中国组织工程研究

中国组织工程研究 ›› 2010, Vol. 14 ›› Issue (39): 7353-7357.doi: 10.3969/j.issn.1673-8225.2010.39.032

• 骨与关节图像与影像 bone and joint imaging • 上一篇    下一篇

DNA算法优化BP网络用于心律失常的识别

师  黎，赵  云，郭  豹   

  1. 郑州大学电气工程学院，河南省郑州市  450001
  • 出版日期:2010-09-24 发布日期:2010-09-24
  • 通讯作者: 赵云，硕士，郑州大学电气工程学院，河南省郑州市 450001 zhaoyun_zzu@163.com
  • 作者简介:师黎☆，女，1964年生，河南省尉氏县人，2007年上海大学毕业，博士，博士生导师，教授，主要从事智能控制及模式识别研究。 shili@zzu.edu.cn
  • 基金资助:

    国家自然科学基金(60841004)：基于基函数超完备集的动物视觉图像重构研究；国家自然科学基金(60971110)：初级视觉皮层中视像整体特征的稀疏表象模型的研究。

Arrhythmia recognition using a BP neural network-based DNA algorithm

Shi Li, Zhao Yun, Guo Bao   

  1. School of Electrical Engineering, Zhengzhou University, Zhengzhou  450001, Henan Province, China　
  • Online:2010-09-24 Published:2010-09-24
  • Contact: Zhao Yun, Master, School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, Henan Province, China zhaoyun_zzu@163.com
  • About author:Shi Li☆, Doctor, Doctoral supervisor, Professor, School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, Henan Province, China shili@zzu.edu.cn
  • Supported by:

    the National Natural Science Foundation of China, No. 60841004*, 60971110*

PDF

482

被引次数

3

摘要：

背景：目前对心律失常的诊断大多是由医生人工完成，费时费力，诊断结果依赖于医生的个人业务水平和责任心。心律失常的自动识别对于心脏病患者的救护和早期治疗具有非常重要的意义。
目的：实现临床心律失常的自动识别和诊断。
方法：首先从心电图中动态提取完整心律失常心拍形态，并采用离散余弦变换和反变换压缩数据；然后设计用于心律失常识别的BP神经网络，并用DNA算法优化该BP网络；最后用MIT/BIH心电数据库中心电图数据对DNA-BP网络进行检验。
结果与结论：对于5种心拍类型，包括正常、左束支阻滞、右束支阻滞、心室跳脱心搏及Paced 心搏，利用DNA-BP网络进行分类，实验达到了很好的识别效果，平均识别正确率达到99%。

关键词: 心电图, 心律失常, 自动识别, BP网络, DNA算法

Abstract:

BACKGROUND: Arrhythmia is commonly diagnosed by doctors, and the diagnosis depends on doctor experience and responsibility. Arrhythmia recognition is great important for rescuing and early treatment of cardiopathy sufferers.
OBJECTIVE: To investigate efficient method of auto-recognizing and diagnosis of arrhythmia.
METHODS: The whole morphology of arrhythmic heartbeat was extracted from electrocardiograph (ECG), and discrete cosine transform and inverse discrete cosine transform were used to compress the data. A BP neural network was designed for arrhythmic heartbeat recognition, and the initial weights and thresholds of network were optimized by DNA algorithm. Finally, the MIT/BIH ECG database was used to test the DNA-BP neural network.
RESULTS AND CONCLUSION: For the five types heart beat, including normal, left bundle branch block, right bundle branch block, ventricular escape heartbeat, Paced heartbeat, the experiment results demonstrate efficient by using DNA-BP neural network, with an average recognition rate of 99%.

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