Implementation methods

Politecnico di MilanoDipartimento di Elettronica, Informazione e Bioingegneria (DEIB)

Biomed Meeting

Sara Bridiosara.bridio@mail.polimi.it

Thursday, May 12, 2016

Giulia Coregiulia.core@mail.polimi.it

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

giulia.core@mail.polimi.itsara.bridio@mail.polimi.it

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Politecnico di Milano, NECST lab, DEIB, building 20, via Ponzio, 34/5, 20133, Milano

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