Independent component analysis on spectral domain

Independent component analysis (ICA) is an effective data-driven method for blind source separation. It has been successfully applied to separate source signals of interest from their mixtures. Most existing ICA procedures are carried out by relying solely on the estimation of the marginal density functions, either parametrically or nonparametrically. In many applications, correlation structures within each source also play an important role besides the marginal distributions. One important example is functional magnetic resonance imaging (fMRI) analysis where the brain-function-related signals are temporally correlated. In this thesis, we propose two novel ICA algorithms that fully exploit the correlation structures within the source signals through spectral density estimation. Our methodology development is two-fold: 1) ICA for auto-correlated sources via parametric spectral density estimation (cICA-YW); 2) ICA for sources with mixed spectra via nonparametric spectral density estimation and atom detection (cICA-LSP). The cICA-YW focuses on the sources with autocorrelation and is implemented using spectral density functions from frequently used time series models such as autoregressive moving average (ARMA) processes. The time series parameters and the mixing matrix are estimated via maximizing the Whittle likelihood function. We illustrate the performance of the proposed method through extensive simulation studies and a real fMRI application. The numerical results indicate that our approach outperforms several popular methods including the most widely used fastICA algorithm. We also establish the sampling properties of the proposed method. For the cICA-LSP, we consider the case of sources with possibly mixed spectra, where ARMA estimates are often unstable. Specifically, we propose to estimate the spectral density functions and the line spectra of the source signals using cubic splines and indicator functions, respectively. The mixed spectra and the mixing matrix are estimated via maximizing the Whittle likelihood function. We illustrate the performance of the proposed method through extensive simulation studies.Doctor of Philosoph

REAL-TIME DATA MINING FOR PROCESS OPERATIONS USING GRAPHICS PROCESSING UNIT (GPU)-BASED HIGH PERFORMANCE COMPUTING

Ph.DDOCTOR OF PHILOSOPH

Towards a better understanding of the precordial leads : an engineering point of view

This thesis provides comprehensive literature review of the electrocardiography evolution to highlight the important theories behind the development of the electrocardiography device. More importantly, it discusses different electrode placement on the chest, and their clinical advantages. This work presents a technical detail of a new ECG device which was developed at MARCS institute and can record the Wilson Central Terminal (WCT) components in addition to the standard 12-lead ECG. This ECG device was used to record from 147 patients at Campbelltown hospital over three years. The first two years of recording contain 92 patients which was published in the Physionet platform under the name of Wilson Central Terminal ECG database (WCTECGdb). This novel dataset was used to demonstrate the WCT signal characterisation and investigate how WCT impacts the precordial leads. Furthermore, the clinical influence of the WCT on precordial leads in patients diagnosed with non-ST segment elevation myocardial infarction (NSTEMI) is discussed. The work presented in this research is intended to revisit some of the ECG theories and investigate the validity of them using the recorded data. Furthermore, the influence of the left leg potential on recording the precordial leads is presented, which lead to investigate whether the WCT and augmented vector foot (aVF) are proportional. Finally, a machine learning approach is proposed to minimise the Wilson Central Terminal