Classifications

• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B5/00—Measuring for diagnostic purposes; Identification of persons
  • A61B5/08—Measuring devices for evaluating the respiratory organs
  • A61B5/0816—Measuring devices for examining respiratory frequency
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B5/00—Measuring for diagnostic purposes; Identification of persons
  • A61B5/08—Measuring devices for evaluating the respiratory organs
  • A61B5/0826—Detecting or evaluating apnoea events
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B5/00—Measuring for diagnostic purposes; Identification of persons
  • A61B5/48—Other medical applications
  • A61B5/4806—Sleep evaluation
  • A61B5/4818—Sleep apnoea
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B5/00—Measuring for diagnostic purposes; Identification of persons
  • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
  • A61B5/7271—Specific aspects of physiological measurement analysis
  • A61B5/7282—Event detection, e.g. detecting unique waveforms indicative of a medical condition
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B7/00—Instruments for auscultation
  • A61B7/003—Detecting lung or respiration noise

Definitions

• Obstructive Sleep Apnea is characterized 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).
• Obesity Hyperventilation Syndrome 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.
• the screening tool may be implemented with an apparatus including a processor and a sensor, such as a microphone.
• the apparatus may be achieved with a programmed application, such as for a multipurpose processor that may operate in a mobile device, such as a smart phone or tablet having an integrated microphone or microphone accessory.
• the start phase of the at least one RRE may comply with at least two or all of the following criteria: the RRE extends for at least a predetermined amount of time; the RRE includes at least a predetermined number of respiration cycles during the at least a predetermined amount of time; and the RRE includes at least a predetermined number of consecutive respiration events during the at least a predetermined amount of time.
• the predetermined amount of time may be two minutes.
• the predetermined number of respiration cycles may be six, over this predetermined amount of time of two minutes.
• the predetermined number of consecutive respiration events may be three during the predetermined amount of time of two minutes.
• a loss of audible signal event is used to mark an end of the RRE, wherein the loss of audible signal event is a period of no audible breathing greater than a predetermined time.
• the processor may be further configured to determine respiratory rate from the one or both of the audible breathing and the snoring.
• the processor may be further configured to analyse the input audio signal to detect an elevated respiration rate after a detected quiet period to confirm an apnoea event.
• the processor may be configured to characterize frames of the input audio signal based upon their correspondence with one or more signal types.
• the one or more signal types include audible breathing, snoring, coughing, noise disturbance, speech, quiet and unknown.
• the processor may be further configured to determine respiration rate for the determined at least one reliable respiration epoch.
• the method may involve analyzing the input audio signal to detect recovery breathing by analysis of audio level, rate and/or frequency after a detected quiet period.
• the method may involve analyzing the input audio signal to detect respiration rate after a detected quiet period to confirm an apnea event.
• the method may involve filtering the input audio signal to remove any one or more frequency components attributable to speech sounds, air handling equipment sounds, traffic sounds, weather sounds.
• the method may involve adjusting, with the processor, a gain control for the input audio signal.
• the method may involve establishing, with the processor, a noise floor and a noise floor threshold with the input audio signal.
• Some versions of the present technology may include a non-transient, computer-readable medium having processor control instructions retrievable therefrom that, when executed by a processing device, cause the processing device to perform any of the methods discussed herein for detecting an event of sleep disordered breathing.
• FIG. 4 illustrates frequency component splitting of the signal of FIG. 3 after transform into discrete bands or frames.
• Upper section A shows breathing signal amplitude with time (an input signal).
• Lower section B shows breathing signal frequency components from 0 Hertz to 4k Hertz with time of the above input signal in frame periods (FF1..FF6) of a suitable time duration (e.g., about 250 ms each).
• FIG. 5 illustrates a process for noise floor detection in the signal of FIG. 4.
• Upper section A shows breathing signal low signal amplitude with time (an input signal).
• Lower section B shows low frequency components from 0 Hertz to 4k Hertz with time of the above input signal.
• FIG. 6H shows an audio signal and frequency spectrogram during quiet breathing. Both the input signal amplitude and frequency components are low because the sound level is low.
• an RRE can be considered a reliable respiration epoch determined from the input audio signal. It might be anything from a period of perfectly normal audible breathing that lasts for just 10 minutes during a whole night of inaudible breathing, to a brief period of consistent snoring followed by a whole night of apnoeic events. [86] Important considerations include that:
• An END section or phase comprising also a period of consistent audible breathing, which may include snoring.
• portions of signal 12 associated with each of quiet breathing (FIG. 6H), snoring (FIG. 6A, 6C), apnea snoring (FIG. 6B), and coughing (FIGs. 6D) may be distinguishable from each other in terms of frequency, time and amplitude.
• Each of these main bands (marked as MB1-MB4) is illustrated in FIG. 6E with reference to the six 0.25s frame files (FF1-FF6) introduced in FIG. 4.
• an RRE is a prolonged epoch of snoring or breathing signals having significant characteristics.
• Several indicative characteristics of an RRE may be: (1) commencement of audible breathing or snoring signals after a number of seconds, typically 60 seconds, of repetitive and uninterrupted breathing or snoring signals;
• the indicia from process 190 may optionally also be output one or more third parties such as via wired or wireless communications link with the screening device 20.
• the indicia may, for example, be sent together with a portion of input signal 12 via transmitter of the device 20 to a database for alternative of subsequent analysis, or other use. Alternatively, portions of the indicia may be concurrently sent to the user and various databases with signal 12 for subsequent analysis or use.

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• Health & Medical Sciences (AREA)
• Life Sciences & Earth Sciences (AREA)
• Engineering & Computer Science (AREA)
• Public Health (AREA)
• Surgery (AREA)
• Veterinary Medicine (AREA)
• General Health & Medical Sciences (AREA)
• Animal Behavior & Ethology (AREA)
• Biomedical Technology (AREA)
• Heart & Thoracic Surgery (AREA)
• Medical Informatics (AREA)
• Molecular Biology (AREA)
• Physics & Mathematics (AREA)
• Pathology (AREA)
• Biophysics (AREA)
• Pulmonology (AREA)
• Physiology (AREA)
• Artificial Intelligence (AREA)
• Computer Vision & Pattern Recognition (AREA)
• Psychiatry (AREA)
• Signal Processing (AREA)
• Measurement Of The Respiration, Hearing Ability, Form, And Blood Characteristics Of Living Organisms (AREA)
• Orthopedics, Nursing, And Contraception (AREA)

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Cited By (10)

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Citations (5)

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• 2016
  • 2016-08-17 JP JP2018508171A patent/JP7100576B2/ja active Active
  • 2016-08-17 US US15/752,647 patent/US20180256069A1/en not_active Abandoned
  • 2016-08-17 CN CN202410315288.5A patent/CN118512166A/zh active Pending
  • 2016-08-17 EP EP16767143.7A patent/EP3337388B1/en active Active
  • 2016-08-17 WO PCT/EP2016/069496 patent/WO2017029317A1/en not_active Ceased
  • 2016-08-17 EP EP22198169.9A patent/EP4176800A1/en active Pending
  • 2016-08-17 CN CN201680057902.6A patent/CN108135486A/zh active Pending
• 2021
  • 2021-05-26 JP JP2021088458A patent/JP7418376B2/ja active Active
• 2024
  • 2024-04-16 US US18/636,890 patent/US20240260850A1/en active Pending

Patent Citations (5)

Non-Patent Citations (1)

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