or coupled to any one or more of the components of the system 100, including a respiration monitoring device 120 if one is included, the positioning structure 35, the control system 110, the external device 180, or any combination thereof.

[0119] The external device 180 of FIG. 4 includes a display device 182. However some examples of the external device 180 may omit a display device. The external device 180 can be, for example, a mobile device such as a smart phone, a tablet, a laptop, or the like. Alternatively, the external device 180 can be an external sensing system, a television (e.g., a smart television) or another smart home device (e.g., a smart speaker(s) such as Google Home, Amazon Echo, Alexa etc.). In some implementations, the external device 180 is a wearable device (e.g., a smart watch). The display device 182 is generally used to display image(s) including still images, video images, or both. In some implementations, the display device 182 acts as a human-machine interface (HMI) that includes a graphical user interface (GUI) configured to display the image(s) and an input interface. The display device 182 can be an LED display, an OLED display, an LCD display, or the like. The input interface can be, for example, a touchscreen or touch-sensitive substrate, a mouse, a keyboard, or any sensor system configured to sense inputs made by a human user interacting with the external device 180. In some implementations, one or more external devices can be used by and / or included in the system 100.

[0120] While the neuromodulation system 100 may include all of the components described above, more or fewer components can be included in a system for aiding a user to fall asleep and maintain the appropriate sleep stages, according to implementations of the present disclosure. For example, a first alternative system includes the control system 110, the memory device 114, the monitoring sensors, and the stimulation arrangement. As another example, a second alternative system includes the control system 110, the memory device 114, the monitoring device 120, the stimulation device 130, and the external device 180. As yet another example, a third alternative system includes the respiration monitoring device 120 and the stimulation device 130. Thus, various systems can be formed using any portion or portions of the components shown and described herein and / or in combination with one or more other components.2.2 Adaptive closed-loop sleep modulation

[0121] The neuromodulation system 100 may be configured to adaptively adjust the stimulation parameters within each functional mode, in response to changes in the user’s sleeppatterns, breathing patterns, or both. This provides adaptive feedback. These patterns may be observed over a period of time. Thus the control system 110 of the neuromodulation system 100 may be configured to execute adaptive algorithms provided in software modules, to process stored data in relation to the user’s sleep or breathing patterns over time. The adaptive algorithms may also process some or all of the available physiological data which were obtained and stored over time.

[0122] The control system 110 may be configured with software modules for an adaptive algorithm to provide adaptive feedback for providing a therapy to improve the user’s sleep architecture. The adaptive feedback may be determined based on machine learning. Using machine learning, the adaptive algorithm analyses EEG data and physiological data over time (e.g. multiple nights), learning the user’s specific sleep patterns and predict stimulation needs. For instance the system may learn from the data over time, when hyperarousal is most likely to occur and how the user responds to different stimulation parameters. The system then finetunes stimulation parameters such as the intensity, frequency, and duration of functional modes (e.g., tDCS, tACS) to optimize both sleep induction and maintenance, reducing sleep disturbances and improving overall sleep quality.

[0123] The adaptive feedback may involve analyzing the EEG data over time and establishing baseline brain wave activities. On the basis of the established baseline, the control system may gradually adjust the stimulation parameters, e.g. over successive nights, to gradually enhance sleep quality. For example, this could be for tACS where the deep sleep quality is gradually enhanced, or for tDCS where gradual adjustment enhances the efficaciousness of brain wave modulation for sleep onset.

[0124] The adaptive feedback may be performed continuously, or may be performed periodically, or may be performed at timings determined by the user or a predetermined schedule which may be stored within the system memory, or when triggered by a determination that a review of the system performance may be required. For example, a review of the system performance may be considered to be required or desirable, when there is a decrease in the efficaciousness of the neuromodulation.

[0125] The control system 110 may be configured with software modules for an adaptive algorithm to provide adaptive feedback for OSA therapy. Again, this may utilize a machine learning model. The neuromodulation system 100 collects sleep data over time, where the machine learning based algorithm learns patterns related to the collected data, so as to utilize the pattern in determining control parameters for one or more of the functional modes. For example, the neuromodulation system 100 collects data in relation to the user’s sleep stages(especially REM), body position (e.g., supine), and apnea frequency, and determines patterns in the data where particular patterns in the REM or body position are correlated with an increased apnea frequency.2.3 Biofeedback-drive stimulation adjustments

[0126] The control system may be configured with software module(s) to analyse physiological data including EEG data, in real time, so as to provide feedback control to dynamically adjust stimulation in real-time. This may be based on biofeedback data. The biofeedback control may be based on physiological data such as the brain wave through EEG monitoring, muscle response through EMG monitoring, heart rate variability, body position, and respiratory patterns. Other physiological or user profile data may also be used to provide the feedback.

