Patent Publication Number: US-2021170138-A1

Title: Method and system for enhancement of slow wave activity and personalized measurement thereof

Description:
CROSS-REFERENCE TO PRIOR APPLICATIONS 
     This application claims the benefit of U.S. Provisional Application No. 62/945,476, filed on 9 Dec. 2019. This application is hereby incorporated by reference herein. 
    
    
     BACKGROUND 
     1. Field 
     The present disclosure pertains to a system and method for enhancing slow wave activity and providing a personalized measurement thereof. 
     2. Description of the Related Art 
     Systems for monitoring sleep and delivering sensory stimulation to subjects during sleep are known. Electroencephalogram (EEG) sensor-based sleep monitoring and sensory stimulation systems are known. 
     SUMMARY 
     Systems and methods described herein may provide enhancements to the slow wave activity of a subject and personalized measurements thereof. Accordingly, one or more aspects of the present disclosure relate to a system configured to measure slow wave activity of a subject during a sleep session. The system comprises one or more sensors, one or more sensory stimulators, one or more processors, and/or other components. The one or more sensors are configured to generate output signals conveying information related to brain activity of the subject during the sleep session. The one or more sensory stimulators are configured to provide the sensory stimulation to the subject during the sleep session. The one or more processors are coupled to the one or more sensors and the one or more sensory stimulators. The one or more processors are configured by machine-readable instructions. The one or more processors are configured to control the one or more sensory stimulators based on stimulation parameters. 
     In some embodiments, the one or more sensors comprise one or more electroencephalogram (EEG) electrodes configured to generate the information related to brain activity. In some embodiments, the one or more processors are further configured to detect deep sleep in the subject. In some embodiments, the one or more processors are configured to determine that the subject has remained in deep sleep for a continuous threshold amount of time during the sleep session. In some embodiments, the one or more processors are further configured to estimate the likelihood of sleep micro-arousals. 
     In some embodiments, detecting deep sleep comprises causing a deep learning algorithm to be trained based on the information related to the brain activity of the subject, as captured by the EEG electrodes. In some embodiments, based on the output signals, the trained deep learning algorithm may determine periods when the subject is experiencing deep sleep during the sleep session. The trained deep learning algorithm comprises an input layer, an output layer, and one or more intermediate layers between the input layer and the output layer. 
     In some embodiments, the one or more processors are configured such that, once deep sleep is detected and the likelihood of sleep micro-arousals is below a threshold, the processors apply stimulations to the subject. In some embodiments, the stimulations may be repeating vibrations, constant vibration, repeating light pulses, constant light stimulation, and/or other repeating or constant stimulations. In some embodiments, repeating stimulations are separated from one another by a constant interval. In some embodiments, the intensity of the stimulations is based upon the depth of sleep. 
     In some embodiments, the one or more processors are configured to detect slow wave activity in the subject during the sleep session. The one or more processors may determine an increase in slow wave activity of the subject throughout the sleep session, where the increase is caused by the sensory stimulation provided to the subject. The increase in slow wave activity is determined based on a baseline model and a personalized model. In some embodiments, the baseline model may describe increases in slow wave activity in a population of subjects (e.g., an age-matched population) as a function of sensory stimulation provided to the population of subjects. In some embodiments, the personalized model may utilize slow wave activity for the subject measured during prior sleep sessions in which the sensory stimulation was provided to the subject, as well as information from the baseline model. In some embodiments, the personalized model may be modified based on the baseline model and the slow wave activity as measured by the one or more sensors during the sleep session. 
     In some embodiments, the one or more processors may provide personalized measurements to the subject following the sleep session based on the baseline model and the modified personalized model. In some embodiments, the sleep quality feedback may comprise a “boost” calculation, which indicates the sleep quality benefit derived from receiving the stimulations during the sleep sessions. In some embodiments, the boost calculation comprises information about slow wave activity enhancement for the age matched population of subjects and information about slow wave activity enhancement for the subject. In some embodiments, the sleep quality feedback may also comprise a score that accounts for other sleep factors. The sleep quality feedback may combine the score and the boost in order to provide the subject with overall quantitative sleep quality feedback for the sleep session. 
     These and other objects, features, and characteristics of the present disclosure, as well as the methods of operation and functions of the related elements of structure and the combination of parts and economies of manufacture, will become more apparent upon consideration of the following description and the appended claims with reference to the accompanying drawings, all of which form a part of this specification, wherein like reference numerals designate corresponding parts in the various figures. It is to be expressly understood, however, that the drawings are for the purpose of illustration and description only and are not intended as a definition of the limits of the disclosure. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a schematic illustration of a system configured to deliver sensory stimulation to a subject during a sleep session, in accordance with one or more embodiments. 
         FIG. 2  illustrates several of the operations performed by the system, in accordance with one or more embodiments. 
         FIG. 3  illustrates slow wave activity enhancement for a subject during a sleep session, in accordance with one or more embodiments. 
         FIG. 4  illustrates contributing factors for sleep quality feedback, in accordance with one or more embodiments. 
         FIG. 5  illustrates components of a sleep boost calculation, in accordance with one or more embodiments. 
         FIG. 6  illustrates a method for measuring slow wave activity of a subject during a sleep session, in accordance with one or more embodiments. 
     
    
    
     DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENTS 
     As used herein, the singular form of “a”, “an”, and “the” include plural references unless the context clearly dictates otherwise. As used herein, the term “or” means “and/or” unless the context clearly dictates otherwise. As used herein, the statement that two or more parts or components are “coupled” shall mean that the parts are joined or operate together either directly or indirectly, i.e., through one or more intermediate parts or components, so long as a link occurs. As used herein, “directly coupled” means that two elements are directly in contact with each other. As used herein, “fixedly coupled” or “fixed” means that two components are coupled to move as one while maintaining a constant orientation relative to each other. 
     Directional phrases used herein, such as, for example and without limitation, top, bottom, left, right, upper, lower, front, back, and derivatives thereof, relate to the orientation of the elements shown in the drawings and are not limiting upon the claims unless expressly recited therein. 
       FIG. 1  is a schematic illustration of a system  10  configured to measure slow wave activity of a subject  12  during a sleep session. System  10  is configured to measure an increase in slow wave activity of the subject during the sleep session based on a baseline model and a personalized model to provide the subject with sleep quality feedback and/or for other purposes. System  10  is configured such that sensory stimulation, which may include auditory, haptic, light, and/or other stimulation, is delivered during sleep. In some embodiments, the stimulation is only delivered to the subject when processors in system  10  (described below) have determined that subject  12  is in deep sleep and that the likelihood of micro-arousals is low (e.g., below a threshold). In some embodiments, system  10  delivers stimulations to subject  12  (e.g., vibrations and/or light pulses). In some embodiments, the stimulations may be repeating stimulations (e.g., repeating vibrations and/or repeating light pulses) and/or constant stimulations delivered to the subject for the duration of the deep sleep period. As described herein, the one or more processors may adjust the intensity of the stimulations based on the depth of sleep (i.e., as sleep becomes deeper the one or more processors increase the intensity of the stimulations). The one or more processors may then determine an increase in slow wave activity caused by the sensory stimulations provided to subject  12 . The increase may be based upon the baseline model and the personalized model. In some embodiments, the baseline model describes increases in slow wave activity in an age matched population of subjects as a function of sensory stimulation provided to the age matched population of subjects. In some embodiments, the personalized model utilizes and/or is based on slow wave activity for subject  12  measured during prior sleep sessions in which the sensory stimulation was provided to subject  12 . In some embodiments, the one or more processors may modify the personalized model based on the baseline model and the slow wave activity as measured by the one or more sensors during the sleep session. The one or more processors may then use the modified personalized model subsequently (e.g., later in the sleep session and/or in subsequent sleep sessions). 
     Providing accurate sleep quality feedback to subject  12  which accounts for enhancement due to sensory stimulation is important to subject  12 &#39;s understanding of their sleep quality. The use of sleep quality information for populations of subject (e.g., age-matched populations) may be accurate for certain subjects but may fail to accurately represent sleep quality for other subjects. The combination of a baseline model (i.e., accounting for sleep information for the age-matched population) with a personalized model (i.e., accounting for sleep information for the subject) allows for more accurate sleep quality feedback for different subjects. The use of a baseline model additionally removes (or at least reduces) the need for a calibration period in which the system learns the habits of the subject and does not provide sleep quality feedback. As shown in  FIG. 1 , system  10  includes one or more of a sensor  14 , a sensory stimulator  16 , external resources  18 , a processor  20 , electronic storage  22 , a subject interface  24 , and/or other components. These components are further described below. 
     Sensor  14  is configured to generate output signals conveying information related to sleep stages of subject  12  during a sleep session. The output signals conveying information related to sleep stages of subject  12  may include information related to brain activity in subject  12 . As such, sensor  14  is configured to generate output signals conveying information related to brain activity. In some embodiments, sensor  14  is configured to generate output signals conveying information related to stimulation provided to subject  12  during sleep sessions. In some embodiments, the information in the output signals from sensor  14  is used to control sensory stimulator  16  to provide sensory stimulation to subject  12  (as described below). 
     Sensor  14  may comprise one or more sensors that generate output signals that convey information related to brain activity in subject  12  directly. For example, sensor  14  may include electroencephalogram (EEG) electrodes configured to detect electrical activity along the scalp of subject  12  resulting from current flows within the brain of subject  12 . Sensor  14  may comprise one or more sensors that generate output signals conveying information related to brain activity of subject  12  indirectly. For example, one or more sensors  14  may comprise a heart rate sensor that generates an output based on a heart rate of subject  12  (e.g., sensor  14  may be a heart rate sensor than can be located on the chest of subject  12 , and/or be configured as a bracelet on a wrist of subject  12 , and/or be located on another limb of subject  12 ), movement of subject  12  (e.g., sensor  14  may comprise an accelerometer that can be carried on a wearable, such as a bracelet around the wrist and/or ankle of subject  12  such that sleep may be analyzed using actigraphy signals), respiration of subject  12 , and/or other characteristics of subject  12 . 
     In some embodiments, sensor  14  may comprise one or more of EEG electrodes, a respiration sensor, a pressure sensor, a vital signs camera, a functional near infra-red sensor (fNIR), a temperature sensor, a microphone and/or other sensors configured to generate output signals related to (e.g., the quantity, frequency, intensity, and/or other characteristics of) the stimulation provided to subject  12 , the brain activity of subject  12 , and/or other sensors. Although sensor  14  is illustrated at a single location near subject  12 , this is not intended to be limiting. Sensor  14  may include sensors disposed in a plurality of locations, such as for example, within (or in communication with) sensory stimulator  16 , coupled (in a removable manner) with clothing of subject  12 , worn by subject  12  (e.g., as a headband, wristband, etc.), positioned to point at subject  12  while subject  12  sleeps (e.g., a camera that conveys output signals related to movement of subject  12 ), coupled with a bed and/or other furniture where subject  12  is sleeping, and/or in other locations. 
     In  FIG. 1 , sensor  14 , sensory stimulator  16 , processor  20 , electronic storage  22 , and subject interface  24  are shown as separate entities. This is not intended to be limiting. Some and/or all of the components of system  10  and/or other components may be grouped into one or more singular devices. For example, these and/or other components may be included in a wearable device  201 . In some embodiments, wearable device  201  may be a headset as illustrated in  FIG. 2  and/or other garments worn by subject  12 . Other garments may include a cap, vest, bracelet, and/or other garment. In some embodiments, wearable device  201  may comprise one or more sensors which may contact the skin of the subject. In some embodiments, wearable device  201  may comprise one or more sensory stimulators, which may provide visual, somatosensory, and or auditory stimulation. For example, wearable device  201  and/or other garments may include, for example, sensing electrodes, a reference electrode, one or more devices associated with an EEG, means to deliver auditory stimulation (e.g., a wired and/or wireless audio device and/or other devices), and one or more audio speakers. In some embodiments, wearable device  201  may comprise means to delivery visual, somatosensory, electric, magnetic, and/or other stimulation to the subject. In this example, the audio speakers may be located in and/or near the ears of subject  12  and/or in other locations. The reference electrode may be located behind the ear of subject  12 , and/or in other locations. In this example, the sensing electrodes may be configured to generate output signals conveying information related to brain activity of subject  12 , and/or other information. The output signals may be transmitted to a processor (e.g., processor  20  shown in  FIG. 1 ), a computing device (e.g., a bedside laptop) which may or may not include the processor, and/or other devices wirelessly and/or via wires. In some embodiments, the processor may be in electric communication with the one or more sensors and the one or more sensory stimulators. In some embodiments, the processor may be located within wearable device  201  and/or located externally. In this example, acoustic stimulation may be delivered to subject  12  via the wireless audio device and/or speakers. In this example, the sensing electrodes, the reference electrode, and the EEG devices may be represented, for example, by sensor  14  in  FIG. 1 . The wireless audio device and the speakers may be represented, for example, by sensory stimulator  16  shown in  FIG. 1 . In this example, a computing device may include processor  20 , electronic storage  22 , subject interface  24 , and/or other components of system  10  shown in  FIG. 1 . 
