公开(公告)号：US20200205728A1

公开(公告)日：2020-07-02

申请号：US16724572

申请日：2019-12-23

申请人： KONINKLIJKE PHILIPS N.V.

发明人： Gary Nelson Garcia MOLINA

IPC分类号： A61B5/00 ,  A61M21/00 ,  G16H50/20 ,  G16H50/30 ,  G06F17/16 ,  G06F17/15

摘要： The present disclosure pertains to optimizing sleep score parameters. In one embodiment, a first set of vectors is obtained, where each vector includes parameter values of sleep-related parameters for calculating a sleep score. For each vector of the first set, a correlation value indicating a degree of a correlation between the vector and a user-indicated sleep score associated with a set of parameter values representative of a sleep metric of the user is determined. A second set of vectors is generated based on a first subset and a second subset of the first set of vectors satisfying a first and second criteria, respectively. A correlation value associated with each vector of the second set is determined. For each sleep-related parameter, a parameter value of a given vector of the second set is assigned based on the correlation values associated with the second set.

公开(公告)号：US20200146619A1

公开(公告)日：2020-05-14

申请号：US16628186

申请日：2018-07-10

申请人： KONINKLIJKE PHILIPS N.V.

发明人： Gary Nelson Garcia MOLINA ,  Cristhian Mauricio POTES BLANDON ,  Pedro Miguel FERREIRA DOS SANTOS DA FONSECA ,  Bryan CONROY ,  Minnan XU

IPC分类号： A61B5/00 ,  A61B5/0205

摘要： A system (400) for monitoring an individual's sleep includes: (i) a patient monitor (410) configured to obtain a patient waveform, the patient waveform comprising information representative of a vital statistic of the patient; a processor (420) in communication with the patient monitor and configured to: (i) process the patient waveform to generate a segmented waveform; (ii) extract at least one feature from a segment of the waveform in a time domain and/or at least one feature from the segment of the waveform in the frequency domain; (iii) classify, using the at least one extracted feature, a sleep stage of the patient for the segment of the waveform; and (iv) generate, from classified sleep stages for a plurality of segments of the waveform, a sleep quality measurement; and a user interface (480) configured to report the generated sleep quality measurement.

公开(公告)号：US20200306494A1

公开(公告)日：2020-10-01

申请号：US16832509

申请日：2020-03-27

申请人： KONINKLIJKE PHILIPS N.V.

发明人： Gary Nelson Garcia MOLINA ,  Ulf GROSSEKATHÖFER ,  Stojan TRAJANOVSKI ,  Jesse SALAZAR ,  Tsvetomira Kirova TSONEVA ,  Sander Theodoor PASTOOR ,  Antonio AQUINO ,  Adrienne HEINRICH ,  Birpal Singh SACHDEV

IPC分类号： A61M21/02 ,  A61B5/00 ,  A61B5/0496 ,  A61B5/0478 ,  A61B5/04 ,  A61B5/048

摘要： 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.