# US 2014/0155774 A1 · Non-invasively determining respiration rate using pressure sensors > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2014/0155774 A1 - Document ID: US20140155774A1 - Status: Published application - Kind: A1 - Application number: 14/094,476 - Filed: 2013-12-02 - Published: 2014-06-05 - Earliest claimed: 2012-11-30 - Applicants: The Regents of the University of California - Inventors: Majid Sarrafzadeh; Lauren Samy; Amir Vajid - Web page: https://patentgrep.com/patent/US20140155774A1 ## CPC - A61B 5/746: {Alarms related to a physiological condition, e.g. details of setting alarm thresholds or avoiding false alarms} - A61B 5/0816: {Measuring devices for examining respiratory frequency (measuring frequency of electric signals G01R23/00)} - A61B 5/4818: {Sleep apnoea} - A61B 5/6892: {Mats} - A61B 5/7207: {of noise induced by motion artifacts} - 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)} - A61B 5/725: {using specific filters therefor, e.g. Kalman or adaptive filters (specific diagnostics methods using using bioelectric or biomagnetic signals A61B5/316)} - A61B 5/7267: {involving training the classification device} - A61B 2562/0247: Pressure sensors - A61B 2562/046: in a matrix array - A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D) ## Abstract A respiration rate of a human subject is derived by: (1) receiving data corresponding to a sequence of two-dimensional pressure distributions of the human subject as applied against an array of pressure sensors over time; (2) converting the data into a one-dimensional waveform; (3) identifying oscillations in the one-dimensional waveform as occurrences of breath of the human subject; and (4) deriving the respiration rate of the human subject based on frequencies of the oscillations. ## Claims ### Claim 1 A non-transitory computer-readable storage medium, comprising executable instructions to: receive data corresponding to a sequence of two-dimensional pressure distributions of a human subject as applied against an array of pressure sensors over time; convert the data into a one-dimensional waveform; identify oscillations in the one-dimensional waveform as occurrences of breath of the human subject; and derive a respiration rate of the human subject based on frequencies of the oscillations. ### Claim 2 (depends on 1) The non-transitory computer-readable storage medium of claim 1, wherein the executable instructions to convert the data into the one-dimensional waveform include executable instructions to: for each two-dimensional pressure distribution of the sequence of two-dimensional pressure distributions, sum pressures values collected from at least a subset of pressure sensors included in the array of pressure sensors. ### Claim 3 (depends on 1) The non-transitory computer-readable storage medium of claim 1, further comprising executable instructions to: apply a low-pass filter to the one-dimensional waveform. ### Claim 4 (depends on 1) The non-transitory computer-readable storage medium of claim 1, further comprising executable instructions to: apply a peak-to-peak amplitude filter to the one-dimensional waveform. ### Claim 5 (depends on 1) The non-transitory computer-readable storage medium of claim 1, further comprising executable instructions to: process the one-dimensional waveform to compensate for logarithmic drift in pressure values collected from the array of pressure sensors. ### Claim 6 (depends on 1) The non-transitory computer-readable storage medium of claim 1, wherein the executable instructions to identify the oscillations in the one-dimensional waveform include executable instructions to: classify each oscillation as an occurrence of breath based on at least one of a frequency of the oscillation and an amplitude of the oscillation. ### Claim 7 (depends on 1) The non-transitory computer-readable storage medium of claim 1, wherein the executable instructions to identify the oscillations in the one-dimensional waveform include executable instructions to: classify each oscillation as an occurrence of breath based on both a frequency of the oscillation and an amplitude of the oscillation. ### Claim 8 (depends on 1) The non-transitory computer-readable storage medium of claim 1, wherein the executable instructions to identify the oscillations in the one-dimensional waveform include executable instructions to: apply a machine learning procedure to the one-dimensional waveform to identify the oscillations as the occurrences of breath. ### Claim 9 (depends on 1) The non-transitory computer-readable storage medium of claim 1, wherein the executable instructions to identify the oscillations in the one-dimensional waveform include executable instructions to: identify an occurrence of non-respiratory movement of the human subject; and identify the oscillations in the one-dimensional waveform by omitting a portion of the one-dimensional waveform corresponding to the occurrence of non-respiratory movement. ### Claim 10 (depends on 1) The non-transitory computer-readable storage medium of claim 1, wherein the executable instructions to derive the respiration rate include executable instructions to: derive the respiration rate as corresponding to a moving average of the frequencies of the oscillations over a specified time interval. ### Claim 11 (depends on 1) The non-transitory computer-readable storage medium of claim 1, further comprising executable instructions to: generate an alert if the respiration rate falls outside of a specified range. ### Claim 12 A non-transitory computer-readable storage medium, comprising executable instructions to: receive time series data corresponding to a sequence of pressure maps of a human subject as applied against a sensor sheet over time; identify an occurrence of non-respiratory movement of the human subject; omit a portion of the time series data corresponding to the occurrence of non-respiratory movement; identify an oscillation in a remaining portion of the time series data as an occurrence of breath of the human subject; and derive a respiration rate of the human subject based on a frequency of the oscillation. ### Claim 13 (depends on 12) The non-transitory computer-readable storage medium of claim 12, further comprising executable instructions to: apply a low-pass filter to the time series data. ### Claim 14 (depends on 12) The non-transitory computer-readable storage medium of claim 12, further comprising executable instructions to: apply a peak-to-peak amplitude filter to the time series data. ### Claim 15 (depends on 12) The non-transitory computer-readable storage medium of claim 12, further comprising executable instructions to: process the time series data to compensate for logarithmic drift in pressure values collected from the sensor sheet. ### Claim 16 (depends on 12) The non-transitory computer-readable storage medium of claim 12, wherein the executable instructions to identify the oscillation include executable instructions to: classify the oscillation as the occurrence of breath based on at least one of the frequency of the oscillation and an amplitude of the oscillation. ### Claim 17 (depends on 12) The non-transitory computer-readable storage medium of claim 12, wherein the executable instructions to identify the oscillation include executable instructions to: apply a machine learning procedure to the time series data to identify the oscillation as the occurrence of breath.