[0127] The control system continuously monitors the physiological signals, e.g., by reading the memory into which a data structure encapsulating the monitored data is saved. This information is fed back to the algorithm for determining the control parameters for the specific functional modes, and for determining the appropriate functional modes. For instance, if the control system detects elevated beta activity, i.e., brainwaves more commonly observed in an awake state, from the EEG, then the tDCS and / or CLAS may be initiated to reduce hyperarousal and promote sleep onset. As another example, the control system may adjust the amplitude and the duration of tDCS and CLAS in real-time to promote alpha and theta activities, if these activities in the brain waves are only observed at sub-optimal levels. This may help to induce awake relaxation and deep relaxation, respectively. If, during a sleep session, frequent awakenings or transitions out of the deep sleep stage are detected, suggesting sleep instability, then the tACS and CLAS can be triggered. These may be applied at a parameter protocol aligned with the user’s baseline delta wave frequency to reinduce and stabilize deep sleep. The choice of the parameter protocol on the basis of the user’s baseline delta may be in accordance with a pre-set control protocol. The control protocol may set out how to select parameter protocols. If an improvement in the levels of the desired brainwave activities is observed, then the system may maintain or adjust the tACS and / or CLAS parameters, or turn off the tACS and / or CLAS, to ensure sustained deep sleep without overstimulation. For example this may be when the EEG data shows improved levels in the delta wave activities.

[0128] Thus, more generally, in some embodiments, to provide real-time adjustments for the stimulations provided, the control system 110 may be configured with software module(s) fora feedback algorithm, to continuously monitor the biofeedback, such as to continuously monitor sleep patterns using the EEG data, to track the user’s sleep stages. The feedback may be used for real-time adjustment to the stimulation parameters being delivered.3.4 Wakefulness management and daytime applications

[0129] The system may be configured to perform longitudinal analysis based on data gathered during the daytime or data entered in relation to the user’s daytime conditions or activities. For example the longitudinal analysis may be performed to determine correlations between particular user lifestyle habits or intakes, or user activities, and the user’s sleep states. For example, this determination may be whether caffeine intake of the day impacts the efficaciousness of the therapy modes or the amount of time it takes for the user to transition from sleep onset into later sleep stages. The significance of detection of particular user activities (e.g., active time being less than a certain amount of time) or reported intake, can be presented to the user via the interface. Further, as mentioned, the user may be able to operate the neuromodulation system, to manually initiate neuromodulation. This means the system provides flexibility which allows the user to use the system 100 at a user-selected time, allowing for possible daytime neuromodulation. For instance, the user may be able to initiate neuromodulation at a selected time, and the initiated neuromodulation will work to utilize biofeedback to decrease beta brain wave activity. Such a process allows the neuromodulation to provide a tool for helping to induce relaxation or help the user calm from a stressed state.3.5 Example operational flows

[0130] FIGs. 5, 6-1 and 6-2 conceptually depict the computing operations performed by the control system 110 to control the components of the neuromodulation system 100, according to some implementations of the present disclosure, wherein the first and second primary modes are provided using tDCS and tACS, and CLAS is a secondary functional mode.

[0131] FIG. 5 depicts an operational flow 200 performed by the control system 110 during an initial period where tDCS and CLAS are the active functional modes, according to some embodiments of the present disclosure. Session(s) performed during this period are “initial” sessions. Operations that occur during the initial sessions are conceptually depicted inside box 202. During the initial period, the system learns about how the user responds to neuromodulation provided using the functional modes of tDCS and CLAS, according to some implementations of the present disclosure. At step 204 the stimulation device in the system ispositioned onto the user (e.g. the user “wears” the stimulation device”). At step 206 the system conducts baseline assessments, e.g., recording baseline data in relation to the user’s sleep. Other baseline physiological data in relation to the user may also be recorded. At step 208, the tDCS and CLAS functional mode, with an initial set of parameters for the stimulations, is activated. The initial set of parameters may be determined based on, e.g., a comparison between the user’s baseline data with population data. Whilst the neuromodulation is being applied using the tDCS and CLAS functional mode, the user’s sleep state from the physiological data is monitored at step 210.

