Acoustic neuromodulation for treatment of sleep apnea

Abstract

Provided herein are systems and methods for treating sleep apnea of a subject using acoustic neuromodulation. Sleep apnea can be treated by enhancing deep sleep, such as by inducing, maintaining, and / or increasing the depth of the deep sleep. The methods may include determining a trigger phase for acoustic neurostimulation and delivering phase-locked acoustic stimulation pulses at the trigger phase. The trigger phase may be determined based on brain signal data and / or changes in biomarker data received from one or more sensors.

Description

ACOUSTIC NEUROMODULATION FOR TREATMENT OF SLEEP APNEACROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims priority to and the benefit of U.S. Provisional Application No. 63 / 541,093, filed September 28, 2023, the contents of which are incorporated herein in its entirety.FIELD

[0002] This disclosure relates generally to acoustic neuromodulation, and more specifically to systems, methods, and device for delivering acoustic neuromodulation to the brain for treatment of sleep apnea.BACKGROUND

[0003] Obstructive Sleep Apnea (OSA) is characterized by randomly occurring apneic events during sleep, such as airway collapse. Apneic obstructive events are not evenly distributed throughout the various phases of sleep. The obstructive events cause the subject to experience fragmented, non-restorative sleep due to frequent arousals caused by airway collapse. A marked improvement in sleep apnea symptoms has been observed during slow wave sleep (SWS), which occurs during sleep stage 3 (N3). Sleep stage 3 is otherwise known as “deep sleep” and is characterized primarily by delta waves with a frequency between about 0.5-4 Hz. Despite the possibility of improvement in symptoms during SWS, the randomly occurring apneic obstructive events of OSA make it challenging to optimize SWS when it occurs.

[0004] OSA is a significant health burden, with approximately 30 million Americans affected, and is associated with negative health outcomes including an increased risk of cardiovascular and metabolic disease. Currently, treatment for OSA includes the use of positive airway pressure (PAP) machines. However, these systems require users to wear a mask throughout the night that is connected to a pressure-generating machine, which can limit motion and cause discomfort. Moreover, a majority of OSA patients using a PAP machine are non-adherent. Other treatment options include surgical interventions or implanted neurostimulators, which are typically indicated for severe cases only. No pharmacological treatment has been approved for the treatment of OSA. Therefore, a substantial need exists for alternative treatments for obstructive sleep apnea. Exemplary acoustic neuromodulation systems and methods are described in greater detail in U.S. PatentNo. 11,166,632 and U.S. Patent Publication No. 2022 / 0225920, the entire contents of each of which are incorporated herein by reference.

[0005] Pharmacological agents have been introduced to attempt to enhance SWS, such as tiagabine, gaboxadol, sodium oxybate, baclofen, olanzapine, and interleukin- 6. However, these agents have demonstrated small effects on SWS and have not been shown to meaningfully improve sleep apnea symptoms. Additionally, they carry a significant risk of unwanted side effects.SUMMARY

[0006] Described herein are systems and methods for treating sleep apnea of a subject using acoustic neuromodulation. The methods may include determining an instantaneous phase of slow wave sleep signals based on received brain signal data, determining a trigger phase for neurostimulation, and delivering phase-locked acoustic stimulation pulses at the trigger phase. Additionally or alternatively, the methods may include determining a trigger phase based on changes in received biomarker data. The phase-locked acoustic stimulation can enhance deep sleep of the subject by inducing deep sleep, increasing the depth of the deep sleep, and / or maintaining the deep sleep, thereby reducing apneic obstruction events.

[0007] As described herein, obstructive sleep apnea (OSA) is characterized by randomly occurring apneic obstruction events, such as airway collapse. These obstructive events can increase subjects’ risk of developing cardiovascular and metabolic disease. It has been observed that these negative symptoms are markedly reduced during slow wave sleep (SWS). Therefore, by monitoring brain signals and delivering phase-locked acoustic neuromodulation in accordance with the signals, slow wave sleep can be prolonged, thereby reducing the severity of sleep apnea and improving health outcomes.

[0008] In some examples, a method for treating sleep apnea in a subject using acoustic neuromodulation is provided, comprising: receiving brain signal data from one or more sensors configured to detect brain waves from the subject; determining one or more characteristics of at least a portion of the brain signal data, the one or more characteristics indicating a stage of sleep of the subject; and delivering a phase-locked acoustic stimulation pulse based on the determined one or more characteristics to enhance slow wave sleep.

[0009] In some examples, an acoustic neuromodulation system for treating sleep apnea in a subject is provided, comprising: one or more sensors configured to detect brain waves from the subject; a processing device configured to: receive brain signal data from the one or more sensors; and determine one or more characteristics of at least a portion of the brain signaldata, the one or more characteristics indicating a stage of sleep of the subject; and one or more auditory stimulators configured to deliver a phase-locked acoustic stimulation pulse based on the determined one or more characteristics to enhance slow wave sleep.

[0010] In some examples, a non-transitory computer-readable storage medium is provided storing instructions that, when executed by a processing device operatively couple to one or more sensors configured to detect brain waves of a subject, cause the device to: receive brain signal data from the one or more sensors; determine one or more characteristics of at least a portion of the brain signal data, the one or more characteristics indicating a stage of sleep of the subject; and deliver a phase-locked acoustic stimulation pulse based on the determined one or more characteristics to enhance slow wave sleep.

[0011] In some examples, a method for treating sleep apnea in a subject using acoustic neuromodulation is provided, comprising: receiving biomarker data from one or more sensors configured to detect biomarkers from the subject; determining one or more characteristics indicating a stage of sleep of the subject based on the received biomarker data; and delivering a phase-locked acoustic stimulation pulse based on the determined one or more characteristics to enhance slow wave sleep.

[0012] In some examples, an acoustic neuromodulation system for treating sleep apnea in a subject is provided, comprising: one or more sensors configured to detect biomarkers from the subject; a processing device configured to: receive biomarker data from the one or more sensors; and determine one or more characteristics indicating a stage of sleep of the subject based on the received biomarker data; and one or more auditory stimulators configured to deliver a phase-locked acoustic stimulation pulse based on the determined one or more characteristics to enhance slow wave sleep.

[0013] In some examples, a non-transitory computer-readable storage medium is provided storing instructions that, when executed by a processing device operatively couple to one or more sensors configured to detect biomarkers of a subject, cause the device to: receive biomarker data from the one or more sensors; determine one or more characteristics indicating a stage of sleep of the subject based on the received biomarker data; and deliver a phase-locked acoustic stimulation pulse based on the determined one or more characteristics to enhance slow wave sleep.BRIEF DESCRIPTION OF THE FIGURES

[0014] Various aspects of the disclosed systems and methods are set forth with particularity in the appended claims. A better understanding of the features and advantages ofthe disclosed systems and methods will be obtained by reference to the detailed description of illustrative embodiments and the accompanying drawings.

[0015] FIG. 1A illustrates an exemplary method for treating sleep apnea in a subject using acoustic neuromodulation, in accordance with some embodiments.

[0016] FIG. IB illustrates an exemplary method for determining characteristics of brain signal data of the subject, in accordance with some embodiments.

[0017] FIG. 1C illustrates another exemplary method for determining characteristics of brain signal data of the subject, in accordance with some embodiments.

[0018] FIG. ID illustrates an exemplary method for delivering phase-locked acoustic stimulation pulses, in accordance with some embodiments.

[0019] FIG. 2A illustrates another exemplary method for treating sleep apnea in a subject using acoustic neuromodulation, in accordance with some embodiments.

[0020] FIG. 2B illustrates an exemplary method for determining characteristics of brain signal data of the subject, in accordance with some embodiments.

[0021] FIG. 3 illustrates an exemplary acoustic neuromodulation system, in accordance with some embodiments.

[0022] FIG. 4 illustrates a computing device, in accordance with some embodiments.DETAILED DESCRIPTION

[0023] Described herein are systems and methods for treating sleep apnea using acoustic neuromodulation. The methods may include receiving brain signal data of a subject, determining characteristics of the brain signal data indicative of the stage of sleep of the subject, and delivering phase-locked acoustic stimulation pulses based on the determined characteristics to enhance slow wave sleep. In some instances, the method may additionally or alternatively include receiving biomarker data, and based on a change in the biomarker data, delivering the phase-locked acoustic stimulation pulses to enhance slow wave sleep of the subject. Enhancing slow wave sleep can include inducing slow wave sleep (e.g., in the instance the patient is not yet in the slow wave sleep stage), maintaining slow wave sleep, and / or increasing the depth of the slow wave sleep. In any instance, by enhancing slow wave sleep, the number of obstructive apneic events may be markedly decreased, thereby treating sleep apnea of the subject.

[0024] The following disclosure describes acoustic neuromodulation and its use as a treatment regimen for sleep apnea. Also described are exemplary acoustic neuromodulation systems and devices configurable for execution, for example for performing the methodsdescribed herein. Additionally, a general computing system that can be configured to execute the methods described herein is described.

[0025] Acoustic neuromodulation involves the delivery of sound pulses to modulate brain signals (i.e., otherwise referred to herein as brain waves). A neuromodulator can generate exogenous signals (e.g., acoustic pulses) that stimulate the neural tissue to suppress, augment, or otherwise maintain neural oscillations in the subject. An electroencephalogram (EEG) may measure brain signals (e.g., electrophysiological signals representative of neural tissue activity), and the acoustic stimulation may be delivered in accordance with these brain signals. In the context of sleep apnea, the brain signals of interest may comprise slow wave sleep signals and / or brain signals that precede slow wave sleep signals, as will be described herein. By characterizing the slow wave oscillations of the signals and delivering pulses in accordance with the phase of the oscillations, slow wave sleep can be enhanced. Particularly, the acoustic pulses may be delivered such that they are locked to the phase of the slow wave oscillations measured by an EEG, hereinafter referred to as “phase-locked acoustic neuromodulation.” For example, in phase-locked acoustic neuromodulation, a difference between the phase of a first signal and the phase of a second signal may be constant (i.e., the two signals have a consistent phase relationship with each other).

[0026] The systems and methods described herein may address obstructive apneic events by increasing the depth and total duration of slow wave sleep, thereby minimizing sleep disturbances, and improving health outcomes of patients suffering from OSA.Methods for Treating Sleep Apnea Using Acoustic Neuromodulation

[0027] FIGS. 1A-1D and 2A-2B illustrate methods for treating sleep apnea using acoustic neuromodulation. FIGS. 1A-1D primarily relate to using characteristics of brain signal data to determine parameters for acoustic neurostimulation, whereas FIGS. 2A-2B more generally relate to the use of biomarkers to determine parameters for the acoustic neurostimulation. Although described with reference to individual sets of Figures, unless explicitly stated otherwise, it is to be understood that concepts described with respect method 100 described with respect to FIGS. 1A-1D may be reasonably encompassed in method 200 described with respect to FIGS. 2A-2B (and vice versa). For example, as will be described in greater detail below, method 100 may include receiving biomarker data (e.g., power of an electroencephalogram (EEG) frequency band, frequency of snoring, blood oxygen saturation, respiratory effort, movement, and / or respiratory pattern) to determine stimulation parameters,and / or method 200 may include receiving brain signal data to determine stimulation parameters.

[0028] As will be described in greater detail herein, each of methods 100 and 200 may be executed by an acoustic neuromodulation system, such as system 300. Method 100 illustrated in FIGS. 1A-1D will first be described, followed by method 200 illustrated in FIGS. 2A-2B.

