Publications by authors named "Anil Kumar Tiwari"

Background And Objectives: Hypertensive Retinopathy (HR) is a retinal manifestation resulting from persistently elevated blood pressure. Severity grading of HR is essential for patient risk stratification, effective management, progression monitoring, timely intervention, and minimizing the risk of vision impairment. Computer-aided diagnosis and artificial intelligence (AI) systems play vital roles in the diagnosis and grading of HR.

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Background: Epilepsy is a neurological illness affecting the brain that makes people more likely to experience frequent, spontaneous seizures. There has to be an accurate automated method for measuring seizures frequency and severity to assess the efficacy of pharmacological therapy for epilepsy. The drug quantities are often derived from patient reports which may cause significant issues owing to inadequate or inaccurate descriptions of seizures and their frequencies.

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Hypertensive Retinopathy (HR) is a retinal disease caused by elevated blood pressure for a prolonged period. There are no obvious signs in the early stages of high blood pressure, but it affects various body parts over time, including the eyes. HR is a biomarker for several illnesses, including retinal diseases, atherosclerosis, strokes, kidney disease, and cardiovascular risks.

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The human musculoskeletal (MSK) system (also known as the locomotor system) provides strength and assistance to perform functional tasks and daily life activities. The MSK health monitoring plays a vital role in maintaining the body mobility and quality of life. Manual approaches for musculoskeletal health monitoring are subjective and require a clinician's intervention.

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Body joint monitoring is essential for disorder diagnosis and assessment of treatment effectiveness. Microsoft Kinect v2 is a low-cost and markerless human motion-tracking RGB-D sensor that provides spatial locations of tracked skeletal joints in the form of 3D coordinates. Sometimes, body tracking of kinect v2 produces erratic 3D coordinates, which affects the real-time tracking performance of the sensor.

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During the screening programme for microbial cultures producing antimicrobial agents, an active microbial strain of Streptomyces was isolated from the agricultural soil of Narnaul, Haryana India. Physiological, biochemical characteristics and 16S ribosomal RNA sequence homology studies revealed that it was similar to Streptomyces levis (sequence similarity 100%). The microbial strain was submitted to Genomebio Technologies Pvt.

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The medicine of cancer is directed in this paper. The pie theory is applied for the proposed medicine. The improbability and un-constancy are the major theories, which are used to design this anti-cancer medicine.

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Introduction: Appendicitis poses a great health problem worldwide. Previous studies demonstrated structural damage to neuronal network and interstitial cell of Cajal in appendicitis. Above observations suggest for the alterations in appendicular motility/contractility in appendicitis.

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This paper presents a system based on Seismocardiography (SCG) to monitor the heart sound signal for the long-term. It uses an accelerometer, which is of small size and low weight and, thus, convenient to wear. Such a system should also be robust to various noises which occur in real life scenarios.

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To diagnose health status of the heart, heart monitoring systems use heart signals produced during each cardiac cycle. Many types of signals are acquired to analyze heart functionality and hence several heart monitoring systems such as phonocardiography, electrocardiography, photoplethysmography and seismocardiography are used in practice. Recently, focus on the at-home monitoring of the heart is increasing for long term monitoring, which minimizes risks associated with the patients diagnosed with cardiovascular diseases.

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Fetal phonocardiography (fPCG) based antenatal care system is economical and has a potential to use for long-term monitoring due to noninvasive nature of the system. The main limitation of this technique is that noise gets superimposed on the useful signal during its acquisition and transmission. Conventional filtering may result into loss of valuable diagnostic information from these signals.

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Continuous foetal monitoring of physiological signals is of particular importance for early detection of complexities related to the foetus or the mother's health. The available conventional methods of monitoring mostly perform off-line analysis and restrict the mobility of subjects within a hospital or a room. Hence, the aim of this paper is to develop a foetal e-health monitoring system using mobile phones and wireless sensors for providing advanced healthcare services in the home environment.

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Over the past few years, various devices and techniques have been developed for electronic fetal monitoring (EFM), which is performed during pregnancy or continuously during labor to ensure normal delivery of a healthy baby. We reviewed and analyzed the performance of currently used EFM techniques with the goal of determining a noninvasive, cost-effective alternative for use in the home environment. This review includes research papers, publications, web sources, product manuals, interviews, formal discussions, and other available literature with the goal of providing a comprehensive comparative analysis of all available EFM techniques.

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