基于多重分形理论的心电诊断系统设计与应用

张春慨; 尹奥; 张景旺; 田攀博; 周颖; 齐常青

doi:10.11959/j.issn.2096-109x.2017.00204

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基于多重分形理论的心电诊断系统设计与应用

学术论文 | 更新时间：2024-06-03

- 基于多重分形理论的心电诊断系统设计与应用
- Design and application of electrocardiograph diagnosis system based on multifractal theory
- 网络与信息安全学报 2017年3卷第10期页码：62-71
- 作者机构：
- 作者简介：
  
  [ "张春慨（1973-），男，山东烟台人，博士，哈尔滨工业大学（深圳）副教授，主要研究方向为流数据挖掘、网络信息安全以及互联网技术。" ]
  [ "尹奥（1993-），男，河北邯郸人，哈尔滨工业大学（深圳）硕士生，主要研究方向为异常检测。" ]
  [ "张景旺（1991-），男，黑龙江哈尔滨人，哈尔滨工业大学（深圳）硕士生，主要研究方向为流数据挖掘、异常检测。" ]
  [ "田攀博（1993-），男，河南周口人，哈尔滨工业大学（深圳）硕士生，主要研究方向为数据挖掘、隐私感知。" ]
  [ "周颖（1994-），女，湖南邵阳人，哈尔滨工业大学（深圳）硕士生，主要研究方向为数据挖掘。" ]
  [ "齐常青（1991-），男，山东泰安人，哈尔滨工业大学（深圳）硕士生，主要研究方向为数据挖掘。" ]
- 基金信息：
  
  深圳市基础研究基金资助项目;Shenzhen Foundation Research Project(JCYJ20170307151518535)
- DOI：10.11959/j.issn.2096-109x.2017.00204
  中图分类号： TP315
- 网络首发：2017-10，
  
  纸质出版：2017-10-15
- 稿件说明：
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张春慨, 尹奥, 张景旺, 等. 基于多重分形理论的心电诊断系统设计与应用[J]. 网络与信息安全学报, 2017,3(10):62-71.

Chun-kai ZHANG, Ao,ZHANGJing-wang YIN, Pan-bo TIAN, et al. Design and application of electrocardiograph diagnosis system based on multifractal theory[J]. Chinese Journal of Network and Information Security, 2017, 3(10): 62-71.
张春慨, 尹奥, 张景旺, 等. 基于多重分形理论的心电诊断系统设计与应用[J]. 网络与信息安全学报, 2017,3(10):62-71. DOI： 10.11959/j.issn.2096-109x.2017.00204.

Chun-kai ZHANG, Ao,ZHANGJing-wang YIN, Pan-bo TIAN, et al. Design and application of electrocardiograph diagnosis system based on multifractal theory[J]. Chinese Journal of Network and Information Security, 2017, 3(10): 62-71. DOI： 10.11959/j.issn.2096-109x.2017.00204.

摘要

设计实现了一种利用差分阈值法进行心电数据自动分段算法，该算法可识别连续心电数据的各个心电周期；基于多重分形理论，获取心电数据的多重分形半谱特征和广义 Hurst 指数特征，用于神经网络模型的训练，以实现心电数据分类，其分类的准确率为97%。实现了心电诊断系统并用于实际应用，该系统能自动识别包含多个周期的心电序列，忽略该心电序列中首尾不完整心电周期数据，并可对心电数据各个周期进行分类标注。

Abstract

An automatic segmentation algorithm for ECG data using differential threshold method was designed

which could identify the various ECG cycles of continuous ECG data.And it could obtain the multifractal features of multiple fractal and generalize hurst index feature of ECG data

these features were used to train artificial neural network in order to classify ECG data

the accuracy of the classifier could reach 97%.An ECG diagnosis system was implemented

which can automatically identify ECG sequences that contain multiple ECG cycles

and can automatically ignore the incomplete ECG cycle data

and could annotate every cycle of ECG data.

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references

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