Learning ECG Patterns with the aid of Multilayer Perceptrons and Classification Trees
This paper presents an approach based on the combination of multilayer perceptrons (MLP) and classification tree (CT) to recognising four electrocardiograms (ECG) patterns: normal, left bundle branch block (LBBB), right bundle branch block (RBBB) and premature ventricular contraction (PVC). This study utilises MIT/BIH arrhythmia database as training and testing data. We first apply MLP and CT respectively to recognise ECG patterns. Next, we collect the ECG signal features which are selected in splitting CTs node, and feed these selected features into MLP for ECG pattern recognition. The aim is twofold: reducing the input attributes of MLP so as to lower computation burden, and understanding which heartbeat features play important roles in recognizing above four ECG patterns. To compare the effectiveness of proposed method, we considered the principal component analysis (PCA) that was frequently used to cut down the input dimension for pattern recognition. Comprehensive computer simulations will justify the feasibility of the proposed approach.
Electrocardiograms (ECG) Pattern Recognition Multilayer Perceptrons (MLP) Classification Tree (CT) Principal Component Analysis (PCA)
Lin, Yu-Jen Tsai, Shun-Ning Yang, Jing-Xiong
Dept. of Electrical Engineering, I-Shou University Kaohsiung County, Taiwan, ROC Dept. of General Courses I-Shou University Kaohsiung County, Taiwan, ROC Dept. of Electrical Engineering I-Shou University Kaohsiung County, Taiwan, ROC
国际会议
上海
英文
1859-1862
2008-05-16（万方平台首次上网日期，不代表论文的发表时间）