implementation methods
TRANSCRIPT
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Politecnico di MilanoDipartimento di Elettronica, Informazione e Bioingegneria (DEIB)
Biomed Meeting
Sara [email protected]
Thursday, May 12, 2016
Giulia [email protected]
Implementation methods
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Scenario
2
Photoplethysmography
Biometric recognition
PPG signal from subject 1
PPG signal from subject 2
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3
Preprocessing
Features extraction
Test definition
Evaluation
Our project
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Preprocessing
fir1( #coefficients , f_cutoff_norm )
filtfilt( filter, 1 , signal )
Filtering
FIR filter
f_cutoff = 8 Hz
Signal frequency 0,001-2 Hz
Low pass filter
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PreprocessingPeak detection algorithm AMPD [1]
Segmentation
256 samples for each segment
Resample
[1] F. Scholkmann, J. Boss and M. Wolf, “An Efficient Algorithm for Automatic Peak Detection in Noisy Periodic and Quasi-Periodic Signals“, 2012
Drift elimination
Segments of PPG signal from subject 1
Segments of PPG signal from subject 1, after drift elimination
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Preprocessing
Normalization
Normalized segments of PPG signal from subject 1
Segments of PPG signal from subject 1 Segments of PPG signal from subject 1, after drift elimination
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Preprocessing
Segments Matrix for each subject
SegmSamples
1 2 … 256
Segm 1
Segm n
…
rows: segments belonging to a subject columns: samples
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Features extractionTemplate creation
PPG signal template
Template = mean(segm_mat)
Segments matrix
Template of PPG signal from subject 1
Template of PPG signal from subject 2
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Features extractionFirst derivative Second derivative
Central finite difference
More accurate
Less sensitive to noise
Matlab function diff(X,ord)
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Features extraction1st derivative template 2nd derivative templateTemplate of 1st derivative of PPG signals from subject 1
Template of 1st derivative of PPG signals from subject 2 Template of 2nd derivative of PPG signals from subject 2
Template of 2nd derivative of PPG signals from subject 1
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Features extractionTemplate matching
3 matrixes
Euclidean distance
3 matrixeswith distances
One for each type of templatePPG signal template1st derivative template2nd derivative template
Sum of distances dij
One for each type of template
i: template subject i
j: template subject j
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Features extraction3 matrixes with distances
T1 T2
T1
T2
…
…
…
…
Tn
Tn
0
0
0
0
0
d12
d12
d1n
d1n
d2n
d2n
…
…
… …
… … …
……
…
…
… …
…
Ti: Template belonging to subject i dij: sum of euclidean distances point to point between Ti and Tj
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Test definition
(*)
(*) www.angelsensor.com
Number of subjects
Acquisition time
Physiological conditions
Stress
Physical
Acquisition trials
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EvaluationClassificator k-nearest neighbors
• Subject 1
Subject 2
Subject 3
Template to assign
• k arbitrarily determined• Euclidean distance calculated between and stored data points• Majority ranking on Euclidean distance: the template is assigned to the class with the majority among the k closest templates
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Preprocessing
Features extraction
Test definition
Evaluation
Success?no yes
Robust recognition
system based on PPG signal
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[email protected]@mail.polimi.it
Emails
https://www.facebook.com/bioreds.project/
Politecnico di Milano, NECST lab, DEIB, building 20, via Ponzio, 34/5, 20133, Milano
https://twitter.com/BioREDs_necst
http://www.slideshare.net/BioREDsSlideshare