舒张期心音自回归建模中的阶数确定
Order determination in autoregressive modeling of diastolic heart sounds.
作者信息
Güler I, Kiymik M K, Güler N F
机构信息
Kahramanmaras Sutcu Imam University, Institute of Science and Technology, Turkey.
出版信息
J Med Syst. 1996 Feb;20(1):11-7. doi: 10.1007/BF02260870.
Previous studies demonstrated that spectral analysis of diastolic heart sounds may provide valuable information for the detection of coronary artery disease. Although parametric modeling methods were successfully used to achieve this goal, and showed considerable performance, the accuracy and precision of the analysis is strongly dependent on the model order selected. In order to investigate the effects of model order selection on the analysis, diastolic heart sound recorded from both normal and diseased patients were analyzed using the AR modeling, which is computationally the most efficient parametric spectral analysis method. The model order were determined by using four different model order selection criteria. The results showed that the four criteria yielded different order for the same data set. On the other hand, different criteria showed different performance in different measurement conditions. Effect of arbitrary order selection was also discussed. As a result, an optimal AR model order that may be used for every case was determined.
先前的研究表明，舒张期心音的频谱分析可为冠状动脉疾病的检测提供有价值的信息。尽管参数建模方法已成功用于实现这一目标，并显示出相当不错的性能，但分析的准确性和精确性在很大程度上取决于所选的模型阶数。为了研究模型阶数选择对分析的影响，使用自回归（AR）建模对正常患者和患病患者记录的舒张期心音进行了分析，AR建模是计算效率最高的参数频谱分析方法。通过使用四种不同的模型阶数选择标准来确定模型阶数。结果表明，对于同一数据集，这四种标准产生了不同的阶数。另一方面，不同的标准在不同的测量条件下表现出不同的性能。还讨论了任意阶数选择的影响。结果，确定了一个可用于每种情况的最佳AR模型阶数。
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