Taleb ALASHKAR 1

Medical Multi-signal Signature Recognition Appliedto Cardiac Diagnosis

Supervisor: Eric Fauvet & Olivier Laligant

Centre universitaire Condorcet,Université de Bourgogne

June-13-2012

Taleb ALASHKAR

Outline

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1. Introduction2. Motivation3. Methodology 4. Results5. Conclusion

Introduction

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What is Electrocardiogram (ECG) Signal

Fig 1. ECG Phases

Introduction

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ECG Interpretation

• RR Line• QT Interval• PR Interval• ST Segment• TP Segment

Fig 2. ECG Interpretation

Motivation

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Statistics• 1/3 people in US has Cardiac Problem• Main reason of mortality in developed countries• Costs of healing an caring of patients

Fig 3. Cardiac Problems Costs in US

Motivation

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Why we need automatic analysis system

• Decrease costs • Increase efficiency of diagnosis systems

Fig 4. ECG Automatic Analysis

Methodology

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Methodology of ECG Features Detection

• 1D Nonlinear Filtering Scheme (NLFS)• Mathematical Model for ECG Features Detection• Real Approach for ECG Features Detection

Methodology

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1D Nonlinear Filtering Scheme (NLFS)

• Edge Detection Approach• Decomposing signal into two signals by:

Y +(z)=T(F +(z)S(z))

Y -(z)=T(-F -(z)S(z))

T: Threshold to select the responseF(z): Detector FilterS(z): Original Signal

Methodology

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1D Nonlinear Filtering Scheme (NLFS)

Original Signal

Y+

Y-

Fig 5. NLFS Signals

Methodology

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Mathematical Model for ECG Features Detection

• Applied on: Synthetic & free of noise ECG Signal

Fig 6. Synthetic ECG

Methodology

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Mathematical Model for ECG Features Detection• QRS Peak Detection

1. NLFS on ECG: Y+

2. Differentiation: difY+

3. Thresholding: TdifY+

4. Linear search: Peaks5. RR line

ECG

Y+

difY+

TdifY+

Fig 7. QRS Peak Detection

Methodology

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Mathematical Model for ECG Features Detection• Onset of P or T-wave Detection

1. Defining search window (w0 ,w1 )

Fig 8. ECG waves

Methodology

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Mathematical Model for ECG Features Detection• Onset of P or T-wave Detection

2. Y+ = Y+ [w0 ,w1 ]3. Differentiation difY+ 4. Linear search

P-wave

Y+

difY+

Fig 9. Onset Detection

Methodology

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Mathematical Model for ECG Features Detection• End of P or T-wave Detection

1. Defining search window (w0 ,w1 )2. Y- = Y- [w0 ,w1 ]3. Differentiation difY- 4. Linear search

T-wave

Y-

difY-

Fig 10. End Detection

Methodology

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Mathematical Model for ECG Features Detection• Challenges1. Work for free of noise signal

Fig 11. a) Real ECG, b) Synthetic

Methodology

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Mathematical Model for ECG Features Detection• Challenges2. Variable Morphologies'

Fig 12. a) Inverted T-wave, b) biphasic T-wave

Methodology

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Real Approach for ECG Features Detection

• Starting from previous Mathematical Model• Modification to overcome challenges

Fig 13. Real ECG

Methodology

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Real Approach for ECG Features Detection

• QRS Peak Detection

1. Smoothing ECG: Average Filtering2. 1D NLFS: to get Y+

3. Y+ Differentiation 4. Thresholding

5. Linear Search: C0 is the end of each peak6. Search window : QRS peak = max (ECG[C0 -5, C0 +5])7. Repeat 5, 6 steps up to end of ECG signal8. Defining RR line

Fig 14. QRS Peak Detection

Methodology

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Real Approach for ECG Features Detection

• Synthetic ECG vs Real ECG

Fig 15. a) Synthetic ECG b) Real ECG

Methodology

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Real Approach for ECG Features Detection• Onset of P or T-wave Detection

1. Defining w0 ,w1

2. Y+ = Y+ [w0 ,w1 ]3. Differentiation by 8 samples step

4. Onset is the index of max value of S(i)Fig 16. S(i) fro Y+

Methodology

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Real Approach for ECG Features Detection• End of P or T-wave Detection

1. Defining w0 ,w1

2. Y- = Y- [w0 ,w1 ]3. Differentiation by 8 S(i)

4. Index min value of S(i)5. Shifting by 8 samples to get the End

point Fig 17. S(i) for Y-

Results

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Testing on Standard Database Testing Database• 12 Records of QTMIT Standard Database• Contains Manual Annotations by expert• Each record contains about 30 annotated beats

Results

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Testing on Standard Database Evaluation Parameters1. Sensitivity:

2. Positive Predictivity

3. Mean Error

4. Standard Deviation

Results

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Testing on Standard Database Standard Accepted ErrorFor deciding Automatic Detection is TP, FP or FN

Table 1. Maximum Accepted Error

Results

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Testing on Standard Database Testing Results

 ME (ms) SD (ms) Se % P+ %

P-onset 1.48 11.55 75.16 75.16

P-end -1.747 13.57 71 71

R peak -3.251 2.487 98.43 98.88

T-end -7.93 12.396 90.7 90.7

Table 2. Results

Results

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Testing on Standard Database Comparing with other methods

Method Parameters P-onset P-end QRS T-end

This workSe (%)P+ (%)m±s (ms)

75.1675.16

1.48±11.5

7171

-1.7±13.5

98.8898.88

-3.2±2.48

90.790.7

-7.9±12.3

WTSe (%)P+ (%)m±s (ms)

98.8791.03

2.0±14.8

98.7591.03

1.9±12.8

`99.9299.88NA

99.7797.79

-1.6±18.1

LPD Se(%)P+ (%)m±s (ms)

97.791.17

14±13.3

97.7091.17

-0.1±12.3NA

99.9097.71

13.5±27.0

BayesSe (%)P+ (%)m±s (ms)

99.6NA

1.7±10.8

99.6NA

2.5±11.2NA

100NA

2.7±13.5

Table 3. Comparing Results

Results

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Testing Against NoiseECG signal with 8 Different Level of additive noise.

Noise Level L1 L2 L3 L4 L5 L6 L7 L8PSNR (dB) 113 106.9 100.9 97.4 94.7 86.9 80.8 77.4

L1 L2 L3 L4 L5 L6 L7 L80

20

40

60

80

100

120

PonPoffRTonToff

Noise Levels

Se(%)

Table 4. Noise Levels

Fig 18. Testing against noise results

Conclusion

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Pros

1. Exploiting 1D NLFS in ECG features Detection2. Fast & Robust to noise approach3. Possibility to improve performance

Cons:

4. Less performance than other method5. Not sufficient for special ECG cases

Conclusion

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Future Work1. Developing to detect QRS onset/end 2. Detection of P and T-waves peaks3. Handling special clinical cases in ECG

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THANKS

Q&A