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A Signal Processing Approach to the Detection of Obstructive Sleep Apnea
NRP: EEE24A
Jovyn Tan Li ShyanHwa Chong Institution
OSA: Obstructive Sleep Apnea
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❏ Sleep disorder where breathing stops for ❏ at least 10 seconds,❏ more than 5 times/hour
1 in 3 Singaporeans
1 billion people worldwide
Heart disease
High blood pressure
Daytime fatigue
Health Complications
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Electromyogram Electro-oculogram Electromyogram Electrocardiogram
Current Form of Diagnosis
Polysomnography (PSG)
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Current Form of Diagnosis
Polysomnography (PSG)
Apnea Hypopnea Index (AHI)
Problems
Distorts OSA condition
Manual data analysis
Cannot analyse all data collected
No differentiation of event severity
Aims of Research
An automated system without full PSG
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Automated 3-class system(Healthy, Hypopnea, Apnea)
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Aims & Objectives
3-class Fisher’s Ratio3
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Electromyogram Electro-oculogram Electromyogram Electrocardiogram
OSA Diagnosis
Oro-nasal thermistor Respiratory effort belts
Proposed method
Data
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14 channels
Data
obs
erva
tions
Sampling rate: 64Hz64Hz * 60s * 60min * 6.2h = 1428480 data points
Methodology
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Signals extracted from data● Oro-nasal airflow● Rib cage movement● Abdomen movement Normalisation
Preparation of data for machine learning
Methodology
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Signals extracted from data● Oro-nasal airflow● Rib cage movement● Abdomen movement
Feature Extraction
Segmentationwindows of 1024
data pointsNormalisation
Preparation of data for machine learning
1024 points= 1 window
Feature Extraction
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❏ 15 features extracted from each signal❏ e.g. mean peak prominence, number of peaks
❏ 5 peak processing thresholds
Preparation of data for machine learning
Methodology
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Signals extracted from data● Oro-nasal airflow● Rib cage movement● Abdomen movement
By box plot analysis
Feature Selection
By 3-class Fisher’s ratio
Conceptualising 3-class Fisher’s Ratio
Segmentationwindows of 1024
data pointsNormalisation
Feature Extraction
Fisher’s Ratio
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❏ Measures discriminating power of a variable
❏ 2-Class FR:
3-Class Fisher’s Ratio
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Feature Selection by Box Plots
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Suitable feature Unsuitable feature
Preparation of data for machine learning
Methodology
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Signals extracted from data● Oro-nasal airflow● Rib cage movement● Abdomen movement
By box plot analysis
Feature Selection
By 3-class Fisher’s ratio
Recursive Feature Elimination
Feature Elimination
Principal Component Analysis (PCA)
Support Vector Machines (SVM) using Matlab
Conceptualising 3-class Fisher’s Ratio
Segmentationwindows of 1024
data pointsNormalisation
Feature Extraction
Classification Results
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2 Classes
3 Classes
Results [2 classes]
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Highest Accuracy: 94.8%Sensitivity: 96%Specificity: 93%
Cubic kernel28/28 features
No PCASelection by FR
Healthy
Hea
lthy
Apn
ea
Apnea
True
Cla
ssPredicted Class
Rib
cage
and
Abd
omen
Mov
emen
ts
Oro-nasal Airflow
Standard Deviation of Peak Prominence of 2 Signals
Results [3 class]
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Highest Accuracy: 83.4%
Medium Gaussian kernel19/28 features
Selection by box plotsPCA enabled
Oro-nasal AirflowHealthy
Hea
lthy
Sev
ere
Apn
ea
Severe Apnea
Mild
Apn
ea
Mild ApneaTr
ue C
lass
Predicted Class
Standard Deviation of Peak Prominence of 2 Signals
Rib
cage
and
Abd
omen
Mov
emen
ts
Conclusion
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An automated system without full PSG1
An automated 3-class system(Healthy, Hypopnea, Apnea)
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3-class Fisher’s Ratio2