公开(公告)号：US11612713B2

公开(公告)日：2023-03-28

申请号：US16832509

申请日：2020-03-27

Applicant: KONINKLIJKE PHILIPS N.V.

Inventor： Gary Nelson Garcia Molina ,  Ulf Grossekathöfer ,  Stojan Trajanovski ,  Jesse Salazar ,  Tsvetomira Kirova Tsoneva ,  Sander Theodoor Pastoor ,  Antonio Aquino ,  Adrienne Heinrich ,  Birpal Singh Sachdev

IPC: A61M21/02 ,  A61B5/00 ,  A61B5/291 ,  A61B5/316 ,  A61B5/374 ,  A61B5/398 ,  A61M21/00

Abstract: Typically, high NREM stage N3 sleep detection accuracy is achieved using a frontal electrode referenced to an electrode at a distant location on the head (e.g., the mastoid, or the earlobe). For comfort and design considerations it is more convenient to have active and reference electrodes closely positioned on the frontal region of the head. This configuration, however, significantly attenuates the signal, which degrades sleep stage detection (e.g., N3) performance. The present disclosure describes a deep neural network (DNN) based solution developed to detect sleep using frontal electrodes only. N3 detection is enhanced through post-processing of the soft DNN outputs. Detection of slow-waves and sleep micro-arousals is accomplished using frequency domain thresholds. Volume modulation uses a high-frequency/low-frequency spectral ratio extracted from the frontal signal.

公开(公告)号：US11123009B2

公开(公告)日：2021-09-21

申请号：US16209522

申请日：2018-12-04

Applicant: KONINKLIJKE PHILIPS N.V.

Inventor： Gary Nelson Garcia Molina ,  Erik Bresch ,  Ulf Grossekathöfer ,  Adrienne Heinrich ,  Sander Theodoor Pastoor

IPC: A61B5/00 ,  A61M21/02 ,  G16H20/00 ,  A61B5/369

Abstract: The present disclosure pertains to a system configured to facilitate prediction of a sleep stage and intervention preparation in advance of the sleep stage's occurrence. The system comprises sensors configured to be placed on a subject and to generate output signals conveying information related to brain activity of the subject; and processors configured to: determine a sample representing the output signals with respect to a first time period of a sleep session; provide the sample to a prediction model at a first time of the sleep session to predict a sleep stage of the subject occurring around a second time; determine intervention information based on the prediction of the sleep stage, the intervention information indicating one or more stimulator parameters related to periheral stimulation; and cause one or more stimulators to provide the intervention to the subject around the second time of the sleep session.

公开(公告)号：US11116935B2

公开(公告)日：2021-09-14

申请号：US16407777

申请日：2019-05-09

Applicant: KONINKLIJKE PHILIPS N.V.

Inventor： Gary Nelson Garcia Molina ,  Sander Theodoor Pastoor ,  Ulf Grossekathöfer ,  Erik Bresch ,  Adrienne Heinrich

IPC: A61M21/02 ,  A61B5/0476 ,  A61B5/00 ,  G06N3/08 ,  G06N3/04 ,  G16H20/70 ,  A61B5/369 ,  A61M21/00

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.

公开(公告)号：US20190156204A1

公开(公告)日：2019-05-23

申请号：US16188835

申请日：2018-11-13

Applicant: KONINKLIJKE PHILIPS N.V.

Inventor： Erik Bresch ,  Ulf Grossekathöfer

IPC: G06N3/08 ,  G06N3/04

Abstract: A system for training a neural network model, comprises a memory comprising instruction data representing a set of instructions and a processor configured to communicate with the memory and to execute the set of instructions. The set of instructions, when executed by the processor, cause the processor to acquire training data, the training data comprising: data, an annotation for the data as determined by a user and auxiliary data, the auxiliary data describing at least one location of interest in the data, as considered by the user when determining the annotation for the data. The set of instructions when executed by the processor, further cause the processor to train the model using the training data, by minimising an auxiliary loss function that compares the at least one location of interest to an output of one or more layers of the model and minimising a main loss function that compares the annotation for the data as determined by the user to an annotation produced by the model.

公开(公告)号：US11657265B2

公开(公告)日：2023-05-23

申请号：US16191542

申请日：2018-11-15

Applicant: KONINKLIJKE PHILIPS N.V.

Inventor： Binyam Gebre ,  Erik Bresch ,  Dimitrios Mavroeidis ,  Teun van den Heuvel ,  Ulf Grossekathöfer

IPC: G06N3/08 ,  G06N3/04

CPC classification number: G06N3/08 ,  G06N3/0454 ,  G06N3/0481

Abstract: Described herein are systems and methods for training first and second neural network models. A system comprises a memory comprising instruction data representing a set of instructions and a processor configured to communicate with the memory and to execute the set of instructions. The set of instructions, when executed by the processor, cause the processor to set a weight in the second model based on a corresponding weight in the first model, train the second model on a first dataset, wherein the training comprises updating the weight in the second model and adjust the corresponding weight in the first model based on the updated weight in the second model.

公开(公告)号：US20190156205A1

公开(公告)日：2019-05-23

申请号：US16191542

申请日：2018-11-15

Applicant: KONINKLIJKE PHILIPS N.V.

Inventor： Binyam Gebre ,  Erik Bresch ,  Dimitrios Mavroeidis ,  Teun van den Heuvel ,  Ulf Grossekathöfer

IPC: G06N3/08 ,  G06N3/04

Abstract: Described herein are systems and methods for training first and second neural network models. A system comprises a memory comprising instruction data representing a set of instructions and a processor configured to communicate with the memory and to execute the set of instructions. The set of instructions, when executed by the processor, cause the processor to set a weight in the second model based on a corresponding weight in the first model, train the second model on a first dataset, wherein the training comprises updating the weight in the second model and adjust the corresponding weight in the first model based on the updated weight in the second model.