COMPUTER-BASED ANALYSIS OF OSCILLATORY VENTILATION

• US 20160166176A1
• Filed: 07/23/2014
• Published: 06/16/2016
• Est. Priority Date: 07/25/2013
• Status: Abandoned Application

• Current Assignee: Mayo Foundation For Medical Education And Research (Mayo Clinic)
• Original Assignee: Mayo Foundation For Medical Education And Research (Mayo Clinic)
• Inventors: Johnson, Bruce D., Olson, Thomas P.
• Application Number:
  US14/907,364
• Publication Number:
  US 20160166176A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: A61B 5/0816 Measuring devices for examining respiratory frequency measuring frequency of electric signals G01R23/00
• CPC Class Codes: A61B 5/083 Measuring rate of metabolism by using breath test, e.g. measuring rate of oxygen consumption
• CPC Class Codes: A61B 5/087 Measuring breath flow
• CPC Class Codes: A61B 5/091 Measuring volume of inspired or expired gases, e.g. to determine lung capacity
• CPC Class Codes: A61B 5/7257 using Fourier transforms
• CPC Class Codes: A61B 5/7275 Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems

Abstract

This document provides systems and techniques that can be used to provide computer-implemented interpretation (e.g., up-to-date, on demand, and real time interpretation) of respiratory profiles of patients using, for example, an application provided by a computer server system, to assess a patient'"'"'s cardiac and/or pulmonary function.

Claims

1. A computer-implemented method, wherein said method comprises:

(a) accessing, by a computer system, cardiopulmonary data for a patient;

(b) identifying, using an oscillation period operation, periods of oscillation from the cardiopulmonary data for the patient;

(c) identifying, using peak detection operation, amplitudes of oscillation from the cardiopulmonary data for the patient;

(d) determining, by the computer system, one or more breathing patterns for the patient based on (i) the identified periods of oscillation and (ii) the identified amplitudes of oscillation; and

(e) outputting, by the computer system, information that identifies the one or more breathing patterns.

2. The computer-implemented method of claim 1, wherein the oscillation period operation comprises a sine wave fitting operation that identifies the periods of oscillation.

3. The computer-implemented method of claim 1, wherein the oscillation period operation comprises Fourier analysis operation that identifies the periods of oscillation.

4. The computer-implemented method of claim 1, wherein the cardiopulmonary data for the patient corresponds to measurements that were taken over a period of time while the patient was exercising.

5. The computer-implemented method of claim 1, wherein the cardiopulmonary data for the patient corresponds to measurements that were taken over a period of time while the patient was resting.

6. The computer-implemented method of claim 1, wherein the cardiopulmonary data comprises spirometry and gas exchange values for the patient.

7. The computer-implemented method of claim 1, wherein the one or more breathing patterns comprise cyclic patterns in breath-by-breath ventilation and gas exchange.

8. The computer-implemented method of claim 1, wherein the determining comprises curve fitting the cardiopulmonary data for the patient by combining the periods of oscillation that are identified using the oscillation period operation with the amplitudes of oscillation that are identified using the peak detection operation.

9. The computer-implemented method of claim 1, wherein the peak detection operation comprises:

(a) for each data point in the cardiopulmonary data, generating a list that includes at least a threshold number of adjacent data points that occur earlier and later in time within the cardiopulmonary data;

(b) sorting the generated list;

(c) discarding data points in the generated list have the greatest and lowest values to generate a resulting list;

(d) identifying local minima and maxima from the resulting list;

(e) determining time differences and magnitude differences between the local minima and maxima; and

(f) averaging the time differences and the magnitude differences to determine magnitude and period values from the cardiopulmonary data.