The ECG features detection and arrhythmia classification system

• Main Authors: Shao-Yung Yen, 顏紹雍
• Other Authors: 江行全
• Format: Others
• Language: zh-TW
• Published: 2007
• Online Access: http://ndltd.ncl.edu.tw/handle/36848125095541950811

碩士 === 元智大學 === 工業工程與管理學系 === 95 === Automated diagnostic system has become an established component of medical technology. The main concept of the medical technology is an inductive engine that learns the decision characteristics of the diseases and then can be used to diagnose future patients with uncertain diseases states. Electrocardiogram (ECG) is an important tool in diagnosing the condition of the heart. It provides valuable information about the functional aspects of the heart and cardiovascular system. This research develops an algorithm to extract the QRS complex wave and combines the clinical judgment criterion of the cardiac arrhythmia to model a classification system. The system input data is ECG signal and output is the classification of cardiac arrhythmia. The method is forward-backward algorithm that is revised So and Chan method and augments forward and backward searching rules and also deletes lower R amplitude to improve the detection performance. The ECG signals are taken form MIT-BIH arrhythmia database, which are used to classify 4 different arrhythmias for training. There are normal, premature ventricular contraction, ventricular flutter/fibrillation and 2o heart block (Tsipouras, 2005). Regard MIT-BIH arrhythmia database as training data in order to set up the parameters, verifying and testing by real medical ECG data. According to QRS complex extraction and clinical judgment criterion of the cardiac arrhythmia, the proposed approach provided 96.3% accuracy of classification on MIT-BIH database and 90.38% accuracy of classification on empirical medical ECG data.