.png){kind=icon}
返回
ECG beat classification using particle swarm optimization and support vector machine
.png){kind=icon}
PRE
AI
DOI：10.1007/s11704-014-2398-1.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In this paper, we propose a novel ECG arrhythmia classification method using power spectral-based features and support vector machine (SVM) classifier. The method extracts electrocardiogram's spectral and three timing interval features. Non-parametric power spectral density (PSD) estimation methods are used to extract spectral features. The proposed approach optimizes the relevant parameters of SVM classifier through an intelligent algorithm using particle swarm optimization (PSO). These parameters are: Gaussian radial basis function (GRBF) kernel parameter sigma and C penalty parameter of SVM classifier. ECG records from the MIT-BIH arrhythmia database are selected as test data. It is observed that the proposed power spectral-based hybrid particle swarm optimization-support vector machine (SVMPSO) classification method offers significantly improved performance over the SVM which has constant and manually extracted parameter.
Keyword:
ECG arrhythmia classification
SVM
PSO
optimization
PSD
期刊
IF:
4.6
论文数:
1.6K
被引数:
2.8K
机构
引用论文
分享收藏An overview of power topologies for micro-hydro turbines.png)
2012 3rd IEEE International Symposium on Power Electronics for Distributed Generation Systems (PEDG)
2012 3rd IEEE International Symposium on Power Electronics for Distributed Generation Systems (PEDG)IF0
PREAI
分享收藏分享收藏分享收藏Recurrent neural networks employing Lyapunov exponents for analysis of ECG signals采用Lyapunov指数的递归神经网络分析ECG信号
PREAI
分享收藏分享收藏
查看更多内容