     Stimulator  16  is configured to provide sensory stimulation to subject  12 . Sensory stimulator  16  is configured to provide auditory, visual, somatosensory, electric, magnetic, and/or sensory stimulation to subject  12  prior to a sleep session, during a sleep session, and/or at other times. In some embodiments, a sleep session may comprise any period of time when subject  12  is sleeping and/or attempting to sleep. Sleep sessions may include nights of sleep, naps, and/or other sleeps sessions. For example, sensory stimulator  16  may be configured to provide stimuli to subject  12  during a sleep session to enhance EEG signals during deep sleep in subject  12 , and/or for other purposes. 
     Sensory stimulator  16  is configured to affect deep sleep in subject  12  through non-invasive brain stimulation and/or other methods. Sensory stimulator  16  may be configured to affect deep sleep through non-invasive brain stimulation using auditory, electric, magnetic, visual, somatosensory, and/or other sensory stimuli. The auditory, electric, magnetic, visual, somatosensory, and/or other sensory stimulation may include auditory stimulation, visual stimulation, somatosensory stimulation, electrical stimulation, magnetic stimulation, a combination of different types of stimulation, and/or other stimulation. The auditory, electric, magnetic, visual, somatosensory, and/or other sensory stimuli include odors, sounds, visual stimulation, touches, tastes, somatosensory stimulation, haptic, electrical, magnetic, and/or other stimuli. The sensory stimulation may have an intensity, a timing, and/or other characteristics. For example, acoustic tones may be provided to subject  12  to affect deep sleep in subject  12 . The acoustic tones may include one or more series of tones of a determined length (e.g., less than a decisecond, 50 milliseconds, etc.) separated from each other by an interval (e.g., one second). The volume (i.e., the intensity) of individual tones may be modulated based on depth of sleep and/or other factors (as described herein). In some embodiments, the initial volume may be imperceptible, set to a default volume, and/or set by the subject via a subject interface (e.g.,  24 , as shown in  FIG. 1 ). The length of individual tones (e.g., the timing), the interval between tones, the pitch of the tones, and the type of tone may also be adjusted. This example is not intended to be limiting, and the stimulation parameters may vary. 
     Examples of sensory stimulator  16  may include one or more of a sound generator, a speaker, a music player, a tone generator, a vibrator (such as a piezoelectric member, for example) to deliver vibratory stimulation, a coil generating a magnetic field to directly stimulate the brain&#39;s cortex, one or more light generators or lamps, a fragrance dispenser, and/or other devices. In some embodiments, sensory stimulator  16  is configured to adjust the intensity, timing, and/or other parameters of the stimulation provided to subject  12  (e.g., as described below). 
     External resources  18  include sources of information (e.g., databases, websites, etc.), external entities participating with system  10  (e.g., one or more the external sleep monitoring devices, a medical records system of a health care provider, etc.), and/or other resources. In some embodiments, external resources  18  include components that facilitate communication of information, one or more servers outside of system  10 , a network (e.g., the internet), electronic storage, equipment related to Wi-Fi technology, equipment related to Bluetooth® technology, data entry devices, sensors, scanners, computing devices associated with individual subjects, and/or other resources. In some implementations, some or all of the functionality attributed herein to external resources  18  may be provided by resources included in system  10 . External resources  18  may be configured to communicate with processor  20 , subject interface  24 , sensor  14 , electronic storage  22 , sensory stimulator  16 , and/or other components of system  10  via wired and/or wireless connections, via a network (e.g., a local area network and/or the internet), via cellular technology, via Wi-Fi technology, and/or via other resources. 
     Processor  20  is configured to provide information processing capabilities in system  10 . As such, processor  20  may comprise one or more of a digital processor, an analog processor, a digital circuit designed to process information, an analog circuit designed to process information, a state machine, and/or other mechanisms for electronically processing information. Although processor  20  is shown in  FIG. 1  as a single entity, this is for illustrative purposes only. In some embodiments, processor  20  may comprise a plurality of processing units. These processing units may be physically located within the same device (e.g., sensory stimulator  16 , subject interface  24 , etc.), or processor  20  may represent processing functionality of a plurality of devices operating in coordination. In some embodiments, processor  20  may be and/or be included in a computing device such as a desktop computer, a laptop computer, a smartphone, a tablet computer, a server, and/or other computing devices. Such computing devices may run one or more electronic applications having graphical subject interfaces configured to facilitate subject interaction with system  10 . 
     As shown in  FIG. 1 , processor  20  is configured to execute one or more computer program components. The computer program components may comprise software programs and/or algorithms coded and/or otherwise embedded in processor  20 , for example. The one or more computer program components may comprise one or more of an information component  30 , a model component  32 , a control component  34 , a modulation component  36 , and/or other components. Processor  20  may be configured to execute components  30 ,  32 ,  34 , and/or  36  by software; hardware; firmware; some combination of software, hardware, and/or firmware; and/or other mechanisms for configuring processing capabilities on processor  20 . 
     It should be appreciated that although components  30 ,  32 ,  34 , and  36  are illustrated in  FIG. 1  as being co-located within a single processing unit, in embodiments in which processor  20  comprises multiple processing units, one or more of components  30 ,  32 ,  34 , and/or  36  may be located remotely from the other components. The description of the functionality provided by the different components  30 ,  32 ,  34 , and/or  36  described below is for illustrative purposes, and is not intended to be limiting, as any of components  30 ,  32 ,  34 , and/or  36  may provide more or less functionality than is described. For example, one or more of components  30 ,  32 ,  34 , and/or  36  may be eliminated, and some or all of its functionality may be provided by other components  30 ,  32 ,  34 , and/or  36 . As another example, processor  20  may be configured to execute one or more additional components that may perform some or all of the functionality attributed below to one of components  30 ,  32 ,  34 , and/or  36 . 
     Information component  30  is configured to determine one or more brain activity parameters of subject  12 , and/or other information. The brain activity parameters are determined based on the output signals from sensor  14  and/or other information. The brain activity parameters indicate depth of sleep in subject  12 . In some embodiments, the information in the output signals related to brain activity indicates sleep depth over time. In some embodiments, the information indicating sleep depth over time is or includes information related to deep sleep in subject  12 . 
     In some embodiments, the information indicating sleep depth over time may be indicative of other sleep stages of subject  12 . For example, the sleep stages of subject  12  may be associated with deep sleep, rapid eye movement (REM) sleep, and/or other sleep. Deep sleep may be stage N3, and/or other deep sleep stages. In some embodiments, the sleep stages of subject  12  may be one or more of stage S1, S2, S3, or S4. In some embodiments, NREM stage 2 and/or 3 (and/or S3 and/or S4) may be slow wave (e.g., deep) sleep. In some embodiments, the information that indicates sleep depth over time is and/or is related to one or more additional brain activity parameters. 
     In some embodiments, the information related to brain activity that indicates sleep depth over time is and/or includes EEG information and/or other information generated during sleep sessions of subject  12  and/or at other times. In some embodiments, brain activity parameters may be determined based on the EEG information and/or other information. In some embodiments, the brain activity parameters may be determined by information component  30  and/or other components of system  10 . In some embodiments, the brain activity parameters may be previously determined and be part of the historical sleep stage information obtained from external resources  18  (described below). In some embodiments, the one or more brain activity parameters are and/or are related to a frequency, amplitude, phase, presence of specific sleep patterns such as eye movements, ponto-geniculo-occipital (PGO) wave, slow wave, and/or other characteristics of an EEG signal. In some embodiments, the one or more brain activity parameters are determined based on the frequency, amplitude, and/or other characteristics of the EEG signal. In some embodiments, the determined brain activity parameters and/or the characteristics of the EEG may be and/or indicate sleep stages that correspond to the deep sleep stage described above. 