[0029] Method 100 may include, at block 110, receiving brain signal data from one or more sensors configured to detect brain waves from the subject. The brain signal data may comprise electrophysiological signals that represent brain waves of the subject. For example, the brain signal data may comprise electroencephalogram (EEG) signals received from one or more sensors (e.g., external electrodes) of an EEG system. In some embodiments, the brain signal received by the system may comprise substantially all of the brain signal data detected by the one or more sensors. In some embodiments, the system may receive a portion of brain signal data from the one or more sensors because the sensors may be configured to begin detecting (and / or transmitting) brain signal data upon a determination that the subject is in and / or approaching a specific stage of sleep, such as slow wave sleep. For example, the brain signal data detected and / or transmitted by the one or more sensors may comprise signals within a frequency band, such as about 0.1-100 Hz. The frequency band may comprise frequencies corresponding to one or more sleep stage(s) of interest (e.g., comprising frequencies corresponding at least to stage N3 and / or stage N4 sleep).

[0030] In some embodiments, the brain signal data received at block 110 may be pre- processed signal data or natural signal data. In other words, the brain signal data transmitted to the system executing method 100 may receive processed brain signal or natural signal data. The method 100 may comprise processing the data. Processing the brain signal data (e.g., the natural signal data) may comprise creating a discrete signal that is a sampling of the received brain signal data and based on the discrete signal, determining an analytical signal and Hilbert transform signal for the discrete signal. Determining the analytical signal and Hilbert transform signal can include utilizing an endpoint-corrected Hilbert transform (ECHT) algorithm, described in greater detail in U.S. Patent No. 11,166,632, incorporated by reference herein in its entirety.

[0031] The ECHT algorithm may comprise a frequency domain ECHT, end-padded time domain ECHT, or front-padded time domain ECHT. The ECHT algorithms described herein can correct (e.g., correct, prevent, mitigate, ameliorate, or otherwise compensate for) distortion in the brain signal data due to Gibbs phenomenon that occurs at the ends of sample data. The ECHT algorithms can be particularly useful for determining instantaneousattributes (e.g., instantaneous phase, amplitude, etc.) of a signal using a fast Fourier transform (FFT) and / or inverse fast Fourier transform (IFFT) procedures.

[0032] Each of the frequency domain ECHT, end-padded time domain ECHT, and front- padded time domain ECHT algorithms may include applying causal filter(s) to the discrete signal. The causal filter may comprise a causal infinite impulse response (IIR) filter or causal finite impulse response (FIR) filter. For example, the IIR or FIR filter may include but is not limited to Butterworth filters, Chebyshev filters, Bessel filters, etc. The causal filter may comprise a linear and time-invariant (LTI) bandpass filter. As described herein, the ECHT method may be used to determine instantaneous attributes of the brain signal data. In this manner, the causal filter may reduce distortion of the instantaneous attributes (e.g., amplitude and / or phase) of the signal inside a passband and suppress frequencies outside of the passband. For example, the passband may comprise the range of frequencies of the received brain signal.

[0033] The ECHT algorithm may be used to determine the characteristic center frequency of a signal within a specific range. For example, the ECHT algorithm may determine the local maximum of the signal in the frequency domain. This characteristic center frequency value can then be used as the center frequency of the causal bandpass filter. By determining the center frequency of the filter, any phase shift that may be introduced by the filter can be minimized. In a non-limiting example, an example peak frequency in the frequency spectrum of a brain signal between 0.4 Hz and 2 Hz may be about 1 Hz. 1 Hz may be the delta center frequency. The center frequency of the bandpass filter would be centered at 1 Hz, for example, by setting cutoff values of frequency to 0.5 Hz and 1.5 Hz. These cutoff values can help minimize phase shift introduced by the filter.

[0034] At block 120, the method 100 may comprise determining one or more characteristics of at least a portion of the brain signal data. For example, the system may be configured to determine characteristics for the portion of brain signal data indicative of sleep stage(s) of the subject that is of interest (e.g., stage N3 and / or stage N4 sleep, otherwise referred to herein as slow wave sleep). This brain signal data may occupy a frequency band between about 0.1-5 Hz, such as about 0.1-4 Hz, 0.1-3 Hz, 0.5-5 Hz, 0.5-4 Hz, or 0.5-3 Hz.

[0035] Determining characteristics of the brain signal data is described in greater detail with respect to FIG. IB. For example, block 120 can include determining an instantaneous phase 112 and determining a trigger phase 118. In some embodiments, the method 100 may additionally comprise determining an oscillation frequency 114 and / or determining an evokedresponse delay 116 (otherwise referred to herein as an evoked response potential, ERP). Each of these determined characteristics are described in greater detail below.

[0036] In some embodiments, determining an instantaneous phase of the brain signal data may comprise using an endpoint-corrected Hilbert transform (EHCT) algorithm. As mentioned above, an ECHT algorithm (e.g., frequency domain ECHT, front-padded time domain ECHT, or end-padded time domain ECHT) can be used to determine an analytic signal based on a discrete signal created for the natural signal (e.g., the brain signal data). The instantaneous phase can be determined, for example, at the end of the analytic signal. The endpoint of the analytic signal may correspond in time to the end of the original natural signal. In some embodiments, an ECHT algorithm can be used to determine instantaneous phase over a range of frequencies of interest. For example, instantaneous phase can be determined at a range of frequencies surrounding a primary frequency of interest of the brain signal data. The bandpass of the causal filter (described above) may be configured to correspond with this range of frequencies.

[0037] In some embodiments, determining an instantaneous phase of the brain signal data may comprise using a phase-locked loop. A phase-locked loop can comprise a phase detector, a proportional-integral-derivative controller (PID controller), and a controlled oscillator. The phase evolution of the brain signal data (i.e., the input signal) may be transferred to the controlled oscillator to generate an output signal that follows the phase of the input signal. The instantaneous phase may be the phase at which the controlled oscillator generates the output signal. The phase detector may measure the phase difference between the two signals. The difference can be compared to a set phase (i.e., a setpoint) to generate an error signal for the PID controller. Based on the error signal, the PID controller can produce a feedback signal to adjust the frequency of the controlled oscillator. In this manner, the controlled oscillator can follow the input signal’s phase and generate an analytic signal at the same frequency as the brain signal data.

[0038] In some embodiments, determining an instantaneous phase of the brain signal data may comprise using a forward prediction in the time or frequency domain. A forward prediction in the time domain can comprise use of an autoregressive model. The most recent segment of a brain signal may be bandpass filtered in the forward and reverse directions (e.g., to eliminate filter-induced phase shifts) and the autoregressive model may be used to predict the future values of the signal in the time domain. These predicted values can then be appended to the most recent segment used to generate them. Then, a Hilbert transform may be applied to estimate the instantaneous phase of the signal at the most recently sampledpoint. A forward prediction in the frequency domain can comprise bandpass filtering the signal of interest, as described above, followed by computing the Fast Fourier Transform (FFT) of the filtered signal. From the FFT of the signal, a dominant frequency (e.g., the frequency of the FFT bin with the largest magnitude), and the phase of the signal may be (e.g., the angle component of its complex value) may be determined. From the dominant frequency and phase, a sine wave can then be generated to forecast the future values, including phase, of the brain signal.

[0039] The determined instantaneous phase may be used to estimate when the phase of a brain signal is about equal to a predetermined trigger phase. The trigger phase may be defined as the phase at which to trigger delivery of an acoustic stimulation pulse. The trigger phase may be chosen to be the phase at which triggering an acoustic stimulation pulse maximizes the power of subsequent slow wave oscillations. . The trigger phase may be programmable and unique for each subject. As will be described in greater detail below, the trigger phase may be selected to induce slow wave sleep, increase a depth of the slow wave sleep, and / or maintain the slow wave sleep.

[0040] In some embodiments, the trigger phase may be chosen to align an evoked response to a sound pulse with a specific up-state or down-state of the slow wave sleep oscillation. To do so, the trigger phase may be determined based on an evoked response delay and an oscillation frequency of the slow wave sleep. In some embodiments, one or more of the evoked response delay and oscillation frequency may be predetermined for the subject. In some embodiments, the method 100 (at block 120) may comprise determining the oscillation frequency 114 and determining the evoked response delay 116.

[0041] The oscillation frequency may be determined by selecting a value in the middle of a range of frequency values of interest, such as the midpoint of the bandpass filter used to filter the brain signal. . Alternatively, the oscillation frequency may be determined from a measurement of the brain signal itself. This can comprise bandpass filtering the brain signal to isolate a frequency range of interest, and then computing the FFT of a segment of the filtered brain signal. The dominant frequency within this frequency range of interest can be determined by finding the frequency of the FFT bin with the largest magnitude. In some embodiments, the frequency spectrum may need to first be de-trended to remove noise present in EEG signals. In some embodiments, the oscillation frequency may be determined using a bank of finite impulse response (FIR) notch filters that span the frequency range of interest. The dominant frequency of the signal within this range can then be estimated by determining a weighted sum of each filter frequency value according to the degree ofattenuation induced on the brain signal by the filter at its notch frequency. Other methods for determining the oscillation frequency may exist, and one of ordinary skill in the art will understand these alternative methods to be encompassed by the scope of the disclosure provide herein.

[0042] The evoked response delay may be determined empirically by computing a stimulus-triggered average of EEG signals following delivery of several fixed-duration acoustic stimulation pulses. In some embodiments, this evoked response delay may be measured across multiple individuals, and the mean or median of these measurements may be interpreted as the evoked response delay. In some embodiments, the evoked response delay may be independently measured for each individual, for example, at a time prior to initiation of sleep. The evoked response delay may in some embodiments be determined by recording segments of EEG activity following each acoustic stimulation pulse. .

[0043] In some embodiments, determining the trigger phase 118 may comprise determining an onset trigger phase 119 and determining an offset trigger phase 120. The onset trigger phase may be determined based on the above-described evoked response delay and the oscillation frequency of the slow wave sleep. For example, the onset trigger phase may be selected to align the peak of the evoked response with the peak of the upcoming slow wave oscillation.

[0044] The offset trigger phase may be based on the onset trigger phase, a pulse duration 222, and a period of the oscillation of the slow wave sleep. The period may be inversely related to the oscillation frequency described above. The pulse duration may be between about 20-500 ms, such as about 50 ms, 75 ms, 100 ms, 125 ms, 150 ms, 175 ms, or any value therebetween. In some embodiments, one or more of the period of the oscillation of the frequency and the pulse duration may be predetermined. Using these characteristics in combination with the aforementioned onset trigger phase, the offset trigger phase may be determined empirically such that the amplitude of the evoked response can be maximized.

[0045] In some embodiments, determining the trigger phase 118 may include receiving biomarker data 124 from one or more sensors configured to detect biomarkers from the subject. Biomarker data 124 may comprise one or more of power of an electroencephalogram (EEG) frequency band, frequency of snoring, blood oxygen saturation, respiratory effort, sound (e.g., snoring), movement, and / or respiratory pattern, each of which will be described in greater detail below with reference to method 200 and FIGS. 2A-2B. The biomarker data 124 may be used to iteratively update stimulation parameters, such as the trigger phase, pulse duration (i.e., offset phase), and pulse pattern (i.e., pulse duty cycle). In a non-limitingexample, the pulse duration may be varied according to a gradient descent (or other) algorithm to maximize EEG power in a particular frequency band, maximize blood oxygen saturation, etc. In another example, one or more of the above-mentioned stimulation parameters may be selected to not only optimize EEG power and / or blood oxygen saturation, but also minimize frequency of snoring, respiratory effort, etc. Example algorithms that may utilize biomarker data 124 can include a parameter search and optimization algorithm, such as a simulated annealing algorithm, particle swarm algorithm, genetic algorithm, etc.

