ETA Laboratory, Faculty of Sciences and Technology, University of Mohamed El Bachir El-Ibrahimi, Bordj Bou Arreridj, Algeria.
Laboratoire PRISME, Université d'Orléans, 12 rue de Blois, BP 6744, 45067, Orléans, France.
Med Eng Phys. 2022 Nov;109:103865. doi: 10.1016/j.medengphy.2022.103865. Epub 2022 Sep 3.
In this paper, we propose a flexible modeling technique for the ECG signals. Modeling is achieved by considering the weighted summation of elementary functions representing the waveforms that describe each component of the cardiac cycle. We thus evaluate the benefit brought by α-stable functions with respect to Gaussian functions in terms of modeling precision. Seven records from the MIT-BIH arrhythmia database have been used to assess the performances of the proposed modeling method, including Normal beat, Premature Ventricular Contraction beat, Right Bundle Block Branch beat and Paced beat. When applied on the chosen records, it turns out that α-stable modeling always outperforms Gaussian modeling. Since each waveform is related to a particular physiological event in the ECG cardiac cycle, we also exploit flexibility of choosing α-stable modeling instead of Gaussian one for some event of medical interest in order to solve compression purpose efficiency-quality compromise. The comparison of the α-stable model applied in compression with other techniques proves the efficiency of the proposed method, mainly in term of quality score.
在本文中，我们提出了一种用于 ECG 信号的灵活建模技术。通过考虑表示描述心动周期每个分量的波形的基本函数的加权和来实现建模。因此，我们根据建模精度评估了 α-稳定函数相对于高斯函数带来的好处。使用来自 MIT-BIH 心律失常数据库的 7 个记录来评估所提出的建模方法的性能，包括正常节拍、室性早搏节拍、右束支阻滞分支节拍和起搏节拍。当应用于所选记录时，事实证明，α-稳定建模始终优于高斯建模。由于每个波形都与 ECG 心动周期中的特定生理事件相关，因此我们还利用选择 α-稳定建模而不是高斯建模的灵活性，以解决某些医学感兴趣的事件的压缩目的效率-质量折衷问题。将应用于压缩的 α-稳定模型与其他技术进行比较证明了所提出方法的效率，主要是在质量评分方面。
