Method for detecting and discriminating breathing patterns from respiratory signals

• US 8,066,647 B2
• Filed: 12/21/2005
• Issued: 11/29/2011
• Est. Priority Date: 12/23/2004
• Status: Expired due to Fees

• Current Assignee: ResMed Pty Ltd. (ResMed Incorporated)
• Original Assignee: Resmed Limited (ResMed Incorporated)
• Inventors: Armitstead, Jeffrey
• Primary Examiner(s): Le; Miranda
• Assistant Examiner(s): BLOCH, MICHAEL RYAN
• Application Number:
  US11/576,210
• Publication Number:
  US 20080177195A1
• Time in Patent Office: 2,169 Days
• Field of Search: 600/481, 600/483, 600/484, 600529-543
• US Class Current: 600/529
• 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 signal representative of a patient'"'"'s respiration is split into equal length epochs. A primary feature is extracted from each epoch that acts as a compressed representation of the signal events. Statistics are applied to the primary feature to produce one or more secondary features that represent the entire epoch. Each secondary feature is grouped with one or more other features that are extracted from the entire epoch rather than selected epoch events. This grouping is the epoch pattern. The pattern is manipulated with suitable classifier algorithm to produce a probability for each class within the algorithm, that the signal may be representative of a disease state (Cheyne-Stokes, OSA etc). The epoch is assigned to the class with the highest probability. Also defined are methods of detecting Cheyne-Stokes breathing by analyzing a signal to detect one or regions of hyperpnoea and if the length of a hyperpnoea exceeds a parameter, Cheyne-Stokes is present.

Claims

1. A method for diagnosing the presence of sleep disorders comprising the steps of:

recording a signal representative of respiration from a patient using a logging device which includes a data-acquisition system and a memory,

processing the respiratory signal either on-board by the recording device or offline using a computer,

dividing the signal into n epochs of equal length,

recording events consisting of an hypopnoea followed by an hyperpnoea,

detecting for each event its beginning and endpoints,

calculating event lengths, and

processing each hyperpnoea to derive shape features;

wherein the shape features are calculated using singular value decomposition of the hyperpnoea ventilation signal by the steps of extracting the hyperpnoea from the respiratory signal, forming a ventilation signal from the respiratory signal,

scaling the ventilation signal to give a vector of values, calculating shape factors from the product of the pseudoinverse of the matrix and the vector of values; and

the matrix of orthogonal functions is;

2. A method for diagnosing the presence of sleep disorders comprising the steps of:

recording a signal representative of respiration from a patient using a logging device which includes a data-acquisition system and a memory,

processing the respiratory signal either on-board by the recording device or off line using a computer,

dividing the signal into n epochs of equal length,

recording events consisting of an hypopnoea followed by an a hyperpnoea,

detecting for each event its beginning and end points,

calculating event lengths,

processing each hyperpnoea to derive shape features, and

deriving a jump feature for each hyperpnoea by the steps of;

extracting the hyperpnoea from the respiratory signal, forming a ventilation signal from an absolute value of the respiratory signal, and approximating an envelope of the ventilation signal with a droopy peak-detector.

3. The method for diagnosing the presence of sleep disorders of claim 2, wherein the following droopy peak-detector is used to approximate the envelope of the ventilation signal:

4. A method for diagnosing the presence of sleep disorders comprising the steps of:

recording a signal representative of respiration from a patient using a logging device which includes a data-acquisition system and a memory,

processing the respiratory signal either on-board by the recording device or off-line using a computer,

dividing the signal into n epochs of equal length,

recording events consisting of an hypopnoea followed by an hyperpnoea,

detecting for each event its beginning and end points,

calculating event lengths,

processing each hyperpnoea to derive shape features, and

calculating an additional feature from the entire epoch signal from a spectrogram of the epoch signal by;

calculating a mean of the respiratory signal and subtracting said mean from the respiratory signal,

chopping the subtracted signal into n slices,

windowing each slice using a Hanning window,

applying a Fourier transform to each windowed slice yielding a complex vector result for each slice,

determining an absolute value of each complex result so as to yield a real valued vector per slice,

averaging said real valued vectors to yield one vector,

taking a natural log of a subsequent vector,

extracting values in a frequency range of 0 Hz to 0.075 Hz to form a sub-vector,

de-trending the sub-vector and determining its mean, and

calculating the feature as a maximum minus the mean of said de-trended sub-vector.