     Information component  30  is configured to obtain historical sleep stage information. In some embodiments, the historical sleep stage information is for subject  1  and/or other subjects. The historical sleep stage information is related to brain activity, and/or other physiological of the population of subjects and/or subject  12  that indicates sleep stages over time during previous sleep sessions of the population of subjects and/or subject  12 . The historical sleep stage information is related to sleep stages and/or other brain parameters of the population of subjects and/or subject  12  during corresponding sleep sessions, and/or other information. 
     In some embodiments, information component  30  is configured to obtain the historical sleep stage information electronically from external resources  18 , electronic storage  22 , and/or other sources of information. In some embodiments, obtaining the historical sleep stage information electronically from external resources  18 , electronic storage  22 , and/or other sources of information comprises querying one more databases and/or servers; uploading information and/or downloading information, facilitating subject input, sending and/or receiving emails, sending and/or receiving text messages, and/or sending and/or receiving other communications, and/or other obtaining operations. In some embodiments, information component  30  is configured to aggregate information from various sources (e.g., one or more of the external resources  18  described above, electronic storage  22 , etc.), arrange the information in one or more electronic databases (e.g., electronic storage  22 , and/or other electronic databases), normalize the information based on one or more features of the historical sleep stage information (e.g., duration of sleep sessions, number of sleep sessions, number of sleep disruptions, duration of various sleep stages, and/or sleep time regularity etc.) and/or perform other operations. 
     Model component  32  is configured such that a trained deep learning algorithm and/or any other supervised machine deep learning algorithms are caused to detect deep sleep in subject  12 . In some embodiments, this may be and/or include determining periods when subject  12  is experiencing deep sleep during the sleep session and/or other operations. The determined deep sleep, and/or timing, indicates whether subject  12  is in deep sleep for stimulation and/or other information. By way of a non-limiting example, a trained deep learning algorithm may be caused to determine deep sleep stages and/or timing of the deep sleep stages for the subject based on the output signals (e.g., using the information in the output signals as input for the model) and/or other information. In some embodiments, model component  32  is configured to provide the information in the output signals to the deep learning algorithm in temporal sets that correspond to individual periods during the sleep session. In some embodiments, model component  32  is configured to cause the trained deep learning algorithm to output the determined sleep stages of deep sleep for subject  12  during the sleep session based on the temporal sets of information. (The functionality of model component  32  is further discussed below relative to  FIG. 2-3 .) 
     As an example, deep learning algorithms may be a deep neural network. A deep neural network may be based on a large collection of neural units (or artificial neurons). Deep learning algorithms may loosely mimic the manner in which a biological brain works (e.g., via large clusters of biological neurons connected by axons). Each neural unit of a deep learning algorithm may be connected with many other neural units of the deep learning algorithm. Such connections can be enforcing or inhibitory in their effect on the activation state of connected neural units. In some embodiments, each individual neural unit may have a summation function that combines the values of all its inputs together. In some embodiments, each connection (or the neural unit itself) may have a threshold function such that a signal must surpass the threshold before it is allowed to propagate to other neural units. These deep learning algorithm systems may be self-learning and trained, rather than explicitly programmed, and can perform significantly better in certain areas of problem solving, as compared to traditional computer programs. In some embodiments, deep learning algorithms may include multiple layers (e.g., where a signal path traverses from front layers to back layers). In some embodiments, back propagation techniques may be utilized by the deep learning algorithms, where forward stimulation is used to reset weights on the “front” neural units. In some embodiments, stimulation and inhibition for deep learning algorithms may be more free flowing, with connections interacting in a more chaotic and complex fashion. 
     As described above, a trained deep neural network may comprise one or more intermediate or hidden layers. The intermediate layers of the trained deep neural network include one or more convolutional layers, one or more recurrent layers, and/or other layers of the trained deep learning algorithm. Individual intermediate layers receive information from another layer as input and generate corresponding outputs. The detected sleep stages of deep sleep are generated based on the information in the output signals from sensor  14  as processed by the layers of the deep learning algorithm. 
     Control component  34  is configured to control stimulator  16  to provide stimulation to subject  12  during sleep and/or at other times. Control component  34  is configured to cause sensory stimulator  16  to provide the sensory stimulation to subject  12  during deep sleep to affect deep sleep in subject  12  during a sleep session. Control component  34  is configured to cause sensory stimulator  16  to provide sensory stimulation to subject  12  based on a detected deep sleep stage (e.g., the output from model component  32 ) and/or other information. Control component  34  is configured to cause sensory stimulator  16  to provide the sensory stimulation to subject  12  based on the detected deep sleep stage and/or other information over time during the sleep session. Control component  34  is configured to cause sensory stimulator  16  to provide sensory stimulation to subject  12  responsive to subject  12  being in, or likely being in, deep sleep for stimulation. For example, control component  34  is configured such that controlling one or more sensory stimulators  16  to provide the sensory stimulation to subject  12  during the deep sleep to affect the deep sleep in subject  12  during the sleep session comprises: determining the periods when subject  12  is experiencing deep sleep, causing one or more sensory stimulators  16  to provide the sensory stimulation to subject  12  during the periods when subject  12  is experiencing deep sleep, and/or causing one or more sensory stimulators  16  to modulate (e.g., as described herein), an amount, a timing, and/or intensity of the sensory stimulation provided to subject  12  based on the one or more values of the one or more intermediate layers. In some embodiments, stimulators  16  are controlled by control component  34  to affect deep sleep through (e.g., peripheral auditory, magnetic, electrical, and/or other) stimulation delivered during deep sleep (as described herein). 
     In some embodiments, control component  34  is configured to control sensory stimulator  16  to deliver sensory stimulation to subject  12  responsive to model component  32  determining that subject  12  has remained in deep sleep for a continuous threshold amount of time during the sleep session. For example, model component  32  and/or control component  34  may be configured such that on detection of deep sleep, model component  32  starts a (physical or virtual) timer configured to track the time subject  12  spends in deep sleep. Control component  34  is configured to deliver auditory stimulation responsive to the duration that subject  12  spends in continuous deep sleep breaching a predefined duration threshold. In some embodiments, the predefined duration threshold is determined at manufacture of system  10  and/or at other times. In some embodiments, the predefined duration threshold is determined based on information from previous sleep sessions of subject  12  and/or subjects demographically similar to subject  12  (e.g., as described above). In some embodiments, the predefined duration threshold is adjustable via subject interface  24  and/or other adjustment mechanisms. 
     In some embodiments, the predefined deep sleep duration threshold may be one minute and/or other durations, for example. By way of a non-limiting example, control component  34  may be configured such that auditory stimulation starts once a minute of continuous deep sleep in subject  12  is detected. In some embodiments, once the stimulation begins, control component  34  is configured to control stimulation parameters of the stimulation. Upon detection of a sleep stage transition (e.g., from deep sleep to some other sleep stage), control component  34  is configured to stop stimulation. Modulation component  36  is configured to cause sensory stimulator  16  to modulate an amount, a timing, and/or intensity of the sensory stimulation. Modulation component  36  is configured to cause sensory stimulator  16  to modulate the amount, timing, and/or intensity of the sensory stimulation based on the brain activity parameters, values output from the intermediate layers of the trained deep learning algorithm, and/or other information. As an example, sensory stimulator  16  is caused to modulate the timing and/or intensity of the sensory stimulation based on the brain activity parameters, the values output from the convolutional layers, the values output from the recurrent layers, and/or other information. For example, modulation component  36  may be configured such that sensory stimulation is delivered with an intensity that is proportional to a predicted probability value (e.g., an output from an intermediate layer of a deep learning algorithm) of a particular sleep stage (e.g., deep sleep). In this example, the higher the probability of deep sleep, the more likely the stimulation continues. If sleep micro-arousals are detected and the sleep stage remains in deep sleep, modulation component  36  may be configured such that the intensity of the stimulation is decreased (by for instance five dBs) responsive to individual micro-arousal detections. 
