English  |  正體中文  |  简体中文  |  Items with full text/Total items : 70548/70548 (100%)
Visitors : 23121690      Online Users : 357
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version

    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/83428

    Title: 以PPG脈搏訊號提取時頻特徵做心血管疾病診斷的神經網路
    Authors: 胡鎧麟;Hu, Kai-Lin
    Contributors: 光機電工程研究所
    Keywords: 小波轉換;總體經驗模態分解;光體積變化描計圖法;倒傳遞神經網路;卷積神經網路;Wavelet transform;Ensemble empirical mode decomposition;Photoplethysmography;Backpropagation neural network;Convolution neural network
    Date: 2020-08-18
    Issue Date: 2020-09-02 15:38:03 (UTC+8)
    Publisher: 國立中央大學
    Abstract: 摘要

    關鍵字: 小波轉換、總體經驗模態分解、光體積變化描計圖法、倒傳遞神經網路、卷積神經網路
    The aim of this thesis is to discuss the details and algorithms of signal processing techniques such as continuous wavelet transform, discrete wavelet transform and ensemble empirical mode decomposition; and how they can be applied to human health condition classification by analyzing human pulse with aforementioned methods. By applying these methods and a new algorithm proposed in this thesis on human pulse dataset measured by Photoplethysmography(PPG), the features in time domain and time-frequency domain of PPG pulse signal can be extracted successfully as the inputs to two kinds of artificial neural network: Backpropagation Neural Network(BPNN) and Convolution Neural Network(CNN) to examine whether the 88 samples could be clustered into three different groups according to the cardiovascular conditions described in the datasets.
    The experiment shows that the testing accuracy of BPNN with inputs vector composed by six features in time domain, eight features in time-frequency domain and proper stopping criterions to avoid over-fitting is about 67% to 73%. On the other hand, the testing accuracy of CNN with inputs are time-frequency mapped with frequency range between 2Hz to 10Hz and time interval is one second can achieve about 61% to 64%.
    Comparing the testing accuracy of two datasets gives us a conclusion that the algorithm used in this paper to extract features affects the classification result is much less than the quality of PPG pulse signal acquired in different circumstances and samples of dataset for analysis.
    Keyword: Wavelet transform, Ensemble empirical mode decomposition, Photoplethysmography, Backpropagation neural network, Convolution neural network
    Appears in Collections:[光機電工程研究所 ] 博碩士論文

    Files in This Item:

    File Description SizeFormat

    All items in NCUIR are protected by copyright, with all rights reserved.

    社群 sharing

    ::: Copyright National Central University. | 國立中央大學圖書館版權所有 | 收藏本站 | 設為首頁 | 最佳瀏覽畫面: 1024*768 | 建站日期:8-24-2009 :::
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback  - 隱私權政策聲明