[0046] Optionally, in some embodiments, determining one or more characteristics of the brain signal data at block 120 of method 100 may comprise determining an amplitude (e.g., an instantaneous amplitude) and / or a spectral power of the brain signal data. Determining the amplitude and / or spectral power of the brain signal data is described in greater detail with respect to FIG. 1C. In some embodiments, determining the amplitude and / or the spectral power of the brain signal (126) may comprise using an endpoint-correct Hilbert transform (ECHT). As mentioned above, an ECHT algorithm (e.g., frequency domain ECHT, front- padded time domain ECHT, or end-padded time domain ECHT) can be used to determine an analytic signal based on a discrete signal created for the natural signal (e.g., the brain signal data). The instantaneous amplitude, similar to the instantaneous phase as described herein, can be determined at the end of the analytic signal. The endpoint of the analytic signal may correspond in time to the end of the original natural signal. In some embodiments, the amplitude of an oscillation can be determined by bandpass filtering the signal within a frequency range of interest (e.g., about 0.5-2.5 Hz, as described herein) and identifying the maximum value of the signal for each cycle.

[0047] The spectral power may be determined using a frequency domain representation (e.g., Fast Fourier Transform, FFT) of a portion of the brain signal data, as described herein. In some embodiments, the spectral power may be determined by computing a spectrogram. For example, a spectrogram can be computed using a multi-taper method and sliding window approach.

[0048] As shown in FIG. 1C at 128, the determined amplitude and / or spectral power of the brain signal data may be compared to a threshold (e.g., an amplitude threshold and a spectral power threshold, respectively). The threshold may be indicative of the sleep stage of the subject (i.e., whether the subject is close to or currently in slow wave sleep). These thresholds can vary, for example, based on electrode position, contact, etc. For example, the threshold may be represented by an increase in amplitude and / or spectral power of the brain signals within about a 0.1 Hz to 4 Hz range, as compared to a previously measured baselineamplitude or spectral power for the individual. The baseline may be determined during a beginning period of sleep, for example, in the first 5, 10, 15, 20, etc. minutes of sleep. In the instance the determined amplitude and / or spectral power of the brain signal data meets or exceeds the predetermined threshold value(s), a phase-locked acoustic stimulation pulse may be delivered (block 130). In the instance the determined amplitude and / or spectral power of the brain signal does not meet or exceed the predetermined threshold value(s), the phase- locked stimulation pulse may not be delivered, and the system may be configured to continue monitoring the amplitude and / or spectral power of the brain signal.

[0049] Method 100 may comprise, at block 130, delivering a phase-locked acoustic stimulation pulse based on the determined one or more characteristics to enhance slow wave sleep. For example, the acoustic stimulation pulse may be delivered in accordance with the determined trigger phase. In some embodiments, the acoustic stimulation pulse may be delivered in accordance with a determined onset trigger phase and a determined offset trigger phase. In some embodiments, the acoustic stimulation pulse may be delivered only if the spectral power and / or the amplitude of the brain signal data meets or exceeds a predetermined threshold value, as described herein with respect to FIG. 1C.

[0050] In some embodiments, delivering the phase-locked acoustic stimulation pulse may comprise delivering a pulse at the determined trigger phase for a fixed duration. For example, the pulse may be delivered for about 50 ms. In some embodiments, the duration of the pulse may be greater than or equal to about 20 ms, 30 ms, 40 ms, 50 ms, 60 ms, 70 ms, 80 ms, 90 ms, 100 ms, 125 ms, 150 ms, 175 ms, 200 ms, 250 ms, 300 ms, 350 ms, 400 ms, or 450 ms, . In some embodiments, the duration of the pulse may be less than or equal to about 30 ms, 40 ms, 50 ms, 60 ms, 70 ms, 80 ms, 90 ms, 100 ms, 125 ms, 150 ms, 175 ms, 200 ms, 250 ms, 300 ms, 350 ms, 400 ms, 450 ms, or 500 ms.

[0051] In some embodiments, the duration of the acoustic stimulation pulse may be variable based on one or more characteristics of the brain signal data, described in greater detail above with respect to the example onset and offset trigger phase. In this embodiment, the acoustic stimulation pulse may begin when a particular onset trigger phase is detected, and end when an offset trigger phase is detected. In some embodiments, a first stimulation pulse may have a first duration, and a second stimulation pulse may have a second duration. The duration of the first stimulation pulse may be greater than, less than, or about equal to the duration of the second stimulation pulse.

[0052] As shown in FIG. ID, phase-locked acoustic stimulation pulses may in some embodiments be delivered in accordance with a pulse train pattern. The pulse train patternmay be predetermined. In some embodiments, the acoustic pulse may be delivered intermittently relative to the oscillation cycle of the stage of sleep (132). For example, the acoustic stimulation pulse may be delivered every 2, 3, 4, 5, 6, 7, or more oscillation cycles of the stage of sleep. In some embodiments, the acoustic stimulation pulse may be delivered for 1, 2, 3, or more (consecutive) oscillation cycles, followed by 1, 2, 3, or more (consecutive) oscillation cycles with no stimulation pulse. This pulse train pattern may repeat.

[0053] In some embodiments, the phase-locked acoustic stimulation pulse may be delivered in each oscillation cycle of the stage of sleep (134). In some embodiments, the pulse train pattern may comprise delivering the stimulation pulse in each oscillation cycle for a plurality of oscillation cycles (e.g., 2, 3, 4, 5, or more consecutive oscillation cycles), and after a given number of oscillation cycles, the stimulation pulse may be delivered intermittently (e.g., at every other oscillation cycle, at every 2, 3, 4 or more oscillation cycles, etc.), or vice versa. The pulse duration of the acoustic stimulation pulse may be fixed or may be variable (as described above).

[0054] The phase-locked acoustic stimulation pulses may be delivered for substantially all of the duration of a stage of sleep, such as slow wave sleep. In some embodiments, acoustic stimulation pulses may only be delivered for a portion of a given stage of sleep (e.g., about 25%, about 50%, about 75%, about 90%, etc.). In some embodiments, as described herein, the stimulation pulses may only be delivered during a given stage of sleep (e.g., slow wave sleep) when the amplitude and / or spectral power of the brain signal data is above a predetermined threshold value. The system may be configured in a closed-loop manner in that the brain signal data may be continuously or iteratively monitored to selectively deliver acoustic stimulation based on, for example, the stage of sleep, one or more characteristics of the stage of sleep (e.g., spectral power and / or amplitude), etc. As used herein, closed-loop may refer to the use or implementation of a closed-loop control system with one or more feedback loops to modulate system outputs and automatically regulate process variables to a desired state or set point. In some cases, the closed-loop control system can comprise a proportional-integral-derivative controller (PID controller).

[0055] In some embodiments, the phase-locked acoustic stimulation pulse may be delivered only if the subject is determined to be in a particular phase of sleep, such as slow wave sleep. In some embodiments, a first type of acoustic stimulation may be delivered if the subject is determined to be in a first stage of sleep (e.g., a stage of sleep approaching slow wave sleep), and a second type of acoustic stimulation may be delivered if the subject is determined to be in a second stage of sleep (e.g., slow wave sleep). The first and second typesof acoustic stimulation may differ in, for example, trigger phase (e.g., onset trigger phase and / or offset trigger phase), pulse duration, pulse train pattern, etc. As described herein, the system executing method 100 may be configured in a closed-loop manner such that the progression between the stages of sleep can be monitored and the acoustic stimulation can be updated in accordance with the detected stage of sleep.

[0056] In some embodiments, auditory stimulation may comprise broad- spectrum sound pulses. For example, the acoustic stimulation pulses may comprise pink and / or white noise pulses. The acoustic stimulation pulses may be in the range of, for example, about 20 decibels (DB) to about 60 DB, such as 20 DB, 25 DB, 30 DB, 35 DB, 40 DB, 45 DB, 50 DB, 55 DB, or 60 DB.

[0057] The trigger phase may be selected to optimally enhance the power of the slow wave oscillation (136). Specifically, the low-frequency spectral power of the brain signal (e.g., frequencies in the range of about 0.25 Hz to 4 Hz) may be enhanced, or increased, in comparison to a baseline spectral power determined prior to the start of stimulation. For example, the low-frequency spectral power of the brain signal may be increased about 20%, 30%, 40%, 50%, 60%, 70%, 75%, 80%, 90%, 95%, 99%, or more. By enhancing the power of the slow wave oscillation, obstructive apneic events can be markedly decreased.Enhancing slow wave sleep can comprise inducing slow wave sleep (137), increasing the depth of the slow wave sleep (138), and / or maintaining the slow wave sleep (139). For example, phase-locked acoustic pulses can be delivered at a trigger phase corresponding to a sleep stage preceding slow wave sleep to cause the subject to enter slow wave sleep. Once it is determined that the user is in the slow wave sleep stage, phase-locked acoustic pulses can be delivered to maintain the slow wave sleep stage. As described herein, in some embodiments the acoustic pulses may be delivered for the duration of the slow wave sleep stage to maintain the slow wave sleep. Alternatively, in some embodiments, the acoustic pulses may be delivered as needed, that is, upon a threshold (e.g., a brain signal amplitude threshold and / or spectral power threshold) being met or exceeded. In some embodiments, acoustic stimulation may be delivered for a predetermined amount of time upon the subject entering the slow wave sleep stage.

[0058] In some embodiments, the acoustic neurostimulation described herein may increase the depth of the slow wave sleep. The depth of sleep may be inversely correlated with the sensory arousal threshold, meaning a louder noise, brighter light, etc. may be required to wake up (e.g., arouse) the subject. The depth of sleep may be measured using this arousal threshold, whether it be light, noise, etc. required to arouse the subject. In someembodiments, delta power and / or odds ratio product (i.e., the relationship of power in different frequency bands) may be used to measure the depth of the slow wave sleep.

[0059] As mentioned above, in some embodiments, parameters for acoustic neurostimulation may be determined based on biomarker data. FIGS. 2A-2B illustrate a method 200 for treating sleep apnea in a subject using acoustic neurostimulation, the stimulation parameters determined using biomarker data. The method 200 may be executed by an acoustic neurostimulation system, such as system 300 described herein with respect to FIG. 3. As mentioned above, unless explicitly stated otherwise, method 200 may comprise any one or more blocks of method 100 described herein with respect to FIGS. 1A-1D. For example, method 200 may comprise receiving brain signal data, and, in some embodiments, processing the brain signal data.

[0060] At block 210, method 200 may comprise receiving biomarker data from one or more sensors configured to detect biomarkers from the subject. The biomarker data may include the brain signal data described herein with respect to FIGS. 1A-1D and method 100. The biomarker data may comprise power of an electroencephalogram (EEG) frequency band, frequency of snoring, blood oxygen saturation, respiratory effort, movement, and / or respiratory pattern of the subject.

