# US 12,109,038 B2 · Systems and methods for using breath events in sleep staging > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,109,038 B2 - Document ID: US12109038B2 - Status: Granted - Kind: B2 - Application number: 16/589,191 - Filed: 2019-10-01 - Published: 2020-04-02 - Granted: 2024-10-08 - Earliest claimed: 2018-10-01 - Assignees: Koninklijke Philips N.V. - Applicants: Koninklijke Philips N.V. - Inventors: Pedro Miguel Ferreira Dos Santos Da Fonseca; Angela Grassi; Gregory Delano Matthews; Michael Thomas Kane; Raymond Charles Vasko, Jr.; Mareike Klee - Web page: https://patentgrep.com/patent/US12109038B2 ## CPC - A61B 5/4818: {Sleep apnoea} - A61B 5/02416: {using photoplethysmograph signals, e.g. generated by infrared radiation (A61B5/14552 takes precedence)} - A61B 5/0245: by using sensing means generating electric signals, {i.e. ECG signals} - A61B 5/0816: {Measuring devices for examining respiratory frequency (measuring frequency of electric signals G01R23/00)} - A61B 5/085: Measuring impedance of respiratory organs or lung elasticity - A61B 5/087: Measuring breath flow - A61B 5/091: Measuring volume of inspired or expired gases, e.g. to determine lung capacity - A61B 5/1102: {Ballistocardiography} - A61B 5/113: occurring during breathing - A61B 5/4812: {Detecting sleep stages or cycles} - A61B 5/4836: {Diagnosis combined with treatment in closed-loop systems or methods (A61B5/0036 takes precedence)} - A61B 5/486: {Biofeedback (using electroencephalography [EEG] A61B5/375)} - A61B 5/7264: {Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems} - A61B 5/7278: {Artificial waveform generation or derivation, e.g. synthesizing signals from measured signals} - A61B 5/7282: {Event detection, e.g. detecting unique waveforms indicative of a medical condition (cough events A61B5/0823; seizures A61B5/4094; sleep apnoea A61B5/4818)} - A61M 16/024: {including calculation means, e.g. using a processor} - A61M 16/026: {specially adapted for predicting, e.g. for determining an information representative of a flow limitation during a ventilation cycle by using a root square technique or a regression analysis} - A61B 5/0871: {Peak expiratory flowmeters} - A61B 5/7267: {involving training the classification device} - A61M 16/0069: {the speed thereof being controlled by respiratory parameters, e.g. by inhalation} - A61M 2016/0027: {pressure meter} - A61M 2016/0033: {electrical} - A61M 2205/3306: Optical measuring means - A61M 2205/3331: Pressure; Flow - A61M 2205/3368: Temperature - A61M 2205/3553: remote, e.g. between patient's home and doctor's office - A61M 2205/3584: using modem, internet or Bluetooth® - A61M 2205/3592: using telemetric means, e.g. radio or optical transmission - A61M 2205/505: Touch-screens; Virtual keyboard or keypads; Virtual buttons; Soft keys; Mouse touches - A61M 2230/04: Heartbeat characteristics, e.g. ECG, blood pressure modulation - A61M 2230/10: Electroencephalographic signals - A61M 2230/205: partial oxygen pressure (P-O2) - A61M 2230/30: Blood pressure (A61M2230/04 takes precedence) - A61M 2230/40: Respiratory characteristics - A61M 2230/50: Temperature - A61M 2230/63: Motion, e.g. physical activity - G16H 50/20: for computer-aided diagnosis, e.g. based on medical expert systems - A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D) - A61M: DEVICES FOR INTRODUCING MEDIA INTO, OR ONTO, THE BODY (introducing media into or onto the bodies of animals A61D7/00; means for inserting tampons A61F13/26; devices for administering food or medicines orally A61J; containers for collecting, storing or administering blood or medical fluids A61J1/05); DEVICES FOR TRANSDUCING BODY MEDIA OR FOR TAKING MEDIA FROM THE BODY (surgery A61B; chemical aspects of surgical articles A61L); DEVICES FOR PRODUCING OR ENDING SLEEP OR STUPOR {(Electrotherapy, e.g. producing anaesthesia by the use of alternating or intermittent currents A61N1/36021)} - G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA ## Abstract The present disclosure describes a sleep staging system. The 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 a distribution of the one or more breathing features over one or more time windows; detect presence of a breathing event based on the output signals; determine sleep states of the subject with a sleep stage classifier model based upon the distribution of the breathing features and the one or more breathing events; and provide feedback indicating the sleep states. ## Claims ### Claim 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. ### Claim 2 (depends on 1) The system of claim 1, wherein the one or more physical computer processors are further configured to: determine at least one measured parameter for one of the breathing events indicating a number of times the category of the one of the breathing events is detected; and determine sleep states of the subject based on the at least one measured parameter and the first and second meta-features. ### Claim 3 (depends on 1) The system of claim 1, wherein the first size and/or the second size is at least 60 seconds. ### Claim 4 (depends on 1) The system of claim 1, wherein the output signals are non-electroencephalogram (non-EEG) signals. ### Claim 5 (depends on 1) The system of claim 1, wherein the one or more breathing parameters include respiratory effort of the subject. ### Claim 6 (depends on 1) The system of claim 1, wherein the one or more breathing parameters include respiratory airflow of the subject. ### Claim 7 (depends on 1) The system of claim 1, wherein the sleep staging system comprises a CPAP device, the CPAP device further comprising a pressure generator configured