[0132] On the basis of the sleep state monitoring, at step 212 the system checks to see whether there has been a decrease in the beta brain wave activities which are typically observed in the awake state. If the beta brain waves are not observed to be decreasing, based on either subjective reports of relaxation, or objective evidence of EEG signal analysis measuring a reduction in the power of the beta wave frequency or EEG band ratios, statistically significantly, then the system will adjust one or more of the stimulation parameters at step 214. The adjustments are made gradually, i.e., incrementally, at each iteration of the operational loop comprising steps 210, 212, and 214. The adjustment may be in accordance with an existing adjustment protocol as part of a control protocol for controlling the stimulation parameters. However the control protocol is adaptable to be personalized to the patient, as mentioned above in relation to the adaptive closed-loop control as more data becomes available. If the beta brain waves are observed to be decreasing statistically significantly, then the system will record the stimulation parameters into system memory at step 216. These parameters, having been shown to be effective in reducing the beta brain wave activities, will be used as starting parameters for the next therapy session.

[0133] Once the parameters found to be effective from the initial sessions are stored, these can be used to provide a personalized combined tDCS and CLAS functional mode. That is, the next time the functional mode is activated (step 220), it will be considered personalized. During an active personalized combined tDCS and CLAS functional mode, the control system will apply real-time adjustments to the tDCS and / or CLAS functional mode parameters, from the biofeedback (step 222). Moreover, adaptive feedback based on session data may also be provided if this has become available (step 224).

[0134] Fig. 6-1 and 6-2 conceptually depict operational flows during a sleep session after the personalized tDCS / CLAS parameters have been initially determined from the initial sessions (see Fig. 5). Fig. 6-1 generally depicts operations to assist with sleep onset. Fig. 6-2 generally depicts operations to assist with sleep maintenance. Referring to Fig. 6-1, when a personalizedcombined tDCS and CLAS functional mode is activated at step 220, the system will monitor the user’s sleep state 226 from the physiological data (e.g., EEG, temperature, heart rate, etc.). The monitoring may be “continuous” in that it ongoingly monitors the physiological data. On the basis of the sleep state monitoring, at step 228 the system checks to see whether there has been a decrease in the beta brain wave activities, an increase in the alpha and / or theta brain waves, or both, which are statistically significant. If no such changes are observed, real-time adjustments are applied to one or more parameters of tDCS and / or CLAS, based the feedback control using the biofeedback data, whilst the system keeps the personalized combined tDCS and CLAS functional modes active. This provides a closed-loop control based on the biofeedback control (222). If a sufficient level of change is detected from step 228, then the system stops the tDCS whilst continuing to monitor the user at step 230, to check for signs of sleep instability within the sleep stage, or frequent awakenings (step 232). If this is not observed, then the system continues the monitoring without activating other functional modes (step 230). However, if sleep instability or frequent awakenings are observed, then the system activates personalized combined tACS and CLAS functional modes at step 234.

[0135] Referring to Fig. 6-2, when the system activates personalized combined tACS and CLAS functional modes, at step 236 the system will perform continuous monitoring of the user’s physiological data to determine whether the therapy intervention applied by the combined tACS and CLAS functional mode is effective. This is determined on the basis of the sleep stability within the sleep stage. The determination of the sleep stability may be on the basis of one or more of EEG based delta wave measurements, sleep spindle density and / or consistency, heart rate variability, respiratory stability, or with actigraphy (wrist based sensing method) to measure sleep fragmentation index). If the intervention is not effective, then the system stays in the active personalized combined tACS and CLAS mode, but applies real-time adjustments to the tACS and / or CLAS functional mode parameters, from the biofeedback (step 238). The operational loop between steps 234 and 236 involving adjustments of the stimulation parameters continues with biofeedback applied, until the intervention has been observed to be effective. If the intervention is effective, then the system will continue the monitoring and detect whether the user is nearing his or her natural awakening (step 240). At a detection that the user is nearing his or her natural awakening, the system will reduce or stop the tACS at step 242 for the user to wake up naturally. At step 244 data from the session, including monitored data and stimulation parameters, will be stored so that the data may be used for longitudinal analysis to adapt the stimulation parameters and the parameter protocol, i.e., the controlprotocol, for further personalization. This analysis is used to implement the adaptive feedback control to the functional modes when they are active (e.g., at step 234, step 220).

[0136] The above may be modified where the sleep onset and sleep maintenance phase operations involve tDCS and tACS only, without utilizing the CLAS.