     By way of a non-limiting example,  FIG. 2  illustrates several of the operations performed by system  10  and described above. In the example shown in system  200  of  FIG. 2 , an EEG signal  202  is processed and/or otherwise provided (e.g., by information component  30  and model component  32  shown in  FIG. 1 ) to a deep learning algorithm  206  in temporal windows  204 . Deep learning algorithm  206  detects sleep stages (e.g., N3, N2, N1, REM, and wakefulness). Determination  210  indicates whether the subject is in deep (N3) sleep. Deep learning algorithm  206  may determine the sleep stage of the subject using methods described in the publication “Recurrent Deep Neural Networks for Real-Time Sleep Stage Classification From Single Channel EEG.”  Frontiers in Computational Neuroscience . Bresch, E., Großekathöfer, U., and Garcia-Molina, G. (2018), which is hereby incorporated by reference in its entirety. 
     As shown in  FIG. 2 , deep learning algorithm  206  outputs soft prediction probabilities  208 . Soft prediction probabilities  208  are prediction probabilities for individual sleep stages. The set of soft prediction probabilities  208  constitute a so-called soft decision vector, which may be translated into a hard decision by determining which sleep stage is associated with a highest probability value (in a continuum of possible values) relative to other sleep stages. These soft decisions make it possible for system  10  to consider different possible sleep states on a continuum rather than being forced to decide which discrete sleep stage “bucket” particular EEG information fits into (as in prior art systems). The terms “soft” and “hard” are not intended to be limiting but may be helpful to use to describe the operations performed by the system. For example, the term “soft output” may be used, because at this stage, any decision is possible. Indeed, the final decision could depend on post-processing of the soft outputs, for example. 
     Determination  210  indicates whether deep sleep is detected. If deep sleep is not detected at determination  210 , system  200  returns to processing EEG signal  202  in temporal window  204  by deep learning algorithm  206 . If deep sleep is detected at determination  210 , the one or more sensory stimulators apply sensory stimulation  212  to the subject. As described above, the sensory stimulation may be repeating stimulations (e.g., vibrations, light pulses, etc.) and/or constant stimulations. Repeating stimulations may be separated from one another be a constant interval, and the intensity (i.e., volume, brightness, etc.) of the stimulations may vary based on the depth of sleep. These parameters (e.g., volume and timing  216  and/or other parameters) are calculated based on features  214  of the EEG. For example, features  214  of the EEG may indicate that the subject has been in deep sleep for a threshold period of time and that the likelihood of microarousals is low. The one or more sensory stimulators may then increase the volume as the depth of sleep increases (e.g., increased slow wave activity). In some embodiments, the increase in volume may be proportional to the depth of sleep or may otherwise correspond to the depth of sleep. 
       FIG. 3  illustrates slow wave activity enhancement for a subject during a sleep session. Example  300  shows the effect of sensory stimulations  304  on slow wave activity  306  of the subject. Slow wave activity  306  in graph  302  has a relatively constant amplitude until time zero, when sensory stimulation  304  is applied. Once sensory stimulation  304  is applied, the amplitude of the EEG signal for slow wave activity  306  increases. In example  300 , ten stimulations are applied with a constant one-second interval separating the stimulations. In some embodiments, the stimulations may be 50 milliseconds in length and may have an intensity (e.g., volume, brightness, etc.) that is adjusted based on the depth of sleep. The duration, timing, intensity, and other factors are not limited to example  300  and may vary. In addition, example  300  depicts ten stimulations followed by a period of no stimulations. In some embodiments, the sensory stimulators may cease providing sensory stimulation if the subject exits deep sleep and/or if micro-arousals are detected. In some embodiments, if the subject remains in deep sleep and no micro-arousals are detected, the repeating and/or constant stimulations may continue to be delivered to the subject. 
     Example  350  shows slow wave activity in a subject during a sleep session with sensory stimulation as compared to slow wave activity in the subject during a sleep session without sensory stimulation. In graph  308 , stimulated slow wave activity  310  is enhanced at the tone locations  314 , i.e., the largest increases in slow wave activity occur at tone locations  314 . Additionally, unstimulated slow wave activity  312  is overall lower than stimulated slow wave activity  310 . Graph  316  shows cumulative slow wave activity information for the subject across the same range of times as graph  308 . Tone locations  322  align temporally with tone locations  314 . As shown in graph  316 , cumulative stimulated slow wave activity  318  increases at tone locations  322 . Cumulative stimulated slow wave activity  318  is overall greater than cumulative unstimulated slow wave activity  320 . 
     Returning to  FIG. 1 , model component  32  is configured such that both the values output from convolutional layers, and the soft decision value outputs, are vectors comprising continuous values as opposed to discrete values such as sleep stages. Consequently, convolutional and recurrent (soft decision) value outputs are available to be used by system  10  to modulate the volume of the stimulation when the deep learning algorithm detects occurrences of deep sleep. In addition, as described herein, parameters determined (e.g., by information component  30  shown in  FIG. 1 ) based on the raw sensor output signals (e.g., EEG signals) can be used to modulate stimulation settings. 
     As described above, modulation component  36  is configured to cause sensory stimulator  16  to modulate an amount, timing, and/or intensity of the sensory stimulation. Modulation component  36  is configured to cause sensory stimulator to modulate the amount, timing, and/or intensity of the sensory stimulation based on the one or more brain activity and/or other parameters, values output from the convolutional and/or recurrent layers of the trained deep learning algorithm, and/or other information. As an example, the interval of auditory stimulation provided to subject  12  may be adjusted and/or otherwise controlled (e.g., modulated) based on value outputs from the deep learning algorithm such as convolutional layer value outputs and recurrent layer value outputs (e.g., sleep stage (soft) prediction probabilities). In some embodiments, modulation component  36  is configured to cause one or more sensory stimulators  16  to modulate the amount, timing, and/or intensity of the sensory stimulation, wherein the modulation comprises adjusting the interval, the stimulation intensity, and/or the stimulation frequency, responsive to an indication subject  12  is experiencing one or more micro-arousals. 
     In some embodiments, modulation component  36  is configured to modulate the sensory stimulation based on the brain activity and/or other parameters alone, which may be determined based on the output signals from sensors  14  (e.g., based on a raw EEG signal). In these embodiments, the output of a deep learning algorithm (and/or other machine learning models) continues to be used to detect sleep stages (e.g., as described above). However, the stimulation intensity and timing are instead modulated based on brain activity and/or other parameters or properties determined based on the sensor output signals. In some embodiments, the information in, or determined based on, the sensor output signals can also be combined with intermediate outputs of the network such as output of the convolution layers or the final outputs (soft stages) to modulate intensity and timing (e.g., as described herein). 
       FIG. 4  illustrates contributing factors for sleep quality feedback. Quantitative sleep quality feedback is useful the subject (e.g.,  12 , as shown in  FIG. 1 ) to assess the quality of their sleep and factors which detract from and/or improve sleep quality. Sleep quality feedback may be provided as a part of a sleep enhancement system (e.g., such as the SmartSleep system) and/or separately. In some embodiments, the contributing factors may include sleep architecture factors and cumulative slow wave activity throughout the sleep session. The factors are combined to produce a sleep quality score for the sleep session. 
