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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”).

Abstract Polyp detection is a challenging task in the diagnosis of Colorectal Cancer (CRC), and it demands clinical expertise due to the diverse nature of polyps. The recent years have witnessed the development of automated polyp detection systems to assist the experts in early diagnosis, considerably reducing the time consumption and diagnostic errors. In automated CRC diagnosis, polyp segmentation is an important step which is carried out with deep learning segmentation models. Recently, Vision Transformers (ViT) are slowly replacing these models due to their ability to capture long range dependencies among image patches. However, the existing ViTs for polyp do not harness the inherent self-attention abilities and incorporate complex attention mechanisms. This paper presents Polyp-Vision Transformer (Polyp-ViT), a novel Transformer model based on the conventional Transformer architecture, which is enhanced with adaptive mechanisms for feature extraction and positional embedding. Polyp-ViT is tested on the Kvasir-seg and CVC-Clinic DB Datasets achieving segmentation accuracies of 0.9891 ± 0.01 and 0.9875 ± 0.71 respectively, outperforming state-of-the-art models. Polyp-ViT is a prospective tool for polyp segmentation which can be adapted to other medical image segmentation tasks as well due to its ability to generalize well.

Retinal blood vessel examination is commonly utilized for retinal disease diagnosis by ophthalmologists. The automated retinal vessel segmentation process becomes an essential tool to identify disease. Several retinal vessel segmentation models suffer from a lack of high generalization abilities and low accuracy due to the presence of complex symmetrical and asymmetrical patterns. Robust vessel segmentation of fundus images is needed to improve diagnostic performance including vein occlusion and diabetic retinopathy (DR). In this aspect, this study concentrates on the design of a sandpiper optimization algorithm with region growing based robust retinal blood vessel segmentation (SPORG-RBVS) approach. The proposed SPORG-RBVS technique involves different stages of pre-processing such as grayscale conversion, Z-score-based data normalization, and multi-scale vessel enhancement filtering. The SPO approach addresses the intricate challenges modeled by difficult symmetrical and asymmetrical patterns in retinal vessel segmentation. This method has been specifically designed to improve the generalization capabilities and accuracy of retinal vessel segmentation manners, vital for the precise detection of retinal diseases like vein occlusion and DR. Through phases of preprocessing comprising grayscale conversion, Z-score-based data normalization, and multi-scale vessel enhancement filtering, the SPORG-RBVS model ensures robust segmentation of fundus images. Particularly, the automated segmentation approach employing SPORG incorporates primary seed point generation and threshold determination using the SPO method, contributing to the overall performance of disease detection. A wide-ranging experimental analysis is executed and the outcomes are examined on three benchmark databases such as Digital Retinal Images for Vessel Extraction (DRIVE), Structured Analysis of the Retina (STARE), and CHASE_DB1 (CHASE). The comparative study stated the supremacy of the SPORG-RBVS method over existing techniques with maximum accuracy of 98.68%, 98.14%%, and 98.34% under DRIVE, STARE, and CHASE datasets, respectively.

The dynamic model of mobile wheelchair technology requires developing and implementing an intelligent control system to improve protection, increasing performance efficiency, and creating precise maneuvering in indoor and outdoor spaces. This work aims to design a robust tracking control algorithm based on a reference model for operating the kinematic model of powered wheelchairs under the variation of system parameters and unknown disturbance signals. The control algorithm was implemented using the pole placement method in combination with the sliding mode control (PP-SMC) approach. The design also adopted a neural network approach to eliminate system uncertainties from perturbations. The designed method utilized the sinewave signal as an essential input signal to the reference model. The stability of a closed-loop control system was achieved by adopting the Goa reaching law. The performance of the proposed tracking control system was evaluated in three scenarios under different conditions. These included assessing the tracking under normal operation conditions, considering the tracking performance by changing the dynamic system's parameters and evaluating the control system in the presence of uncertainties and external disturbances. The findings demonstrated that the proposed control method efficiently tracked the reference signal within a small error based on mean absolute error (MAE) measurements, where the range of MAE was between 0.08 and 0.12 in the presence of uncertainties or perturbations.

Scoliosis is a medical condition marked by an abnormal lateral curvature of the spine, typically forming a sideways “S” or “C” shape. Mechanically, it manifests as a three-dimensional deformation of the spine, potentially leading to diverse clinical issues such as pain, diminished lung capacity, and postural abnormalities. This research specifically concentrates on the Adolescent Idiopathic Scoliosis (AIS) population, as existing literature indicates a tendency for this type of scoliosis to deteriorate over time. The principal aim of this investigation is to pinpoint the biomechanical factors contributing to the progression of scoliosis by employing Finite Element Analysis (FEA) on computed tomography (CT) data collected from adolescent patients. By accurately modeling the spinal curvature and related deformities, the stresses and strains experienced by vertebral and intervertebral structures under diverse loading conditions can be simulated and quantified. The transient simulation incorporated damping and inertial terms, along with the static stiffness matrix, to enhance comprehension of the response. The findings of this study indicate a significant reduction in the Cobb angle, halving from its initial value, decreasing from 35° to 17°. In degenerative scoliosis, failure was predicted at 109 cycles, with the Polypropylene brace deforming by 10.34 mm, while the Nitinol brace exhibited significantly less deformation at 7.734 mm. This analysis contributes to a better understanding of the biomechanical mechanisms involved in scoliosis development and can assist in the formulation of more effective treatment strategies. The FEA simulation emerges as a valuable supplementary tool for exploring various hypothetical scenarios by applying diverse loads at different locations to enhance comprehension of the effectiveness of proposed interventions.

