Classification of Electrocardiogram (ECG) Waveforms for the Detection of Cardiac Problems By Enda Moloney
Dec 23, 2015
Classification of Electrocardiogram (ECG) Waveforms for the Detection of Cardiac Problems
By Enda Moloney
Contents
Project aims The Heart & ECG ECG Signals MIT-BIH arrhythmia Database QRS Detection Pan-Tomkins Algorithm Artificial Neural Network Timeline Question
Project Aims
Analyse ECG waveform to detect abnormalities Using sample waveform from MIT-BIH database Process waveforms to make it easier to classify them Extract information from ECG waves e.g. QRS
complex Use the artificial Neural Network to classify the ECG
waves into different classes Translate the ECG classification system from Matlab
to C Possible development of a suitable of
hardware/software system and Database
Heart & ECG
Determining if the heart is performing normally or suffering from abnormalities e.g. skipped heartbeats.
Indicating previous damage to the heart muscle.
Providing information on the physical condition of the heart.
Been used to detect non-cardiac diseases
ECG Signal
An ECG is measuring the electrical potential between various points of the body using leads. The normal ECG wave is composed of
The P wave QRS complex The T wave
The relationship between P waves and QRS complexes helps distinguish various
cardiac irregularities.
MIT-BIH Arrhythmia Database
This is a waveform of the data 101 of the MIT-BIH Database of ECG waveforms
Using matlab programme is used to extract information for the MIT-BIH arrhythmia database.
0 10 20 30 40 50 60 70 80-1
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0
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1.5
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281111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111
Time / s
Vol
tage
/ m
V
ECG signal 101.dat
ECG signal 101.dat
0 10 20 30 40 50 60 70 80-1
-0.5
0
0.5
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1.5
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281111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111
Time / s
Vol
tage
/ m
VECG signal 101.dat
QRS Detection
The QRS complex is the most important complex in the ECG. The duration and amplitude sure be measure as accurate as possible.
There are two methods:
the Pan-Tompkins algorithm
the derivation-based method.
Pan-Tompkins algorithm
Pan-Tompkins algorithm proposes a real-time QRS detection algorithm based on slope, amplitude and width of the QRS complexes
After Squaring
0 0.5 1 1.5 2 2.5 3
x 104
0
0.02
0.04
0.06
0.08
0.1
0.12
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0.16
0.18
After the implementing the Bandpass filter & differentiation this suppresses P and T waves.
Squaring makes all the results positive and emphasising from large differences arising for the QRS complexes
Artificial Neural Network
ANN is an adaptive system that changes its structure based on external or internal information that flows through the network during the learning phase
When the ECG waves have been processed, they must be classified into Two classes Normal Abnormal
Timeline
30-Jan-09 Transfer the ECG system from Matlab to C, as a
real-time Implementation. The neural network needs to be in C.
15-Feb-09 Develop hardware circuit to interact with the
software, thus a circuit that has a ECG sensor 9-Mar-09
Investigate possible extensions of the system. Eg. Web-based database system that could be used story cardiology records and analysis
Question