[1]杨雨诺,张国林,孙科学,等.基于深度学习网络的心音智能分析平台构建[J].计算机技术与发展,2019,29(07):130-134.[doi:10. 3969 / j. issn. 1673-629X. 2019. 07. 026]
　YANG Yu-nuo,ZHANG Guo-lin,SUN Ke-xue,et al.Construction of Heart Sound Intelligent Analysis Platform Based on Deep Learning Network[J].,2019,29(07):130-134.[doi:10. 3969 / j. issn. 1673-629X. 2019. 07. 026]
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基于深度学习网络的心音智能分析平台构建()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
29
期数:
2019年07期
页码:
130-134
栏目:
应用开发研究
出版日期:
2019-07-10

文章信息/Info

Title:
Construction of Heart Sound Intelligent Analysis Platform Based on Deep Learning Network
文章编号:
1673-629X(2019)07-0130-05
作者:
杨雨诺1 ; 张国林1 ; 孙科学1; 2; 3 ; 成谢锋1; 2; 3
1. 南京邮电大学 电子与光学工程学院,江苏 南京 210023; 2. 南京邮电大学 信息电子技术国家级虚拟仿真实验教学中心,江苏 南京 210023; 3. 射频集成与微组装技术国家地方联合工程实验室,江苏 南京 210023
Author(s):
YANG Yu-nuo 1 ; ZHANG Guo-lin 1 ; SUN Ke-xue 1; 2; 3 ; CHENG Xie-feng 1; 2; 3
1. School of Electronics and Optical Engineering,Nanjing University of Posts and Telecommunications,Nanjing 210023,China; 2. The National Virtual Simulation Experiment Teaching Center of Information Electronic Technology,Nanjing University of Posts and Te
关键词:
心音信号; 深度学习; 模式识别; 信号分析; 实验平台; 进程择优算法
Keywords:
heart sound signal; deep learning; pattern recognition; signal analysis; experimental platform; process optimization algorithm
分类号:
TP274+.2
DOI:
10. 3969 / j. issn. 1673-629X. 2019. 07. 026
摘要:
为了将深度学习和心音研究相结合以提高心音识别算法处理数据的能力,设计了一种深度学习网络的模块化实验平台。 信号采集模块、数据处理模块和深度学习网络模块共同组成实验平台系统,使用实验室制作的传感器进行心音信号的采集和去噪声处理,随后由数据处理模块完成信号的特征提取并建立信号的特征数据库,心音信号的识别过程与数据处理在深度学习网络模块中实现。 心音智能分析平台以心音深度学习网络为核心,采用进程择优算法,分类器选用BP 神经网络,可对心音信号进行分类处理,实现了基于深度学习网络的心音信号识别与数据处理。 与此同时,该平台的实现对心音与深度学习的结合、提高心音识别算法在自然环境下处理大数据的能力,具有积极的意义。
Abstract:
In order to combine deep learning and heart sound research to improve the ability of heart sound recognition algorithm to process data,a modular experimental platform for deep learning network is designed. The experimental platform system consists of signal acquisition module,data processing module and deep learning network module,and uses the sensors produced by the laboratory to perform heart sound signal acquisition and denoising. Then the data processing module completes the feature extraction of the signal and establishes a characteristic database of the signal. The recognition and data processing of the heart sound signal are implemented in the deep learning network module. The heart sound intelligent analysis platform takes the heart sound deep learning network as the core, adopts the process optimization algorithm and BP neural network as the classifier,which can classify the heart sound signal and realize the heart sound signal recognition and data processing based on the deep learning network. At the same time,the implementation of the platform has a positive significance for the combination of heart sound and deep learning,and the ability of the heart sound recognition algorithm to process big data in the natural environment.

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[1]朱冰莲 刘倩.心音信号的自适应小波去噪[J].计算机技术与发展,2006,(10):83.
　ZHU Bing-lian,LIU Oian.Denoising of Heart Sound Signal Based on Adaptive Wavelet[J].,2006,(07):83.
[2]姬汉贵,成谢锋. 心音信号周期增量序列的多尺度化研究[J].计算机技术与发展,2015,25(09):48.
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　CHEN Hao-xiang,CAI Jian-ming,LIU Keng-ran,et al. Deep Learning and Recognition of Handwritten Numeral Features[J].,2016,26(07):19.
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更新日期/Last Update: 2019-07-10