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The Motion artifacts in ECG data can lead to inaccurate circulatory state analysis. Recently, there has been growing interest in methods to mitigate motion distortion in ECG signals. One major challenge is to reduce motion artifacts without affecting the underlying ECG signal, as the motion artifacts and the ECG signal often overlap. Adaptive noise cancellers have proven effective in reducing motion artifacts, provided that an appropriate noise reference, which correlates with the noise in the ECG signal, is available. However, the correlation between motion distortion and the noise reference is not always consistent, and using an inappropriate noise reference can contaminate the ECG data. In this research, we present NARX_CSOFC, an advanced approach that significantly enhances the capabilities of the nonlinear autoregressive network with exogenous inputs (NARX) architecture by incorporating nonlinear combinations of input variables to globally estimate any nonlinear function. The method employs cat swarm optimization with fractional calculus (CSOFC) to find optimal solutions. The proposed NARX_CSOFC demonstrates substantial improvements in artifact reduction: achieving a 12 dB improvement for ECG artifacts, a 16 dB improvement for EMG artifacts, and a 15 dB improvement for random noise artifacts, as measured by Signal-to-Noise Ratio (SNR) and Mean Squared Error (MSE) compared to existing techniques.

Knee osteoarthritis (KOA) is a degenerative joint disease predominantly affecting the elderly and is often managed through knee replacement surgeries. West Asians, who frequently engage in activities involving bending and kneeling during their prayers, tend to exhibit distinct bone anatomy compared to the Caucasian population. This research posits that patient-specific, customized knee implants with articular cartilages may lead to reduced post-surgical discomfort and a better implant fit compared to conventional standard implants. This study presents a novel concept and approach for the development of a customized total knee implant with articular cartilages, specifically tailored to simulate loading conditions using finite element analysis (FEA) for the Saudi Arabian population. The research analyses patient-specific customized knee implants with articular cartilages under both pre- and post-implant conditions using a finite element model (FEM). Computed tomography (CT) images of patients were utilized to create a solid model, which was then analysed under various constraints and conditions. The meshing process employed tetrahedral elements, converging with 76,197 nodes and 43,009 elements. The analysis was conducted under different body weights, specifically 75-80 kg and 100-105 kg. Two sets of force and moment were applied: the first with a force of 1000 N and a moment of 1.5 N-m, and the second with a force of 750 N and a moment of 0.8 N-m. The results indicated a 5% reduction in stress with implants designed for a 100 kg body weight, along with a significant reduction in ligament strain when compared to conventional knee joint stresses. This study offers a promising pathway toward reducing post-surgical discomfort. The proposed innovative solution has the potential to revolutionize total knee implant technology, offering enhanced functionality and improved patient outcomes for the Saudi Arabian population.

Presents corrections to the paper, (Corrections to “Sandpiper Optimization Algorithm With Region Growing Based Robust Retinal Blood Vessel Segmentation Approach”).