醫療科技的進步為早期的疾病偵測提供了更多元的方法,但生理訊號 的複雜性和對專業領域的依賴性也讓實際偵測上碰到許多困難。為了找出一個自動化且能正確判斷心律不整的方式,本篇論文提出了一個結合 Hilbert-Huang Transform 以及 Convolutional Neural Networks 的心電圖判別架構。透過 Hilbert-Huang Transform 來處理複雜且非平穩的生理訊號並轉換出 Hilbert Spectrum,再使用 Hilbert Spectrum 以訓練 Convolutional Neural Networks Model 來學習其特徵並判別心律不整的類別。最後透過實驗結果以驗證透過機器學習取代傳統由專家判別特徵的可行性及效率,同時分析所提出架構的準確度並加以探討。;In this thesis, a novel approach to arrhythmia-based signal classification is introduced. The objective is to properly identify three classes of patients exhibiting normal sinus rhythm, atrial fibrillation, and other rhythm. The proposed method apply Hilbert-Huang transform on raw signal to generate noise-free reconstruction of the original containing temporal variations as input for classification mechanism to learn representative features. The features are directly learned by a computer vision technique known as Convolutional Neural Network, thus replacing traditional methods of relying on experts to handcraft features. To summarize, this thesis contains two major processes: utilize a nonlinear and non-stationary signal processing technique to produce input, and to feed reconstructed signal containing representative features to CNN for multi-classification task. The experimental results indicate the effectiveness of this method, removing the need of human involvement in the process of feature selection. Through analyses and stimulations, the effectiveness of the proposed ECG-classification method is evaluated.
