An Arrhythmia Recognition System Based on K-means Clustering、Wavelet Transform and Support Vector Machine

• Main Author: 張家熏
• Other Authors: 吳順德
• Format: Others
• Language: zh-TW
• Published: 2011
• Online Access: http://ndltd.ncl.edu.tw/handle/60026233899445480611

碩士 === 國立臺灣師範大學 === 機電科技研究所 === 99 === This paper described an arrhythmia classification system based on the technologies of wavelet transform, k means clustering and support vector machine for the purpose of heartbeat recognition. The method consists of three stages. At the first stage, the waveform of a single heartbeat in each main group is classified into subgroups using k-means clustering technology. At the second stage, the time-frequency features of each heartbeat were extracted by using wavelet transform. At the third stage, the model of the proposed classification system is obtained by using support vector machine (SVM). The training vector of SVM is the combinations of morphological features and time-frequency features extracted using wavelet transform. Three experiments were done to examine the performance and reliability of the proposed classification system. Experiments show that the efficiency and feasibility of this proposed classification system.