Acoustic Sleep Apnea Diagnosis
Sleep apnea syndrome is a common and serious respiratory disorder. Apnea is a cessation of airflow to the lungs (usually during sleep) which lasts for at least 10 seconds. Polysomnography (PSG) during the entire night is currently the accepted Gold Standard diagnostic method of sleep apnea. The standard PSG consists of recording various physiological parameters including EEG, ECG, EMG of chins and legs’ muscles, nasal airflow, electro-oculogram (EOG), abdominal and thoracic movements, and blood oxygen saturation (SaO2) and usually snoring sounds. However, the high cost of the system, discomfort of the electrodes connecting to the body and the high amount of information required to be analyzed are the main disadvantages of this method. To obtain a full sleep study in Manitoba there is currently a long waiting list (3300 patients) and long waiting times (3.4 to 8.3 years). Hence, healthcare providers and payers are seeking alternative methods, portable devices, and automated/intelligent systems in which sleep apnea testing can be done in the patient’s home. We have been one of the first groups who started to use respiratory sounds for apnea/hypopnea detection. Our work on this topic has been recognized and funded by industry (TRLabs and Sasktel, and BCC) to develop a prototype of a system including a portable device that has the capability to record respiratory and snore sounds as well as SaO2, and transmit data to a center, in which a smart expert software detects apnea/hypopnea events and present the apnea index and other clinical information to a physician. This system has been patented in US and Europe. The advantages of our proposed system to other portable devices are that it is as accurate as PSG but without the need for supervision, is very convenient for patients and also has a fast and user friendly analysis.
Since 2010, we started to use tracheal breathing sounds and advance signal processing with machine learning to develop a quick screening tool to detect OSA in patients whilst awake. That has resulted a technology that we call it AWakeOSA (patented) and published in several journal papers. AWakeOSA technology records a few breathing sounds during wakefulness and within a few seconds can identify people with OSA and in need of treatment with an accuracy above 90%. To learn more about our acoustic OSA tecnology you may click on this link and see the slides.
Here are the list of our publications related to this topic:
Patents
-
Systems and Methods for Screening Obstructive Sleep Apnea During Wakefulness Using Anthropometric Information and Tracheal Breathing Sounds,” Moussavi Z. and Elwali A., Application #: 3089395 filed by University of Manitoba on Aug 7, 2020
-
Acoustic System and Methodology for identifying the risk of obstructive sleep apnea during wakefulness, Moussavi Z. and Karimi D., US 20140142452 A1, May 2014.
-
System and Methods for estimating respiratory airflow, Moussavi Z. and Yadollahi A, US 20140330095 A1, Issued June 2018, Licensed to BresoTec, Ltd.
-
System and Methods of Acoustical Screening for Obstructive Sleep Apnea during Wakefulness, Moussavi Z. and MontazeriPouragha A., US 20130253357 A1, Filed Sept. 2013, Approved Dec. 2017.
-
Breathing Sound Analysis for detection of sleep apnea/hypopnea events, Moussavi Z., Yadollahi A, and Camorlinga, TRLabs, US, 7559903, July 2009 (issued), Licensed to BresoTec, Ltd.
Selected Full Papers Published in Refereed Journals
-
Elwali A. and Moussavi Z., “Predicting Polysomnography parameters from anthropometric features and breathing sounds recorded during wakefulness,” J Diagnostics, Accepted April 2021.
