School of Computing Science and Engineering, VIT Bhopal University, Kothrikalan, Sehore, Madhya Pradesh 466114, India.
Department of Clinical Laboratory Sciences, The Faculty of Applied Medical Sciences, Taif University, Taif, Saudi Arabia.
Comput Intell Neurosci. 2022 Jun 28;2022:3773883. doi: 10.1155/2022/3773883. eCollection 2022.
ECG (electrocardiogram) identifies and traces targets and is commonly employed in cardiac disease detection. It is necessary for monitoring precise target trajectories. Estimations of ECG are nonlinear as the parameters TDEs (time delays) and Doppler shifts are computed on receipt of echoes where EKFs (extended Kalman filters) and electrocardiogram have not been examined for computations. ECG, certain times, results in poor accuracies and low SNRs (signal-to-noise ratios), especially while encountering complicated environments. This work proposes to track online filter performances while using optimization techniques to enhance outcomes with the removal of noise in the signal. The use of cost functions can assist state corrections while lowering costs. A new parameter is optimized using IMCEHOs (Improved Mutation Chaotic Elephant Herding Optimizations) by linearly approximating system nonlinearity where multi-iterative function (Optimized Iterative UKFs) predicts a target's unknown parameters. To obtain optimal solutions theoretically, multi-iterative function takes less iteration, resulting in shorter execution times. The proposed multi-iterative function provides numerical approximations, which are derivative-free implementations. Signals are updated in the cloud environment; the updates are received by the patients from home. The simulation evaluation results with estimators show better performances in terms of reduced NMSEs (normalized mean square errors), RMSEs (root mean squared errors), SNRs, variances, and better accuracies than current approaches. Machine learning algorithms have been used to predict the stages of heart disease, which is updated to the patient in the cloud environment. The proposed work has a 91.0% accuracy rate with an error rate of 0.05% by reducing noise levels.
心电图（electrocardiogram）可识别和追踪目标，常用于心脏病检测。它是监测精确目标轨迹所必需的。由于 TDEs（时间延迟）和多普勒频移的参数是在接收回波时计算的，而 EKFs（扩展卡尔曼滤波器）和心电图尚未进行计算，因此 ECG 的估计是非线性的。心电图在某些情况下会导致精度较差和 SNR（信噪）较低，尤其是在遇到复杂环境时。这项工作提出了在线跟踪滤波器性能的方法，并使用优化技术来提高结果，同时去除信号中的噪声。使用代价函数可以辅助状态校正，同时降低成本。使用改进的突变混沌象群优化算法（IMCEHOs）来优化新参数，通过线性逼近系统非线性，其中多迭代函数（优化迭代 UKFs）预测目标的未知参数。为了在理论上获得最优解，多迭代函数需要较少的迭代次数，从而缩短执行时间。所提出的多迭代函数提供了数值逼近，这是无导数的实现。信号在云环境中进行更新，患者从家中接收更新。与估计器的仿真评估结果相比，该方法在降低 NMSEs（归一化均方误差）、RMSEs（均方根误差）、SNR、方差和提高精度方面具有更好的性能，优于当前方法。机器学习算法已被用于预测心脏病的阶段，并在云环境中更新给患者。通过降低噪声水平，该方法的准确率达到 91.0%，错误率为 0.05%。
