1) The document discusses obstructive sleep apnea (OSA) and methods to detect and classify the severity of OSA.
2) Features were extracted from breathing sound recordings and used to build three classifiers to distinguish between mild, moderate, and severe OSA.
3) The classifiers were tested on new subject data and achieved sensitivity and specificity rates ranging from 70-95% depending on the classifier and OSA severity group, demonstrating the potential for accurate OSA detection and assessment using breathing sound analysis.
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From Helmholtz resonance
equation, we got that
First formant frequency
is shifted down as AHI
increases
As AHI is increased,
the airway get narrower
So increasing variability
as AHI increases
Forcing air to flow through a
deformable tube, will
generate spontaneous
oscillations.
21. 21
Mild & Severe
subjects
Mild & Moderate
subjects
Moderate &
Severe subjects
2 features 3 features2 features
F(1,81) = 30.58, P = 3.81*10-7
22. 22
Feature 1 is the ratio between mean of the
inspiration and expiration phases’ summation at
130-250 Hz over 1200-1300 Hz during breathing
from mouth.
Feature 2 is the
skewness in frequency
within 120-400 Hz.
Expiration phase from
nose
Feature 3 is the slope
within 210-370 Hz.
Inspiration phase from
mouth
Feature 4 is the mean of the
subtraction of signals come out from
mouth and nose during inspiration
phase within 1100-1350 Hz.
Feature 5 is the slope within
210-400 Hz.
Inspiration phase from mouth
Feature 6 is the skewness in
frequency within 120-400 Hz.
Inspiration phase from mouth
23. 23
6 features’
values /subject
Classifier 3
using
features 4, 5, & 6
Classifier 2
using
features 1 & 3
Classifier 1
using
features 1 & 2
votingmild-OSA or moderate-OSA
moderate-OSA or severe-OSA
mild-OSA or severe-OSA
Decision
Mild, Moderate, and Severe OSA groups Mild and High OSA groups
AHI>10
Majority voting
Mild Mild X Mild
Else High
24. 24
Assigned Class Total
True Class
AHI < 5 10 ≤ AHI ≤
25
AHI ≥ 30
AHI < 5 47 6 3 56
10 ≤ AHI ≤
25
4 20 3 27
AHI ≥ 30 1 5 16 22
Assigned Class Total
True Class
AHI < 5 AHI ≥ 10
AHI < 5 47 9 56
AHI ≥ 10 5 43 49
Sensitivity = 87.8% Specificity = 84%
Data set
Classifier 1
Mild & Severe
Classifier 2
Mild & Moderate
Classifier 3
Moderate & Severe
Sensitivity 95.5% 85.2% 88.9%
Specificity 82.1% 84% 77.3%
Classifying training data (105
subjects)
Total classification accuracy79.1% 85.7%
25. 25
Classifying 30 new
subjects
Total classification accuracy66.7% 83.78%
Classifying 37 new
subjectsData set
Classifier 1
Mild & Severe
Classifier 2
Mild & Moderate
Classifier 3
Moderate & Severe
Sensitivity 90% 80% 70%
Specificity 90% 90% 70%
Assigned Class Total
True Class
AHI < 5 10 ≤ AHI ≤
25
AHI ≥ 30
AHI < 5 8 1 1 10
10 ≤ AHI ≤
25
2 5 3 10
AHI ≥ 30 0 3 7 10
Assigned Class Total
True Class
AHI < 5 AHI ≥ 10
AHI < 5 8 2 10
AHI ≥ 10 4 23 27
Sensitivity = 85.2% Specificity = 80%
From Passive electrical resistance
analogy, Power is higher
at wider airway cavity systems
Power is maintained
at high frequencies
in high compliance systems
From Hook’s law, Power is higher
at high frequencies
in high stiffness systems