基于多分辨分析量子跃迁神经网络的心电图智能检测研究  

Research of Automatic Detection of ECG Based on Quantum Neural Networks with Multiresolution Analysis

作　　者：王淑艳[1] 王玉松[1] 

机构地区：[1]中国民航大学天津市智能信号与图像处理重点实验室,天津300300

出　　处：《生物医学工程学杂志》2009年第3期480-483,共4页Journal of Biomedical Engineering

基　　金：天津市自然科学基金资助项目(06YFJMJC00800);校科研启动基金资助项目(06qd09s)

摘　　要：为了提高心电信号的正确检测效率,本文在BP神经网络模型基础上,引入多量子能级传递函数,使产生的量子跃迁神经网络具有更有效的学习和泛化能力,克服了经典BP网络在心电图模糊分类方面的局限性,使网络输出"跃变"跳出特征空间不确定区域而"坍缩"到确定区域,提高了心电信号正确检测效率。并且本文为了进一步优化网络,利用具有多分辨特性的小波变换对心电信号进行预处理,减少了网络神经元选取个数,优化网络,提高了收敛速度,实验结果验证了理论的可行性。An automatic detection of Electrocardiogram （ECG） using Quantum Neural Networks（QNN） with multiresolution analysis is given in the paper. QNN originates from exploiting BP neural networks. With the quantum neurons, QNN can model the levels of uncertainty arising from complex classification problems. Its potential advantage over conventional methods is based on the argument for reliability. The fuzzy feature is expected to enhance the reliability of the network, which is eritical for improving desirable diagnosis accuracy. And wavelet transform with multiresolution anaIysis is adopted in ECG pretreatment for reducing the numbers of neurons. So it can improve convergence speed of the network. The results of simulation confirmed the feasibility of the proposed approach.

关 键 词：多分辨率分析 小波分解 量子跃迁神经网络 心电图 智能检测 

分 类 号：TP29-AI[自动化与计算机技术—检测技术与自动化装置]