personal authentication by SINGLE- CHANNEL ecg

BACKGROUND INFORMATION ❑False Acceptance Rate FAR FAR = Number of False Acceptances Number of Identification Attemps ❑False Reject Rate FRR FRR = Number of False Rejections Biometric aut

future

1 BACKGROUND INFORMATION

❑False Acceptance Rate (FAR)

FAR = Number of False Acceptances

Number of Identification Attemps

❑False Reject Rate (FRR)

FRR = Number of False Rejections

Biometric authentication:

"Are you indeed Mr or Mrs A?"

Biometric identification

"Who are you?"

Feature extraction

Find features

P, Q, R, S, T peaks Statistical data

3 ECG ACQUISITION

❖ Kardia mobile

o To converts electrical impulses from fingertips into

ultrasound signals transmitted to the mobile device’s

microphone

o Specifications

Input Dynamic Range 10 mV Frequency Response 0.5Hz - 40 Hz A/D Sampling Rate 300 Hz Resolution 16 bit Heart Rate Range 30- 300 bpm Battery Type 3V Coin Cell Battery life 12 months typical use

3 ECG ACQUISITION

❖ Kardia mobile

3 ECG ACQUISITION

❖ Web plot digitizer

▪ To digitize the signals into numeric

format

▪ Sample rate: 350 Hz

3 ECG ACQUISITION

❖ Web plot digitizer

EXPERIMENT SET UP

❑Object sit on the chair, put two hands on

the table, the device is in front of the object

and next to the phone

❑4 steps:

• Step 1: Press on “Record your EKG” in

Kadia app

• Step 2: Put your fingers on device as Figure

15 and adjust posture until having

continuous signal to start to run There are 2

seconds to stabilize device and relax

• Step 3: After that, keep posture in 1 minute

recording

• Step 4: When recording finishes, fill up your

individual information as instructor in app

=> Divide into 2 group: the authenticated person and the others

4 PRE-PROCESSING

❑Filter: Band-pass filter

• High-pass filter: 0.05 and 0.5 Hz (low-frequency cutoff )

• Low-pass filters : 40, 100, and 150 Hz (high-frequency cutoff)

Filter configuration

0.05–40 Hz 0.5–40 Hz 0.05–100 Hz 0.5–100 Hz 0.05–150 Hz 0.5–150 Hz

Choose

4 PRE- PROCESSING

❑ Pre-processing : Chebychev band-pass filter 0.5- 40 Hz

The power spectrum of original signal The power spectrum of filtered signal

5 FEATURE EXTRACTION

Scaling function of Daubechies Wavelet:

A progression {αk; kϵZ} satisfying the following four conditions for all integer N≥2:

The expression relating the mother wavelet to the scaling function is:

5 FEATURE EXTRACTION

5 FEATURE EXTRACTION

• Find R peak in decomposition level 4

Find peak >=

60% max value

Let first peak be R peak

Find the other peaks based on the minimum and maximum

Find peaks inversely on original signal

From R peaks, find the other peaks based

on duration

of them

Mean values of

P, Q, R, S, T peaks

7 RESULTS

o Total data: 150 samples / 60 samples of

authenticated person

✓120 training data : 60 data of authenticated

person, 6 data/ each other ( total 60)

✓32 testing data: the ratio 16 /16data

o Feature extraction: 11 features

✓MeanP, MeanQ, MeanR, MeanS, MeanT

✓Mean, Median, SD, MAD, Skewness, Kurtosis

7 RESULTS

Accuracy of train and different validate

Test/ train ration: 30/ 70

Weighted KNN, Medium Gausisan SVM

(Test/ train ratio)

KNN

7 RESULTS

Trained model: Medium Gaussian SVM Trained model: Weighted KNN

8 CONCLUSION

❑Classification accuracy of SVM is higher than one of KNN

❑Base on biometric criteria: KNN is better

❑Some causes affect to results:

▪ Small number of samples

▪ Reconstruction

▪ Timer for cutting segments

▪ Feature extraction method

THANK YOU

REFERENCES

o Joseph D B.( n.d ) Biomedical engineering fundamental (3rd edition) Trinity college, Hartfort, Conecticuit ,U.S.A.

o https://www.gemalto.com/govt/inspired/biometrics

o http://cimss.ssec.wisc.edu/wxwise/class/aos340/spr00/whatismatlab.htm

o Ruben J.M.D , Neson E.V.P, Erika U.,Wavelet Daubechies (db4) Transform Assessment for WorldView-2 Images Fusion, Distrital University

Francisco José de Caldas, Carrera 7 No 40B – 53, Bogotá D.C., Colombia.

o Zardoshti-Kermani, M.; Wheeler, B.C.; Badie, K.; Hashemi, R.M EMG feature evaluation for movement control of upper extremity

prostheses IEEE Trans Rehabil Eng 1995, 3, 324–333.

o Englehart, K.; Hudgins, B.; Parker, P.A.; Stevenson, M Classification of the myoelectric signal using time-frequency based representations Med Eng Phys 1999, 21, 431–438

o Phinyomark, A.; Phukpattaranont, P.; Limsakul, C Feature reduction and selection for EMG signal classification Expert Syst Appl 2012,

39, 7420–7431.