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EP-4740985-A2 - METHODS AND APPARATUS FOR ACOUSTIC MONITORING OF RESPIRATORY THERAPY DEVICES

EP4740985A2EP 4740985 A2EP4740985 A2EP 4740985A2EP-4740985-A2

Abstract

The present invention discloses a method of a computer system for determining a status of a respiratory therapy apparatus comprising a pressure generator for generating a flow of air through an air circuit to a patient interface, the method comprising: receiving, via a communication network, data from the respiratory therapy apparatus, the data representing data set outputs, the data set outputs comprising one or more of: (a) processing output from applying, to a frequency domain representation, an integer-multiple function of a fundamental frequency attributable to operation of a motor of the pressure generator, (b) processing output from applying, to the frequency domain representation, a non-integer-multiple function of the fundamental frequency attributable to operation of the motor of the pressure generator, (c) processing output from applying, to the frequency domain representation and one or more predetermined fault signature spectra, a statistical correlation function, and (d) processing output from applying, to the frequency domain representation, a resonant frequency function; wherein the frequency domain representation is computed from a sound signal representing sound in the air circuit during generation of the flow of air; classifying a noise vector to obtain a status indicator of at least a component of the pressure generator, wherein the noise vector is derived with the data set outputs; and generating an output based on the status indicator.

Inventors

  • TARAKCI, CEM
  • KENYON, BARTON JOHN
  • SHARMA, ANIKET
  • SHADIE, TIMOTHY NICHOLAS

Assignees

  • ResMed Pty Ltd

Dates

Publication Date
20260513
Application Date
20210907

Claims (15)

  1. A method of a computer system for determining a status of a respiratory therapy apparatus comprising a pressure generator for generating a flow of air through an air circuit to a patient interface, the method comprising: receiving, via a communication network, data from the respiratory therapy apparatus, the data representing data set outputs, the data set outputs comprising one or more of: (a) processing output from applying, to a frequency domain representation, an integer-multiple function of a fundamental frequency attributable to operation of a motor of the pressure generator, (b) processing output from applying, to the frequency domain representation, a non-integer-multiple function of the fundamental frequency attributable to operation of the motor of the pressure generator, (c) processing output from applying, to the frequency domain representation and one or more predetermined fault signature spectra, a statistical correlation function, and (d) processing output from applying, to the frequency domain representation, a resonant frequency function; wherein the frequency domain representation is computed from a sound signal representing sound in the air circuit during generation of the flow of air; classifying a noise vector to obtain a status indicator of at least a component of the pressure generator, wherein the noise vector is derived with the data set outputs; and generating an output based on the status indicator.
  2. The method of claim 1, wherein the data set outputs comprises values of peak ratios.
  3. The method of claim 2, wherein at least one peak ratio of the peak ratios is computed by dividing a height of a peak by a power in a band surrounding the peak.
  4. The method of claim 3, wherein the power is an average power or a root mean square (RMS) power.
  5. The method of any one of claims 1 to 4, wherein the noise vector is derived, for the classifying, with data set outputs from the applying the integer-multiple function, and wherein the integer-multiple function extracts harmonic peak ratios from peaks in the frequency domain representation at integer multiples of the fundamental frequency.
  6. The method of any one of claims 1 to 5, wherein the noise vector is derived, for the classifying, with data set outputs from the applying the non-integer-multiple function, and wherein the non-integer-multiple function extracts non-harmonic peak ratios from peaks in the frequency domain representation at non-integer multiples of the fundamental frequency, and optionally, wherein the non-integer-multiple function identifies location information of the non-harmonic peak ratios.
  7. The method of claim 6, wherein the noise vector is derived with data set output from the resonant frequency function, and, optionally, wherein the data set output from the resonant frequency function comprises power data for one or more predetermined resonant regions, and further, optionally, wherein the power data comprises a noise ratio.
  8. The method of claim 7, wherein the noise ratio comprises an average power of a predetermined resonant region divided by a power of a reference region.
  9. The method of any one of claims 1 to 8, wherein the noise vector is derived, for the classifying, with output data from applying a statistical correlation function to the frequency domain representation and the one or more accessed predetermined fault signature spectra, and optionally, wherein the classifying the noise vector comprises comparing the output data from the applying of the statistical correlation function to one or more correlation thresholds.
  10. The method of any one of claims 1 to 6, wherein the applying the non-integer-multiple function further comprises thresholding peak ratios using one or more predetermined first ratio thresholds.
  11. The method of any one of claims 1 to 10, wherein: (i) the classifying comprises counting harmonic peak ratios in the noise vector that exceed one or more second predetermined thresholds; (ii) the classifying comprises forming a weighted sum from harmonic peak ratios generated with the integer-multiple function; (iii) the output generated based on the status indicator comprises communicating the status indicator to a controller that (a) controls an operation of the pressure generator based on the status indicator; (b) deactivates the pressure generator based on the status indicator; and/or (c) limits operation of the pressure generator based on the status indicator; and/or (iv) the output generated based on the status indicator comprises at least one of (a) output on a display of a remote external device, or (b) output on a display coupled with the pressure generator.
  12. The method of any one of claims 1 to 11, wherein the classifying: (a) evaluates a weighed sum of integer multiple peak ratios, non-integer multiple peak ratios, and noise ratios; and/or (b) evaluates each of a weighed sum of integer multiple peak ratios, a weighted sum of non-integer multiple peak ratios, and a weighted sum of noise ratios.
  13. A respiratory therapy system for treating a respiratory disorder in a patient, the system comprising: a computer system, the computer system configured to perform the method of any one of claims 1 to 12 with the respiratory therapy apparatus; wherein the respiratory therapy apparatus comprises: a pressure generator configured to generate a flow of air through an air circuit to a patient interface for treating the respiratory disorder, the pressure generator comprising a motor; a transducer configured to generate a signal representing sound in the air circuit generated by the pressure generator during generation of the flow of air; and a controller configured to control the pressure generator to generate the flow of air.
  14. The respiratory therapy system of claim 13, wherein the computer system comprises a server.
  15. The respiratory therapy system of any one of claims 13 to 14, wherein the classifier is trained using noise vectors obtained from blowers of known status using machine learning.