[0137] The operational flows may be initiated (e.g. start of process 200 or start of the process in Fig 6-1), manually by the user, or it may be initiated based on a timer, or it may be initiated automatically on the basis of a combination of sensor data regarding the environment that the user is in and / or the activity of the user or lack of, and / or a position of the user, and / or one or more physiological measurements such as the heart rate.

[0138] Embodiments of the neuromodulation system may be configured to control the stimulation in any one or more of the functional modes such that the stimulation parameters are adjusted to acclimatize the user to a particular stimulation target, during an acclimatization phase. The system may be configured to apply stimulation over a plurality of time frames (i.e., sessions) over a ramp-up period, such that the level of the stimulation applied is gradually increased over the time frames, until a target stimulation level is reached. The target stimulation level may be a preset target stimulation level. The pre-set target level may be a baseline level chosen on the basis of population data, i.e., general parameters set for most users. This ramp up period provides an acclimatization phase, giving users time to adjust to the sensations and avoid overstimulation.

[0139] As the system ramps up the stimulation levels during the acclimatization phase, the user’s physiological responses may be monitored using biofeedback such as the HRV, EEG, etc. If signs of discomfort or overstimulation occur, the system may adjust the ramping process on the fly, such as reduce the frequency of stimulation, pausing the stimulation, or lowering the intensity of the stimulation. On the other hand, if the biofeedback shows a tolerable and beneficial response, the system may accelerate or fine-tune the ramping process to reach the preset target more quickly.

[0140] The system may utilize adaptive control over the acclimatization phase. For example, in the early sessions of the acclimatization phase, the system may gradually increase the stimulation levels for ramping to a preset target, and monitor the user’s real-time biofeedback during these early sessions. The biofeedback data gathered is used to determine the user’s longitudinal response to this initial ramping. The system may be configured to accumulate the data over a minimum number of acclimatization sessions before determining the user response, to determine whether to apply adjustments to the ramping process. For example, the system may be configured to determine, based on the data, whether the current rampingprocess is effective, or if it needs to adjust the intensity or speed of the ramp-up in future sessions. Therefore, based on the accumulated data gathered over the early sessions, the system may modify the ramping process in subsequent sessions. For example, if the user consistently responds well to the stimulation settings during the initial sessions, the system may keep the same target stimulation level, but fine-tune how quickly or slowly it increases the stimulation. In embodiments utilizing adaptive control over the acclimatization phase, as the system continues to monitor the user’s responses across multiple sessions, it may keep refining one or both of the ramping process (e.g., how fast or slowly the stimulation level is ramped up) and the target stimulation level. This further contributes toward the ability of the system to personalize the neuromodulation to the users’ needs.

[0141] Embodiments of the neuromodulations system, offers a holistic approach to sleep management. Embodiments involving the combination of the multi-modal neuromodulation, with the biofeedback and adaptive control, further advantageously tailors sleep therapy to individual needs, including for those with chronic sleep issues, or those individuals seeking to optimize their sleep. By integrating electrical and / or auditory stimulation, EEG-based closed-loop feedback, and adaptive sleep modulation, the described system enhances both sleep induction and maintenance, optimizing restorative sleep phases and minimizing disturbances.

[0142] While the present disclosure has been described with reference to one or more particular embodiments or implementations, those skilled in the art will recognize that many changes may be made thereto without departing from the spirit and scope of the present disclosure. Each of these implementations and obvious variations thereof is contemplated as falling within the spirit and scope of the present disclosure. It is also contemplated that additional implementations according to aspects of the present disclosure may combine any number of features from any of the implementations described herein.

Claims

CLAIMSWHAT IS CLAIMED IS:

1. A neuromodulation system, comprising: a stimulation arrangement configured to deliver two or more stimulation modes; a memory storing machine-readable instructions; and a control system including one or more processors, configured to execute the machine-readable instructions to: control the stimulation arrangement to deliver stimulation in one of the stimulation modes as a first primary mode to support an onset of sleep or relaxation for the user, and to deliver stimulation in another one of the stimulation modes as a second primary mode to maintain the user’s relaxation or sleep after the onset.

2. A neuromodulation system according to claim 1, the control system being further configured to execute the machine-readable instructions to: monitor physiological data from one or more sensors sensing physiological data from the user, including physiological data correlated with sleep or relaxation states of the user; and apply a feedback control to the stimulation delivered, by processing the physiological data to determine a responsive stimulation profile of stimulations to be delivered by the stimulation arrangement, and adjusting the stimulation being delivered based on the responsive stimulation profile.

