Automatic ECG Analysis using Principal Component Analysis and Wavelet Transformation
By Antoun Khawaja
Publisher: Universitätsverlag Karlsruhe
Number Of Pages:
Publication Date: 2007
ISBN-10 / ASIN: 3866441320
ISBN-13 / EAN: 9783866441323
Binding: Broschiert
Kurzfassung in englisch
ECG signal processing algorithms form an important part of systems for monitoring of patients who suffer from a life-threatening condition. The life-threatening condition can be pronounced by a drug-induced ventricular tachyarrhythmia. This kind of tachyarrhythmia is called Torsade de Pointes (TDP). Two main features of TDP are pronounced first with marked prolongation of the duration between ventricular depolarization and repolarization, known as QT interval, and second with large morphology changes of the T wave, respresenting the variance of ventricular repolarization in ECG signal from one cardiac cycle, also called beat, to another. In particular, QT interval has been identified as a surrogate marker for possible proarrhythmic effects, i.e. for clinical assessment of drug safety. In fact, QT interval is the simplest clinical measure that is available at present. On the other hand, analysing T wave morphology (TWM) changes in beat-to-beat manner seems to be more complicated than measuring simply QT interval and appears to play a more important role in accessing the electrical stability of the ventricles and furthermore in detecting predisposition to TDP. That is, analysing the beat-to-beat variability in TWM seems to be a robust precursor to TDP as noticed in ECG signal. The main objective of this thesis is developing methods to analyse and detect small changes in ECG waves and complexes that indicate cardiac diseases and disorders. Detecting predisposition to Torsade de Points (TDP) by analysing the beat-to-beat variability in T wave morphology before and after TDP episode is the main core of this thesis. Detecting small changes in QRS complex and predicting future QRS complexes of patients from a time series of ECG signals is the second main topic of this research thesis. The third main point is to cluster similar ECG components, namely T waves, depending on their morphologies in different groups and to find the main dominant T wave morphology or morphologies for every ECG signal. In order to establish and achieve the mentioned aims, the following objectives have to be fulfilled: 1- ECG Signal Preconditioning: Novel techniques for low-frequency and high-frequency noise cancellation as well as ECG fiducial points detection have been developed using the power of the time-frequency analysis, namely Wavelet transformation. Some other new preconditioning algorithms for detecting outliers in ECG signal and for ECG wave and complex alignment were also carried out. 2- Morphological Feature Extraction: Morphological features have been extracted from ECG signals after applying the preconditioning stage. The extraction is based on using Principal Component Analysis (PCA), also called Karhunen-Love transform (KLT). This technique is a multivariate statistical technique that allows for the identification of key variables, or combinations of variables, in a multidimensional data set that best explains the small differences between individual observations. In this study, the observations are ECG waves or complexes from all cardiac beats of an ECG signal. 3- Analysis of the Morphological Features: After extracting the morphological features from similar ECG components, further analysis will be applied depending on the application. As mentioned already, this research thesis is based on using PCA as a linear transformation technique in extracting morphological features from ECG signals. More and further investigations will be done in the future by using nonlinear techniques in addition to PCA in order to examine any inherently nonlinear underlying structure in ECG signal. The thesis is divided into four parts. The first part, including chapter 3 and chapter 4, provides the medical and technical basics and foundations necessary for the understanding of ECG signal, the electrophysiological processes in the heart and the terminology used throughout the thesis. Chapter 3 describes the anatomy and the physiology of the human heart, ECG lead systems and normal ECG signal, normal heart rhythms and different arrhythmias as well as heartbeat morphologies. Chapter 4 addresses the technical aspects of ECG recording including ECG electrodes, ECG artifacts and interference and ECG amplifiers. Chapter 4 includes also the databases used in this thesis. The second part includes chapter 5 and chapter 6. Chapter 5 describes the mathematical background of all the methods used in this thesis including Wavelet transformation, PCA etc... Whereas, chapter 6 provides the state of the art in ECG signal processing. The third part of this thesis, chapter 7, includes all the ECG signal preconditioning developed and used in this thesis. The fourth and the last part, chapter 8 and chapter 9, addresses the methods for detecting predisposition to Torsade de Points (TDP), T wave clustering, QRS complex temporal and spatiotemporal analysis as well as the analysis for predicting future QRS complexes along with their results.
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