長時間動態信號倒頻譜特徵擷取及分類研究; The Research on Cepstrum Feature Extraction and Classification Techniques for Long Term Dynamic Signals

NCUIR > Engineering College > Graduate Institute of Mechanical Engineering > Electronic Thesis & Dissertation > Item 987654321/2497

Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/2497

Title:	長時間動態信號倒頻譜特徵擷取及分類研究;The Research on Cepstrum Feature Extraction and Classification Techniques for Long Term Dynamic Signals
Authors:	任國光;Kuo-Kuang Jen
Contributors:	機械工程研究所
Keywords:	分類;倒頻譜;特徵值;心電圖;DTW;Feature Extraction;Cepstrum;ECG
Date:	2008-05-30
Issue Date:	2009-09-21 11:48:33 (UTC+8)
Publisher:	國立中央大學圖書館
Abstract:	心電訊號處理的論文，大部份針對單一波形(單一QRS COMPLEX)來求取特徵參數後進行辨認，這類文章已有許多方法被提出，但此些方法均僅觀看單一波形，常無法看出波形間隱藏的資訊，本文提出利用倒頻譜參數來求取長時間的心電信號特徵參數，並用此參數向量來作病症辨認，利用本法針對一段時間的心電訊號作特徵參數求取，如此可看出波形間隱藏的特性且可將其存成心電信號資料庫，再利用DTW(Dynamic Time Warping, DTW)的方法來加以辨認，文中使用MIT/BIH(麻省理工學院醫學中心與貝色以色列醫院)的不整脈心電圖資料庫中正常及PACED BEAT的資料來進行測試，確實能有效區分兩種不同訊號，因此本研究所提出的方法可有效執行長時間心電訊號特徵參數的求取，並有助於作好病症分類。其次, 在分類辨識方面,本研究利用監督式倒傳遞網路,以MIT/BIH資料庫做類神經網路的訓練學習與評估測試。選擇現有資料庫所儲存的病症本研究將針對將針對正常正常(NOR)、左束枝傳導阻滯(LBBB)、右束枝傳導阻滯(RBBB)、心室頻脈(VF)等四類病症作診斷。整個心電圖自動診斷即是以倒頻譜轉換自動偵測擷取特徵值再以類神經網路進行分類辨識。使用MIT/BIH資料庫評估本自動診斷系統，即使心電圖訊號受高頻或低頻準線漂移的干擾，自動偵測分類的正確率達97.5% 以上。因此本研究所提出的方法可有效提供執行長時間動態信號特徵參數的擷取與辨識。 Current ECG signal processing methods for feature extraction of QRS complex are mainly focusing on a short-term single heartbeat rather than a long-term multiple heartbeats, in which much useful information is hiding between QRS complexes and only retrievable through long-term monitoring. This dissertation proposes a cepstrum coefficients method to extract the feature from a long term ECG signal. Utilizing this method, one can identify the characteristics hiding inside the ECG signal and classify the signal by its feature vector by DTW (Dynamic Time Warping) and ANN (artificial neural network). First, Normal and Paced-Beat data in the MIT/BIH (Massachusetts Institute of Technology and Beth Israel Hospital) arrhythmia database are used to evaluate the proposed algorithm, and the experiment results have demonstrated successful classification for different cardiac diseases and proved the algorithm to be a fast valid method for long term ECG signal feature extraction. Second, an integrated system for ECG diagnosis that combines cepstrum coefficients method for feature extraction from long-term ECG signals and ANN models for the classification is proposed. Unlike the previous methods using only one single heartbeat for analysis, we analyze a meaningful ECG segment data, usually containing 5-6 heartbeats, to obtain the corresponding cepstrum coefficients and classify the cardiac systems through ANN models. Utilizing the proposed method, one can identify the characteristics hiding inside an ECG signal and then classify the signal as well as diagnose the abnormalities. To evaluate this method, the NOR, LBBB, RBBB and VF types of ECG data from the MIT/BIH database were used for verification. The experiment results showed that the accuracy of diagnosing cardiac disease was above 97.5%. The proposed method successfully extracted the corresponding feature vectors, distinguished the difference and classified long term dynamic signals correctly.
Appears in Collections:	[Graduate Institute of Mechanical Engineering] Electronic Thesis & Dissertation

Files in This Item:

File	Size	Format

社群 sharing

Loading...