System and method for enhancing sensory stimulation delivered to a user using neural networks

Patent metadata

Field	Value
Publication number	US-11116935-B2
Application number	US-201916407777-A
Country	US
Kind code	B2
Filing date	May 9, 2019
Priority date	May 10, 2018
Publication date	Sep 14, 2021
Grant date	Sep 14, 2021

Abstract

The present disclosure pertains to a system and method for delivering sensory stimulation to a user during a sleep session. The system comprises one or more sensors, one or more sensory stimulators, and one or more hardware processors. The processor(s) are configured to: determine one or more brain activity parameters indicative of sleep depth in the user based on output signals from the sensors; cause a neural network to indicate sleep stages predicted to occur at future times for the user during the sleep session; cause the sensory stimulator(s) to provide the sensory stimulation to the user based on the predicted sleep stages over time during the sleep session, and cause the sensory stimulator(s) to modulate a timing and/or intensity of the sensory stimulation based on the one or more brain activity parameters and values output from one or more intermediate layers of the neural network.

First claim

What is claimed is:

1. A system configured to deliver sensory stimulation to a user during a sleep session, the system comprising:
one or more sensors configured to generate output signals conveying information related to brain activity of the user during the sleep session;
one or more sensory stimulators configured to provide the sensory stimulation to the user during the sleep session; and
one or more hardware processors coupled to the one or more sensors and the one or more sensory stimulators, the one or more hardware processors configured by machine-readable instructions to:
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, the trained neural network to predict future times during the sleep session at which the user 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, one or more values generated by the one or more intermediate layers of the trained neural network; and
cause the one or more sensory stimulators to provide the sensory stimulation to the user 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 values of the one or more intermediate layers.

2. The system of claim 1 , wherein the one or more hardware processors are further configured to determine one or more brain activity parameters of the user based on the output signals, the one or more brain activity parameters indicative of sleep depth in the user; and cause the one or more sensory stimulators to modulate the timing and/or intensity of the sensory stimulation during the sleep session based on the one or more values of the one or more intermediate layers and the one or more brain activity parameters.

3. The system of claim 2 , wherein the one or more hardware processors are configured such that the one or more values from the one or more intermediate layers of the trained neural network include values from one or more convolutional layers and values from one or more recurrent layers of the trained neural network, and the one or more sensory stimulators are caused to modulate the timing and/or intensity of the sensory stimulation based on the one or more brain activity parameters, the values from the one or more convolutional layers, and the values from the one or more recurrent layers.

4. The system of claim 3 , wherein the one or more hardware processors:
(i) are configured such that the values from the one or more convolutional layers comprise two or more individual values from two or more corresponding convolutional layers,
(ii) determine a ratio of a value from one convolutional layer to a value from another convolutional layer, and
(iii) cause the one or more sensory stimulators to modulate the timing and/or intensity of the sensory stimulation based on the ratio.

5. The system of claim 3 , wherein the one or more hardware processors are further configured to weight the one or more brain activity parameters, the values from the one or more convolutional layers, and the values from the one or more recurrent layers relative to each other, and cause the one or more sensory stimulators to modulate the sensory stimulation based on the weighted one or more brain activity parameters, the weighted values from the one or more convolutional layers, and the weighted values from the one or more recurrent layers.

6. The system of claim 1 , wherein the one or more hardware processors are configured to provide the information in the output signals to the neural network in temporal sets that correspond to individual periods of time during the sleep session, and cause the trained neural network to predict the future times based on the temporal sets of information.

7. The system of claim 1 , wherein the one or more sensory stimulators are configured such that the sensory stimulation comprises audible tones, and the one or more hardware processors are configured such that causing the one or more sensory stimulators to modulate the timing and/or intensity of the sensory stimulation comprises decreasing an inter tone interval and/or increasing a tone volume responsive to an indication the user is in deep sleep.

8. A method for delivering sensory stimulation to a user during a sleep session with a delivery system, the system comprising one or more sensors, one or more sensory stimulators, and one or more hardware processors coupled to the one or more sensors and the one or more sensory stimulators, the one or more processors configured by machine readable instructions, the method comprising:
generating, with the one or more sensors, output signals conveying information related to brain activity of the user during the sleep session;
providing, with the one or more sensory stimulators, the sensory stimulation to the user during the sleep session;
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, the trained neural network to predict future times during the sleep session at which the user 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, one or more values generated by the one or more intermediate layers of the trained neural network; and
causing, with the one or more hardware processors, the one or more sensory stimulators to provide the sensory stimulation to the user 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 values of the one or more intermediate layers.

9. The method of claim 8 , further comprising determining, with the one or more hardware processors, one or more brain activity parameters of the user based on the output signals, the one or more brain activity parameters indicative of sleep depth in the user; and
causing, with the one or more hardware processors, the one or more sensory stimulators to modulate the timing and/or intensity of the sensory stimulation during the sleep session based on the one or more values of the one or more intermediate layers and the one or more brain activity parameters.

10. The method of claim 9 , wherein the one or more values from the one or more intermediate layers of the trained neural network include values from one or more convolutional layers and values from one or more recurrent layers of the trained neural network, and the one or more sensory stimulators are caused to modulate the timing and/or intensity of the sensory stimulation based on the one or more brain activity parameters, the va

Assignees

• Koninklijke Philips Nv

Inventors

• Garcia Molina Gary Nelson
• Pastoor Sander Theodoor
• Grossekathöfer Ulf
• Bresch Erik
• Heinrich Adrienne

Classifications

• A61B5/372

  Analysis of electroencephalograms · CPC title

• G06N3/08

  Learning methods · CPC title

• G06N3/0464

  Convolutional networks [CNN, ConvNet] · CPC title

• G06N3/09

  Supervised learning · CPC title

• G06N3/0442

  characterised by memory or gating, e.g. long short-term memory [LSTM] or gated recurrent units [GRU] · CPC title