US 12,109,038 B2
Systems and methods for using breath events in sleep staging
Pedro Miguel Ferreira Dos Santos Da Fonseca, Borgerhout (BE); Angela Grassi, Eindhoven (NL); Gregory Delano Matthews, Pittsburgh, PA (US); Michael Thomas Kane, Harrison City, PA (US); Raymond Charles Vasko, Jr., Turtle Creek, PA (US); and Mareike Klee, Straelen (DE)
Assigned to KONINKLIJKE PHILIPS N.V., Eindhoven (NL)
Filed by KONINKLIJKE PHILIPS N.V., Eindhoven (NL)
Filed on Oct. 1, 2019, as Appl. No. 16/589,191.
Claims priority of provisional application 62/739,591, filed on Oct. 1, 2018.
Prior Publication US 2020/0100728 A1, Apr. 2, 2020
Int. Cl. A61B 5/00 (2006.01); A61B 5/085 (2006.01); A61B 5/087 (2006.01); A61B 5/091 (2006.01); A61M 16/00 (2006.01)
CPC A61B 5/4818 (2013.01) [A61B 5/085 (2013.01); A61B 5/087 (2013.01); A61B 5/091 (2013.01); A61B 5/4812 (2013.01); A61B 5/486 (2013.01); A61B 5/7264 (2013.01); A61M 16/024 (2017.08); A61M 16/026 (2017.08); A61M 2230/40 (2013.01)] 31 Claims
OG exemplary drawing
 
1. A sleep staging system comprising:
one or more sensors configured to generate output signals conveying information related to one or more breathing parameters of subject; and
one or more physical computer processors operatively connected with the one or more sensors, the one or more physical computer processors configured by computer readable instructions to:
determine, based on the output signals, one or more breathing features of individual breaths of the subject;
determine at least a first meta-feature from the one or more breathing features over a number of first time windows each having a first size and a second meta-feature from the one or more breathing features over a number of second time windows each having a second size different than the first size, wherein the first meta-feature is a first statistic that describes a first shape of a first distribution of the one or more breathing features over the number of first time windows and is not just a variance or standard deviation of the one or more breathing features over time, and wherein the second meta-feature is a second statistic obtained from the one or more breathing features that describes a shape of a distribution of the one or more breathing features over the number of second time windows and is not just the variance or standard deviation of the one or more breathing features over time;
detect presence of one or more breathing events based on the output signals and indicate a category of a type of each of the one or more breathing events; and
determine sleep states of the subject with a sleep stage classifier model based upon the first meta-feature, the second meta-feature, and the category of each of the one or more breathing events, wherein the sleep stage classifier model is based on a history of prior sleep states and is not a Markov classification system.
 
9. The system of claim 1, wherein the one or more physical computer processors are further configured to provide feedback indicating the sleep states.
 
22. A sleep staging system comprising:
means for sensing, configured for generating output signals conveying information related to one or more breathing parameters of subject;
means for determining, configured for determining, based on the output signals, one or more breathing features of individual breaths of the subject;
means for determining, configured for determining at least a first meta-feature from the one or more breathing features over a number of first time windows each having a first size and a second meta-feature from the one or more breathing features over a number of second time windows each having a second size different than the first size, wherein the first meta-feature is a first statistic that describes a first shape of a first distribution of the one or more breathing features over the number of first time windows and is not just a variance or standard deviation of the one or more breathing features over time, and wherein the second meta-feature is a second statistic obtained from the one or more breathing features that describes a shape of a distribution of the one or more breathing features over the number of second time windows and is not just the variance or standard deviation of the one or more breathing features over time;
means for detecting, configured for detecting presence of one or more breathing events based on the output signals and indicating a category of a type of each of the one or more breathing events; and
means for determining, configured for determining sleep states of the subject with a sleep stage classifier model based upon the first meta-feature, the second meta-feature, and the category of each of the one or more breathing events, wherein the sleep stage classifier model is based on a history of prior sleep states and is not a Markov classification system.