SLEEP APNEA MONITORING AND DIAGNOSIS BASED ON PULSE OXIMETERY AND TRACHEAL SOUND SIGNALS

• US 20120071741A1
• Filed: 09/21/2010
• Published: 03/22/2012
• Est. Priority Date: 09/21/2010
• Status: Abandoned Application

• Current Assignee: TR Technologies Incorporated
• Original Assignee: TR Technologies Incorporated
• Inventors: Moussavi, Zahra, Yadollahi, Azadeh
• Application Number:
  US12/886,640
• Publication Number:
  US 20120071741A1
• Time in Patent Office: Days
• US Class Current: 600/340
• CPC Class Codes: A61B 5/14551 for measuring blood gases
• CPC Class Codes: A61B 5/4818 Sleep apnoea
• CPC Class Codes: A61B 5/6826 Finger
• CPC Class Codes: A61B 5/7264 Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems neural networks per se G06N3/00; expert systems per se G06N5/00
• CPC Class Codes: A61B 5/7282 Event detection, e.g. detecting unique waveforms indicative of a medical condition A61B5/4094, A61B5/4818, A61B5/349, A61B5/0823 take precedence
• CPC Class Codes: A61B 7/003 Detecting lung or respiration noise

Abstract

Detection of apnea/hypopnea events to calculate an apnea/hypopnea index is obtained by analysis of breathing pattern of a patient from breathing and snore sounds and a finger probe recording the SaO2 signal. A detector analyzes microphone signals to detect breath, snore and noise sounds in response to a detected drop in the SaO2 level greater than 2% and to extract and analyze the breathing sounds from a limited time period starting prior to the drop of the SaO2 signal and ending at least at the end of each drop. Separated time periods are divided phases with snore sounds and those with breathing sounds and an estimated breathing volume adjacent to a snore phase is used to estimate the airflow of the snore phase. The relative and absolute energy and duration of the sound periods is used to classify the sound periods into the three groups of breath, snore and noise.

Claims

25. Apparatus for use in analysis of breathing pattern of a patient during sleep for detection of apnea/hypopnea events comprising:

a microphone arranged to be located on the patient for generating signals in response to breathing and snore sounds from the patient;

a detector module for receiving and analyzing the microphone signals to extract data relating to the breathing;

wherein the detector module is arranged to extract and separate time periods and to divide those periods into groups with snore sounds, groups with breathing sounds and groups with noise;

and wherein the detector module is arranged to calculate the relative and absolute energy and duration of the sound periods to classify the sound periods into the three groups of breath, snore and noise.

1. Apparatus for use in analysis of breathing pattern of a patient during sleep for detection of apnea/hypopnea events comprising:

a microphone arranged to be located on the patient for generating signals in response to breathing and snore sounds from the patient;

a finger probe Oximeter to be located on the patient'"'"'s finger for recording the patient'"'"'s blood SaO2 signal;

a detector module for receiving and analyzing the SaO2 signals and for receiving and analyzing the microphone signals to extract data relating to the breathing;

wherein the detector module is arranged to analyze the SaO2 signal for detecting the drops in the Oxygen level of the patient;

and wherein the detector module is arranged to analyze the microphone signals to detect breath, snore and noise sounds in response to a detected drop in the SaO2 level.

2. The apparatus according to claim 1 wherein the detector module is arranged to extract the drops in the SaO2 signal or greater than a predetermined level and to extract and analyze the breathing sounds from a limited time period starting prior to the drop of the SaO2 signal and ending at least at the end of each drop.

3. The apparatus according to claim 1 wherein the detector module is arranged to calculate from the analysis of the breathing sounds and SaO2 signal an apnea/hypopnea index.

4. The apparatus according to claim 3 wherein the index is calculated from the amplitude of SaO2 and the amount of its drop in the time period.

5. The apparatus according to claim 2 wherein the drop is at least of the order of 2%.

6. The apparatus according to claim 2 wherein the time period is at least of the order of 10 seconds before the drop.

7. The apparatus according to claim 1 wherein the detector module is arranged to extract and separate time periods into groups with snore sounds and groups without snore sounds.

