| dc.description.abstract | With the global prevalence of the wearable and portable devices in recent years, we can achieve long-term monitoring, telemedicine or even individualized treatment with the assistance of cloud computing. Nevertheless, the biomedical signals gathered from these wearable and portable gadgets are prone to be contaminated by various kinds of perturbation, such as motion artifacts, intermittency, baseline wandering or white noise, thereby causing further analysis, including feature extraction and finding indicators, to be more complicated.
In this study, we develop a distinctive method based on the quasi-periodicity and Empirical Mode Decomposition (EMD)-based method, Uniform Phase EMD (UPEMD), to adopt the nonlinearity and adaptiveness on biomedical signal analysis. This study aims at applying our proposed method in two biomedical topics, the first one is to develop a universal beat detection algorithm that is suitable for different types of biomedical signals, such as electrocardiography (ECG), photoplethysmography (PPG), arterial blood pressure (ABP) and remote photoplethysmography (Remote PPG). The second topic is harnessing our proposed technique on the smartphone usage records to estimate the sleep-wake cycles as well as the sleep parameters.
In view of the non-stationary property in the physiological signals, EMD tends to be a satisfactory choice that can accommodate the unpredicted variations. The rationale of EMD is to segregate the signal into a finite number of Intrinsic Mode Function (IMF) through the iterative sifting processes, meanwhile considering the time scale characteristics of the data. To put it bluntly, EMD serves as a data-driven technique rather than relying on a priori basis. Our proposed method not merely avoid the cumbersome and rigorous statements and threshold settings of the existing algorithms but also automatically and adaptively decide the filter bank to extract the specific component, meanwhile retaining the nonstationary and nonlinear properties of the physiological signal without being interfered by the intermittency. | en_US |