- 上一篇：Labview身管直线度测试系统硬件设计
- 下一篇：labview735MM身管缠度系统设计软件与硬件部分
摘要窦房结电图的自动识别与分析技术是心脏电生理检测与诊断的发展方向，属于国际生物医学工程领域的前沿课题。本文基于超微心电仪的记录技术，主要研究窦房结电图信号预处理与特征波形识别的算法。61401
信号预处理方面，提出了小波自适应滤波的思想，将工频干扰和基线漂移同时滤除；特征波形识别方面，利用小波变换和信号奇异性理论识别QRS、P、T波，另外结合时域阈值分析法，探索实现P前波的自动识别。
本文探索性地研究了窦房结电图的自动分析算法，具有一定程度的创新性，为后续信号的研究奠定了基础。
毕业论文关键词 SNE 小波自适应 信号奇异性 特征波形识别
毕业设计说明书（论文）外文摘要
Title Intelligent Supermicro ECG The Research of SNE Signal Processing and Analysis Algorithms
Abstract The research of Sinus Node Electrogram (SNE) automatic identification and analysis is the direction of detection and diagnosis of cardiac electrophysiology, is the frontier topic of international biomedical engineering field. Based on the recording technology of Intelligent Ultramicro ECG, this paper carries on the algorithms of SNE signal processing and feature extraction.
A wavelet-based adaptive filtering algorithm is designed with the considering of a variety interference such as 50 Hz interference and baseline drift when the SNE is acquired practically. In the feature extraction, QRS、P、T wave is identified based on the theory of wavelet transform and signal singularity. Combined with analysis time domain and threshold, the pre-P wave is identified.
In the paper, some innovative research of SNE automatic analysis is exposed. The results we achieved is a foundation for the next study.
Keywords: SNE Wavelet-adaptive Signal Singularity feature extraction recognition
1 绪论 1
1.1 研究背景及意义 1
1.2 窦房结电图基本知识 2
1.3 SNE信号处理技术的发展现状 3
1.4 本课题的研究工作 7
2 窦房结电图信号的预处理 9
2.1 引言 9
2.2 窦房结电图噪声及干扰的类型与分析 9
2.3 小波自适应滤波器的设计 10
2.4 小结 23
3 窦房结电图特征波形自动识别 24
3.1 引言 24
3.2 QRS波检测 24
3.3 P波、T波检测 32
3.4 P前波检测 36
3.5 小结 39
结论 40