8. The apparatus according to claim 1 wherein the detector module is arranged to extract and separate time periods and to divide those periods into groups with snore sounds, groups with breathing sounds and groups with noise.

9. The apparatus according to claim 8 wherein a weighted average of the groups and the SaO2 drop and amplitude are used to detect apnea/hypopnea events.

10. The apparatus according to claim 8 wherein the detector module is arranged to calculate the relative and absolute energy and duration of the sound segments to classify the sound segments into the three groups of breath, snore and noise.

11. The apparatus according to claim 10 wherein the detector module is arranged to calculate the energy, number of zero crossing rate (ZCR) and first formant of the sounds in a plurality of separate windows of data, to classify the sound segments into the groups of breath and snore.

12. The apparatus according to claim 11 wherein the detector module is arranged to use the Fisher Linear Discriminant (FLD) method to transform the three features into a new 1-dimential space and then minimize the Bayesian error to classify the sound segments into the groups of breath and snore.

13. The apparatus according to claim 1 wherein the detector module is arranged to filter extraneous sounds related to high frequency noises and/or heart sounds and movements.

14. The apparatus according to claim 1 wherein the detector module divides the microphone signals into separate windows and uses the log of the variance (LogVar) of the sound in every window of data.

15. The apparatus according to claim 1 wherein the detector module is arranged to calculate a flow estimate by the equation from the first few breaths of the patient during the wake time at a self-calibration state to estimate the relative amount of airflow for monitoring the patient'"'"'s breathing pattern.

16. The apparatus according to claim 1 wherein the detector module uses an estimated breathing volume in adjacent phases to a snore phase to correctly estimate the airflow of the snore phase.

17. The apparatus according to claim 16 wherein the detector module is arranged to use the estimated airflow to detect periods of apnea and/or hypopnea.

18. The apparatus according to claim 1 wherein the detector module includes a display of the relative airflow and the detected apnea/hypopnea episodes and other statistical info for a clinician.

19. The apparatus according to claim 18 wherein the display is capable of playing the breathing and classified snoring sounds in any zoomed-in or zoomed-out data window.

20. The apparatus according to claim 1 wherein the detector module is arranged to display the extracted information about the frequency and duration of apnea/hypopnea episodes, and their association with the level of oximetry data in a separate window for the clinician.

21. The apparatus according to claim 1 wherein the microphone is wireless.

22. The apparatus according to claim 1 wherein there is provided additionally a microphone to collect lung sounds from the patient.

23. The apparatus according to claim 1 wherein there is provided a third microphone arranged to receive sounds from the patient in the vicinity of the patient so as to be sensitive to snoring and ambient noises and wherein the detector module is arranged to use adaptive filtering to extract the signals relating to the snoring and ambient noises from the signals including the breathing sounds, snoring sounds and noises.

24. The apparatus according to claim 1 wherein the microphone is arranged to collect tracheal sounds from the neck.

26. The apparatus according to claim 25 wherein the detector module is arranged to calculate the energy, number of zero crossing rate (ZCR) and first formant of the sounds in a plurality of separate windows of data, to classify the sound segments into the groups of breath and snore.

27. The apparatus according to claim 26 wherein the detector module is arranged to use the Fisher Linear Discriminant (FLD) method to transform the three features into a new 1-dimential space and then minimize the Bayesian error to classify the sound segments into the groups of breath and snore.

28. Apparatus for use in analysis of breathing pattern of a patient during sleep for detection of apnea/hypopnea events comprising:

a microphone arranged to be located on the patient for generating signals in response to breathing and snore sounds from the patient;

a detector module for receiving and analyzing the microphone signals to extract data relating to the breathing;

wherein the detector module is arranged to extract and separate time periods and to divide those periods into at least groups with snore sounds and groups with breathing sounds;

wherein the detector module uses an estimated breathing volume in adjacent phases to a snore phase to correctly estimate the airflow of the snore phase.

28-1. The apparatus according to claim 27 wherein the detector module is arranged to use the estimated airflow to detect periods of apnea and/or hypopnea.