Download Time Frequency Analysis and Wavelet Transforms Oral Presentation

Time Frequency Analysis and Wavelet Transforms
Oral Presentation
Applications of Discrete Wavelet Transform
in ECG Signal Processing
Presenter: Chia-Chun Hsu 徐嘉駿
E-mail: [email protected]
Graduate Institute of Communication Engineering
National Taiwan University
Taipei, Taiwan
November 26,2015
Outline

Introduction to ECG signal

Motivation

Discrete Wavelet Transform



ECG Signal Processing




Conventional Discrete Wavelet Transform
Stationary Discrete Wavelet Transform
Baseline Wandering Problem
Power line noise and noise interference
Feature detection
Reference
Outline

Introduction to ECG signal

Motivation

Discrete Wavelet Transform



ECG Signal Processing




Conventional Discrete Wavelet Transform
Stationary Discrete Wavelet Transform
Baseline Wandering Problem
Power line noise and noise interference
Feature detection
Reference
Introduction to ECG signal

Electrocardiography(ECG) is the process of recording the
electrical activity of the heart on graph sheets or some
monitors over a period of time by placing the electrodes on
some specific locations of body of a person.
Fig1. ECG monitor
From: http://www2.ntin.edu.tw/constructing/%E6%80%A5%E9%87%8D%E7%97%87%E8%AD%B7%E7%90%86%E8%A8%AD%E5%82%99.htm
Introduction to ECG signal

Relationship between the polarization of the
heart and the ECG signal.
Fig2. Animation of a norml ECG wave
From: https://en.wikipedia.org/wiki/Electrocardiography
Introduction to ECG signal

Normal ECG signal
Fig3. Normal ECG signal
Introduction to ECG signal
Table1 Feature of ECG signal and its description
Feature
Description
RR interval
The interval between an R wave
and the next R wave. (bpm)
P wave
During normal atrial
depolarization.
QRS wave
(QRS complex)
Rapid depolarization of the
right and left ventricles.
Has the largest amplitude.
PR interval
From the sinus node through
the AV node and entering the
ventricles.
T wave
ST interval
Represents the repolarization of the ventricles.
Repolarization of the
ventricles.
Outline

Introduction to ECG signal

Motivation

Discrete Wavelet Transform



ECG Signal Processing




Conventional Discrete Wavelet Transform
Stationary Discrete Wavelet Transform
Baseline Wandering Problem
Power line noise and noise interference
Feature detection
Reference
Motivation


The ECG signal is a very weak signal,
amounting to only 0.5 and 2mV at the skin
surface.
Hence, ECG signal is usually contaminated
by the external disturbance.(e.g. Power line
noise, respiration, etc.)
Motivation

Problem of ECG signal processing

Power line noise

High frequency noise interference

Baseline wander problem

QRS complex extraction
Motivation


Power line noise(PLI): In general , Power
line noise is caused by the electromagnetic
field of 50/60Hz power line.
American Heart Association recommends
that ECG recorder should have a 3dB
frequency range extending from 0.67 to
150Hz.
Motivation
Fig4. ECG signal with 50Hz PLI noise
Motivation

High frequency noise interference
Fig5. ECG signal contaminated by high frequency noise
Motivation

Baseline wander(BW):Commonly caused by
electrode-skin impedance changes due to
perspiration, patient movement, and
respiration.
From:MIT-BIH Arrhythmia Database Fig5. Baseline wander of ECG signal
Motivation

Flow chart of ECG signal processing
ECG
signal
Baseline
correction
Determine the
symptoms.
Noise
removal
Classification
(e.g. M.L)
QRS complex
extraction
P,Q,S,T
extraction
Note : M.L = Machine Learning
Outline

Introduction to ECG signal

Motivation

Discrete Wavelet Transform



ECG Signal Processing




Conventional Discrete Wavelet Transform
Stationary Discrete Wavelet Transform
Baseline Wandering Problem
Power line noise and noise interference
Feature detection
Reference
Discrete Wavelet Transform

Conventional discrete wavelet transform(DWT)
L-points
lowpass filter
g[n]
N-points
x[n]
L-points
highpass filter
h[n]
down sampling
xL[n]
xH[n]
2
down sampling
2
x1,L[n]:Approximation(Low frequency part)
x1,H[n]:Details (high frequency part)
x1,L[n] ~N/2-points
x1,H[n] ~N/2-points
In general,
N>>L.
Discrete Wavelet Transform

The multi-level decompositions of the DWT
loss the characteristic of the original signal
at the high level.
(a)
Fig6. (a)Non-stationary signal
(b)
(b) DWT of (a) [1]
Stationary Discrete Wavelet Transform


The stationary discrete wavelet transform
(STW) is similar to the DWT but without the
decimation.
Filters in each level are up-sampled version of
the previous.
Stationary Discrete Wavelet Transform
(a)
(b)
Fig7. (a)A 3 level SWT filter bank (b) SWT filters
→The up-sampling scheme is achieved by inserting zeros between every adjacent
pair of elements of the filter.
Outline