[0061] At block 220, method 200 may comprise determining one or more characteristics indicating a stage of sleep of the subject based on the received biomarker data. For example, determining the one or more characteristics may comprise determining a trigger phase at which to trigger delivery of an acoustic stimulation pulse (218). The trigger phase may be determined iteratively based on changes in the received biomarker data. For example, an initial acoustic stimulation pulse may be delivered at an initial trigger phase, and the received biomarker data may be measured for changes in the biomarker data based on the initial acoustic stimulation. As described herein, the initial trigger phase may be determined, for example, by aligning the peak of an evoked response potential (ERP) with an upcoming slow-wave oscillation peak. The ERP delay may be independently determined for each individual in a prior session, or may be an average ERP delay measured from one or more individuals. Based on an observed change in the received biomarker data, the trigger phase can be updated. As described herein, biomarker data can be used to iteratively update stimulation parameters such as trigger phase using a parameter search and optimization algorithm, such as a simulated annealing algorithm, particle swarm algorithm, genetic algorithm, etc. Determining the trigger phase at 218 may be executed in this manner until the received biomarker data that corresponds to the apneic state of the individual is maximized orminimized (227). In some embodiments, the trigger phase may be iteratively determined until a threshold for a given biomarker of the received biomarker data is met or exceeded. As described herein, the trigger phase may be iteratively adjusted to achieve an optimal combination of biomarkers, such as low respiratory effort and high oxygen saturation. Once a trigger phase is determined, this determined trigger phase may be used to trigger delivery of acoustic stimulation, as described below. The trigger phase may be updated at any time during acoustic stimulation because the biomarker data (including brain signal data) may be continuously monitored in the closed-loop system.

[0062] In some embodiments, determining one or more characteristics indicating the stage of sleep may comprise determining an oscillation frequency 214 and / or determining an evoked response delay 216. The oscillation frequency and / or the evoked response delay may be determined based on brain signal data. Determining the oscillation frequency and the evoked response delay are described in greater detail with respect to blocks 114 and 116, respectively, illustrated in FIG. IB. For example, determining the evoked response delay 216 may include delivering an initial stimulation 217 to the subject, and based on the evoked response to the stimulation, determining a delay between the time of stimulation and the time of response.

[0063] The evoked response delay and the oscillation frequency may be determined in order to determine an onset trigger phase (219). As described herein with respect to FIGS. 1A and 1C, determining a trigger phase for acoustic neurostimulation may comprise determining an onset trigger phase 219 and an offset trigger phase 220. The onset trigger phase may be determined based on the evoked response delay and the oscillation frequency of the stage of sleep (e.g., slow wave sleep). The offset trigger phase may be determined based on the onset trigger phase, a pulse duration, and a period of oscillation of the stage of sleep (e.g., slow wave sleep).

[0064] In some embodiments, determining one or more characteristics indicating a stage of sleep may include determining a spectral power and / or an amplitude of the brain signal data. Determining the spectral power and the amplitude of the brain signal data is described herein in greater detail with respect to method 100 and FIG. 1C and is understood to be optionally encompassed in method 200. The spectral power and / or amplitude of the brain signal may be determined to, for example, control when acoustic stimulation is delivered. For example, a phase-locked acoustic stimulation pulse may be delivered based on a determination that one or more of the amplitude and / or the spectral power of the brain signaldata meet or exceed a predetermined threshold value. Example amplitude and spectral power threshold values are described in greater detail above.

[0065] At block 230, method 200 may comprise delivering a phase-locked acoustic stimulation pulse based on the determined one or more characteristics to enhance slow wave sleep. Delivering the acoustic stimulation may comprise any one or more methods described with respect to block 130 illustrated in FIGS. 1A and ID. For example, phase-locked acoustic stimulation pulses may be delivered for a fixed duration or variable duration. The phase- locked acoustic stimulation pulses may be delivered in each oscillation cycle of the sleep stage, intermittently based on a pulse train pattern, etc. Example pulse train patterns are described in greater detail above. The phase-locked acoustic stimulation pulse may be a broad- spectrum sound pulse, such as a pink noise and / or white noise pulse. The acoustic neurostimulation delivered at block 230 of method 200 may enhance slow wave sleep by inducing the slow wave sleep, increasing the depth of the slow wave sleep, and / or maintaining the slow wave sleep. In doing so, obstructive sleep apneic events can be decreased, thereby treating sleep apnea.Acoustic Neuromodulation Systems and Devices

[0066] FIG. 3 illustrates a schematic of an exemplary acoustic neuromodulation system 300 that can be configured to execute methods 100 and / or 200. The system 300 may be embodied in a wearable medical device and / or a tabletop device, described in greater detail below. The wearable medical device may be an electroencephalogram (EEG)-based device. In some embodiments, the wearable medical device may be a headband and / or a CPAP mask. For example, a subject may wear the headband device configured to measure brain (EEG) signals from one or more positions on the scalp.

[0067] The acoustic neuromodulation system 300 may comprise one or more hardware components known to one of ordinary skill in the art for signal processing, including but not limited to one or more processors (e.g., microcontrollers), an analog-to-digital converter (ADC), a digital signal processor (DSP), a digital-analog-converter (DAC), and a transducer configured to provide a stimulation signal to an output device.

[0068] The system 300 may comprise one or more processors 305 embodied within the tabletop device and / or wearable medical device. The one or more processors 305 may be configured to receive inputs such as brain signal data (e.g., electrophysiological signals, such as EEG signals) from one or more sensors 310 (e.g., electrodes). For example, the one or more sensors 310 may be sensors of the wearable medical device (e.g., a headband, a CPAPmask, etc.), an electroencephalography device, and / or the external tabletop device. The one or more sensors may be configured to detect neural activity, such as neural activity associated with sleep. Based on the detected neural activity, the neuromodulation device may be configured to determine (and record) the phase of neural oscillations in real-time to enhance deep sleep and treat sleep apnea.

[0069] In some embodiments, the processor(s) 305 may be configured to receive biomarker data from one or more biomarker sensors 315. The biomarker sensors may be sensors of an EEG machine, a heart rate monitor, a pulse oximeter, an audio recording device, etc. The one or more sensors may be configured to detect and record physiological data, such as EEG signals, heart rate, oxygen saturation, respiratory rate and effort, motion, sound (e.g., snoring sound), echocardiogram (ECG), and / or electromyogram (EMG). For example, one or more of the aforementioned signals may be used to monitor symptoms of sleep apnea experienced by the subject. In some embodiments, the biomarker sensors may be embodied in the aforementioned headband device and / or CPAP mask.

[0070] The system 300 may be configured to receive one or more audio files 320 containing sounds to be delivered as acoustic stimulation. The system 300 may be configured to receive configuration scripts 325. The configurations scripts 325 may be usable by system 300 to define initial input and output parameters, such as initial trigger phase, audio volume, pulse duration, parameters for determining the onset of slow-wave sleep (e.g., delta power and / or slow wave amplitude), etc. The configuration scripts 325 and audio files 320 may be stored, for example in a memory 330 of system 300. The memory 330 may be communicatively coupled (e.g., via a wired or wireless connection, such as a USB connection, a Bluetooth connection, etc.) to the one or more processors 305 to allow data to be passed between the memory 330 and the processors 305. Memory 330 of system 300 may be of the aforementioned external tabletop device and / or of the wearable device.

[0071] The one or more processors 305 may be configured to receive configuration scripts stored in the memory to configure the acoustic neuromodulation system. The configuration scripts usable by the one or more processors 305 may in some embodiments initially be set based on a user input indicating a desired output type. For example, the user may request that the system 300 be configured to deliver acoustic neurostimulation for enhancing slow wave sleep and treating sleep apnea. The active configuration scripts may be used to configure the system for determining the parameters (e.g., trigger phase) for enhancing slow wave sleep, thereby treating sleep apnea. In some embodiments, the active configuration scripts may be based on population normative brain signals and correspondingbiomarkers. For example, the configuration scripts may be set initially based on parameters that may be, on average, optimal for a population of users. As described herein, the system 300 may be configured to iteratively refine the configuration scripts, for example, based on received brain signal data and / or biomarker data. For example, the received brain signal data may be processed using one or more causal filters, an endpoint-corrected Hilbert transform algorithm (ECHT), etc. as described in greater detail above. Based on this processed signal, instantaneous parameters of the brain signal data, such the instantaneous phase, amplitude, etc. may be identified and used to determine a trigger phase for acoustic neurostimulation. The neuromodulating audio signal may be phase-locked with the endogenous signals (e.g., brain wave signals) of the subject. In some embodiments, the configuration scripts can be refined over the course of a single neuromodulation session, or over the course of a plurality of sessions.

[0072] In some implementations, the one or more processors 305 may be configured to generate the acoustic signal and drive a closed-loop playback system. The generated acoustic signal may be transmitted as instructions to an output device 335 (e.g., an auditory stimulator, such as a speaker). The auditory stimulator may be embodied in a wearable device (e.g., a headband, CPAP mask, etc.) described herein and / or provided as an external tabletop device. In some embodiments, the acoustic stimulation may be provided via one or more boneconduction drivers (e.g., embodied in a wearable headband). As described herein, the delivered acoustic stimulation may be phase-locked to phases of oscillatory activity.

[0073] In some embodiments, the one or more processors may be configured to record and transmit sensor data (e.g., brain signal sensors, biomarker sensors, environmental sensors, etc.) to the memory 330 and / or an external storage or device. For example, sensor data (e.g., biomarker data, brain signal data, etc.) may be transmitted to a smartphone, tablet, desktop, or other web application for presentation to the subject, caregiver, and / or a clinician.Computing Device

[0074] FIG. 4 depicts a computing device 400, according to one or more examples of the disclosure. In one or more examples, device 400 may be understood to encompass one or more components of the acoustic neurostimulation system 300 illustrated in FIG. 3. In one or more examples, computing device 400 may be configured to implement a method for treating sleep apnea using acoustic neurostimulation, such as the method 100 of FIGS. 1A-1D and / or method 200 of FIGS. 2A-2B.

[0075] Device 400 can be a host computer connected to a network. Device 400 can be a client computer or a server. As shown in FIG. 4, device 400 can be any suitable type of microprocessor-based device, such as a personal computer, workstation, server, or handheld computing device (portable electronic device) such as a phone or tablet. The device can include, for example, one or more of processors 402, input device 406, output device 408, storage 410, and communication device 404. Input device 406 and output device 408 can generally correspond to those described above and can either be connectable or integrated with the computer. For example, the one or more processors 402 may comprise processors 305. The input device 406 may comprise the one or more sensors (e.g., brain signal sensors 310 and / or biomarker sensors 315 described herein with respect at least to system 300 and FIG. 3). The output device 408 may comprise the output device 335. The storage 410 may comprise at least the memory 330 described herein with respect to system 300 illustrated in FIG. 3.

[0076] Input device 406 can be any suitable device that provides an input, such as a touch screen, keyboard or keypad, mouse, or voice-recognition device. Output device 408 can be any suitable device that provides output, such as a touch screen, haptics device, or speaker.

[0077] Storage 410 can be any suitable device that provides storage, such as an electrical, magnetic, or optical memory, including a RAM, cache, hard drive, or removable storage disk. Communication device 404 can include any suitable device capable of transmitting and receiving signals over a network, such as a network interface chip or device. The components of the computer can be connected in any suitable manner, such as via a physical bus or wirelessly.

[0078] Software 412, which can be stored in storage 410 and executed by processor 402, can include, for example, the programming that embodies the functionality of the present disclosure (e.g., as embodied in the devices as described above).

[0079] Software 412 can also be stored and / or transported within any non-transitory computer-readable storage medium for use by or in connection with an instruction execution system, apparatus, or device, such as those described above, that can fetch instructions associated with the software from the instruction execution system, apparatus, or device and execute the instructions. In the context of this disclosure, a computer-readable storage medium can be any medium, such as storage 410, that can contain or store programming for use by or in connection with an instruction execution system, apparatus, or device.

[0080] Software 412 can also be propagated within any transport medium for use by or in connection with an instruction execution system, apparatus, or device, such as thosedescribed above, that can fetch instructions associated with the software from the instruction execution system, apparatus, or device and execute the instructions. In the context of this disclosure, a transport medium can be any medium that can communicate, propagate, or transport programming for use by or in connection with an instruction execution system, apparatus, or device. The transport readable medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, or infrared wired or wireless propagation medium.