Scoliosis is a medical condition in which a person's spine has an abnormal curvature and Cobb angle is a measurement used to evaluate the severity of a spinal curvature. Presently, automatic Existing Cobb angle measurement techniques require huge dataset, time-consuming, and needs significant effort. So, it is important to develop an unsupervised method for the measurement of Cobb angle with good accuracy. In this work, an unsupervised local center of mass (LCM) technique is proposed to segment the spine region and further novel Cobb angle measurement method is proposed for accurate measurement. Validation of the proposed method was carried out on 2D X-ray images from the Saudi Arabian population. Segmentation results were compared with GMM-Based Hidden Markov Random Field (GMM-HMRF) segmentation method based on sensitivity, specificity, and dice score. Based on the findings, it can be observed that our proposed segmentation method provides an overall accuracy of 97.3% whereas GMM-HMRF has an accuracy of 89.19%. Also, the proposed method has a higher dice score of 0.54 compared to GMM-HMRF. To further evaluate the effectiveness of the approach in the Cobb angle measurement, the results were compared with Senior Scoliosis Surgeon at Multispecialty Hospital in Saudi Arabia. The findings indicated that the segmentation of the scoliotic spine was nearly flawless, and the Cobb angle measurements obtained through manual examination by the expert and the algorithm were nearly identical, with a discrepancy of only ± 3 degrees. Our proposed method can pave the way for accurate spinal segmentation and Cobb angle measurement among scoliosis patients by reducing observers' variability.

Mild cognitive impairment (MCI) is the precursor to the advanced stage of Alzheimer’s disease (AD), and it is important to detect the transition to the MCI condition as early as possible. Trends in daily routines/activities provide a measurement of cognitive/functional status, particularly in older adults. In this study, activity data from longitudinal monitoring through in-home ambient sensors are leveraged in predicting the transition to the MCI stage at a future time point. The activity dataset from the Oregon Center for Aging and Technology (ORCATECH) includes measures representing various domains such as walk, sleep, etc. Each sensor-captured activity measure is constructed as a time series, and a variety of summary statistics is computed. The similarity between one individual’s activity time series and that of the remaining individuals is also computed as distance measures. The long short-term memory (LSTM) recurrent neural network is trained with time series statistics and distance measures for the prediction modeling, and performance is evaluated by classification accuracy. The model outcomes are explained using the SHapley Additive exPlanations (SHAP) framework. LSTM model trained using the time series statistics and distance measures outperforms other modeling scenarios, including baseline classifiers, with an overall prediction accuracy of 83.84%. SHAP values reveal that sleep-related features contribute the most to the prediction of the cognitive stage at the future time point, and this aligns with the findings in the literature. Findings from this study not only demonstrate that a practical, less expensive, longitudinal monitoring of older adults’ activity routines can benefit immensely in modeling AD progression but also unveil the most contributing features that are medically applicable and meaningful.

This paper presents the feasibility of automated and accurate in vivo measurements of vascular parameters using an ultrasound sensor. The continuous and non-invasive monitoring of certain parameters, such as pulse wave velocity (PWV), blood pressure (BP), arterial compliance (AC), and stiffness index (SI), is crucial for assessing cardiovascular disorders during surgeries and follow-up procedures. Traditional methods, including cuff-based or invasive catheter techniques, serve as the gold standard for measuring BP, which is then manually used to calculate AC and SI through imaging algorithms. In this context, the Continuous and Non-Invasive Vascular Stiffness and Arterial Compliance Screener (CaNVAS) is developed to provide continuous and non-invasive measurements of these parameters using an ultrasound sensor. By driving 5 MHz (ranging from 2.2 to 10 MHz) acoustic waves through the arterial walls, capturing the reflected echoes, and employing pre-processing techniques, the frequency shift is utilized to calculate PWV. It is observed that PWV measured by CaNVAS correlates exponentially with BP values obtained from the sphygmomanometer (BPMR-120), enabling the computation of instantaneous BP values. The proposed device is validated through measurements conducted on 250 subjects under pre- and post-exercise conditions, demonstrating an accuracy of 95% and an average coefficient of variation of 12.5%. This validates the reliability and precision of CaNVAS in assessing vascular parameters.

