- 學位論文
以類神經網路建構二導程心電圖判讀系統
Using Artificial Neural Networks in Arrhythmias Classification with Two Leads ECG Signals
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摘要
本篇提出一個可自動化心電圖判讀系統，利用2個導程Lead II及V1來判別心電圖病變，其所面臨的問題包含有如何抓取心電圖的QRS波位置和如何從心電圖辨別心律不整的種類，由於所錄製的心電圖常受到身體晃動的低頻漂移雜訊，和肌電訊號耦合的高頻雜訊影響，因此心電圖訊號會先經前置處理來達到訊號穩定性，再利用支援向量機(Support Vector Machines，SVM)做QRS波位置的辨別，以此為心電圖的波形標記基準，來擷取辨識的波形。利用自我組織學習模糊類神經網路 (Self-constructing Neural Fuzzy Inference Network，SoNFIN) 分類器做波形的辨識，包含正常節律(Normal Sinus Rhythm)、早發性心室收縮(Premature Ventricular Contraction)、左束枝傳導阻滯(Left Bundle Branch Block)、右束枝傳導阻滯(Right Bundle Branch Block)等4種，研究中所採用的心電圖來自MIT-BIH Arrhythmia資料庫，共選用了29個病患的資料，在QRS波位置辨識正確率達99.8%，在心電圖的心律不整波形辨識率亦可達到98.8%。
並列摘要
In this thesis, an automated system was built for electrocardiogram (ECG) arrhythmias classification that use ECG’s Lead II and V1 signals as input. Two problems must be overcome how to pick up ECG’QRS position and how to classify different arrhythmias. The ECG signals often coupled the different noises, like as the body motion causing the based line shift, or the electromyogram causing the high frequency noise. Therefore, the ECG signals will pass through the pre-processing to achieve the signal stability, first. Support vector machine was used to determine the QRS wave position. Self-constructing neural fuzzy inference network (SoNFIN) was used to classify 4 different arrhythmias including Normal Sinus Rhythm, Premature Ventricular Contraction, Left Bundle Branch Block, Right Bundle Branch Block. The ECG signals taken from MIT-BIH arrhythmia database. There are 29 patients to test the performance of our proposed system. The results show that the accuracy could achieve 99.8% for determining the QRS wave position, and 98.8% for classifying the arrhythmias’ types.
參考文獻
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