personal authentication by SINGLE- CHANNEL ecg

Hanoi University of Science and Technology
Telecommunication and Electronics Department

personal authentication by
SINGLE- CHANNEL ecg

4/28/23

Students:
Class:

Vu th i m in h
BME K58

Instructor:

DR.

Nguyen

viet

dung

1


Purposes

Research about biometric using ECG signal
To authenticate a person lead to identify person in the future

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8

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Background information

•

Block diagram

•

ECG acquisition

•

Pre-processing

•

Feature extraction

•

Classifcation

•

Results

•

Conclusion

•

6

5

4

3

2

1

outline

1. Background information
Biometric
authentication:
"Are you indeed
Mr or Mrs A?"

Biometric

Biometrics

identifcation

•

 



False Acceptance Rate (FAR) 

"Who are you?"

FAR = 



False Reject Rate (FRR) 

FRR = 

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1. Background information

Bio- signal
A material carrier of the information about the state of the
analyzed biological systems.
Give more detailed characteristics about the system

ECG

Non- electric bio- signals
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Electrical bio-signals
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1. Background information
ELECTROCARDIOGRAM (ECG)


f: 0.05 Hz- 100 Hz



A: 1- 10 mV ( dynamic range)



5 peaks and valleys: P, Q, R, S, T

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1. Background information
ELECTROCARDIOGRAM (ECG)


PR: 0.12- 0.25s



QRS: 0.08- 0.12s



QT: 0.35-0.44s



ST: 0.05- 0.15s

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2. Block diagram

Classifcation

Feature
extraction
Pre- processing
Classify ‘A’ or ‘not A’
Find features

ECG Record

P, Q, R, S, T peaks

Record from Kardia

Filter to remove noises

Statistical data

mobile

Digitize data

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3. ECG Acquisition
 Kardia mobile
o To converts electrical impulses from fngertips into ultrasound signals
transmitted to the mobile device’s microphone.

o

Specifcations

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ECG Channels

Single Channel

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

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3. ECG Acquisition
 Kardia mobile

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3. ECG Acquisition
 Web plot digitizer
 To digitize the signals into numeric format

 Sample rate: 350 Hz

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3. ECG Acquisition
 Web plot digitizer

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Exp eriment s et 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 fngers 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 fnishes, fll up your individual

information as instructor in app.
=> Divide into 2 group: the authenticated person and the others.

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4. Pre-processing
Filter: Band-pass flter.
• High-pass flter: 0.05 and 0.5 Hz (low-frequency cutoff )
• Low-pass flters : 40, 100, and 150 Hz (high-frequency cutoff).

Filter confguration

0.05–40 Hz

0.5–40 Hz

0.05–100 Hz

0.5–100 Hz

0.05–150 Hz

0.5–150 Hz

Cutting segments: from 20s to 50s

Choose

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4. Pre- processing
 Pre-processing : Chebychev band-pass flter 0.5- 40 Hz.
The power spectrum of original signal

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The power spectrum of fltered signal

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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:

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5. Feature extraction

Daubechies 4

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Daubechies 6

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5. Feature extraction
Decomposition level 4

•
•
•
•

S: original signal/ time series data
Ai: approximation low frequency content
Di: Detail high frequency content
Level 1 decomposition:
S = A1+ D1

•

Level decomposition 2:
S = A2 + D2 +D1

•

Level decomposition n- level:

S = An + Dn +Dn-1 + Dn-2+…+ D1

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5. Feature extraction
•

Find R peak in decomposition level 4

From R peaks,

Find peak >=

Find peaks

fnd the other

60% max

inversely on

peaks based

value

original signal

on duration of
them

Let frst
peak be R
peak

Find the other
peaks based on
the minimum and
maximum

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Mean values of
P, Q, R, S, T
peaks

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5. Feature extraction
o

Mean

o

Median absolute deviation (MAD)

o

Standard deviation (SD)

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Skewness: a measure for the degree of symmetry in the variable distribution.

o

Kurtosis: a measure for the degree of tailedness in the variable distribution

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6. Classif cation

Classifcation

Unsupervised

Supervised

classifcation

classifcation

Support vector
machine (SVM)

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K- Nearest Neighbor

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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

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Feature extraction: 11 features



MeanP, MeanQ, MeanR, MeanS, MeanT



Mean, Median, SD, MAD, Skewness, Kurtosis

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7. Results

Classification

Train

Validate
(Test/ train ratio)
Accuracy of train and different validate

10/90

20/80

30/70

40/60

100%

90.9%

91.3%

94.3%

93.5%

99.1%

90.9%

91.3%

97.1%

91.3%

KNN
SVM

⇒
⇒

Test/ train ration: 30/ 70
Weighted KNN, Medium Gausisan SVM

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7. Results
Trained model: Medium Gaussian SVM

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Trained model: Weighted KNN

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7. Results

Results of test 30 samples with 15 samples of authenticated person

Trial

SVM

KNN

87.50 %

78.13%

0.125

0.125

0.000

0.094

Accuracy (%)

FAR

FRR

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