基於傳統方法和卷積神經網絡的心房顫動和充血性心力衰竭識別之比較;Comparison of Atrial Fibrillation and Congestive Heart Failure Recognization Based on Traditional Method and Convolutional Neural Network

Abstract

[[abstract]]本研究針對兩種疾病:心房纖維顫動(Atrial Fibrillation)、心力衰竭(Heart Failure)，探討相對於正常心律（Normal Sinus Rhythm）的疾病辨識方法。本研究提出兩個藉由生理訊號辨識疾病之方法，並對這兩種方法比較與探討。由於正常人與患有心臟疾病的人在心率變異上有極顯著的差異，因此一方面我們可擷取大量心率變異性特徵並且搭配分類器來識別有無病症。另一方面，最近深度學習廣泛應用在數位訊號分析領域，本研究也探討深度學習適用於生理時間序列的可行性。 本研究提出以心電圖(Electrocardiogram)上RR間隔為主要分析的訊號。以傳統方法包含特徵擷取、特徵萃取、分類。而所擷取的特徵有:時間統計特徵、頻域特徵、熵特徵、Poincare、高階頻譜特徵，特徵萃取則是使用線性鑑別分析(Linear Discriminant Analysis)，分類器選擇支持向量機(Support Vector Machine)、類神經網絡(Neural Network)。此外，在分三類的項目中，提出使用兩階段式分類法能更加提升分類結果的正確率。 本研究另一個系統採用卷積神經網絡(Convolutional Neural Network)，以RR間隔和其短時間傅立葉轉換(Short Time Fourier Transform)為對象，企圖從中提取特徵並分類。本篇使用Leave-one-out 以及Two-fold 做驗證，深入比較兩種系統的優劣和效能。 研究結果顯示，使用卷積神經網絡之系統優於傳統系統，在AF和NSR的分類以Leave-one-out和Two-fold驗證出最佳正確率分別為97.44%、98.08%，CHF和NSR分類的最佳正確率分別為93.10%、95.69%，兩階段分三類則分別可達到為91.00%、92.50%。 This study focus on two diseases: atrial fibrillation (AF) and congestive heart failure (CHF), and try to develop a disease identification system to identify them from normal sinus rhythm. This study presents two methods that identify disease based on physiological signals. The performance of these two systems are compared. There is a significant difference in heart rate variability between people who is normal and who has heart disease. On the other hand, we can extract a variety of characteristics and use a classifier to identify the presence of disease. On the other hand, deep learning is widely used in the field of digital signals analysis, this study also explores the feasibility of applying deep learning to physiological time series. This study uses the RR interval extracted from the electrocardiogram (ECG) as the main signal for analysis. The traditional method includes feature extraction, feature discrimination, and classification. We employ time statistical, frequency domain, Entropy, Poincare, High-order spectral features and use the linear discriminant analysis (LDA) to increase the discrimination capability. Selecting support vector machine (SVM) and neural network (NN) are employed the classifiers. In addition, in the classification of three categories, we proposed a two-stage classification method to further improve the accuracy of classification results. In this study, we also propose another system using convolutional neural network (CNN). The RR interval and its short time fourier transform (STFT) are ued as inputs and their features are extracted for classification. The verification methods include leave-one-out and two-fold cross-validation. The advantages and disadvantages of both systems are compared. The results show that the system using convolution neural network is superior to the traditional system. The classification of AF and NSR get accuracies of 97.44% and 98.08%, using leave-one-out and two-fold, respectively. The best result for CHF and NSR classification, accuracy rates are 93.10% and 95.69%, respectively. Moreover, using two-stage scheme to classify three classes can achieve accuracy rates of up to 91.00% and 92.50% ,respectively