School of Electrical and Computer Engineering, Shiraz University, Shiraz, Iran.
Comput Biol Med. 2013 Oct;43(10):1453-61. doi: 10.1016/j.compbiomed.2013.06.017. Epub 2013 Jul 5.
In this paper a general framework is presented for morphological modeling of cardiac signals from a signal decomposition perspective. General properties of a desired morphological model are presented and special cases of the model are studied in detail. The presented approach is studied for modeling the morphology of electrocardiogram (ECG) signals. Specifically, three types of ECG modeling techniques, including polynomial spline models, sinusoidal model and a model previously presented by McSharry et al., are studied within this framework. The proposed method is applied to datasets from the PhysioNet ECG database for compression and modeling of normal and abnormal ECG signals. Quantitative and qualitative results of these applications are also presented and discussed.
本文从信号分解的角度提出了一种用于心脏信号形态建模的通用框架。本文提出了期望形态模型的一般性质，并详细研究了模型的特殊情况。所提出的方法用于对心电图（ECG）信号的形态进行建模。具体来说，本文在该框架内研究了三种 ECG 建模技术，包括多项式样条模型、正弦模型和 McSharry 等人之前提出的模型。该方法应用于 PhysioNet ECG 数据库中的数据集，用于正常和异常 ECG 信号的压缩和建模。本文还给出并讨论了这些应用的定量和定性结果。
