| dc.description.abstract | As rapid development in clinical medicine, the ultimate goal of clinicians turns to tailor an individualized treatment for each patient rather than using standardized therapy which neglects the heterogeneity of subjects. Although molecular biomarkers, especially generated from high-throughput screening technologies, have achieved a certain degree of success in providing useful information about disease status or finding the subgroups of patients with same diseases, most of those biomarkers are still difficult to gain widespread acceptance because of the resource-consuming analysis and procedure. Furthermore, it is impractical for monitoring or following up the changes of disease processes. Complementary ways to explore the characteristics of patients in different statuses may expedite the progress in achieving the goal.
Analysis of physiological signals, the outputs of specific control systems of biological entity in order to maintain internal homeostasis under the environments, should be a promising way to probe the characteristics of the subjects during normal and diseased conditions and may provide crucial information of the underlying physiological and pathological mechanisms. Moreover, most of the physiological signals, such as electrocardiogram (ECG), electro-encephalogram (EEG), and respiratory signal, can be non-invasively and easily acquired and the digitalized signals can be stored for a long time by in-expensive data storage devices. The demands of approaches to analyze those signals inevitably become stronger by clinical practice. However, the major challenge of contemporary methods is the daunting nonlinearity and nonstationarity of those physiological signals which continuously interact with the varying environment.
An innovated method, Hilbert-Huang Transform (HHT), consists of Empirical Mode Decomposition (EMD) and Hilbert Spectral Analysis. EMD, the key step of HHT, can adaptively decompose the signal into many different intrinsic mode functions (IMFs) operated in different time-scales. Instead of integral of a priori basis to reconstruct the signal, it gives better time-frequency-energy representation for the nonlinear and nonstationary systems. Moreover, the instantaneous frequency derived from HHT can provide a better route to analyze the underlying information of the signal, especially for physiological systems that are nonlinear by nature and interconnect with other systems under the perturbation of the ever-changing environments. However, only limited studies adopt the concept of nonlinear and adaptive data analysis. The aims of the study are, therefore, trying to apply HHT in several biomedical topics including (1) probing the dynamical characteristics of aging processes or pathological changes of heart rate dynamics; (2) developing a data-driven detrending method to assist the existed nonlinear analysis that is vulnerable to nonstationary trends; (3) extracting the main features of the highly nonstationary ECG signals from automated extracorporeal defibrillator.
It is suggested that the underlying mechanisms of physiological signals are better described by HHT and dynamical information of the signals may serve as alternative biomarkers for aging or some diseases. Moreover, HHT can also be applied as adaptive filter to extract the important features or eliminate the unwanted trends of the signals. The elicited components of the signals can provide more crucial information of clinical relevant issues and the sensitivity of the existed nonlinear analysis for the reconstructed signals can be improved.
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