Ning James, Atanasov Nikolay, Ning Taikang
Thayer School of Engineering, Dartmouth College, Hanover, NH 03755, USA.
Annu Int Conf IEEE Eng Med Biol Soc. 2009;2009:958-61. doi: 10.1109/IEMBS.2009.5332562.
A quantitative approach integrating AR modeling and wavelet transform is presented in this paper to analyze the digitized phonocardiogram. The recognition of the first and the second heart sounds (S(1) and S(2)) were facilitated with wavelet transform without referring to the QRS waveform. We found that the Daubechies wavelet is most effective in identifying S(1) and S(2). In addition, the boundaries of S(1), S(2), and the onset and duration of the systolic murmur thus identified within the systole could be marked using the wavelet-filtered signal's strength. Furthermore, quantitative measures derived from a 2(nd) order AR model were used to delineate the configuration and pitch of the systolic murmur found within through piecewise segmentation. The proposed approach was tested and proved effective in delineating a set of clinically diagnosed systolic murmurs. The suggested AR and wavelet transform combined approach can be generalized with minor adjustments to delineate diastolic murmurs as well.
本文提出了一种将自回归（AR）建模与小波变换相结合的定量方法，用于分析数字化心音图。利用小波变换辅助识别第一和第二心音（S(1)和S(2)），无需参考QRS波形。我们发现，Daubechies小波在识别S(1)和S(2)方面最为有效。此外，利用小波滤波信号的强度，可以标记出S(1)、S(2)的边界以及由此确定的收缩期杂音在收缩期内的起始点和持续时间。此外，通过二阶AR模型得出的定量指标用于通过分段分割来描绘收缩期杂音的形态和音调。所提出的方法经过测试，证明在描绘一组临床诊断的收缩期杂音方面是有效的。所建议的AR与小波变换相结合的方法只需稍作调整就可以推广应用于描绘舒张期杂音。
