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基于小波的充血性心力衰竭识别的三种确认函数方法

Wavelet Based Method for Congestive Heart Failure Recognition by Three Confirmation Functions.

作者信息

Daqrouq K, Dobaie A

机构信息

Electrical and Computer Engineering Department, King Abdulaziz University, P.O. Box 80230, Jeddah 21589, Saudi Arabia.

出版信息

Comput Math Methods Med. 2016;2016:7359516. doi: 10.1155/2016/7359516. Epub 2016 Feb 2.

DOI:10.1155/2016/7359516
PMID:26949412
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC4754477/
Abstract

An investigation of the electrocardiogram (ECG) signals and arrhythmia characterization by wavelet energy is proposed. This study employs a wavelet based feature extraction method for congestive heart failure (CHF) obtained from the percentage energy (PE) of terminal wavelet packet transform (WPT) subsignals. In addition, the average framing percentage energy (AFE) technique is proposed, termed WAFE. A new classification method is introduced by three confirmation functions. The confirmation methods are based on three concepts: percentage root mean square difference error (PRD), logarithmic difference signal ratio (LDSR), and correlation coefficient (CC). The proposed method showed to be a potential effective discriminator in recognizing such clinical syndrome. ECG signals taken from MIT-BIH arrhythmia dataset and other databases are utilized to analyze different arrhythmias and normal ECGs. Several known methods were studied for comparison. The best recognition rate selection obtained was for WAFE. The recognition performance was accomplished as 92.60% accurate. The Receiver Operating Characteristic curve as a common tool for evaluating the diagnostic accuracy was illustrated, which indicated that the tests are reliable. The performance of the presented system was investigated in additive white Gaussian noise (AWGN) environment, where the recognition rate was 81.48% for 5 dB.

摘要

本文提出了一种基于小波能量的心电图（ECG）信号分析及心律失常特征提取方法。本研究采用基于小波的特征提取方法，通过对终端小波包变换（WPT）子信号的能量百分比（PE）来获取充血性心力衰竭（CHF）的特征。此外，还提出了平均帧能量百分比（AFE）技术，称为WAFE。通过三种确认函数引入了一种新的分类方法。这些确认方法基于三个概念：百分比均方根误差（PRD）、对数差分信号比（LDSR）和相关系数（CC）。所提出的方法在识别此类临床综合征方面显示出潜在的有效性。利用取自麻省理工学院 - 贝斯以色列女执事医疗中心（MIT - BIH）心律失常数据集及其他数据库的ECG信号来分析不同的心律失常和正常心电图。研究了几种已知方法进行比较。获得的最佳识别率选择是WAFE。识别性能达到了92.60%的准确率。绘制了作为评估诊断准确性常用工具的接收者操作特征曲线，表明测试是可靠的。在加性高斯白噪声（AWGN）环境中研究了所提出系统的性能，在5 dB时识别率为81.48%。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a7a2/4754477/e923f72f08a8/CMMM2016-7359516.004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a7a2/4754477/de57f535fbb4/CMMM2016-7359516.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a7a2/4754477/d2abc741bdd0/CMMM2016-7359516.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a7a2/4754477/5a2167d90202/CMMM2016-7359516.003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a7a2/4754477/e923f72f08a8/CMMM2016-7359516.004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a7a2/4754477/de57f535fbb4/CMMM2016-7359516.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a7a2/4754477/d2abc741bdd0/CMMM2016-7359516.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a7a2/4754477/5a2167d90202/CMMM2016-7359516.003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a7a2/4754477/e923f72f08a8/CMMM2016-7359516.004.jpg

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