[0081] Device 400 may be connected to a network, which can be any suitable type of interconnected communication system. The network can implement any suitable communications protocol and can be secured by any suitable security protocol. The network can comprise network links of any suitable arrangement that can implement the transmission and reception of network signals, such as wireless network connections, T1 or T3 lines, cable networks, DSL, or telephone lines.

[0082] Device 400 can implement any operating system suitable for operating on the network. Software 412 can be written in any suitable programming language, such as C, C++, Java, or Python. In various embodiments, application software embodying the functionality of the present disclosure can be deployed in different configurations, such as in a client / server arrangement or through a Web browser as a Web-based application or Web service, for example.Definitions

[0083] As used herein, the singular forms “a”, “an”, and “the” include the plural reference unless the context clearly dictates otherwise.

[0084] Reference to “about” a value or parameter herein includes (and describes) variations that are directed to that value or parameter per se. For example, description referring to “about X” includes description of “X”.

[0085] It is understood that aspects and variations of the invention described herein include “consisting” and / or “consisting essentially of’ aspects and variations.

[0086] The term “real time” or “real-time,” as used interchangeably herein, generally refers to an event (e.g., an operation, a process, a method, a technique, a computation, a calculation, an analysis, a visualization, an optimization, etc.) that is performed using recently obtained (e.g., collected or received) data. In some cases, a real time event may be performed almost immediately or within a short enough time span, such as within at least 1 millisecond (ms), 5 ms, 0.01 seconds, 0.05 seconds, 0.1 seconds, 0.5 seconds, 1 second, 0.1 minute, 0.5minutes, 1 minute, or more. In some cases, a real time event may be performed almost immediately or within a short enough time span, such as within at most 1 second, 0.5 seconds, 0.1 seconds, 0.05 seconds, 0.01 seconds, 5 ms, 1 ms, or less.

[0087] The term “treat,” “treatment,” and / or “treating” is used herein generally refers to reducing the severity of sleep apnea. Reducing the severity of sleep apnea may include reducing the frequency or duration of apnea or hypopnea events and / or reducing the severity of symptoms relating to or resulting from sleep apnea. Reducing the severity of sleep apnea may in some instances involve measurable changes in diagnostic markers used to detect the presence of sleep apnea.

[0088] When a range of values or values is provided, it is to be understood that each intervening value between the upper and lower limit of that range, and any other stated or intervening value in that stated range, is encompassed within the scope of the present disclosure. Where the stated range includes upper or lower limits, ranges excluding either of those included limits are also included in the present disclosure.

[0089] The entire disclosure of the patents and publications referred in this application are hereby incorporated herein by reference for all purposes. To the extent that any reference incorporated by reference conflicts with the instant disclosure, the instant disclosure shall control.EXEMPLARY EMBODIMENTS

[0090] The following embodiments are exemplary and are not intended to limit the scope of any invention described herein.

[0091] Embodiment 1. A method for treating sleep apnea in a subject using acoustic neuromodulation, comprising: receiving brain signal data from one or more sensors configured to detect brain waves from the subject; determining one or more characteristics of at least a portion of the brain signal data, the one or more characteristics indicating a stage of sleep of the subject; and delivering a phase-locked acoustic stimulation pulse based on the determined one or more characteristics to enhance slow wave sleep.

[0092] Embodiment 2. The method of embodiment 1, wherein the at least a portion of the brain signal data comprises brain signal data occupying a frequency band between 0.1-5 Hz.

[0093] Embodiment 3. The method of embodiment 1 or 2, wherein determining the one or more characteristics comprises determining an instantaneous phase of the at least a portion of the brain signal data.

[0094] Embodiment 4. The method of any one of embodiments 1-3, wherein determining the one more characteristics comprises determining a trigger phase to induce the slow wave sleep, increase a depth of the slow wave sleep, or maintain the slow wave sleep.

[0095] Embodiment 5. The method of embodiment 4, wherein the trigger phase is determined based on an evoked response delay and an oscillation frequency of the slow wave sleep.

[0096] Embodiment 6. The method of embodiment 4 or 5, wherein the determined trigger phase aligns an evoked response with an up-state of the oscillation of the slow wave sleep or a down-state of the oscillation of the slow wave sleep.

[0097] Embodiment 7. The method of any one of embodiments 1-6, wherein determining the one or more characteristics comprises determining an onset trigger phase and an offset trigger phase, the onset trigger phase based on the evoked response delay and the oscillation frequency of the slow wave sleep, and the offset trigger phase based on the onset trigger phase, a pulse duration, and a period of the oscillation of the slow wave sleep.

[0098] Embodiment 8. The method of any one of embodiments 1-7, comprising receiving biomarker data from one or more sensors configured to detect biomarkers from the subject.

[0099] Embodiment 9. The method of embodiment 8, wherein the one or more sensors are configured to detect biomarkers comprising: power of an electroencephalogram (EEG) frequency band, frequency of snoring, blood oxygen saturation, respiratory effort, sound, movement, or respiratory pattern.

[0100] Embodiment 10. The method of any one of embodiments 4-9, wherein delivering the phase-locked acoustic stimulation pulse comprises delivering an acoustic stimulation pulse at the determined trigger phase for a fixed duration.

[0101] Embodiment 11. The method of any one of embodiments 1-10, wherein delivering the phase-locked acoustic stimulation pulse comprises delivering the phase-locked acoustic stimulation pulse intermittently in accordance with a predetermined pulse train pattern relative to an oscillation cycle of the stage of sleep.

[0102] Embodiment 12. The method of any one of embodiments 1-10, wherein delivering the phase-locked acoustic stimulation pulse comprises delivering the phase-locked acoustic stimulation pulse in each oscillation cycle of the stage of sleep.

[0103] Embodiment 13. The method of any one of embodiments 1-12, wherein the one or more characteristics comprises an amplitude and / or a spectral power of the at least a portion of the brain signal data.

[0104] Embodiment 14. The method of embodiment 13, wherein delivering the phase- locked acoustic stimulation pulse comprises delivering the phase-locked acoustic stimulation pulse in accordance with the amplitude and / or the spectral power reaching a predetermined threshold value.

[0105] Embodiment 15. The method of any one of embodiments 1-14, wherein the phase- locked acoustic stimulation pulse is a broad- spectrum sound pulse.

[0106] Embodiment 16. The method of any one of embodiments 1-15, wherein the slow wave sleep comprises stage N3 and / or stage N4 sleep.

[0107] Embodiment 17. The method of any one of embodiments 1-15, comprising using a wearable device on the subject that comprises the one or more sensors to receive the brain signal data and one or more auditory stimulators to deliver the phase-locked acoustic stimulation pulse.

[0108] Embodiment 18. An acoustic neuromodulation system for treating sleep apnea in a subject, comprising: one or more sensors configured to detect brain waves from the subject; a processing device configured to: receive brain signal data from the one or more sensors; and determine one or more characteristics of at least a portion of the brain signal data, the one or more characteristics indicating a stage of sleep of the subject; and one or more auditory stimulators configured to deliver a phase-locked acoustic stimulation pulse based on the determined one or more characteristics to enhance slow wave sleep.

[0109] Embodiment 19. The acoustic neuromodulation system of embodiment 1, wherein at least the one or more sensors and the auditory stimulators are provided in a wearable device wearable by the subject and / or a device proximate to the subject.

[0110] Embodiment 20. The acoustic neuromodulation system of embodiment 18 or 19, wherein the at least a portion of the brain signal data comprises brain signal data occupying a frequency band between 0.1-5 Hz.

[0111] Embodiment 21. The acoustic neuromodulation system of any one of embodiments 18-20, wherein determining the one or more characteristics comprises determining an instantaneous phase of the at least a portion of the brain signal data.

[0112] Embodiment 22. The acoustic neuromodulation system of embodiment 21, wherein determining the one more characteristics comprises determining a trigger phase to induce the slow wave sleep, increase a depth of the slow wave sleep, or maintain the slow wave sleep.

[0113] Embodiment 23. The acoustic neuromodulation system of embodiment 22, wherein the trigger phase is determined based on an evoked response delay and an oscillation frequency of the slow wave sleep.

[0114] Embodiment 24. The acoustic neuromodulation system of embodiment 22 or 23, wherein the determined trigger phase aligns an evoked response with an up-state of the oscillation of the slow wave sleep or a down-state of the oscillation of the slow wave sleep.

[0115] Embodiment 25. The acoustic neuromodulation system of any one of embodiments 18-24, wherein determining the one or more characteristics comprises determining an onset trigger phase and an offset trigger phase, the onset trigger phase based on the evoked response delay and the oscillation frequency of the slow wave sleep, and the offset trigger phase based on the onset trigger phase, a pulse duration, and a period of the oscillation of the slow wave sleep.

[0116] Embodiment 26. The acoustic neuromodulation system of any one of embodiments 18-25, comprising receiving biomarker data from one or more sensors configured to detect biomarkers from the subject.

[0117] Embodiment 27. The acoustic neuromodulation system of embodiment 26, wherein the one or more sensors are configured to detect biomarkers comprising: power of an electroencephalogram (EEG) frequency band, frequency of snoring, blood oxygen saturation, respiratory effort, sound, movement, or respiratory pattern.

[0118] Embodiment 28. The acoustic neuromodulation system of any one of embodiments 22-27, wherein delivering the phase-locked acoustic stimulation pulse comprises delivering an acoustic stimulation pulse at the determined trigger phase for a fixed duration.

[0119] Embodiment 29. The acoustic neuromodulation system of any one of embodiments 18-28, wherein delivering the phase-locked acoustic stimulation pulse comprises delivering the phase-locked acoustic stimulation pulse intermittently in accordance with a predetermined pulse train pattern relative to an oscillation cycle of the stage of sleep.

[0120] Embodiment 30. The acoustic neuromodulation system of any one of embodiments 18-28, wherein delivering the phase-locked acoustic stimulation pulse comprises delivering the phase-locked acoustic stimulation pulse in each oscillation cycle of the stage of sleep.

[0121] Embodiment 31. The acoustic neuromodulation system of any one of embodiments 18-30, wherein the one or more characteristics comprises an amplitude and / or a spectral power of the at least a portion of the brain signal data.

[0122] Embodiment 32. The acoustic neuromodulation system of embodiment 31, wherein delivering the phase-locked acoustic stimulation pulse comprises delivering the phase-locked acoustic stimulation pulse in accordance with the amplitude and / or the spectral power reaching a predetermined threshold value.

[0123] Embodiment 33. The acoustic neuromodulation system of any one of embodiments 18-32, wherein the phase-locked acoustic stimulation pulse is a broad-spectrum sound pulse.

[0124] Embodiment 34. The acoustic neuromodulation system of any one of embodiments 18-33, wherein the stage of slow wave sleep comprises stage N3 and / or stage N4 sleep.

[0125] Embodiment 35. A non-transitory computer-readable storage medium storing instructions that, when executed by a processing device operatively couple to one or more sensors configured to detect brain waves of a subject, cause the device to: receive brain signal data from the one or more sensors; determine one or more characteristics of at least a portion of the brain signal data, the one or more characteristics indicating a stage of sleep of the subject; and deliver a phase-locked acoustic stimulation pulse based on the determined one or more characteristics to enhance slow wave sleep.

[0126] Embodiment 36. The non-transitory computer-readable storage medium of embodiment 35, wherein at least the one or more sensors and the auditory stimulators are provided in a wearable device wearable by the subject and / or a device proximate to the subject.

