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 ;Abstract 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