     System  400  illustrates one method of combining relevant factors to calculate a sleep quality score. System  400  may begin with score  402 . Score  402  may be an initial score (e.g., 100%) which represents a perfect score. In some embodiments, score  402  represents the sleep quality of a perfect night of sleep (e.g., having a sufficiently long duration, with no disruptions, etc.). Other inputs include subject history database  408  and reference database  410 . Subject history database  408  may provide information such as regular bedtimes and wakeup times, as well as typical slow wave activity of the subject (e.g.,  12 , as shown in  FIG. 1 ) across sleep sessions. Reference database  410  may store and/or provide information related to matched populations (e.g., such as gender-matched populations, age-matched populations, and/or other matched populations). In some embodiments, the information related to increases in slow wave activity in the age matched population of subjects is received by reference database  410  and/or is preprogrammed within reference database  410 . Stored information related to the matched populations may include typical sleep architecture information for the matched populations (e.g., bedtimes and wakeup times, slow wave activity of the populations across sleep sessions, and/or other information). In addition, sleep architecture metrics  406  for the subject are also factored into the sleep feedback calculation. Sleep architecture metrics may include information about the slow wave activity, cumulative slow wave activity, duration, disruptions, and regularity of the subject&#39;s sleep session and/or sessions. In some embodiments, sleep architecture metrics  406  may be based upon EEG signals measured by one or more sensors (e.g.,  14 , as shown in  FIG. 1 ). 
     In some embodiments, system  400  may use the information received from sleep architecture metrics  406 , subject history database  408 , and reference database  410  to determine deductions  404 . In some embodiments, deductions  404  are subtracted from sleep score  402 . Deductions  404  may be features identified from sleep architecture metrics  406 , subject history database  408 , and reference database  410  which have a negative impact on sleep quality. Deductions  404  may include total sleep duration, wake after sleep onset, sleep onset latency, number of sleep disruptions, deep sleep duration, REM sleep duration, bedtime regularity, wakeup time regularity, and/or other factors. In some embodiments, each factor has a pre-defined value and/or range of values that is subtracted from sleep score  402  if identified within the subject&#39;s sleep session and/or sessions. In some embodiments, the subject may input values and/or ranges of values for deductions  404  via a subject interface (e.g.,  24 , as shown in  FIG. 1 ). 
     In some embodiments, once deductions  404  have been subtracted from score  402 , the resulting score is combined with a boost calculation  412 . The boost calculation  412  is representative of the improvement to sleep quality of the sleep session and/or sessions that resulted from the sensory stimulations provided to the subject (e.g.,  12 , as shown in  FIG. 1 ). For example, boost calculation  412  may represent increases in cumulative slow wave activity in the subject during the sleep session and/or sessions as a result of the stimulation provided to the subject via sensory stimulators (e.g.,  16 , as shown in  FIG. 1 ). In some embodiments, boost calculation  412  may be based upon a matched population (e.g., age-matched, gender-matched, BMI-matched, and/or other matched population). In some embodiments, boost calculation  412  may be based upon slow wave activity information that is specific to the subject. In some embodiments, boost calculation  412  may be based upon another source of sleep enhancement information. In some embodiments, boost calculation  412  may be based upon any combination of the aforementioned sources. In some embodiments, the one or more processors (e.g.,  20 , as shown in  FIG. 1 ), may convert the slow wave activity information into a boost score (e.g., compatible with score  402  and deductions  404 ). The one or more processors may then combine boost calculation  412  with the combination of score  402  and deductions  404 . The resulting score, sleep quality feedback score  418 , represents negative effects on the subject&#39;s sleep session and/or sessions as well as the positive effects of the sensory stimulation (e.g., via sensory stimulators  16 ). 
     Boost calculation  412  may be calculated using various techniques. These techniques may alter the accuracy of boost calculation  412  and resulting sleep quality feedback score  418 . A technique which combines several sources of sleep quality information, as described in  FIG. 5 , may provide increased accuracy of sleep quality feedback scores for certain subjects. 
       FIG. 5  illustrates components of a sleep boost calculation (e.g., boost calculation  412 , as shown in  FIG. 4 ). System  500  shows the combination of a baseline model  504  (i.e., matched population sleep information) and a personalized model  510  (i.e., specific to the subject). In some embodiments, baseline model  504  may comprise information about increases in the slow wave activity throughout deep sleep for different age ranges as a function of sensory stimulation provided to the age matched population of subjects. In some embodiments, the information related to increases in slow wave activity in the age matched population of subjects is received or preprogrammed. The system may select the age group that corresponds to the subject (e.g.,  12 , as shown in  FIG. 1 ) and may use the associated slow wave activity information for the age group as an approximation of the slow wave activity of the subject during the sleep session. An example of using data from an age matched population of subject to determine an increase in slow wave activity in a subject is described in European Pat. App. Pub. No. 3457411, which is hereby incorporated by reference in its entirety. Baseline model  504  may utilize inputs  502 , which may include cumulative slow wave activity information for a sleep session for the selected age group, a number of stimulations delivered to the subject during the sleep session and/or a duration of stimulations delivered to the subject during the sleep session, deep sleep information (e.g., depth of sleep, duration of deep sleep, and/or other deep sleep factors), and/or additional inputs. The one or more processors (e.g.,  20 , as shown in  FIG. 1 ), may combine inputs  502  to produce a slow wave activity enhancement value for the sleep session according to the baseline model. This slow wave activity enhancement value may be baseline boost  506 . In some embodiments, baseline boost  506  may be used as the value for boost calculation  412 . In some embodiments, baseline boost  506  may be combined with a slow wave activity enhancement value for the sleep session according to the personalized model in order to calculate boost calculation  412 . 
     In some embodiments, a threshold amount of sleep data for the subject (e.g.,  12 , as shown in  FIG. 1 ) is needed before the system can create personalized model  512 . Therefore, system  500  may use initial model  518  to collect and analyze the subject&#39;s initial sleep data. For example, initial model  518  may utilize inputs  516  which include data for a number of sleep sessions. In some embodiments, the number of sleep sessions comprising the data for inputs  516  may be the first several sleep sessions (e.g., the first seven, ten, or another number of sleep sessions) for which the subject is receiving sensory stimulation. In some embodiments, the number of initial sleep sessions may vary. In some embodiments, only valid sleep sessions are included in the initial sleep sessions. In some embodiments, if a pre-determined number of valid sleep sessions is exceeded, only the most recent valid sleep sessions are included. In some embodiments, data collected from the initial sleep sessions may comprise cumulative slow wave activity information for the initial sleep sessions and the duration and/or number of stimulations provided to the subject during the initial sleep sessions. This data may be included in inputs  516 . Baseline boost  506  may also be included in inputs  516 . In some embodiments, initial model  518  may collect inputs  516  until a threshold number of initial sleep sessions is reached, at which time, initial model  518  may generate personalized model  512 . 
     In some embodiments, personalized model  512  may initially utilize the data collected by initial model  518 . The data collected by initial model  518  may comprise baseline boost  506 , cumulative slow wave activity, stimulation information for the initial sleep sessions, and/or other information (i.e., inputs  516 ). Personalized model  512  may combine this data to calculate personalized boost  514 . In some embodiments, personalized model  512  may use various equations to combine inputs  516  to generate personalized boost  514 . In some embodiments, personalized model  512  may use one or more of equations 1 and 2 and/or other equations. 
                     Personalized                 Boost     =       β   0     +       β   1     ×   Tones     +       β   2     ×   CSW                   A   :                 Equation                 1                                β   2     =                     〈     Tones   2     〉     ×     〈     CSW                 A   ×   Boost     〉       -                 〈     Tones   ×   CSW                 A     〉          〈     Tones   ×   Boost     〉                   〈     Tones   2     〉          〈     CSW                   A   2       〉       -       〈     Tones   ×   CSW                 A     〉     2         :     
                     β   1       =                 〈     CSW                   A   2       〉     ×     〈     CSW                 A   ×   Boost     〉       -                 〈     Tones   ×   CSW                 A     〉          〈     CSW                 A   ×   Boost     〉                   〈     Tones   2     〉          〈     CSW                   A   2       〉       -       〈     Tones   ×   CSW                 A     〉     2                  
                       β   0     =       〈   Boost   〉     -       β   1     ×     〈   Tones   〉       -       β   2     ×     〈     CSW                 A     〉                     Equation                 2               where: 
     