3. A neuromodulation system, comprising: a stimulation arrangement configured to deliver two or more stimulation modes; a memory storing machine-readable instructions; and a control system including one or more processors, configured to execute the machine-readable instructions to: control the stimulation applied in at least one of the one or more stimulation modes, in accordance with a control protocol, the control protocol defining control parameters used to control stimulation applied in one or more of the stimulation modes; monitor physiological data from one or more sensors sensing physiological data from the user, including physiological data correlated with sleep states of the user; and apply a feedback control to the stimulation arrangement, by processing the physiological data to determine a responsive stimulation profile of stimulations to be delivered, and adjusting the stimulation being delivered based on the responsive stimulation profile.

4. A neuromodulation system according to any preceding claim, wherein: a. the first and second primary modes are of different stimulation modalities; or b. the first and second primary modes are of the same stimulation modality, and have different stimulation targets and / or different stimulation profiles.

5. A neuromodulation system according to any of claims 1, 2, and 4, the stimulation arrangement being configured to deliver stimulation in a further one of the stimulation modes as a secondary stimulation mode, in combination with or in place of the first or second primary mode.

6. A neuromodulation system according to claim 5, wherein the secondary stimulation mode is delivered responsive to feedback from physiological data indicating stimulations delivered in the first primary mode are not causing an expected sleep or relaxation onset, or indicating stimulations delivered in the second primary mode are not maintaining sleep or relaxation stability.

7. A neuromodulation system according to any preceding claim, wherein stimulation applied in at least one of the one or more stimulation modes is in accordance with a control protocol, defining control parameters used to control stimulation applied in one or more of the stimulation modes.

8. A neuromodulation system according to claim 7, the control protocol being a pre-set protocol.

9. A neuromodulation system according to claim 7 or 8, the control system being further configured to execute the machine-readable instructions to: apply an adaptive control to tune the control protocol based on historical data, wherein the historical data comprises one or more of: historical physiological data correlated with sleep states of the user measured over a historical time frame, historical stimulation parameters of stimulations applied during the historical time frame, physiological responses to changes in stimulation parameters of stimulations applied during the historical time frame, self-reported information in relation to the user during the historical time frame.

10. A neuromodulation system, comprising: a stimulation arrangement configured to deliver two or more stimulation modes; a memory storing machine-readable instructions; and a control system including one or more processors configured to execute the machine- readable instructions to:control the stimulation applied in at least one of the one or more stimulation modes, in accordance with a control protocol, defining control parameters used to control stimulation applied in one or more of the stimulation modes; and apply an adaptive control to tune the control protocol based on historical data, wherein the historical data comprises one or more of: historical physiological data correlated with sleep states of the user measured over a historical time frame, historical stimulation parameters of stimulations applied during the historical time frame, physiological responses to changes in stimulation parameters of stimulations applied during the historical time frame, self-reported information in relation to the user during the historical time frame.

11. A neuromodulation system according to claim 9 or 10, further comprising long term memory configured to record stimulation data comprising stimulation profiles of the stimulations applied, and physiological data acquired, over one or more sleep sessions.

12. A neuromodulation system according to claim 11, wherein the control system is configured to process the recorded stimulation data and physiological data to determine one or more patterns between the stimulation data and the physiological data.

13. A neuromodulation system according to claim 12, wherein applying the adaptive control comprises modifying the control protocol for adjusting one or more parameters of at least one of stimulation modes, based on at least one of the determined one or more patterns.

14. A neuromodulation system according to any preceding claim, wherein at least one of the stimulation modes is provided via neurostimulation, and stimulation current parameters for the neurostimulation include one or more of: number of stimulation currents, frequency of each stimulation current, frequency difference between two stimulation currents, amplitude of each stimulation current, waveform shape of each stimulation current and waveform width of each stimulation current.

15. A neuromodulation system according to any preceding claim, wherein the stimulation arrangement comprises transcranial stimulating elements.

16. A neuromodulation system according to claim 15, wherein the stimulation arrangement is configurable to provide transcranial stimulation to target the dorsolateral prefrontal cortex (DLPFC) of the user.

17. A neuromodulation system according to claim 15 or 16, wherein the stimulation arrangement comprises transcranial stimulating elements which are configurable to provide both tDCS and tACs, and are adapted to be controlled to provide either tDCS or tACS by the control system.

