a signal processing approach to the detection of …...classification of respiratory signals by...
TRANSCRIPT
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A Signal Processing Approach to the Detection of Pulmonary Edema
EEE24B
Nicole Lim Sze Ting
Dr Ser Wee
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Pulmonary Edemathe accumulation of excess fluid in the lungs
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Limitations of current methods
Accuracy dependent on
clinician's experience
Costly, require bulky machines
Time-consuming
Exposure to radiation
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Proposed method:
ML algorithm
Faster
More consistent and reliable
Portable;
Suitable for ambulatory use
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ACC 95.7% with 5 features
via kNN (k = 3)
Previous studies
Yang, F., Ser, W., Yu, J., Foo, D., Yeo, D., Chia, P., & Wong, J. (n.d.). Lung Water Detection using Acoustic Techniques (Rep.).
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AIM: Improve existing algorithm
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Methodology
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1
Data Collection & Pre-Processing
40s
Audio recordings of lung sounds
are labelled by a doctor.
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1
Data Collection & Pre-Processing
Recordings are divided into 3
second samples.
40s
40s
40s
3s
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1
Data Collection & Pre-Processing
40s
40s
3s
1024
x x x xx
x xx
x x xx
1024
x x x xx
x xx
x x xx
1024
x x x xx
x xx
x x xx
1024
x x x xx
x xx
x x xx
Samples are divided into windows
of 1024 points, with 50% overlap.
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1
Data Collection & Pre-Processing
100
y
x
t
1
(t
1
, y
1
)
(t
1
, ✕ 100)
y
1
y
max
Windows are normalised such that
values range from 0 to 100.
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3s
fnNo. of windows
=
∑
Average feature value of windows
gives final feature value of sample.
1024
x x x xx
x xx
x x xxfn1024
x x x xx
x xx
x x xxfn1024
x x x xx
x xx
x x xxfn1024
x x x xx
x xx
x x xxfn
Feature Extraction
2
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3
Feature Selection
(mean
A
- mean
B
)
2
variance
A
+ variance
B
Fisher’s ratio =
Fisher's Ratio
Higher FR Lower FR
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Feature Extraction
2
Features used in the detection of wheezing
Features previously used in the detection of PE
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Aydore, S., Sen, I., Kahya, Y., &
Mihcak, M. (n.d.). Classification of
Respiratory Signals by Linear
Analysis (Rep.).
Feature Extraction
2
Previous algorithms for the detection of wheezing
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Feature Extraction
2
Features used in the detection of wheezingKurtosisDegree of peakedness of distribution
Renyi EntropyRandomness of system
Mean Crossing Irregularity & FrequencyMean-crossing behaviour
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Feature Extraction
2
Fisher's Ratio of features used in the detection of wheezingKurtosisRenyi EntropyMCI
MCF
0.0010
0.0053
0.0086
0.0349
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Feature Extraction
2
Features previously used in the detection of PE13 Mel-Frequency Cepstral Coefficients (MFCCs) Mimic doctor’s logarithmic
perception of lung sounds during
auscultation
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Feature Extraction
2
Features previously used in the detection of PE13 Mel-Frequency Cepstral Coefficients (MFCCs) Mel scale:
Approximated
frequency resolution
of the human auditory
system
Mels
kHz
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Feature Extraction
2
Features previously used in the detection of PE
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Feature Extraction
2
Features previously used in the detection of PERatio & Difference between MFCCs● Hypothesised to have higher
discriminating power
● Derived from top 6 MFCC
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Feature Extraction
2
Features previously used in the detection of PE
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Using Fisher's Ratio in Feature Selection
3
Feature Selection
● Features with higher FR values
are added first
○ Reduce number of features
○ Higher classification accuracy
○ Shorten training time
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3
Feature Selection
Final feature ranking by FR
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Signal Classifiersk-Nearest Neighbours (kNN)Most common label amongst k
nearest neighbours
Support Vector Machines (SVM)Decision boundary/hyperplane
determined by support vectors
Signal Classification
4
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Evaluation metrics
Model Evaluation
5
TPRTrue positive rate
TNRTrue negative rate
ACCDetection accuracy
10-fold cross validation