[0127] Embodiment 37. The non-transitory computer-readable storage medium of embodiment 35 or 36, wherein the at least a portion of the brain signal data comprises brain signal data occupying a frequency band between 0.1-5 Hz.

[0128] Embodiment 38. The non-transitory computer-readable storage medium of any one of embodiments 35-37, wherein determining the one or more characteristics comprises determining an instantaneous phase of the at least a portion of the brain signal data.

[0129] Embodiment 39. The non-transitory computer-readable storage medium of embodiment 38, wherein determining the one more characteristics comprises determining a trigger phase to induce the slow wave sleep, increase a depth of the slow wave sleep, or maintain the slow wave sleep.

[0130] Embodiment 40. The non-transitory computer-readable storage medium of embodiment 39, wherein the trigger phase is determined based on an evoked response delay and an oscillation frequency of the slow wave sleep.

[0131] Embodiment 41. The non-transitory computer-readable storage medium of embodiment 39 or 40, wherein the determined trigger phase aligns an evoked response with an up-state of the oscillation of the slow wave sleep or a down-state of the oscillation of the slow wave sleep.

[0132] Embodiment 42. The non-transitory computer-readable storage medium of any one of embodiments 35-41, wherein determining the one or more characteristics comprises determining an onset trigger phase and an offset trigger phase, the onset trigger phase based on the evoked response delay and the oscillation frequency of the slow wave sleep, and the offset trigger phase based on the onset trigger phase, a pulse duration, and a period of the oscillation of the slow wave sleep.

[0133] Embodiment 43. The non-transitory computer-readable storage medium of any one of embodiments 35-42, comprising receiving biomarker data from one or more sensors configured to detect biomarkers from the subject.

[0134] Embodiment 44. The non-transitory computer-readable storage medium of embodiment 43, wherein the one or more sensors are configured to detect biomarkers comprising: power of an electroencephalogram (EEG) frequency band, frequency of snoring, blood oxygen saturation, respiratory effort, sound, movement, or respiratory pattern.

[0135] Embodiment 45. The non-transitory computer-readable storage medium of any one of embodiments 39-44, wherein delivering the phase-locked acoustic stimulation pulse comprises delivering an acoustic stimulation pulse at the determined trigger phase for a fixed duration.

[0136] Embodiment 46. The non-transitory computer-readable storage medium of any one of embodiments 35-45, wherein delivering the phase-locked acoustic stimulation pulse comprises delivering the phase-locked acoustic stimulation pulse intermittently in accordance with a predetermined pulse train pattern relative to an oscillation cycle of the stage of sleep.

[0137] Embodiment 47. The non-transitory computer-readable storage medium of any one of embodiments 35-45, wherein delivering the phase-locked acoustic stimulation pulse comprises delivering the phase-locked acoustic stimulation pulse in each oscillation cycle of the stage of sleep.

[0138] Embodiment 48. The non-transitory computer-readable storage medium of any one of embodiments 35-47, wherein the one or more characteristics comprises an amplitude and / or a spectral power of the at least a portion of the brain signal data.

[0139] Embodiment 49. The non-transitory computer-readable storage medium of embodiment 48, wherein delivering the phase-locked acoustic stimulation pulse comprises 1delivering the phase-locked acoustic stimulation pulse in accordance with the amplitude and / or the spectral power reaching a predetermined threshold value.

[0140] Embodiment 50. The non-transitory computer-readable storage medium of any one of embodiments 35-49, wherein the phase-locked acoustic stimulation pulse is a broadspectrum sound pulse.

[0141] Embodiment 51. The non-transitory computer-readable storage medium of any one of embodiments 35-50, wherein the stage of slow wave sleep comprises stage N3 and / or stage N4 sleep.

[0142] Embodiment 52. A method for treating sleep apnea in a subject using acoustic neuromodulation, comprising: receiving biomarker data from one or more sensors configured to detect biomarkers from the subject; determining one or more characteristics indicating a stage of sleep of the subject based on the received biomarker data; and delivering a phase- locked acoustic stimulation pulse based on the determined one or more characteristics to enhance slow wave sleep.

[0143] Embodiment 53. The method of embodiment 52, wherein the one or more characteristics are determined based on a change in the received biomarker data.

[0144] Embodiment 54. The method of embodiment 52 or 53, wherein determining the one or more characteristics comprises determining a trigger phase that induces the slow wave sleep, increases a depth of the slow wave sleep, or maintains the slow wave sleep.

[0145] Embodiment 55. The method of embodiment 54, wherein the determined trigger phase aligns an evoked response with an up-state of the oscillation of the slow wave sleep or a down-state of the oscillation of the slow wave sleep.

[0146] Embodiment 56. The method of any one of embodiments 52-55, wherein determining the one or more characteristics comprises determining an onset trigger phase and an offset trigger phase, the onset trigger phase based on the evoked response delay and the oscillation frequency of the slow wave sleep, and the offset trigger phase based on the onset trigger phase, a pulse duration, and a period of the oscillation of the slow wave sleep.

[0147] Embodiment 57. The method of any one of embodiments 52-55, wherein determining the one or more characteristics comprises determining the trigger phase that maximizes or minimizes the received biomarker data.

[0148] Embodiment 58. The method of any one of embodiments 52-57, wherein the received biomarker data comprises brain signal data received from one or more sensors configured to detect brain waves from the subject.

[0149] Embodiment 59. The method of any one of embodiments 54-58, wherein delivering the phase-locked acoustic stimulation pulse comprises delivering an acoustic stimulation pulse at the determined trigger phase for a fixed duration.

[0150] Embodiment 60. The method of any one of embodiments 52-59, wherein delivering the phase-locked acoustic stimulation pulse comprises delivering the phase-locked acoustic stimulation pulse intermittently in accordance with a predetermined pulse train pattern relative to an oscillation cycle of the stage of sleep.

[0151] Embodiment 61. The method of any one of embodiments 52-59, wherein delivering the phase-locked acoustic stimulation pulse comprises delivering the phase-locked acoustic stimulation pulse in each oscillation cycle of the stage of sleep.

[0152] Embodiment 62. The method of any one of embodiments 52-61, wherein the one or more sensors are configured to detect biomarkers comprising: power of an electroencephalogram (EEG) frequency band, frequency of snoring, blood oxygen saturation, respiratory effort, sound, movement, or respiratory pattern.

[0153] Embodiment 63. The method of any one of embodiments 52-62, wherein the phase-locked acoustic stimulation pulse is a broad- spectrum sound pulse.

[0154] Embodiment 64. The method of any one of embodiments 52-63, wherein the stage of slow wave sleep comprises stage N3 and / or stage N4 sleep.

[0155] Embodiment 65. The method of any one of embodiments 52-64, comprising using a wearable device on the subject and / or a device proximate to the subject that comprises the one or more sensors to receive the biomarker data and one or more auditory stimulators to deliver the phase-locked acoustic stimulation pulse.

[0156] Embodiment 66. An acoustic neuromodulation system for treating sleep apnea in a subject, comprising: one or more sensors configured to detect biomarkers from the subject; a processing device configured to: receive biomarker data from the one or more sensors; and determine one or more characteristics indicating a stage of sleep of the subject based on the received biomarker data; and one or more auditory stimulators configured to deliver a phase- locked acoustic stimulation pulse based on the determined one or more characteristics to enhance slow wave sleep.

[0157] Embodiment 67. The acoustic neuromodulation system of embodiment 66, wherein at least the one or more sensors and the one or more auditory stimulators are provided in a device wearable by the subject and / or a device proximate to the subject.

[0158] Embodiment 68. The acoustic neuromodulation system of embodiment 66 or 67, wherein the one or more characteristics are determined based on a change in the received biomarker data.

[0159] Embodiment 69. The acoustic neuromodulation system of any one of embodiments 66-68, wherein determining the one or more characteristics comprises determining a trigger phase that induces the slow wave sleep, increases a depth of the slow wave sleep, or maintains the slow wave sleep.

[0160] Embodiment 70. The acoustic neuromodulation system of embodiment 69, wherein the determined trigger phase aligns an evoked response with an up-state of the oscillation of the slow wave sleep or a down-state of the oscillation of the slow wave sleep.

[0161] Embodiment 71. The acoustic neuromodulation system of any one of embodiments 66-70, wherein determining the one or more characteristics comprises determining an onset trigger phase and an offset trigger phase, the onset trigger phase based on the evoked response delay and the oscillation frequency of the slow wave sleep, and the offset trigger phase based on the onset trigger phase, a pulse duration, and a period of the oscillation of the slow wave sleep.

[0162] Embodiment 72. The acoustic neuromodulation system of any one of embodiments 66-70, wherein determining the one or more characteristics comprises determining the trigger phase that maximizes or minimizes the received biomarker data.

[0163] Embodiment 73. The acoustic neuromodulation system of any one of embodiments 66-72, wherein the received biomarker data comprises brain signal data received from one or more sensors configured to detect brain waves from the subject.

[0164] Embodiment 74. The acoustic neuromodulation system of any one of embodiments 69-73, wherein delivering the phase-locked acoustic stimulation pulse comprises delivering an acoustic stimulation pulse at the determined trigger phase for a fixed duration.

[0165] Embodiment 75. The acoustic neuromodulation system of any one of embodiments 66-74, wherein delivering the phase-locked acoustic stimulation pulse comprises delivering the phase-locked acoustic stimulation pulse intermittently in accordance with a predetermined pulse train pattern relative to an oscillation cycle of the stage of sleep.

[0166] Embodiment 76. The acoustic neuromodulation system of any one of embodiments 66-74, wherein delivering the phase-locked acoustic stimulation pulse comprises delivering the phase-locked acoustic stimulation pulse in each oscillation cycle of the stage of sleep.

[0167] Embodiment 77. The acoustic neuromodulation system of any one of embodiments 66-76, wherein the one or more sensors are configured to detect biomarkers comprising: power of an electroencephalogram (EEG) frequency band, frequency of snoring, blood oxygen saturation, respiratory effort, movement, or respiratory pattern.

[0168] Embodiment 78. The acoustic neuromodulation system of any one of embodiments 66-77, wherein the phase-locked acoustic stimulation pulse is a broad-spectrum sound pulse.

[0169] Embodiment 79. The acoustic neuromodulation system of any one of embodiments 66-78, wherein the stage of slow wave sleep comprises stage N3 and / or stage N4 sleep.

[0170] Embodiment 80. A non-transitory computer-readable storage medium storing instructions that, when executed by a processing device operatively couple to one or more sensors configured to detect biomarkers of a subject, cause the device to: receive biomarker data from the one or more sensors; determine one or more characteristics indicating a stage of sleep of the subject based on the received biomarker data; and deliver a phase-locked acoustic stimulation pulse based on the determined one or more characteristics to enhance slow wave sleep.

[0171] Embodiment 81. The non-transitory computer-readable storage medium of embodiment 80, wherein at least the one or more sensors and the one or more auditory stimulators are provided in a device wearable by the subject and / or a device proximate to the subject.

[0172] Embodiment 82. The non-transitory computer-readable storage medium of embodiment 80 or 81, wherein the one or more characteristics are determined based on a change in the received biomarker data.

[0173] Embodiment 83. The non-transitory computer-readable storage medium of any one of embodiments 80-82, wherein determining the one or more characteristics comprises determining a trigger phase that induces the slow wave sleep, increases a depth of the slow wave sleep, or maintains the slow wave sleep.

[0174] Embodiment 84. The non-transitory computer-readable storage medium of embodiment 83, wherein the determined trigger phase aligns an evoked response with an upstate of the oscillation of the slow wave sleep or a down-state of the oscillation of the slow wave sleep.

