Laboratoire de Traitement du Signal, Département d'Electronique, Université des Frères Mentouri, Constantine, Algeria.
Université Saad Dahlab Blida 1, Blida, Algeria.
Biomed Tech (Berl). 2021 Feb 22;66(4):363-373. doi: 10.1515/bmt-2020-0170. Print 2021 Aug 26.
It is well known that many physiological phenomena are modeled accurately and effectively using fractional operators and systems. This type of modeling is due mainly to the dynamical link between fractional-order systems and the fractal structures of the physiological systems. The automatic characterization of the premature ventricular contraction (PVC) is very important for early diagnosis of patients with different life-threatening cardiac diseases. In this paper, a classification scheme of normal and PVC beats of the electrocardiogram (ECG) signal is proposed. The clustering features used for normal and PVC beats discrimination are the parameters of the commensurate order linear fractional model of the frequency content of the QRS complex of the ECG signal. A series of tests and comparisons have been performed to evaluate and validate the efficiency of the proposed PVC classification algorithm using the MIT-BIH arrhythmia database. The proposed PVC classification method has achieved an overall accuracy of 94.745%, a specificity of 95.178% and a sensitivity of 90.021% using all the 48 records of the database.
众所周知，许多生理现象都可以通过分数阶算子和系统进行准确有效地建模。这种建模类型主要归因于分数阶系统与生理系统的分形结构之间的动态联系。对室性早搏（PVC）的自动特征描述对于不同危及生命的心脏病患者的早期诊断非常重要。本文提出了一种心电图（ECG）信号中正常和 PVC 搏动的分类方案。用于正常和 PVC 搏动区分的聚类特征是 ECG 信号 QRS 复合频率内容的同阶线性分数模型的参数。使用 MIT-BIH 心律失常数据库进行了一系列测试和比较，以评估和验证所提出的 PVC 分类算法的效率。使用数据库的所有 48 个记录，所提出的 PVC 分类方法的整体准确性为 94.745%，特异性为 95.178%，灵敏度为 90.021%。