to generate a pressurized flow of breathable gas for delivery to the airway of a subject, the one or more physical computer processors being operatively connected with the pressure generator to control delivery of the pressurized flow of breathable gas to an airway of the subject based on the output signals from the one or more sensors. ### Claim 8 (depends on 1) The system of claim 1, wherein the one or more breathing features of the individual breaths comprise breath duration, duration of inspiration vs expiration, minimum and maximum flow values, and/or tidal volume of each individual breath. ### Claim 9 (depends on 1) The system of claim 1, wherein the one or more physical computer processors are further configured to provide feedback indicating the sleep states. ### Claim 10 (depends on 1) The system of claim 1, wherein the first statistic and the second statistic is each a median or a number of percentiles. ### Claim 11 (depends on 1) The system of claim 1, wherein the number of first time windows does not overlap the number of second time windows. ### Claim 12 A method comprising: generating, with one or more sensors, output signals conveying information related to one or more breathing parameters of subject; determining, with one or more physical computer processors, based on the output signals, one or more breathing features of individual breaths of the subject; determining, with one or more physical computer processors, 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; detecting, with one or more physical computer processors, 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 determining, with one or more physical computer processors, 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. ### Claim 13 (depends on 12) The method of claim 12, wherein the method further comprises: determining, with one or more physical computer processors, at least one measured parameter for one of the breathing events indicating a number of times the category of the one of the breathing events is detected; and determining, with one or more physical computer processors, sleep states of the subject based on the at least one measured parameter and the first and second meta-features. ### Claim 14 (depends on 12) The method of claim 12, wherein the first size and/or the second size is at least 60 seconds. ### Claim 15 (depends on 12) The method of claim 12, wherein the output signals are non-electroencephalogram (non-EEG) signals. ### Claim 16 (depends on 12) The method of claim 12, wherein the one or more breathing parameters include respiratory effort of the subject. ### Claim 17 (depends on 12) The method of claim 12, wherein the one or more breathing parameters include respiratory airflow of the subject. ### Claim 18 (depends on 12) The method of claim 12, further comprising generating a pressurized flow of breathable gas for delivery to the airway of a subject, the one or more physical computer processors being operatively connected with a pressure generator to control deliver of the pressurized flow of breathable gas to an airway of the subject based on the output signals. ### Claim 19 (depends on 12) The method of claim 12, further comprising providing, with one or more physical computer processors, feedback indicating the sleep states. ### Claim 20 (depends on 12) The method of claim 12, wherein the first statistic and the second statistic is each a median or a number of percentiles. ### Claim 21 (depends on 12) The method of claim 12, wherein the number of first time windows does not overlap the number of second time windows. ### Claim 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. ### Claim 23 (depends on 22) The system of claim 22, further comprising: means for determining, configured for determining at least one measured parameter for one of the breathing events indicating a number of times the category of the one of the breathing events is detected; and means for determining, configured for determining sleep states of the subject based on the at least one measured parameter and the first and second meta-features. ### Claim 24 (depends on 22) The system of claim 22, wherein the first size and/or the second size is: at least 60 seconds. ### Claim 25 (depends on 22) The system of claim 22, wherein the output signals are non-electroencephalogram (non-EEG) signals. ### Claim 26 (depends on 22) The system of claim 22, wherein the one or more breathing parameters include respiratory effort of the subject. ### Claim 27 (depends on 22) The system of claim 22, wherein the one or more breathing parameters include respiratory airflow of the subject. ### Claim 28 (depends on 22) The system of claim 22, further comprising: the sleep staging system comprises a CPAP device (100), the CPAP device further comprising a pressure generating means configured to generate a pressurized flow of breathable gas for delivery to the airway of a subject, the CPAP device comprising one or more physical computer processors being operatively connected with the pressure generating means to control deliver of the pressurized flow of breathable gas to an airway of the subject based on the output signals from the sensing means. ### Claim 29 (depends on 22) The system of claim 22, further comprising means for providing feedback configured to provide feedback indicating the sleep states. ### Claim 30 (depends on 22) The system of claim 22, wherein the first statistic and the second statistic is each a median or a number of percentiles. ### Claim 31 (depends on 22) The system of claim 22, wherein the number of first time windows does not overlap the number of second time windows.