18. A neuromodulation system according to any one of claims 15 to 17, wherein the control system is configured to apply an auditory stimulation during a sleep onset period and / or a sleep maintenance period for the user.

19. A neuromodulation system according to claim 18, wherein the feedback control is configured to adjust the auditory stimulation in real time to enhance a physiological effect of the transcranial stimulation.

20. A neuromodulation system according to claim 4 or any one of claims 5 to 19 when dependent on claim 4, wherein the control system is configured to activate the secondary mode and turn off the first primary mode, when the first primary mode does not result in a reduction of a beta activity measured from the user.

21. A neuromodulation system according to claim 4 or any one of claims 5 to 19 when dependent on claim 4, wherein the control system is configured to apply the secondary mode integrated with the second primary mode during a sleep maintenance period for the user where the user is in an asleep state.

22. A neuromodulation system according to claim 21 when dependent on claim 2, wherein the feedback control is configured to adjust the secondary mode in real time to enhance a physical effect of the second primary mode responsive to the feedback control.

23. A neuromodulation system according to any preceding claim, wherein a stimulation profile of the second primary mode is adjusted based on the user’s delta brain wave activity from the physiological data signal.

24. A neuromodulation system according to any preceding claim, wherein the control system is configured to reduce or stop the second primary mode near an end of a sleep session for the user.

25. A neuromodulation system according to any preceding claim, comprising auditory stimulation elements, configured to conduct stimulation by air conduction or by bone conduction.

26. A neuromodulation system according to any preceding claim, wherein comprising a positioning structure, configured to hold at least part of the stimulation arrangement in close contact with the user’s skin.

27. A neuromodulation system according to claim 26, wherein the positioning structure is configured to be wearable during a sleep session.

28. A neuromodulation system according to claim 27, wherein the positioning structure is a headband or a sleep mask.

29. A neuromodulation system according to claim 26 or 27, wherein the positioning structure is configured to house the transcranial stimulating elements and the stimulating elements to be applied to user’s ears.

30. A neuromodulation system according to any one of claims 26 to 29, wherein the positioning structure also houses one or more sensors configured to acquire an electroencephalogram (EEG) of the user.

31. A neuromodulation method for providing a sleep therapy to a user, comprising: controlling a stimulation arrangement to deliver stimulation in a first primary mode during a sleep or relaxation onset period to aid sleep or relaxation onset, and controlling the stimulation arrangement to deliver stimulation in a second primary mode during a sleep or relaxation maintenance period to aid sleep or relaxation stability.

32. A neuromodulation method in accordance with claim 31, further comprising monitoring physiological data from one or more sensors collecting physiological data from the user, including physiological data correlated with sleep states of the user, wherein the controlling of the stimulation arrangement is on the basis of the monitored physiological data.

33. A neuromodulation method in accordance with claim 32, wherein the controlling of the stimulation arrangement comprises applying a feedback control to the first and / or second primary modes on the basis of physiological data from the one or more sensors during delivery of the stimulation, by determining a responsive stimulation profile for the stimulation delivered by the stimulation arrangement, and controlling the stimulation arrangement to deliver the determined responsive stimulation profile.

34. A neuromodulation system, comprising: a stimulation arrangement configured to deliver two or more stimulation modes; a memory storing machine-readable instructions; and a control system including one or more processors configured to execute the machine- readable instructions to control the stimulation applied in at least one of the one or more stimulation modes to apply stimulation over a plurality of time frames, wherein stimulation applied during the time frames is increased in stimulation level over the time frames, up to a target stimulation level.

35. A neuromodulation system in accordance with claim 34, the control system further being configured to execute the machine-readable instructions to: monitor physiological data from one or more sensors sensing physiological data from the user, including physiological data correlated with sleep states of the user; andapply a feedback control to the stimulation delivered, by processing the physiological data to determine a responsive stimulation profile of stimulations to be delivered under the control protocol, and adjusting the stimulation being delivered based on the responsive stimulation profile; wherein the target stimulation level is set based on the responsive stimulation profile determined in the initial session.

36. A neuromodulation system in accordance with claim 35, the control system further being configured to execute the machine-readable instructions to: monitor the physiological data over an initial one or more of the plurality of time frames, during which the target stimulation level is set to a preset target; determine an updated target from the responsive stimulation profiles determined during the initial one or more of the plurality of time frames; and apply stimulation over remaining one or ones of the plurality of time frames where the target stimulation level is set to the adjusted target.

Images