NON-BLIND SOURCE SEPARATION AND FEATURE EXTRACTION: THEORY, APPROACH AND CASE STUDIES IN CARDIAC SIGNALS
Tesi di Dottorato
Data di Pubblicazione:
2015
Citazione:
NON-BLIND SOURCE SEPARATION AND FEATURE EXTRACTION: THEORY, APPROACH AND CASE STUDIES IN CARDIAC SIGNALS / M.w. Rivolta ; supervisor: R. Sassi, L. T. Mainardi, JP Couderc, F. Castells ; coordinator: E. Damiani. DIPARTIMENTO DI INFORMATICA, 2015 Mar 13. 27. ciclo, Anno Accademico 2014. [10.13130/rivolta-massimo-walter_phd2015-03-13].
Abstract:
Source separation (SS) and feature extraction (FE) are tools employed in digital signal processing. The former permits to estimate the values of some sources that have been mixed, and the latter extracts features from a set of measurements. SS and FE are widely applied on biomedical signals such as electrocardiogram (ECG), electroencephalogram, arterial blood pressure, etc., because these signals are collected in noisy environments. For instance, ECG recordings show the electrical activity generated by the whole heart at once. Yet, there are cardiac pathologies or arrhythmias related to only the atrial or ventricular chambers and thus, tools capable to separate them become fundamental for the diagnosis, prognosis and prediction of life-threatening events. Therefore, the quality of treatments depends on the reliability of the features extracted from the signals and then, the reduction of possible interferences becomes very relevant in this context. The study and the development of new SS technique play an important role in those application in which the components of a measurement cannot be splitted using classical temporal or frequency analysis. In addition, non-blind SS aims to employ further information and to develop mathematical and statistical model to make the estimates more reliable. Features extraction is fundamental for the classification task. In biomedical signals, features are used to characterize the status of the subjects in either healthy or pathological condition. For example, features to predict the risk of developing cardiac arrhythmias
are continuously encouraged by regulatory agencies as the US Food and Drug Administration. The study of reliability and feasibility of features requires an extensive use of tests. These tests are necessary to evaluate some properties, e.g., the capability of the feature to be resilient to noise, variability of the estimate, classification power, etc. The aim of this thesis is to study, develop, validate and test new SS techniques and features applicable to different kind of signals. The new algorithms and features are extensively studied to characterize their properties from a methodological point
of view. In addition, simulated and real data are considered as a test bench. Cardiac signals will be the specific field of application. First, a new algorithm for non-blind SS will be presented and discussed. In particular, this new methodology is an extension of a well-known algorithm, i.e., template, matching and subtraction (TMS), normally used to estimate transient sources, i.e., sources
that are located only somewhere in the signals, in stationary conditions. TMS estimates the values of the source by averaging a set of measurements in which it is known to be constant over time. However, there are situations in which this assumption does not hold and the results obtained are not “good” estimates. In order to track changes over time, we proposed a method based on a multi-goal optimization problem to modulate the estimate provided by the classic TMS. The multi-goal optimization problem has been defined as a weighted sum of three subgoals measuring: i) difference in the power of the residue and that of the signal when the transient source is not present; ii) difference in the power of the first derivative of the residue and that of the signal when the transient source is not present; and iii) difference between the estimate provided by the classic TMS and its modulated version. A multi-particle swarm optimization algorithm was used to find the solution of a non-linear problem in a very high dimensional space (a vector in R^100). This technique employs a set of particles that moves in the search space with an heuristic rule and it has been shown to be highly robust to local optim
Tipologia IRIS:
Tesi di dottorato
Keywords:
Source Separation; Signal Processing; Biomedical Signal Processing; Computer Science
Elenco autori:
M.W. Rivolta
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