Gholami Maryam, Maleki Mahsa, Amirkhani Saeed, Chaibakhsh Ali
Department of Engineering, Islamic Azad University of Kazerun, Kazerun, Fars Iran.
Faculty of Mechanical Engineering, University of Guilan, P.O. Box 41938-33697, Rasht, Guilan Iran.
Biomed Eng Lett. 2022 Mar 7;12(2):205-215. doi: 10.1007/s13534-022-00223-1. eCollection 2022 May.
This study investigates a nonlinear modelbased feature extraction approach for the accurate classification of four types of heartbeats. The features are the morphological parameters of ECG signal derived from the nonlinear ECG model using an optimization-based inverse problem solution. In the model-based methods, high feature extraction time is a crucial issue. In order to reduce the feature extraction time, a new structure was employed in the optimization algorithms. Using the proposed structure has considerably increased the speed of feature extraction. In the following, the effectiveness of two types of optimization methods (genetic algorithm and particle swarm optimization) and the McSharry ECG model has been studied and compared in terms of speed and accuracy of diagnosis. In the classification section, the adaptive neuro-fuzzy inference system and fuzzy c-mean clustering methods, along with the principal component analysis data reduction method, have been utilized. The obtained results reveal that using an adaptive neuro-fuzzy inference system with data obtained from particle swarm optimization will have the shortest process time and the best diagnosis, with a mean accuracy of 99% and a mean sensitivity of 99.11%.
本研究探讨了一种基于非线性模型的特征提取方法，用于对四种类型的心跳进行准确分类。这些特征是使用基于优化的反问题解决方案从非线性心电图模型导出的心电图信号的形态学参数。在基于模型的方法中，高特征提取时间是一个关键问题。为了减少特征提取时间，在优化算法中采用了一种新结构。使用所提出的结构大大提高了特征提取的速度。在下文中，研究并比较了两种优化方法（遗传算法和粒子群优化）以及McSharry心电图模型在诊断速度和准确性方面的有效性。在分类部分，利用了自适应神经模糊推理系统和模糊c均值聚类方法，以及主成分分析数据约简方法。所得结果表明，使用基于粒子群优化获得的数据的自适应神经模糊推理系统将具有最短的处理时间和最佳的诊断效果，平均准确率为99%，平均灵敏度为99.11%。