       
         
           
             
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     In some embodiments, n may be a number of initial sleep sessions for which initial model  518  must receive data before generating personalized model  512 . In some embodiments, for calculations of the initial sleep sessions, the boost values in equations 1 and 2 are baseline boost values (i.e., baseline boost  506 ), which relate to the matched population. 
     As shown in system  500 , personalized boost  514  may be combined with baseline boost  506  to generate boost  508 . In some embodiments, system  500  may use equation 3 and/or other equations to calculate boost  508 . 
       Boost=(1−λ)×baseline boost+λ×personalized boost  Equation 3:
 
     In some embodiments, lambda may be a constant between zero and one. In some embodiments, lambda controls the relative importance of baseline boost and personalized boost to overall boost. In some embodiments, lambda may be a variable. For example, the system may initially use a small value of lambda (e.g., 0.1) in order to increase the relative importance of baseline boost for an initial number of sleep sessions. Thereafter, the system may increase the value of lambda such that the personalized boost plays an increasingly important role in calculating overall boost. In some embodiments, boost  508  may be used for boost calculation  412 , as shown in  FIG. 4 . 
     In some embodiments, coefficients β 0 , β 1 , and β 2  may be empirically estimated with available data and may be continuously updated as new data becomes available. In some embodiments, system  500  may use equation 4 and/or other equations to continuously update the calculations above. 
       Personalized Boost n+1 =β 0 +β 1 ×Tones n+1  
 