[0175] Embodiment 85. The non-transitory computer-readable storage medium of any one of embodiments 80-84, wherein determining the one or more characteristics comprisesdetermining an onset trigger phase and an offset trigger phase, the onset trigger phase based on the evoked response delay and the oscillation frequency of the slow wave sleep, and the offset trigger phase based on the onset trigger phase, a pulse duration, and a period of the oscillation of the slow wave sleep.

[0176] Embodiment 86. The non-transitory computer-readable storage medium of any one of embodiments 80-84, wherein determining the one or more characteristics comprises determining the trigger phase that maximizes or minimizes the received biomarker data.

[0177] Embodiment 87. The non-transitory computer-readable storage medium of any one of embodiments 80-86, wherein the received biomarker data comprises brain signal data received from one or more sensors configured to detect brain waves from the subject.

[0178] Embodiment 88. The non-transitory computer-readable storage medium of any one of embodiments 83-87, wherein delivering the phase-locked acoustic stimulation pulse comprises delivering an acoustic stimulation pulse at the determined trigger phase for a fixed duration.

[0179] Embodiment 89. The non-transitory computer-readable storage medium of any one of embodiments 80-88, wherein delivering the phase-locked acoustic stimulation pulse comprises delivering the phase-locked acoustic stimulation pulse intermittently in accordance with a predetermined pulse train pattern relative to an oscillation cycle of the stage of sleep.

[0180] Embodiment 90. The non-transitory computer-readable storage medium of any one of embodiments 80-88, wherein delivering the phase-locked acoustic stimulation pulse comprises delivering the phase-locked acoustic stimulation pulse in each oscillation cycle of the stage of sleep.

[0181] Embodiment 91. The non-transitory computer-readable storage medium of any one of embodiments 80-90, wherein the one or more sensors are configured to detect biomarkers comprising: power of an electroencephalogram (EEG) frequency band, frequency of snoring, blood oxygen saturation, respiratory effort, movement, or respiratory pattern.

[0182] Embodiment 92. The non-transitory computer-readable storage medium of any one of embodiments 80-91, wherein the phase-locked acoustic stimulation pulse is a broadspectrum sound pulse.

[0183] Embodiment 93. The non-transitory computer-readable storage medium of any one of embodiments 80-92, wherein the stage of slow wave sleep comprises stage N3 and / or stage N4 sleep.Conclusion

[0184] The section headings used herein are for organization purposes only and are not to be construed as limiting the subject matter described. The description is presented to enable one of ordinary skill in the art to make and use the invention and is provided in the context of a patent application and its requirements. Various modifications to the described embodiments will be readily apparent to those persons skilled in the art and the generic principles herein may be applied to other embodiments. Thus, the present invention is not intended to be limited to the embodiment shown but is to be accorded the widest scope consistent with the principles and features described herein.

[0185] The figures illustrate processes according to various embodiments. In the exemplary processes, some blocks are, optionally, combined, the order of some blocks is, optionally, changed, and some blocks are, optionally, omitted. In some examples, additional steps may be performed in combination with the exemplary processes. Accordingly, the operations as illustrated (and described in greater detail below) are exemplary by nature and, as such, should not be viewed as limiting.

[0186] Although the disclosure and examples have been fully described with reference to the accompanying figures, it is to be noted that various changes and modifications will become apparent to those skilled in the art. Such changes and modifications are to be understood as being included within the scope of the disclosure and examples as defined by the claims.

Claims

CLAIMSWhat is claimed is:

1. A method for treating sleep apnea in a subject using acoustic neuromodulation, comprising: receiving brain signal data from one or more sensors configured to detect brain waves from the subject; determining one or more characteristics of at least a portion of the brain signal data, the one or more characteristics indicating a stage of sleep of the subject; and delivering a phase-locked acoustic stimulation pulse based on the determined one or more characteristics to enhance slow wave sleep.

2. The method of claim 1, wherein the at least a portion of the brain signal data comprises brain signal data occupying a frequency band between 0.1-5 Hz.

3. The method of claim 1 or 2, wherein determining the one or more characteristics comprises determining an instantaneous phase of the at least a portion of the brain signal data.

4. The method of any one of claims 1-3, wherein determining the one more characteristics comprises determining a trigger phase to induce the slow wave sleep, increase a depth of the slow wave sleep, or maintain the slow wave sleep.

5. The method of claim 4, wherein the trigger phase is determined based on an evoked response delay and an oscillation frequency of the slow wave sleep.

6. The method of claim 4 or 5, wherein the determined trigger phase aligns an evoked response with an up-state of the oscillation of the slow wave sleep or a down-state of the oscillation of the slow wave sleep.

7. The method of any one of claims 1-6, wherein determining the one or more characteristics comprises determining an onset trigger phase and an offset trigger phase, the onset trigger phase based on the evoked response delay and the oscillation frequency of the slow wave sleep, and the offset trigger phase based on the onset trigger phase, a pulse duration, and a period of the oscillation of the slow wave sleep.

8. The method of any one of claims 1-7, comprising receiving biomarker data from one or more sensors configured to detect biomarkers from the subject.

9. The method of claim 8, wherein the one or more sensors are configured to detect biomarkers comprising: power of an electroencephalogram (EEG) frequency band, frequency of snoring, blood oxygen saturation, respiratory effort, sound, movement, or respiratory pattern.

10. The method of any one of claims 4-9, wherein delivering the phase-locked acoustic stimulation pulse comprises delivering an acoustic stimulation pulse at the determined trigger phase for a fixed duration.

11. The method of any one of claims 1-10, wherein delivering the phase-locked acoustic stimulation pulse comprises delivering the phase-locked acoustic stimulation pulse intermittently in accordance with a predetermined pulse train pattern relative to an oscillation cycle of the stage of sleep.

12. The method of any one of claims 1-10, wherein delivering the phase-locked acoustic stimulation pulse comprises delivering the phase-locked acoustic stimulation pulse in each oscillation cycle of the stage of sleep.

13. The method of any one of claims 1-12, wherein the one or more characteristics comprises an amplitude and / or a spectral power of the at least a portion of the brain signal data.

14. The method of claim 13, wherein delivering the phase-locked acoustic stimulation pulse comprises delivering the phase-locked acoustic stimulation pulse in accordance with the amplitude and / or the spectral power reaching a predetermined threshold value.

15. The method of any one of claims 1-14, wherein the phase-locked acoustic stimulation pulse is a broad-spectrum sound pulse.

16. The method of any one of claims 1-15, wherein the slow wave sleep comprises stage N3 and / or stage N4 sleep.

17. The method of any one of claims 1-15, comprising using a wearable device on the subject that comprises the one or more sensors to receive the brain signal data and one or more auditory stimulators to deliver the phase-locked acoustic stimulation pulse.

18. An acoustic neuromodulation system for treating sleep apnea in a subject, comprising: one or more sensors configured to detect brain waves from the subject; a processing device configured to: receive brain signal data from the one or more sensors; and determine one or more characteristics of at least a portion of the brain signal data, the one or more characteristics indicating a stage of sleep of the subject; and one or more auditory stimulators configured to deliver a phase-locked acoustic stimulation pulse based on the determined one or more characteristics to enhance slow wave sleep.

19. The acoustic neuromodulation system of claim 1, wherein at least the one or more sensors and the auditory stimulators are provided in a wearable device wearable by the subject and / or a device proximate to the subject.

20. The acoustic neuromodulation system of claim 18 or 19, wherein the at least a portion of the brain signal data comprises brain signal data occupying a frequency band between 0.1- 5 Hz.

21. The acoustic neuromodulation system of any one of claims 18-20, wherein determining the one or more characteristics comprises determining an instantaneous phase of the at least a portion of the brain signal data.

22. The acoustic neuromodulation system of claim 21, wherein determining the one more characteristics comprises determining a trigger phase to induce the slow wave sleep, increase a depth of the slow wave sleep, or maintain the slow wave sleep.

23. The acoustic neuromodulation system of claim 22, wherein the trigger phase is determined based on an evoked response delay and an oscillation frequency of the slow wave sleep.

24. The acoustic neuromodulation system of claim 22 or 23, wherein the determined trigger phase aligns an evoked response with an up-state of the oscillation of the slow wave sleep or a down-state of the oscillation of the slow wave sleep.

25. The acoustic neuromodulation system of any one of claims 18-24, wherein determining the one or more characteristics comprises determining an onset trigger phase and an offset trigger phase, the onset trigger phase based on the evoked response delay and the oscillation frequency of the slow wave sleep, and the offset trigger phase based on the onset trigger phase, a pulse duration, and a period of the oscillation of the slow wave sleep.

26. The acoustic neuromodulation system of any one of claims 18-25, comprising receiving biomarker data from one or more sensors configured to detect biomarkers from the subject.

27. The acoustic neuromodulation system of claim 26, wherein the one or more sensors are configured to detect biomarkers comprising: power of an electroencephalogram (EEG) frequency band, frequency of snoring, blood oxygen saturation, respiratory effort, sound, movement, or respiratory pattern.

28. The acoustic neuromodulation system of any one of claims 22-27, wherein delivering the phase-locked acoustic stimulation pulse comprises delivering an acoustic stimulation pulse at the determined trigger phase for a fixed duration.

29. The acoustic neuromodulation system of any one of claims 18-28, wherein delivering the phase-locked acoustic stimulation pulse comprises delivering the phase-locked acoustic stimulation pulse intermittently in accordance with a predetermined pulse train pattern relative to an oscillation cycle of the stage of sleep.

30. The acoustic neuromodulation system of any one of claims 18-28, wherein delivering the phase-locked acoustic stimulation pulse comprises delivering the phase-locked acoustic stimulation pulse in each oscillation cycle of the stage of sleep.

31. The acoustic neuromodulation system of any one of claims 18-30, wherein the one or more characteristics comprises an amplitude and / or a spectral power of the at least a portion of the brain signal data.

32. The acoustic neuromodulation system of claim 31, wherein delivering the phase- locked acoustic stimulation pulse comprises delivering the phase-locked acoustic stimulation pulse in accordance with the amplitude and / or the spectral power reaching a predetermined threshold value.

33. The acoustic neuromodulation system of any one of claims 18-32, wherein the phase- locked acoustic stimulation pulse is a broad- spectrum sound pulse.

34. The acoustic neuromodulation system of any one of claims 18-33, wherein the stage of slow wave sleep comprises stage N3 and / or stage N4 sleep.

35. A non-transitory computer-readable storage medium storing instructions that, when executed by a processing device operatively couple to one or more sensors configured to detect brain waves of a subject, cause the device to: receive brain signal data from the one or more sensors; determine one or more characteristics of at least a portion of the brain signal data, the one or more characteristics indicating a stage of sleep of the subject; and deliver a phase-locked acoustic stimulation pulse based on the determined one or more characteristics to enhance slow wave sleep.

36. The non-transitory computer-readable storage medium of claim 35, wherein at least the one or more sensors and the auditory stimulators are provided in a wearable device wearable by the subject and / or a device proximate to the subject.

37. The non-transitory computer-readable storage medium of claim 35 or 36, wherein the at least a portion of the brain signal data comprises brain signal data occupying a frequency band between 0.1-5 Hz.

38. The non-transitory computer-readable storage medium of any one of claims 35-37, wherein determining the one or more characteristics comprises determining an instantaneous phase of the at least a portion of the brain signal data.

39. The non-transitory computer-readable storage medium of claim 38, wherein determining the one more characteristics comprises determining a trigger phase to induce the slow wave sleep, increase a depth of the slow wave sleep, or maintain the slow wave sleep.

40. The non-transitory computer-readable storage medium of claim 39, wherein the trigger phase is determined based on an evoked response delay and an oscillation frequency of the slow wave sleep.

