Selection of Dynamic Features Based on Time-Frequency Representations for Heart Murmur Detection from Phonocardiographic Signals
Selection of Dynamic Features Based on Time-Frequency Representations for Heart Murmur Detection from Phonocardiographic Signals
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DOI：
10.1007/s10439-009-9838-3
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发表时间：
2010-01-01
影响因子：
3.8
通讯作者：
Castellanos-Dominguez, G.
中科院分区：
文献类型：
--
作者：
Quiceno-Manrique, A. F.;Godino-Llorente, J. I.;Castellanos-Dominguez, G.
这项工作讨论了一种方法，用于选择的动态功能，通过时间的频谱功率的计算的基础上应用于检测收缩期杂音从心音图记录。为了研究杂音时频谱功率的动态特性，研究了几种二次能量分布，即Wigner-Ville分布、Choi-Williams分布、平滑伪Wigner-Ville分布、指数分布和双曲T分布。分类性能进行了比较，使用短时傅立叶变换和连续小波变换表示。此外，这项工作讨论了各种非参数技术来估计的频谱功率轮廓的动态特征，表征心音（HS）：瞬时能量，特征向量，瞬时频率，等效带宽，子带频谱质心，和梅尔倒谱系数。以这种方式，通过使用简单的k-最近邻分类器检测杂音存在的能力来评估上述时间-频率表示及其动态特征。此外，建议的动态功能的相关性进行了评估，使用随时间变化的主成分分析。所提出的工作是使用包含22个心音图记录（16个正常和6个记录杂音）的数据库进行的，分段提取402个有代表性的单个节拍（每类201个）。结果表明，由二次能量分布给出的平滑显著提高了HS中杂音检测的分类性能。此外，它示出的功率动态功能，使最好的整体分类性能的MFCC轮廓。因此，所提出的方法可以实现作为一个简单的诊断工具，用于初级卫生保健的目的，具有高精度（高达98%）区分正常和病理性的节拍。
This work discusses a method for the selection of dynamic features, based on the calculation of the spectral power through time applied to the detection of systolic murmurs from phonocardiographic recordings. To investigate the dynamic properties of the spectral power during murmurs, several quadratic energy distributions have been studied, namely Wigner-Ville, Choi-Williams, smoothed pseudo Wigner-Ville, exponential, and hyperbolic T-distribution. The classification performance has been compared with that using a Short Time Fourier Transform and Continuous Wavelet Transform representations. Furthermore, this work discusses a variety of nonparametric techniques to estimate the spectral power contours as dynamic features that characterize the heart sounds (HS): instantaneous energy, eigenvectors, instantaneous frequency, equivalent bandwidth, subband spectral centroids, and Mel cepstral coefficients. In this way, the aforementioned time-frequency representations and their dynamic features were evaluated by means of their ability to detect the presence of murmurs using a simple k-Nearest Neighbors classifier. Moreover, the relevancies of the proposed dynamic features have been evaluated using a time-varying principal component analysis. The work presented is carried out using a database containing 22 phonocardiographic recordings (16 normal and 6 records with murmurs), segmented to extract 402 representative individual beats (201 per class). The results suggest that the smoothing given by the quadratic energy distribution significantly improves the classification performance for the detection of murmurs in HS. Moreover, it is shown that the power dynamic features which give the best overall classification performance are the MFCC contours. As a result, the proposed method can be implemented as a simple diagnostic tool for primary health-care purposes with high accuracy (up to 98%) discriminating between normal and pathologic beats.