       Boost n+1 =(1−λ)×baseline boost n+1 +λ×personalized boost n+1   Equation 4:
 
     Once data is available for a subsequent sleep session (i.e., each sleep session after the initial sleep sessions), system  500  must update equation 2. In some embodiments, system  500  may use equation 5 and/or other equations to update equation 2. 
         Tones =(1−θ)× Tones +θ×Tones n+1  
 
         CSWA =(1−θ)× CSWA +θ×CSWA n+1  
 
         Boost =(1−θ)× Boost +θ×Boost n+1  
 
         Tones×Boost =(1−θ)× Tones×Boost +θ×Tones n+1 ×Boost n+1  
 
         CSWA×Boost =(1−θ)× CSWA×Boost +θ×CSWA n+1 ×Boost n+1  
 
         Tones×CSWA =(1−θ)× Tones×CSWA +θ×Tones n+1 ×CSWA n+1  
 
         Tones 2   =(1−θ)× Tones 2   +θ×Tones n+1 ×Tones n+1  
 
         CSWA 2   =(1−θ)×CSWA+θ×CSWA n+1 ×CSWA n+1   Equation 5:
 
     In some embodiments, θ is a constant between zero and one which controls the influence of the new data on the updating of personalized model  512 . For example, a higher value of θ leads to a higher influence of the updating of personalized model  512 . In some embodiments, a default value of θ may be 0.1 (e.g., if there are approximately ten valid sleep sessions factored into equation 2). In some embodiments, the value of θ and/or the number of valid sleep sessions factored into equation 2 may vary. 
     Returning to  FIG. 4 , in some embodiments, for sleep sessions falling within the initial sleep sessions, any sleep quality feedback delivered to the subject may utilize baseline boost  506  for boost calculation  412 . In some embodiments, for sleep sessions that occur after the initial sleep sessions, system  400  may utilize boost  508 , which includes boost information for the matched population as well as information specific to the subject, for boost calculation  412 . 
     In some embodiments, the sleep quality feedback score  418  is calculated for each sleep session of the subject. In some embodiments, sleep quality feedback score  418  is calculated for several sleep sessions of the subject. In some embodiments, sleep quality feedback score  418  represents the subject&#39;s sleep quality over time and may be updated with information from each new sleep session. In some embodiments, sleep quality feedback score  418  is provided to the subject after each sleep session. Sleep quality feedback score  418  may be provided to the subject via the same device that is used to provide sensory stimulation to the subject (e.g., headset  201 , as shown in  FIG. 2 ). In some embodiments, sleep quality feedback score  418  is provided to the subject via a separate application (e.g., on a mobile phone, tablet, computer, etc.). In some embodiments, sleep quality feedback score  418  may be delivered to the subject as a message (e.g., via text message or email). Sleep quality feedback score  418  may be provided to the subject using any combination of the aforementioned methods and/or other methods. 
     In some embodiments, the one or more processors (e.g.,  20 , as shown in  FIG. 1 ) may be configured to control the one or more sensory stimulators (e.g.,  16 , as shown in  FIG. 1 ) based on baseline model  504  and updated personalized model  512 . For example, if sleep feedback score  418  indicates poor sleep quality, the one or more processors may adjust stimulation parameters (e.g., by increasing the intensity) for the sensory stimulation. The sensory stimulators may then deliver the sensory stimulation to the subject (e.g., in a subsequent sleep session) according to the adjusted stimulation parameters. 
     Returning to  FIG. 1 , electronic storage  22  comprises electronic storage media that electronically stores information. The electronic storage media of electronic storage  22  may comprise one or both of system storage that is provided integrally (i.e., substantially non-removable) with system  10  and/or removable storage that is removably connectable to system  10  via, for example, a port (e.g., a USB port, a firewire port, etc.) or a drive (e.g., a disk drive, etc.). Electronic storage  22  may comprise one or more of optically readable storage media (e.g., optical disks, etc.), magnetically readable storage media (e.g., magnetic tape, magnetic hard drive, floppy drive, etc.), electrical charge-based storage media (e.g., EPROM, RAM, etc.), solid-state storage media (e.g., flash drive, etc.), cloud storage, and/or other electronically readable storage media. Electronic storage  22  may store software algorithms, information determined by processor  20 , information received via subject interface  24  and/or external computing systems (e.g., external resources  18 ), and/or other information that enables system  10  to function as described herein. Electronic storage  22  may be (in whole or in part) a separate component within system  10 , or electronic storage  22  may be provided (in whole or in part) integrally with one or more other components of system  10  (e.g., processor  20 ). 
     Subject interface  24  is configured to provide an interface between system  10  and subject  12 , and/or other subjects through which subject  12  and/or other subjects may provide information to and receive information from system  10 . This enables data, cues, results, and/or instructions and any other communicable items, collectively referred to as “information,” to be communicated between a subject (e.g., subject  12 ) and one or more of sensor  14 , sensory stimulator  16 , external resources  18 , processor  20 , and/or other components of system  10 . For example, a hypnogram, EEG data, deep sleep stage probability, and/or other information may be displayed for subject  12  or other subjects via subject interface  24 . As another example, subject interface  24  may be and/or be included in a computing device such as a desktop computer, a laptop computer, a smartphone, a tablet computer, and/or other computing devices. Such computing devices may run one or more electronic applications having graphical subject interfaces configured to provide information to and/or receive information from subjects. 
     Examples of interface devices suitable for inclusion in subject interface  24  comprise a keypad, buttons, switches, a keyboard, knobs, levers, a display screen, a touch screen, speakers, a microphone, an indicator light, an audible alarm, a printer, a tactile feedback device, and/or other interface devices. In some embodiments, subject interface  24  comprises a plurality of separate interfaces. In some embodiments, subject interface  24  comprises at least one interface that is provided integrally with processor  20  and/or other components of system  10 . In some embodiments, subject interface  24  is configured to communicate wirelessly with processor  20  and/or other components of system  10 . 
     It is to be understood that other communication techniques, either hard-wired or wireless, are also contemplated by the present disclosure as subject interface  24 . For example, the present disclosure contemplates that subject interface  24  may be integrated with a removable storage interface provided by electronic storage  22 . In this example, information may be loaded into system  10  from removable storage (e.g., a smart card, a flash drive, a removable disk, etc.) that enables the subject(s) to customize the implementation of system  10 . Other exemplary input devices and techniques adapted for use with system  10  as subject interface  24  comprise, but are not limited to, an RS-232 port, RF link, an IR link, modem (telephone, cable or other). In short, any technique for communicating information with system  10  is contemplated by the present disclosure as subject interface  24 . 
       FIG. 6  illustrates method  600  for measuring slow wave activity of a subject during a sleep session. The system comprises one or more sensors, one or more sensory stimulators, one or more processors configured by machine-readable instructions, and/or other components. The one or more processors are configured to execute computer program components. The computer program components comprise an information component, a model component, a control component, a modulation component, and/or other components. The operations of method  600  presented below are intended to be illustrative. In some embodiments, method  600  may be accomplished with one or more additional operations not described, and/or without one or more of the operations discussed. Additionally, the order in which the operations of method  600  are illustrated in  FIG. 6  and described below is not intended to be limiting. 
     In some embodiments, method  600  may be implemented in one or more processing devices such as one or more processors  20  described herein (e.g., a digital processor, an analog processor, a digital circuit designed to process information, an analog circuit designed to process information, a state machine, and/or other mechanisms for electronically processing information). The one or more processing devices may include one or more devices executing some or all of the operations of method  600  in response to instructions stored electronically on an electronic storage medium. The one or more processing devices may include one or more devices configured through hardware, firmware, and/or software to be specifically designed for execution of one or more of the operations of method  600 . 
     At an operation  602 , output signals conveying information related to brain activity of the subject during the sleep session are generated. The output signals are generated during a sleep session of the subject and/or at other times. In some embodiments, operation  602  is performed by sensors the same as or similar to sensors  14  (shown in  FIG. 1  and described herein). 
     In some embodiments, operation  602  includes providing the information in the output signals to the deep learning algorithm in temporal sets that correspond to individual periods of time during the sleep session. In some embodiments, operation  602  includes causing the trained deep learning algorithm to output the detected deep sleep for the subject during the sleep session based on the temporal sets of information. In some embodiments, operation  602  is performed by a processor component the same as or similar to model component  32  (shown in  FIG. 1  and described herein). 
     At an operation  604 , slow wave activity is detected in the subject during a sleep session based on the output signals. In some embodiments, the slow wave activity indicates a sleep stage (e.g., N3, N2, N1, REM, or wakefulness). If the slow wave activity of the subject indicates deep sleep (e.g., N3 sleep stage), the one or more processors may control the one or more sensory stimulators to provide sensory stimulation to the subject during the deep sleep. In some embodiments, the one or more processors may determine that the subject has been in deep sleep for a threshold amount of time before controlling the sensory stimulators to provide sensory stimulation. In some embodiments, the one or more processors may determine that the likelihood of microarousals is below a threshold before controlling the sensory stimulators to provide sensory stimulation. In some embodiments, operation  604  is performed by a processor component the same as or similar to control component  34  (shown in  FIG. 1  and described herein). 
     At an operation  606 , sensory stimulation is delivered to the subject to increase the slow wave activity. In some embodiments, the sensory stimulation may be in the form of auditory vibrations, haptic vibrations, light pulses, and/or another type of sensory stimulation. In some embodiments, the sensory stimulation may be provided to the subject as repeating stimulations with a constant interval between stimulations and/or constant stimulations. In some embodiments, the sensory stimulators may vary the intensity of the stimulations based on the depth of sleep (e.g., as detected by the one or more sensors). In some embodiments, the parameters (e.g., amount, timing, intensity, etc.) may be modulated by the sensory stimulators (e.g.,  16 , as shown in  FIG. 1 ). In some embodiments, operation  606  is performed by a processor component the same as or similar to modulation component  36  (shown in  FIG. 1  and described herein). 
     At an operation  608 , baseline information is obtained via a baseline model, wherein the baseline information is related to a baseline slow wave activity increase derived from sensory stimulation provided to an age matched population of subject. The baseline information may comprise information about increases in the slow wave activity during deep sleep for different age ranges as a function of sensory stimulation provided to the age matched population of subjects. In some embodiments, the information related to increases in slow wave activity in the age matched population of subjects is received or preprogrammed. The system may select the age group that corresponds to the subject. In some embodiments, operation  608  is performed by a processor component the same as or similar to control component  34  (shown in  FIG. 1  and described herein). 
     At an operation  610 , a personalized model is generated, where the personalized model is based on the baseline information obtained via the baseline model, an amount of sensory stimulation delivered to the subject, and an amount of slow wave activity of the subject during prior sleep sessions in which sensory stimulation was provided to the subject. In some embodiments, the personalized model is configured to provide personalized information related to a slow wave activity increase derived from the sensory stimulation provided to the subject. In some embodiments, operation  610  is performed by a processor component the same as or similar to control component  34  (shown in  FIG. 1  and described herein. 
     In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word “comprising” or “including” does not exclude the presence of elements or steps other than those listed in a claim. In a device claim enumerating several means, several of these means may be embodied by one and the same item of hardware. The word “a” or “an” preceding an element does not exclude the presence of a plurality of such elements. In any device claim enumerating several means, several of these means may be embodied by one and the same item of hardware. The mere fact that certain elements are recited in mutually different dependent claims does not indicate that these elements cannot be used in combination. 
     Although the description provided above provides detail for the purpose of illustration based on what is currently considered to be the most practical and preferred embodiments, it is to be understood that such detail is solely for that purpose and that the disclosure is not limited to the expressly disclosed embodiments, but, on the contrary, is intended to cover modifications and equivalent arrangements that are within the spirit and scope of the appended claims. For example, it is to be understood that the present disclosure contemplates that, to the extent possible, one or more features of any embodiment can be combined with one or more features of any other embodiment.