The projects goal was to develop a system that will assist a medical doctor to find problems in the heart operation.
Introduction
The projects goal was to develop a system that will assist a medical doctor to find problems
in the heart operation.
This system is based on advanced ECG signal processing techniques.
The main analysis technique used in the system is “Wavelet transform for ECG Characterization”.
On the base of the ECG analysis techniques a GUI was built.
This GUI enables a convenient and simple access to the results of the ECG analysis.
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The ECG
The ECG is a representation of the electrical activity of the heart. The ECG gives us information about the condition of the heart muscle. During myocardial depolarization and repolarization, deflections or waves are inscribed on the ECG.
The ECG consists of (figure 1) a P-wave (representing the depolarization of the Atria), the QRS-complex (representing the depolarization of the Ventricles), the T-wave (representing the repolarization of the Ventricles) and the U-wave.
The repolarization of the atria is not visible because it happens at the same time that the ventricles depolarizes.
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The wavelet transform for ECG characterization
The wavelet transform
Wavelet analysis is a very promising mathematical tool that gives good estimation of time and frequency localization.
Wavelet transformation is a linear operation that decomposes a signal into components that appear in different scales.
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ECG characterization
The automatic detection of ECG waves is important to cardiac disease diagnosis.
A good performance of an automatic ECG analyzing system depends heavily upon the accurate and reliable detection of the QRS complex, as well as T and P waves.
The detection of the QRS complex is the most important task in automatic ECG signal analysis.
Once the QRS complex has been identified, a more detailed examination of the ECG signal, including the heart rate can be performed.
The zero crossing of the wavelet transform is used to detect the location of the QRS complex.
Figure 2 shows the wavelet transform of an ECG signal (green) and the ECG signal (blue).
It can be seen that a sharp change in the ECG signal (QRS complex), results a local maximum, and after a local minimum with a zero crossing between them.
In this way the QRS complexes are found. In a similar way the P and T wave are found.
A “natural” wavelet kernel was used for the wavelet transform – Mexican hat (second derivative of gaussian function).
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ECG analyzer GUI
After implementing the analysis algorithm, a GUI (Graphic User Interface) was built.
The main features of ECG analyzer GUI are:
1. Control of the parameters of analysis.
2. Convenient display of processed data.
The GUI performs the following types of analysis of ECG signal:
- Simple ECG plot (without analysis)
- ECG plot with ECG character identification
- ECG plot of the signal interval (“slicing” the ECG signal to intervals and displaying the intervals one over the other)
- Short time fourier transform of the ECG signal
- R-R spectrum plot
- Heart rate plot
The next example (figure 3) shows the QRS complex detection , and the GUI that was developed.
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Conclusions
In this project the following topics had been achieved:
- Learn about the Heart activity and ECG signal
- Learn about the Wavelet transform in general, and an algorithm for ECG characterization in particular
- Learn useful signal processing techniques, as Short Time Fourier Transform and Spectrum Estimation
- Implement A Graphic User Interface, on MATLAB 5.3 platform
A further work can be done based on this project:
- Add more signal processing techniques that are usefull for ECG signal analysis
- Implement the GUI in Real Time environment, such as Visual C++
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Acknowledgement
I would like to thank my supervisor , Elad Yom-Tov , for his patience and guidance through the project.
I would also like to thank Johanan Erez and the rest of the laboratory staff for their help and support.
Finally I wish to thank the Ollendorff Minerva Center for their support.