Evaluation of effect of unsupervised dimensionality reduction techniques on automated arrhythmia classification
Evaluation of effect of unsupervised dimensionality reduction techniques on automated arrhythmia classification
复制标题
DOI：
10.1016/j.bspc.2016.12.017
复制
发表时间：
2017-04-01
影响因子：
5.1
通讯作者：
Ranganathan, Vidhyapriya
中科院分区：
文献类型：
--
作者：
Rajagopal, Rekha;Ranganathan, Vidhyapriya
心律失常分类的自动化有助于医疗专业人员对患者的健康做出准确的决定。本工作的目的是评估五种不同的线性和非线性无监督降维（DR）技术的性能，即主成分分析（PCA），具有切向、峰度和高斯对比度函数的快速独立成分分析（fastICA），具有多项式核的核PCA（KPCA），采用概率神经网络分类器（PNN）对心律失常进行分级非线性主成分分析（hNLPCA）和主多项式分析（PPA）。分类模型的设计阶段包括以下阶段：通过消除包含噪声的细节系数对心脏信号进行预处理，通过Daubechies小波变换进行特征提取，通过无监督DR技术进行降维，以及使用PNN进行心律失常分类。PCA是一种广泛使用的DR技术，用于将高维数据映射到其低维表示。但是像心电图（ECG）信号这样的真实的世界数据本质上是复杂的和非线性的。本文主要研究了四种非线性DR技术和传统线性PCA技术在心律失常分类中的性能分析。整个MIT-BIH心律失常数据库用于实验。实验结果表明，PNN分类器（在扩展参数，sigma = 0.4）和快速ICA DR技术与切向对比度函数的组合表现出最高的F分数为99.83%，最小的10个维度。hNLPCA和KPCA对于低维映射需要更多的计算时间。PPA的性能比PCA好约10%，介于线性和非线性技术之间。(C)2016爱思唯尔有限公司版权所有
Automation in cardiac arrhythmia classification helps medical professionals to make accurate decisions upon the patient's health. The aim of this work is to evaluate the performance of five different linear and nonlinear unsupervised dimensionality reduction (DR) techniques namely principal component analysis (PCA), fast independent component analysis (fastICA) with tangential, kurtosis and Gaussian contrast functions, kernel PCA (KPCA) with polynomial kernel, hierarchical nonlinear PCA (hNLPCA) and principal polynomial analysis (PPA) on classification of cardiac arrhythmias using probabilistic neural network classifier (PNN). The design phase of the classification model comprises of the following stages: preprocessing of the cardiac signal by eliminating detail coefficients that contain noise, feature extraction through Daubechies wavelet transform, dimensionality reduction through unsupervised DR techniques, and arrhythmia classification using PNN. PCA is a widely used DR technique for mapping high dimensional data to its low dimensional representation. But real world data like electrocardiogram (ECG) signals are complex and nonlinear in nature. This work concentrates on performance analysis of four nonlinear DR techniques and conventional linear PCA technique on classification of cardiac arrhythmias. Entire MIT-BIH arrhythmia database is used for experimentation. The experimental results demonstrate that the combination of PNN classifier (at spread parameter, sigma = 0.4) and fastICA DR technique with tangential contrast function exhibit highest F score of 99.83% with a minimum of 10 dimensions. hNLPCA and KPCA requires more computation time for low dimensional mapping. PPA performs about 10% better than PCA and serves intermediate between linear and nonlinear techniques. (C) 2016 Elsevier Ltd. All rights reserved.