Robust Methods for Nonlinear Behavior Identification in Clinical Applications

• Main Authors: Yi-Chung Chang, 張儀中
• Other Authors: Jen-Ho Tsao
• Format: Others
• Language: en_US
• Published: 2015
• Online Access: http://ndltd.ncl.edu.tw/handle/45583848105922130080

博士 === 國立臺灣大學 === 電信工程學研究所 === 103 === In recent years, the complexity of human body has been continuous revealed and discussed in many fields, it may eventually lead to a complete theory through the studies on pathogenesis and molecular biology of disease. On the other hand, the complex theory combined with the homeostasis mechanism has been used for biomedical signal analysis trying to identify such complex phenomena and underlying information behind the clinic data. These methods can help to extract non-linear feature from ambiguous information as the disease assessments, some of them have been accepted to have more advantages than traditional ones. However, such refining procedure are subject to many restrictions in clinical conditions and environments, such as limited data length, information may be occasional uneven or noise interfered. In addition, the extraction itself can also lead to distortions, the interference from other mechanism may not be effectively removed which raised the difficulty on the subsequent analysis. Therefore, this thesis proposes several robust methods to identify the specific nonlinear features in clinic data series and try to fulfill the clinical requirements. The first portion of nonlinear feature is quantization of multi-scale correlation. It was derived from the entropy in information theory as well as the coarse-graining in chaos-fractal theory to quantify the complexity of a system through the correlations at different time scale. In the first study, a novel approach has been proposed to decrease the length of data in complexity calculation of pulse wave velocity (PWV) such that the time for data acquisition can be substantially reduced to 12 minutes. It utilized a smaller sample size (i.e. 600 consecutive signals) with remarkable preservation of sensitivity in differentiating among the healthy, aged, and diabetic populations compared with the conventional method (i.e. 1000 consecutive signals). The second study utilized the multi-scale correlation of heart beat intervals (RRI) on critical patients whose life continuation relies on extracorporeal membrane oxygenator (ECMO). This study propose a new approach to estimate the complexity in a signal by analyzing the irregularity of the sign time series of coarse-grained time series at different time scales. Without removing any outliers due to ectopic beats, the method is able to detect a degradation of cardiac control in patients with congestive heart failure and a more degradation. Moreover, the derived complexity measures can predict the mortality of ECMO patients. These results indicate that the proposed method may serve as a promising tool for monitoring cardiac function of patients in clinical settings. In the second portion of nonlinear feature, the trajectories on phase space have been used for calculating statistical properties of the orbits in a dynamic system. In the first study, a novel method been proposed to noninvasively derive the fetus ECG signals from the maternal abdominal ECG during the cesarean section (CS). The heart beat series derived from the noisy signal were then quantified by several heart rate variability (HRV) methods. Moat parameters tell that the HRV increased 5 minutes after anesthesia and 5 minutes before delivery. These results shows that the proposed method may serve as a promising tool to obtain significant information about the fetal condition during labor. In the second study, a nonlinear-based waveform similarity analysis of the local electrograms has been proposed, aiming to detect crucial complex fractionated atrial electrograms (CFEs) in atrial fibrillation (AF) ablation. This method firstly identify each cycle of orbits in the dynamic system and then calculate the statistical properties (similarity index, SI) of these trajectories on phase space. The result shows the average SI of the targeted CFEs was higher in termination patients, and they had a better outcome. This study suggested that sites with a high level of fibrillation electrogram similarity at the CFE sites were important for AF maintenance.