Description

1 CROSS-REFERENCE TO RELATED APPLICATIONS This application claims the benefit of United States Provisional Application No. 62/706,809, filed 11 September 2020, the entire disclosure of which is hereby incorporated herein by reference. 2 BACKGROUND OF THE TECHNOLOGY 2.1 FIELD OF THE TECHNOLOGY The present technology relates to one or more of the screening, diagnosis, monitoring, treatment, prevention and amelioration of respiratory-related disorders. The present technology also relates to monitoring medical devices or apparatus. 2.2 DESCRIPTION OF THE RELATED ART 2.2.1 Human Respiratory System and its Disorders The respiratory system of the body facilitates gas exchange. The nose and mouth form the entrance to the airways of a patient. The airways include a series of branching tubes, which become narrower, shorter and more numerous as they penetrate deeper into the lung. The prime function of the lung is gas exchange, allowing oxygen to move from the inhaled air into the venous blood and carbon dioxide to move in the opposite direction. The trachea divides into right and left main bronchi, which further divide eventually into terminal bronchioles. The bronchi make up the conducting airways, and do not take part in gas exchange. Further divisions of the airways lead to the respiratory bronchioles, and eventually to the alveoli. The alveolated region of the lung is where the gas exchange takes place, and is referred to as the respiratory zone. See "Respiratory Physiology", by John B. West, Lippincott Williams & Wilkins, 9th edition published 2012. A range of respiratory disorders exist. Certain disorders may be characterised by particular events, e.g. apneas, hypopneas, and hyperpneas. Examples of respiratory disorders include Obstructive Sleep Apnea (OSA), Cheyne-Stokes Respiration (CSR), respiratory insufficiency, Obesity Hyperventilation Syndrome (OHS), Chronic Obstructive Pulmonary Disease (COPD), Neuromuscular Disease (NMD) and Chest wall disorders. Obstructive Sleep Apnea (OSA), a form of Sleep Disordered Breathing (SDB), is characterised by events including occlusion or obstruction of the upper air passage during sleep. It results from a combination of an abnormally small upper airway and the normal loss of muscle tone in the region of the tongue, soft palate and posterior oropharyngeal wall during sleep. The condition causes the affected patient to stop breathing for periods typically of 30 to 120 seconds in duration, sometimes 200 to 300 times per night. It often causes excessive daytime somnolence, and it may cause cardiovascular disease and brain damage. The syndrome is a common disorder, particularly in middle aged overweight males, although a person affected may have no awareness of the problem. See US Patent No. 4,944,310 (Sullivan). Cheyne-Stokes Respiration (CSR) is another form of sleep disordered breathing. CSR is a disorder of a patient's respiratory controller in which there are rhythmic alternating periods of waxing and waning ventilation known as CSR cycles. CSR is characterised by repetitive de-oxygenation and re-oxygenation of the arterial blood. It is possible that CSR is harmful because of the repetitive hypoxia. In some patients CSR is associated with repetitive arousal from sleep, which causes severe sleep disruption, increased sympathetic activity, and increased afterload. See US Patent No. 6,532,959 (Berthon-Jones). Respiratory failure is an umbrella term for respiratory disorders in which the lungs are unable to inspire sufficient oxygen or exhale sufficient CO2 to meet the patient's needs. Respiratory failure may encompass some or all of the following disorders. A patient with respiratory insufficiency (a form of respiratory failure) may experience abnormal shortness of breath on exercise. Obesity Hyperventilation Syndrome (OHS) is defined as the combination of severe obesity and awake chronic hypercapnia, in the absence of other known causes for hypoventilation. Symptoms include dyspnea, morning headache and excessive daytime sleepiness. Chronic Obstructive Pulmonary Disease (COPD) encompasses any of a group of lower airway diseases that have certain characteristics in common. These include increased resistance to air movement, extended expiratory phase of respiration, and loss of the normal elasticity of the lung. Examples of COPD are emphysema and chronic bronchitis. COPD is caused by chronic tobacco smoking (primary risk factor), occupational exposures, air pollution and genetic factors. Symptoms include: dyspnea on exertion, chronic cough and sputum production. Neuromuscular Disease (NMD) is a broad term that encompasses many diseases and ailments that impair the functioning of the muscles either directly via intrinsic muscle pathology, or indirectly via nerve pathology. Some NMD patients are characterised by progressive muscular impairment leading to loss of ambulation, being wheelchair-bound, swallowing difficulties, respiratory muscle weakness and, eventually, death from