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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
Scenario
2
Photoplethysmography
Biometric recognition
PPG signal from subject 1
PPG signal from subject 2
3
Preprocessing
Features extraction
Test definition
Evaluation
Our project
4
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
5
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
6
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
7
Preprocessing
Segments Matrix for each subject
SegmSamples
1 2 … 256
Segm 1
Segm n
…
rows: segments belonging to a subject columns: samples
8
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
9
Features extractionFirst derivative Second derivative
Central finite difference
More accurate
Less sensitive to noise
Matlab function diff(X,ord)
10
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
11
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
12
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
13
Test definition
(*)
(*) www.angelsensor.com
Number of subjects
Acquisition time
Physiological conditions
Stress
Physical
Acquisition trials
14
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
15
Preprocessing
Features extraction
Test definition
Evaluation
Success?no yes
Robust recognition
system based on PPG signal
[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
