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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/85757

    Title: 實踐經驗模態分解於高度非穩態生理訊號之訊號特徵擷取;Harnessing Empirical Mode Decomposition on Feature Extraction of Highly Nonstationary Biomedical Signals
    Authors: 王子文;Wang, Zih-Wen
    Contributors: 生物醫學工程研究所
    Keywords: 經驗模態分解;峰值檢測;自適應性;晝夜節律;睡眠分析;Empirical Mode Decomposition (EMD);beat detection;adaptive;circadian rhythm;sleep analysis
    Date: 2021-07-23
    Issue Date: 2021-12-07 11:21:45 (UTC+8)
    Publisher: 國立中央大學
    Abstract: 隨著科技的進步與蓬勃發展,穿戴式裝置、微型化裝置以及智慧型手機已然成為普及世界的存在,藉由這些便攜型的非侵入式與智慧型裝置來長期量測生物醫學訊號是未來醫療領域的發展主軸之一,除有利於長期監測及個人化醫療,對於遠距醫療與居家檢測的助益也十分可觀;有鑑於日常生活中擷取的生理訊號具有非穩態與非線性特質,因此對該類訊號進行分析甚至是特徵擷取與指標量化計算有其難度,本研究目的在於應用經驗模態分解於數種生物醫學訊號相關主題,包含:(1)發展普適性峰值檢測演算法並應用於心電圖、光體積變化描記圖、動脈血壓波以及遠端光體積變化描記圖與 (2) 應用日常手機使用量於作息節律監控與睡眠分析。
    ;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.
    Appears in Collections:[生物醫學工程研究所 ] 博碩士論文

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