-
Hajipour F, Elwali A., Jafari-Jozani M. and Moussavi Z., “Regularized Logistic Regression for Obstructive Sleep Apnea Screening during Wakefulness Using Daytime Tracheal Breathing Sounds and Anthropometric Information,” J Medical & Biological Engineering & Computing, 57(12):2641-2655, 2019. DOI: 1007/s11517-019-02052-4
-
Elwali A. and Moussavi Z., “A novel diagnostic decision making procedure for screening obstructive sleep apnea using anthropometric information and tracheal breathing sounds during wakefulness,” Scientific Reports, 9: 11467 (2019) Aug. 2019. https://doi.org/10.1038/s41598-019-47998-5
-
Elwali A. and Moussavi Z., “The effects of anthropometric parameters on the breathing sound features while screening obstructive sleep apnea during wakefulness,” J Medicine and Biological Engineering (JMBE), 39(2):230-237, March 2019. https://doi.org/10.1007/s40846-018-0410-1
-
Elwali A. and Moussavi Z., “Obstructive Sleep Apnea Screening and Airway Structure Characterization during Wakefulness Using Tracheal Breathing Sounds,” Annals in Biomedical Eng, 45(3):851-858, 2017, DOI: 10.1007/s10439-016-1770-8
-
Hajipour F, Giannouli E. and Moussavi Z., “Acoustic characterization of upper airway variation from wakefulness to sleep with respect to obstructive sleep apnea,” J Medical & Biological Engineering & Computing, 58:2375-2385, July 2020. DOI: https://doi.org/10.1007/s11517-020-02234-5
-
Hajipour F, Jafari-Jozani M. and Moussavi Z., “A comparison of regularized logistic regression and random forest machine learning models for daytime diagnosis of obstructive sleep apnea,” J Medical & Biological Engineering & Computing, 58: 2517:2529; Aug. 2020. DOI: https://doi.org/10.1007/s11517-020-02206-9
-
Hajipour F. and Moussavi Z., “Spectral and Phasic Characteristics of Expiratory Tracheal Breathing Sounds during Wakefulness and Sleep in People with Different Levels of Obstructive Sleep Apnea,” J Medicine and Biological Engineering (JMBE), 39(2):244-250, March 2019. https://doi.org/10.1007/s40846-018-0409-7
-
Yadollahi A., Azarbarzin A., Montazeri A and Moussavi, Z., “Respiratory flow-sound relationship during both wakefulness and sleep and its variation in relation to sleep apnea,” J. Ann Biomed Eng., 41(3):537-546, DOI: 10.1007/s10439-012-0692-3, 2013
-
Azarbarzin A., Ostrowski M., Moussavi Z., Hanly P. and Younes M., “Contribution of Arousal from sleep to post-event trachycardia in patients with obstructive sleep apnea,” Sleep, 36(6):881-889, 2013
-
Azarbarzin A. and Moussavi Z., “Snoring Sounds Variability as a Signature of Obstructive Sleep Apnea,” J Med Eng Phys, DOI:10.1016/j.medengphy.2012.06., 35(4):479-85, 2013
-
Azarbarzin A. and Moussavi Z., “Snoring Sounds’ Intra-Subject Variability in Relation to Body Position, Sleep Stage, and Blood Oxygen Level,” J Med Biol Eng Comp, 51(4):429-439, DOI: 10.1007/s11517-012-1011-8, 2013
-
Azarbarzin A. and Moussavi Z., “Snoring Sounds’ Statistical Characteristics Depend on Anthropometric Parameters”, J Biomedical Sciences & Engineering, 5(5):245-254, May 2012
-
Montazeri A., Giannouli E. and Moussavi Z., “Assessment of Obstructive Sleep Apnea by Respiratory Sound Analysis during Wakefulness,” J. Annals on Biomed. Eng., Vol 4, No.4, PP:916-924, (doi:10.1007 /s10439-011-0456-5) 2012
-
Yadollahi A. and Moussavi Z. "The effect of anthropometric variation on acoustical flow estimation," J IEEE Trans. BME, Vol 58, No. 6, PP: 1663-1670, June 2011
-
Yadollahi A., Giannouli E. and Moussavi Z., "Sleep Apnea Monitoring and Diagnosis based on Pulse Oximetry and Tracheal Sound Signals", J. Med. Biol. Eng. Comput., Vol 48, No 11, PP:1087-97, Nov. 2010
-
Yadollahi A. and Moussavi Z., "Automatic Breath and Snore Sounds Classification from Tracheal Sounds Recordings, J. Medical Engineering & Physics, Vol 32, No 9, PP:985-990, Nov. 2010
-
Montazeri A., Giannouli E. and Moussavi Z., “Assessment of Obstructive Sleep Apnea by Respiratory Sound Analysis during Wakefulness,” J. Annals on Biomed. Eng., Vol 4, No.4, PP:916-924, (doi:10.1007 /s10439-011-0456-5) 2012