Introduction to ECG signal

Motivation

Discrete Wavelet Transform



ECG Signal Processing




Conventional Discrete Wavelet Transform
Stationary Discrete Wavelet Transform
Baseline Wandering Problem
Power line noise and noise interference
Feature detection
Reference
ECG Signal Processing-Baseline problem(1/5)

Baseline problem: Baseline can be viewed as
the low frequency part in the ECG signal.
Fig8. Noisy sine wave
ECG Signal Processing-Baseline problem(2/5)

Conventional methods using moving
average filter to estimate the baseline.
→Sensitive to the abrupt peak.(e.g. R peak)


DWT use the filter banks for construction of
the multi-resolution analysis. [2]
Separate the ECG signal into its
Approximation and Details.
ECG Signal Processing-Baseline problem(3/5)

From MIT-BIH Database [8]
(a) (b)
(c) (d)
Fig9. Each level of
SWT result
(a)Original signal
(b)Level1
(c)Level 5
(d)Level9
ECG Signal Processing-Baseline problem(4/5)

MATLAB Code
ECG Signal Processing-Baseline problem(5/5)
Fig10. Examples of
baseline drift
correction.
(Wavelet method)
ECG Signal Processing-Noise Removal(1/7)


For the power line noise, the notch filter is
used to filter out the 50/60Hz noise.
To deal with the high frequency noise
problem, the naïve method is using the Low
pass filter.
ECG Signal Processing-Noise Removal(2/7)

Wavelet approach(proposed by Donoho)[3]

Hard thresholding
cD j
ˆ
cD j  
 0
for |cD j | t
for |cD j | t
t is some thresholding
→Noise : High frequency(detail) with small amplitude
→R peak : High frequency(detail) with larger amplitude than the noise.

soft thresholding
sgn(cD j ) | (cD j )  t | for |cD j | t
ˆ
cD j  
for |cD j | t
0

ECG Signal Processing-Noise Removal(3/7)
(a)
(b)
(c)
Fig10. Denoising of
ECG signal by using
wavelet transform
thresholding
techneque.[4]
(a)Raw data
(b)Noisy ECG signal
(c)Denoising by
wavelet transform
ECG Signal Processing-Feature detection (4/7)


In ECG feature detection, R-wave peak is the
most important job.
When the R peak postion is found, the
location of P,Q,S,T can be found by the
relative position from R peak to each others.
ECG Signal Processing-Feature detection (5/7)

Time domain detection [5]

Derivative method + peak height thresholding
→Very sensitive to the noise.(Your pre-processing must have good performance)

Frequency domain detection


Hilbert Transform method [6]
Discrete Wavelet Transform Method [7]
ECG Signal Processing-Feature detection (6/7)


Discrete Wavelet Transform Method: In order to
detect the R-wave peak, using DWT to separate
the ECG signal to details(High Frequency) and
approximation(Low frequency).
Find the R peak characteristic in the detail parts of
DWT.
ECG Signal Processing-Feature detection (7/7)
Level5 has high similarity to the R peak!!→
Fig11.
Multiresolution
decomposition
of ECG signal
Using D6
containing
short burst of
noise.
Reference
[1] Nason, Guy P., and Bernard W. Silverman. "The stationary wavelet
transform and some statistical applications." LECTURE NOTES IN
STATISTICS-NEW YORK-SPRINGER VERLAG- (1995): 281-281.
[2] Chowdhury, Shubhajit Roy, and Dipankar Chakrabarti. "Daubechies
wavelet decomposition based baseline wander correction of transabdominal maternal ECG." Electrical and Computer Engineering (ICECE),
2010 International Conference on. IEEE, 2010.
[3] Lin, H-Y., et al. "Discrete-wavelet-transform-based noise removal and
feature extraction for ECG signals." IRBM 35.6 (2014): 351-361.
[4] Georgieva-Tsaneva, Galya, and Krassimir Tcheshmedjiev.
"Denoising of electrocardiogram data with methods of wavelet
transform." International Conference on Computer Systems and
Technologies. 2013.
Reference
[5] J.P.Pan, “A Real-Time QRS Dection Algorithm”, IEEE Transaction
Biomedical Engineering, pp. 230-236, 1985.
[6] Benitez, D., et al. "The use of the Hilbert transform in ECG signal
analysis."Computers in biology and medicine 31.5 (2001): 399-406.
[7] S. Mahmoodabadi , A. Ahmadian , M. Abolhasani , M. Eslami and J.
Bidgoli "ECG feature extraction based on multiresolution wavelet
transform", Proc. IEEE Eng. Med. Biol. Soc., pp.3902 -3905 2005
[8] Moody GB, Mark RG. The impact of the MIT-BIH Arrhythmia
Database. IEEE Eng in Med and Biol 20(3):45-50 (May-June 2001).
Available from: https://www.physionet.org/physiobank/database/mitdb/
[9] 國立台灣大學電信工程研究所，數位影像與訊號處理實驗室，課程專區，
時頻分析與小波轉換，Tutorial專區:Time-Frequency Analysis for ECG signals。
Thank you for your attention!!