基於機器學習與經驗模態分解的心律異常數據分類之研究;A Study of Machine Learning-Based Arrhythmia Data Classification with Empirical Mode Decomposition

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Title:	基於機器學習與經驗模態分解的心律異常數據分類之研究;A Study of Machine Learning-Based Arrhythmia Data Classification with Empirical Mode Decomposition
Authors:	盧盈君;Lu, Ying-Jiun
Contributors:	通訊工程學系
Keywords:	心律異常分類;心電圖訊號;經驗模態分解;長短期記憶網路;機器學習;arrhythmia classification;electrocardiogram (ECG);empirical mode decomposition (EMD);long short-term memory (LSTM);machine learning
Date:	2021-09-06
Issue Date:	2021-12-07 12:50:41 (UTC+8)
Publisher:	國立中央大學
Abstract:	心電圖訊號的心跳分類對於診斷心律異常十分重要，為了有效針對多種心律異常的類別進行分類，本論文提出一種使用機器學習長短期記憶網路(LSTM)的心律異常分類方法，每個心電圖訊號透過經驗模態分解 (Empirical Mode Decomposition, EMD) 進行分解，並將感興趣的本質模態函數 (Intrinsic Mode Function, IMF) 結合成一個經過修潤的心電圖訊號。這段預處理後的心電圖訊號則可作為機器學習網路的輸入訊號。利用LSTM對時間序列與長期依賴性的能力，有效地將輸入的心電圖訊號進行分類。依據觀察，與原始訊號相比，EMD能濾除高頻訊號與校正基準線，使每個心跳的QRS波群的特徵變得更加清楚，以提升分類的準確度。相較於先前的方法，我們所提出的方法，在MIT-BIH心律異常資料庫中，達到98.79%的準確度。;To reduce the high mortality rate caused by heart diseases, classifying heartbeats of ECG signals is crucial for arrhythmia diagnosis. This study in this paper proposes a machine learning-based approach to classify a variety of arrhythmia using the long short- term memory (LSTM) technique. Each ECG signal is decomposed by empirical mode decomposition (EMD), and the intrinsic mode function (IMF) of interest is combined into a revised ECG signal. The pre-processed ECG signal is used as the input signal of the network model. In light of time series and long-term dependence among input signals, LSTM can effectively classify the input ECG signals. This study observes that EMD can filter out the high-frequency signals and correct the baseline by contrast to the original signals. Thus, the QRS characteristics of each beat can become clear, thereby improving the accuracy of classification. Compared with the previous methods, our proposed method achieves high accuracy of 98.79% in the MIT- BIH arrhythmia database.
Appears in Collections:	[Graduate Institute of Communication Engineering] Electronic Thesis & Dissertation

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