In this work, we developed a prototype that adopted sound-based systems for localization of visually impaired individuals. The system was implemented based on a wireless ultrasound network, which helped the blind and visually impaired to navigate and maneuver autonomously. Ultrasonic-based systems use high-frequency sound waves to detect obstacles in the environment and provide location information to the user. Voice recognition and long short-term memory (LSTM) techniques were used to design the algorithms. The Dijkstra algorithm was also used to determine the shortest distance between two places. Assistive hardware tools, which included an ultrasonic sensor network, a global positioning system (GPS), and a digital compass, were utilized to implement this method. For indoor evaluation, three nodes were localized on the doors of different rooms inside the house, including the kitchen, bathroom, and bedroom. The coordinates (interactive latitude and longitude points) of four outdoor areas (mosque, laundry, supermarket, and home) were identified and stored in a microcomputer’s memory to evaluate the outdoor settings. The results showed that the root mean square error for indoor settings after 45 trials is about 0.192. In addition, the Dijkstra algorithm determined that the shortest distance between two places was within an accuracy of 97%.

In this study, we present a sensorless, robust, and physiological tracking control method to drive the operational speed of implantable rotary blood pumps (IRBPs) for patients with heart failure (HF). The method used sensorless measurements of the pump flow to track the desired reference flow (Qr). A dynamical estimator model was used to estimate the average pump flow (Q^est) based on pulse-width modulation (PWM) signals. A proportional-integral (PI) controller integrated with a fuzzy logic control (FLC) system was developed to automatically adapt the pump flow. The Qr was modeled as a constant and trigonometric function using an elastance function (E(t)) to achieve a variation in the metabolic demand. The proposed method was evaluated in silico using a lumped parameter model of the cardiovascular system (CVS) under rest and exercise scenarios. The findings demonstrated that the proposed control system efficiently updated the pump speed of the IRBP to avoid suction or overperfusion. In all scenarios, the numerical results for the left atrium pressure (Pla), aortic pressure (Pao), and left ventricle pressure (Plv) were clinically accepted. The Q^est accurately tracked the Qr within an error of 0.25 L/min.

Installing and developing a sophisticated control system to optimize left ventricular assist device (LVAD) pump speed to meet changes in metabolic demand is essential for advancing LVAD technology. This paper aims to design and implement a physiological control method for LVAD pumps to provide optimal cardiac output. The method is designed to adjust the pump speed by regulating the pump flow based on a predefined set point (operating point). The Frank–Starling mechanism technique was adopted to control the set point within a safe operating zone (green square), and it mimics the physiological demand of the patient. This zone is predefined by preload control lines, which are known as preload lines. A proportional–integral (PI) controller was utilized to control the operating point within safe limits to prevent suction or overperfusion. In addition, a PI type 1 fuzzy logic controller was designed and implemented to drive the LVAD pump. To evaluate the design method, rest, moderate, and exercise scenarios of heart failure (HF) were simulated by varying the hemodynamic parameters in one cardiac cycle. This evaluation was conducted using a lumped parameter model of the cardiovascular system (CVS). The results demonstrated that the proposed control method efficiently drives an LVAD pump under accepted clinical conditions. In both scenarios, the left ventricle pressure recorded 112 mmHg for rest and 55 mmHg for exercise, and the systematic flow recorded 5.5 L/min for rest and 1.75 L/min for exercise.

Skeletal muscle is considered as a near-constant volume system, and the contractions of the muscle are related to the changes in tissue thickness. Assessment of the skeletal muscle contractile parameters such as maximum contraction thickness ( T h ), contraction time ( T c ), contraction velocity ( V c ), sustain time ( T s ), and half-relaxation ( T r ) provides valuable information for various medical applications. This paper presents a single-element wearable ultrasonic sensor (WUS) and a method to measure the skeletal muscle contractile parameters in A-mode ultrasonic data acquisition. The developed WUS was made of double-layer polyvinylidene fluoride (PVDF) piezoelectric polymer films with a simple and low-cost fabrication process. A flexible, lightweight, thin, and small size WUS would provide a secure attachment to the skin surface without affecting the muscle contraction dynamics of interest. The developed WUS was employed to monitor the contractions of gastrocnemius (GC) muscle of a human subject. The GC muscle contractions were evoked by the electrical muscle stimulation (EMS) at varying EMS frequencies from 2 Hz up to 30 Hz. The tissue thickness changes due to the muscle contractions were measured by utilizing a time-of-flight method in the ultrasonic through-transmission mode. The developed WUS demonstrated the capability to monitor the tissue thickness changes during the unfused and fused tetanic contractions. The tetanic progression level was quantitatively assessed using the parameter of the fusion index (FI) obtained. In addition, the contractile parameters ( T h , T c , V c , T s , and T r ) were successfully extracted from the measured tissue thickness changes. In addition, the unfused and fused tetanus frequencies were estimated from the obtained FI-EMS frequency curve. The WUS and ultrasonic method proposed in this study could be a valuable tool for inexpensive, non-invasive, and continuous monitoring of the skeletal muscle contractile properties.