计算机科学与探索2017，Vol.11Issue(11)：1783-1791,9.DOI:10.3778/j.issn.1673-9418.1608083
循环谱分析在心律失常分类中的应用研究
ECG Arrhythmias Classification with Cyclic Spectral Analysis
摘要
Abstract
The performance of ECG arrhythmia classification mainly depends on both the effective feature extrac-tion and the optimal design of the classifier. Most of the classic methods extract the time domain features or frequency domain features directly to achieve the arrhythmia classification, but the classification performance still needs to be improved for multi-classification tasks. For this issue, the cyclic spectrum analysis technique is used to achieve the multi-arrhythmia classification. The method assumes that the signal is in non-stationary state, and arrhythmia classi-fication can be implemented through establishing a model to capture the hidden period in the ECG signal, which is more appropriate with the actual state of ECG signals. In order to implement the arrhythmia classification, the mor-phological features and wavelet coefficients time-frequency domain features are extracted. In addition, the cyclic spectrum technology is adopted for extracting the spectral correlation features for the subsequent multi-classification task. Besides, a comparison on the classification performance is also conducted among the artificial neural net-works, the traditional support vector machine classifier and extreme learning machine. Experimental results show that the proposed method based on the extreme learning machine can classify ten types of arrhythmias and achieve an average classification accuracy of 98.13%on the MIT-BIH arrhythmia benchmark dataset.关键词
心律失常分类/循环谱/超限学习机Key words
arrhythmia classification/cyclic spectral/extreme learning machine分类
信息技术与安全科学引用本文复制引用
褚晶辉,卢莉莉,吕卫,李喆..循环谱分析在心律失常分类中的应用研究[J].计算机科学与探索,2017,11(11):1783-1791,9.基金项目
The National Natural Science Foundation of China under Grant No. 61271069 (国家自然科学基金). （国家自然科学基金）