41. The non-transitory computer-readable storage medium of claim 39 or 40, wherein the determined trigger phase aligns an evoked response with an up-state of the oscillation of the slow wave sleep or a down-state of the oscillation of the slow wave sleep.

42. The non-transitory computer-readable storage medium of any one of claims 35-41, wherein determining the one or more characteristics comprises determining an onset trigger phase and an offset trigger phase, the onset trigger phase based on the evoked response delay and the oscillation frequency of the slow wave sleep, and the offset trigger phase based on the onset trigger phase, a pulse duration, and a period of the oscillation of the slow wave sleep.

43. The non-transitory computer-readable storage medium of any one of claims 35-42, comprising receiving biomarker data from one or more sensors configured to detect biomarkers from the subject.

44. The non-transitory computer-readable storage medium of claim 43, wherein the one or more sensors are configured to detect biomarkers comprising: power of an electroencephalogram (EEG) frequency band, frequency of snoring, blood oxygen saturation, respiratory effort, sound, movement, or respiratory pattern.

45. The non-transitory computer-readable storage medium of any one of claims 39-44, wherein delivering the phase-locked acoustic stimulation pulse comprises delivering an acoustic stimulation pulse at the determined trigger phase for a fixed duration.

46. The non-transitory computer-readable storage medium of any one of claims 35-45, wherein delivering the phase-locked acoustic stimulation pulse comprises delivering the phase-locked acoustic stimulation pulse intermittently in accordance with a predetermined pulse train pattern relative to an oscillation cycle of the stage of sleep.

47. The non-transitory computer-readable storage medium of any one of claims 35-45, wherein delivering the phase-locked acoustic stimulation pulse comprises delivering the phase-locked acoustic stimulation pulse in each oscillation cycle of the stage of sleep.

48. The non-transitory computer-readable storage medium of any one of claims 35-47, wherein the one or more characteristics comprises an amplitude and / or a spectral power of the at least a portion of the brain signal data.

49. The non-transitory computer-readable storage medium of claim 48, wherein delivering the phase-locked acoustic stimulation pulse comprises delivering the phase-locked acoustic stimulation pulse in accordance with the amplitude and / or the spectral power reaching a predetermined threshold value.

50. The non-transitory computer-readable storage medium of any one of claims 35-49, wherein the phase-locked acoustic stimulation pulse is a broad-spectrum sound pulse.

51. The non-transitory computer-readable storage medium of any one of claims 35-50, wherein the stage of slow wave sleep comprises stage N3 and / or stage N4 sleep.

52. A method for treating sleep apnea in a subject using acoustic neuromodulation, comprising: receiving biomarker data from one or more sensors configured to detect biomarkers from the subject;determining one or more characteristics indicating a stage of sleep of the subject based on the received biomarker data; and delivering a phase-locked acoustic stimulation pulse based on the determined one or more characteristics to enhance slow wave sleep.

53. The method of claim 52, wherein the one or more characteristics are determined based on a change in the received biomarker data.

54. The method of claim 52 or 53, wherein determining the one or more characteristics comprises determining a trigger phase that induces the slow wave sleep, increases a depth of the slow wave sleep, or maintains the slow wave sleep.

55. The method of claim 54, wherein the determined trigger phase aligns an evoked response with an up-state of the oscillation of the slow wave sleep or a down-state of the oscillation of the slow wave sleep.

56. The method of any one of claims 52-55, wherein determining the one or more characteristics comprises determining an onset trigger phase and an offset trigger phase, the onset trigger phase based on the evoked response delay and the oscillation frequency of the slow wave sleep, and the offset trigger phase based on the onset trigger phase, a pulse duration, and a period of the oscillation of the slow wave sleep.

57. The method of any one of claims 52-55, wherein determining the one or more characteristics comprises determining the trigger phase that maximizes or minimizes the received biomarker data.

58. The method of any one of claims 52-57, wherein the received biomarker data comprises brain signal data received from one or more sensors configured to detect brain waves from the subject.

59. The method of any one of claims 54-58, wherein delivering the phase-locked acoustic stimulation pulse comprises delivering an acoustic stimulation pulse at the determined trigger phase for a fixed duration.

60. The method of any one of claims 52-59, wherein delivering the phase-locked acoustic stimulation pulse comprises delivering the phase-locked acoustic stimulation pulse intermittently in accordance with a predetermined pulse train pattern relative to an oscillation cycle of the stage of sleep.

61. The method of any one of claims 52-59, wherein delivering the phase-locked acoustic stimulation pulse comprises delivering the phase-locked acoustic stimulation pulse in each oscillation cycle of the stage of sleep.

62. The method of any one of claims 52-61, wherein the one or more sensors are configured to detect biomarkers comprising: power of an electroencephalogram (EEG) frequency band, frequency of snoring, blood oxygen saturation, respiratory effort, sound, movement, or respiratory pattern.

63. The method of any one of claims 52-62, wherein the phase-locked acoustic stimulation pulse is a broad-spectrum sound pulse.

64. The method of any one of claims 52-63, wherein the stage of slow wave sleep comprises stage N3 and / or stage N4 sleep.

65. The method of any one of claims 52-64, comprising using a wearable device on the subject and / or a device proximate to the subject that comprises the one or more sensors to receive the biomarker data and one or more auditory stimulators to deliver the phase-locked acoustic stimulation pulse.

66. An acoustic neuromodulation system for treating sleep apnea in a subject, comprising: one or more sensors configured to detect biomarkers from the subject; a processing device configured to: receive biomarker data from the one or more sensors; and determine one or more characteristics indicating a stage of sleep of the subject based on the received biomarker data; and one or more auditory stimulators configured to deliver a phase-locked acoustic stimulation pulse based on the determined one or more characteristics to enhance slow wave sleep.

67. The acoustic neuromodulation system of claim 66, wherein at least the one or more sensors and the one or more auditory stimulators are provided in a device wearable by the subject and / or a device proximate to the subject.

68. The acoustic neuromodulation system of claim 66 or 67, wherein the one or more characteristics are determined based on a change in the received biomarker data.

69. The acoustic neuromodulation system of any one of claims 66-68, wherein determining the one or more characteristics comprises determining a trigger phase that induces the slow wave sleep, increases a depth of the slow wave sleep, or maintains the slow wave sleep.

70. The acoustic neuromodulation system of claim 69, wherein the determined trigger phase aligns an evoked response with an up-state of the oscillation of the slow wave sleep or a down-state of the oscillation of the slow wave sleep.

71. The acoustic neuromodulation system of any one of claims 66-70, wherein determining the one or more characteristics comprises determining an onset trigger phase and an offset trigger phase, the onset trigger phase based on the evoked response delay and the oscillation frequency of the slow wave sleep, and the offset trigger phase based on the onset trigger phase, a pulse duration, and a period of the oscillation of the slow wave sleep.

72. The acoustic neuromodulation system of any one of claims 66-70, wherein determining the one or more characteristics comprises determining the trigger phase that maximizes or minimizes the received biomarker data.

73. The acoustic neuromodulation system of any one of claims 66-72, wherein the received biomarker data comprises brain signal data received from one or more sensors configured to detect brain waves from the subject.

74. The acoustic neuromodulation system of any one of claims 69-73, wherein delivering the phase-locked acoustic stimulation pulse comprises delivering an acoustic stimulation pulse at the determined trigger phase for a fixed duration.

75. The acoustic neuromodulation system of any one of claims 66-74, wherein delivering the phase-locked acoustic stimulation pulse comprises delivering the phase-locked acoustic stimulation pulse intermittently in accordance with a predetermined pulse train pattern relative to an oscillation cycle of the stage of sleep.

76. The acoustic neuromodulation system of any one of claims 66-74, wherein delivering the phase-locked acoustic stimulation pulse comprises delivering the phase-locked acoustic stimulation pulse in each oscillation cycle of the stage of sleep.

77. The acoustic neuromodulation system of any one of claims 66-76, wherein the one or more sensors are configured to detect biomarkers comprising: power of an electroencephalogram (EEG) frequency band, frequency of snoring, blood oxygen saturation, respiratory effort, movement, or respiratory pattern.

78. The acoustic neuromodulation system of any one of claims 66-77, wherein the phase- locked acoustic stimulation pulse is a broad- spectrum sound pulse.

79. The acoustic neuromodulation system of any one of claims 66-78, wherein the stage of slow wave sleep comprises stage N3 and / or stage N4 sleep.

80. A non-transitory computer-readable storage medium storing instructions that, when executed by a processing device operatively couple to one or more sensors configured to detect biomarkers of a subject, cause the device to: receive biomarker data from the one or more sensors; determine one or more characteristics indicating a stage of sleep of the subject based on the received biomarker data; and deliver a phase-locked acoustic stimulation pulse based on the determined one or more characteristics to enhance slow wave sleep.

81. The non-transitory computer-readable storage medium of claim 80, wherein at least the one or more sensors and the one or more auditory stimulators are provided in a device wearable by the subject and / or a device proximate to the subject.

82. The non-transitory computer-readable storage medium of claim 80 or 81, wherein the one or more characteristics are determined based on a change in the received biomarker data.

83. The non-transitory computer-readable storage medium of any one of claims 80-82, wherein determining the one or more characteristics comprises determining a trigger phase that induces the slow wave sleep, increases a depth of the slow wave sleep, or maintains the slow wave sleep.

84. The non-transitory computer-readable storage medium of claim 83, wherein the determined trigger phase aligns an evoked response with an up-state of the oscillation of the slow wave sleep or a down-state of the oscillation of the slow wave sleep.

85. The non-transitory computer-readable storage medium of any one of claims 80-84, wherein determining the one or more characteristics comprises determining an onset trigger phase and an offset trigger phase, the onset trigger phase based on the evoked response delay and the oscillation frequency of the slow wave sleep, and the offset trigger phase based on the onset trigger phase, a pulse duration, and a period of the oscillation of the slow wave sleep.

86. The non-transitory computer-readable storage medium of any one of claims 80-84, wherein determining the one or more characteristics comprises determining the trigger phase that maximizes or minimizes the received biomarker data.

87. The non-transitory computer-readable storage medium of any one of claims 80-86, wherein the received biomarker data comprises brain signal data received from one or more sensors configured to detect brain waves from the subject.

88. The non-transitory computer-readable storage medium of any one of claims 83-87, wherein delivering the phase-locked acoustic stimulation pulse comprises delivering an acoustic stimulation pulse at the determined trigger phase for a fixed duration.

89. The non-transitory computer-readable storage medium of any one of claims 80-88, wherein delivering the phase-locked acoustic stimulation pulse comprises delivering the phase-locked acoustic stimulation pulse intermittently in accordance with a predetermined pulse train pattern relative to an oscillation cycle of the stage of sleep.

90. The non-transitory computer-readable storage medium of any one of claims 80-88, wherein delivering the phase-locked acoustic stimulation pulse comprises delivering the phase-locked acoustic stimulation pulse in each oscillation cycle of the stage of sleep.

91. The non-transitory computer-readable storage medium of any one of claims 80-90, wherein the one or more sensors are configured to detect biomarkers comprising: power of an electroencephalogram (EEG) frequency band, frequency of snoring, blood oxygen saturation, respiratory effort, movement, or respiratory pattern.

92. The non-transitory computer-readable storage medium of any one of claims 80-91, wherein the phase-locked acoustic stimulation pulse is a broad-spectrum sound pulse.

93. The non-transitory computer-readable storage medium of any one of claims 80-92, wherein the stage of slow wave sleep comprises stage N3 and / or stage N4 sleep.

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