基于自回归功率谱密度的心音特征提取与分类

2014 The 1st International Conference on Information Technology, Computer, and Electrical Engineering Pub Date : 2014-10-21 DOI:10.1109/ICITACEE.2014.7065730

Laurentius Kuncoro Probo Saputra, H. A. Nugroho, M. Wulandari

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引用次数: 3

摘要

心音是诊断心脏异常的重要信息。本文是在前人研究的基础上，对各类异常心音的特征进行改进。小波分解用于去噪。通过AR-PSD提取特征并将其作为分类的输入。最后将13种异常心音分为13类。心音数据集取自密歇根声音心脏数据库。在本研究中，频率幅度和峰度被用作附加特征。结果表明，该分类器系统的准确率达到了92.31%。

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Feature extraction and classification of heart sound based on autoregressive power spectral density (AR-PSD)

Heart sound has an important information that can help in diagnosis of the abnormality. This paper is developed based on the previous research to improve the feature in each types of abnormal heart sound. Wavelet decomposition is used for noise removal. Features are extracted by AR-PSD and used as inputs for classification. Finally 13 types of abnormal heart sound are classified into 13 categories. Data set of heart sound is taken from Michigan Sound Heart Database. In this research, magnitude of frequency and kurtosis are used as additional features. The result shows that classifier system achives the accuracy of 92.31%.

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来源期刊

2014 The 1st International Conference on Information Technology, Computer, and Electrical Engineering

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