Application US20200306494

ENHANCING DEEP SLEEP BASED ON INFORMATION FROM FRONTAL BRAIN ACTIVITY MONITORING SENSORS
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US20200306494

1. A system configured to enhance non-rapid eye movement (NREM) sleep by delivering sensory stimulation to a subject during a sleep session, the system comprising: (4) (4)

first and second sensors configured to generate output signals conveying information related to brain activity of the subject during the sleep session, the first and second sensors configured to engage a forehead of the subject;

one or more sensory stimulators configured to provide the sensory stimulation to the subject during the sleep session; and

one or more hardware processors coupled to the first and second sensors and the one or more sensory stimulators, the one or more hardware processors configured by machine-readable instructions to:

detect NREM sleep in the subject during the sleep session based on the output signals from the first and second sensors, and control the one or more sensory stimulators to provide the sensory stimulation to the subject during the NREM sleep to enhance the NREM sleep in the subject during the sleep session.

2. The system of claim 1, wherein the first and second sensors are configured to engage the forehead of the subject at a distance of less than or equal to 10 centimeters from each other (0)

3. The system of claim 1 wherein the first sensor comprises a mid-frontal (FPz) electrode, and the second sensor comprises a right ocular electrode (EOGR) or a left ocular electrode (EOGL). (0)

4. The system of claim 1, wherein either the first sensor or the second sensor is a reference electrode. (0)

5. The system of claim 1, wherein the one or more hardware processors are further configured by machine-readable instructions to: (3) (5)

obtain historical sleep depth information for a population of users, the historical sleep depth information being related to brain activity of the population of users that indicates sleep depth over time during sleep sessions of the population of users;

cause a neural network to be trained based on the historical sleep depth information by providing the historical sleep depth information as input to the neural network;

cause, based on the output signals from the first and second sensors, the trained neural network to predict future times during the sleep session at which the subject will be in a deep sleep stage, the trained neural network comprising an input layer, an output layer, and one or more intermediate layers between the input layer and the output layer;

determine, with respect to each of the future times, a predicted sleep stage generated by the output layer of the trained neural network, and sleep stage probability values generated by the one or more intermediate layers of the trained neural network; and

responsive to the predicted sleep stage being N3, or the predicted sleep stage being N2 with a ratio of a probability of N3 sleep to a probability of N2 sleep being at least 0.5, cause the one or more sensory stimulators to provide the sensory stimulation to the subject at the future times and to modulate a timing and/or intensity of the sensory stimulation during the sleep session based on the one or more probability values generated by the one or more intermediate layers.

6. The system of claim 5, wherein the one or more hardware processors are further configured by machine-readable instructions to detect sleep micro-arousals based on the information in the output signals from the first and second sensors, and control, based on the detected sleep micro-arousals, the one or more sensory stimulators to provide the sensory stimulation to the subject during N3 sleep to enhance the N3 sleep in the subject during the sleep session, wherein a sleep micro-arousal is detected: (0) (1)

7. The system of claim 5, wherein the one or more hardware processors are further configured by machine-readable instructions to detect slow waves based on the information in the output signals from the first and second sensors, and control, based on the detected slow waves, the one or more sensory stimulators to provide the sensory stimulation to the subject during N3 sleep to enhance the N3 sleep in the subject during the sleep session, wherein a slow wave is detected: (0) (3)

8. The system of claim 5, wherein the one or more hardware processors are further configured by machine-readable instructions: (0) (3)

9. A method for enhancing non-rapid eye movement (NREM) sleep by delivering sensory stimulation to a subject during a sleep session with an enhancement system, the system comprising first and second sensors, one or more sensory stimulators, and one or more hardware processors coupled to the first and second sensors and the one or more sensory stimulators, the method comprising: (3) (3)

generating, with the first and second sensors, output signals conveying information related to brain activity of the subject during the sleep session, the first and second sensors configured to engage a forehead of the subject;

detecting, with the one or more hardware processors, NREM sleep in the subject during the sleep session based on the output signals from the first and second sensors; and

controlling, with the one or more processors, the one or more sensory stimulators to provide the sensory stimulation to the subject during the NREM sleep to enhance the NREM sleep in the subject during the sleep session.

10. The method of claim 9, wherein the first sensor comprises a mid-frontal (FPz) electrode, and the second sensor comprises a right ocular electrode (EOGR) or a left ocular electrode (EOGL). (0)

11. The method of claim 9, wherein either the first sensor or the second sensor is a reference electrode. (0)

12. The method of claim 9, further comprising: (3) (5)

obtaining, with the one or more hardware processors, historical sleep depth information for a population of users, the historical sleep depth information being related to brain activity of the population of users that indicates sleep depth over time during sleep sessions of the population of users;

causing, with the one or more hardware processors, a neural network to be trained based on the historical sleep depth information by providing the historical sleep depth information as input to the neural network;

causing, with the one or more hardware processors, based on the output signals from the first and second sensors, the trained neural network to predict future times during the sleep session at which the subject will be in a deep sleep stage, the trained neural network comprising an input layer, an output layer, and one or more intermediate layers between the input layer and the output layer;

determining, with the one or more hardware processors, with respect to each of the future times, a predicted sleep stage generated by the output layer of the trained neural network, and sleep stage probability values generated by the one or more intermediate layers of the trained neural network; and

responsive to the predicted sleep stage being N3, or the predicted sleep stage being N2 with a ratio of a probability of N3 sleep to a probability of N2 sleep being at least 0.5, causing, with the one or more hardware processors, the one or more sensory stimulators to provide the sensory stimulation to the subject at the future times and to modulate a timing and/or intensity of the sensory stimulation during the sleep session based on the one or more probability values generated by the one or more intermediate layers.

13. The method of claim 12, further comprising detecting, with the one or more hardware processors, sleep micro-arousals based on the information in the output signals from the first and second sensors, and controlling, with the one or more hardware processors, based on the detected sleep micro-arousals, the one or more sensory stimulators to provide the sensory stimulation to the subject during N3 sleep to enhance the N3 sleep in the subject during the sleep session, wherein a sleep micro-arousal is detected: (0) (2)

14. The method of claim 12, further comprising detecting, with the one or more hardware processors, slow waves based on the information in the output signals from the first and second sensors, and controlling, with the one or more hardware processors, based on the detected slow waves, the one or more sensory stimulators to provide the sensory stimulation to the subject during N3 sleep to enhance the N3 sleep in the subject during the sleep session, wherein a slow wave is detected: (0) (3)

15. The method of claim 12, wherein: (0) (3)

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Application US20200306494

ENHANCING DEEP SLEEP BASED ON INFORMATION FROM FRONTAL BRAIN ACTIVITY MONITORING SENSORS

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