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Evaluation metrics
Model Evaluation
5ACC
ACC
Healthy Unhealthy
Algorithm
Health
yU
nh
ealth
y
Do
cto
r
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Evaluation metrics
Model Evaluation
5Missed
diagnosis TPR
Healthy Unhealthy
Algorithm
Health
yU
nh
ealth
y
Do
cto
r
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Evaluation metrics
Model Evaluation
5
False alarmsTNR
Healthy Unhealthy
Algorithm
Health
yU
nh
ealth
y
Do
cto
r
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Evaluation metrics
Model Evaluation
5
TPRTrue positive rate
TNRTrue negative rate
ACCDetection accuracy
10-fold cross validation
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10-fold cross validation
Model Evaluation
5 training training training
training training training
training training training
training
testing
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10-fold cross validation
Model Evaluation
5 training training training
training training training
training training training
training
testing testing testing
testing testing testing
testing testing testing
testing
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Removing features that cause decrease in ACC
Performance Improvement
6
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Removing features that cause decrease in ACC
Performance Improvement
6 1. Rank features by decrease in
ACC caused
2. Remove features from the
model in order of descending
decrease caused
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Results & Discussion
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Best Model:kNN
ACC
86
TPR
89
TNR
82
kNN (k = 1)on 24 features
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Best Model:kNN
Effect of removing 6 features
TPR
89 88
ACC
86 85
TNR
82 80
24 18 24 18 24 18
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Best Model:SVM
SVM (C = 1)on 26 features
ACC
86
TPR
88
TNR
82
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Best Model:SVM
Effect of removing 9 features
TPR
88 88
ACC
86 86
TNR
82 83
26 17 26 17 26 17
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Comparison to results of previous
works
kNN
ACC
85 85
13 18TPR
85 88
13 18TNR
85 80
13 18
YF ME
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Comparison to results of previous
works
SVM
TPR
88 88
ACC
87 86
TNR
84 83
12 17 12 17 12 17
YF ME
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Comparison to results of previous
works
● New features with higher FR
values via ratio/difference of
MFCC
● Did not improve ACC
Evaluation of my approach
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Strategies hypothesised to improve ACC
Strategy Evaluation
Features used for the detection of other breathing anomalies
Derivation of features using ratio and difference
Removal of features that decrease ACC to improve performance
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Strategies hypothesised to improve ACCFeatures used for the detection of other breathing anomalies ● Not useful in distinguishing
healthy and unhealthy signals
○ PE: crackle sounds
Strategy Evaluation
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Strategies hypothesised to improve ACCDerivation of features using ratio and difference● Can derive features with higher
FR ⇒ ↑ discriminating power
● Time-consuming
○ Comparison of boxplots
Strategy Evaluation
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Strategies hypothesised to improve ACC
Renyi entropy Randomness of system
Mean Crossing Irregularity (MCI) & Mean Crossing Frequency (MCF) Mean-crossing behaviour
Removal of features that decrease ACC to improve performance● ↓ training time
● ↓ algorithm complexity
● Can potentially improve ACC
○ SVM algorithm
Strategy Evaluation
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Limitations
Uncertainty in performance evaluation due to validation method
Quality of data & reliability of doctor
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Future Work
Automate derivation of ratio/ difference-based features
Vary number of MFCCs
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Conclusion
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Yang Feng's algorithm performed better
Proposed algorithm● SVM (C = 1) ● 17 features ● ACC 85.8, TPR 88, TNR 83
Key points
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SVM > kNN
TPR
88
ACC
85
TNR
80
24 18 24 18 24 18
Key points
SVM kNN
8886 83
17 17 17
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Wheeze detection features for pulmonary edema detection
Key points
Difference/ratio-based feature derivation for features with higher Fisher's ratio values
Removing features that decrease accuracy to improve performance
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Dr Ser WeeResearch mentor
AcknowledgementsDr Shi Wen
Advice on MATLAB and data handling
Mr Low Kay SiangTeacher-mentor
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Q&A