METHOD FOR DETECTING AND DISCRIMINATING BREATHING PATTERNS FROM RESPIRATORY SIGNALS

• US 20120088992A1
• Filed: 10/04/2011
• Published: 04/12/2012
• Est. Priority Date: 12/23/2004
• Status: Abandoned Application

• Current Assignee: ResMed Pty Ltd. (ResMed Incorporated)
• Original Assignee: ResMed Pty Ltd. (ResMed Incorporated)
• Inventors: ARMITSTEAD, Jeffrey
• Application Number:
  US13/252,640
• Publication Number:
  US 20120088992A1
• Time in Patent Office: Days
• US Class Current: 600/323
• CPC Class Codes: A61B 5/087 Measuring breath flow
• CPC Class Codes: A61B 5/145 Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissuemeasuring of blood pressure or blood flow A61B5/02; non-radiation detecting or locating of foreign bodies in blood A61B5/06
• CPC Class Codes: A61B 5/4818 Sleep apnoea
• CPC Class Codes: A61B 5/7232 involving compression of the physiological signal, e.g. to extend the signal recording period
• CPC Class Codes: A61B 5/726 using Wavelet transforms
• CPC Class Codes: A61B 5/7267 involving training the classification device
• CPC Class Codes: A61M 16/021 operated by electrical means A61M16/202 – A61M16/205 take precedence
• CPC Class Codes: A61M 2230/40 Respiratory characteristics
• CPC Class Codes: G16H 50/70 for mining of medical data, e.g. analysing previous cases of other patients

Abstract

A Cheyne-Stokes (CS) diagnosis system classifies periods of CS-like breathing by examining a signal indicative of a respiratory parameter. For example, nasal flow data is processed to classify it as unambiguously CS breathing or nearly so and to display the classification Processing may detect and display: apnoeas, hypopnoeas, flow-limitation and snore. The signal may be split into equal length epochs and event features are extracted. Statistics are applied to these primary feature(s) to produce secondary feature(s) representing the entire epoch. Each secondary feature is grouped with other feature(s) extracted from the entire epoch rather than from the epoch events. This final group of features is the epoch pattern. The epoch pattern is classified to produce a probability for possible event classes (e.g., Cheyne-Stokes breathing, OSA, etc.). The epoch is assigned to the class with the highest probability, which may both be reported as an indication of disease state.

Claims

1-51. -51. (canceled)

52. A method for a controlled ventilatory apparatus to automatically determine the absence or the presence of Cheyne-Stokes breathing in a person comprising the steps of:

capturing a signal indicative of respiratory flow of a person;

determining the absence or presence of an apnea or an hypopnea from said signal and the rate of increase of flow following the determined presence of an apnea or an hypopnea;

determining any peak in a frequency spectrum of said signal; and

determining the absence of Cheyne-Stokes breathing from a sudden increase in flow following an apnea or an hypopnea, or determining the presence of Cheyne-Stokes breathing from a combination of a peak in the frequency spectrum and a slow rate of increase in flow following an apnea or an hypopnea.

53. The method of claim 52 wherein said signal indicative of respiratory flow is a nasal flow signal.

54. The method of claim 52 wherein said step of capturing a signal indicative of respiratory flow of the person encompasses pulse oximetry.

55. The method of claim 52 wherein the step of determining a peak in a frequency spectrum of said signal comprises a step of calculating a spectrogram of said signal and determining any peak from said spectrogram.

56. The method of claim 52 further comprising the step of determining whether the signal has a peak in a frequency range of 0 Hz to 0.075 Hz.

57. The method of claim 52 wherein the step of determining said peak in a frequency spectrum of said signal includes calculating a Fourier transform of the signal.

58. The method of claim 52 wherein said step of determining a rate of increase of flow is determined from the signal of the respiratory flow in a region of hypopnea to hyperpnoea.

59. The method of claim 52 further comprising the step of calculating a shape of an envelope of said signal and utilizing said shape as an indication of central apnea.

60. A ventilatory apparatus for automatically detecting the absence of the presence of Cheyne-Stokes breathing in a person comprising:

a sensor for sensing a signal indicative of the respiratory flow of a person;

a detector for determining the presence of apnea or hypopnea in said signal; and

a processor for determining (a) any peak in a frequency spectrum of said signal, (b) any sudden increase in said signal, (c) the presence of any slow increase in said signal; and

(d) the absence of Cheyne-Stokes breathing from a sudden increase in flow following an apnea or an hypopnea, or the presence of Cheyne-Stokes breathing from the combination of a peak in the frequency spectrum and a slow rate of increase in flow following an apnea or an hypopnea.

61. The apparatus of claim 60 wherein said respiratory flow is nasal flow.

62. The apparatus of claim 60 wherein said signal indicative of respiratory flow encompasses pulse oximetry.

63. The apparatus of claim 60 wherein said processor determines a peak in a frequency spectrum of said signal by calculating a spectrogram of said signal.

64. The apparatus of claim 60 wherein said processor determines whether the signal has a peak in a frequency range of 0 Hz to 0.075 Hz.

65. The apparatus of claim 60 wherein said processor determines a peak in a frequency spectrum of said signal by calculating a Fourier transform of said signal.

66. The apparatus of claim 60 wherein said processor determines a rate of increase in flow in a region of hypopnea to hyperpnoea.

67. The apparatus of claim 60 wherein said processor calculates a shape of an envelope of said signal and utilizes said shape as an indication of central apnea.